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.

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 33	71.25 68	95.06 1	94.23 5	78.38 39	92.78 5	95.74 14	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 3	70.98 76	93.57 9	94.06 15	77.24 65	93.10 1	95.72 16	82.99 1	97.44 7	89.07 25	96.63 4	94.88 20

MED-MVS		89.78 3	90.41 3	87.89 24	94.57 19	71.43 63	93.28 13	94.36 3	77.30 62	92.25 16	95.87 8	81.59 7	97.39 11	88.15 40	96.28 17	94.85 25

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 6	71.25 68	93.49 11	92.73 83	77.33 60	92.12 18	95.78 12	80.98 10	97.40 9	89.08 22	96.41 12	93.33 134
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 5	89.91 6	88.30 10	94.28 36	73.46 19	92.90 22	94.11 11	80.27 11	91.35 23	94.16 61	78.35 24	96.77 33	89.59 17	94.22 73	94.67 44
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 6	89.98 5	88.01 16	94.80 12	72.69 33	91.59 52	94.10 13	75.90 115	92.29 14	95.66 18	81.67 6	97.38 13	87.44 52	96.34 16	93.95 91
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MM		89.16 7	89.23 10	88.97 4	90.79 105	73.65 11	92.66 29	91.17 166	86.57 1	87.39 67	94.97 32	71.70 81	97.68 1	92.19 1	95.63 34	95.57 2

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 24	71.69 57	93.83 5	93.96 18	75.70 122	91.06 26	96.03 5	76.84 32	97.03 25	89.09 21	95.65 33	94.47 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 9	89.23 10	88.61 6	94.25 37	73.73 10	92.40 30	93.63 31	74.77 158	92.29 14	95.97 7	74.28 49	97.24 16	88.58 33	96.91 1	94.87 22
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 10	89.15 12	88.63 5	95.01 10	76.03 1	92.38 33	92.85 77	80.26 12	87.78 58	94.27 54	75.89 37	96.81 32	87.45 51	96.44 9	93.05 159

aaEdge-Enhanced		88.98 11	89.39 8	87.75 30	94.54 22	71.43 63	91.61 50	94.25 4	76.30 105	90.62 30	95.03 29	78.06 26	97.07 24	88.15 40	95.96 23	94.75 36

CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 13	73.82 9	90.51 71	93.00 64	80.90 7	88.06 53	94.06 66	76.43 34	96.84 30	88.48 36	95.99 22	94.34 69

TestfortrainingZip a		88.85 13	89.24 9	87.68 37	94.57 19	71.25 68	93.28 13	93.91 21	76.30 105	91.13 25	95.87 8	78.92 18	96.95 27	86.12 63	93.07 84	94.85 25

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 81	72.96 27	93.73 6	93.67 30	80.19 13	88.10 52	94.80 34	73.76 54	97.11 22	87.51 50	95.82 27	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 15	88.74 16	87.64 39	92.78 73	71.95 54	92.40 30	94.74 2	75.71 120	89.16 38	95.10 27	75.65 39	96.19 58	87.07 55	96.01 21	94.79 29

DeepPCF-MVS	80.84 1	88.10 16	88.56 18	86.73 60	92.24 80	69.03 114	89.57 100	93.39 42	77.53 55	89.79 34	94.12 63	78.98 17	96.58 46	85.66 64	95.72 30	94.58 53

lecture		88.09 17	88.59 17	86.58 63	93.26 58	69.77 100	93.70 7	94.16 7	77.13 70	89.76 35	95.52 22	72.26 71	96.27 55	86.87 56	94.65 59	93.70 109

SD-MVS		88.06 18	88.50 19	86.71 61	92.60 78	72.71 31	91.81 47	93.19 51	77.87 44	90.32 32	94.00 70	74.83 42	93.78 168	87.63 48	94.27 72	93.65 115
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 18	88.01 23	88.24 11	94.41 28	73.62 12	91.22 63	92.83 78	81.50 5	85.79 83	93.47 88	73.02 62	97.00 26	84.90 70	94.94 49	94.10 82

ACMMP_NAP		88.05 20	88.08 22	87.94 19	93.70 47	73.05 24	90.86 66	93.59 33	76.27 108	88.14 51	95.09 28	71.06 91	96.67 39	87.67 47	96.37 14	94.09 83

TSAR-MVS + MP.		88.02 21	88.11 21	87.72 33	93.68 49	72.13 50	91.41 59	92.35 102	74.62 162	88.90 42	93.85 78	75.75 38	96.00 66	87.80 46	94.63 61	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 22	87.85 25	88.20 12	94.39 30	73.33 21	93.03 20	93.81 24	76.81 80	85.24 88	94.32 51	71.76 79	96.93 28	85.53 67	95.79 28	94.32 71

MP-MVS		87.71 23	87.64 27	87.93 21	94.36 32	73.88 7	92.71 28	92.65 89	77.57 51	83.84 122	94.40 48	72.24 72	96.28 54	85.65 65	95.30 43	93.62 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MGCNet		87.69 24	87.55 30	88.12 13	89.45 142	71.76 56	91.47 58	89.54 225	82.14 3	86.65 77	94.28 53	68.28 140	97.46 6	90.81 6	95.31 42	95.15 9

MP-MVS-pluss		87.67 25	87.72 26	87.54 40	93.64 50	72.04 53	89.80 91	93.50 37	75.17 143	86.34 79	95.29 25	70.86 93	96.00 66	88.78 31	96.04 20	94.58 53
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 26	87.47 32	87.94 19	94.58 17	73.54 17	93.04 18	93.24 49	76.78 82	84.91 93	94.44 46	70.78 94	96.61 43	84.53 78	94.89 53	93.66 111

reproduce-ours		87.47 27	87.61 28	87.07 51	93.27 56	71.60 58	91.56 55	93.19 51	74.98 147	88.96 39	95.54 20	71.20 89	96.54 47	86.28 60	93.49 79	93.06 157

our_new_method		87.47 27	87.61 28	87.07 51	93.27 56	71.60 58	91.56 55	93.19 51	74.98 147	88.96 39	95.54 20	71.20 89	96.54 47	86.28 60	93.49 79	93.06 157

ACMMPR		87.44 29	87.23 37	88.08 15	94.64 14	73.59 13	93.04 18	93.20 50	76.78 82	84.66 101	94.52 39	68.81 131	96.65 41	84.53 78	94.90 52	94.00 88

APD-MVS		87.44 29	87.52 31	87.19 48	94.24 38	72.39 43	91.86 46	92.83 78	73.01 216	88.58 44	94.52 39	73.36 55	96.49 49	84.26 81	95.01 46	92.70 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 31	87.26 35	87.89 24	94.12 42	72.97 26	92.39 32	93.43 40	76.89 78	84.68 98	93.99 72	70.67 96	96.82 31	84.18 85	95.01 46	93.90 94

region2R		87.42 31	87.20 38	88.09 14	94.63 15	73.55 15	93.03 20	93.12 58	76.73 85	84.45 106	94.52 39	69.09 124	96.70 37	84.37 80	94.83 56	94.03 86

fmvsm_s_conf0.5_n_9		87.39 33	87.95 24	85.70 83	89.48 141	67.88 158	88.59 149	89.05 255	80.19 13	90.70 28	95.40 23	74.56 44	93.92 160	91.54 2	92.07 103	95.31 6

MCST-MVS		87.37 34	87.25 36	87.73 31	94.53 23	72.46 42	89.82 89	93.82 23	73.07 213	84.86 96	92.89 103	76.22 35	96.33 52	84.89 72	95.13 45	94.40 65

reproduce_model		87.28 35	87.39 34	86.95 55	93.10 64	71.24 73	91.60 51	93.19 51	74.69 159	88.80 43	95.61 19	70.29 100	96.44 50	86.20 62	93.08 83	93.16 149

MTAPA		87.23 36	87.00 40	87.90 22	94.18 41	74.25 6	86.58 236	92.02 126	79.45 23	85.88 81	94.80 34	68.07 142	96.21 57	86.69 58	95.34 40	93.23 138

XVS		87.18 37	86.91 45	88.00 17	94.42 26	73.33 21	92.78 24	92.99 67	79.14 27	83.67 126	94.17 60	67.45 148	96.60 44	83.06 93	94.50 64	94.07 84

HPM-MVS		87.11 38	86.98 42	87.50 43	93.88 45	72.16 49	92.19 39	93.33 44	76.07 112	83.81 123	93.95 75	69.77 111	96.01 65	85.15 68	94.66 58	94.32 71
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 38	86.92 44	87.68 37	94.20 40	73.86 8	93.98 4	92.82 81	76.62 88	83.68 125	94.46 43	67.93 143	95.95 69	84.20 84	94.39 68	93.23 138

DeepC-MVS	79.81 2	87.08 40	86.88 46	87.69 36	91.16 94	72.32 47	90.31 80	93.94 19	77.12 71	82.82 148	94.23 57	72.13 75	97.09 23	84.83 73	95.37 39	93.65 115
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 41	86.62 51	87.76 29	93.52 52	72.37 45	91.26 60	93.04 59	76.62 88	84.22 113	93.36 92	71.44 85	96.76 34	80.82 122	95.33 41	94.16 78
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 42	86.99 41	86.15 72	91.24 93	67.61 167	90.51 71	92.90 74	77.26 64	87.44 66	91.63 146	71.27 88	96.06 61	85.62 66	95.01 46	94.78 30

SR-MVS		86.73 43	86.67 49	86.91 56	94.11 43	72.11 51	92.37 34	92.56 94	74.50 164	86.84 74	94.65 38	67.31 150	95.77 71	84.80 74	92.85 88	92.84 174

CS-MVS		86.69 44	86.95 43	85.90 80	90.76 106	67.57 169	92.83 23	93.30 47	79.67 20	84.57 105	92.27 117	71.47 84	95.02 108	84.24 83	93.46 81	95.13 11

PGM-MVS		86.68 45	86.27 57	87.90 22	94.22 39	73.38 20	90.22 82	93.04 59	75.53 125	83.86 121	94.42 47	67.87 145	96.64 42	82.70 105	94.57 63	93.66 111

mPP-MVS		86.67 46	86.32 55	87.72 33	94.41 28	73.55 15	92.74 26	92.22 115	76.87 79	82.81 149	94.25 56	66.44 163	96.24 56	82.88 98	94.28 71	93.38 129

fmvsm_s_conf0.5_n_8		86.56 47	87.17 39	84.73 125	87.76 230	65.62 220	89.20 116	92.21 117	79.94 18	89.74 36	94.86 33	68.63 134	94.20 145	90.83 5	91.39 117	94.38 66

CANet		86.45 48	86.10 63	87.51 42	90.09 118	70.94 80	89.70 95	92.59 93	81.78 4	81.32 173	91.43 156	70.34 98	97.23 18	84.26 81	93.36 82	94.37 67

train_agg		86.43 49	86.20 58	87.13 50	93.26 58	72.96 27	88.75 140	91.89 134	68.69 330	85.00 91	93.10 96	74.43 46	95.41 87	84.97 69	95.71 31	93.02 161

PHI-MVS		86.43 49	86.17 61	87.24 47	90.88 102	70.96 78	92.27 38	94.07 14	72.45 223	85.22 89	91.90 132	69.47 114	96.42 51	83.28 92	95.94 25	94.35 68

CSCG		86.41 51	86.19 60	87.07 51	92.91 69	72.48 39	90.81 67	93.56 35	73.95 181	83.16 140	91.07 170	75.94 36	95.19 96	79.94 136	94.38 69	93.55 123

fmvsm_s_conf0.5_n_10		86.38 52	86.76 47	85.24 98	87.33 256	67.30 180	89.50 102	90.98 172	76.25 109	90.56 31	94.75 36	68.38 137	94.24 144	90.80 7	92.32 100	94.19 77

fmvsm_s_conf0.5_n_3		86.36 53	87.46 33	83.09 217	87.08 271	65.21 234	89.09 125	90.21 202	79.67 20	89.98 33	95.02 31	73.17 59	91.71 285	91.30 3	91.60 112	92.34 194

NormalMVS		86.29 54	85.88 68	87.52 41	93.26 58	72.47 40	91.65 48	92.19 120	79.31 25	84.39 108	92.18 123	64.64 189	95.53 78	80.70 125	94.65 59	94.56 57

SPE-MVS-test		86.29 54	86.48 52	85.71 82	91.02 98	67.21 186	92.36 35	93.78 26	78.97 34	83.51 133	91.20 164	70.65 97	95.15 98	81.96 109	94.89 53	94.77 31

Casviewmamba		86.09 56	86.04 65	86.24 67	88.17 203	68.05 151	89.44 105	92.79 82	80.30 10	84.71 97	92.78 110	72.83 66	95.05 106	82.81 99	90.57 133	95.62 1

fmvsm_s_conf0.5_n_11		86.06 57	86.75 48	84.00 178	87.78 227	66.09 203	89.96 87	90.80 181	77.37 59	86.72 75	94.20 59	72.51 69	92.78 236	89.08 22	92.33 98	93.13 153

fmvsm_l_conf0.5_n_3		86.02 58	86.32 55	85.14 101	87.20 262	68.54 134	89.57 100	90.44 191	75.31 133	87.49 64	94.39 49	72.86 64	92.72 237	89.04 27	90.56 134	94.16 78

EC-MVSNet		86.01 59	86.38 53	84.91 116	89.31 151	66.27 201	92.32 36	93.63 31	79.37 24	84.17 115	91.88 133	69.04 128	95.43 84	83.93 87	93.77 76	93.01 163

MVSMamba_PlusPlus		85.99 60	85.96 67	86.05 75	91.09 95	67.64 166	89.63 98	92.65 89	72.89 219	84.64 102	91.71 141	71.85 77	96.03 62	84.77 75	94.45 67	94.49 61

casdiffmvs_mvg		85.99 60	86.09 64	85.70 83	87.65 238	67.22 185	88.69 145	93.04 59	79.64 22	85.33 87	92.54 113	73.30 56	94.50 133	83.49 89	91.14 122	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 62	85.88 68	86.22 69	92.69 75	69.53 103	91.93 43	92.99 67	73.54 196	85.94 80	94.51 42	65.80 177	95.61 74	83.04 95	92.51 93	93.53 125

test_fmvsmconf_n		85.92 63	86.04 65	85.57 89	85.03 327	69.51 104	89.62 99	90.58 186	73.42 200	87.75 60	94.02 68	72.85 65	93.24 205	90.37 8	90.75 130	93.96 89

sasdasda		85.91 64	85.87 70	86.04 76	89.84 128	69.44 109	90.45 77	93.00 64	76.70 86	88.01 55	91.23 160	73.28 57	93.91 161	81.50 112	88.80 168	94.77 31

canonicalmvs		85.91 64	85.87 70	86.04 76	89.84 128	69.44 109	90.45 77	93.00 64	76.70 86	88.01 55	91.23 160	73.28 57	93.91 161	81.50 112	88.80 168	94.77 31

ACMMP		85.89 66	85.39 79	87.38 44	93.59 51	72.63 35	92.74 26	93.18 55	76.78 82	80.73 189	93.82 79	64.33 192	96.29 53	82.67 106	90.69 131	93.23 138
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 67	86.63 50	83.46 197	87.12 270	66.01 206	88.56 151	89.43 229	75.59 124	89.32 37	94.32 51	72.89 63	91.21 314	90.11 11	92.33 98	93.16 149

SR-MVS-dyc-post		85.77 68	85.61 75	86.23 68	93.06 66	70.63 86	91.88 44	92.27 108	73.53 197	85.69 84	94.45 44	65.00 186	95.56 75	82.75 101	91.87 108	92.50 187

CDPH-MVS		85.76 69	85.29 84	87.17 49	93.49 53	71.08 74	88.58 150	92.42 99	68.32 338	84.61 103	93.48 86	72.32 70	96.15 60	79.00 153	95.43 38	94.28 74

TSAR-MVS + GP.		85.71 70	85.33 81	86.84 57	91.34 91	72.50 38	89.07 126	87.28 313	76.41 97	85.80 82	90.22 202	74.15 52	95.37 92	81.82 110	91.88 107	92.65 180

dcpmvs_2		85.63 71	86.15 62	84.06 170	91.71 87	64.94 247	86.47 240	91.87 136	73.63 191	86.60 78	93.02 101	76.57 33	91.87 279	83.36 90	92.15 101	95.35 4

test_fmvsmconf0.1_n		85.61 72	85.65 74	85.50 90	82.99 384	69.39 111	89.65 96	90.29 200	73.31 204	87.77 59	94.15 62	71.72 80	93.23 206	90.31 9	90.67 132	93.89 95

fmvsm_l_mol_unc0.5_1		85.55 73	86.37 54	83.10 216	86.42 290	62.98 311	85.89 262	84.85 363	76.48 95	92.88 3	96.67 1	74.16 51	92.46 249	87.11 54	92.90 87	93.85 96

fmvsm_s_conf0.5_n_6		85.55 73	86.20 58	83.60 191	87.32 258	65.13 237	88.86 132	91.63 150	75.41 129	88.23 50	93.45 89	68.56 135	92.47 248	89.52 18	92.78 89	93.20 143

alignmvs		85.48 75	85.32 82	85.96 79	89.51 138	69.47 106	89.74 93	92.47 95	76.17 110	87.73 62	91.46 155	70.32 99	93.78 168	81.51 111	88.95 164	94.63 50

3Dnovator+	77.84 4	85.48 75	84.47 97	88.51 7	91.08 96	73.49 18	93.18 17	93.78 26	80.79 8	76.66 277	93.37 91	60.40 264	96.75 36	77.20 176	93.73 77	95.29 7

MSLP-MVS++		85.43 77	85.76 72	84.45 137	91.93 84	70.24 89	90.71 68	92.86 76	77.46 57	84.22 113	92.81 107	67.16 152	92.94 226	80.36 129	94.35 70	90.16 281

DELS-MVS		85.41 78	85.30 83	85.77 81	88.49 188	67.93 157	85.52 277	93.44 39	78.70 35	83.63 128	89.03 236	74.57 43	95.71 73	80.26 133	94.04 74	93.66 111
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 79	85.75 73	84.30 149	86.70 282	65.83 213	88.77 138	89.78 214	75.46 128	88.35 46	93.73 81	69.19 123	93.06 221	91.30 3	88.44 177	94.02 87

SymmetryMVS		85.38 80	84.81 90	87.07 51	91.47 90	72.47 40	91.65 48	88.06 291	79.31 25	84.39 108	92.18 123	64.64 189	95.53 78	80.70 125	90.91 128	93.21 141

HPM-MVS_fast		85.35 81	84.95 89	86.57 64	93.69 48	70.58 88	92.15 41	91.62 151	73.89 185	82.67 152	94.09 64	62.60 216	95.54 77	80.93 119	92.93 86	93.57 121

test_fmvsm_n_1920		85.29 82	85.34 80	85.13 104	86.12 298	69.93 96	88.65 147	90.78 182	69.97 292	88.27 48	93.98 73	71.39 86	91.54 296	88.49 35	90.45 136	93.91 92

fmvsm_s_conf0.5_n_5		85.22 83	85.55 76	84.25 156	86.26 292	67.40 176	89.18 117	89.31 238	72.50 222	88.31 47	93.86 77	69.66 112	91.96 271	89.81 13	91.05 123	93.38 129

MVS_111021_HR		85.14 84	84.75 91	86.32 66	91.65 88	72.70 32	85.98 258	90.33 197	76.11 111	82.08 159	91.61 149	71.36 87	94.17 148	81.02 118	92.58 92	92.08 210

hybridcas		85.11 85	85.18 85	84.90 117	87.47 250	65.68 218	88.53 153	92.38 100	77.91 43	84.27 112	92.48 114	72.19 73	93.88 165	80.37 128	90.97 125	95.15 9

casdiffmvs		85.11 85	85.14 86	85.01 109	87.20 262	65.77 217	87.75 185	92.83 78	77.84 45	84.36 111	92.38 116	72.15 74	93.93 159	81.27 117	90.48 135	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 87	84.96 88	85.45 91	92.07 82	68.07 149	89.78 92	90.86 179	82.48 2	84.60 104	93.20 95	69.35 116	95.22 95	71.39 249	90.88 129	93.07 156

MGCFI-Net		85.06 88	85.51 77	83.70 189	89.42 143	63.01 305	89.43 106	92.62 92	76.43 96	87.53 63	91.34 158	72.82 67	93.42 198	81.28 116	88.74 171	94.66 47

DPM-MVS		84.93 89	84.29 98	86.84 57	90.20 116	73.04 25	87.12 210	93.04 59	69.80 296	82.85 147	91.22 163	73.06 61	96.02 64	76.72 188	94.63 61	91.46 232

baseline		84.93 89	84.98 87	84.80 122	87.30 260	65.39 227	87.30 206	92.88 75	77.62 49	84.04 118	92.26 118	71.81 78	93.96 153	81.31 115	90.30 138	95.03 13

ETV-MVS		84.90 91	84.67 92	85.59 88	89.39 146	68.66 131	88.74 142	92.64 91	79.97 17	84.10 116	85.71 334	69.32 117	95.38 89	80.82 122	91.37 118	92.72 175

test_fmvsmconf0.01_n		84.73 92	84.52 96	85.34 95	80.25 429	69.03 114	89.47 103	89.65 221	73.24 208	86.98 72	94.27 54	66.62 159	93.23 206	90.26 10	89.95 146	93.78 106

fmvsm_l_conf0.5_n		84.47 93	84.54 93	84.27 153	85.42 314	68.81 120	88.49 154	87.26 318	68.08 340	88.03 54	93.49 85	72.04 76	91.77 281	88.90 29	89.14 163	92.24 201

BP-MVS1		84.32 94	83.71 113	86.17 70	87.84 222	67.85 159	89.38 111	89.64 222	77.73 47	83.98 119	92.12 128	56.89 294	95.43 84	84.03 86	91.75 111	95.24 8

fmvsm_l_mol_unc0.5_2		84.30 95	84.54 93	83.56 195	88.31 196	64.89 252	84.22 315	91.07 171	73.17 211	86.68 76	95.18 26	68.96 130	91.94 275	80.86 121	92.44 96	93.84 99

E5new		84.22 96	84.12 100	84.51 132	87.60 240	65.36 229	87.45 195	92.31 104	76.51 91	83.53 129	92.26 118	69.25 121	93.50 188	79.88 137	88.26 179	94.69 39

E6new		84.22 96	84.12 100	84.52 130	87.60 240	65.36 229	87.45 195	92.30 106	76.51 91	83.53 129	92.26 118	69.26 119	93.49 190	79.88 137	88.26 179	94.69 39

E6		84.22 96	84.12 100	84.52 130	87.60 240	65.36 229	87.45 195	92.30 106	76.51 91	83.53 129	92.26 118	69.26 119	93.49 190	79.88 137	88.26 179	94.69 39

E5		84.22 96	84.12 100	84.51 132	87.60 240	65.36 229	87.45 195	92.31 104	76.51 91	83.53 129	92.26 118	69.25 121	93.50 188	79.88 137	88.26 179	94.69 39

EI-MVSNet-Vis-set		84.19 100	83.81 110	85.31 96	88.18 202	67.85 159	87.66 187	89.73 219	80.05 16	82.95 143	89.59 220	70.74 95	94.82 117	80.66 127	84.72 256	93.28 136

fmvsm_l_conf0.5_n_a		84.13 101	84.16 99	84.06 170	85.38 315	68.40 137	88.34 162	86.85 330	67.48 348	87.48 65	93.40 90	70.89 92	91.61 287	88.38 37	89.22 160	92.16 208

E4		84.10 102	83.99 106	84.45 137	87.58 248	64.99 243	86.54 238	92.25 111	76.38 101	83.37 134	92.09 129	69.88 109	93.58 177	79.78 142	88.03 190	94.77 31

fmvsm_s_conf0.5_n_2		84.04 103	84.11 104	83.81 187	86.17 296	65.00 242	86.96 217	87.28 313	74.35 169	88.25 49	94.23 57	61.82 232	92.60 240	89.85 12	88.09 187	93.84 99

test_fmvsmvis_n_1920		84.02 104	83.87 107	84.49 136	84.12 345	69.37 112	88.15 171	87.96 295	70.01 290	83.95 120	93.23 94	68.80 132	91.51 299	88.61 32	89.96 145	92.57 181

E2		84.00 105	83.87 107	84.39 140	87.70 235	64.95 244	86.40 245	92.23 112	75.85 116	83.21 136	91.78 137	70.09 104	93.55 182	79.52 146	88.05 188	94.66 47

E3		84.00 105	83.87 107	84.39 140	87.70 235	64.95 244	86.40 245	92.23 112	75.85 116	83.21 136	91.78 137	70.09 104	93.55 182	79.52 146	88.05 188	94.66 47

balanced_ft_v1		83.98 107	83.64 116	85.03 107	89.76 131	65.86 212	88.31 164	91.71 146	74.41 168	80.41 198	90.82 179	62.90 214	94.90 112	83.04 95	91.37 118	94.32 71

viewcassd2359sk11		83.89 108	83.74 112	84.34 145	87.76 230	64.91 251	86.30 249	92.22 115	75.47 127	83.04 142	91.52 151	70.15 102	93.53 185	79.26 148	87.96 191	94.57 55

nrg030		83.88 109	83.53 119	84.96 111	86.77 280	69.28 113	90.46 76	92.67 86	74.79 157	82.95 143	91.33 159	72.70 68	93.09 219	80.79 124	79.28 342	92.50 187

EI-MVSNet-UG-set		83.81 110	83.38 122	85.09 106	87.87 220	67.53 171	87.44 200	89.66 220	79.74 19	82.23 156	89.41 229	70.24 101	94.74 123	79.95 135	83.92 271	92.99 167

fmvsm_s_conf0.1_n_2		83.80 111	83.79 111	83.83 185	85.62 308	64.94 247	87.03 214	86.62 337	74.32 170	87.97 57	94.33 50	60.67 256	92.60 240	89.72 14	87.79 194	93.96 89

fmvsm_s_conf0.5_n		83.80 111	83.71 113	84.07 167	86.69 283	67.31 179	89.46 104	83.07 394	71.09 254	86.96 73	93.70 82	69.02 129	91.47 302	88.79 30	84.62 258	93.44 128

E3new		83.78 113	83.60 117	84.31 147	87.76 230	64.89 252	86.24 252	92.20 118	75.15 144	82.87 145	91.23 160	70.11 103	93.52 187	79.05 149	87.79 194	94.51 60

viewmacassd2359aftdt		83.76 114	83.66 115	84.07 167	86.59 286	64.56 258	86.88 222	91.82 139	75.72 119	83.34 135	92.15 127	68.24 141	92.88 229	79.05 149	89.15 162	94.77 31

CPTT-MVS		83.73 115	83.33 124	84.92 115	93.28 55	70.86 82	92.09 42	90.38 193	68.75 329	79.57 209	92.83 105	60.60 260	93.04 224	80.92 120	91.56 115	90.86 250

EPNet		83.72 116	82.92 132	86.14 74	84.22 343	69.48 105	91.05 65	85.27 355	81.30 6	76.83 272	91.65 144	66.09 171	95.56 75	76.00 195	93.85 75	93.38 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmanbaseed2359cas		83.66 117	83.55 118	84.00 178	86.81 278	64.53 259	86.65 232	91.75 144	74.89 153	83.15 141	91.68 142	68.74 133	92.83 234	79.02 151	89.24 159	94.63 50

patch_mono-2		83.65 118	84.54 93	80.99 289	90.06 123	65.83 213	84.21 316	88.74 274	71.60 242	85.01 90	92.44 115	74.51 45	83.50 437	82.15 108	92.15 101	93.64 117

HQP_MVS		83.64 119	83.14 125	85.14 101	90.08 119	68.71 127	91.25 61	92.44 96	79.12 29	78.92 221	91.00 174	60.42 262	95.38 89	78.71 157	86.32 223	91.33 233

fmvsm_s_conf0.5_n_a		83.63 120	83.41 121	84.28 151	86.14 297	68.12 147	89.43 106	82.87 399	70.27 285	87.27 69	93.80 80	69.09 124	91.58 289	88.21 38	83.65 279	93.14 152

casdiffseed414692147		83.62 121	83.02 128	85.40 93	87.31 259	67.50 172	88.70 144	91.72 145	76.97 75	82.77 150	91.72 140	66.85 156	93.71 175	73.06 229	88.12 186	94.98 14

Effi-MVS+		83.62 121	83.08 126	85.24 98	88.38 194	67.45 173	88.89 131	89.15 251	75.50 126	82.27 155	88.28 262	69.61 113	94.45 136	77.81 168	87.84 193	93.84 99

fmvsm_s_conf0.1_n		83.56 123	83.38 122	84.10 161	84.86 329	67.28 181	89.40 110	83.01 395	70.67 269	87.08 70	93.96 74	68.38 137	91.45 303	88.56 34	84.50 259	93.56 122

GDP-MVS		83.52 124	82.64 137	86.16 71	88.14 206	68.45 136	89.13 123	92.69 84	72.82 220	83.71 124	91.86 135	55.69 303	95.35 93	80.03 134	89.74 150	94.69 39

OPM-MVS		83.50 125	82.95 131	85.14 101	88.79 177	70.95 79	89.13 123	91.52 155	77.55 54	80.96 183	91.75 139	60.71 254	94.50 133	79.67 144	86.51 220	89.97 297
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 126	82.80 134	85.43 92	90.25 115	68.74 125	90.30 81	90.13 205	76.33 104	80.87 186	92.89 103	61.00 251	94.20 145	72.45 241	90.97 125	93.35 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 127	83.45 120	83.28 205	92.74 74	62.28 325	88.17 169	89.50 227	75.22 137	81.49 170	92.74 112	66.75 157	95.11 101	72.85 231	91.58 114	92.45 191

EPP-MVSNet		83.40 128	83.02 128	84.57 128	90.13 117	64.47 264	92.32 36	90.73 183	74.45 167	79.35 215	91.10 167	69.05 127	95.12 99	72.78 232	87.22 205	94.13 80

3Dnovator	76.31 5	83.38 129	82.31 145	86.59 62	87.94 217	72.94 30	90.64 69	92.14 125	77.21 67	75.47 303	92.83 105	58.56 276	94.72 124	73.24 227	92.71 91	92.13 209

viewdifsd2359ckpt09		83.34 130	82.55 140	85.70 83	87.64 239	67.72 164	88.43 155	91.68 148	71.91 236	81.65 168	90.68 183	67.10 154	94.75 122	76.17 191	87.70 197	94.62 52

fmvsm_s_conf0.5_n_7		83.34 130	84.03 105	81.28 280	85.73 305	65.13 237	85.40 278	89.90 212	74.96 149	82.13 158	93.89 76	66.65 158	87.92 390	86.56 59	91.05 123	90.80 251

fmvsm_s_conf0.1_n_a		83.32 132	82.99 130	84.28 151	83.79 353	68.07 149	89.34 113	82.85 400	69.80 296	87.36 68	94.06 66	68.34 139	91.56 292	87.95 45	83.46 285	93.21 141

KinetiMVS		83.31 133	82.61 139	85.39 94	87.08 271	67.56 170	88.06 173	91.65 149	77.80 46	82.21 157	91.79 136	57.27 289	94.07 151	77.77 169	89.89 148	94.56 57

EIA-MVS		83.31 133	82.80 134	84.82 120	89.59 134	65.59 221	88.21 167	92.68 85	74.66 161	78.96 219	86.42 320	69.06 126	95.26 94	75.54 202	90.09 142	93.62 118

h-mvs33		83.15 135	82.19 148	86.02 78	90.56 108	70.85 83	88.15 171	89.16 249	76.02 113	84.67 99	91.39 157	61.54 237	95.50 80	82.71 103	75.48 393	91.72 221

MVS_Test		83.15 135	83.06 127	83.41 202	86.86 275	63.21 300	86.11 256	92.00 128	74.31 171	82.87 145	89.44 228	70.03 106	93.21 208	77.39 175	88.50 176	93.81 102

IS-MVSNet		83.15 135	82.81 133	84.18 159	89.94 126	63.30 298	91.59 52	88.46 284	79.04 31	79.49 210	92.16 125	65.10 183	94.28 139	67.71 288	91.86 110	94.95 15

DP-MVS Recon		83.11 138	82.09 152	86.15 72	94.44 25	70.92 81	88.79 137	92.20 118	70.53 274	79.17 217	91.03 173	64.12 194	96.03 62	68.39 285	90.14 141	91.50 228

PRO-TEST		83.03 139	82.63 138	84.23 157	88.20 200	66.81 194	87.41 201	90.93 174	73.55 195	80.73 189	88.90 242	66.17 169	92.85 230	78.39 162	89.36 157	93.02 161

PAPM_NR		83.02 140	82.41 142	84.82 120	92.47 79	66.37 199	87.93 179	91.80 140	73.82 186	77.32 260	90.66 184	67.90 144	94.90 112	70.37 260	89.48 155	93.19 145

VDD-MVS		83.01 141	82.36 144	84.96 111	91.02 98	66.40 198	88.91 130	88.11 287	77.57 51	84.39 108	93.29 93	52.19 337	93.91 161	77.05 179	88.70 172	94.57 55

viewdifsd2359ckpt13		82.91 142	82.29 146	84.77 123	86.96 274	66.90 193	87.47 192	91.62 151	72.19 229	81.68 167	90.71 182	66.92 155	93.28 201	75.90 196	87.15 207	94.12 81

MVSFormer		82.85 143	82.05 153	85.24 98	87.35 251	70.21 90	90.50 73	90.38 193	68.55 332	81.32 173	89.47 223	61.68 234	93.46 195	78.98 154	90.26 139	92.05 211

viewdifsd2359ckpt07		82.83 144	82.78 136	82.99 224	86.51 288	62.58 316	85.09 286	90.83 180	75.22 137	82.28 154	91.63 146	69.43 115	92.03 267	77.71 170	86.32 223	94.34 69

OMC-MVS		82.69 145	81.97 156	84.85 119	88.75 180	67.42 174	87.98 175	90.87 178	74.92 152	79.72 207	91.65 144	62.19 226	93.96 153	75.26 206	86.42 221	93.16 149

PVSNet_Blended_VisFu		82.62 146	81.83 158	84.96 111	90.80 104	69.76 101	88.74 142	91.70 147	69.39 307	78.96 219	88.46 257	65.47 179	94.87 116	74.42 213	88.57 173	90.24 279

MVS_111021_LR		82.61 147	82.11 149	84.11 160	88.82 171	71.58 60	85.15 283	86.16 345	74.69 159	80.47 197	91.04 171	62.29 223	90.55 341	80.33 131	90.08 143	90.20 280

HQP-MVS		82.61 147	82.02 154	84.37 142	89.33 148	66.98 189	89.17 118	92.19 120	76.41 97	77.23 263	90.23 201	60.17 265	95.11 101	77.47 173	85.99 234	91.03 243

RRT-MVS		82.60 149	82.10 151	84.10 161	87.98 216	62.94 312	87.45 195	91.27 162	77.42 58	79.85 205	90.28 198	56.62 297	94.70 126	79.87 141	88.15 185	94.67 44

viewmamba		82.38 150	82.11 149	83.19 211	83.30 366	64.26 269	84.62 300	89.16 249	75.24 134	80.97 182	91.10 167	67.12 153	91.63 286	81.36 114	86.13 229	93.67 110

diffmvs_AUTHOR		82.38 150	82.27 147	82.73 243	83.26 368	63.80 279	83.89 323	89.76 216	73.35 203	82.37 153	90.84 177	66.25 166	90.79 333	82.77 100	87.93 192	93.59 120

CLD-MVS		82.31 152	81.65 160	84.29 150	88.47 189	67.73 163	85.81 267	92.35 102	75.78 118	78.33 236	86.58 315	64.01 196	94.35 137	76.05 194	87.48 201	90.79 252
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
onestephybrid01		82.22 153	81.81 159	83.46 197	83.16 374	64.93 250	84.64 299	89.19 248	73.95 181	81.48 171	90.63 185	66.00 175	91.92 276	80.33 131	86.93 211	93.53 125

VNet		82.21 154	82.41 142	81.62 269	90.82 103	60.93 350	84.47 304	89.78 214	76.36 103	84.07 117	91.88 133	64.71 188	90.26 346	70.68 257	88.89 165	93.66 111

diffmvs		82.10 155	81.88 157	82.76 241	83.00 380	63.78 281	83.68 328	89.76 216	72.94 217	82.02 160	89.85 207	65.96 176	90.79 333	82.38 107	87.30 204	93.71 108
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		82.08 156	81.27 163	84.50 134	89.23 156	68.76 123	90.22 82	91.94 132	75.37 131	76.64 278	91.51 152	54.29 316	94.91 110	78.44 159	83.78 272	89.83 302

FIs		82.07 157	82.42 141	81.04 288	88.80 176	58.34 383	88.26 166	93.49 38	76.93 77	78.47 233	91.04 171	69.92 108	92.34 257	69.87 269	84.97 250	92.44 192

PS-MVSNAJss		82.07 157	81.31 162	84.34 145	86.51 288	67.27 182	89.27 114	91.51 156	71.75 237	79.37 214	90.22 202	63.15 207	94.27 140	77.69 171	82.36 300	91.49 229

API-MVS		81.99 159	81.23 164	84.26 155	90.94 100	70.18 95	91.10 64	89.32 237	71.51 244	78.66 226	88.28 262	65.26 180	95.10 104	64.74 316	91.23 121	87.51 374

SSM_0404		81.91 160	80.84 173	85.13 104	89.24 155	68.26 141	87.84 184	89.25 243	71.06 256	80.62 192	90.39 195	59.57 267	94.65 128	72.45 241	87.19 206	92.47 190

UniMVSNet_NR-MVSNet		81.88 161	81.54 161	82.92 228	88.46 190	63.46 294	87.13 209	92.37 101	80.19 13	78.38 234	89.14 232	71.66 83	93.05 222	70.05 265	76.46 376	92.25 199

MAR-MVS		81.84 162	80.70 174	85.27 97	91.32 92	71.53 61	89.82 89	90.92 175	69.77 299	78.50 230	86.21 325	62.36 222	94.52 132	65.36 310	92.05 104	89.77 305
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		81.82 163	81.23 164	83.57 194	91.89 85	63.43 296	89.84 88	81.85 413	77.04 74	83.21 136	93.10 96	52.26 336	93.43 197	71.98 244	89.95 146	93.85 96

hse-mvs2		81.72 164	80.94 171	84.07 167	88.72 181	67.68 165	85.87 263	87.26 318	76.02 113	84.67 99	88.22 265	61.54 237	93.48 193	82.71 103	73.44 421	91.06 241

GeoE		81.71 165	81.01 170	83.80 188	89.51 138	64.45 265	88.97 128	88.73 276	71.27 250	78.63 227	89.76 213	66.32 165	93.20 211	69.89 268	86.02 233	93.74 107

xiu_mvs_v2_base		81.69 166	81.05 168	83.60 191	89.15 159	68.03 152	84.46 306	90.02 207	70.67 269	81.30 176	86.53 318	63.17 206	94.19 147	75.60 201	88.54 174	88.57 347

PS-MVSNAJ		81.69 166	81.02 169	83.70 189	89.51 138	68.21 146	84.28 314	90.09 206	70.79 265	81.26 177	85.62 339	63.15 207	94.29 138	75.62 200	88.87 166	88.59 346

PAPR		81.66 168	80.89 172	83.99 180	90.27 114	64.00 273	86.76 229	91.77 143	68.84 328	77.13 270	89.50 221	67.63 146	94.88 115	67.55 290	88.52 175	93.09 155

UniMVSNet (Re)		81.60 169	81.11 167	83.09 217	88.38 194	64.41 266	87.60 188	93.02 63	78.42 38	78.56 229	88.16 266	69.78 110	93.26 204	69.58 272	76.49 375	91.60 223

SSM_0407		81.58 170	80.48 181	84.87 118	88.81 172	67.96 154	87.37 202	89.25 243	71.06 256	79.48 211	90.39 195	59.57 267	94.48 135	72.45 241	85.93 236	92.18 204

Elysia		81.53 171	80.16 190	85.62 86	85.51 311	68.25 143	88.84 135	92.19 120	71.31 247	80.50 195	89.83 208	46.89 406	94.82 117	76.85 181	89.57 152	93.80 104

StellarMVS		81.53 171	80.16 190	85.62 86	85.51 311	68.25 143	88.84 135	92.19 120	71.31 247	80.50 195	89.83 208	46.89 406	94.82 117	76.85 181	89.57 152	93.80 104

FC-MVSNet-test		81.52 173	82.02 154	80.03 314	88.42 193	55.97 423	87.95 177	93.42 41	77.10 72	77.38 258	90.98 176	69.96 107	91.79 280	68.46 284	84.50 259	92.33 195

VDDNet		81.52 173	80.67 175	84.05 173	90.44 111	64.13 272	89.73 94	85.91 348	71.11 253	83.18 139	93.48 86	50.54 369	93.49 190	73.40 224	88.25 183	94.54 59

ACMP	74.13 6	81.51 175	80.57 178	84.36 143	89.42 143	68.69 130	89.97 86	91.50 159	74.46 166	75.04 325	90.41 193	53.82 322	94.54 130	77.56 172	82.91 292	89.86 301
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
hybridnocas07		81.44 176	81.13 166	82.37 252	82.13 402	63.11 304	83.45 337	88.74 274	72.54 221	80.71 191	90.73 180	65.14 182	90.74 338	80.35 130	86.41 222	93.27 137

jason		81.39 177	80.29 187	84.70 126	86.63 285	69.90 98	85.95 259	86.77 331	63.24 411	81.07 179	89.47 223	61.08 250	92.15 263	78.33 163	90.07 144	92.05 211
jason: jason.
lupinMVS		81.39 177	80.27 188	84.76 124	87.35 251	70.21 90	85.55 273	86.41 339	62.85 418	81.32 173	88.61 252	61.68 234	92.24 261	78.41 161	90.26 139	91.83 214

test_yl		81.17 179	80.47 182	83.24 208	89.13 160	63.62 283	86.21 253	89.95 210	72.43 226	81.78 165	89.61 218	57.50 286	93.58 177	70.75 255	86.90 212	92.52 185

DCV-MVSNet		81.17 179	80.47 182	83.24 208	89.13 160	63.62 283	86.21 253	89.95 210	72.43 226	81.78 165	89.61 218	57.50 286	93.58 177	70.75 255	86.90 212	92.52 185

guyue		81.13 181	80.64 177	82.60 246	86.52 287	63.92 277	86.69 231	87.73 303	73.97 180	80.83 188	89.69 214	56.70 295	91.33 308	78.26 167	85.40 247	92.54 183

DU-MVS		81.12 182	80.52 180	82.90 229	87.80 224	63.46 294	87.02 215	91.87 136	79.01 32	78.38 234	89.07 234	65.02 184	93.05 222	70.05 265	76.46 376	92.20 202

hybrid		81.05 183	80.66 176	82.22 256	81.97 404	62.99 309	83.42 338	88.68 277	70.76 267	80.56 194	90.40 194	64.49 191	90.48 342	79.57 145	86.06 231	93.19 145

PVSNet_Blended		80.98 184	80.34 184	82.90 229	88.85 168	65.40 225	84.43 309	92.00 128	67.62 345	78.11 241	85.05 355	66.02 173	94.27 140	71.52 246	89.50 154	89.01 327

FA-MVS(test-final)		80.96 185	79.91 197	84.10 161	88.30 198	65.01 241	84.55 303	90.01 208	73.25 207	79.61 208	87.57 282	58.35 278	94.72 124	71.29 250	86.25 226	92.56 182

QAPM		80.88 186	79.50 211	85.03 107	88.01 215	68.97 118	91.59 52	92.00 128	66.63 362	75.15 321	92.16 125	57.70 283	95.45 82	63.52 322	88.76 170	90.66 259

TranMVSNet+NR-MVSNet		80.84 187	80.31 185	82.42 250	87.85 221	62.33 323	87.74 186	91.33 161	80.55 9	77.99 245	89.86 206	65.23 181	92.62 238	67.05 297	75.24 403	92.30 197

UGNet		80.83 188	79.59 209	84.54 129	88.04 212	68.09 148	89.42 108	88.16 286	76.95 76	76.22 289	89.46 225	49.30 388	93.94 156	68.48 283	90.31 137	91.60 223
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		80.81 189	80.14 192	82.80 235	86.05 300	63.96 274	86.46 241	85.90 349	73.71 189	80.85 187	90.56 189	54.06 320	91.57 291	79.72 143	83.97 270	92.86 172

Fast-Effi-MVS+		80.81 189	79.92 196	83.47 196	88.85 168	64.51 261	85.53 275	89.39 231	70.79 265	78.49 231	85.06 354	67.54 147	93.58 177	67.03 298	86.58 218	92.32 196

XVG-OURS-SEG-HR		80.81 189	79.76 202	83.96 182	85.60 309	68.78 122	83.54 336	90.50 189	70.66 272	76.71 276	91.66 143	60.69 255	91.26 309	76.94 180	81.58 310	91.83 214

IMVS_0403		80.80 192	80.12 193	82.87 231	87.13 265	63.59 287	85.19 280	89.33 233	70.51 275	78.49 231	89.03 236	63.26 203	93.27 203	72.56 237	85.56 243	91.74 217

xiu_mvs_v1_base_debu		80.80 192	79.72 205	84.03 175	87.35 251	70.19 92	85.56 270	88.77 268	69.06 320	81.83 161	88.16 266	50.91 362	92.85 230	78.29 164	87.56 198	89.06 322

xiu_mvs_v1_base		80.80 192	79.72 205	84.03 175	87.35 251	70.19 92	85.56 270	88.77 268	69.06 320	81.83 161	88.16 266	50.91 362	92.85 230	78.29 164	87.56 198	89.06 322

xiu_mvs_v1_base_debi		80.80 192	79.72 205	84.03 175	87.35 251	70.19 92	85.56 270	88.77 268	69.06 320	81.83 161	88.16 266	50.91 362	92.85 230	78.29 164	87.56 198	89.06 322

ACMM	73.20 8	80.78 196	79.84 200	83.58 193	89.31 151	68.37 138	89.99 85	91.60 153	70.28 284	77.25 261	89.66 216	53.37 327	93.53 185	74.24 216	82.85 293	88.85 335
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		80.68 197	79.62 208	83.83 185	85.07 326	68.01 153	86.99 216	88.83 265	70.36 280	81.38 172	87.99 273	50.11 374	92.51 247	79.02 151	86.89 214	90.97 246

114514_t		80.68 197	79.51 210	84.20 158	94.09 44	67.27 182	89.64 97	91.11 169	58.75 461	74.08 340	90.72 181	58.10 279	95.04 107	69.70 270	89.42 156	90.30 277

IMVS_0407		80.61 199	79.90 198	82.75 242	87.13 265	63.59 287	85.33 279	89.33 233	70.51 275	77.82 247	89.03 236	61.84 230	92.91 227	72.56 237	85.56 243	91.74 217

CANet_DTU		80.61 199	79.87 199	82.83 232	85.60 309	63.17 303	87.36 203	88.65 280	76.37 102	75.88 296	88.44 258	53.51 325	93.07 220	73.30 225	89.74 150	92.25 199

VPA-MVSNet		80.60 201	80.55 179	80.76 295	88.07 211	60.80 353	86.86 223	91.58 154	75.67 123	80.24 200	89.45 227	63.34 200	90.25 347	70.51 259	79.22 343	91.23 236

mvsmamba		80.60 201	79.38 214	84.27 153	89.74 132	67.24 184	87.47 192	86.95 326	70.02 289	75.38 309	88.93 241	51.24 359	92.56 243	75.47 204	89.22 160	93.00 164

PVSNet_BlendedMVS		80.60 201	80.02 194	82.36 253	88.85 168	65.40 225	86.16 255	92.00 128	69.34 309	78.11 241	86.09 329	66.02 173	94.27 140	71.52 246	82.06 303	87.39 377

AdaColmap		80.58 204	79.42 212	84.06 170	93.09 65	68.91 119	89.36 112	88.97 261	69.27 311	75.70 299	89.69 214	57.20 291	95.77 71	63.06 331	88.41 178	87.50 375

EI-MVSNet		80.52 205	79.98 195	82.12 257	84.28 341	63.19 302	86.41 242	88.95 262	74.18 176	78.69 224	87.54 285	66.62 159	92.43 251	72.57 235	80.57 324	90.74 256

viewmambaseed2359dif		80.41 206	79.84 200	82.12 257	82.95 386	62.50 319	83.39 339	88.06 291	67.11 351	80.98 181	90.31 197	66.20 168	91.01 323	74.62 210	84.90 251	92.86 172

XVG-OURS		80.41 206	79.23 220	83.97 181	85.64 307	69.02 116	83.03 352	90.39 192	71.09 254	77.63 253	91.49 154	54.62 315	91.35 306	75.71 198	83.47 284	91.54 226

SDMVSNet		80.38 208	80.18 189	80.99 289	89.03 165	64.94 247	80.45 392	89.40 230	75.19 141	76.61 280	89.98 204	60.61 259	87.69 394	76.83 184	83.55 281	90.33 275

PCF-MVS	73.52 7	80.38 208	78.84 229	85.01 109	87.71 233	68.99 117	83.65 329	91.46 160	63.00 415	77.77 251	90.28 198	66.10 170	95.09 105	61.40 360	88.22 184	90.94 248
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
viewdifsd2359ckpt11		80.37 210	79.73 203	82.30 254	83.70 357	62.39 320	84.20 317	86.67 333	73.22 209	80.90 184	90.62 186	63.00 212	91.56 292	76.81 185	78.44 349	92.95 169

viewmsd2359difaftdt		80.37 210	79.73 203	82.30 254	83.70 357	62.39 320	84.20 317	86.67 333	73.22 209	80.90 184	90.62 186	63.00 212	91.56 292	76.81 185	78.44 349	92.95 169

X-MVStestdata		80.37 210	77.83 252	88.00 17	94.42 26	73.33 21	92.78 24	92.99 67	79.14 27	83.67 126	12.47 541	67.45 148	96.60 44	83.06 93	94.50 64	94.07 84

test_djsdf		80.30 213	79.32 217	83.27 206	83.98 349	65.37 228	90.50 73	90.38 193	68.55 332	76.19 290	88.70 248	56.44 298	93.46 195	78.98 154	80.14 330	90.97 246

v2v482		80.23 214	79.29 218	83.05 221	83.62 359	64.14 271	87.04 213	89.97 209	73.61 192	78.18 240	87.22 293	61.10 249	93.82 166	76.11 192	76.78 372	91.18 237

NR-MVSNet		80.23 214	79.38 214	82.78 239	87.80 224	63.34 297	86.31 248	91.09 170	79.01 32	72.17 367	89.07 234	67.20 151	92.81 235	66.08 304	75.65 389	92.20 202

Anonymous20240529		80.19 216	78.89 228	84.10 161	90.60 107	64.75 256	88.95 129	90.90 176	65.97 372	80.59 193	91.17 166	49.97 376	93.73 174	69.16 276	82.70 297	93.81 102

testing915		80.13 217	80.30 186	79.64 332	89.00 167	58.38 381	87.08 212	84.16 375	74.04 179	80.14 203	89.37 231	64.04 195	90.08 350	66.04 305	88.82 167	90.45 269

IterMVS-LS		80.06 218	79.38 214	82.11 259	85.89 301	63.20 301	86.79 226	89.34 232	74.19 175	75.45 306	86.72 305	66.62 159	92.39 253	72.58 234	76.86 369	90.75 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dtuplus		80.04 219	79.40 213	81.97 263	83.08 376	62.61 315	83.63 332	87.98 293	67.47 349	81.02 180	90.50 192	64.86 187	90.77 336	71.28 251	84.76 255	92.53 184

Effi-MVS+-dtu		80.03 220	78.57 233	84.42 139	85.13 324	68.74 125	88.77 138	88.10 288	74.99 146	74.97 327	83.49 392	57.27 289	93.36 199	73.53 221	80.88 318	91.18 237

v1144		80.03 220	79.03 224	83.01 223	83.78 354	64.51 261	87.11 211	90.57 188	71.96 235	78.08 243	86.20 326	61.41 241	93.94 156	74.93 208	77.23 363	90.60 262

v8		79.97 222	79.02 225	82.80 235	84.09 346	64.50 263	87.96 176	90.29 200	74.13 178	75.24 318	86.81 302	62.88 215	93.89 164	74.39 214	75.40 398	90.00 293

OpenMVS	72.83 10	79.77 223	78.33 240	84.09 165	85.17 320	69.91 97	90.57 70	90.97 173	66.70 356	72.17 367	91.91 131	54.70 313	93.96 153	61.81 355	90.95 127	88.41 351

v10		79.74 224	78.67 230	82.97 227	84.06 347	64.95 244	87.88 182	90.62 185	73.11 212	75.11 322	86.56 316	61.46 240	94.05 152	73.68 219	75.55 391	89.90 299

ECVR-MVS		79.61 225	79.26 219	80.67 297	90.08 119	54.69 439	87.89 181	77.44 463	74.88 154	80.27 199	92.79 108	48.96 394	92.45 250	68.55 282	92.50 94	94.86 23

BH-RMVSNet		79.61 225	78.44 236	83.14 214	89.38 147	65.93 209	84.95 290	87.15 321	73.56 194	78.19 239	89.79 212	56.67 296	93.36 199	59.53 377	86.74 216	90.13 283

v1192		79.59 227	78.43 237	83.07 220	83.55 361	64.52 260	86.93 220	90.58 186	70.83 263	77.78 250	85.90 330	59.15 271	93.94 156	73.96 218	77.19 365	90.76 254

ab-mvs		79.51 228	78.97 226	81.14 285	88.46 190	60.91 351	83.84 324	89.24 245	70.36 280	79.03 218	88.87 245	63.23 205	90.21 348	65.12 312	82.57 298	92.28 198

WR-MVS		79.49 229	79.22 221	80.27 307	88.79 177	58.35 382	85.06 287	88.61 282	78.56 36	77.65 252	88.34 260	63.81 199	90.66 340	64.98 314	77.22 364	91.80 216

v144192		79.47 230	78.37 238	82.78 239	83.35 364	63.96 274	86.96 217	90.36 196	69.99 291	77.50 255	85.67 337	60.66 257	93.77 170	74.27 215	76.58 373	90.62 260

BH-untuned		79.47 230	78.60 232	82.05 260	89.19 158	65.91 210	86.07 257	88.52 283	72.18 230	75.42 307	87.69 279	61.15 248	93.54 184	60.38 368	86.83 215	86.70 404

test1111		79.43 232	79.18 222	80.15 312	89.99 124	53.31 452	87.33 205	77.05 467	75.04 145	80.23 201	92.77 111	48.97 393	92.33 258	68.87 279	92.40 97	94.81 28

mvs_anonymous		79.42 233	79.11 223	80.34 305	84.45 340	57.97 389	82.59 354	87.62 305	67.40 350	76.17 293	88.56 255	68.47 136	89.59 360	70.65 258	86.05 232	93.47 127

thisisatest0530		79.40 234	77.76 257	84.31 147	87.69 237	65.10 240	87.36 203	84.26 373	70.04 288	77.42 257	88.26 264	49.94 377	94.79 121	70.20 263	84.70 257	93.03 160

tttt0517		79.40 234	77.91 248	83.90 184	88.10 209	63.84 278	88.37 161	84.05 376	71.45 245	76.78 274	89.12 233	49.93 379	94.89 114	70.18 264	83.18 290	92.96 168

V42		79.38 236	78.24 242	82.83 232	81.10 421	65.50 223	85.55 273	89.82 213	71.57 243	78.21 238	86.12 328	60.66 257	93.18 214	75.64 199	75.46 395	89.81 304

mamba_0408		79.37 237	77.52 264	84.93 114	88.81 172	67.96 154	65.03 498	88.66 278	70.96 260	79.48 211	89.80 210	58.69 273	94.65 128	70.35 261	85.93 236	92.18 204

jajsoiax		79.29 238	77.96 246	83.27 206	84.68 334	66.57 197	89.25 115	90.16 204	69.20 316	75.46 305	89.49 222	45.75 424	93.13 217	76.84 183	80.80 320	90.11 285

v1921920		79.22 239	78.03 245	82.80 235	83.30 366	63.94 276	86.80 225	90.33 197	69.91 294	77.48 256	85.53 341	58.44 277	93.75 172	73.60 220	76.85 370	90.71 258

AUN-MVS		79.21 240	77.60 262	84.05 173	88.71 182	67.61 167	85.84 265	87.26 318	69.08 319	77.23 263	88.14 270	53.20 329	93.47 194	75.50 203	73.45 420	91.06 241

TAPA-MVS	73.13 9	79.15 241	77.94 247	82.79 238	89.59 134	62.99 309	88.16 170	91.51 156	65.77 373	77.14 269	91.09 169	60.91 252	93.21 208	50.26 447	87.05 209	92.17 207
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 242	77.77 256	83.22 210	84.70 333	66.37 199	89.17 118	90.19 203	69.38 308	75.40 308	89.46 225	44.17 436	93.15 215	76.78 187	80.70 322	90.14 282

UniMVSNet_ETH3D		79.10 243	78.24 242	81.70 268	86.85 276	60.24 365	87.28 207	88.79 267	74.25 174	76.84 271	90.53 191	49.48 383	91.56 292	67.98 286	82.15 301	93.29 135

CDS-MVSNet		79.07 244	77.70 259	83.17 213	87.60 240	68.23 145	84.40 312	86.20 344	67.49 347	76.36 286	86.54 317	61.54 237	90.79 333	61.86 354	87.33 203	90.49 267
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 245	77.88 251	82.38 251	83.07 377	64.80 255	84.08 322	88.95 262	69.01 323	78.69 224	87.17 296	54.70 313	92.43 251	74.69 209	80.57 324	89.89 300

v1240		78.99 246	77.78 255	82.64 244	83.21 370	63.54 291	86.62 234	90.30 199	69.74 302	77.33 259	85.68 336	57.04 292	93.76 171	73.13 228	76.92 367	90.62 260

Anonymous20231211		78.97 247	77.69 260	82.81 234	90.54 109	64.29 268	90.11 84	91.51 156	65.01 390	76.16 294	88.13 271	50.56 368	93.03 225	69.68 271	77.56 362	91.11 239

v7n		78.97 247	77.58 263	83.14 214	83.45 363	65.51 222	88.32 163	91.21 164	73.69 190	72.41 363	86.32 323	57.93 280	93.81 167	69.18 275	75.65 389	90.11 285

icg_test_0407_2		78.92 249	78.93 227	78.90 347	87.13 265	63.59 287	76.58 442	89.33 233	70.51 275	77.82 247	89.03 236	61.84 230	81.38 454	72.56 237	85.56 243	91.74 217

TAMVS		78.89 250	77.51 266	83.03 222	87.80 224	67.79 162	84.72 294	85.05 360	67.63 344	76.75 275	87.70 278	62.25 224	90.82 332	58.53 389	87.13 208	90.49 267

c3_l		78.75 251	77.91 248	81.26 281	82.89 387	61.56 337	84.09 321	89.13 253	69.97 292	75.56 301	84.29 369	66.36 164	92.09 266	73.47 223	75.48 393	90.12 284

tt0805		78.73 252	77.83 252	81.43 274	85.17 320	60.30 364	89.41 109	90.90 176	71.21 251	77.17 268	88.73 247	46.38 413	93.21 208	72.57 235	78.96 344	90.79 252

v148		78.72 253	77.80 254	81.47 273	82.73 390	61.96 331	86.30 249	88.08 289	73.26 206	76.18 291	85.47 343	62.46 220	92.36 255	71.92 245	73.82 417	90.09 287

VPNet		78.69 254	78.66 231	78.76 349	88.31 196	55.72 427	84.45 307	86.63 336	76.79 81	78.26 237	90.55 190	59.30 270	89.70 359	66.63 299	77.05 366	90.88 249

ET-MVSNet_ETH3D		78.63 255	76.63 287	84.64 127	86.73 281	69.47 106	85.01 288	84.61 366	69.54 304	66.51 443	86.59 313	50.16 373	91.75 282	76.26 190	84.24 267	92.69 178

anonymousdsp		78.60 256	77.15 272	82.98 226	80.51 427	67.08 187	87.24 208	89.53 226	65.66 376	75.16 320	87.19 295	52.52 331	92.25 260	77.17 177	79.34 341	89.61 309

miper_ehance_all_eth		78.59 257	77.76 257	81.08 287	82.66 392	61.56 337	83.65 329	89.15 251	68.87 327	75.55 302	83.79 383	66.49 162	92.03 267	73.25 226	76.39 378	89.64 308

VortexMVS		78.57 258	77.89 250	80.59 298	85.89 301	62.76 314	85.61 268	89.62 223	72.06 233	74.99 326	85.38 345	55.94 302	90.77 336	74.99 207	76.58 373	88.23 355

WR-MVS_H		78.51 259	78.49 234	78.56 354	88.02 213	56.38 417	88.43 155	92.67 86	77.14 69	73.89 342	87.55 284	66.25 166	89.24 367	58.92 384	73.55 419	90.06 291

GBi-Net		78.40 260	77.40 267	81.40 276	87.60 240	63.01 305	88.39 158	89.28 239	71.63 239	75.34 311	87.28 289	54.80 309	91.11 315	62.72 336	79.57 334	90.09 287

test1		78.40 260	77.40 267	81.40 276	87.60 240	63.01 305	88.39 158	89.28 239	71.63 239	75.34 311	87.28 289	54.80 309	91.11 315	62.72 336	79.57 334	90.09 287

Vis-MVSNet (Re-imp)		78.36 262	78.45 235	78.07 366	88.64 184	51.78 464	86.70 230	79.63 445	74.14 177	75.11 322	90.83 178	61.29 245	89.75 357	58.10 394	91.60 112	92.69 178

Anonymous202405211		78.25 263	77.01 274	81.99 262	91.03 97	60.67 357	84.77 293	83.90 378	70.65 273	80.00 204	91.20 164	41.08 457	91.43 304	65.21 311	85.26 248	93.85 96

CP-MVSNet		78.22 264	78.34 239	77.84 370	87.83 223	54.54 441	87.94 178	91.17 166	77.65 48	73.48 348	88.49 256	62.24 225	88.43 384	62.19 347	74.07 412	90.55 264

BH-w/o		78.21 265	77.33 270	80.84 293	88.81 172	65.13 237	84.87 291	87.85 300	69.75 300	74.52 335	84.74 361	61.34 243	93.11 218	58.24 393	85.84 239	84.27 446

FMVSNet2		78.20 266	77.21 271	81.20 283	87.60 240	62.89 313	87.47 192	89.02 257	71.63 239	75.29 317	87.28 289	54.80 309	91.10 318	62.38 344	79.38 340	89.61 309

MVS		78.19 267	76.99 276	81.78 266	85.66 306	66.99 188	84.66 296	90.47 190	55.08 484	72.02 370	85.27 347	63.83 198	94.11 150	66.10 303	89.80 149	84.24 447

Baseline_NR-MVSNet		78.15 268	78.33 240	77.61 376	85.79 303	56.21 421	86.78 227	85.76 351	73.60 193	77.93 246	87.57 282	65.02 184	88.99 372	67.14 296	75.33 400	87.63 368

CNLPA		78.08 269	76.79 281	81.97 263	90.40 112	71.07 75	87.59 189	84.55 367	66.03 370	72.38 364	89.64 217	57.56 285	86.04 411	59.61 376	83.35 286	88.79 338

cl22		78.07 270	77.01 274	81.23 282	82.37 400	61.83 333	83.55 334	87.98 293	68.96 326	75.06 324	83.87 379	61.40 242	91.88 278	73.53 221	76.39 378	89.98 296

PLC	70.83 11	78.05 271	76.37 293	83.08 219	91.88 86	67.80 161	88.19 168	89.46 228	64.33 399	69.87 395	88.38 259	53.66 323	93.58 177	58.86 385	82.73 295	87.86 364
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 272	76.49 288	82.62 245	83.16 374	66.96 191	86.94 219	87.45 310	72.45 223	71.49 376	84.17 376	54.79 312	91.58 289	67.61 289	80.31 327	89.30 318

PS-CasMVS		78.01 273	78.09 244	77.77 372	87.71 233	54.39 443	88.02 174	91.22 163	77.50 56	73.26 350	88.64 251	60.73 253	88.41 385	61.88 353	73.88 416	90.53 265

HY-MVS	69.67 12	77.95 274	77.15 272	80.36 304	87.57 249	60.21 366	83.37 341	87.78 302	66.11 367	75.37 310	87.06 300	63.27 202	90.48 342	61.38 361	82.43 299	90.40 272

eth_miper_zixun_eth		77.92 275	76.69 285	81.61 271	83.00 380	61.98 330	83.15 345	89.20 247	69.52 305	74.86 329	84.35 368	61.76 233	92.56 243	71.50 248	72.89 425	90.28 278

FMVSNet3		77.88 276	76.85 279	80.97 291	86.84 277	62.36 322	86.52 239	88.77 268	71.13 252	75.34 311	86.66 311	54.07 319	91.10 318	62.72 336	79.57 334	89.45 313

miper_enhance_ethall		77.87 277	76.86 278	80.92 292	81.65 409	61.38 341	82.68 353	88.98 259	65.52 378	75.47 303	82.30 413	65.76 178	92.00 270	72.95 230	76.39 378	89.39 315

FE-MVS		77.78 278	75.68 300	84.08 166	88.09 210	66.00 207	83.13 346	87.79 301	68.42 337	78.01 244	85.23 349	45.50 427	95.12 99	59.11 382	85.83 240	91.11 239

PEN-MVS		77.73 279	77.69 260	77.84 370	87.07 273	53.91 446	87.91 180	91.18 165	77.56 53	73.14 352	88.82 246	61.23 246	89.17 369	59.95 372	72.37 427	90.43 270

cl____		77.72 280	76.76 282	80.58 299	82.49 397	60.48 361	83.09 348	87.87 298	69.22 314	74.38 338	85.22 350	62.10 227	91.53 297	71.09 252	75.41 397	89.73 307

DIV-MVS_self_test		77.72 280	76.76 282	80.58 299	82.48 398	60.48 361	83.09 348	87.86 299	69.22 314	74.38 338	85.24 348	62.10 227	91.53 297	71.09 252	75.40 398	89.74 306

sd_testset		77.70 282	77.40 267	78.60 352	89.03 165	60.02 367	79.00 414	85.83 350	75.19 141	76.61 280	89.98 204	54.81 308	85.46 419	62.63 340	83.55 281	90.33 275

PAPM		77.68 283	76.40 292	81.51 272	87.29 261	61.85 332	83.78 325	89.59 224	64.74 392	71.23 378	88.70 248	62.59 217	93.66 176	52.66 431	87.03 210	89.01 327

SSM_04072		77.67 284	77.52 264	78.12 364	88.81 172	67.96 154	65.03 498	88.66 278	70.96 260	79.48 211	89.80 210	58.69 273	74.23 495	70.35 261	85.93 236	92.18 204

FBQ-MVS		77.66 285	76.04 296	82.50 248	88.78 179	63.76 282	86.60 235	84.86 362	70.85 262	77.63 253	82.83 405	47.83 399	92.10 265	60.18 371	84.82 254	91.65 222

CHOSEN 1792x2688		77.63 286	75.69 299	83.44 199	89.98 125	68.58 133	78.70 419	87.50 308	56.38 478	75.80 298	86.84 301	58.67 275	91.40 305	61.58 358	85.75 241	90.34 274

HyFIR lowres test		77.53 287	75.40 307	83.94 183	89.59 134	66.62 195	80.36 393	88.64 281	56.29 479	76.45 283	85.17 351	57.64 284	93.28 201	61.34 362	83.10 291	91.91 213

FMVSNet1		77.44 288	76.12 295	81.40 276	86.81 278	63.01 305	88.39 158	89.28 239	70.49 279	74.39 337	87.28 289	49.06 392	91.11 315	60.91 364	78.52 347	90.09 287

TR-MVS		77.44 288	76.18 294	81.20 283	88.24 199	63.24 299	84.61 301	86.40 340	67.55 346	77.81 249	86.48 319	54.10 318	93.15 215	57.75 397	82.72 296	87.20 387

1112_ss		77.40 290	76.43 290	80.32 306	89.11 164	60.41 363	83.65 329	87.72 304	62.13 430	73.05 353	86.72 305	62.58 218	89.97 353	62.11 350	80.80 320	90.59 263

thisisatest0515		77.33 291	75.38 308	83.18 212	85.27 319	63.80 279	82.11 362	83.27 388	65.06 388	75.91 295	83.84 381	49.54 382	94.27 140	67.24 294	86.19 227	91.48 230

test2506		77.30 292	76.49 288	79.74 327	90.08 119	52.02 458	87.86 183	63.10 512	74.88 154	80.16 202	92.79 108	38.29 475	92.35 256	68.74 281	92.50 94	94.86 23

pm-mvs1		77.25 293	76.68 286	78.93 346	84.22 343	58.62 379	86.41 242	88.36 285	71.37 246	73.31 349	88.01 272	61.22 247	89.15 370	64.24 320	73.01 424	89.03 326

IMVS_0404		77.16 294	76.42 291	79.37 338	87.13 265	63.59 287	77.12 439	89.33 233	70.51 275	66.22 446	89.03 236	50.36 371	82.78 442	72.56 237	85.56 243	91.74 217

LCM-MVSNet-Re		77.05 295	76.94 277	77.36 380	87.20 262	51.60 465	80.06 398	80.46 431	75.20 140	67.69 422	86.72 305	62.48 219	88.98 373	63.44 324	89.25 158	91.51 227

DTE-MVSNet		76.99 296	76.80 280	77.54 379	86.24 293	53.06 456	87.52 190	90.66 184	77.08 73	72.50 361	88.67 250	60.48 261	89.52 361	57.33 401	70.74 439	90.05 292

baseline1		76.98 297	76.75 284	77.66 374	88.13 207	55.66 428	85.12 284	81.89 411	73.04 214	76.79 273	88.90 242	62.43 221	87.78 393	63.30 326	71.18 437	89.55 311

LS3D		76.95 298	74.82 318	83.37 203	90.45 110	67.36 178	89.15 122	86.94 327	61.87 433	69.52 398	90.61 188	51.71 352	94.53 131	46.38 469	86.71 217	88.21 357

GA-MVS		76.87 299	75.17 315	81.97 263	82.75 389	62.58 316	81.44 374	86.35 342	72.16 232	74.74 330	82.89 403	46.20 418	92.02 269	68.85 280	81.09 315	91.30 235

DP-MVS		76.78 300	74.57 321	83.42 200	93.29 54	69.46 108	88.55 152	83.70 380	63.98 405	70.20 386	88.89 244	54.01 321	94.80 120	46.66 466	81.88 307	86.01 417

cascas		76.72 301	74.64 320	82.99 224	85.78 304	65.88 211	82.33 358	89.21 246	60.85 439	72.74 357	81.02 426	47.28 402	93.75 172	67.48 291	85.02 249	89.34 317

testing91		76.54 302	75.66 302	79.18 343	88.43 192	55.89 424	81.08 379	83.00 396	73.76 188	75.34 311	84.29 369	46.20 418	90.07 351	64.33 318	84.50 259	91.58 225

1314		76.53 303	75.30 313	80.21 310	83.93 350	62.32 324	84.66 296	88.81 266	60.23 444	70.16 389	84.07 378	55.30 306	90.73 339	67.37 292	83.21 289	87.59 371

thres100view900		76.50 304	75.55 304	79.33 339	89.52 137	56.99 406	85.83 266	83.23 389	73.94 183	76.32 287	87.12 297	51.89 348	91.95 272	48.33 457	83.75 275	89.07 320

thres600view7		76.50 304	75.44 305	79.68 330	89.40 145	57.16 403	85.53 275	83.23 389	73.79 187	76.26 288	87.09 298	51.89 348	91.89 277	48.05 462	83.72 278	90.00 293

thres400		76.50 304	75.37 309	79.86 320	89.13 160	57.65 397	85.17 281	83.60 381	73.41 201	76.45 283	86.39 321	52.12 338	91.95 272	48.33 457	83.75 275	90.00 293

MonoMVSNet		76.49 307	75.80 297	78.58 353	81.55 412	58.45 380	86.36 247	86.22 343	74.87 156	74.73 331	83.73 385	51.79 351	88.73 378	70.78 254	72.15 430	88.55 348

usedtu_dtu_shiyan1		76.43 308	75.32 311	79.76 325	83.00 380	60.72 354	81.74 366	88.76 272	68.99 324	72.98 354	84.19 374	56.41 299	90.27 344	62.39 342	79.40 338	88.31 352

FE-MVSNET3		76.43 308	75.32 311	79.76 325	83.00 380	60.72 354	81.74 366	88.76 272	68.99 324	72.98 354	84.19 374	56.41 299	90.27 344	62.39 342	79.40 338	88.31 352

tfpn200view9		76.42 310	75.37 309	79.55 336	89.13 160	57.65 397	85.17 281	83.60 381	73.41 201	76.45 283	86.39 321	52.12 338	91.95 272	48.33 457	83.75 275	89.07 320

Test_1112_low_res		76.40 311	75.44 305	79.27 340	89.28 153	58.09 385	81.69 369	87.07 324	59.53 452	72.48 362	86.67 310	61.30 244	89.33 364	60.81 366	80.15 329	90.41 271

F-COLMAP		76.38 312	74.33 327	82.50 248	89.28 153	66.95 192	88.41 157	89.03 256	64.05 403	66.83 435	88.61 252	46.78 408	92.89 228	57.48 398	78.55 346	87.67 367

LTVRE_ROB	69.57 13	76.25 313	74.54 323	81.41 275	88.60 185	64.38 267	79.24 409	89.12 254	70.76 267	69.79 397	87.86 275	49.09 391	93.20 211	56.21 413	80.16 328	86.65 406
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		76.12 314	74.46 325	81.13 286	85.37 316	69.79 99	84.42 311	87.95 296	65.03 389	67.46 426	85.33 346	53.28 328	91.73 284	58.01 395	83.27 288	81.85 473
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 315	74.27 328	81.62 269	83.20 371	64.67 257	83.60 333	89.75 218	69.75 300	71.85 371	87.09 298	32.78 491	92.11 264	69.99 267	80.43 326	88.09 359

testing99		76.09 316	75.12 316	79.00 344	88.16 204	55.50 430	80.79 383	81.40 418	73.30 205	75.17 319	84.27 372	44.48 433	90.02 352	64.28 319	84.22 268	91.48 230

ACMH+	68.96 14	76.01 317	74.01 329	82.03 261	88.60 185	65.31 233	88.86 132	87.55 306	70.25 286	67.75 421	87.47 287	41.27 455	93.19 213	58.37 391	75.94 386	87.60 369

ACMH	67.68 16	75.89 318	73.93 331	81.77 267	88.71 182	66.61 196	88.62 148	89.01 258	69.81 295	66.78 436	86.70 309	41.95 452	91.51 299	55.64 414	78.14 355	87.17 389
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 319	73.36 340	83.31 204	84.76 332	66.03 204	83.38 340	85.06 359	70.21 287	69.40 399	81.05 425	45.76 423	94.66 127	65.10 313	75.49 392	89.25 319
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		75.70 320	73.83 334	81.30 279	83.26 368	61.79 334	82.57 355	80.65 426	66.81 353	66.88 434	83.42 393	57.86 282	92.19 262	63.47 323	79.57 334	89.91 298

WTY-MVS		75.65 321	75.68 300	75.57 397	86.40 291	56.82 408	77.92 432	82.40 404	65.10 387	76.18 291	87.72 277	63.13 210	80.90 457	60.31 369	81.96 304	89.00 329

thres200		75.55 322	74.47 324	78.82 348	87.78 227	57.85 392	83.07 350	83.51 384	72.44 225	75.84 297	84.42 364	52.08 341	91.75 282	47.41 464	83.64 280	86.86 399

test_vis1_n_1920		75.52 323	75.78 298	74.75 411	79.84 436	57.44 401	83.26 343	85.52 353	62.83 419	79.34 216	86.17 327	45.10 429	79.71 461	78.75 156	81.21 314	87.10 395

EPNet_dtu		75.46 324	74.86 317	77.23 383	82.57 395	54.60 440	86.89 221	83.09 393	71.64 238	66.25 445	85.86 332	55.99 301	88.04 389	54.92 419	86.55 219	89.05 325
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 325	73.87 333	80.11 313	82.69 391	64.85 254	81.57 371	83.47 385	69.16 317	70.49 383	84.15 377	51.95 344	88.15 387	69.23 274	72.14 431	87.34 382

XXY-MVS		75.41 326	75.56 303	74.96 406	83.59 360	57.82 393	80.59 389	83.87 379	66.54 363	74.93 328	88.31 261	63.24 204	80.09 460	62.16 348	76.85 370	86.97 397

reproduce_monomvs		75.40 327	74.38 326	78.46 359	83.92 351	57.80 394	83.78 325	86.94 327	73.47 199	72.25 366	84.47 363	38.74 471	89.27 366	75.32 205	70.53 440	88.31 352

TransMVSNet (Re)		75.39 328	74.56 322	77.86 369	85.50 313	57.10 405	86.78 227	86.09 347	72.17 231	71.53 375	87.34 288	63.01 211	89.31 365	56.84 407	61.83 482	87.17 389

CostFormer		75.24 329	73.90 332	79.27 340	82.65 393	58.27 384	80.80 382	82.73 402	61.57 434	75.33 315	83.13 398	55.52 304	91.07 321	64.98 314	78.34 354	88.45 349

testing11		75.14 330	74.01 329	78.53 356	88.16 204	56.38 417	80.74 386	80.42 433	70.67 269	72.69 360	83.72 386	43.61 440	89.86 354	62.29 346	83.76 274	89.36 316

testing3-2		75.12 331	75.19 314	74.91 407	90.40 112	45.09 495	80.29 395	78.42 455	78.37 41	76.54 282	87.75 276	44.36 434	87.28 399	57.04 404	83.49 283	92.37 193

D2MVS		74.82 332	73.21 341	79.64 332	79.81 437	62.56 318	80.34 394	87.35 312	64.37 398	68.86 405	82.66 408	46.37 414	90.10 349	67.91 287	81.24 313	86.25 410

pmmvs6		74.69 333	73.39 338	78.61 351	81.38 416	57.48 400	86.64 233	87.95 296	64.99 391	70.18 387	86.61 312	50.43 370	89.52 361	62.12 349	70.18 442	88.83 336

SD_0403		74.65 334	74.77 319	74.29 415	86.20 295	47.42 484	83.71 327	85.12 357	69.30 310	68.50 412	87.95 274	59.40 269	86.05 410	49.38 451	83.35 286	89.40 314

tfpnnormal		74.39 335	73.16 342	78.08 365	86.10 299	58.05 386	84.65 298	87.53 307	70.32 283	71.22 379	85.63 338	54.97 307	89.86 354	43.03 483	75.02 405	86.32 409

IterMVS		74.29 336	72.94 345	78.35 360	81.53 413	63.49 293	81.58 370	82.49 403	68.06 341	69.99 392	83.69 387	51.66 353	85.54 417	65.85 307	71.64 434	86.01 417
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 337	72.42 351	79.80 322	83.76 355	59.59 372	85.92 261	86.64 335	66.39 364	66.96 433	87.58 281	39.46 466	91.60 288	65.76 308	69.27 445	88.22 356

SCA		74.22 338	72.33 352	79.91 318	84.05 348	62.17 326	79.96 401	79.29 449	66.30 365	72.38 364	80.13 438	51.95 344	88.60 381	59.25 380	77.67 361	88.96 331

mmtdpeth		74.16 339	73.01 344	77.60 378	83.72 356	61.13 343	85.10 285	85.10 358	72.06 233	77.21 267	80.33 435	43.84 438	85.75 413	77.14 178	52.61 502	85.91 420

miper_lstm_enhance		74.11 340	73.11 343	77.13 384	80.11 432	59.62 371	72.23 468	86.92 329	66.76 355	70.40 384	82.92 402	56.93 293	82.92 441	69.06 277	72.63 426	88.87 334

testing222		74.04 341	72.66 348	78.19 362	87.89 219	55.36 431	81.06 380	79.20 450	71.30 249	74.65 333	83.57 391	39.11 470	88.67 380	51.43 439	85.75 241	90.53 265

EG-PatchMatch MVS		74.04 341	71.82 356	80.71 296	84.92 328	67.42 174	85.86 264	88.08 289	66.04 369	64.22 461	83.85 380	35.10 487	92.56 243	57.44 399	80.83 319	82.16 471

pmmvs4		74.03 343	71.91 355	80.39 302	81.96 405	68.32 139	81.45 373	82.14 409	59.32 453	69.87 395	85.13 352	52.40 334	88.13 388	60.21 370	74.74 408	84.73 442

MS-PatchMatch		73.83 344	72.67 347	77.30 382	83.87 352	66.02 205	81.82 364	84.66 365	61.37 437	68.61 408	82.82 406	47.29 401	88.21 386	59.27 379	84.32 266	77.68 490

test_cas_vis1_n_1920		73.76 345	73.74 335	73.81 422	75.90 469	59.77 369	80.51 390	82.40 404	58.30 463	81.62 169	85.69 335	44.35 435	76.41 479	76.29 189	78.61 345	85.23 432

myMVS_eth3d28		73.62 346	73.53 337	73.90 421	88.20 200	47.41 485	78.06 429	79.37 447	74.29 173	73.98 341	84.29 369	44.67 430	83.54 436	51.47 437	87.39 202	90.74 256

sss		73.60 347	73.64 336	73.51 424	82.80 388	55.01 436	76.12 444	81.69 414	62.47 425	74.68 332	85.85 333	57.32 288	78.11 468	60.86 365	80.93 316	87.39 377

RPMNet		73.51 348	70.49 380	82.58 247	81.32 419	65.19 235	75.92 446	92.27 108	57.60 470	72.73 358	76.45 468	52.30 335	95.43 84	48.14 461	77.71 358	87.11 393

WBMVS		73.43 349	72.81 346	75.28 403	87.91 218	50.99 471	78.59 422	81.31 420	65.51 380	74.47 336	84.83 358	46.39 412	86.68 403	58.41 390	77.86 356	88.17 358

blended_shiyan8		73.38 350	71.17 368	80.02 315	78.36 452	61.51 339	82.43 356	87.28 313	65.40 382	68.61 408	77.53 463	51.91 347	91.00 326	63.28 327	65.76 464	87.53 373

blended_shiyan6		73.38 350	71.17 368	80.01 316	78.36 452	61.48 340	82.43 356	87.27 316	65.40 382	68.56 410	77.55 462	51.94 346	91.01 323	63.27 328	65.76 464	87.55 372

SixPastTwentyTwo		73.37 352	71.26 367	79.70 329	85.08 325	57.89 391	85.57 269	83.56 383	71.03 258	65.66 449	85.88 331	42.10 450	92.57 242	59.11 382	63.34 476	88.65 344

CR-MVSNet		73.37 352	71.27 366	79.67 331	81.32 419	65.19 235	75.92 446	80.30 436	59.92 448	72.73 358	81.19 423	52.50 332	86.69 402	59.84 373	77.71 358	87.11 393

MSDG		73.36 354	70.99 371	80.49 301	84.51 339	65.80 215	80.71 387	86.13 346	65.70 375	65.46 451	83.74 384	44.60 431	90.91 329	51.13 440	76.89 368	84.74 441

SSC-MVS3.2		73.35 355	73.39 338	73.23 425	85.30 318	49.01 480	74.58 459	81.57 415	75.21 139	73.68 345	85.58 340	52.53 330	82.05 448	54.33 423	77.69 360	88.63 345

usedtu_blend_shiyan5		73.29 356	70.96 372	80.25 308	77.80 459	62.16 327	84.44 308	87.38 311	64.41 396	68.09 415	76.28 472	51.32 355	91.23 311	63.21 329	65.76 464	87.35 379

tpm2		73.26 357	71.46 361	78.63 350	83.34 365	56.71 411	80.65 388	80.40 434	56.63 477	73.55 347	82.02 418	51.80 350	91.24 310	56.35 412	78.42 352	87.95 361

gbinet_0.2-2-1-0.02		73.24 358	70.86 375	80.39 302	78.03 457	61.62 336	83.10 347	86.69 332	65.98 371	69.29 402	76.15 475	49.77 380	91.51 299	62.75 335	66.00 462	88.03 360

RPSCF		73.23 359	71.46 361	78.54 355	82.50 396	59.85 368	82.18 361	82.84 401	58.96 457	71.15 380	89.41 229	45.48 428	84.77 426	58.82 386	71.83 433	91.02 245

PatchmatchNet		73.12 360	71.33 364	78.49 358	83.18 372	60.85 352	79.63 404	78.57 454	64.13 400	71.73 372	79.81 443	51.20 360	85.97 412	57.40 400	76.36 383	88.66 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nomal-1		73.10 361	71.76 357	77.13 384	82.58 394	65.50 223	73.53 465	79.64 444	66.14 366	72.17 367	81.27 422	46.45 411	81.47 453	62.08 351	81.93 306	84.42 445

UBG		73.08 362	72.27 353	75.51 399	88.02 213	51.29 469	78.35 426	77.38 464	65.52 378	73.87 343	82.36 411	45.55 425	86.48 406	55.02 418	84.39 265	88.75 340

COLMAP_ROB	66.92 17	73.01 363	70.41 382	80.81 294	87.13 265	65.63 219	88.30 165	84.19 374	62.96 416	63.80 466	87.69 279	38.04 476	92.56 243	46.66 466	74.91 406	84.24 447
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 364	72.58 349	74.25 416	84.28 341	50.85 472	86.41 242	83.45 386	44.56 505	73.23 351	87.54 285	49.38 385	85.70 414	65.90 306	78.44 349	86.19 412

wanda-best-256-512		72.94 365	70.66 376	79.79 323	77.80 459	61.03 348	81.31 376	87.15 321	65.18 385	68.09 415	76.28 472	51.32 355	90.97 327	63.06 331	65.76 464	87.35 379

FE-blended-shiyan7		72.94 365	70.66 376	79.79 323	77.80 459	61.03 348	81.31 376	87.15 321	65.18 385	68.09 415	76.28 472	51.32 355	90.97 327	63.06 331	65.76 464	87.35 379

test-LLR		72.94 365	72.43 350	74.48 412	81.35 417	58.04 387	78.38 423	77.46 461	66.66 357	69.95 393	79.00 450	48.06 397	79.24 462	66.13 301	84.83 252	86.15 413

FE-MVSNET2		72.88 368	71.28 365	77.67 373	78.30 454	57.78 395	84.43 309	88.92 264	69.56 303	64.61 458	81.67 420	46.73 410	88.54 383	59.33 378	67.99 454	86.69 405

test_0402		72.79 369	70.44 381	79.84 321	88.13 207	65.99 208	85.93 260	84.29 371	65.57 377	67.40 429	85.49 342	46.92 405	92.61 239	35.88 499	74.38 411	80.94 478

tpmrst		72.39 370	72.13 354	73.18 429	80.54 426	49.91 476	79.91 402	79.08 451	63.11 413	71.69 373	79.95 440	55.32 305	82.77 443	65.66 309	73.89 415	86.87 398

PatchMatch-RL		72.38 371	70.90 373	76.80 388	88.60 185	67.38 177	79.53 405	76.17 474	62.75 421	69.36 400	82.00 419	45.51 426	84.89 425	53.62 426	80.58 323	78.12 489

CL-MVSNet_self_test		72.37 372	71.46 361	75.09 405	79.49 443	53.53 448	80.76 385	85.01 361	69.12 318	70.51 382	82.05 417	57.92 281	84.13 430	52.27 433	66.00 462	87.60 369

tpm		72.37 372	71.71 358	74.35 414	82.19 401	52.00 459	79.22 410	77.29 465	64.56 394	72.95 356	83.68 388	51.35 354	83.26 440	58.33 392	75.80 387	87.81 365

blend_shiyan4		72.29 374	69.65 388	80.21 310	78.24 455	62.16 327	82.29 359	87.27 316	65.41 381	68.43 414	76.42 471	39.91 464	91.23 311	63.21 329	65.66 469	87.22 386

ETVMVS		72.25 375	71.05 370	75.84 393	87.77 229	51.91 461	79.39 407	74.98 477	69.26 312	73.71 344	82.95 401	40.82 459	86.14 409	46.17 470	84.43 264	89.47 312

sc_t1		72.19 376	69.51 389	80.23 309	84.81 330	61.09 345	84.68 295	80.22 438	60.70 440	71.27 377	83.58 390	36.59 482	89.24 367	60.41 367	63.31 477	90.37 273

UWE-MVS		72.13 377	71.49 360	74.03 419	86.66 284	47.70 482	81.40 375	76.89 469	63.60 409	75.59 300	84.22 373	39.94 463	85.62 416	48.98 454	86.13 229	88.77 339

PVSNet	64.34 18	72.08 378	70.87 374	75.69 395	86.21 294	56.44 415	74.37 461	80.73 425	62.06 431	70.17 388	82.23 415	42.86 444	83.31 439	54.77 420	84.45 263	87.32 383

WB-MVSnew		71.96 379	71.65 359	72.89 431	84.67 337	51.88 462	82.29 359	77.57 460	62.31 427	73.67 346	83.00 400	53.49 326	81.10 456	45.75 474	82.13 302	85.70 424

pmmvs5		71.55 380	70.20 385	75.61 396	77.83 458	56.39 416	81.74 366	80.89 422	57.76 468	67.46 426	84.49 362	49.26 389	85.32 421	57.08 403	75.29 401	85.11 436

test-mter		71.41 381	70.39 383	74.48 412	81.35 417	58.04 387	78.38 423	77.46 461	60.32 443	69.95 393	79.00 450	36.08 485	79.24 462	66.13 301	84.83 252	86.15 413

K. test v3		71.19 382	68.51 396	79.21 342	83.04 379	57.78 395	84.35 313	76.91 468	72.90 218	62.99 469	82.86 404	39.27 467	91.09 320	61.65 357	52.66 501	88.75 340

dmvs_re		71.14 383	70.58 378	72.80 432	81.96 405	59.68 370	75.60 450	79.34 448	68.55 332	69.27 403	80.72 431	49.42 384	76.54 476	52.56 432	77.79 357	82.19 470

tpmvs		71.09 384	69.29 391	76.49 389	82.04 403	56.04 422	78.92 417	81.37 419	64.05 403	67.18 431	78.28 456	49.74 381	89.77 356	49.67 450	72.37 427	83.67 454

AllTest		70.96 385	68.09 402	79.58 334	85.15 322	63.62 283	84.58 302	79.83 441	62.31 427	60.32 480	86.73 303	32.02 492	88.96 375	50.28 445	71.57 435	86.15 413

0.4-1-1-0.1		70.93 386	67.94 406	79.91 318	79.35 445	61.27 342	78.95 416	82.19 408	63.36 410	67.50 424	69.40 496	39.83 465	91.04 322	62.44 341	68.40 451	87.40 376

test_fmvs1		70.93 386	70.52 379	72.16 436	73.71 481	55.05 435	80.82 381	78.77 453	51.21 496	78.58 228	84.41 365	31.20 496	76.94 474	75.88 197	80.12 331	84.47 444

test_fmvs1_n		70.86 388	70.24 384	72.73 433	72.51 493	55.28 433	81.27 378	79.71 443	51.49 495	78.73 223	84.87 357	27.54 502	77.02 473	76.06 193	79.97 332	85.88 421

Patchmtry		70.74 389	69.16 393	75.49 400	80.72 423	54.07 445	74.94 457	80.30 436	58.34 462	70.01 390	81.19 423	52.50 332	86.54 404	53.37 428	71.09 438	85.87 422

MIMVSNet		70.69 390	69.30 390	74.88 408	84.52 338	56.35 419	75.87 448	79.42 446	64.59 393	67.76 420	82.41 410	41.10 456	81.54 451	46.64 468	81.34 311	86.75 403

tpm cat1		70.57 391	68.31 398	77.35 381	82.41 399	57.95 390	78.08 428	80.22 438	52.04 491	68.54 411	77.66 461	52.00 343	87.84 392	51.77 434	72.07 432	86.25 410

OpenMVS_ROB	64.09 19	70.56 392	68.19 399	77.65 375	80.26 428	59.41 375	85.01 288	82.96 398	58.76 460	65.43 452	82.33 412	37.63 478	91.23 311	45.34 478	76.03 385	82.32 468

pmmvs-eth3d		70.50 393	67.83 409	78.52 357	77.37 465	66.18 202	81.82 364	81.51 416	58.90 458	63.90 465	80.42 433	42.69 445	86.28 408	58.56 388	65.30 471	83.11 460

tt0320		70.49 394	68.03 403	77.89 368	84.78 331	59.12 376	83.55 334	80.44 432	58.13 465	67.43 428	80.41 434	39.26 468	87.54 396	55.12 416	63.18 478	86.99 396

USDC		70.33 395	68.37 397	76.21 391	80.60 425	56.23 420	79.19 411	86.49 338	60.89 438	61.29 475	85.47 343	31.78 494	89.47 363	53.37 428	76.21 384	82.94 464

Patchmatch-RL test		70.24 396	67.78 411	77.61 376	77.43 464	59.57 373	71.16 472	70.33 491	62.94 417	68.65 407	72.77 487	50.62 367	85.49 418	69.58 272	66.58 459	87.77 366

CMPMVS	51.72 21	70.19 397	68.16 400	76.28 390	73.15 488	57.55 399	79.47 406	83.92 377	48.02 501	56.48 493	84.81 359	43.13 442	86.42 407	62.67 339	81.81 308	84.89 439
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tt0320-xc		70.11 398	67.45 417	78.07 366	85.33 317	59.51 374	83.28 342	78.96 452	58.77 459	67.10 432	80.28 436	36.73 481	87.42 397	56.83 408	59.77 490	87.29 384

ppachtmachnet_test		70.04 399	67.34 419	78.14 363	79.80 438	61.13 343	79.19 411	80.59 427	59.16 455	65.27 453	79.29 447	46.75 409	87.29 398	49.33 452	66.72 457	86.00 419

0.3-1-1-0.015		70.03 400	66.80 425	79.72 328	78.18 456	61.07 346	77.63 434	82.32 407	62.65 423	65.50 450	67.29 497	37.62 479	90.91 329	61.99 352	68.04 453	87.19 388

0.4-1-1-0.2		70.01 401	66.86 424	79.44 337	77.61 462	60.64 358	76.77 441	82.34 406	62.40 426	65.91 448	66.65 498	40.05 462	90.83 331	61.77 356	68.24 452	86.86 399

dtuonly		69.95 402	69.98 386	69.85 452	73.09 489	49.46 479	74.55 460	76.40 471	57.56 472	67.82 419	86.31 324	50.89 366	74.23 495	61.46 359	81.71 309	85.86 423

gg-mvs-nofinetune		69.95 402	67.96 404	75.94 392	83.07 377	54.51 442	77.23 438	70.29 492	63.11 413	70.32 385	62.33 501	43.62 439	88.69 379	53.88 425	87.76 196	84.62 443

TESTMET0.1,1		69.89 404	69.00 394	72.55 434	79.27 447	56.85 407	78.38 423	74.71 481	57.64 469	68.09 415	77.19 465	37.75 477	76.70 475	63.92 321	84.09 269	84.10 450

test_vis1_n		69.85 405	69.21 392	71.77 439	72.66 492	55.27 434	81.48 372	76.21 473	52.03 492	75.30 316	83.20 397	28.97 499	76.22 481	74.60 211	78.41 353	83.81 453

FMVSNet5		69.50 406	67.96 404	74.15 417	82.97 385	55.35 432	80.01 400	82.12 410	62.56 424	63.02 467	81.53 421	36.92 480	81.92 449	48.42 456	74.06 413	85.17 435

mvs5depth		69.45 407	67.45 417	75.46 401	73.93 479	55.83 425	79.19 411	83.23 389	66.89 352	71.63 374	83.32 394	33.69 490	85.09 422	59.81 374	55.34 498	85.46 428

PMMVS		69.34 408	68.67 395	71.35 444	75.67 472	62.03 329	75.17 452	73.46 484	50.00 498	68.68 406	79.05 448	52.07 342	78.13 467	61.16 363	82.77 294	73.90 497

our_test_3		69.14 409	67.00 422	75.57 397	79.80 438	58.80 377	77.96 430	77.81 458	59.55 451	62.90 470	78.25 457	47.43 400	83.97 431	51.71 435	67.58 456	83.93 452

EPMVS		69.02 410	68.16 400	71.59 440	79.61 441	49.80 478	77.40 436	66.93 503	62.82 420	70.01 390	79.05 448	45.79 422	77.86 470	56.58 410	75.26 402	87.13 392

KD-MVS_self_test		68.81 411	67.59 415	72.46 435	74.29 478	45.45 490	77.93 431	87.00 325	63.12 412	63.99 464	78.99 452	42.32 447	84.77 426	56.55 411	64.09 475	87.16 391

Anonymous20240521		68.80 412	67.22 421	73.55 423	74.33 477	54.11 444	83.18 344	85.61 352	58.15 464	61.68 474	80.94 428	30.71 497	81.27 455	57.00 405	73.34 423	85.28 431

Anonymous20231206		68.60 413	67.80 410	71.02 447	80.23 430	50.75 473	78.30 427	80.47 430	56.79 476	66.11 447	82.63 409	46.35 415	78.95 464	43.62 481	75.70 388	83.36 457

MIMVSNet1		68.58 414	66.78 426	73.98 420	80.07 433	51.82 463	80.77 384	84.37 368	64.40 397	59.75 483	82.16 416	36.47 483	83.63 434	42.73 484	70.33 441	86.48 408

testing3		68.56 415	67.67 413	71.22 446	87.33 256	42.87 500	83.06 351	71.54 489	70.36 280	69.08 404	84.38 366	30.33 498	85.69 415	37.50 497	75.45 396	85.09 437

EU-MVSNet		68.53 416	67.61 414	71.31 445	78.51 451	47.01 487	84.47 304	84.27 372	42.27 508	66.44 444	84.79 360	40.44 460	83.76 432	58.76 387	68.54 450	83.17 458

PatchT		68.46 417	67.85 407	70.29 450	80.70 424	43.93 498	72.47 467	74.88 478	60.15 445	70.55 381	76.57 467	49.94 377	81.59 450	50.58 441	74.83 407	85.34 430

dtuonlycased		68.45 418	67.29 420	71.92 437	80.18 431	54.90 437	79.76 403	80.38 435	60.11 446	62.57 472	76.44 470	49.34 386	82.31 445	55.05 417	61.77 483	78.53 488

test_fmvs2		68.35 419	67.48 416	70.98 448	69.50 497	51.95 460	80.05 399	76.38 472	49.33 499	74.65 333	84.38 366	23.30 511	75.40 490	74.51 212	75.17 404	85.60 425

Syy-MVS		68.05 420	67.85 407	68.67 460	84.68 334	40.97 506	78.62 420	73.08 486	66.65 360	66.74 437	79.46 445	52.11 340	82.30 446	32.89 502	76.38 381	82.75 465

test0.0.03 1		68.00 421	67.69 412	68.90 457	77.55 463	47.43 483	75.70 449	72.95 488	66.66 357	66.56 439	82.29 414	48.06 397	75.87 485	44.97 479	74.51 410	83.41 456

TDRefinement		67.49 422	64.34 435	76.92 386	73.47 485	61.07 346	84.86 292	82.98 397	59.77 449	58.30 487	85.13 352	26.06 503	87.89 391	47.92 463	60.59 488	81.81 474

test20.03		67.45 423	66.95 423	68.94 456	75.48 474	44.84 496	77.50 435	77.67 459	66.66 357	63.01 468	83.80 382	47.02 404	78.40 466	42.53 487	68.86 449	83.58 455

UnsupCasMVSNet_eth		67.33 424	65.99 429	71.37 442	73.48 484	51.47 467	75.16 453	85.19 356	65.20 384	60.78 477	80.93 430	42.35 446	77.20 472	57.12 402	53.69 500	85.44 429

TinyColmap		67.30 425	64.81 433	74.76 410	81.92 407	56.68 412	80.29 395	81.49 417	60.33 442	56.27 495	83.22 395	24.77 507	87.66 395	45.52 475	69.47 444	79.95 484

FE-MVSNET		67.25 426	65.33 431	73.02 430	75.86 470	52.54 457	80.26 397	80.56 428	63.80 408	60.39 478	79.70 444	41.41 454	84.66 428	43.34 482	62.62 480	81.86 472

myMVS_eth3d		67.02 427	66.29 428	69.21 455	84.68 334	42.58 501	78.62 420	73.08 486	66.65 360	66.74 437	79.46 445	31.53 495	82.30 446	39.43 493	76.38 381	82.75 465

dp		66.80 428	65.43 430	70.90 449	79.74 440	48.82 481	75.12 455	74.77 479	59.61 450	64.08 463	77.23 464	42.89 443	80.72 458	48.86 455	66.58 459	83.16 459

MDA-MVSNet-bldmvs		66.68 429	63.66 440	75.75 394	79.28 446	60.56 360	73.92 463	78.35 456	64.43 395	50.13 503	79.87 442	44.02 437	83.67 433	46.10 471	56.86 492	83.03 462

testgi		66.67 430	66.53 427	67.08 467	75.62 473	41.69 505	75.93 445	76.50 470	66.11 367	65.20 456	86.59 313	35.72 486	74.71 492	43.71 480	73.38 422	84.84 440

CHOSEN 280x420		66.51 431	64.71 434	71.90 438	81.45 414	63.52 292	57.98 507	68.95 498	53.57 487	62.59 471	76.70 466	46.22 417	75.29 491	55.25 415	79.68 333	76.88 492

PM-MVS		66.41 432	64.14 436	73.20 428	73.92 480	56.45 414	78.97 415	64.96 509	63.88 407	64.72 457	80.24 437	19.84 515	83.44 438	66.24 300	64.52 474	79.71 485

JIA-IIPM		66.32 433	62.82 446	76.82 387	77.09 466	61.72 335	65.34 496	75.38 475	58.04 467	64.51 459	62.32 502	42.05 451	86.51 405	51.45 438	69.22 446	82.21 469

KD-MVS_2432*1600		66.22 434	63.89 438	73.21 426	75.47 475	53.42 450	70.76 475	84.35 369	64.10 401	66.52 441	78.52 454	34.55 488	84.98 423	50.40 443	50.33 505	81.23 476

miper_refine_blended		66.22 434	63.89 438	73.21 426	75.47 475	53.42 450	70.76 475	84.35 369	64.10 401	66.52 441	78.52 454	34.55 488	84.98 423	50.40 443	50.33 505	81.23 476

ADS-MVSNet2		66.20 436	63.33 441	74.82 409	79.92 434	58.75 378	67.55 487	75.19 476	53.37 488	65.25 454	75.86 477	42.32 447	80.53 459	41.57 488	68.91 447	85.18 433

UWE-MVS-28		65.32 437	64.93 432	66.49 468	78.70 449	38.55 508	77.86 433	64.39 510	62.00 432	64.13 462	83.60 389	41.44 453	76.00 483	31.39 504	80.89 317	84.92 438

YYNet1		65.03 438	62.91 444	71.38 441	75.85 471	56.60 413	69.12 483	74.66 482	57.28 474	54.12 497	77.87 459	45.85 421	74.48 493	49.95 448	61.52 485	83.05 461

MDA-MVSNet_test_wron		65.03 438	62.92 443	71.37 442	75.93 468	56.73 409	69.09 484	74.73 480	57.28 474	54.03 498	77.89 458	45.88 420	74.39 494	49.89 449	61.55 484	82.99 463

Patchmatch-test		64.82 440	63.24 442	69.57 453	79.42 444	49.82 477	63.49 502	69.05 497	51.98 493	59.95 482	80.13 438	50.91 362	70.98 501	40.66 490	73.57 418	87.90 363

usedtu_dtu_shiyan2		64.75 441	61.63 450	74.10 418	70.64 495	53.18 455	82.10 363	81.27 421	56.22 480	56.39 494	74.67 482	27.94 501	83.56 435	42.71 485	62.73 479	85.57 426

ADS-MVSNet		64.36 442	62.88 445	68.78 459	79.92 434	47.17 486	67.55 487	71.18 490	53.37 488	65.25 454	75.86 477	42.32 447	73.99 497	41.57 488	68.91 447	85.18 433

LF4IMVS		64.02 443	62.19 447	69.50 454	70.90 494	53.29 453	76.13 443	77.18 466	52.65 490	58.59 485	80.98 427	23.55 510	76.52 477	53.06 430	66.66 458	78.68 487

UnsupCasMVSNet_bld		63.70 444	61.53 451	70.21 451	73.69 482	51.39 468	72.82 466	81.89 411	55.63 482	57.81 489	71.80 489	38.67 472	78.61 465	49.26 453	52.21 503	80.63 480

test_fmvs3		63.36 445	61.82 448	67.98 464	62.51 506	46.96 488	77.37 437	74.03 483	45.24 504	67.50 424	78.79 453	12.16 523	72.98 500	72.77 233	66.02 461	83.99 451

dmvs_testset		62.63 446	64.11 437	58.19 478	78.55 450	24.76 523	75.28 451	65.94 506	67.91 342	60.34 479	76.01 476	53.56 324	73.94 498	31.79 503	67.65 455	75.88 494

mvsany_test1		62.30 447	61.26 452	65.41 470	69.52 496	54.86 438	66.86 490	49.78 523	46.65 502	68.50 412	83.21 396	49.15 390	66.28 508	56.93 406	60.77 486	75.11 495

new-patchmatchnet		61.73 448	61.73 449	61.70 474	72.74 491	24.50 524	69.16 482	78.03 457	61.40 435	56.72 492	75.53 480	38.42 473	76.48 478	45.95 472	57.67 491	84.13 449

PVSNet_0	57.27 20	61.67 449	59.27 453	68.85 458	79.61 441	57.44 401	68.01 485	73.44 485	55.93 481	58.54 486	70.41 493	44.58 432	77.55 471	47.01 465	35.91 513	71.55 501

test_vis1_rt		60.28 450	58.42 454	65.84 469	67.25 500	55.60 429	70.44 477	60.94 515	44.33 506	59.00 484	66.64 499	24.91 506	68.67 506	62.80 334	69.48 443	73.25 498

ttmdpeth		59.91 451	57.10 456	68.34 462	67.13 501	46.65 489	74.64 458	67.41 502	48.30 500	62.52 473	85.04 356	20.40 513	75.93 484	42.55 486	45.90 511	82.44 467

MVS-HIRNet		59.14 452	57.67 455	63.57 472	81.65 409	43.50 499	71.73 469	65.06 508	39.59 512	51.43 500	57.73 509	38.34 474	82.58 444	39.53 491	73.95 414	64.62 507

pmmvs3		57.79 453	54.26 459	68.37 461	64.02 505	56.72 410	75.12 455	65.17 507	40.20 510	52.93 499	69.86 495	20.36 514	75.48 488	45.45 476	55.25 499	72.90 499

DSMNet-mixed		57.77 454	56.90 457	60.38 476	67.70 499	35.61 512	69.18 481	53.97 521	32.30 523	57.49 490	79.88 441	40.39 461	68.57 507	38.78 494	72.37 427	76.97 491

MVStest1		56.63 455	52.76 462	68.25 463	61.67 507	53.25 454	71.67 470	68.90 499	38.59 513	50.59 502	83.05 399	25.08 505	70.66 502	36.76 498	38.56 512	80.83 479

WB-MVS		54.94 456	54.72 458	55.60 485	73.50 483	20.90 526	74.27 462	61.19 514	59.16 455	50.61 501	74.15 483	47.19 403	75.78 486	17.31 522	35.07 514	70.12 502

LCM-MVSNet		54.25 457	49.68 468	67.97 465	53.73 515	45.28 493	66.85 491	80.78 424	35.96 517	39.45 513	62.23 503	8.70 527	78.06 469	48.24 460	51.20 504	80.57 482

mvsany_test3		53.99 458	51.45 464	61.61 475	55.51 511	44.74 497	63.52 501	45.41 527	43.69 507	58.11 488	76.45 468	17.99 516	63.76 512	54.77 420	47.59 507	76.34 493

SSC-MVS		53.88 459	53.59 460	54.75 488	72.87 490	19.59 527	73.84 464	60.53 516	57.58 471	49.18 505	73.45 486	46.34 416	75.47 489	16.20 525	32.28 516	69.20 503

FPMVS		53.68 460	51.64 463	59.81 477	65.08 503	51.03 470	69.48 480	69.58 495	41.46 509	40.67 511	72.32 488	16.46 519	70.00 505	24.24 515	65.42 470	58.40 512

APD_test1		53.31 461	49.93 467	63.42 473	65.68 502	50.13 475	71.59 471	66.90 504	34.43 519	40.58 512	71.56 490	8.65 528	76.27 480	34.64 501	55.36 497	63.86 508

N_pmnet		52.79 462	53.26 461	51.40 490	78.99 448	7.68 540	69.52 479	3.89 549	51.63 494	57.01 491	74.98 481	40.83 458	65.96 509	37.78 495	64.67 473	80.56 483

test_f		52.09 463	50.82 465	55.90 483	53.82 514	42.31 504	59.42 506	58.31 519	36.45 516	56.12 496	70.96 492	12.18 522	57.79 516	53.51 427	56.57 494	67.60 504

EGC-MVSNET		52.07 464	47.05 469	67.14 466	83.51 362	60.71 356	80.50 391	67.75 500	0.07 573	0.43 568	75.85 479	24.26 508	81.54 451	28.82 506	62.25 481	59.16 510

new_pmnet		50.91 465	50.29 466	52.78 489	68.58 498	34.94 514	63.71 500	56.63 520	39.73 511	44.95 506	65.47 500	21.93 512	58.48 515	34.98 500	56.62 493	64.92 506

ANet_high		50.57 466	46.10 471	63.99 471	48.67 520	39.13 507	70.99 474	80.85 423	61.39 436	31.18 515	57.70 510	17.02 518	73.65 499	31.22 505	15.89 529	79.18 486

test_vis3_rt		49.26 467	47.02 470	56.00 482	54.30 512	45.27 494	66.76 492	48.08 524	36.83 515	44.38 507	53.20 516	7.17 530	64.07 511	56.77 409	55.66 495	58.65 511

testf1		45.72 468	41.96 473	57.00 479	56.90 509	45.32 491	66.14 493	59.26 517	26.19 524	30.89 516	60.96 505	4.14 533	70.64 503	26.39 513	46.73 509	55.04 514

APD_test2		45.72 468	41.96 473	57.00 479	56.90 509	45.32 491	66.14 493	59.26 517	26.19 524	30.89 516	60.96 505	4.14 533	70.64 503	26.39 513	46.73 509	55.04 514

dongtai		45.42 470	45.38 472	45.55 492	73.36 486	26.85 521	67.72 486	34.19 529	54.15 486	49.65 504	56.41 513	25.43 504	62.94 513	19.45 520	28.09 518	46.86 521

Gipuma		45.18 471	41.86 475	55.16 486	77.03 467	51.52 466	32.50 517	80.52 429	32.46 522	27.12 519	35.02 529	9.52 526	75.50 487	22.31 517	60.21 489	38.45 526
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 472	40.28 477	55.82 484	40.82 523	42.54 503	65.12 497	63.99 511	34.43 519	24.48 521	57.12 511	3.92 535	76.17 482	17.10 523	55.52 496	48.75 518
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		44.04 473	39.87 479	56.58 481	50.92 519	36.22 511	59.86 505	27.68 533	33.67 521	42.15 510	71.07 491	3.10 538	59.10 514	45.79 473	24.54 520	74.41 496

ArgMatch-Sym		43.72 474	39.92 478	55.10 487	52.36 517	37.56 510	61.93 504	23.00 535	35.80 518	43.62 508	70.22 494	3.22 536	55.93 518	45.35 477	23.80 522	71.81 500

PMMVS2		40.82 475	38.86 480	46.69 491	53.84 513	16.45 531	48.61 510	49.92 522	37.49 514	31.67 514	60.97 504	8.14 529	56.42 517	28.42 507	30.72 517	67.19 505

kuosan		39.70 476	40.40 476	37.58 497	64.52 504	26.98 519	65.62 495	33.02 530	46.12 503	42.79 509	48.99 520	24.10 509	46.56 523	12.16 530	26.30 519	39.20 525

DenseAffine		31.97 477	28.22 484	43.21 494	43.10 522	27.10 518	46.21 511	11.36 539	24.92 526	27.70 518	58.81 508	1.09 542	46.50 524	26.95 510	13.85 533	56.02 513

E-PMN		31.77 478	30.64 481	35.15 499	52.87 516	27.67 517	57.09 508	47.86 525	24.64 527	16.40 535	33.05 530	11.23 524	54.90 519	14.46 526	18.15 527	22.87 533

test_method		31.52 479	29.28 483	38.23 496	27.03 530	6.50 545	20.94 523	62.21 513	4.05 544	22.35 525	52.50 517	13.33 520	47.58 521	27.04 509	34.04 515	60.62 509

EMVS		30.81 480	29.65 482	34.27 500	50.96 518	25.95 522	56.58 509	46.80 526	24.01 528	15.53 536	30.68 533	12.47 521	54.43 520	12.81 529	17.05 528	22.43 534

MVE	26.22 23	30.37 481	25.89 486	43.81 493	44.55 521	35.46 513	28.87 522	39.07 528	18.20 532	18.58 532	40.18 525	2.68 539	47.37 522	17.07 524	23.78 523	48.60 519
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
RoMa-SfM		28.67 482	25.38 487	38.54 495	32.61 527	22.48 525	40.24 512	7.23 543	21.81 529	26.66 520	60.46 507	0.96 543	41.72 525	26.47 512	11.95 534	51.40 517

LoFTR		27.52 483	24.27 488	37.29 498	34.75 526	19.27 528	33.78 516	21.60 536	12.42 536	21.61 527	56.59 512	0.91 544	40.37 526	13.94 527	22.80 524	52.22 516

DKM		25.67 484	23.01 489	33.64 501	32.08 528	19.25 529	37.50 514	5.52 545	18.67 530	23.58 524	55.44 514	0.64 549	34.02 527	23.95 516	9.73 536	47.66 520

PDCNetPlus		24.75 485	22.46 490	31.64 502	35.53 525	17.00 530	32.00 518	9.46 540	18.43 531	18.56 533	51.31 518	1.65 540	33.00 529	26.51 511	8.70 538	44.91 522

MatchFormer		22.13 486	19.86 492	28.93 503	28.66 529	15.74 532	31.91 519	17.10 538	7.75 537	18.87 531	47.50 523	0.62 551	33.92 528	7.49 537	18.87 526	37.14 527

RoMa-HiRes		21.63 487	19.64 493	27.59 504	22.40 532	14.25 533	29.71 520	4.10 547	15.42 534	21.09 528	54.77 515	0.72 547	28.87 530	21.01 518	7.52 542	39.65 524

DKM-HiRes		20.87 488	19.15 494	26.02 506	25.34 531	14.13 534	29.63 521	3.62 552	14.53 535	20.13 529	50.55 519	0.47 557	24.22 534	20.96 519	7.15 543	39.70 523

cdsmvs_eth3d_5k		19.96 489	26.61 485	0.00 544	0.00 569	0.00 573	0.00 557	89.26 242	0.00 577	0.00 571	88.61 252	61.62 236	0.00 569	0.00 567	0.00 572	0.00 568

tmp_tt		18.61 490	21.40 491	10.23 514	4.82 562	10.11 535	34.70 515	30.74 532	1.48 550	23.91 523	26.07 534	28.42 500	13.41 538	27.12 508	15.35 531	7.17 544

wuyk23d		16.82 491	15.94 496	19.46 509	58.74 508	31.45 515	39.22 513	3.74 551	6.84 538	6.04 543	2.70 564	1.27 541	24.29 533	10.54 535	14.40 532	2.63 551

VLMVS_CLIP		15.14 492	16.11 495	12.23 513	12.32 539	7.35 541	15.53 526	20.73 537	4.02 545	22.32 526	31.59 531	4.37 532	21.02 536	11.59 532	22.52 525	8.32 537

ELoFTR		14.23 493	11.56 500	22.24 507	11.02 540	6.56 544	13.59 529	7.57 542	5.55 540	11.96 539	39.09 526	0.21 562	24.93 532	9.43 536	5.66 547	35.22 528

PMatch-SfM		14.15 494	12.67 499	18.59 510	12.84 538	7.03 542	17.41 524	2.28 554	6.63 539	12.96 537	43.56 524	0.09 574	16.11 537	13.90 528	4.38 553	32.63 530

MASt3R-SfM		13.55 495	13.93 497	12.41 512	10.54 543	5.97 546	16.61 525	6.07 544	4.50 542	16.53 534	48.67 521	0.73 546	9.44 540	11.56 533	10.18 535	21.81 535

GLUNet-SfM		12.90 496	10.00 501	21.62 508	13.58 537	8.30 538	10.19 533	9.30 541	4.31 543	12.18 538	30.90 532	0.50 555	22.76 535	4.89 538	4.14 554	33.79 529

MVS_clip		11.37 497	13.03 498	6.40 518	15.78 536	6.79 543	11.98 532	1.47 562	1.89 547	19.38 530	35.95 528	3.13 537	3.09 546	12.10 531	15.54 530	9.34 536

PMatch-Up-SfM		10.76 498	9.99 502	13.09 511	9.50 546	4.83 547	12.94 531	1.40 563	4.65 541	10.16 540	37.54 527	0.07 577	10.94 539	10.71 534	2.92 564	23.50 532

ALIKED-LG		8.61 499	8.70 504	8.33 515	20.63 533	8.70 537	15.50 527	4.61 546	2.19 546	5.84 544	18.70 535	0.80 545	8.06 541	1.03 548	8.97 537	8.25 538

ALIKED-MNN		7.86 500	7.83 507	7.97 516	19.40 534	8.86 536	14.48 528	3.90 548	1.59 548	4.74 549	16.49 536	0.59 552	7.65 542	0.91 549	8.34 540	7.39 541

ALIKED-NN		7.51 501	7.61 508	7.21 517	18.26 535	8.10 539	13.45 530	3.88 550	1.50 549	4.87 547	16.47 537	0.64 549	7.00 543	0.88 550	8.50 539	6.52 546

ab-mvs-re		7.23 502	9.64 503	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	86.72 305	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

test123		6.12 503	8.11 505	0.14 542	0.06 568	0.09 570	71.05 473	0.03 580	0.04 575	0.25 570	1.30 566	0.05 579	0.03 568	0.21 559	0.01 571	0.29 566

testmvs		6.04 504	8.02 506	0.10 543	0.08 567	0.03 572	69.74 478	0.04 579	0.05 574	0.31 569	1.68 565	0.02 580	0.04 567	0.24 553	0.02 570	0.25 567

pcd_1.5k_mvsjas		5.26 505	7.02 509	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	63.15 207	0.00 569	0.00 567	0.00 572	0.00 568

VLMVS		4.54 506	4.93 510	3.37 525	4.86 561	2.23 554	3.38 547	1.77 561	0.23 572	7.94 541	11.34 543	4.62 531	2.44 547	2.43 540	7.76 541	5.44 548

XFeat-MNN		4.39 507	4.49 511	4.10 519	2.88 565	1.91 561	5.86 539	2.57 553	1.06 552	5.04 545	13.99 539	0.43 559	4.47 544	2.00 541	6.55 545	5.92 547

SP-DiffGlue		4.29 508	4.46 512	3.77 523	3.68 563	2.12 555	5.97 538	2.22 555	1.10 551	4.89 546	13.93 540	0.66 548	1.95 552	2.47 539	5.24 548	7.22 543

SP-LightGlue		4.27 509	4.41 513	3.86 520	10.99 541	1.99 558	8.19 534	2.06 557	0.98 554	2.37 551	8.29 544	0.56 553	2.10 549	1.27 544	4.99 549	7.48 540

SP-SuperGlue		4.24 510	4.38 514	3.81 522	10.75 542	2.00 557	8.18 535	2.09 556	1.00 553	2.41 550	8.29 544	0.56 553	2.05 551	1.27 544	4.91 550	7.39 541

SP-MNN		4.14 511	4.24 515	3.82 521	10.32 544	1.83 562	8.11 536	1.99 558	0.82 556	2.23 552	8.27 546	0.47 557	2.14 548	1.20 546	4.77 551	7.49 539

SP-NN		4.00 512	4.12 516	3.63 524	9.92 545	1.81 563	7.94 537	1.90 560	0.86 555	2.15 553	8.00 547	0.50 555	2.09 550	1.20 546	4.63 552	6.98 545

XFeat-NN		3.78 513	3.96 518	3.23 526	2.65 566	1.53 566	4.99 540	1.92 559	0.81 557	4.77 548	12.37 542	0.38 560	3.39 545	1.64 542	6.13 546	4.77 549

MVS_baseline		3.29 514	4.00 517	1.16 540	3.08 564	0.09 570	1.26 556	0.24 578	0.04 575	6.52 542	16.19 538	0.30 561	0.00 569	1.53 543	6.83 544	3.39 550

SIFT-NN		2.77 515	2.92 519	2.34 527	8.70 547	3.08 548	4.46 541	1.01 565	0.68 558	1.46 554	5.49 548	0.16 563	1.65 553	0.26 551	4.04 555	2.27 552

SIFT-MNN		2.63 516	2.75 520	2.25 528	8.10 548	2.84 549	4.08 542	1.02 564	0.68 558	1.28 555	5.34 551	0.15 564	1.64 554	0.26 551	3.88 557	2.27 552

SIFT-NN-NCMNet		2.52 517	2.64 521	2.14 529	7.53 550	2.74 550	4.00 543	0.98 566	0.65 561	1.24 557	5.08 554	0.14 565	1.60 555	0.23 554	3.94 556	2.07 556

SIFT-NCM-Cal		2.40 518	2.52 522	2.05 530	7.74 549	2.54 551	3.75 545	0.84 567	0.65 561	0.89 562	4.78 557	0.13 568	1.60 555	0.19 562	3.71 558	2.01 558

SIFT-NN-CMatch		2.31 519	2.41 523	2.00 531	6.59 554	2.34 553	3.48 546	0.83 568	0.65 561	1.28 555	5.09 552	0.14 565	1.52 557	0.23 554	3.41 560	2.14 554

SIFT-NN-UMatch		2.26 520	2.39 524	1.89 533	6.21 556	2.08 556	3.76 544	0.83 568	0.66 560	1.04 559	5.09 552	0.14 565	1.52 557	0.23 554	3.51 559	2.07 556

SIFT-ConvMatch		2.25 521	2.37 525	1.90 532	7.29 551	2.37 552	3.21 550	0.75 570	0.65 561	1.03 560	4.91 555	0.12 571	1.51 559	0.22 557	3.13 562	1.81 559

SIFT-UMatch		2.16 522	2.30 526	1.72 535	6.99 552	1.97 560	3.32 548	0.70 572	0.64 565	0.91 561	4.86 556	0.12 571	1.49 560	0.22 557	2.97 563	1.72 561

SIFT-NN-PointCN		2.07 523	2.18 527	1.74 534	5.75 557	1.65 565	3.27 549	0.73 571	0.60 568	1.07 558	4.62 558	0.13 568	1.43 561	0.21 559	3.22 561	2.12 555

SIFT-CM-Cal		2.02 524	2.13 528	1.67 536	6.79 553	1.99 558	2.79 552	0.64 573	0.63 566	0.87 563	4.48 560	0.13 568	1.41 562	0.19 562	2.70 565	1.61 563

SIFT-UM-Cal		1.97 525	2.12 529	1.52 537	6.57 555	1.67 564	2.93 551	0.57 575	0.62 567	0.83 564	4.55 559	0.11 573	1.37 563	0.20 561	2.69 566	1.53 564

SIFT-PCN-Cal		1.72 526	1.82 531	1.39 538	5.64 558	1.19 568	2.39 554	0.53 576	0.55 570	0.72 565	3.90 561	0.09 574	1.22 565	0.17 564	2.42 568	1.76 560

SIFT-PointCN		1.72 526	1.83 530	1.36 539	5.55 559	1.22 567	2.59 553	0.59 574	0.55 570	0.71 566	3.77 562	0.08 576	1.24 564	0.17 564	2.48 567	1.63 562

SIFT-NCMNet		1.44 528	1.56 532	1.08 541	5.14 560	1.07 569	1.97 555	0.32 577	0.56 569	0.64 567	3.23 563	0.07 577	1.01 566	0.14 566	1.95 569	1.15 565

mmdepth		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

monomultidepth		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

test_blank		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

uanet_test		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

DCPMVS		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

sosnet-low-res		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

sosnet		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

uncertanet		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

Regformer		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

uanet		0.00 529	0.00 533	0.00 544	0.00 569	0.00 573	0.00 557	0.00 581	0.00 577	0.00 571	0.00 567	0.00 581	0.00 569	0.00 567	0.00 572	0.00 568

testLo									77.30 62			79.32 15

testloun									75.24 134

aa1122									68.52 335

11-2								93.94 19	67.85 343			78.23 25			96.35 15	94.72 38

aa112-1								94.15 9	76.29 107			78.62 23			96.28 17	94.89 19

aa112								93.80 25				77.22 30			93.56 78	93.19 145

11-21									74.59 163

11-1								93.18 55	69.80 296	92.48 9	96.29 3	77.53 28	96.76 34	87.57 49	95.19 44	93.20 143

testa						74.80 5

aa-1101								93.78 26	74.95 150	92.47 10	96.42 2	77.11 31	97.24 16		94.91 50	93.00 164

aa-1031								93.32 45	72.21 228			77.30 29			91.98 105

test-Zhanyuan												78.70 22

100007								93.18 55	65.76 374	92.41 12		79.65 13				93.00 164

aaa1007												78.92 18

RA-MVS			88.76 15		95.68 1	73.57 14	94.07 3	93.39 42	70.82 264	90.99 27	95.81 10	75.13 41	97.22 19	88.08 43	94.91 50

Meshroom													0.00 569
: In preparation.
AliceVision / Meshro													0.00 569
: In preparation.
AliceVision_Meshroom													0.00 569
: In preparation.
PatchmatchNet2					0.00 569	30.51 516	67.30 489	67.46 501	50.92 497
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														37.67 496	64.79 472	80.58 481
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													65.90 510
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					94.58 17	71.43 63		94.16 7		90.64 29		79.61 14	97.25 15	88.08 43	96.28 17

testmeadow								93.30 47	69.51 306	92.51 8	96.02 6	78.75 20	97.15 21	87.21 53	95.56 35	93.19 145

testFor bridge								93.52 36	73.04 214	92.54 7	96.21 4	78.06 26	97.22 19	88.21 38	95.49 36	93.38 129

aaatest				87.86 27	94.57 19	71.43 63	93.28 13	93.59 33	75.24 134	92.25 16	95.03 29		97.39 11	88.15 40	95.96 23	94.75 36

testtestaaa								93.78 26	74.95 150	92.47 10		79.30 16

TestfortrainingZip				87.28 46	92.85 70	72.05 52	93.28 13	93.32 45	76.52 90	88.91 41	93.52 84	78.75 20	96.67 39		91.98 105	93.13 153

WAC-MVS						42.58 501								39.46 492

FOURS1					95.00 11	72.39 43	95.06 1	93.84 22	74.49 165	91.30 24

MSC_two_6792asdad				89.16 1	94.34 33	75.53 2		92.99 67					97.53 2	89.67 15	96.44 9	94.41 63

PC_three_1452									68.21 339	92.02 21	94.00 70	82.09 5	95.98 68	84.58 77	96.68 2	94.95 15

No_MVS				89.16 1	94.34 33	75.53 2		92.99 67					97.53 2	89.67 15	96.44 9	94.41 63

test_one_0601					95.07 8	71.46 62		94.14 10	78.27 42	92.05 20	95.74 14	80.83 12

eth-test2					0.00 569

eth-test					0.00 569

ZD-MVS					94.38 31	72.22 48		92.67 86	70.98 259	87.75 60	94.07 65	74.01 53	96.70 37	84.66 76	94.84 55

RE-MVS-def			85.48 78		93.06 66	70.63 86	91.88 44	92.27 108	73.53 197	85.69 84	94.45 44	63.87 197		82.75 101	91.87 108	92.50 187

IU-MVS					95.30 3	71.25 68		92.95 73	66.81 353	92.39 13				88.94 28	96.63 4	94.85 25

OPU-MVS				89.06 3	94.62 16	75.42 4	93.57 9				94.02 68	82.45 3	96.87 29	83.77 88	96.48 8	94.88 20

test_241102_TWO								94.06 15	77.24 65	92.78 5	95.72 16	81.26 9	97.44 7	89.07 25	96.58 6	94.26 75

test_241102_ONE					95.30 3	70.98 76		94.06 15	77.17 68	93.10 1	95.39 24	82.99 1	97.27 14

9.14			88.26 20		92.84 72		91.52 57	94.75 1	73.93 184	88.57 45	94.67 37	75.57 40	95.79 70	86.77 57	95.76 29

save fliter					93.80 46	72.35 46	90.47 75	91.17 166	74.31 171

test_0728_THIRD									78.38 39	92.12 18	95.78 12	81.46 8	97.40 9	89.42 19	96.57 7	94.67 44

test_0728_SECOND				87.71 35	95.34 2	71.43 63	93.49 11	94.23 5					97.49 4	89.08 22	96.41 12	94.21 76

test0726					95.27 6	71.25 68	93.60 8	94.11 11	77.33 60	92.81 4	95.79 11	80.98 10

GSMVS																88.96 331

test_part2					95.06 9	72.65 34				91.80 22

sam_mvs1												51.32 355				88.96 331

sam_mvs												50.01 375

ambc				75.24 404	73.16 487	50.51 474	63.05 503	87.47 309		64.28 460	77.81 460	17.80 517	89.73 358	57.88 396	60.64 487	85.49 427

MTGPA								92.02 126

test_post1							78.90 418				5.43 550	48.81 396	85.44 420	59.25 380

test_post											5.46 549	50.36 371	84.24 429

patchmatchnet-post											74.00 484	51.12 361	88.60 381

GG-mvs-BLEND				75.38 402	81.59 411	55.80 426	79.32 408	69.63 494		67.19 430	73.67 485	43.24 441	88.90 377	50.41 442	84.50 259	81.45 475

MTMP							92.18 40	32.83 531

gm-plane-assit					81.40 415	53.83 447			62.72 422		80.94 428		92.39 253	63.40 325

test9_res														84.90 70	95.70 32	92.87 171

TEST9					93.26 58	72.96 27	88.75 140	91.89 134	68.44 336	85.00 91	93.10 96	74.36 48	95.41 87

test_8					93.13 62	72.57 37	88.68 146	91.84 138	68.69 330	84.87 95	93.10 96	74.43 46	95.16 97

agg_prior2														82.91 97	95.45 37	92.70 176

agg_prior					92.85 70	71.94 55		91.78 142		84.41 107			94.93 109

TestCases				79.58 334	85.15 322	63.62 283		79.83 441	62.31 427	60.32 480	86.73 303	32.02 492	88.96 375	50.28 445	71.57 435	86.15 413

test_prior4						72.60 36	89.01 127

test_prior2							88.85 134		75.41 129	84.91 93	93.54 83	74.28 49		83.31 91	95.86 26

test_prior				86.33 65	92.61 77	69.59 102		92.97 72					95.48 81			93.91 92

旧先验2							86.56 237		58.10 466	87.04 71			88.98 373	74.07 217

新几何2							86.29 251

新几何1				83.42 200	93.13 62	70.71 84		85.48 354	57.43 473	81.80 164	91.98 130	63.28 201	92.27 259	64.60 317	92.99 85	87.27 385

旧先验1					91.96 83	65.79 216		86.37 341			93.08 100	69.31 118			92.74 90	88.74 342

无先验							87.48 191	88.98 259	60.00 447				94.12 149	67.28 293		88.97 330

原ACMM2							86.86 223

原ACMM1				84.35 144	93.01 68	68.79 121		92.44 96	63.96 406	81.09 178	91.57 150	66.06 172	95.45 82	67.19 295	94.82 57	88.81 337

test222					91.50 89	68.26 141	84.16 319	83.20 392	54.63 485	79.74 206	91.63 146	58.97 272			91.42 116	86.77 402

testdata2													91.01 323	62.37 345

segment_acmp												73.08 60

testdata				79.97 317	90.90 101	64.21 270		84.71 364	59.27 454	85.40 86	92.91 102	62.02 229	89.08 371	68.95 278	91.37 118	86.63 407

testdata1							84.14 320		75.71 120

test12				86.80 59	92.63 76	70.70 85		91.79 141		82.71 151		71.67 82	96.16 59		94.50 64	93.54 124

plane_prior7					90.08 119	68.51 135

plane_prior6					89.84 128	68.70 129						60.42 262

plane_prior5								92.44 96					95.38 89	78.71 157	86.32 223	91.33 233

plane_prior4											91.00 174

plane_prior3						68.60 132			78.44 37	78.92 221

plane_prior2							91.25 61		79.12 29

plane_prior1					89.90 127

plane_prior						68.71 127	90.38 79		77.62 49						86.16 228

n2								0.00 581

nn								0.00 581

door-mid								69.98 493

lessismore_v0				78.97 345	81.01 422	57.15 404		65.99 505		61.16 476	82.82 406	39.12 469	91.34 307	59.67 375	46.92 508	88.43 350

LGP-MVS_train				84.50 134	89.23 156	68.76 123		91.94 132	75.37 131	76.64 278	91.51 152	54.29 316	94.91 110	78.44 159	83.78 272	89.83 302

test11								92.23 112

door								69.44 496

HQP5-MVS						66.98 189

HQP-NCC					89.33 148		89.17 118		76.41 97	77.23 263

ACMP_Plane					89.33 148		89.17 118		76.41 97	77.23 263

BP-MVS														77.47 173

HQP4-MVS										77.24 262			95.11 101			91.03 243

HQP3-MVS								92.19 120							85.99 234

HQP2-MVS												60.17 265

NP-MVS					89.62 133	68.32 139					90.24 200

MDTV_nov1_ep13_2view						37.79 509	75.16 453		55.10 483	66.53 440		49.34 386		53.98 424		87.94 362

MDTV_nov1_ep13			69.97 387		83.18 372	53.48 449	77.10 440	80.18 440	60.45 441	69.33 401	80.44 432	48.89 395	86.90 401	51.60 436	78.51 348

ACMMP++_ref															81.95 305

ACMMP++															81.25 312

Test By Simon												64.33 192

ITE_SJBPF				78.22 361	81.77 408	60.57 359		83.30 387	69.25 313	67.54 423	87.20 294	36.33 484	87.28 399	54.34 422	74.62 409	86.80 401

DeepMVS_CX				27.40 505	40.17 524	26.90 520		24.59 534	17.44 533	23.95 522	48.61 522	9.77 525	26.48 531	18.06 521	24.47 521	28.83 531

High-resolution multi-view benchmark