High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

RA-MVStest		90.27 1	90.71 3	88.98 4	95.68 1	74.80 5	94.07 3	94.15 9	77.30 62	92.54 7	96.42 2	81.63 7	97.24 16	88.21 38	96.35 15	94.89 20

DVP-MVS++		90.23 2	91.01 1	87.89 25	94.34 34	71.25 69	95.06 1	94.23 5	78.38 39	92.78 5	95.74 16	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

SED-MVS		90.08 3	90.85 2	87.77 29	95.30 4	70.98 77	93.57 10	94.06 16	77.24 69	93.10 1	95.72 18	82.99 1	97.44 7	89.07 25	96.63 4	94.88 22

MED-MVS		89.78 4	90.41 4	87.89 25	94.57 20	71.43 64	93.28 14	94.36 3	77.30 62	92.25 17	95.87 9	81.59 9	97.39 11	88.15 41	96.28 18	94.85 27

DVP-MVS		89.60 5	90.35 5	87.33 46	95.27 7	71.25 69	93.49 12	92.73 84	77.33 60	92.12 19	95.78 14	80.98 13	97.40 9	89.08 22	96.41 12	93.33 136
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSP-MVS		89.51 6	89.91 7	88.30 11	94.28 37	73.46 20	92.90 23	94.11 12	80.27 11	91.35 24	94.16 63	78.35 33	96.77 34	89.59 17	94.22 75	94.67 46
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		89.48 7	89.98 6	88.01 17	94.80 13	72.69 34	91.59 53	94.10 14	75.90 120	92.29 15	95.66 20	81.67 6	97.38 13	87.44 53	96.34 17	93.95 93
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MM		89.16 8	89.23 11	88.97 5	90.79 106	73.65 12	92.66 30	91.17 167	86.57 1	87.39 69	94.97 34	71.70 94	97.68 1	92.19 1	95.63 35	95.57 2

APDe-MVS		89.15 9	89.63 8	87.73 32	94.49 25	71.69 58	93.83 6	93.96 19	75.70 127	91.06 27	96.03 6	76.84 44	97.03 26	89.09 21	95.65 34	94.47 64
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 10	89.23 11	88.61 7	94.25 38	73.73 11	92.40 31	93.63 32	74.77 163	92.29 15	95.97 8	74.28 61	97.24 16	88.58 33	96.91 1	94.87 24
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HPM-MVS++		89.02 11	89.15 13	88.63 6	95.01 11	76.03 1	92.38 34	92.85 78	80.26 12	87.78 60	94.27 56	75.89 49	96.81 33	87.45 52	96.44 9	93.05 161

aaEdge-Enhanced		88.98 12	89.39 9	87.75 31	94.54 23	71.43 64	91.61 51	94.25 4	76.30 110	90.62 31	95.03 31	78.06 36	97.07 25	88.15 41	95.96 24	94.75 38

CNVR-MVS		88.93 13	89.13 14	88.33 9	94.77 14	73.82 10	90.51 72	93.00 65	80.90 7	88.06 55	94.06 68	76.43 46	96.84 31	88.48 36	95.99 23	94.34 71

TestfortrainingZip a		88.85 14	89.24 10	87.68 38	94.57 20	71.25 69	93.28 14	93.91 22	76.30 110	91.13 26	95.87 9	78.92 25	96.95 28	86.12 64	93.07 86	94.85 27

SteuartSystems-ACMMP		88.72 15	88.86 15	88.32 10	92.14 82	72.96 28	93.73 7	93.67 31	80.19 13	88.10 54	94.80 36	73.76 66	97.11 23	87.51 51	95.82 28	94.90 19
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 16	88.74 17	87.64 40	92.78 74	71.95 55	92.40 31	94.74 2	75.71 125	89.16 40	95.10 29	75.65 51	96.19 59	87.07 56	96.01 22	94.79 31

DeepPCF-MVS	80.84 1	88.10 17	88.56 19	86.73 61	92.24 81	69.03 115	89.57 101	93.39 43	77.53 55	89.79 36	94.12 65	78.98 23	96.58 47	85.66 65	95.72 31	94.58 55

lecture		88.09 18	88.59 18	86.58 64	93.26 59	69.77 101	93.70 8	94.16 7	77.13 74	89.76 37	95.52 24	72.26 84	96.27 56	86.87 57	94.65 61	93.70 111

SD-MVS		88.06 19	88.50 20	86.71 62	92.60 79	72.71 32	91.81 48	93.19 52	77.87 44	90.32 34	94.00 72	74.83 54	93.78 170	87.63 49	94.27 74	93.65 117
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NCCC		88.06 19	88.01 24	88.24 12	94.41 29	73.62 13	91.22 64	92.83 79	81.50 5	85.79 85	93.47 90	73.02 75	97.00 27	84.90 71	94.94 51	94.10 84

ACMMP_NAP		88.05 21	88.08 23	87.94 20	93.70 48	73.05 25	90.86 67	93.59 34	76.27 113	88.14 53	95.09 30	71.06 105	96.67 40	87.67 48	96.37 14	94.09 85

TSAR-MVS + MP.		88.02 22	88.11 22	87.72 34	93.68 50	72.13 51	91.41 60	92.35 103	74.62 167	88.90 44	93.85 80	75.75 50	96.00 67	87.80 47	94.63 63	95.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		87.94 23	87.85 26	88.20 13	94.39 31	73.33 22	93.03 21	93.81 25	76.81 85	85.24 90	94.32 53	71.76 92	96.93 29	85.53 68	95.79 29	94.32 73

MP-MVS		87.71 24	87.64 28	87.93 22	94.36 33	73.88 8	92.71 29	92.65 90	77.57 51	83.84 124	94.40 50	72.24 85	96.28 55	85.65 66	95.30 45	93.62 120
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MGCNet		87.69 25	87.55 31	88.12 14	89.45 143	71.76 57	91.47 59	89.54 227	82.14 3	86.65 79	94.28 55	68.28 154	97.46 6	90.81 6	95.31 43	95.15 9

MP-MVS-pluss		87.67 26	87.72 27	87.54 41	93.64 51	72.04 54	89.80 92	93.50 38	75.17 148	86.34 81	95.29 27	70.86 107	96.00 67	88.78 31	96.04 21	94.58 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 27	87.47 33	87.94 20	94.58 18	73.54 18	93.04 19	93.24 50	76.78 87	84.91 95	94.44 48	70.78 108	96.61 44	84.53 79	94.89 55	93.66 113

reproduce-ours		87.47 28	87.61 29	87.07 52	93.27 57	71.60 59	91.56 56	93.19 52	74.98 152	88.96 41	95.54 22	71.20 103	96.54 48	86.28 61	93.49 81	93.06 159

our_new_method		87.47 28	87.61 29	87.07 52	93.27 57	71.60 59	91.56 56	93.19 52	74.98 152	88.96 41	95.54 22	71.20 103	96.54 48	86.28 61	93.49 81	93.06 159

ACMMPR		87.44 30	87.23 38	88.08 16	94.64 15	73.59 14	93.04 19	93.20 51	76.78 87	84.66 103	94.52 41	68.81 145	96.65 42	84.53 79	94.90 54	94.00 90

APD-MVS		87.44 30	87.52 32	87.19 49	94.24 39	72.39 44	91.86 47	92.83 79	73.01 221	88.58 46	94.52 41	73.36 68	96.49 50	84.26 82	95.01 48	92.70 178
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 32	87.26 36	87.89 25	94.12 43	72.97 27	92.39 33	93.43 41	76.89 83	84.68 100	93.99 74	70.67 110	96.82 32	84.18 86	95.01 48	93.90 96

region2R		87.42 32	87.20 39	88.09 15	94.63 16	73.55 16	93.03 21	93.12 59	76.73 90	84.45 108	94.52 41	69.09 138	96.70 38	84.37 81	94.83 58	94.03 88

fmvsm_s_conf0.5_n_9		87.39 34	87.95 25	85.70 84	89.48 142	67.88 159	88.59 150	89.05 257	80.19 13	90.70 29	95.40 25	74.56 56	93.92 162	91.54 2	92.07 105	95.31 6

MCST-MVS		87.37 35	87.25 37	87.73 32	94.53 24	72.46 43	89.82 90	93.82 24	73.07 218	84.86 98	92.89 105	76.22 47	96.33 53	84.89 73	95.13 47	94.40 67

reproduce_model		87.28 36	87.39 35	86.95 56	93.10 65	71.24 74	91.60 52	93.19 52	74.69 164	88.80 45	95.61 21	70.29 114	96.44 51	86.20 63	93.08 85	93.16 151

MTAPA		87.23 37	87.00 41	87.90 23	94.18 42	74.25 7	86.58 238	92.02 127	79.45 23	85.88 83	94.80 36	68.07 156	96.21 58	86.69 59	95.34 41	93.23 140

XVS		87.18 38	86.91 46	88.00 18	94.42 27	73.33 22	92.78 25	92.99 68	79.14 27	83.67 128	94.17 62	67.45 162	96.60 45	83.06 94	94.50 66	94.07 86

HPM-MVS		87.11 39	86.98 43	87.50 44	93.88 46	72.16 50	92.19 40	93.33 45	76.07 117	83.81 125	93.95 77	69.77 125	96.01 66	85.15 69	94.66 60	94.32 73
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 39	86.92 45	87.68 38	94.20 41	73.86 9	93.98 5	92.82 82	76.62 93	83.68 127	94.46 45	67.93 157	95.95 70	84.20 85	94.39 70	93.23 140

DeepC-MVS	79.81 2	87.08 41	86.88 47	87.69 37	91.16 95	72.32 48	90.31 81	93.94 20	77.12 76	82.82 150	94.23 59	72.13 88	97.09 24	84.83 74	95.37 40	93.65 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 42	86.62 52	87.76 30	93.52 53	72.37 46	91.26 61	93.04 60	76.62 93	84.22 115	93.36 94	71.44 99	96.76 35	80.82 123	95.33 42	94.16 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
BridgeMVS		86.78 43	86.99 42	86.15 73	91.24 94	67.61 168	90.51 72	92.90 75	77.26 68	87.44 68	91.63 148	71.27 102	96.06 62	85.62 67	95.01 48	94.78 32

SR-MVS		86.73 44	86.67 50	86.91 57	94.11 44	72.11 52	92.37 35	92.56 95	74.50 169	86.84 76	94.65 40	67.31 164	95.77 72	84.80 75	92.85 90	92.84 176

CS-MVS		86.69 45	86.95 44	85.90 81	90.76 107	67.57 170	92.83 24	93.30 48	79.67 20	84.57 107	92.27 119	71.47 98	95.02 109	84.24 84	93.46 83	95.13 11

PGM-MVS		86.68 46	86.27 58	87.90 23	94.22 40	73.38 21	90.22 83	93.04 60	75.53 130	83.86 123	94.42 49	67.87 159	96.64 43	82.70 106	94.57 65	93.66 113

mPP-MVS		86.67 47	86.32 56	87.72 34	94.41 29	73.55 16	92.74 27	92.22 116	76.87 84	82.81 151	94.25 58	66.44 177	96.24 57	82.88 99	94.28 73	93.38 131

fmvsm_s_conf0.5_n_8		86.56 48	87.17 40	84.73 126	87.76 232	65.62 221	89.20 117	92.21 118	79.94 18	89.74 38	94.86 35	68.63 148	94.20 146	90.83 5	91.39 119	94.38 68

CANet		86.45 49	86.10 64	87.51 43	90.09 119	70.94 81	89.70 96	92.59 94	81.78 4	81.32 175	91.43 158	70.34 112	97.23 19	84.26 82	93.36 84	94.37 69

train_agg		86.43 50	86.20 59	87.13 51	93.26 59	72.96 28	88.75 141	91.89 135	68.69 335	85.00 93	93.10 98	74.43 58	95.41 88	84.97 70	95.71 32	93.02 163

PHI-MVS		86.43 50	86.17 62	87.24 48	90.88 103	70.96 79	92.27 39	94.07 15	72.45 228	85.22 91	91.90 134	69.47 128	96.42 52	83.28 93	95.94 26	94.35 70

CSCG		86.41 52	86.19 61	87.07 52	92.91 70	72.48 40	90.81 68	93.56 36	73.95 186	83.16 142	91.07 172	75.94 48	95.19 97	79.94 137	94.38 71	93.55 125

fmvsm_s_conf0.5_n_10		86.38 53	86.76 48	85.24 99	87.33 258	67.30 181	89.50 103	90.98 173	76.25 114	90.56 32	94.75 38	68.38 151	94.24 145	90.80 7	92.32 102	94.19 79

fmvsm_s_conf0.5_n_3		86.36 54	87.46 34	83.09 219	87.08 273	65.21 235	89.09 126	90.21 203	79.67 20	89.98 35	95.02 33	73.17 72	91.71 287	91.30 3	91.60 114	92.34 196

NormalMVS		86.29 55	85.88 69	87.52 42	93.26 59	72.47 41	91.65 49	92.19 121	79.31 25	84.39 110	92.18 125	64.64 203	95.53 79	80.70 126	94.65 61	94.56 59

SPE-MVS-test		86.29 55	86.48 53	85.71 83	91.02 99	67.21 187	92.36 36	93.78 27	78.97 34	83.51 135	91.20 166	70.65 111	95.15 99	81.96 110	94.89 55	94.77 33

Casviewmamba		86.09 57	86.04 66	86.24 68	88.17 205	68.05 152	89.44 106	92.79 83	80.30 10	84.71 99	92.78 112	72.83 79	95.05 107	82.81 100	90.57 135	95.62 1

fmvsm_s_conf0.5_n_11		86.06 58	86.75 49	84.00 180	87.78 229	66.09 204	89.96 88	90.80 182	77.37 59	86.72 77	94.20 61	72.51 82	92.78 238	89.08 22	92.33 100	93.13 155

fmvsm_l_conf0.5_n_3		86.02 59	86.32 56	85.14 102	87.20 264	68.54 135	89.57 101	90.44 192	75.31 138	87.49 66	94.39 51	72.86 77	92.72 239	89.04 27	90.56 136	94.16 80

EC-MVSNet		86.01 60	86.38 54	84.91 117	89.31 152	66.27 202	92.32 37	93.63 32	79.37 24	84.17 117	91.88 135	69.04 142	95.43 85	83.93 88	93.77 78	93.01 165

MVSMamba_PlusPlus		85.99 61	85.96 68	86.05 76	91.09 96	67.64 167	89.63 99	92.65 90	72.89 224	84.64 104	91.71 143	71.85 90	96.03 63	84.77 76	94.45 69	94.49 63

casdiffmvs_mvg		85.99 61	86.09 65	85.70 84	87.65 240	67.22 186	88.69 146	93.04 60	79.64 22	85.33 89	92.54 115	73.30 69	94.50 134	83.49 90	91.14 124	95.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 63	85.88 69	86.22 70	92.69 76	69.53 104	91.93 44	92.99 68	73.54 201	85.94 82	94.51 44	65.80 191	95.61 75	83.04 96	92.51 95	93.53 127

test_fmvsmconf_n		85.92 64	86.04 66	85.57 90	85.03 329	69.51 105	89.62 100	90.58 187	73.42 205	87.75 62	94.02 70	72.85 78	93.24 207	90.37 8	90.75 132	93.96 91

sasdasda		85.91 65	85.87 71	86.04 77	89.84 129	69.44 110	90.45 78	93.00 65	76.70 91	88.01 57	91.23 162	73.28 70	93.91 163	81.50 113	88.80 170	94.77 33

canonicalmvs		85.91 65	85.87 71	86.04 77	89.84 129	69.44 110	90.45 78	93.00 65	76.70 91	88.01 57	91.23 162	73.28 70	93.91 163	81.50 113	88.80 170	94.77 33

ACMMP		85.89 67	85.39 80	87.38 45	93.59 52	72.63 36	92.74 27	93.18 56	76.78 87	80.73 191	93.82 81	64.33 206	96.29 54	82.67 107	90.69 133	93.23 140
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
fmvsm_l_conf0.5_n_9		85.84 68	86.63 51	83.46 199	87.12 272	66.01 207	88.56 152	89.43 231	75.59 129	89.32 39	94.32 53	72.89 76	91.21 316	90.11 11	92.33 100	93.16 151

SR-MVS-dyc-post		85.77 69	85.61 76	86.23 69	93.06 67	70.63 87	91.88 45	92.27 109	73.53 202	85.69 86	94.45 46	65.00 200	95.56 76	82.75 102	91.87 110	92.50 189

CDPH-MVS		85.76 70	85.29 85	87.17 50	93.49 54	71.08 75	88.58 151	92.42 100	68.32 343	84.61 105	93.48 88	72.32 83	96.15 61	79.00 154	95.43 39	94.28 76

TSAR-MVS + GP.		85.71 71	85.33 82	86.84 58	91.34 92	72.50 39	89.07 127	87.28 315	76.41 102	85.80 84	90.22 204	74.15 64	95.37 93	81.82 111	91.88 109	92.65 182

dcpmvs_2		85.63 72	86.15 63	84.06 172	91.71 88	64.94 248	86.47 242	91.87 137	73.63 196	86.60 80	93.02 103	76.57 45	91.87 281	83.36 91	92.15 103	95.35 4

test_fmvsmconf0.1_n		85.61 73	85.65 75	85.50 91	82.99 386	69.39 112	89.65 97	90.29 201	73.31 209	87.77 61	94.15 64	71.72 93	93.23 208	90.31 9	90.67 134	93.89 97

fmvsm_l_mol_unc0.5_1		85.55 74	86.37 55	83.10 218	86.42 292	62.98 313	85.89 264	84.85 365	76.48 100	92.88 3	96.67 1	74.16 63	92.46 251	87.11 55	92.90 89	93.85 98

fmvsm_s_conf0.5_n_6		85.55 74	86.20 59	83.60 193	87.32 260	65.13 238	88.86 133	91.63 151	75.41 134	88.23 52	93.45 91	68.56 149	92.47 250	89.52 18	92.78 91	93.20 145

alignmvs		85.48 76	85.32 83	85.96 80	89.51 139	69.47 107	89.74 94	92.47 96	76.17 115	87.73 64	91.46 157	70.32 113	93.78 170	81.51 112	88.95 166	94.63 52

3Dnovator+	77.84 4	85.48 76	84.47 99	88.51 8	91.08 97	73.49 19	93.18 18	93.78 27	80.79 8	76.66 279	93.37 93	60.40 278	96.75 37	77.20 177	93.73 79	95.29 7

MSLP-MVS++		85.43 78	85.76 73	84.45 138	91.93 85	70.24 90	90.71 69	92.86 77	77.46 57	84.22 115	92.81 109	67.16 166	92.94 228	80.36 130	94.35 72	90.16 283

DELS-MVS		85.41 79	85.30 84	85.77 82	88.49 189	67.93 158	85.52 279	93.44 40	78.70 35	83.63 130	89.03 238	74.57 55	95.71 74	80.26 134	94.04 76	93.66 113
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
fmvsm_s_conf0.5_n_4		85.39 80	85.75 74	84.30 150	86.70 284	65.83 214	88.77 139	89.78 216	75.46 133	88.35 48	93.73 83	69.19 137	93.06 223	91.30 3	88.44 179	94.02 89

SymmetryMVS		85.38 81	84.81 92	87.07 52	91.47 91	72.47 41	91.65 49	88.06 293	79.31 25	84.39 110	92.18 125	64.64 203	95.53 79	80.70 126	90.91 130	93.21 143

HPM-MVS_fast		85.35 82	84.95 91	86.57 65	93.69 49	70.58 89	92.15 42	91.62 152	73.89 190	82.67 154	94.09 66	62.60 230	95.54 78	80.93 120	92.93 88	93.57 123

test_fmvsm_n_1920		85.29 83	85.34 81	85.13 105	86.12 300	69.93 97	88.65 148	90.78 183	69.97 297	88.27 50	93.98 75	71.39 100	91.54 298	88.49 35	90.45 138	93.91 94

fmvsm_s_conf0.5_n_5		85.22 84	85.55 77	84.25 157	86.26 294	67.40 177	89.18 118	89.31 240	72.50 227	88.31 49	93.86 79	69.66 126	91.96 273	89.81 13	91.05 125	93.38 131

MVS_111021_HR		85.14 85	84.75 93	86.32 67	91.65 89	72.70 33	85.98 260	90.33 198	76.11 116	82.08 161	91.61 151	71.36 101	94.17 149	81.02 119	92.58 94	92.08 212

hybridcas		85.11 86	85.18 86	84.90 118	87.47 252	65.68 219	88.53 154	92.38 101	77.91 43	84.27 114	92.48 116	72.19 86	93.88 167	80.37 129	90.97 127	95.15 9

casdiffmvs		85.11 86	85.14 88	85.01 110	87.20 264	65.77 218	87.75 187	92.83 79	77.84 45	84.36 113	92.38 118	72.15 87	93.93 161	81.27 118	90.48 137	95.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		85.08 88	84.96 90	85.45 92	92.07 83	68.07 150	89.78 93	90.86 180	82.48 2	84.60 106	93.20 97	69.35 130	95.22 96	71.39 250	90.88 131	93.07 158

MGCFI-Net		85.06 89	85.51 78	83.70 191	89.42 144	63.01 307	89.43 107	92.62 93	76.43 101	87.53 65	91.34 160	72.82 80	93.42 200	81.28 117	88.74 173	94.66 49

fmvsm_s_mol_unc0.5_1		84.94 90	85.17 87	84.23 158	88.40 195	63.74 284	87.78 186	90.14 206	77.13 74	90.33 33	95.80 12	71.64 97	94.05 153	70.03 268	95.31 43	94.91 18

DPM-MVS		84.93 91	84.29 100	86.84 58	90.20 117	73.04 26	87.12 212	93.04 60	69.80 301	82.85 149	91.22 165	73.06 74	96.02 65	76.72 189	94.63 63	91.46 234

baseline		84.93 91	84.98 89	84.80 123	87.30 262	65.39 228	87.30 208	92.88 76	77.62 49	84.04 120	92.26 120	71.81 91	93.96 155	81.31 116	90.30 140	95.03 13

ETV-MVS		84.90 93	84.67 94	85.59 89	89.39 147	68.66 132	88.74 143	92.64 92	79.97 17	84.10 118	85.71 336	69.32 131	95.38 90	80.82 123	91.37 120	92.72 177

test_fmvsmconf0.01_n		84.73 94	84.52 98	85.34 96	80.25 431	69.03 115	89.47 104	89.65 223	73.24 213	86.98 74	94.27 56	66.62 173	93.23 208	90.26 10	89.95 148	93.78 108

fmvsm_l_conf0.5_n		84.47 95	84.54 95	84.27 154	85.42 316	68.81 121	88.49 155	87.26 320	68.08 345	88.03 56	93.49 87	72.04 89	91.77 283	88.90 29	89.14 165	92.24 203

BP-MVS1		84.32 96	83.71 115	86.17 71	87.84 224	67.85 160	89.38 112	89.64 224	77.73 47	83.98 121	92.12 130	56.89 308	95.43 85	84.03 87	91.75 113	95.24 8

fmvsm_l_mol_unc0.5_2		84.30 97	84.54 95	83.56 197	88.31 198	64.89 253	84.22 317	91.07 172	73.17 216	86.68 78	95.18 28	68.96 144	91.94 277	80.86 122	92.44 98	93.84 101

E5new		84.22 98	84.12 102	84.51 133	87.60 242	65.36 230	87.45 197	92.31 105	76.51 96	83.53 131	92.26 120	69.25 135	93.50 190	79.88 138	88.26 181	94.69 41

E6new		84.22 98	84.12 102	84.52 131	87.60 242	65.36 230	87.45 197	92.30 107	76.51 96	83.53 131	92.26 120	69.26 133	93.49 192	79.88 138	88.26 181	94.69 41

E6		84.22 98	84.12 102	84.52 131	87.60 242	65.36 230	87.45 197	92.30 107	76.51 96	83.53 131	92.26 120	69.26 133	93.49 192	79.88 138	88.26 181	94.69 41

E5		84.22 98	84.12 102	84.51 133	87.60 242	65.36 230	87.45 197	92.31 105	76.51 96	83.53 131	92.26 120	69.25 135	93.50 190	79.88 138	88.26 181	94.69 41

EI-MVSNet-Vis-set		84.19 102	83.81 112	85.31 97	88.18 204	67.85 160	87.66 189	89.73 221	80.05 16	82.95 145	89.59 222	70.74 109	94.82 118	80.66 128	84.72 258	93.28 138

fmvsm_l_conf0.5_n_a		84.13 103	84.16 101	84.06 172	85.38 317	68.40 138	88.34 163	86.85 332	67.48 353	87.48 67	93.40 92	70.89 106	91.61 289	88.38 37	89.22 162	92.16 210

E4		84.10 104	83.99 108	84.45 138	87.58 250	64.99 244	86.54 240	92.25 112	76.38 106	83.37 136	92.09 131	69.88 123	93.58 179	79.78 143	88.03 192	94.77 33

fmvsm_s_conf0.5_n_2		84.04 105	84.11 106	83.81 189	86.17 298	65.00 243	86.96 219	87.28 315	74.35 174	88.25 51	94.23 59	61.82 246	92.60 242	89.85 12	88.09 189	93.84 101

test_fmvsmvis_n_1920		84.02 106	83.87 109	84.49 137	84.12 347	69.37 113	88.15 172	87.96 297	70.01 295	83.95 122	93.23 96	68.80 146	91.51 301	88.61 32	89.96 147	92.57 183

E2		84.00 107	83.87 109	84.39 141	87.70 237	64.95 245	86.40 247	92.23 113	75.85 121	83.21 138	91.78 139	70.09 118	93.55 184	79.52 147	88.05 190	94.66 49

E3		84.00 107	83.87 109	84.39 141	87.70 237	64.95 245	86.40 247	92.23 113	75.85 121	83.21 138	91.78 139	70.09 118	93.55 184	79.52 147	88.05 190	94.66 49

balanced_ft_v1		83.98 109	83.64 118	85.03 108	89.76 132	65.86 213	88.31 165	91.71 147	74.41 173	80.41 200	90.82 181	62.90 228	94.90 113	83.04 96	91.37 120	94.32 73

viewcassd2359sk11		83.89 110	83.74 114	84.34 146	87.76 232	64.91 252	86.30 251	92.22 116	75.47 132	83.04 144	91.52 153	70.15 116	93.53 187	79.26 149	87.96 193	94.57 57

nrg030		83.88 111	83.53 121	84.96 112	86.77 282	69.28 114	90.46 77	92.67 87	74.79 162	82.95 145	91.33 161	72.70 81	93.09 221	80.79 125	79.28 344	92.50 189

EI-MVSNet-UG-set		83.81 112	83.38 124	85.09 107	87.87 222	67.53 172	87.44 202	89.66 222	79.74 19	82.23 158	89.41 231	70.24 115	94.74 124	79.95 136	83.92 273	92.99 169

fmvsm_s_conf0.1_n_2		83.80 113	83.79 113	83.83 187	85.62 310	64.94 248	87.03 216	86.62 339	74.32 175	87.97 59	94.33 52	60.67 270	92.60 242	89.72 14	87.79 196	93.96 91

fmvsm_s_conf0.5_n		83.80 113	83.71 115	84.07 169	86.69 285	67.31 180	89.46 105	83.07 396	71.09 259	86.96 75	93.70 84	69.02 143	91.47 304	88.79 30	84.62 260	93.44 130

E3new		83.78 115	83.60 119	84.31 148	87.76 232	64.89 253	86.24 254	92.20 119	75.15 149	82.87 147	91.23 162	70.11 117	93.52 189	79.05 150	87.79 196	94.51 62

viewmacassd2359aftdt		83.76 116	83.66 117	84.07 169	86.59 288	64.56 259	86.88 224	91.82 140	75.72 124	83.34 137	92.15 129	68.24 155	92.88 231	79.05 150	89.15 164	94.77 33

CPTT-MVS		83.73 117	83.33 126	84.92 116	93.28 56	70.86 83	92.09 43	90.38 194	68.75 334	79.57 211	92.83 107	60.60 274	93.04 226	80.92 121	91.56 117	90.86 252

EPNet		83.72 118	82.92 134	86.14 75	84.22 345	69.48 106	91.05 66	85.27 357	81.30 6	76.83 274	91.65 146	66.09 185	95.56 76	76.00 196	93.85 77	93.38 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmanbaseed2359cas		83.66 119	83.55 120	84.00 180	86.81 280	64.53 260	86.65 234	91.75 145	74.89 158	83.15 143	91.68 144	68.74 147	92.83 236	79.02 152	89.24 161	94.63 52

patch_mono-2		83.65 120	84.54 95	80.99 291	90.06 124	65.83 214	84.21 318	88.74 276	71.60 247	85.01 92	92.44 117	74.51 57	83.50 439	82.15 109	92.15 103	93.64 119

HQP_MVS		83.64 121	83.14 127	85.14 102	90.08 120	68.71 128	91.25 62	92.44 97	79.12 29	78.92 223	91.00 176	60.42 276	95.38 90	78.71 158	86.32 225	91.33 235

fmvsm_s_conf0.5_n_a		83.63 122	83.41 123	84.28 152	86.14 299	68.12 148	89.43 107	82.87 401	70.27 290	87.27 71	93.80 82	69.09 138	91.58 291	88.21 38	83.65 281	93.14 154

casdiffseed414692147		83.62 123	83.02 130	85.40 94	87.31 261	67.50 173	88.70 145	91.72 146	76.97 80	82.77 152	91.72 142	66.85 170	93.71 177	73.06 230	88.12 188	94.98 14

Effi-MVS+		83.62 123	83.08 128	85.24 99	88.38 196	67.45 174	88.89 132	89.15 253	75.50 131	82.27 157	88.28 264	69.61 127	94.45 137	77.81 169	87.84 195	93.84 101

fmvsm_s_conf0.1_n		83.56 125	83.38 124	84.10 163	84.86 331	67.28 182	89.40 111	83.01 397	70.67 274	87.08 72	93.96 76	68.38 151	91.45 305	88.56 34	84.50 261	93.56 124

GDP-MVS		83.52 126	82.64 139	86.16 72	88.14 208	68.45 137	89.13 124	92.69 85	72.82 225	83.71 126	91.86 137	55.69 317	95.35 94	80.03 135	89.74 152	94.69 41

OPM-MVS		83.50 127	82.95 133	85.14 102	88.79 178	70.95 80	89.13 124	91.52 156	77.55 54	80.96 185	91.75 141	60.71 268	94.50 134	79.67 145	86.51 222	89.97 299
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 128	82.80 136	85.43 93	90.25 116	68.74 126	90.30 82	90.13 207	76.33 109	80.87 188	92.89 105	61.00 265	94.20 146	72.45 242	90.97 127	93.35 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 129	83.45 122	83.28 207	92.74 75	62.28 327	88.17 170	89.50 229	75.22 142	81.49 172	92.74 114	66.75 171	95.11 102	72.85 232	91.58 116	92.45 193

EPP-MVSNet		83.40 130	83.02 130	84.57 129	90.13 118	64.47 265	92.32 37	90.73 184	74.45 172	79.35 217	91.10 169	69.05 141	95.12 100	72.78 233	87.22 207	94.13 82

3Dnovator	76.31 5	83.38 131	82.31 147	86.59 63	87.94 219	72.94 31	90.64 70	92.14 126	77.21 71	75.47 305	92.83 107	58.56 290	94.72 125	73.24 228	92.71 93	92.13 211

viewdifsd2359ckpt09		83.34 132	82.55 142	85.70 84	87.64 241	67.72 165	88.43 156	91.68 149	71.91 241	81.65 170	90.68 185	67.10 168	94.75 123	76.17 192	87.70 199	94.62 54

fmvsm_s_conf0.5_n_7		83.34 132	84.03 107	81.28 282	85.73 307	65.13 238	85.40 280	89.90 214	74.96 154	82.13 160	93.89 78	66.65 172	87.92 392	86.56 60	91.05 125	90.80 253

fmvsm_s_conf0.1_n_a		83.32 134	82.99 132	84.28 152	83.79 355	68.07 150	89.34 114	82.85 402	69.80 301	87.36 70	94.06 68	68.34 153	91.56 294	87.95 46	83.46 287	93.21 143

KinetiMVS		83.31 135	82.61 141	85.39 95	87.08 273	67.56 171	88.06 174	91.65 150	77.80 46	82.21 159	91.79 138	57.27 303	94.07 152	77.77 170	89.89 150	94.56 59

EIA-MVS		83.31 135	82.80 136	84.82 121	89.59 135	65.59 222	88.21 168	92.68 86	74.66 166	78.96 221	86.42 322	69.06 140	95.26 95	75.54 203	90.09 144	93.62 120

h-mvs33		83.15 137	82.19 150	86.02 79	90.56 109	70.85 84	88.15 172	89.16 251	76.02 118	84.67 101	91.39 159	61.54 251	95.50 81	82.71 104	75.48 395	91.72 223

MVS_Test		83.15 137	83.06 129	83.41 204	86.86 277	63.21 302	86.11 258	92.00 129	74.31 176	82.87 147	89.44 230	70.03 120	93.21 210	77.39 176	88.50 178	93.81 104

IS-MVSNet		83.15 137	82.81 135	84.18 161	89.94 127	63.30 300	91.59 53	88.46 286	79.04 31	79.49 212	92.16 127	65.10 197	94.28 140	67.71 290	91.86 112	94.95 15

DP-MVS Recon		83.11 140	82.09 154	86.15 73	94.44 26	70.92 82	88.79 138	92.20 119	70.53 279	79.17 219	91.03 175	64.12 208	96.03 63	68.39 287	90.14 143	91.50 230

PRO-TEST		83.03 141	82.63 140	84.23 158	88.20 202	66.81 195	87.41 203	90.93 175	73.55 200	80.73 191	88.90 244	66.17 183	92.85 232	78.39 163	89.36 159	93.02 163

PAPM_NR		83.02 142	82.41 144	84.82 121	92.47 80	66.37 200	87.93 180	91.80 141	73.82 191	77.32 262	90.66 186	67.90 158	94.90 113	70.37 261	89.48 157	93.19 147

VDD-MVS		83.01 143	82.36 146	84.96 112	91.02 99	66.40 199	88.91 131	88.11 289	77.57 51	84.39 110	93.29 95	52.19 351	93.91 163	77.05 180	88.70 174	94.57 57

viewdifsd2359ckpt13		82.91 144	82.29 148	84.77 124	86.96 276	66.90 194	87.47 194	91.62 152	72.19 234	81.68 169	90.71 184	66.92 169	93.28 203	75.90 197	87.15 209	94.12 83

MVSFormer		82.85 145	82.05 155	85.24 99	87.35 253	70.21 91	90.50 74	90.38 194	68.55 337	81.32 175	89.47 225	61.68 248	93.46 197	78.98 155	90.26 141	92.05 213

viewdifsd2359ckpt07		82.83 146	82.78 138	82.99 226	86.51 290	62.58 318	85.09 288	90.83 181	75.22 142	82.28 156	91.63 148	69.43 129	92.03 269	77.71 171	86.32 225	94.34 71

OMC-MVS		82.69 147	81.97 158	84.85 120	88.75 181	67.42 175	87.98 176	90.87 179	74.92 157	79.72 209	91.65 146	62.19 240	93.96 155	75.26 207	86.42 223	93.16 151

PVSNet_Blended_VisFu		82.62 148	81.83 160	84.96 112	90.80 105	69.76 102	88.74 143	91.70 148	69.39 312	78.96 221	88.46 259	65.47 193	94.87 117	74.42 214	88.57 175	90.24 281

MVS_111021_LR		82.61 149	82.11 151	84.11 162	88.82 172	71.58 61	85.15 285	86.16 347	74.69 164	80.47 199	91.04 173	62.29 237	90.55 343	80.33 132	90.08 145	90.20 282

HQP-MVS		82.61 149	82.02 156	84.37 143	89.33 149	66.98 190	89.17 119	92.19 121	76.41 102	77.23 265	90.23 203	60.17 279	95.11 102	77.47 174	85.99 236	91.03 245

RRT-MVS		82.60 151	82.10 153	84.10 163	87.98 218	62.94 314	87.45 197	91.27 163	77.42 58	79.85 207	90.28 200	56.62 311	94.70 127	79.87 142	88.15 187	94.67 46

viewmamba		82.38 152	82.11 151	83.19 213	83.30 368	64.26 270	84.62 302	89.16 251	75.24 139	80.97 184	91.10 169	67.12 167	91.63 288	81.36 115	86.13 231	93.67 112

diffmvs_AUTHOR		82.38 152	82.27 149	82.73 245	83.26 370	63.80 280	83.89 325	89.76 218	73.35 208	82.37 155	90.84 179	66.25 180	90.79 335	82.77 101	87.93 194	93.59 122

CLD-MVS		82.31 154	81.65 162	84.29 151	88.47 190	67.73 164	85.81 269	92.35 103	75.78 123	78.33 238	86.58 317	64.01 210	94.35 138	76.05 195	87.48 203	90.79 254
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
onestephybrid01		82.22 155	81.81 161	83.46 199	83.16 376	64.93 251	84.64 301	89.19 250	73.95 186	81.48 173	90.63 187	66.00 189	91.92 278	80.33 132	86.93 213	93.53 127

VNet		82.21 156	82.41 144	81.62 271	90.82 104	60.93 352	84.47 306	89.78 216	76.36 108	84.07 119	91.88 135	64.71 202	90.26 348	70.68 258	88.89 167	93.66 113

diffmvs		82.10 157	81.88 159	82.76 243	83.00 382	63.78 282	83.68 330	89.76 218	72.94 222	82.02 162	89.85 209	65.96 190	90.79 335	82.38 108	87.30 206	93.71 110
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		82.08 158	81.27 165	84.50 135	89.23 157	68.76 124	90.22 83	91.94 133	75.37 136	76.64 280	91.51 154	54.29 330	94.91 111	78.44 160	83.78 274	89.83 304

FIs		82.07 159	82.42 143	81.04 290	88.80 177	58.34 385	88.26 167	93.49 39	76.93 82	78.47 235	91.04 173	69.92 122	92.34 259	69.87 271	84.97 252	92.44 194

PS-MVSNAJss		82.07 159	81.31 164	84.34 146	86.51 290	67.27 183	89.27 115	91.51 157	71.75 242	79.37 216	90.22 204	63.15 221	94.27 141	77.69 172	82.36 302	91.49 231

API-MVS		81.99 161	81.23 166	84.26 156	90.94 101	70.18 96	91.10 65	89.32 239	71.51 249	78.66 228	88.28 264	65.26 194	95.10 105	64.74 318	91.23 123	87.51 376

SSM_0404		81.91 162	80.84 175	85.13 105	89.24 156	68.26 142	87.84 185	89.25 245	71.06 261	80.62 194	90.39 197	59.57 281	94.65 129	72.45 242	87.19 208	92.47 192

UniMVSNet_NR-MVSNet		81.88 163	81.54 163	82.92 230	88.46 191	63.46 296	87.13 211	92.37 102	80.19 13	78.38 236	89.14 234	71.66 96	93.05 224	70.05 266	76.46 378	92.25 201

MAR-MVS		81.84 164	80.70 176	85.27 98	91.32 93	71.53 62	89.82 90	90.92 176	69.77 304	78.50 232	86.21 327	62.36 236	94.52 133	65.36 312	92.05 106	89.77 307
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
LFMVS		81.82 165	81.23 166	83.57 196	91.89 86	63.43 298	89.84 89	81.85 415	77.04 79	83.21 138	93.10 98	52.26 350	93.43 199	71.98 245	89.95 148	93.85 98

hse-mvs2		81.72 166	80.94 173	84.07 169	88.72 182	67.68 166	85.87 265	87.26 320	76.02 118	84.67 101	88.22 267	61.54 251	93.48 195	82.71 104	73.44 423	91.06 243

GeoE		81.71 167	81.01 172	83.80 190	89.51 139	64.45 266	88.97 129	88.73 278	71.27 255	78.63 229	89.76 215	66.32 179	93.20 213	69.89 270	86.02 235	93.74 109

xiu_mvs_v2_base		81.69 168	81.05 170	83.60 193	89.15 160	68.03 153	84.46 308	90.02 209	70.67 274	81.30 178	86.53 320	63.17 220	94.19 148	75.60 202	88.54 176	88.57 349

PS-MVSNAJ		81.69 168	81.02 171	83.70 191	89.51 139	68.21 147	84.28 316	90.09 208	70.79 270	81.26 179	85.62 341	63.15 221	94.29 139	75.62 201	88.87 168	88.59 348

PAPR		81.66 170	80.89 174	83.99 182	90.27 115	64.00 274	86.76 231	91.77 144	68.84 333	77.13 272	89.50 223	67.63 160	94.88 116	67.55 292	88.52 177	93.09 157

UniMVSNet (Re)		81.60 171	81.11 169	83.09 219	88.38 196	64.41 267	87.60 190	93.02 64	78.42 38	78.56 231	88.16 268	69.78 124	93.26 206	69.58 274	76.49 377	91.60 225

SSM_0407		81.58 172	80.48 183	84.87 119	88.81 173	67.96 155	87.37 204	89.25 245	71.06 261	79.48 213	90.39 197	59.57 281	94.48 136	72.45 242	85.93 238	92.18 206

Elysia		81.53 173	80.16 192	85.62 87	85.51 313	68.25 144	88.84 136	92.19 121	71.31 252	80.50 197	89.83 210	46.89 420	94.82 118	76.85 182	89.57 154	93.80 106

StellarMVS		81.53 173	80.16 192	85.62 87	85.51 313	68.25 144	88.84 136	92.19 121	71.31 252	80.50 197	89.83 210	46.89 420	94.82 118	76.85 182	89.57 154	93.80 106

FC-MVSNet-test		81.52 175	82.02 156	80.03 316	88.42 194	55.97 425	87.95 178	93.42 42	77.10 77	77.38 260	90.98 178	69.96 121	91.79 282	68.46 286	84.50 261	92.33 197

VDDNet		81.52 175	80.67 177	84.05 175	90.44 112	64.13 273	89.73 95	85.91 350	71.11 258	83.18 141	93.48 88	50.54 383	93.49 192	73.40 225	88.25 185	94.54 61

ACMP	74.13 6	81.51 177	80.57 180	84.36 144	89.42 144	68.69 131	89.97 87	91.50 160	74.46 171	75.04 327	90.41 195	53.82 336	94.54 131	77.56 173	82.91 294	89.86 303
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
hybridnocas07		81.44 178	81.13 168	82.37 254	82.13 404	63.11 306	83.45 339	88.74 276	72.54 226	80.71 193	90.73 182	65.14 196	90.74 340	80.35 131	86.41 224	93.27 139

jason		81.39 179	80.29 189	84.70 127	86.63 287	69.90 99	85.95 261	86.77 333	63.24 416	81.07 181	89.47 225	61.08 264	92.15 265	78.33 164	90.07 146	92.05 213
jason: jason.
lupinMVS		81.39 179	80.27 190	84.76 125	87.35 253	70.21 91	85.55 275	86.41 341	62.85 423	81.32 175	88.61 254	61.68 248	92.24 263	78.41 162	90.26 141	91.83 216

test_yl		81.17 181	80.47 184	83.24 210	89.13 161	63.62 285	86.21 255	89.95 212	72.43 231	81.78 167	89.61 220	57.50 300	93.58 179	70.75 256	86.90 214	92.52 187

DCV-MVSNet		81.17 181	80.47 184	83.24 210	89.13 161	63.62 285	86.21 255	89.95 212	72.43 231	81.78 167	89.61 220	57.50 300	93.58 179	70.75 256	86.90 214	92.52 187

guyue		81.13 183	80.64 179	82.60 248	86.52 289	63.92 278	86.69 233	87.73 305	73.97 185	80.83 190	89.69 216	56.70 309	91.33 310	78.26 168	85.40 249	92.54 185

DU-MVS		81.12 184	80.52 182	82.90 231	87.80 226	63.46 296	87.02 217	91.87 137	79.01 32	78.38 236	89.07 236	65.02 198	93.05 224	70.05 266	76.46 378	92.20 204

hybrid		81.05 185	80.66 178	82.22 258	81.97 406	62.99 311	83.42 340	88.68 279	70.76 272	80.56 196	90.40 196	64.49 205	90.48 344	79.57 146	86.06 233	93.19 147

PVSNet_Blended		80.98 186	80.34 186	82.90 231	88.85 169	65.40 226	84.43 311	92.00 129	67.62 350	78.11 243	85.05 357	66.02 187	94.27 141	71.52 247	89.50 156	89.01 329

FA-MVS(test-final)		80.96 187	79.91 199	84.10 163	88.30 200	65.01 242	84.55 305	90.01 210	73.25 212	79.61 210	87.57 284	58.35 292	94.72 125	71.29 251	86.25 228	92.56 184

QAPM		80.88 188	79.50 213	85.03 108	88.01 217	68.97 119	91.59 53	92.00 129	66.63 367	75.15 323	92.16 127	57.70 297	95.45 83	63.52 324	88.76 172	90.66 261

TranMVSNet+NR-MVSNet		80.84 189	80.31 187	82.42 252	87.85 223	62.33 325	87.74 188	91.33 162	80.55 9	77.99 247	89.86 208	65.23 195	92.62 240	67.05 299	75.24 405	92.30 199

UGNet		80.83 190	79.59 211	84.54 130	88.04 214	68.09 149	89.42 109	88.16 288	76.95 81	76.22 291	89.46 227	49.30 402	93.94 158	68.48 285	90.31 139	91.60 225
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
AstraMVS		80.81 191	80.14 194	82.80 237	86.05 302	63.96 275	86.46 243	85.90 351	73.71 194	80.85 189	90.56 191	54.06 334	91.57 293	79.72 144	83.97 272	92.86 174

Fast-Effi-MVS+		80.81 191	79.92 198	83.47 198	88.85 169	64.51 262	85.53 277	89.39 233	70.79 270	78.49 233	85.06 356	67.54 161	93.58 179	67.03 300	86.58 220	92.32 198

XVG-OURS-SEG-HR		80.81 191	79.76 204	83.96 184	85.60 311	68.78 123	83.54 338	90.50 190	70.66 277	76.71 278	91.66 145	60.69 269	91.26 311	76.94 181	81.58 312	91.83 216

IMVS_0403		80.80 194	80.12 195	82.87 233	87.13 267	63.59 289	85.19 282	89.33 235	70.51 280	78.49 233	89.03 238	63.26 217	93.27 205	72.56 238	85.56 245	91.74 219

xiu_mvs_v1_base_debu		80.80 194	79.72 207	84.03 177	87.35 253	70.19 93	85.56 272	88.77 270	69.06 325	81.83 163	88.16 268	50.91 376	92.85 232	78.29 165	87.56 200	89.06 324

xiu_mvs_v1_base		80.80 194	79.72 207	84.03 177	87.35 253	70.19 93	85.56 272	88.77 270	69.06 325	81.83 163	88.16 268	50.91 376	92.85 232	78.29 165	87.56 200	89.06 324

xiu_mvs_v1_base_debi		80.80 194	79.72 207	84.03 177	87.35 253	70.19 93	85.56 272	88.77 270	69.06 325	81.83 163	88.16 268	50.91 376	92.85 232	78.29 165	87.56 200	89.06 324

ACMM	73.20 8	80.78 198	79.84 202	83.58 195	89.31 152	68.37 139	89.99 86	91.60 154	70.28 289	77.25 263	89.66 218	53.37 341	93.53 187	74.24 217	82.85 295	88.85 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		80.68 199	79.62 210	83.83 187	85.07 328	68.01 154	86.99 218	88.83 267	70.36 285	81.38 174	87.99 275	50.11 388	92.51 249	79.02 152	86.89 216	90.97 248

114514_t		80.68 199	79.51 212	84.20 160	94.09 45	67.27 183	89.64 98	91.11 170	58.75 466	74.08 342	90.72 183	58.10 293	95.04 108	69.70 272	89.42 158	90.30 279

IMVS_0407		80.61 201	79.90 200	82.75 244	87.13 267	63.59 289	85.33 281	89.33 235	70.51 280	77.82 249	89.03 238	61.84 244	92.91 229	72.56 238	85.56 245	91.74 219

CANet_DTU		80.61 201	79.87 201	82.83 234	85.60 311	63.17 305	87.36 205	88.65 282	76.37 107	75.88 298	88.44 260	53.51 339	93.07 222	73.30 226	89.74 152	92.25 201

VPA-MVSNet		80.60 203	80.55 181	80.76 297	88.07 213	60.80 355	86.86 225	91.58 155	75.67 128	80.24 202	89.45 229	63.34 214	90.25 349	70.51 260	79.22 345	91.23 238

mvsmamba		80.60 203	79.38 216	84.27 154	89.74 133	67.24 185	87.47 194	86.95 328	70.02 294	75.38 311	88.93 243	51.24 373	92.56 245	75.47 205	89.22 162	93.00 166

PVSNet_BlendedMVS		80.60 203	80.02 196	82.36 255	88.85 169	65.40 226	86.16 257	92.00 129	69.34 314	78.11 243	86.09 331	66.02 187	94.27 141	71.52 247	82.06 305	87.39 379

AdaColmap		80.58 206	79.42 214	84.06 172	93.09 66	68.91 120	89.36 113	88.97 263	69.27 316	75.70 301	89.69 216	57.20 305	95.77 72	63.06 333	88.41 180	87.50 377

EI-MVSNet		80.52 207	79.98 197	82.12 259	84.28 343	63.19 304	86.41 244	88.95 264	74.18 181	78.69 226	87.54 287	66.62 173	92.43 253	72.57 236	80.57 326	90.74 258

viewmambaseed2359dif		80.41 208	79.84 202	82.12 259	82.95 388	62.50 321	83.39 341	88.06 293	67.11 356	80.98 183	90.31 199	66.20 182	91.01 325	74.62 211	84.90 253	92.86 174

XVG-OURS		80.41 208	79.23 222	83.97 183	85.64 309	69.02 117	83.03 354	90.39 193	71.09 259	77.63 255	91.49 156	54.62 329	91.35 308	75.71 199	83.47 286	91.54 228

SDMVSNet		80.38 210	80.18 191	80.99 291	89.03 166	64.94 248	80.45 394	89.40 232	75.19 146	76.61 282	89.98 206	60.61 273	87.69 396	76.83 185	83.55 283	90.33 277

PCF-MVS	73.52 7	80.38 210	78.84 231	85.01 110	87.71 235	68.99 118	83.65 331	91.46 161	63.00 420	77.77 253	90.28 200	66.10 184	95.09 106	61.40 362	88.22 186	90.94 250
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
viewdifsd2359ckpt11		80.37 212	79.73 205	82.30 256	83.70 359	62.39 322	84.20 319	86.67 335	73.22 214	80.90 186	90.62 188	63.00 226	91.56 294	76.81 186	78.44 351	92.95 171

viewmsd2359difaftdt		80.37 212	79.73 205	82.30 256	83.70 359	62.39 322	84.20 319	86.67 335	73.22 214	80.90 186	90.62 188	63.00 226	91.56 294	76.81 186	78.44 351	92.95 171

X-MVStestdata		80.37 212	77.83 254	88.00 18	94.42 27	73.33 22	92.78 25	92.99 68	79.14 27	83.67 128	12.47 543	67.45 162	96.60 45	83.06 94	94.50 66	94.07 86

test_djsdf		80.30 215	79.32 219	83.27 208	83.98 351	65.37 229	90.50 74	90.38 194	68.55 337	76.19 292	88.70 250	56.44 312	93.46 197	78.98 155	80.14 332	90.97 248

v2v482		80.23 216	79.29 220	83.05 223	83.62 361	64.14 272	87.04 215	89.97 211	73.61 197	78.18 242	87.22 295	61.10 263	93.82 168	76.11 193	76.78 374	91.18 239

NR-MVSNet		80.23 216	79.38 216	82.78 241	87.80 226	63.34 299	86.31 250	91.09 171	79.01 32	72.17 369	89.07 236	67.20 165	92.81 237	66.08 306	75.65 391	92.20 204

Anonymous20240529		80.19 218	78.89 230	84.10 163	90.60 108	64.75 257	88.95 130	90.90 177	65.97 377	80.59 195	91.17 168	49.97 390	93.73 176	69.16 278	82.70 299	93.81 104

testing915		80.13 219	80.30 188	79.64 334	89.00 168	58.38 383	87.08 214	84.16 377	74.04 184	80.14 205	89.37 233	64.04 209	90.08 352	66.04 307	88.82 169	90.45 271

IterMVS-LS		80.06 220	79.38 216	82.11 261	85.89 303	63.20 303	86.79 228	89.34 234	74.19 180	75.45 308	86.72 307	66.62 173	92.39 255	72.58 235	76.86 371	90.75 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dtuplus		80.04 221	79.40 215	81.97 265	83.08 378	62.61 317	83.63 334	87.98 295	67.47 354	81.02 182	90.50 194	64.86 201	90.77 338	71.28 252	84.76 257	92.53 186

Effi-MVS+-dtu		80.03 222	78.57 235	84.42 140	85.13 326	68.74 126	88.77 139	88.10 290	74.99 151	74.97 329	83.49 394	57.27 303	93.36 201	73.53 222	80.88 320	91.18 239

v1144		80.03 222	79.03 226	83.01 225	83.78 356	64.51 262	87.11 213	90.57 189	71.96 240	78.08 245	86.20 328	61.41 255	93.94 158	74.93 209	77.23 365	90.60 264

v8		79.97 224	79.02 227	82.80 237	84.09 348	64.50 264	87.96 177	90.29 201	74.13 183	75.24 320	86.81 304	62.88 229	93.89 166	74.39 215	75.40 400	90.00 295

OpenMVS	72.83 10	79.77 225	78.33 242	84.09 167	85.17 322	69.91 98	90.57 71	90.97 174	66.70 361	72.17 369	91.91 133	54.70 327	93.96 155	61.81 357	90.95 129	88.41 353

v10		79.74 226	78.67 232	82.97 229	84.06 349	64.95 245	87.88 183	90.62 186	73.11 217	75.11 324	86.56 318	61.46 254	94.05 153	73.68 220	75.55 393	89.90 301

ECVR-MVS		79.61 227	79.26 221	80.67 299	90.08 120	54.69 441	87.89 182	77.44 465	74.88 159	80.27 201	92.79 110	48.96 408	92.45 252	68.55 284	92.50 96	94.86 25

BH-RMVSNet		79.61 227	78.44 238	83.14 216	89.38 148	65.93 210	84.95 292	87.15 323	73.56 199	78.19 241	89.79 214	56.67 310	93.36 201	59.53 379	86.74 218	90.13 285

v1192		79.59 229	78.43 239	83.07 222	83.55 363	64.52 261	86.93 222	90.58 187	70.83 268	77.78 252	85.90 332	59.15 285	93.94 158	73.96 219	77.19 367	90.76 256

ab-mvs		79.51 230	78.97 228	81.14 287	88.46 191	60.91 353	83.84 326	89.24 247	70.36 285	79.03 220	88.87 247	63.23 219	90.21 350	65.12 314	82.57 300	92.28 200

WR-MVS		79.49 231	79.22 223	80.27 309	88.79 178	58.35 384	85.06 289	88.61 284	78.56 36	77.65 254	88.34 262	63.81 213	90.66 342	64.98 316	77.22 366	91.80 218

v144192		79.47 232	78.37 240	82.78 241	83.35 366	63.96 275	86.96 219	90.36 197	69.99 296	77.50 257	85.67 339	60.66 271	93.77 172	74.27 216	76.58 375	90.62 262

BH-untuned		79.47 232	78.60 234	82.05 262	89.19 159	65.91 211	86.07 259	88.52 285	72.18 235	75.42 309	87.69 281	61.15 262	93.54 186	60.38 370	86.83 217	86.70 406

test1111		79.43 234	79.18 224	80.15 314	89.99 125	53.31 454	87.33 207	77.05 469	75.04 150	80.23 203	92.77 113	48.97 407	92.33 260	68.87 281	92.40 99	94.81 30

mvs_anonymous		79.42 235	79.11 225	80.34 307	84.45 342	57.97 391	82.59 356	87.62 307	67.40 355	76.17 295	88.56 257	68.47 150	89.59 362	70.65 259	86.05 234	93.47 129

thisisatest0530		79.40 236	77.76 259	84.31 148	87.69 239	65.10 241	87.36 205	84.26 375	70.04 293	77.42 259	88.26 266	49.94 391	94.79 122	70.20 264	84.70 259	93.03 162

tttt0517		79.40 236	77.91 250	83.90 186	88.10 211	63.84 279	88.37 162	84.05 378	71.45 250	76.78 276	89.12 235	49.93 393	94.89 115	70.18 265	83.18 292	92.96 170

V42		79.38 238	78.24 244	82.83 234	81.10 423	65.50 224	85.55 275	89.82 215	71.57 248	78.21 240	86.12 330	60.66 271	93.18 216	75.64 200	75.46 397	89.81 306

mamba_0408		79.37 239	77.52 266	84.93 115	88.81 173	67.96 155	65.03 500	88.66 280	70.96 265	79.48 213	89.80 212	58.69 287	94.65 129	70.35 262	85.93 238	92.18 206

jajsoiax		79.29 240	77.96 248	83.27 208	84.68 336	66.57 198	89.25 116	90.16 205	69.20 321	75.46 307	89.49 224	45.75 438	93.13 219	76.84 184	80.80 322	90.11 287

v1921920		79.22 241	78.03 247	82.80 237	83.30 368	63.94 277	86.80 227	90.33 198	69.91 299	77.48 258	85.53 343	58.44 291	93.75 174	73.60 221	76.85 372	90.71 260

AUN-MVS		79.21 242	77.60 264	84.05 175	88.71 183	67.61 168	85.84 267	87.26 320	69.08 324	77.23 265	88.14 272	53.20 343	93.47 196	75.50 204	73.45 422	91.06 243

TAPA-MVS	73.13 9	79.15 243	77.94 249	82.79 240	89.59 135	62.99 311	88.16 171	91.51 157	65.77 378	77.14 271	91.09 171	60.91 266	93.21 210	50.26 449	87.05 211	92.17 209
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 244	77.77 258	83.22 212	84.70 335	66.37 200	89.17 119	90.19 204	69.38 313	75.40 310	89.46 227	44.17 450	93.15 217	76.78 188	80.70 324	90.14 284

UniMVSNet_ETH3D		79.10 245	78.24 244	81.70 270	86.85 278	60.24 367	87.28 209	88.79 269	74.25 179	76.84 273	90.53 193	49.48 397	91.56 294	67.98 288	82.15 303	93.29 137

CDS-MVSNet		79.07 246	77.70 261	83.17 215	87.60 242	68.23 146	84.40 314	86.20 346	67.49 352	76.36 288	86.54 319	61.54 251	90.79 335	61.86 356	87.33 205	90.49 269
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 247	77.88 253	82.38 253	83.07 379	64.80 256	84.08 324	88.95 264	69.01 328	78.69 226	87.17 298	54.70 327	92.43 253	74.69 210	80.57 326	89.89 302

v1240		78.99 248	77.78 257	82.64 246	83.21 372	63.54 293	86.62 236	90.30 200	69.74 307	77.33 261	85.68 338	57.04 306	93.76 173	73.13 229	76.92 369	90.62 262

Anonymous20231211		78.97 249	77.69 262	82.81 236	90.54 110	64.29 269	90.11 85	91.51 157	65.01 395	76.16 296	88.13 273	50.56 382	93.03 227	69.68 273	77.56 364	91.11 241

v7n		78.97 249	77.58 265	83.14 216	83.45 365	65.51 223	88.32 164	91.21 165	73.69 195	72.41 365	86.32 325	57.93 294	93.81 169	69.18 277	75.65 391	90.11 287

icg_test_0407_2		78.92 251	78.93 229	78.90 349	87.13 267	63.59 289	76.58 444	89.33 235	70.51 280	77.82 249	89.03 238	61.84 244	81.38 456	72.56 238	85.56 245	91.74 219

TAMVS		78.89 252	77.51 268	83.03 224	87.80 226	67.79 163	84.72 296	85.05 362	67.63 349	76.75 277	87.70 280	62.25 238	90.82 334	58.53 391	87.13 210	90.49 269

c3_l		78.75 253	77.91 250	81.26 283	82.89 389	61.56 339	84.09 323	89.13 255	69.97 297	75.56 303	84.29 371	66.36 178	92.09 268	73.47 224	75.48 395	90.12 286

tt0805		78.73 254	77.83 254	81.43 276	85.17 322	60.30 366	89.41 110	90.90 177	71.21 256	77.17 270	88.73 249	46.38 427	93.21 210	72.57 236	78.96 346	90.79 254

v148		78.72 255	77.80 256	81.47 275	82.73 392	61.96 333	86.30 251	88.08 291	73.26 211	76.18 293	85.47 345	62.46 234	92.36 257	71.92 246	73.82 419	90.09 289

VPNet		78.69 256	78.66 233	78.76 351	88.31 198	55.72 429	84.45 309	86.63 338	76.79 86	78.26 239	90.55 192	59.30 284	89.70 361	66.63 301	77.05 368	90.88 251

ET-MVSNet_ETH3D		78.63 257	76.63 289	84.64 128	86.73 283	69.47 107	85.01 290	84.61 368	69.54 309	66.51 445	86.59 315	50.16 387	91.75 284	76.26 191	84.24 269	92.69 180

anonymousdsp		78.60 258	77.15 274	82.98 228	80.51 429	67.08 188	87.24 210	89.53 228	65.66 381	75.16 322	87.19 297	52.52 345	92.25 262	77.17 178	79.34 343	89.61 311

miper_ehance_all_eth		78.59 259	77.76 259	81.08 289	82.66 394	61.56 339	83.65 331	89.15 253	68.87 332	75.55 304	83.79 385	66.49 176	92.03 269	73.25 227	76.39 380	89.64 310

VortexMVS		78.57 260	77.89 252	80.59 300	85.89 303	62.76 316	85.61 270	89.62 225	72.06 238	74.99 328	85.38 347	55.94 316	90.77 338	74.99 208	76.58 375	88.23 357

WR-MVS_H		78.51 261	78.49 236	78.56 356	88.02 215	56.38 419	88.43 156	92.67 87	77.14 73	73.89 344	87.55 286	66.25 180	89.24 369	58.92 386	73.55 421	90.06 293

GBi-Net		78.40 262	77.40 269	81.40 278	87.60 242	63.01 307	88.39 159	89.28 241	71.63 244	75.34 313	87.28 291	54.80 323	91.11 317	62.72 338	79.57 336	90.09 289

test1		78.40 262	77.40 269	81.40 278	87.60 242	63.01 307	88.39 159	89.28 241	71.63 244	75.34 313	87.28 291	54.80 323	91.11 317	62.72 338	79.57 336	90.09 289

Vis-MVSNet (Re-imp)		78.36 264	78.45 237	78.07 368	88.64 185	51.78 466	86.70 232	79.63 447	74.14 182	75.11 324	90.83 180	61.29 259	89.75 359	58.10 396	91.60 114	92.69 180

Anonymous202405211		78.25 265	77.01 276	81.99 264	91.03 98	60.67 359	84.77 295	83.90 380	70.65 278	80.00 206	91.20 166	41.08 471	91.43 306	65.21 313	85.26 250	93.85 98

CP-MVSNet		78.22 266	78.34 241	77.84 372	87.83 225	54.54 443	87.94 179	91.17 167	77.65 48	73.48 350	88.49 258	62.24 239	88.43 386	62.19 349	74.07 414	90.55 266

BH-w/o		78.21 267	77.33 272	80.84 295	88.81 173	65.13 238	84.87 293	87.85 302	69.75 305	74.52 337	84.74 363	61.34 257	93.11 220	58.24 395	85.84 241	84.27 448

FMVSNet2		78.20 268	77.21 273	81.20 285	87.60 242	62.89 315	87.47 194	89.02 259	71.63 244	75.29 319	87.28 291	54.80 323	91.10 320	62.38 346	79.38 342	89.61 311

MVS		78.19 269	76.99 278	81.78 268	85.66 308	66.99 189	84.66 298	90.47 191	55.08 489	72.02 372	85.27 349	63.83 212	94.11 151	66.10 305	89.80 151	84.24 449

Baseline_NR-MVSNet		78.15 270	78.33 242	77.61 378	85.79 305	56.21 423	86.78 229	85.76 353	73.60 198	77.93 248	87.57 284	65.02 198	88.99 374	67.14 298	75.33 402	87.63 370

CNLPA		78.08 271	76.79 283	81.97 265	90.40 113	71.07 76	87.59 191	84.55 369	66.03 375	72.38 366	89.64 219	57.56 299	86.04 413	59.61 378	83.35 288	88.79 340

cl22		78.07 272	77.01 276	81.23 284	82.37 402	61.83 335	83.55 336	87.98 295	68.96 331	75.06 326	83.87 381	61.40 256	91.88 280	73.53 222	76.39 380	89.98 298

PLC	70.83 11	78.05 273	76.37 295	83.08 221	91.88 87	67.80 162	88.19 169	89.46 230	64.33 404	69.87 397	88.38 261	53.66 337	93.58 179	58.86 387	82.73 297	87.86 366
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 274	76.49 290	82.62 247	83.16 376	66.96 192	86.94 221	87.45 312	72.45 228	71.49 378	84.17 378	54.79 326	91.58 291	67.61 291	80.31 329	89.30 320

PS-CasMVS		78.01 275	78.09 246	77.77 374	87.71 235	54.39 445	88.02 175	91.22 164	77.50 56	73.26 352	88.64 253	60.73 267	88.41 387	61.88 355	73.88 418	90.53 267

HY-MVS	69.67 12	77.95 276	77.15 274	80.36 306	87.57 251	60.21 368	83.37 343	87.78 304	66.11 372	75.37 312	87.06 302	63.27 216	90.48 344	61.38 363	82.43 301	90.40 274

eth_miper_zixun_eth		77.92 277	76.69 287	81.61 273	83.00 382	61.98 332	83.15 347	89.20 249	69.52 310	74.86 331	84.35 370	61.76 247	92.56 245	71.50 249	72.89 427	90.28 280

FMVSNet3		77.88 278	76.85 281	80.97 293	86.84 279	62.36 324	86.52 241	88.77 270	71.13 257	75.34 313	86.66 313	54.07 333	91.10 320	62.72 338	79.57 336	89.45 315

miper_enhance_ethall		77.87 279	76.86 280	80.92 294	81.65 411	61.38 343	82.68 355	88.98 261	65.52 383	75.47 305	82.30 415	65.76 192	92.00 272	72.95 231	76.39 380	89.39 317

FE-MVS		77.78 280	75.68 302	84.08 168	88.09 212	66.00 208	83.13 348	87.79 303	68.42 342	78.01 246	85.23 351	45.50 441	95.12 100	59.11 384	85.83 242	91.11 241

PEN-MVS		77.73 281	77.69 262	77.84 372	87.07 275	53.91 448	87.91 181	91.18 166	77.56 53	73.14 354	88.82 248	61.23 260	89.17 371	59.95 374	72.37 429	90.43 272

cl____		77.72 282	76.76 284	80.58 301	82.49 399	60.48 363	83.09 350	87.87 300	69.22 319	74.38 340	85.22 352	62.10 241	91.53 299	71.09 253	75.41 399	89.73 309

DIV-MVS_self_test		77.72 282	76.76 284	80.58 301	82.48 400	60.48 363	83.09 350	87.86 301	69.22 319	74.38 340	85.24 350	62.10 241	91.53 299	71.09 253	75.40 400	89.74 308

sd_testset		77.70 284	77.40 269	78.60 354	89.03 166	60.02 369	79.00 416	85.83 352	75.19 146	76.61 282	89.98 206	54.81 322	85.46 421	62.63 342	83.55 283	90.33 277

PAPM		77.68 285	76.40 294	81.51 274	87.29 263	61.85 334	83.78 327	89.59 226	64.74 397	71.23 380	88.70 250	62.59 231	93.66 178	52.66 433	87.03 212	89.01 329

SSM_04072		77.67 286	77.52 266	78.12 366	88.81 173	67.96 155	65.03 500	88.66 280	70.96 265	79.48 213	89.80 212	58.69 287	74.23 497	70.35 262	85.93 238	92.18 206

FBQ-MVS		77.66 287	76.04 298	82.50 250	88.78 180	63.76 283	86.60 237	84.86 364	70.85 267	77.63 255	82.83 407	47.83 413	92.10 267	60.18 373	84.82 256	91.65 224

CHOSEN 1792x2688		77.63 288	75.69 301	83.44 201	89.98 126	68.58 134	78.70 421	87.50 310	56.38 483	75.80 300	86.84 303	58.67 289	91.40 307	61.58 360	85.75 243	90.34 276

HyFIR lowres test		77.53 289	75.40 309	83.94 185	89.59 135	66.62 196	80.36 395	88.64 283	56.29 484	76.45 285	85.17 353	57.64 298	93.28 203	61.34 364	83.10 293	91.91 215

FMVSNet1		77.44 290	76.12 297	81.40 278	86.81 280	63.01 307	88.39 159	89.28 241	70.49 284	74.39 339	87.28 291	49.06 406	91.11 317	60.91 366	78.52 349	90.09 289

TR-MVS		77.44 290	76.18 296	81.20 285	88.24 201	63.24 301	84.61 303	86.40 342	67.55 351	77.81 251	86.48 321	54.10 332	93.15 217	57.75 399	82.72 298	87.20 389

1112_ss		77.40 292	76.43 292	80.32 308	89.11 165	60.41 365	83.65 331	87.72 306	62.13 435	73.05 355	86.72 307	62.58 232	89.97 355	62.11 352	80.80 322	90.59 265

thisisatest0515		77.33 293	75.38 310	83.18 214	85.27 321	63.80 280	82.11 364	83.27 390	65.06 393	75.91 297	83.84 383	49.54 396	94.27 141	67.24 296	86.19 229	91.48 232

test2506		77.30 294	76.49 290	79.74 329	90.08 120	52.02 460	87.86 184	63.10 514	74.88 159	80.16 204	92.79 110	38.29 489	92.35 258	68.74 283	92.50 96	94.86 25

pm-mvs1		77.25 295	76.68 288	78.93 348	84.22 345	58.62 381	86.41 244	88.36 287	71.37 251	73.31 351	88.01 274	61.22 261	89.15 372	64.24 322	73.01 426	89.03 328

IMVS_0404		77.16 296	76.42 293	79.37 340	87.13 267	63.59 289	77.12 441	89.33 235	70.51 280	66.22 448	89.03 238	50.36 385	82.78 444	72.56 238	85.56 245	91.74 219

LCM-MVSNet-Re		77.05 297	76.94 279	77.36 382	87.20 264	51.60 467	80.06 400	80.46 433	75.20 145	67.69 424	86.72 307	62.48 233	88.98 375	63.44 326	89.25 160	91.51 229

DTE-MVSNet		76.99 298	76.80 282	77.54 381	86.24 295	53.06 458	87.52 192	90.66 185	77.08 78	72.50 363	88.67 252	60.48 275	89.52 363	57.33 403	70.74 441	90.05 294

baseline1		76.98 299	76.75 286	77.66 376	88.13 209	55.66 430	85.12 286	81.89 413	73.04 219	76.79 275	88.90 244	62.43 235	87.78 395	63.30 328	71.18 439	89.55 313

LS3D		76.95 300	74.82 320	83.37 205	90.45 111	67.36 179	89.15 123	86.94 329	61.87 438	69.52 400	90.61 190	51.71 366	94.53 132	46.38 471	86.71 219	88.21 359

GA-MVS		76.87 301	75.17 317	81.97 265	82.75 391	62.58 318	81.44 376	86.35 344	72.16 237	74.74 332	82.89 405	46.20 432	92.02 271	68.85 282	81.09 317	91.30 237

DP-MVS		76.78 302	74.57 323	83.42 202	93.29 55	69.46 109	88.55 153	83.70 382	63.98 410	70.20 388	88.89 246	54.01 335	94.80 121	46.66 468	81.88 309	86.01 419

cascas		76.72 303	74.64 322	82.99 226	85.78 306	65.88 212	82.33 360	89.21 248	60.85 444	72.74 359	81.02 428	47.28 416	93.75 174	67.48 293	85.02 251	89.34 319

testing91		76.54 304	75.66 304	79.18 345	88.43 193	55.89 426	81.08 381	83.00 398	73.76 193	75.34 313	84.29 371	46.20 432	90.07 353	64.33 320	84.50 261	91.58 227

1314		76.53 305	75.30 315	80.21 312	83.93 352	62.32 326	84.66 298	88.81 268	60.23 449	70.16 391	84.07 380	55.30 320	90.73 341	67.37 294	83.21 291	87.59 373

thres100view900		76.50 306	75.55 306	79.33 341	89.52 138	56.99 408	85.83 268	83.23 391	73.94 188	76.32 289	87.12 299	51.89 362	91.95 274	48.33 459	83.75 277	89.07 322

thres600view7		76.50 306	75.44 307	79.68 332	89.40 146	57.16 405	85.53 277	83.23 391	73.79 192	76.26 290	87.09 300	51.89 362	91.89 279	48.05 464	83.72 280	90.00 295

thres400		76.50 306	75.37 311	79.86 322	89.13 161	57.65 399	85.17 283	83.60 383	73.41 206	76.45 285	86.39 323	52.12 352	91.95 274	48.33 459	83.75 277	90.00 295

MonoMVSNet		76.49 309	75.80 299	78.58 355	81.55 414	58.45 382	86.36 249	86.22 345	74.87 161	74.73 333	83.73 387	51.79 365	88.73 380	70.78 255	72.15 432	88.55 350

usedtu_dtu_shiyan1		76.43 310	75.32 313	79.76 327	83.00 382	60.72 356	81.74 368	88.76 274	68.99 329	72.98 356	84.19 376	56.41 313	90.27 346	62.39 344	79.40 340	88.31 354

FE-MVSNET3		76.43 310	75.32 313	79.76 327	83.00 382	60.72 356	81.74 368	88.76 274	68.99 329	72.98 356	84.19 376	56.41 313	90.27 346	62.39 344	79.40 340	88.31 354

tfpn200view9		76.42 312	75.37 311	79.55 338	89.13 161	57.65 399	85.17 283	83.60 383	73.41 206	76.45 285	86.39 323	52.12 352	91.95 274	48.33 459	83.75 277	89.07 322

Test_1112_low_res		76.40 313	75.44 307	79.27 342	89.28 154	58.09 387	81.69 371	87.07 326	59.53 457	72.48 364	86.67 312	61.30 258	89.33 366	60.81 368	80.15 331	90.41 273

F-COLMAP		76.38 314	74.33 329	82.50 250	89.28 154	66.95 193	88.41 158	89.03 258	64.05 408	66.83 437	88.61 254	46.78 422	92.89 230	57.48 400	78.55 348	87.67 369

LTVRE_ROB	69.57 13	76.25 315	74.54 325	81.41 277	88.60 186	64.38 268	79.24 411	89.12 256	70.76 272	69.79 399	87.86 277	49.09 405	93.20 213	56.21 415	80.16 330	86.65 408
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
MVP-Stereo		76.12 316	74.46 327	81.13 288	85.37 318	69.79 100	84.42 313	87.95 298	65.03 394	67.46 428	85.33 348	53.28 342	91.73 286	58.01 397	83.27 290	81.85 475
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 317	74.27 330	81.62 271	83.20 373	64.67 258	83.60 335	89.75 220	69.75 305	71.85 373	87.09 300	32.78 505	92.11 266	69.99 269	80.43 328	88.09 361

testing99		76.09 318	75.12 318	79.00 346	88.16 206	55.50 432	80.79 385	81.40 420	73.30 210	75.17 321	84.27 374	44.48 447	90.02 354	64.28 321	84.22 270	91.48 232

ACMH+	68.96 14	76.01 319	74.01 331	82.03 263	88.60 186	65.31 234	88.86 133	87.55 308	70.25 291	67.75 423	87.47 289	41.27 469	93.19 215	58.37 393	75.94 388	87.60 371

ACMH	67.68 16	75.89 320	73.93 333	81.77 269	88.71 183	66.61 197	88.62 149	89.01 260	69.81 300	66.78 438	86.70 311	41.95 466	91.51 301	55.64 416	78.14 357	87.17 391
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 321	73.36 342	83.31 206	84.76 334	66.03 205	83.38 342	85.06 361	70.21 292	69.40 401	81.05 427	45.76 437	94.66 128	65.10 315	75.49 394	89.25 321
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
baseline2		75.70 322	73.83 336	81.30 281	83.26 370	61.79 336	82.57 357	80.65 428	66.81 358	66.88 436	83.42 395	57.86 296	92.19 264	63.47 325	79.57 336	89.91 300

WTY-MVS		75.65 323	75.68 302	75.57 399	86.40 293	56.82 410	77.92 434	82.40 406	65.10 392	76.18 293	87.72 279	63.13 224	80.90 459	60.31 371	81.96 306	89.00 331

thres200		75.55 324	74.47 326	78.82 350	87.78 229	57.85 394	83.07 352	83.51 386	72.44 230	75.84 299	84.42 366	52.08 355	91.75 284	47.41 466	83.64 282	86.86 401

test_vis1_n_1920		75.52 325	75.78 300	74.75 413	79.84 438	57.44 403	83.26 345	85.52 355	62.83 424	79.34 218	86.17 329	45.10 443	79.71 463	78.75 157	81.21 316	87.10 397

EPNet_dtu		75.46 326	74.86 319	77.23 385	82.57 397	54.60 442	86.89 223	83.09 395	71.64 243	66.25 447	85.86 334	55.99 315	88.04 391	54.92 421	86.55 221	89.05 327
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 327	73.87 335	80.11 315	82.69 393	64.85 255	81.57 373	83.47 387	69.16 322	70.49 385	84.15 379	51.95 358	88.15 389	69.23 276	72.14 433	87.34 384

XXY-MVS		75.41 328	75.56 305	74.96 408	83.59 362	57.82 395	80.59 391	83.87 381	66.54 368	74.93 330	88.31 263	63.24 218	80.09 462	62.16 350	76.85 372	86.97 399

reproduce_monomvs		75.40 329	74.38 328	78.46 361	83.92 353	57.80 396	83.78 327	86.94 329	73.47 204	72.25 368	84.47 365	38.74 485	89.27 368	75.32 206	70.53 442	88.31 354

TransMVSNet (Re)		75.39 330	74.56 324	77.86 371	85.50 315	57.10 407	86.78 229	86.09 349	72.17 236	71.53 377	87.34 290	63.01 225	89.31 367	56.84 409	61.83 484	87.17 391

CostFormer		75.24 331	73.90 334	79.27 342	82.65 395	58.27 386	80.80 384	82.73 404	61.57 439	75.33 317	83.13 400	55.52 318	91.07 323	64.98 316	78.34 356	88.45 351

testing11		75.14 332	74.01 331	78.53 358	88.16 206	56.38 419	80.74 388	80.42 435	70.67 274	72.69 362	83.72 388	43.61 454	89.86 356	62.29 348	83.76 276	89.36 318

testing3-2		75.12 333	75.19 316	74.91 409	90.40 113	45.09 497	80.29 397	78.42 457	78.37 41	76.54 284	87.75 278	44.36 448	87.28 401	57.04 406	83.49 285	92.37 195

D2MVS		74.82 334	73.21 343	79.64 334	79.81 439	62.56 320	80.34 396	87.35 314	64.37 403	68.86 407	82.66 410	46.37 428	90.10 351	67.91 289	81.24 315	86.25 412

pmmvs6		74.69 335	73.39 340	78.61 353	81.38 418	57.48 402	86.64 235	87.95 298	64.99 396	70.18 389	86.61 314	50.43 384	89.52 363	62.12 351	70.18 444	88.83 338

SD_0403		74.65 336	74.77 321	74.29 417	86.20 297	47.42 486	83.71 329	85.12 359	69.30 315	68.50 414	87.95 276	59.40 283	86.05 412	49.38 453	83.35 288	89.40 316

tfpnnormal		74.39 337	73.16 344	78.08 367	86.10 301	58.05 388	84.65 300	87.53 309	70.32 288	71.22 381	85.63 340	54.97 321	89.86 356	43.03 485	75.02 407	86.32 411

IterMVS		74.29 338	72.94 347	78.35 362	81.53 415	63.49 295	81.58 372	82.49 405	68.06 346	69.99 394	83.69 389	51.66 367	85.54 419	65.85 309	71.64 436	86.01 419
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 339	72.42 353	79.80 324	83.76 357	59.59 374	85.92 263	86.64 337	66.39 369	66.96 435	87.58 283	39.46 480	91.60 290	65.76 310	69.27 447	88.22 358

SCA		74.22 340	72.33 354	79.91 320	84.05 350	62.17 328	79.96 403	79.29 451	66.30 370	72.38 366	80.13 440	51.95 358	88.60 383	59.25 382	77.67 363	88.96 333

mmtdpeth		74.16 341	73.01 346	77.60 380	83.72 358	61.13 345	85.10 287	85.10 360	72.06 238	77.21 269	80.33 437	43.84 452	85.75 415	77.14 179	52.61 504	85.91 422

miper_lstm_enhance		74.11 342	73.11 345	77.13 386	80.11 434	59.62 373	72.23 470	86.92 331	66.76 360	70.40 386	82.92 404	56.93 307	82.92 443	69.06 279	72.63 428	88.87 336

testing222		74.04 343	72.66 350	78.19 364	87.89 221	55.36 433	81.06 382	79.20 452	71.30 254	74.65 335	83.57 393	39.11 484	88.67 382	51.43 441	85.75 243	90.53 267

EG-PatchMatch MVS		74.04 343	71.82 358	80.71 298	84.92 330	67.42 175	85.86 266	88.08 291	66.04 374	64.22 463	83.85 382	35.10 501	92.56 245	57.44 401	80.83 321	82.16 473

pmmvs4		74.03 345	71.91 357	80.39 304	81.96 407	68.32 140	81.45 375	82.14 411	59.32 458	69.87 397	85.13 354	52.40 348	88.13 390	60.21 372	74.74 410	84.73 444

MS-PatchMatch		73.83 346	72.67 349	77.30 384	83.87 354	66.02 206	81.82 366	84.66 367	61.37 442	68.61 410	82.82 408	47.29 415	88.21 388	59.27 381	84.32 268	77.68 492

test_cas_vis1_n_1920		73.76 347	73.74 337	73.81 424	75.90 471	59.77 371	80.51 392	82.40 406	58.30 468	81.62 171	85.69 337	44.35 449	76.41 481	76.29 190	78.61 347	85.23 434

myMVS_eth3d28		73.62 348	73.53 339	73.90 423	88.20 202	47.41 487	78.06 431	79.37 449	74.29 178	73.98 343	84.29 371	44.67 444	83.54 438	51.47 439	87.39 204	90.74 258

sss		73.60 349	73.64 338	73.51 426	82.80 390	55.01 438	76.12 446	81.69 416	62.47 430	74.68 334	85.85 335	57.32 302	78.11 470	60.86 367	80.93 318	87.39 379

RPMNet		73.51 350	70.49 382	82.58 249	81.32 421	65.19 236	75.92 448	92.27 109	57.60 475	72.73 360	76.45 470	52.30 349	95.43 85	48.14 463	77.71 360	87.11 395

WBMVS		73.43 351	72.81 348	75.28 405	87.91 220	50.99 473	78.59 424	81.31 422	65.51 385	74.47 338	84.83 360	46.39 426	86.68 405	58.41 392	77.86 358	88.17 360

blended_shiyan8		73.38 352	71.17 370	80.02 317	78.36 454	61.51 341	82.43 358	87.28 315	65.40 387	68.61 410	77.53 465	51.91 361	91.00 328	63.28 329	65.76 466	87.53 375

blended_shiyan6		73.38 352	71.17 370	80.01 318	78.36 454	61.48 342	82.43 358	87.27 318	65.40 387	68.56 412	77.55 464	51.94 360	91.01 325	63.27 330	65.76 466	87.55 374

SixPastTwentyTwo		73.37 354	71.26 369	79.70 331	85.08 327	57.89 393	85.57 271	83.56 385	71.03 263	65.66 451	85.88 333	42.10 464	92.57 244	59.11 384	63.34 478	88.65 346

CR-MVSNet		73.37 354	71.27 368	79.67 333	81.32 421	65.19 236	75.92 448	80.30 438	59.92 453	72.73 360	81.19 425	52.50 346	86.69 404	59.84 375	77.71 360	87.11 395

MSDG		73.36 356	70.99 373	80.49 303	84.51 341	65.80 216	80.71 389	86.13 348	65.70 380	65.46 453	83.74 386	44.60 445	90.91 331	51.13 442	76.89 370	84.74 443

SSC-MVS3.2		73.35 357	73.39 340	73.23 427	85.30 320	49.01 482	74.58 461	81.57 417	75.21 144	73.68 347	85.58 342	52.53 344	82.05 450	54.33 425	77.69 362	88.63 347

usedtu_blend_shiyan5		73.29 358	70.96 374	80.25 310	77.80 461	62.16 329	84.44 310	87.38 313	64.41 401	68.09 417	76.28 474	51.32 369	91.23 313	63.21 331	65.76 466	87.35 381

tpm2		73.26 359	71.46 363	78.63 352	83.34 367	56.71 413	80.65 390	80.40 436	56.63 482	73.55 349	82.02 420	51.80 364	91.24 312	56.35 414	78.42 354	87.95 363

gbinet_0.2-2-1-0.02		73.24 360	70.86 377	80.39 304	78.03 459	61.62 338	83.10 349	86.69 334	65.98 376	69.29 404	76.15 477	49.77 394	91.51 301	62.75 337	66.00 464	88.03 362

RPSCF		73.23 361	71.46 363	78.54 357	82.50 398	59.85 370	82.18 363	82.84 403	58.96 462	71.15 382	89.41 231	45.48 442	84.77 428	58.82 388	71.83 435	91.02 247

PatchmatchNet		73.12 362	71.33 366	78.49 360	83.18 374	60.85 354	79.63 406	78.57 456	64.13 405	71.73 374	79.81 445	51.20 374	85.97 414	57.40 402	76.36 385	88.66 345
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nomal-1		73.10 363	71.76 359	77.13 386	82.58 396	65.50 224	73.53 467	79.64 446	66.14 371	72.17 369	81.27 424	46.45 425	81.47 455	62.08 353	81.93 308	84.42 447

UBG		73.08 364	72.27 355	75.51 401	88.02 215	51.29 471	78.35 428	77.38 466	65.52 383	73.87 345	82.36 413	45.55 439	86.48 408	55.02 420	84.39 267	88.75 342

COLMAP_ROB	66.92 17	73.01 365	70.41 384	80.81 296	87.13 267	65.63 220	88.30 166	84.19 376	62.96 421	63.80 468	87.69 281	38.04 490	92.56 245	46.66 468	74.91 408	84.24 449
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 366	72.58 351	74.25 418	84.28 343	50.85 474	86.41 244	83.45 388	44.56 510	73.23 353	87.54 287	49.38 399	85.70 416	65.90 308	78.44 351	86.19 414

wanda-best-256-512		72.94 367	70.66 378	79.79 325	77.80 461	61.03 350	81.31 378	87.15 323	65.18 390	68.09 417	76.28 474	51.32 369	90.97 329	63.06 333	65.76 466	87.35 381

FE-blended-shiyan7		72.94 367	70.66 378	79.79 325	77.80 461	61.03 350	81.31 378	87.15 323	65.18 390	68.09 417	76.28 474	51.32 369	90.97 329	63.06 333	65.76 466	87.35 381

test-LLR		72.94 367	72.43 352	74.48 414	81.35 419	58.04 389	78.38 425	77.46 463	66.66 362	69.95 395	79.00 452	48.06 411	79.24 464	66.13 303	84.83 254	86.15 415

FE-MVSNET2		72.88 370	71.28 367	77.67 375	78.30 456	57.78 397	84.43 311	88.92 266	69.56 308	64.61 460	81.67 422	46.73 424	88.54 385	59.33 380	67.99 456	86.69 407

test_0402		72.79 371	70.44 383	79.84 323	88.13 209	65.99 209	85.93 262	84.29 373	65.57 382	67.40 431	85.49 344	46.92 419	92.61 241	35.88 501	74.38 413	80.94 480

tpmrst		72.39 372	72.13 356	73.18 431	80.54 428	49.91 478	79.91 404	79.08 453	63.11 418	71.69 375	79.95 442	55.32 319	82.77 445	65.66 311	73.89 417	86.87 400

PatchMatch-RL		72.38 373	70.90 375	76.80 390	88.60 186	67.38 178	79.53 407	76.17 476	62.75 426	69.36 402	82.00 421	45.51 440	84.89 427	53.62 428	80.58 325	78.12 491

CL-MVSNet_self_test		72.37 374	71.46 363	75.09 407	79.49 445	53.53 450	80.76 387	85.01 363	69.12 323	70.51 384	82.05 419	57.92 295	84.13 432	52.27 435	66.00 464	87.60 371

tpm		72.37 374	71.71 360	74.35 416	82.19 403	52.00 461	79.22 412	77.29 467	64.56 399	72.95 358	83.68 390	51.35 368	83.26 442	58.33 394	75.80 389	87.81 367

blend_shiyan4		72.29 376	69.65 390	80.21 312	78.24 457	62.16 329	82.29 361	87.27 318	65.41 386	68.43 416	76.42 473	39.91 478	91.23 313	63.21 331	65.66 471	87.22 388

ETVMVS		72.25 377	71.05 372	75.84 395	87.77 231	51.91 463	79.39 409	74.98 479	69.26 317	73.71 346	82.95 403	40.82 473	86.14 411	46.17 472	84.43 266	89.47 314

sc_t1		72.19 378	69.51 391	80.23 311	84.81 332	61.09 347	84.68 297	80.22 440	60.70 445	71.27 379	83.58 392	36.59 496	89.24 369	60.41 369	63.31 479	90.37 275

UWE-MVS		72.13 379	71.49 362	74.03 421	86.66 286	47.70 484	81.40 377	76.89 471	63.60 414	75.59 302	84.22 375	39.94 477	85.62 418	48.98 456	86.13 231	88.77 341

PVSNet	64.34 18	72.08 380	70.87 376	75.69 397	86.21 296	56.44 417	74.37 463	80.73 427	62.06 436	70.17 390	82.23 417	42.86 458	83.31 441	54.77 422	84.45 265	87.32 385

WB-MVSnew		71.96 381	71.65 361	72.89 433	84.67 339	51.88 464	82.29 361	77.57 462	62.31 432	73.67 348	83.00 402	53.49 340	81.10 458	45.75 476	82.13 304	85.70 426

pmmvs5		71.55 382	70.20 387	75.61 398	77.83 460	56.39 418	81.74 368	80.89 424	57.76 473	67.46 428	84.49 364	49.26 403	85.32 423	57.08 405	75.29 403	85.11 438

test-mter		71.41 383	70.39 385	74.48 414	81.35 419	58.04 389	78.38 425	77.46 463	60.32 448	69.95 395	79.00 452	36.08 499	79.24 464	66.13 303	84.83 254	86.15 415

K. test v3		71.19 384	68.51 398	79.21 344	83.04 381	57.78 397	84.35 315	76.91 470	72.90 223	62.99 471	82.86 406	39.27 481	91.09 322	61.65 359	52.66 503	88.75 342

dmvs_re		71.14 385	70.58 380	72.80 434	81.96 407	59.68 372	75.60 452	79.34 450	68.55 337	69.27 405	80.72 433	49.42 398	76.54 478	52.56 434	77.79 359	82.19 472

tpmvs		71.09 386	69.29 393	76.49 391	82.04 405	56.04 424	78.92 419	81.37 421	64.05 408	67.18 433	78.28 458	49.74 395	89.77 358	49.67 452	72.37 429	83.67 456

AllTest		70.96 387	68.09 404	79.58 336	85.15 324	63.62 285	84.58 304	79.83 443	62.31 432	60.32 482	86.73 305	32.02 506	88.96 377	50.28 447	71.57 437	86.15 415

0.4-1-1-0.1		70.93 388	67.94 408	79.91 320	79.35 447	61.27 344	78.95 418	82.19 410	63.36 415	67.50 426	69.40 498	39.83 479	91.04 324	62.44 343	68.40 453	87.40 378

test_fmvs1		70.93 388	70.52 381	72.16 438	73.71 483	55.05 437	80.82 383	78.77 455	51.21 501	78.58 230	84.41 367	31.20 510	76.94 476	75.88 198	80.12 333	84.47 446

test_fmvs1_n		70.86 390	70.24 386	72.73 435	72.51 495	55.28 435	81.27 380	79.71 445	51.49 500	78.73 225	84.87 359	27.54 516	77.02 475	76.06 194	79.97 334	85.88 423

Patchmtry		70.74 391	69.16 395	75.49 402	80.72 425	54.07 447	74.94 459	80.30 438	58.34 467	70.01 392	81.19 425	52.50 346	86.54 406	53.37 430	71.09 440	85.87 424

MIMVSNet		70.69 392	69.30 392	74.88 410	84.52 340	56.35 421	75.87 450	79.42 448	64.59 398	67.76 422	82.41 412	41.10 470	81.54 453	46.64 470	81.34 313	86.75 405

tpm cat1		70.57 393	68.31 400	77.35 383	82.41 401	57.95 392	78.08 430	80.22 440	52.04 496	68.54 413	77.66 463	52.00 357	87.84 394	51.77 436	72.07 434	86.25 412

OpenMVS_ROB	64.09 19	70.56 394	68.19 401	77.65 377	80.26 430	59.41 377	85.01 290	82.96 400	58.76 465	65.43 454	82.33 414	37.63 492	91.23 313	45.34 480	76.03 387	82.32 470

pmmvs-eth3d		70.50 395	67.83 411	78.52 359	77.37 467	66.18 203	81.82 366	81.51 418	58.90 463	63.90 467	80.42 435	42.69 459	86.28 410	58.56 390	65.30 473	83.11 462

tt0320		70.49 396	68.03 405	77.89 370	84.78 333	59.12 378	83.55 336	80.44 434	58.13 470	67.43 430	80.41 436	39.26 482	87.54 398	55.12 418	63.18 480	86.99 398

USDC		70.33 397	68.37 399	76.21 393	80.60 427	56.23 422	79.19 413	86.49 340	60.89 443	61.29 477	85.47 345	31.78 508	89.47 365	53.37 430	76.21 386	82.94 466

Patchmatch-RL test		70.24 398	67.78 413	77.61 378	77.43 466	59.57 375	71.16 474	70.33 493	62.94 422	68.65 409	72.77 489	50.62 381	85.49 420	69.58 274	66.58 461	87.77 368

CMPMVS	51.72 21	70.19 399	68.16 402	76.28 392	73.15 490	57.55 401	79.47 408	83.92 379	48.02 506	56.48 495	84.81 361	43.13 456	86.42 409	62.67 341	81.81 310	84.89 441
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tt0320-xc		70.11 400	67.45 419	78.07 368	85.33 319	59.51 376	83.28 344	78.96 454	58.77 464	67.10 434	80.28 438	36.73 495	87.42 399	56.83 410	59.77 492	87.29 386

ppachtmachnet_test		70.04 401	67.34 421	78.14 365	79.80 440	61.13 345	79.19 413	80.59 429	59.16 460	65.27 455	79.29 449	46.75 423	87.29 400	49.33 454	66.72 459	86.00 421

0.3-1-1-0.015		70.03 402	66.80 427	79.72 330	78.18 458	61.07 348	77.63 436	82.32 409	62.65 428	65.50 452	67.29 499	37.62 493	90.91 331	61.99 354	68.04 455	87.19 390

0.4-1-1-0.2		70.01 403	66.86 426	79.44 339	77.61 464	60.64 360	76.77 443	82.34 408	62.40 431	65.91 450	66.65 500	40.05 476	90.83 333	61.77 358	68.24 454	86.86 401

dtuonly		69.95 404	69.98 388	69.85 454	73.09 491	49.46 481	74.55 462	76.40 473	57.56 477	67.82 421	86.31 326	50.89 380	74.23 497	61.46 361	81.71 311	85.86 425

gg-mvs-nofinetune		69.95 404	67.96 406	75.94 394	83.07 379	54.51 444	77.23 440	70.29 494	63.11 418	70.32 387	62.33 503	43.62 453	88.69 381	53.88 427	87.76 198	84.62 445

TESTMET0.1,1		69.89 406	69.00 396	72.55 436	79.27 449	56.85 409	78.38 425	74.71 483	57.64 474	68.09 417	77.19 467	37.75 491	76.70 477	63.92 323	84.09 271	84.10 452

test_vis1_n		69.85 407	69.21 394	71.77 441	72.66 494	55.27 436	81.48 374	76.21 475	52.03 497	75.30 318	83.20 399	28.97 513	76.22 483	74.60 212	78.41 355	83.81 455

FMVSNet5		69.50 408	67.96 406	74.15 419	82.97 387	55.35 434	80.01 402	82.12 412	62.56 429	63.02 469	81.53 423	36.92 494	81.92 451	48.42 458	74.06 415	85.17 437

mvs5depth		69.45 409	67.45 419	75.46 403	73.93 481	55.83 427	79.19 413	83.23 391	66.89 357	71.63 376	83.32 396	33.69 504	85.09 424	59.81 376	55.34 500	85.46 430

PMMVS		69.34 410	68.67 397	71.35 446	75.67 474	62.03 331	75.17 454	73.46 486	50.00 503	68.68 408	79.05 450	52.07 356	78.13 469	61.16 365	82.77 296	73.90 499

our_test_3		69.14 411	67.00 424	75.57 399	79.80 440	58.80 379	77.96 432	77.81 460	59.55 456	62.90 472	78.25 459	47.43 414	83.97 433	51.71 437	67.58 458	83.93 454

EPMVS		69.02 412	68.16 402	71.59 442	79.61 443	49.80 480	77.40 438	66.93 505	62.82 425	70.01 392	79.05 450	45.79 436	77.86 472	56.58 412	75.26 404	87.13 394

KD-MVS_self_test		68.81 413	67.59 417	72.46 437	74.29 480	45.45 492	77.93 433	87.00 327	63.12 417	63.99 466	78.99 454	42.32 461	84.77 428	56.55 413	64.09 477	87.16 393

Anonymous20240521		68.80 414	67.22 423	73.55 425	74.33 479	54.11 446	83.18 346	85.61 354	58.15 469	61.68 476	80.94 430	30.71 511	81.27 457	57.00 407	73.34 425	85.28 433

Anonymous20231206		68.60 415	67.80 412	71.02 449	80.23 432	50.75 475	78.30 429	80.47 432	56.79 481	66.11 449	82.63 411	46.35 429	78.95 466	43.62 483	75.70 390	83.36 459

MIMVSNet1		68.58 416	66.78 428	73.98 422	80.07 435	51.82 465	80.77 386	84.37 370	64.40 402	59.75 485	82.16 418	36.47 497	83.63 436	42.73 486	70.33 443	86.48 410

testing3		68.56 417	67.67 415	71.22 448	87.33 258	42.87 502	83.06 353	71.54 491	70.36 285	69.08 406	84.38 368	30.33 512	85.69 417	37.50 499	75.45 398	85.09 439

EU-MVSNet		68.53 418	67.61 416	71.31 447	78.51 453	47.01 489	84.47 306	84.27 374	42.27 513	66.44 446	84.79 362	40.44 474	83.76 434	58.76 389	68.54 452	83.17 460

PatchT		68.46 419	67.85 409	70.29 452	80.70 426	43.93 500	72.47 469	74.88 480	60.15 450	70.55 383	76.57 469	49.94 391	81.59 452	50.58 443	74.83 409	85.34 432

dtuonlycased		68.45 420	67.29 422	71.92 439	80.18 433	54.90 439	79.76 405	80.38 437	60.11 451	62.57 474	76.44 472	49.34 400	82.31 447	55.05 419	61.77 485	78.53 490

test_fmvs2		68.35 421	67.48 418	70.98 450	69.50 499	51.95 462	80.05 401	76.38 474	49.33 504	74.65 335	84.38 368	23.30 525	75.40 492	74.51 213	75.17 406	85.60 427

Syy-MVS		68.05 422	67.85 409	68.67 462	84.68 336	40.97 508	78.62 422	73.08 488	66.65 365	66.74 439	79.46 447	52.11 354	82.30 448	32.89 504	76.38 383	82.75 467

test0.0.03 1		68.00 423	67.69 414	68.90 459	77.55 465	47.43 485	75.70 451	72.95 490	66.66 362	66.56 441	82.29 416	48.06 411	75.87 487	44.97 481	74.51 412	83.41 458

TDRefinement		67.49 424	64.34 437	76.92 388	73.47 487	61.07 348	84.86 294	82.98 399	59.77 454	58.30 489	85.13 354	26.06 517	87.89 393	47.92 465	60.59 490	81.81 476

test20.03		67.45 425	66.95 425	68.94 458	75.48 476	44.84 498	77.50 437	77.67 461	66.66 362	63.01 470	83.80 384	47.02 418	78.40 468	42.53 489	68.86 451	83.58 457

UnsupCasMVSNet_eth		67.33 426	65.99 431	71.37 444	73.48 486	51.47 469	75.16 455	85.19 358	65.20 389	60.78 479	80.93 432	42.35 460	77.20 474	57.12 404	53.69 502	85.44 431

TinyColmap		67.30 427	64.81 435	74.76 412	81.92 409	56.68 414	80.29 397	81.49 419	60.33 447	56.27 497	83.22 397	24.77 521	87.66 397	45.52 477	69.47 446	79.95 486

FE-MVSNET		67.25 428	65.33 433	73.02 432	75.86 472	52.54 459	80.26 399	80.56 430	63.80 413	60.39 480	79.70 446	41.41 468	84.66 430	43.34 484	62.62 482	81.86 474

myMVS_eth3d		67.02 429	66.29 430	69.21 457	84.68 336	42.58 503	78.62 422	73.08 488	66.65 365	66.74 439	79.46 447	31.53 509	82.30 448	39.43 495	76.38 383	82.75 467

dp		66.80 430	65.43 432	70.90 451	79.74 442	48.82 483	75.12 457	74.77 481	59.61 455	64.08 465	77.23 466	42.89 457	80.72 460	48.86 457	66.58 461	83.16 461

MDA-MVSNet-bldmvs		66.68 431	63.66 442	75.75 396	79.28 448	60.56 362	73.92 465	78.35 458	64.43 400	50.13 505	79.87 444	44.02 451	83.67 435	46.10 473	56.86 494	83.03 464

testgi		66.67 432	66.53 429	67.08 469	75.62 475	41.69 507	75.93 447	76.50 472	66.11 372	65.20 458	86.59 315	35.72 500	74.71 494	43.71 482	73.38 424	84.84 442

CHOSEN 280x420		66.51 433	64.71 436	71.90 440	81.45 416	63.52 294	57.98 509	68.95 500	53.57 492	62.59 473	76.70 468	46.22 431	75.29 493	55.25 417	79.68 335	76.88 494

PM-MVS		66.41 434	64.14 438	73.20 430	73.92 482	56.45 416	78.97 417	64.96 511	63.88 412	64.72 459	80.24 439	19.84 529	83.44 440	66.24 302	64.52 476	79.71 487

JIA-IIPM		66.32 435	62.82 448	76.82 389	77.09 468	61.72 337	65.34 498	75.38 477	58.04 472	64.51 461	62.32 504	42.05 465	86.51 407	51.45 440	69.22 448	82.21 471

KD-MVS_2432*1600		66.22 436	63.89 440	73.21 428	75.47 477	53.42 452	70.76 477	84.35 371	64.10 406	66.52 443	78.52 456	34.55 502	84.98 425	50.40 445	50.33 507	81.23 478

miper_refine_blended		66.22 436	63.89 440	73.21 428	75.47 477	53.42 452	70.76 477	84.35 371	64.10 406	66.52 443	78.52 456	34.55 502	84.98 425	50.40 445	50.33 507	81.23 478

ADS-MVSNet2		66.20 438	63.33 443	74.82 411	79.92 436	58.75 380	67.55 489	75.19 478	53.37 493	65.25 456	75.86 479	42.32 461	80.53 461	41.57 490	68.91 449	85.18 435

UWE-MVS-28		65.32 439	64.93 434	66.49 470	78.70 451	38.55 510	77.86 435	64.39 512	62.00 437	64.13 464	83.60 391	41.44 467	76.00 485	31.39 506	80.89 319	84.92 440

YYNet1		65.03 440	62.91 446	71.38 443	75.85 473	56.60 415	69.12 485	74.66 484	57.28 479	54.12 499	77.87 461	45.85 435	74.48 495	49.95 450	61.52 487	83.05 463

MDA-MVSNet_test_wron		65.03 440	62.92 445	71.37 444	75.93 470	56.73 411	69.09 486	74.73 482	57.28 479	54.03 500	77.89 460	45.88 434	74.39 496	49.89 451	61.55 486	82.99 465

Patchmatch-test		64.82 442	63.24 444	69.57 455	79.42 446	49.82 479	63.49 504	69.05 499	51.98 498	59.95 484	80.13 440	50.91 376	70.98 503	40.66 492	73.57 420	87.90 365

usedtu_dtu_shiyan2		64.75 443	61.63 452	74.10 420	70.64 497	53.18 457	82.10 365	81.27 423	56.22 485	56.39 496	74.67 484	27.94 515	83.56 437	42.71 487	62.73 481	85.57 428

ADS-MVSNet		64.36 444	62.88 447	68.78 461	79.92 436	47.17 488	67.55 489	71.18 492	53.37 493	65.25 456	75.86 479	42.32 461	73.99 499	41.57 490	68.91 449	85.18 435

LF4IMVS		64.02 445	62.19 449	69.50 456	70.90 496	53.29 455	76.13 445	77.18 468	52.65 495	58.59 487	80.98 429	23.55 524	76.52 479	53.06 432	66.66 460	78.68 489

UnsupCasMVSNet_bld		63.70 446	61.53 453	70.21 453	73.69 484	51.39 470	72.82 468	81.89 413	55.63 487	57.81 491	71.80 491	38.67 486	78.61 467	49.26 455	52.21 505	80.63 482

test_fmvs3		63.36 447	61.82 450	67.98 466	62.51 508	46.96 490	77.37 439	74.03 485	45.24 509	67.50 426	78.79 455	12.16 537	72.98 502	72.77 234	66.02 463	83.99 453

dmvs_testset		62.63 448	64.11 439	58.19 480	78.55 452	24.76 525	75.28 453	65.94 508	67.91 347	60.34 481	76.01 478	53.56 338	73.94 500	31.79 505	67.65 457	75.88 496

mvsany_test1		62.30 449	61.26 454	65.41 472	69.52 498	54.86 440	66.86 492	49.78 525	46.65 507	68.50 414	83.21 398	49.15 404	66.28 510	56.93 408	60.77 488	75.11 497

new-patchmatchnet		61.73 450	61.73 451	61.70 476	72.74 493	24.50 526	69.16 484	78.03 459	61.40 440	56.72 494	75.53 482	38.42 487	76.48 480	45.95 474	57.67 493	84.13 451

PVSNet_0	57.27 20	61.67 451	59.27 455	68.85 460	79.61 443	57.44 403	68.01 487	73.44 487	55.93 486	58.54 488	70.41 495	44.58 446	77.55 473	47.01 467	35.91 515	71.55 503

test_vis1_rt		60.28 452	58.42 456	65.84 471	67.25 502	55.60 431	70.44 479	60.94 517	44.33 511	59.00 486	66.64 501	24.91 520	68.67 508	62.80 336	69.48 445	73.25 500

ttmdpeth		59.91 453	57.10 458	68.34 464	67.13 503	46.65 491	74.64 460	67.41 504	48.30 505	62.52 475	85.04 358	20.40 527	75.93 486	42.55 488	45.90 513	82.44 469

MVS-HIRNet		59.14 454	57.67 457	63.57 474	81.65 411	43.50 501	71.73 471	65.06 510	39.59 517	51.43 502	57.73 511	38.34 488	82.58 446	39.53 493	73.95 416	64.62 509

pmmvs3		57.79 455	54.26 461	68.37 463	64.02 507	56.72 412	75.12 457	65.17 509	40.20 515	52.93 501	69.86 497	20.36 528	75.48 490	45.45 478	55.25 501	72.90 501

DSMNet-mixed		57.77 456	56.90 459	60.38 478	67.70 501	35.61 514	69.18 483	53.97 523	32.30 528	57.49 492	79.88 443	40.39 475	68.57 509	38.78 496	72.37 429	76.97 493

MVStest1		56.63 457	52.76 464	68.25 465	61.67 509	53.25 456	71.67 472	68.90 501	38.59 518	50.59 504	83.05 401	25.08 519	70.66 504	36.76 500	38.56 514	80.83 481

WB-MVS		54.94 458	54.72 460	55.60 487	73.50 485	20.90 528	74.27 464	61.19 516	59.16 460	50.61 503	74.15 485	47.19 417	75.78 488	17.31 524	35.07 516	70.12 504

LCM-MVSNet		54.25 459	49.68 470	67.97 467	53.73 517	45.28 495	66.85 493	80.78 426	35.96 522	39.45 515	62.23 505	8.70 541	78.06 471	48.24 462	51.20 506	80.57 484

mvsany_test3		53.99 460	51.45 466	61.61 477	55.51 513	44.74 499	63.52 503	45.41 529	43.69 512	58.11 490	76.45 470	17.99 530	63.76 514	54.77 422	47.59 509	76.34 495

SSC-MVS		53.88 461	53.59 462	54.75 490	72.87 492	19.59 529	73.84 466	60.53 518	57.58 476	49.18 507	73.45 488	46.34 430	75.47 491	16.20 527	32.28 518	69.20 505

FPMVS		53.68 462	51.64 465	59.81 479	65.08 505	51.03 472	69.48 482	69.58 497	41.46 514	40.67 513	72.32 490	16.46 533	70.00 507	24.24 517	65.42 472	58.40 514

APD_test1		53.31 463	49.93 469	63.42 475	65.68 504	50.13 477	71.59 473	66.90 506	34.43 524	40.58 514	71.56 492	8.65 542	76.27 482	34.64 503	55.36 499	63.86 510

N_pmnet		52.79 464	53.26 463	51.40 492	78.99 450	7.68 542	69.52 481	3.89 551	51.63 499	57.01 493	74.98 483	40.83 472	65.96 511	37.78 497	64.67 475	80.56 485

test_f		52.09 465	50.82 467	55.90 485	53.82 516	42.31 506	59.42 508	58.31 521	36.45 521	56.12 498	70.96 494	12.18 536	57.79 518	53.51 429	56.57 496	67.60 506

EGC-MVSNET		52.07 466	47.05 471	67.14 468	83.51 364	60.71 358	80.50 393	67.75 502	0.07 578	0.43 570	75.85 481	24.26 522	81.54 453	28.82 508	62.25 483	59.16 512

new_pmnet		50.91 467	50.29 468	52.78 491	68.58 500	34.94 516	63.71 502	56.63 522	39.73 516	44.95 508	65.47 502	21.93 526	58.48 517	34.98 502	56.62 495	64.92 508

ANet_high		50.57 468	46.10 473	63.99 473	48.67 522	39.13 509	70.99 476	80.85 425	61.39 441	31.18 517	57.70 512	17.02 532	73.65 501	31.22 507	15.89 531	79.18 488

test_vis3_rt		49.26 469	47.02 472	56.00 484	54.30 514	45.27 496	66.76 494	48.08 526	36.83 520	44.38 509	53.20 518	7.17 544	64.07 513	56.77 411	55.66 497	58.65 513

testf1		45.72 470	41.96 475	57.00 481	56.90 511	45.32 493	66.14 495	59.26 519	26.19 529	30.89 518	60.96 507	4.14 547	70.64 505	26.39 515	46.73 511	55.04 516

APD_test2		45.72 470	41.96 475	57.00 481	56.90 511	45.32 493	66.14 495	59.26 519	26.19 529	30.89 518	60.96 507	4.14 547	70.64 505	26.39 515	46.73 511	55.04 516

dongtai		45.42 472	45.38 474	45.55 494	73.36 488	26.85 523	67.72 488	34.19 531	54.15 491	49.65 506	56.41 515	25.43 518	62.94 515	19.45 522	28.09 520	46.86 523

Gipuma		45.18 473	41.86 477	55.16 488	77.03 469	51.52 468	32.50 519	80.52 431	32.46 527	27.12 521	35.02 531	9.52 540	75.50 489	22.31 519	60.21 491	38.45 528
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 474	40.28 479	55.82 486	40.82 525	42.54 505	65.12 499	63.99 513	34.43 524	24.48 523	57.12 513	3.92 549	76.17 484	17.10 525	55.52 498	48.75 520
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		44.04 475	39.87 481	56.58 483	50.92 521	36.22 513	59.86 507	27.68 535	33.67 526	42.15 512	71.07 493	3.10 552	59.10 516	45.79 475	24.54 522	74.41 498

ArgMatch-Sym		43.72 476	39.92 480	55.10 489	52.36 519	37.56 512	61.93 506	23.00 537	35.80 523	43.62 510	70.22 496	3.22 550	55.93 520	45.35 479	23.80 524	71.81 502

PMMVS2		40.82 477	38.86 482	46.69 493	53.84 515	16.45 533	48.61 512	49.92 524	37.49 519	31.67 516	60.97 506	8.14 543	56.42 519	28.42 509	30.72 519	67.19 507

kuosan		39.70 478	40.40 478	37.58 499	64.52 506	26.98 521	65.62 497	33.02 532	46.12 508	42.79 511	48.99 522	24.10 523	46.56 525	12.16 532	26.30 521	39.20 527

DenseAffine		31.97 479	28.22 486	43.21 496	43.10 524	27.10 520	46.21 513	11.36 541	24.92 531	27.70 520	58.81 510	1.09 556	46.50 526	26.95 512	13.85 535	56.02 515

E-PMN		31.77 480	30.64 483	35.15 501	52.87 518	27.67 519	57.09 510	47.86 527	24.64 532	16.40 537	33.05 532	11.23 538	54.90 521	14.46 528	18.15 529	22.87 535

test_method		31.52 481	29.28 485	38.23 498	27.03 532	6.50 547	20.94 525	62.21 515	4.05 549	22.35 527	52.50 519	13.33 534	47.58 523	27.04 511	34.04 517	60.62 511

EMVS		30.81 482	29.65 484	34.27 502	50.96 520	25.95 524	56.58 511	46.80 528	24.01 533	15.53 538	30.68 535	12.47 535	54.43 522	12.81 531	17.05 530	22.43 536

MVE	26.22 23	30.37 483	25.89 488	43.81 495	44.55 523	35.46 515	28.87 524	39.07 530	18.20 537	18.58 534	40.18 527	2.68 553	47.37 524	17.07 526	23.78 525	48.60 521
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
RoMa-SfM		28.67 484	25.38 489	38.54 497	32.61 529	22.48 527	40.24 514	7.23 545	21.81 534	26.66 522	60.46 509	0.96 557	41.72 527	26.47 514	11.95 536	51.40 519

LoFTR		27.52 485	24.27 490	37.29 500	34.75 528	19.27 530	33.78 518	21.60 538	12.42 541	21.61 529	56.59 514	0.91 558	40.37 528	13.94 529	22.80 526	52.22 518

DKM		25.67 486	23.01 491	33.64 503	32.08 530	19.25 531	37.50 516	5.52 547	18.67 535	23.58 526	55.44 516	0.64 563	34.02 529	23.95 518	9.73 538	47.66 522

PDCNetPlus		24.75 487	22.46 492	31.64 504	35.53 527	17.00 532	32.00 520	9.46 542	18.43 536	18.56 535	51.31 520	1.65 554	33.00 531	26.51 513	8.70 540	44.91 524

MatchFormer		22.13 488	19.86 494	28.93 505	28.66 531	15.74 534	31.91 521	17.10 540	7.75 542	18.87 533	47.50 525	0.62 565	33.92 530	7.49 539	18.87 528	37.14 529

RoMa-HiRes		21.63 489	19.64 495	27.59 506	22.40 534	14.25 535	29.71 522	4.10 549	15.42 539	21.09 530	54.77 517	0.72 561	28.87 532	21.01 520	7.52 544	39.65 526

DKM-HiRes		20.87 490	19.15 496	26.02 508	25.34 533	14.13 536	29.63 523	3.62 554	14.53 540	20.13 531	50.55 521	0.47 571	24.22 536	20.96 521	7.15 545	39.70 525

cdsmvs_eth3d_5k		19.96 491	26.61 487	0.00 546	0.00 571	0.00 575	0.00 559	89.26 244	0.00 582	0.00 573	88.61 254	61.62 250	0.00 571	0.00 569	0.00 574	0.00 570

tmp_tt		18.61 492	21.40 493	10.23 516	4.82 564	10.11 537	34.70 517	30.74 534	1.48 555	23.91 525	26.07 536	28.42 514	13.41 540	27.12 510	15.35 533	7.17 546

wuyk23d		16.82 493	15.94 498	19.46 511	58.74 510	31.45 517	39.22 515	3.74 553	6.84 543	6.04 545	2.70 566	1.27 555	24.29 535	10.54 537	14.40 534	2.63 553

VLMVS_CLIP		15.14 494	16.11 497	12.23 515	12.32 541	7.35 543	15.53 528	20.73 539	4.02 550	22.32 528	31.59 533	4.37 546	21.02 538	11.59 534	22.52 527	8.32 539

ELoFTR		14.23 495	11.56 502	22.24 509	11.02 542	6.56 546	13.59 531	7.57 544	5.55 545	11.96 541	39.09 528	0.21 576	24.93 534	9.43 538	5.66 549	35.22 530

PMatch-SfM		14.15 496	12.67 501	18.59 512	12.84 540	7.03 544	17.41 526	2.28 556	6.63 544	12.96 539	43.56 526	0.09 588	16.11 539	13.90 530	4.38 555	32.63 532

MASt3R-SfM		13.55 497	13.93 499	12.41 514	10.54 545	5.97 548	16.61 527	6.07 546	4.50 547	16.53 536	48.67 523	0.73 560	9.44 542	11.56 535	10.18 537	21.81 537

GLUNet-SfM		12.90 498	10.00 503	21.62 510	13.58 539	8.30 540	10.19 535	9.30 543	4.31 548	12.18 540	30.90 534	0.50 569	22.76 537	4.89 540	4.14 556	33.79 531

MVS_clip		11.37 499	13.03 500	6.40 520	15.78 538	6.79 545	11.98 534	1.47 564	1.89 552	19.38 532	35.95 530	3.13 551	3.09 548	12.10 533	15.54 532	9.34 538

PMatch-Up-SfM		10.76 500	9.99 504	13.09 513	9.50 548	4.83 549	12.94 533	1.40 565	4.65 546	10.16 542	37.54 529	0.07 591	10.94 541	10.71 536	2.92 566	23.50 534

ALIKED-LG		8.61 501	8.70 506	8.33 517	20.63 535	8.70 539	15.50 529	4.61 548	2.19 551	5.84 546	18.70 537	0.80 559	8.06 543	1.03 550	8.97 539	8.25 540

ALIKED-MNN		7.86 502	7.83 509	7.97 518	19.40 536	8.86 538	14.48 530	3.90 550	1.59 553	4.74 551	16.49 538	0.59 566	7.65 544	0.91 551	8.34 542	7.39 543

ALIKED-NN		7.51 503	7.61 510	7.21 519	18.26 537	8.10 541	13.45 532	3.88 552	1.50 554	4.87 549	16.47 539	0.64 563	7.00 545	0.88 552	8.50 541	6.52 548

ab-mvs-re		7.23 504	9.64 505	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	86.72 307	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

test123		6.12 505	8.11 507	0.14 544	0.06 570	0.09 572	71.05 475	0.03 582	0.04 580	0.25 572	1.30 568	0.05 593	0.03 570	0.21 561	0.01 573	0.29 568

testmvs		6.04 506	8.02 508	0.10 545	0.08 569	0.03 574	69.74 480	0.04 581	0.05 579	0.31 571	1.68 567	0.02 594	0.04 569	0.24 555	0.02 572	0.25 569

pcd_1.5k_mvsjas		5.26 507	7.02 511	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	63.15 221	0.00 571	0.00 569	0.00 574	0.00 570

VLMVS		4.54 508	4.93 512	3.37 527	4.86 563	2.23 556	3.38 549	1.77 563	0.23 577	7.94 543	11.34 545	4.62 545	2.44 549	2.43 542	7.76 543	5.44 550

XFeat-MNN		4.39 509	4.49 513	4.10 521	2.88 567	1.91 563	5.86 541	2.57 555	1.06 557	5.04 547	13.99 541	0.43 573	4.47 546	2.00 543	6.55 547	5.92 549

SP-DiffGlue		4.29 510	4.46 514	3.77 525	3.68 565	2.12 557	5.97 540	2.22 557	1.10 556	4.89 548	13.93 542	0.66 562	1.95 554	2.47 541	5.24 550	7.22 545

SP-LightGlue		4.27 511	4.41 515	3.86 522	10.99 543	1.99 560	8.19 536	2.06 559	0.98 559	2.37 553	8.29 546	0.56 567	2.10 551	1.27 546	4.99 551	7.48 542

SP-SuperGlue		4.24 512	4.38 516	3.81 524	10.75 544	2.00 559	8.18 537	2.09 558	1.00 558	2.41 552	8.29 546	0.56 567	2.05 553	1.27 546	4.91 552	7.39 543

SP-MNN		4.14 513	4.24 517	3.82 523	10.32 546	1.83 564	8.11 538	1.99 560	0.82 561	2.23 554	8.27 548	0.47 571	2.14 550	1.20 548	4.77 553	7.49 541

SP-NN		4.00 514	4.12 518	3.63 526	9.92 547	1.81 565	7.94 539	1.90 562	0.86 560	2.15 555	8.00 549	0.50 569	2.09 552	1.20 548	4.63 554	6.98 547

XFeat-NN		3.78 515	3.96 520	3.23 528	2.65 568	1.53 568	4.99 542	1.92 561	0.81 562	4.77 550	12.37 544	0.38 574	3.39 547	1.64 544	6.13 548	4.77 551

MVS_baseline		3.29 516	4.00 519	1.16 542	3.08 566	0.09 572	1.26 558	0.24 580	0.04 580	6.52 544	16.19 540	0.30 575	0.00 571	1.53 545	6.83 546	3.39 552

SIFT-NN		2.77 517	2.92 521	2.34 529	8.70 549	3.08 550	4.46 543	1.01 567	0.68 563	1.46 556	5.49 550	0.16 577	1.65 555	0.26 553	4.04 557	2.27 554

SIFT-MNN		2.63 518	2.75 522	2.25 530	8.10 550	2.84 551	4.08 544	1.02 566	0.68 563	1.28 557	5.34 553	0.15 578	1.64 556	0.26 553	3.88 559	2.27 554

SIFT-NN-NCMNet		2.52 519	2.64 523	2.14 531	7.53 552	2.74 552	4.00 545	0.98 568	0.65 566	1.24 559	5.08 556	0.14 579	1.60 557	0.23 556	3.94 558	2.07 558

SIFT-NCM-Cal		2.40 520	2.52 524	2.05 532	7.74 551	2.54 553	3.75 547	0.84 569	0.65 566	0.89 564	4.78 559	0.13 582	1.60 557	0.19 564	3.71 560	2.01 560

SIFT-NN-CMatch		2.31 521	2.41 525	2.00 533	6.59 556	2.34 555	3.48 548	0.83 570	0.65 566	1.28 557	5.09 554	0.14 579	1.52 559	0.23 556	3.41 562	2.14 556

SIFT-NN-UMatch		2.26 522	2.39 526	1.89 535	6.21 558	2.08 558	3.76 546	0.83 570	0.66 565	1.04 561	5.09 554	0.14 579	1.52 559	0.23 556	3.51 561	2.07 558

SIFT-ConvMatch		2.25 523	2.37 527	1.90 534	7.29 553	2.37 554	3.21 552	0.75 572	0.65 566	1.03 562	4.91 557	0.12 585	1.51 561	0.22 559	3.13 564	1.81 561

SIFT-UMatch		2.16 524	2.30 528	1.72 537	6.99 554	1.97 562	3.32 550	0.70 574	0.64 570	0.91 563	4.86 558	0.12 585	1.49 562	0.22 559	2.97 565	1.72 563

SIFT-NN-PointCN		2.07 525	2.18 529	1.74 536	5.75 559	1.65 567	3.27 551	0.73 573	0.60 573	1.07 560	4.62 560	0.13 582	1.43 563	0.21 561	3.22 563	2.12 557

SIFT-CM-Cal		2.02 526	2.13 530	1.67 538	6.79 555	1.99 560	2.79 554	0.64 575	0.63 571	0.87 565	4.48 562	0.13 582	1.41 564	0.19 564	2.70 567	1.61 565

SIFT-UM-Cal		1.97 527	2.12 531	1.52 539	6.57 557	1.67 566	2.93 553	0.57 577	0.62 572	0.83 566	4.55 561	0.11 587	1.37 565	0.20 563	2.69 568	1.53 566

SIFT-PCN-Cal		1.72 528	1.82 533	1.39 540	5.64 560	1.19 570	2.39 556	0.53 578	0.55 575	0.72 567	3.90 563	0.09 588	1.22 567	0.17 566	2.42 570	1.76 562

SIFT-PointCN		1.72 528	1.83 532	1.36 541	5.55 561	1.22 569	2.59 555	0.59 576	0.55 575	0.71 568	3.77 564	0.08 590	1.24 566	0.17 566	2.48 569	1.63 564

SIFT-NCMNet		1.44 530	1.56 534	1.08 543	5.14 562	1.07 571	1.97 557	0.32 579	0.56 574	0.64 569	3.23 565	0.07 591	1.01 568	0.14 568	1.95 571	1.15 567

mmdepth		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

monomultidepth		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

test_blank		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

uanet_test		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

DCPMVS		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

sosnet-low-res		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

sosnet		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

uncertanet		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

Regformer		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

uanet		0.00 531	0.00 535	0.00 546	0.00 571	0.00 575	0.00 559	0.00 583	0.00 582	0.00 573	0.00 569	0.00 595	0.00 571	0.00 569	0.00 574	0.00 570

ratest												81.63 7

4221559												81.59 9

4211027												78.06 36

1152												78.74 29

115												77.63 39

testmerge												78.20 35

test114-1038												78.52 32

test11-410.03												78.98 23

test114												79.46 19

113-2									77.30 62			79.85 16

test113									77.30 62			79.39 20

testL									77.30 62			73.73 67

testLo									77.30 62			79.32 21

testloun									75.24 139

aa1122									68.52 340

11-2								93.94 20	67.85 348			78.23 34			96.35 15	94.72 40

aa112-1								94.15 9	76.29 112			78.62 31			96.28 18	94.89 20

aa112								93.80 26				77.22 42			93.56 80	93.19 147

11-21									74.59 168

11-1								93.18 56	69.80 301	92.48 10	96.29 4	77.53 40	96.76 35	87.57 50	95.19 46	93.20 145

testa						74.80 5

aa-1101								93.78 27	74.95 155	92.47 11	96.42 2	77.11 43	97.24 16		94.91 52	93.00 166

aa-1031								93.32 46	72.21 233			77.30 41			91.98 107

test-Zhanyuan												78.70 30

100007								93.18 56	65.76 379	92.41 13		79.65 17				93.00 166

aaa1007												78.92 25

RA-MVS			88.76 16		95.68 1	73.57 15	94.07 3	93.39 43	70.82 269	90.99 28	95.81 11	75.13 53	97.22 20	88.08 44	94.91 52

Meshroom													0.00 571
: In preparation.
AliceVision / Meshro													0.00 571
: In preparation.
AliceVision_Meshroom													0.00 571
: In preparation.
PatchmatchNet2					0.00 571	30.51 518	67.30 491	67.46 503	50.92 502
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														37.67 498	64.79 474	80.58 483
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													65.90 512
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					94.58 18	71.43 64		94.16 7		90.64 30		79.61 18	97.25 15	88.08 44	96.28 18

testmeadow								93.30 48	69.51 311	92.51 9	96.02 7	78.75 27	97.15 22	87.21 54	95.56 36	93.19 147

testFor bridge								93.52 37	73.04 219	92.54 7	96.21 5	78.06 36	97.22 20	88.21 38	95.49 37	93.38 131

aaatest				87.86 28	94.57 20	71.43 64	93.28 14	93.59 34	75.24 139	92.25 17	95.03 31		97.39 11	88.15 41	95.96 24	94.75 38

testtestaaa								93.78 27	74.95 155	92.47 11		79.30 22

TestfortrainingZip				87.28 47	92.85 71	72.05 53	93.28 14	93.32 46	76.52 95	88.91 43	93.52 86	78.75 27	96.67 40		91.98 107	93.13 155

WAC-MVS						42.58 503								39.46 494

FOURS1					95.00 12	72.39 44	95.06 1	93.84 23	74.49 170	91.30 25

MSC_two_6792asdad				89.16 1	94.34 34	75.53 2		92.99 68					97.53 2	89.67 15	96.44 9	94.41 65

PC_three_1452									68.21 344	92.02 22	94.00 72	82.09 5	95.98 69	84.58 78	96.68 2	94.95 15

No_MVS				89.16 1	94.34 34	75.53 2		92.99 68					97.53 2	89.67 15	96.44 9	94.41 65

test_one_0601					95.07 9	71.46 63		94.14 11	78.27 42	92.05 21	95.74 16	80.83 15

eth-test2					0.00 571

eth-test					0.00 571

ZD-MVS					94.38 32	72.22 49		92.67 87	70.98 264	87.75 62	94.07 67	74.01 65	96.70 38	84.66 77	94.84 57

RE-MVS-def			85.48 79		93.06 67	70.63 87	91.88 45	92.27 109	73.53 202	85.69 86	94.45 46	63.87 211		82.75 102	91.87 110	92.50 189

IU-MVS					95.30 4	71.25 69		92.95 74	66.81 358	92.39 14				88.94 28	96.63 4	94.85 27

OPU-MVS				89.06 3	94.62 17	75.42 4	93.57 10				94.02 70	82.45 3	96.87 30	83.77 89	96.48 8	94.88 22

test_241102_TWO								94.06 16	77.24 69	92.78 5	95.72 18	81.26 12	97.44 7	89.07 25	96.58 6	94.26 77

test_241102_ONE					95.30 4	70.98 77		94.06 16	77.17 72	93.10 1	95.39 26	82.99 1	97.27 14

9.14			88.26 21		92.84 73		91.52 58	94.75 1	73.93 189	88.57 47	94.67 39	75.57 52	95.79 71	86.77 58	95.76 30

save fliter					93.80 47	72.35 47	90.47 76	91.17 167	74.31 176

test_0728_THIRD									78.38 39	92.12 19	95.78 14	81.46 11	97.40 9	89.42 19	96.57 7	94.67 46

test_0728_SECOND				87.71 36	95.34 3	71.43 64	93.49 12	94.23 5					97.49 4	89.08 22	96.41 12	94.21 78

test0726					95.27 7	71.25 69	93.60 9	94.11 12	77.33 60	92.81 4	95.79 13	80.98 13

GSMVS																88.96 333

test_part2					95.06 10	72.65 35				91.80 23

sam_mvs1												51.32 369				88.96 333

sam_mvs												50.01 389

ambc				75.24 406	73.16 489	50.51 476	63.05 505	87.47 311		64.28 462	77.81 462	17.80 531	89.73 360	57.88 398	60.64 489	85.49 429

MTGPA								92.02 127

test_post1							78.90 420				5.43 552	48.81 410	85.44 422	59.25 382

test_post											5.46 551	50.36 385	84.24 431

patchmatchnet-post											74.00 486	51.12 375	88.60 383

GG-mvs-BLEND				75.38 404	81.59 413	55.80 428	79.32 410	69.63 496		67.19 432	73.67 487	43.24 455	88.90 379	50.41 444	84.50 261	81.45 477

MTMP							92.18 41	32.83 533

gm-plane-assit					81.40 417	53.83 449			62.72 427		80.94 430		92.39 255	63.40 327

test9_res														84.90 71	95.70 33	92.87 173

TEST9					93.26 59	72.96 28	88.75 141	91.89 135	68.44 341	85.00 93	93.10 98	74.36 60	95.41 88

test_8					93.13 63	72.57 38	88.68 147	91.84 139	68.69 335	84.87 97	93.10 98	74.43 58	95.16 98

agg_prior2														82.91 98	95.45 38	92.70 178

agg_prior					92.85 71	71.94 56		91.78 143		84.41 109			94.93 110

TestCases				79.58 336	85.15 324	63.62 285		79.83 443	62.31 432	60.32 482	86.73 305	32.02 506	88.96 377	50.28 447	71.57 437	86.15 415

test_prior4						72.60 37	89.01 128

test_prior2							88.85 135		75.41 134	84.91 95	93.54 85	74.28 61		83.31 92	95.86 27

test_prior				86.33 66	92.61 78	69.59 103		92.97 73					95.48 82			93.91 94

旧先验2							86.56 239		58.10 471	87.04 73			88.98 375	74.07 218

新几何2							86.29 253

新几何1				83.42 202	93.13 63	70.71 85		85.48 356	57.43 478	81.80 166	91.98 132	63.28 215	92.27 261	64.60 319	92.99 87	87.27 387

旧先验1					91.96 84	65.79 217		86.37 343			93.08 102	69.31 132			92.74 92	88.74 344

无先验							87.48 193	88.98 261	60.00 452				94.12 150	67.28 295		88.97 332

原ACMM2							86.86 225

原ACMM1				84.35 145	93.01 69	68.79 122		92.44 97	63.96 411	81.09 180	91.57 152	66.06 186	95.45 83	67.19 297	94.82 59	88.81 339

test222					91.50 90	68.26 142	84.16 321	83.20 394	54.63 490	79.74 208	91.63 148	58.97 286			91.42 118	86.77 404

testdata2													91.01 325	62.37 347

segment_acmp												73.08 73

testdata				79.97 319	90.90 102	64.21 271		84.71 366	59.27 459	85.40 88	92.91 104	62.02 243	89.08 373	68.95 280	91.37 120	86.63 409

testdata1							84.14 322		75.71 125

test12				86.80 60	92.63 77	70.70 86		91.79 142		82.71 153		71.67 95	96.16 60		94.50 66	93.54 126

plane_prior7					90.08 120	68.51 136

plane_prior6					89.84 129	68.70 130						60.42 276

plane_prior5								92.44 97					95.38 90	78.71 158	86.32 225	91.33 235

plane_prior4											91.00 176

plane_prior3						68.60 133			78.44 37	78.92 223

plane_prior2							91.25 62		79.12 29

plane_prior1					89.90 128

plane_prior						68.71 128	90.38 80		77.62 49						86.16 230

n2								0.00 583

nn								0.00 583

door-mid								69.98 495

lessismore_v0				78.97 347	81.01 424	57.15 406		65.99 507		61.16 478	82.82 408	39.12 483	91.34 309	59.67 377	46.92 510	88.43 352

LGP-MVS_train				84.50 135	89.23 157	68.76 124		91.94 133	75.37 136	76.64 280	91.51 154	54.29 330	94.91 111	78.44 160	83.78 274	89.83 304

test11								92.23 113

door								69.44 498

HQP5-MVS						66.98 190

HQP-NCC					89.33 149		89.17 119		76.41 102	77.23 265

ACMP_Plane					89.33 149		89.17 119		76.41 102	77.23 265

BP-MVS														77.47 174

HQP4-MVS										77.24 264			95.11 102			91.03 245

HQP3-MVS								92.19 121							85.99 236

HQP2-MVS												60.17 279

NP-MVS					89.62 134	68.32 140					90.24 202

MDTV_nov1_ep13_2view						37.79 511	75.16 455		55.10 488	66.53 442		49.34 400		53.98 426		87.94 364

MDTV_nov1_ep13			69.97 389		83.18 374	53.48 451	77.10 442	80.18 442	60.45 446	69.33 403	80.44 434	48.89 409	86.90 403	51.60 438	78.51 350

ACMMP++_ref															81.95 307

ACMMP++															81.25 314

Test By Simon												64.33 206

ITE_SJBPF				78.22 363	81.77 410	60.57 361		83.30 389	69.25 318	67.54 425	87.20 296	36.33 498	87.28 401	54.34 424	74.62 411	86.80 403

DeepMVS_CX				27.40 507	40.17 526	26.90 522		24.59 536	17.44 538	23.95 524	48.61 524	9.77 539	26.48 533	18.06 523	24.47 523	28.83 533

High-resolution multi-view benchmark