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++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 68	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 29

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 37

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 14	89.23 24	81.51 2	88.44 31	88.09 45
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
DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 72	87.85 5	85.03 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 159
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
DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 93
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 42

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	89.70 21	79.85 6	91.48 1	88.19 39
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
MM		80.20 8	80.28 11	79.99 2	82.19 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 95
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		80.04 10	80.36 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 52	82.27 18	90.57 25	61.90 16	89.88 19	77.02 34	89.43 22	88.10 42

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 86	86.84 11	85.86 21	60.95 102	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 48

HPM-MVS++		79.88 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 77	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 171

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 87

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 73	90.06 14	78.42 23	89.02 27	87.69 59
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	74.21 62	88.67 30	88.12 41

SF-MVS		78.82 16	79.22 15	77.60 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 85	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 51

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 46	75.08 60	90.47 33	53.96 80	88.68 31	76.48 39	89.63 20	87.16 84

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 104	88.39 34	79.34 9	90.52 13	86.78 96

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 50	66.73 8	74.67 73	89.38 58	55.30 62	89.18 25	74.19 63	87.34 50	86.38 111

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 91	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCNet		78.45 21	78.28 22	78.98 29	80.73 114	57.91 89	84.68 41	81.64 128	68.35 2	75.77 50	90.38 34	53.98 78	90.26 13	81.30 3	87.68 46	88.77 16

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 72	59.34 151	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		78.35 23	78.46 20	78.03 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 77	76.41 48	91.51 11	52.47 106	86.78 75	80.66 4	89.64 19	87.80 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 83	63.89 39	73.60 94	90.60 23	54.85 68	86.72 76	77.20 31	88.06 40	85.74 145
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 35	63.56 43	74.29 79	90.03 47	52.56 103	88.53 33	74.79 59	88.34 33	86.63 104

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 112	79.05 26	90.30 38	55.54 61	88.32 36	73.48 70	87.03 52	84.83 186
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 52	62.82 60	73.96 84	90.50 31	53.20 94	88.35 35	74.02 65	87.05 51	86.13 126

lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 114	78.99 27	91.45 12	51.51 125	87.78 51	75.65 49	87.55 47	87.10 86

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 96	90.56 29	49.80 148	88.24 37	74.02 65	87.03 52	86.32 119

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 121	79.89 22	89.38 58	54.97 66	85.58 113	76.12 45	84.94 70	86.33 117
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 55	62.81 62	73.30 100	90.58 24	49.90 145	88.21 38	73.78 67	87.03 52	86.29 123

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 203	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 105

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 38	62.57 65	73.09 111	89.97 50	50.90 136	87.48 57	75.30 53	86.85 57	87.33 79
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 45	61.98 84	73.06 112	88.88 66	53.72 86	89.06 27	68.27 103	88.04 41	87.42 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	90.01 49	47.95 171	88.01 44	71.55 88	86.74 59	86.37 113

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 86	62.44 69	72.68 121	90.50 31	48.18 169	87.34 58	73.59 69	85.71 66	84.76 190

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 108	75.27 55	84.83 176	60.76 18	86.56 81	67.86 115	87.87 45	86.06 128

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 157

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 157

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 252	80.97 154	65.13 15	75.77 50	90.88 20	48.63 164	86.66 78	77.23 30	88.17 37	84.81 187

PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 74	61.71 86	72.45 127	90.34 37	48.48 167	88.13 41	72.32 78	86.85 57	85.78 139

balanced_conf03		76.58 42	76.55 41	76.68 66	81.73 95	52.90 187	80.94 99	85.70 28	61.12 100	74.90 66	87.17 109	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 118	62.90 57	71.77 134	90.26 39	46.61 196	86.55 84	71.71 86	85.66 67	84.97 182

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 94	67.78 3	70.09 155	86.34 139	54.92 67	88.90 29	72.68 75	84.55 73	87.76 57

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 109	59.65 141	77.31 39	91.43 13	49.62 150	87.24 59	71.99 82	83.75 85	85.14 173

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 219	74.05 82	88.98 63	53.34 92	87.92 47	69.23 101	88.42 32	87.59 65

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 143	55.86 227	74.93 63	88.81 67	53.70 87	84.68 136	75.24 55	88.33 34	83.65 233

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 98	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 112	87.55 47	87.42 71

CS-MVS		76.25 49	75.98 47	77.06 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 100	87.27 101	55.06 64	86.30 93	71.78 85	84.58 72	89.25 6

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 254	51.83 217	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 124	72.75 74	83.93 82	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS		76.13 51	75.70 52	77.40 57	85.87 41	61.20 29	85.52 33	82.19 119	59.99 134	75.10 59	90.35 36	47.66 176	86.52 85	71.64 87	82.99 90	84.47 199

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 49	63.04 54	69.80 165	89.74 55	45.43 210	87.16 65	72.01 81	82.87 95	85.14 173
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
PHI-MVS		75.87 53	75.36 55	77.41 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 225	73.41 97	86.58 130	50.94 135	88.54 32	70.79 93	89.71 17	87.79 56

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 157	52.65 196	83.73 61	86.08 19	63.47 45	72.77 120	87.25 106	53.13 95	87.93 46	71.97 83	85.57 68	86.66 102

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 211	89.24 60	42.03 249	89.38 23	64.07 155	86.50 63	89.69 3

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 184	78.62 137	85.13 37	59.65 141	71.53 140	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 13

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 81	71.49 141	86.03 150	53.83 82	86.36 91	67.74 116	86.91 56	88.19 39

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 213	72.46 125	86.76 117	56.89 39	87.86 49	66.36 135	88.91 29	83.64 234

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 272	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 112	83.88 83	85.85 136

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 184	73.95 304	61.40 92	79.46 23	90.14 41	57.07 37	81.15 226	80.00 5	79.31 149	88.51 28

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 95	58.41 169	73.71 92	90.14 41	45.62 203	85.99 103	69.64 97	82.85 96	85.78 139

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 204	58.58 166	74.32 78	84.51 191	55.94 58	87.22 62	67.11 126	84.48 77	85.52 153

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 268	50.37 246	78.17 154	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 156	69.46 100	82.79 97	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DELS-MVS		74.76 64	74.46 67	75.65 88	77.84 197	52.25 207	75.59 234	84.17 54	63.76 40	73.15 106	82.79 226	59.58 23	86.80 74	67.24 124	86.04 65	87.89 49
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
OPM-MVS		74.73 65	74.25 71	76.19 76	80.81 113	59.01 75	82.60 77	83.64 80	63.74 41	72.52 124	87.49 91	47.18 187	85.88 106	69.47 99	80.78 117	83.66 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 66	74.98 61	73.71 153	78.94 155	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 206	66.01 139	82.12 101	88.58 26

canonicalmvs		74.67 66	74.98 61	73.71 153	78.94 155	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 206	66.01 139	82.12 101	88.58 26

baseline		74.61 68	74.70 64	74.34 121	75.70 263	49.99 256	77.54 174	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 152	69.49 98	82.74 98	89.20 8

SR-MVS-dyc-post		74.57 69	73.90 79	76.58 70	83.49 72	59.87 54	84.29 48	81.36 136	58.07 175	73.14 107	90.07 43	44.74 220	85.84 107	68.20 104	81.76 108	84.03 211

dcpmvs_2		74.55 70	75.23 58	72.48 192	82.34 87	53.34 176	77.87 163	81.46 132	57.80 186	75.49 52	86.81 116	62.22 15	77.75 306	71.09 91	82.02 104	86.34 115

ETV-MVS		74.46 71	73.84 81	76.33 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 122	79.46 309	53.65 90	87.87 48	67.45 123	82.91 93	85.89 134

HQP_MVS		74.31 72	73.73 83	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 174	86.10 147	45.26 214	87.21 63	68.16 108	80.58 123	84.65 191

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 138	75.33 275	52.89 189	78.24 146	77.32 237	61.65 87	78.13 32	88.90 65	52.82 100	81.54 216	78.46 22	78.67 171	87.60 64

HPM-MVS_fast		74.30 73	73.46 89	76.80 63	84.45 64	59.04 74	83.65 63	81.05 151	60.15 130	70.43 151	89.84 52	41.09 271	85.59 112	67.61 119	82.90 94	85.77 142

fmvsm_s_conf0.5_n_10		74.11 75	73.98 78	74.48 118	74.61 295	52.86 191	78.10 158	77.06 241	57.14 196	78.24 31	88.79 70	52.83 99	82.26 202	77.79 28	81.30 113	88.32 32

E5new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

E6new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 135	88.77 16

E6		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 135	88.77 16

E5		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

MVS_111021_HR		74.02 80	73.46 89	75.69 86	83.01 80	60.63 40	77.29 185	78.40 215	61.18 98	70.58 150	85.97 153	54.18 75	84.00 149	67.52 120	82.98 92	82.45 267

MG-MVS		73.96 81	73.89 80	74.16 128	85.65 43	49.69 265	81.59 93	81.29 142	61.45 91	71.05 144	88.11 77	51.77 120	87.73 52	61.05 194	83.09 88	85.05 178

E4		73.91 82	73.83 82	74.15 130	77.13 230	50.47 243	77.15 191	83.79 73	62.21 76	73.61 93	87.19 108	56.08 52	83.03 168	67.91 114	79.35 147	88.94 11

alignmvs		73.86 83	73.99 77	73.45 167	78.20 182	50.50 242	78.57 139	82.43 116	59.40 149	76.57 46	86.71 123	56.42 44	81.23 225	65.84 142	81.79 107	88.62 23

MSLP-MVS++		73.77 84	73.47 88	74.66 108	83.02 79	59.29 63	82.30 85	81.88 123	59.34 151	71.59 138	86.83 115	45.94 201	83.65 155	65.09 148	85.22 69	81.06 298

E2		73.72 85	73.60 86	74.06 135	77.16 224	50.40 244	76.97 196	83.74 74	61.64 88	73.36 98	86.75 120	56.14 48	82.99 170	67.50 121	79.18 157	88.80 13

E3		73.72 85	73.60 86	74.06 135	77.16 224	50.40 244	76.97 196	83.74 74	61.64 88	73.36 98	86.76 117	56.13 49	82.99 170	67.50 121	79.18 157	88.80 13

viewcassd2359sk11		73.56 87	73.41 91	74.00 139	77.13 230	50.35 247	76.86 203	83.69 78	61.23 97	73.14 107	86.38 138	56.09 51	82.96 173	67.15 125	79.01 162	88.70 22

fmvsm_s_conf0.5_n_3		73.55 88	74.39 68	71.03 243	74.09 313	51.86 216	77.77 168	75.60 268	61.18 98	78.67 29	88.98 63	55.88 59	77.73 307	78.69 16	78.68 170	83.50 237

HQP-MVS		73.45 89	72.80 101	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 78	67.85 205	85.54 168	45.46 208	86.93 71	67.04 127	80.35 127	84.32 201

viewdifsd2359ckpt09		73.42 90	72.45 107	76.30 75	77.25 222	53.27 178	80.36 106	82.48 115	57.96 180	72.24 128	85.73 162	53.22 93	86.27 94	63.79 165	79.06 161	89.36 5

E3new		73.41 91	73.22 94	73.95 142	77.06 235	50.31 248	76.78 206	83.66 79	60.90 104	72.93 115	86.02 151	55.99 53	82.95 175	66.89 132	78.77 167	88.61 24

BP-MVS1		73.41 91	72.25 109	76.88 61	76.68 247	53.70 163	79.15 127	81.07 150	60.66 111	71.81 133	87.39 96	40.93 272	87.24 59	71.23 90	81.29 114	89.71 2

CLD-MVS		73.33 93	72.68 103	75.29 96	78.82 159	53.33 177	78.23 151	84.79 46	61.30 95	70.41 152	81.04 275	52.41 107	87.12 66	64.61 154	82.49 100	85.41 163
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 94	72.54 105	75.62 89	77.87 195	53.64 165	79.62 122	79.61 176	61.63 90	72.02 132	82.61 231	56.44 43	85.97 104	63.99 158	79.07 160	87.25 81

fmvsm_l_conf0.5_n_9		73.27 95	73.66 85	72.09 201	73.82 314	52.72 195	77.45 178	74.28 297	56.61 212	77.10 43	88.16 76	56.17 47	77.09 320	78.27 24	81.13 115	86.48 109

fmvsm_l_conf0.5_n_3		73.23 96	73.13 96	73.55 163	74.40 302	55.13 141	78.97 129	74.96 287	56.64 206	74.76 71	88.75 71	55.02 65	78.77 289	76.33 41	78.31 181	86.74 97

fmvsm_s_conf0.5_n_11		73.16 97	73.35 92	72.58 187	75.48 270	52.41 206	78.84 131	76.85 245	58.64 164	73.58 95	87.25 106	54.09 77	79.47 264	76.19 44	79.27 150	85.86 135

viewmacassd2359aftdt		73.15 98	73.16 95	73.11 176	75.15 281	49.31 272	77.53 176	83.21 98	60.42 117	73.20 104	87.34 98	53.82 83	81.05 231	67.02 129	80.79 116	88.96 10

UA-Net		73.13 99	72.93 98	73.76 148	83.58 71	51.66 219	78.75 132	77.66 227	67.75 4	72.61 123	89.42 56	49.82 147	83.29 163	53.61 262	83.14 87	86.32 119

EPNet		73.09 100	72.16 110	75.90 79	75.95 260	56.28 114	83.05 67	72.39 323	66.53 10	65.27 263	87.00 111	50.40 140	85.47 118	62.48 181	86.32 64	85.94 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 101	72.59 104	74.27 124	71.28 368	55.88 124	78.21 153	75.56 270	54.31 274	74.86 67	87.80 87	54.72 69	80.23 252	78.07 26	78.48 176	86.70 98

nrg030		72.96 102	73.01 97	72.84 182	75.41 273	50.24 249	80.02 111	82.89 111	58.36 171	74.44 75	86.73 121	58.90 27	80.83 238	65.84 142	74.46 236	87.44 70

viewmanbaseed2359cas		72.92 103	72.89 99	73.00 178	75.16 279	49.25 275	77.25 188	83.11 106	59.52 148	72.93 115	86.63 126	54.11 76	80.98 232	66.63 133	80.67 120	88.76 21

test_fmvsmconf0.1_n		72.81 104	72.33 108	74.24 125	69.89 391	55.81 125	78.22 152	75.40 275	54.17 276	75.00 62	88.03 83	53.82 83	80.23 252	78.08 25	78.34 180	86.69 99

CPTT-MVS		72.78 105	72.08 112	74.87 102	84.88 61	61.41 26	84.15 54	77.86 223	55.27 245	67.51 217	88.08 79	41.93 252	81.85 209	69.04 102	80.01 132	81.35 289

LPG-MVS_test		72.74 106	71.74 117	75.76 83	80.22 123	57.51 96	82.55 78	83.40 88	61.32 93	66.67 235	87.33 99	39.15 291	86.59 79	67.70 117	77.30 199	83.19 245

h-mvs33		72.71 107	71.49 121	76.40 72	81.99 92	59.58 57	76.92 200	76.74 250	60.40 118	74.81 68	85.95 154	45.54 206	85.76 109	70.41 95	70.61 301	83.86 221

fmvsm_s_conf0.5_n_5		72.69 108	72.80 101	72.37 197	74.11 312	53.21 180	78.12 155	73.31 311	53.98 279	76.81 45	88.05 80	53.38 91	77.37 315	76.64 38	80.78 117	86.53 107

GDP-MVS		72.64 109	71.28 128	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 244	71.38 142	86.97 112	39.94 278	87.00 70	67.02 129	79.20 154	88.89 12

PAPM_NR		72.63 110	71.80 115	75.13 97	81.72 96	53.42 175	79.91 115	83.28 96	59.14 153	66.31 242	85.90 155	51.86 117	86.06 100	57.45 227	80.62 121	85.91 133

fmvsm_s_conf0.5_n_6		72.59 111	72.87 100	71.73 212	75.14 282	51.96 214	76.28 216	77.12 240	57.63 190	73.85 90	86.91 113	51.54 124	77.87 303	77.18 32	80.18 131	85.37 165

VDD-MVS		72.50 112	72.09 111	73.75 150	81.58 97	49.69 265	77.76 169	77.63 228	63.21 50	73.21 103	89.02 62	42.14 248	83.32 162	61.72 188	82.50 99	88.25 35

3Dnovator	64.47 5	72.49 113	71.39 124	75.79 82	77.70 202	58.99 76	80.66 104	83.15 103	62.24 75	65.46 259	86.59 129	42.38 247	85.52 114	59.59 208	84.72 71	82.85 255

MGCFI-Net		72.45 114	73.34 93	69.81 268	77.77 199	43.21 352	75.84 231	81.18 147	59.59 146	75.45 53	86.64 124	57.74 31	77.94 299	63.92 159	81.90 106	88.30 33

MVS_Test		72.45 114	72.46 106	72.42 196	74.88 284	48.50 290	76.28 216	83.14 104	59.40 149	72.46 125	84.68 181	55.66 60	81.12 227	65.98 141	79.66 140	87.63 62

EI-MVSNet-Vis-set		72.42 116	71.59 118	74.91 100	78.47 171	54.02 157	77.05 194	79.33 182	65.03 18	71.68 136	79.35 313	52.75 101	84.89 131	66.46 134	74.23 240	85.83 138

viewdifsd2359ckpt13		72.40 117	71.79 116	74.22 126	75.63 265	51.77 218	78.67 135	83.13 105	57.08 197	71.59 138	85.36 172	53.10 96	82.64 193	63.07 175	78.51 175	88.24 36

ACMP	63.53 6	72.30 118	71.20 130	75.59 91	80.28 121	57.54 94	82.74 74	82.84 112	60.58 113	65.24 267	86.18 144	39.25 289	86.03 102	66.95 131	76.79 207	83.22 243
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 119	71.21 129	75.31 94	78.50 169	55.93 122	81.63 90	82.12 120	56.24 222	70.02 159	85.68 164	47.05 189	84.34 142	65.27 147	74.41 239	85.67 148

Vis-MVSNet		72.18 120	71.37 125	74.61 111	81.29 104	55.41 136	80.90 100	78.28 218	60.73 109	69.23 177	88.09 78	44.36 226	82.65 192	57.68 225	81.75 110	85.77 142
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 121	71.50 120	74.16 128	67.96 415	55.58 133	78.06 159	74.67 290	54.19 275	74.54 74	88.23 74	50.35 142	80.24 251	78.07 26	77.46 194	86.65 103

API-MVS		72.17 121	71.41 123	74.45 119	81.95 93	57.22 99	84.03 56	80.38 165	59.89 139	68.40 188	82.33 244	49.64 149	87.83 50	51.87 276	84.16 81	78.30 344

EPP-MVSNet		72.16 123	71.31 127	74.71 105	78.68 163	49.70 263	82.10 86	81.65 127	60.40 118	65.94 249	85.84 157	51.74 121	86.37 90	55.93 238	79.55 143	88.07 47

DP-MVS Recon		72.15 124	70.73 139	76.40 72	86.57 25	57.99 88	81.15 98	82.96 107	57.03 200	66.78 230	85.56 165	44.50 224	88.11 42	51.77 278	80.23 130	83.10 250

fmvsm_s_conf0.5_n_4		72.04 125	71.85 114	72.58 187	73.74 317	52.49 202	76.69 207	72.42 322	56.42 217	75.32 54	87.04 110	52.13 113	78.01 298	79.29 12	73.65 250	87.26 80

EI-MVSNet-UG-set		71.92 126	71.06 133	74.52 117	77.98 193	53.56 168	76.62 208	79.16 183	64.40 29	71.18 143	78.95 318	52.19 111	84.66 138	65.47 145	73.57 253	85.32 167

viewdifsd2359ckpt07		71.90 127	71.97 113	71.69 215	74.81 288	48.08 296	75.30 239	80.49 162	60.00 133	71.63 137	86.33 140	56.34 45	79.25 269	65.40 146	77.41 195	87.76 57

VDDNet		71.81 128	71.33 126	73.26 174	82.80 83	47.60 305	78.74 133	75.27 277	59.59 146	72.94 114	89.40 57	41.51 264	83.91 150	58.75 220	82.99 90	88.26 34

EIA-MVS		71.78 129	70.60 141	75.30 95	79.85 132	53.54 169	77.27 187	83.26 97	57.92 182	66.49 237	79.39 311	52.07 114	86.69 77	60.05 202	79.14 159	85.66 149

LFMVS		71.78 129	71.59 118	72.32 198	83.40 75	46.38 314	79.75 118	71.08 332	64.18 34	72.80 119	88.64 72	42.58 244	83.72 153	57.41 228	84.49 76	86.86 92

test_fmvsm_n_1920		71.73 131	71.14 131	73.50 164	72.52 339	56.53 111	75.60 233	76.16 257	48.11 370	77.22 40	85.56 165	53.10 96	77.43 312	74.86 57	77.14 201	86.55 106

PAPR		71.72 132	70.82 137	74.41 120	81.20 108	51.17 222	79.55 124	83.33 93	55.81 230	66.93 229	84.61 185	50.95 134	86.06 100	55.79 241	79.20 154	86.00 129

IS-MVSNet		71.57 133	71.00 134	73.27 173	78.86 157	45.63 325	80.22 109	78.69 197	64.14 37	66.46 238	87.36 97	49.30 155	85.60 111	50.26 289	83.71 86	88.59 25

MAR-MVS		71.51 134	70.15 152	75.60 90	81.84 94	59.39 60	81.38 95	82.90 109	54.90 262	68.08 201	78.70 319	47.73 174	85.51 115	51.68 280	84.17 80	81.88 278
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
MVSFormer		71.50 135	70.38 146	74.88 101	78.76 160	57.15 104	82.79 72	78.48 208	51.26 326	69.49 168	83.22 221	43.99 230	83.24 164	66.06 137	79.37 144	84.23 205

RRT-MVS		71.46 136	70.70 140	73.74 151	77.76 200	49.30 273	76.60 209	80.45 163	61.25 96	68.17 193	84.78 178	44.64 222	84.90 130	64.79 150	77.88 187	87.03 87

PVSNet_Blended_VisFu		71.45 137	70.39 145	74.65 109	82.01 90	58.82 79	79.93 114	80.35 166	55.09 250	65.82 255	82.16 252	49.17 158	82.64 193	60.34 200	78.62 173	82.50 266

OMC-MVS		71.40 138	70.60 141	73.78 146	76.60 250	53.15 181	79.74 119	79.78 172	58.37 170	68.75 182	86.45 136	45.43 210	80.60 242	62.58 179	77.73 188	87.58 66

KinetiMVS		71.26 139	70.16 151	74.57 114	74.59 296	52.77 194	75.91 228	81.20 146	60.72 110	69.10 180	85.71 163	41.67 259	83.53 158	63.91 161	78.62 173	87.42 71

UniMVSNet_NR-MVSNet		71.11 140	71.00 134	71.44 225	79.20 147	44.13 340	76.02 226	82.60 114	66.48 11	68.20 191	84.60 188	56.82 40	82.82 188	54.62 252	70.43 303	87.36 78

hse-mvs2		71.04 141	69.86 155	74.60 112	79.58 137	57.12 106	73.96 271	75.25 278	60.40 118	74.81 68	81.95 257	45.54 206	82.90 181	70.41 95	66.83 356	83.77 226

diffmvs_AUTHOR		71.02 142	70.87 136	71.45 224	69.89 391	48.97 281	73.16 293	78.33 217	57.79 187	72.11 131	85.26 173	51.84 118	77.89 302	71.00 92	78.47 178	87.49 68

GeoE		71.01 143	70.15 152	73.60 161	79.57 138	52.17 208	78.93 130	78.12 220	58.02 177	67.76 214	83.87 204	52.36 108	82.72 190	56.90 230	75.79 221	85.92 132

fmvsm_l_conf0.5_n		70.99 144	70.82 137	71.48 221	71.45 361	54.40 151	77.18 190	70.46 338	48.67 360	75.17 57	86.86 114	53.77 85	76.86 328	76.33 41	77.51 193	83.17 249

PCF-MVS	61.88 8	70.95 145	69.49 162	75.35 93	77.63 206	55.71 127	76.04 225	81.81 125	50.30 338	69.66 166	85.40 171	52.51 104	84.89 131	51.82 277	80.24 129	85.45 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		70.84 146	69.41 165	75.12 98	79.20 147	53.86 159	77.89 162	80.00 170	53.88 281	69.40 171	84.61 185	43.21 236	86.56 81	58.80 218	77.68 190	84.95 183

test_fmvsmvis_n_1920		70.84 146	70.38 146	72.22 200	71.16 369	55.39 137	75.86 229	72.21 325	49.03 355	73.28 102	86.17 145	51.83 119	77.29 317	75.80 46	78.05 184	83.98 214

114514_t		70.83 148	69.56 160	74.64 110	86.21 32	54.63 148	82.34 81	81.81 125	48.22 368	63.01 303	85.83 158	40.92 273	87.10 67	57.91 224	79.79 137	82.18 272

FIs		70.82 149	71.43 122	68.98 283	78.33 179	38.14 402	76.96 198	83.59 82	61.02 101	67.33 219	86.73 121	55.07 63	81.64 212	54.61 254	79.22 153	87.14 85

ACMM	61.98 7	70.80 150	69.73 157	74.02 137	80.59 120	58.59 82	82.68 75	82.02 122	55.46 240	67.18 224	84.39 194	38.51 299	83.17 166	60.65 198	76.10 217	80.30 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 151	70.43 144	71.46 222	69.45 398	48.95 282	72.93 296	78.46 210	57.27 194	71.69 135	83.97 203	51.48 126	77.92 301	70.70 94	77.95 186	87.53 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet (Re)		70.63 152	70.20 149	71.89 205	78.55 168	45.29 328	75.94 227	82.92 108	63.68 42	68.16 194	83.59 212	53.89 81	83.49 160	53.97 258	71.12 294	86.89 91

xiu_mvs_v2_base		70.52 153	69.75 156	72.84 182	81.21 107	55.63 130	75.11 245	78.92 190	54.92 261	69.96 162	79.68 304	47.00 193	82.09 205	61.60 190	79.37 144	80.81 303

PS-MVSNAJ		70.51 154	69.70 158	72.93 180	81.52 98	55.79 126	74.92 252	79.00 188	55.04 256	69.88 163	78.66 321	47.05 189	82.19 203	61.61 189	79.58 141	80.83 302

fmvsm_l_conf0.5_n_a		70.50 155	70.27 148	71.18 237	71.30 367	54.09 156	76.89 201	69.87 342	47.90 374	74.37 77	86.49 134	53.07 98	76.69 334	75.41 52	77.11 202	82.76 256

v2v482		70.50 155	69.45 164	73.66 156	72.62 336	50.03 255	77.58 171	80.51 161	59.90 135	69.52 167	82.14 253	47.53 180	84.88 133	65.07 149	70.17 311	86.09 127

v1144		70.42 157	69.31 166	73.76 148	73.22 324	50.64 237	77.83 166	81.43 133	58.58 166	69.40 171	81.16 272	47.53 180	85.29 123	64.01 157	70.64 299	85.34 166

SSM_0407		70.41 158	68.96 175	74.75 104	78.65 164	53.46 171	77.28 186	80.00 170	53.88 281	68.14 195	84.61 185	43.21 236	86.26 95	58.80 218	76.11 214	84.54 193

TranMVSNet+NR-MVSNet		70.36 159	70.10 154	71.17 238	78.64 167	42.97 356	76.53 211	81.16 149	66.95 6	68.53 186	85.42 170	51.61 123	83.07 167	52.32 270	69.70 323	87.46 69

v8		70.33 160	69.28 167	73.49 165	73.15 326	50.22 250	78.62 137	80.78 157	60.79 107	66.45 239	82.11 255	49.35 154	84.98 127	63.58 168	68.71 339	85.28 169

Fast-Effi-MVS+		70.28 161	69.12 171	73.73 152	78.50 169	51.50 220	75.01 248	79.46 180	56.16 224	68.59 183	79.55 307	53.97 79	84.05 145	53.34 264	77.53 192	85.65 150

X-MVStestdata		70.21 162	67.28 221	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	6.49 488	47.95 171	88.01 44	71.55 88	86.74 59	86.37 113

v10		70.21 162	69.02 172	73.81 145	73.51 320	50.92 228	78.74 133	81.39 134	60.05 132	66.39 240	81.83 260	47.58 178	85.41 121	62.80 178	68.86 338	85.09 177

Elysia		70.19 164	68.29 194	75.88 80	74.15 309	54.33 153	78.26 143	83.21 98	55.04 256	67.28 220	83.59 212	30.16 392	86.11 98	63.67 166	79.26 151	87.20 82

StellarMVS		70.19 164	68.29 194	75.88 80	74.15 309	54.33 153	78.26 143	83.21 98	55.04 256	67.28 220	83.59 212	30.16 392	86.11 98	63.67 166	79.26 151	87.20 82

QAPM		70.05 166	68.81 178	73.78 146	76.54 252	53.43 174	83.23 65	83.48 84	52.89 297	65.90 251	86.29 141	41.55 263	86.49 87	51.01 283	78.40 179	81.42 283

DU-MVS		70.01 167	69.53 161	71.44 225	78.05 190	44.13 340	75.01 248	81.51 131	64.37 30	68.20 191	84.52 189	49.12 161	82.82 188	54.62 252	70.43 303	87.37 76

AdaColmap		69.99 168	68.66 182	73.97 141	84.94 58	57.83 90	82.63 76	78.71 196	56.28 221	64.34 282	84.14 197	41.57 261	87.06 69	46.45 325	78.88 163	77.02 365

v1192		69.97 169	68.68 181	73.85 143	73.19 325	50.94 226	77.68 170	81.36 136	57.51 192	68.95 181	80.85 282	45.28 213	85.33 122	62.97 177	70.37 305	85.27 170

Anonymous20240529		69.91 170	69.02 172	72.56 189	80.19 126	47.65 303	77.56 173	80.99 153	55.45 241	69.88 163	86.76 117	39.24 290	82.18 204	54.04 257	77.10 203	87.85 52

patch_mono-2		69.85 171	71.09 132	66.16 324	79.11 152	54.80 147	71.97 314	74.31 295	53.50 290	70.90 146	84.17 196	57.63 34	63.31 418	66.17 136	82.02 104	80.38 312

fmvsm_s_conf0.5_n_2		69.82 172	69.27 168	71.46 222	72.00 351	51.08 223	73.30 286	67.79 361	55.06 255	75.24 56	87.51 90	44.02 229	77.00 324	75.67 48	72.86 268	86.31 122

FA-MVS(test-final)		69.82 172	68.48 185	73.84 144	78.44 172	50.04 254	75.58 236	78.99 189	58.16 173	67.59 215	82.14 253	42.66 242	85.63 110	56.60 231	76.19 213	85.84 137

FC-MVSNet-test		69.80 174	70.58 143	67.46 303	77.61 211	34.73 435	76.05 224	83.19 102	60.84 106	65.88 253	86.46 135	54.52 72	80.76 241	52.52 269	78.12 183	86.91 90

v144192		69.71 175	68.51 184	73.33 172	73.10 327	50.13 252	77.54 174	80.64 158	56.65 205	68.57 185	80.55 285	46.87 194	84.96 129	62.98 176	69.66 324	84.89 185

test_yl		69.69 176	69.13 169	71.36 231	78.37 176	45.74 321	74.71 256	80.20 167	57.91 183	70.01 160	83.83 205	42.44 245	82.87 184	54.97 248	79.72 138	85.48 155

DCV-MVSNet		69.69 176	69.13 169	71.36 231	78.37 176	45.74 321	74.71 256	80.20 167	57.91 183	70.01 160	83.83 205	42.44 245	82.87 184	54.97 248	79.72 138	85.48 155

VNet		69.68 178	70.19 150	68.16 295	79.73 134	41.63 370	70.53 337	77.38 234	60.37 121	70.69 147	86.63 126	51.08 132	77.09 320	53.61 262	81.69 112	85.75 144

jason		69.65 179	68.39 191	73.43 169	78.27 181	56.88 108	77.12 192	73.71 307	46.53 392	69.34 173	83.22 221	43.37 234	79.18 271	64.77 151	79.20 154	84.23 205
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 180	69.01 174	71.52 220	71.66 356	51.04 224	73.39 285	67.14 367	55.02 259	75.11 58	87.64 89	42.94 241	77.01 323	75.55 50	72.63 274	86.52 108

Effi-MVS+-dtu		69.64 180	67.53 211	75.95 78	76.10 258	62.29 15	80.20 110	76.06 261	59.83 140	65.26 266	77.09 352	41.56 262	84.02 148	60.60 199	71.09 297	81.53 282

fmvsm_s_conf0.5_n		69.58 182	68.84 177	71.79 210	72.31 347	52.90 187	77.90 161	62.43 411	49.97 343	72.85 118	85.90 155	52.21 110	76.49 337	75.75 47	70.26 310	85.97 130

lupinMVS		69.57 183	68.28 196	73.44 168	78.76 160	57.15 104	76.57 210	73.29 313	46.19 395	69.49 168	82.18 249	43.99 230	79.23 270	64.66 152	79.37 144	83.93 216

fmvsm_s_conf0.5_n_7		69.54 184	69.67 159	69.15 282	73.47 322	51.41 221	70.35 341	73.34 310	57.05 199	68.41 187	85.83 158	49.86 146	72.84 358	71.86 84	76.83 206	83.19 245

fmvsm_s_conf0.5_n_a		69.54 184	68.74 180	71.93 204	72.47 341	53.82 161	78.25 145	62.26 413	49.78 345	73.12 110	86.21 143	52.66 102	76.79 330	75.02 56	68.88 336	85.18 172

NR-MVSNet		69.54 184	68.85 176	71.59 219	78.05 190	43.81 345	74.20 267	80.86 156	65.18 14	62.76 307	84.52 189	52.35 109	83.59 157	50.96 285	70.78 298	87.37 76

MVS_111021_LR		69.50 187	68.78 179	71.65 217	78.38 174	59.33 61	74.82 254	70.11 340	58.08 174	67.83 210	84.68 181	41.96 250	76.34 341	65.62 144	77.54 191	79.30 335

v1921920		69.47 188	68.17 198	73.36 171	73.06 328	50.10 253	77.39 179	80.56 159	56.58 214	68.59 183	80.37 287	44.72 221	84.98 127	62.47 182	69.82 319	85.00 179

test_djsdf		69.45 189	67.74 204	74.58 113	74.57 298	54.92 145	82.79 72	78.48 208	51.26 326	65.41 260	83.49 217	38.37 301	83.24 164	66.06 137	69.25 331	85.56 152

fmvsm_s_conf0.1_n		69.41 190	68.60 183	71.83 207	71.07 370	52.88 190	77.85 165	62.44 410	49.58 348	72.97 113	86.22 142	51.68 122	76.48 338	75.53 51	70.10 313	86.14 125

fmvsm_s_conf0.1_n_a		69.32 191	68.44 189	71.96 202	70.91 372	53.78 162	78.12 155	62.30 412	49.35 351	73.20 104	86.55 133	51.99 115	76.79 330	74.83 58	68.68 341	85.32 167

Anonymous20231211		69.28 192	68.47 187	71.73 212	80.28 121	47.18 309	79.98 112	82.37 117	54.61 267	67.24 222	84.01 201	39.43 285	82.41 200	55.45 246	72.83 269	85.62 151

EI-MVSNet		69.27 193	68.44 189	71.73 212	74.47 299	49.39 270	75.20 243	78.45 211	59.60 143	69.16 178	76.51 365	51.29 128	82.50 197	59.86 207	71.45 291	83.30 240

v1240		69.24 194	67.91 203	73.25 175	73.02 330	49.82 257	77.21 189	80.54 160	56.43 216	68.34 190	80.51 286	43.33 235	84.99 125	62.03 186	69.77 322	84.95 183

IterMVS-LS		69.22 195	68.48 185	71.43 227	74.44 301	49.40 269	76.23 218	77.55 229	59.60 143	65.85 254	81.59 267	51.28 129	81.58 215	59.87 206	69.90 318	83.30 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt11		69.13 196	68.38 192	71.38 229	71.57 358	48.61 287	73.22 291	73.18 314	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 295	85.74 145

viewmsd2359difaftdt		69.13 196	68.38 192	71.38 229	71.57 358	48.61 287	73.22 291	73.18 314	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 295	85.74 145

IMVS_0403		69.09 198	68.14 199	71.95 203	77.06 235	49.73 259	74.51 260	78.60 200	52.70 299	66.69 233	82.58 232	46.43 197	83.38 161	59.20 213	75.46 227	82.74 257

VPA-MVSNet		69.02 199	69.47 163	67.69 300	77.42 216	41.00 377	74.04 269	79.68 174	60.06 131	69.26 176	84.81 177	51.06 133	77.58 310	54.44 255	74.43 238	84.48 198

v7n		69.01 200	67.36 218	73.98 140	72.51 340	52.65 196	78.54 141	81.30 141	60.26 127	62.67 309	81.62 264	43.61 232	84.49 139	57.01 229	68.70 340	84.79 188

viewmambaseed2359dif		68.91 201	68.18 197	71.11 240	70.21 383	48.05 299	72.28 309	75.90 263	51.96 311	70.93 145	84.47 192	51.37 127	78.59 290	61.55 192	74.97 232	86.68 100

IMVS_0407		68.90 202	67.93 202	71.82 208	77.06 235	49.73 259	74.40 265	78.60 200	52.70 299	66.19 243	82.58 232	45.17 216	83.00 169	59.20 213	75.46 227	82.74 257

OpenMVS	61.03 9	68.85 203	67.56 208	72.70 186	74.26 307	53.99 158	81.21 97	81.34 140	52.70 299	62.75 308	85.55 167	38.86 295	84.14 144	48.41 305	83.01 89	79.97 322

XVG-OURS-SEG-HR		68.81 204	67.47 214	72.82 184	74.40 302	56.87 109	70.59 336	79.04 187	54.77 264	66.99 227	86.01 152	39.57 284	78.21 295	62.54 180	73.33 260	83.37 239

BH-RMVSNet		68.81 204	67.42 215	72.97 179	80.11 129	52.53 200	74.26 266	76.29 256	58.48 168	68.38 189	84.20 195	42.59 243	83.83 151	46.53 324	75.91 219	82.56 261

UGNet		68.81 204	67.39 216	73.06 177	78.33 179	54.47 149	79.77 117	75.40 275	60.45 116	63.22 296	84.40 193	32.71 369	80.91 237	51.71 279	80.56 125	83.81 222
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
XVG-OURS		68.76 207	67.37 217	72.90 181	74.32 305	57.22 99	70.09 345	78.81 193	55.24 246	67.79 212	85.81 161	36.54 324	78.28 294	62.04 185	75.74 222	83.19 245

V42		68.65 208	67.35 219	72.56 189	68.93 406	50.18 251	72.90 298	79.47 179	56.92 202	69.45 170	80.26 291	46.29 199	82.99 170	64.07 155	67.82 347	84.53 196

PVSNet_Blended		68.59 209	67.72 205	71.19 236	77.03 241	50.57 238	72.51 305	81.52 129	51.91 312	64.22 288	77.77 343	49.13 159	82.87 184	55.82 239	79.58 141	80.14 320

xiu_mvs_v1_base_debu		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 283	51.61 316	70.04 156	81.41 269	32.79 365	79.02 282	63.81 162	77.31 196	81.22 292

xiu_mvs_v1_base		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 283	51.61 316	70.04 156	81.41 269	32.79 365	79.02 282	63.81 162	77.31 196	81.22 292

xiu_mvs_v1_base_debi		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 283	51.61 316	70.04 156	81.41 269	32.79 365	79.02 282	63.81 162	77.31 196	81.22 292

PVSNet_BlendedMVS		68.56 213	67.72 205	71.07 242	77.03 241	50.57 238	74.50 261	81.52 129	53.66 289	64.22 288	79.72 303	49.13 159	82.87 184	55.82 239	73.92 244	79.77 330

WR-MVS		68.47 214	68.47 187	68.44 290	80.20 125	39.84 385	73.75 279	76.07 260	64.68 24	68.11 199	83.63 211	50.39 141	79.14 276	49.78 290	69.66 324	86.34 115

mvsmamba		68.47 214	66.56 236	74.21 127	79.60 136	52.95 185	74.94 251	75.48 273	52.09 310	60.10 341	83.27 220	36.54 324	84.70 135	59.32 212	77.69 189	84.99 181

AUN-MVS		68.45 216	66.41 243	74.57 114	79.53 139	57.08 107	73.93 274	75.23 279	54.44 272	66.69 233	81.85 259	37.10 319	82.89 182	62.07 184	66.84 355	83.75 227

c3_l		68.33 217	67.56 208	70.62 252	70.87 373	46.21 317	74.47 262	78.80 194	56.22 223	66.19 243	78.53 326	51.88 116	81.40 219	62.08 183	69.04 334	84.25 204

BH-untuned		68.27 218	67.29 220	71.21 235	79.74 133	53.22 179	76.06 223	77.46 232	57.19 195	66.10 246	81.61 265	45.37 212	83.50 159	45.42 341	76.68 209	76.91 369

jajsoiax		68.25 219	66.45 239	73.66 156	75.62 266	55.49 135	80.82 101	78.51 207	52.33 307	64.33 283	84.11 198	28.28 412	81.81 211	63.48 169	70.62 300	83.67 230

LuminaMVS		68.24 220	66.82 233	72.51 191	73.46 323	53.60 167	76.23 218	78.88 191	52.78 298	68.08 201	80.13 293	32.70 370	81.41 218	63.16 174	75.97 218	82.53 263

v148		68.24 220	67.19 228	71.40 228	70.43 380	47.77 302	75.76 232	77.03 242	58.91 157	67.36 218	80.10 295	48.60 166	81.89 208	60.01 203	66.52 359	84.53 196

CANet_DTU		68.18 222	67.71 207	69.59 271	74.83 287	46.24 316	78.66 136	76.85 245	59.60 143	63.45 294	82.09 256	35.25 334	77.41 313	59.88 205	78.76 168	85.14 173

mvs_tets		68.18 222	66.36 245	73.63 159	75.61 267	55.35 139	80.77 102	78.56 205	52.48 306	64.27 285	84.10 199	27.45 420	81.84 210	63.45 170	70.56 302	83.69 229

guyue		68.10 224	67.23 227	70.71 251	73.67 319	49.27 274	73.65 281	76.04 262	55.62 237	67.84 209	82.26 247	41.24 269	78.91 288	61.01 195	73.72 248	83.94 215

SDMVSNet		68.03 225	68.10 201	67.84 297	77.13 230	48.72 286	65.32 389	79.10 184	58.02 177	65.08 270	82.55 237	47.83 173	73.40 355	63.92 159	73.92 244	81.41 284

miper_ehance_all_eth		68.03 225	67.24 225	70.40 256	70.54 377	46.21 317	73.98 270	78.68 198	55.07 253	66.05 247	77.80 340	52.16 112	81.31 222	61.53 193	69.32 328	83.67 230

mvs_anonymous		68.03 225	67.51 212	69.59 271	72.08 349	44.57 337	71.99 313	75.23 279	51.67 314	67.06 226	82.57 236	54.68 70	77.94 299	56.56 234	75.71 223	86.26 124

ET-MVSNet_ETH3D		67.96 228	65.72 257	74.68 107	76.67 248	55.62 132	75.11 245	74.74 288	52.91 296	60.03 343	80.12 294	33.68 354	82.64 193	61.86 187	76.34 211	85.78 139

thisisatest0530		67.92 229	65.78 256	74.33 122	76.29 255	51.03 225	76.89 201	74.25 298	53.67 288	65.59 257	81.76 262	35.15 335	85.50 116	55.94 237	72.47 275	86.47 110

PAPM		67.92 229	66.69 235	71.63 218	78.09 188	49.02 278	77.09 193	81.24 145	51.04 330	60.91 335	83.98 202	47.71 175	84.99 125	40.81 378	79.32 148	80.90 301

AstraMVS		67.86 231	66.83 232	70.93 245	73.50 321	49.34 271	73.28 289	74.01 302	55.45 241	68.10 200	83.28 219	38.93 294	79.14 276	63.22 173	71.74 286	84.30 203

tttt0517		67.83 232	65.66 258	74.33 122	76.69 246	50.82 230	77.86 164	73.99 303	54.54 270	64.64 280	82.53 240	35.06 336	85.50 116	55.71 242	69.91 317	86.67 101

mamba_0408		67.78 233	65.42 262	74.85 103	78.65 164	53.46 171	50.83 461	79.09 185	53.75 284	68.14 195	83.83 205	41.79 257	86.56 81	56.58 232	76.11 214	84.54 193

tt0805		67.77 234	67.24 225	69.34 276	74.87 285	40.08 382	77.36 180	81.37 135	55.31 243	66.33 241	84.65 183	37.35 313	82.55 196	55.65 244	72.28 280	85.39 164

ECVR-MVS		67.72 235	67.51 212	68.35 291	79.46 140	36.29 425	74.79 255	66.93 369	58.72 160	67.19 223	88.05 80	36.10 326	81.38 220	52.07 273	84.25 78	87.39 74

eth_miper_zixun_eth		67.63 236	66.28 249	71.67 216	71.60 357	48.33 292	73.68 280	77.88 222	55.80 231	65.91 250	78.62 324	47.35 186	82.88 183	59.45 209	66.25 360	83.81 222

UniMVSNet_ETH3D		67.60 237	67.07 230	69.18 280	77.39 217	42.29 361	74.18 268	75.59 269	60.37 121	66.77 231	86.06 149	37.64 309	78.93 287	52.16 272	73.49 255	86.32 119

VPNet		67.52 238	68.11 200	65.74 334	79.18 149	36.80 417	72.17 311	72.83 319	62.04 82	67.79 212	85.83 158	48.88 163	76.60 336	51.30 281	72.97 267	83.81 222

cl22		67.47 239	66.45 239	70.54 254	69.85 393	46.49 313	73.85 277	77.35 235	55.07 253	65.51 258	77.92 335	47.64 177	81.10 228	61.58 191	69.32 328	84.01 213

Fast-Effi-MVS+-dtu		67.37 240	65.33 266	73.48 166	72.94 331	57.78 92	77.47 177	76.88 244	57.60 191	61.97 321	76.85 356	39.31 287	80.49 246	54.72 251	70.28 309	82.17 274

MVS		67.37 240	66.33 246	70.51 255	75.46 271	50.94 226	73.95 272	81.85 124	41.57 432	62.54 313	78.57 325	47.98 170	85.47 118	52.97 267	82.05 103	75.14 385

test1111		67.21 242	67.14 229	67.42 304	79.24 146	34.76 434	73.89 276	65.65 379	58.71 162	66.96 228	87.95 84	36.09 327	80.53 243	52.03 274	83.79 84	86.97 89

GBi-Net		67.21 242	66.55 237	69.19 277	77.63 206	43.33 349	77.31 181	77.83 224	56.62 209	65.04 272	82.70 227	41.85 254	80.33 248	47.18 318	72.76 270	83.92 217

test1		67.21 242	66.55 237	69.19 277	77.63 206	43.33 349	77.31 181	77.83 224	56.62 209	65.04 272	82.70 227	41.85 254	80.33 248	47.18 318	72.76 270	83.92 217

cl____		67.18 245	66.26 250	69.94 263	70.20 384	45.74 321	73.30 286	76.83 247	55.10 248	65.27 263	79.57 306	47.39 184	80.53 243	59.41 211	69.22 332	83.53 236

DIV-MVS_self_test		67.18 245	66.26 250	69.94 263	70.20 384	45.74 321	73.29 288	76.83 247	55.10 248	65.27 263	79.58 305	47.38 185	80.53 243	59.43 210	69.22 332	83.54 235

MVSTER		67.16 247	65.58 260	71.88 206	70.37 382	49.70 263	70.25 343	78.45 211	51.52 319	69.16 178	80.37 287	38.45 300	82.50 197	60.19 201	71.46 290	83.44 238

miper_enhance_ethall		67.11 248	66.09 252	70.17 260	69.21 401	45.98 319	72.85 299	78.41 214	51.38 323	65.65 256	75.98 375	51.17 131	81.25 223	60.82 197	69.32 328	83.29 242

Baseline_NR-MVSNet		67.05 249	67.56 208	65.50 338	75.65 264	37.70 408	75.42 237	74.65 291	59.90 135	68.14 195	83.15 224	49.12 161	77.20 318	52.23 271	69.78 320	81.60 280

WR-MVS_H		67.02 250	66.92 231	67.33 307	77.95 194	37.75 406	77.57 172	82.11 121	62.03 83	62.65 310	82.48 241	50.57 139	79.46 265	42.91 364	64.01 377	84.79 188

anonymousdsp		67.00 251	64.82 271	73.57 162	70.09 387	56.13 117	76.35 214	77.35 235	48.43 365	64.99 275	80.84 283	33.01 362	80.34 247	64.66 152	67.64 349	84.23 205

FMVSNet2		66.93 252	66.31 248	68.79 286	77.63 206	42.98 355	76.11 221	77.47 230	56.62 209	65.22 269	82.17 251	41.85 254	80.18 254	47.05 322	72.72 273	83.20 244

BH-w/o		66.85 253	65.83 255	69.90 266	79.29 142	52.46 203	74.66 258	76.65 251	54.51 271	64.85 277	78.12 329	45.59 205	82.95 175	43.26 360	75.54 225	74.27 399

Anonymous202405211		66.84 254	65.99 253	69.40 275	80.19 126	42.21 363	71.11 328	71.31 331	58.80 159	67.90 203	86.39 137	29.83 397	79.65 259	49.60 296	78.78 166	86.33 117

CDS-MVSNet		66.80 255	65.37 264	71.10 241	78.98 154	53.13 183	73.27 290	71.07 333	52.15 309	64.72 278	80.23 292	43.56 233	77.10 319	45.48 339	78.88 163	83.05 251
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 256	65.27 267	71.33 234	79.16 151	53.67 164	73.84 278	69.59 346	52.32 308	65.28 262	81.72 263	44.49 225	77.40 314	42.32 368	78.66 172	82.92 252

FMVSNet1		66.70 257	65.87 254	69.19 277	77.49 214	43.33 349	77.31 181	77.83 224	56.45 215	64.60 281	82.70 227	38.08 307	80.33 248	46.08 329	72.31 279	83.92 217

ab-mvs		66.65 258	66.42 242	67.37 305	76.17 257	41.73 367	70.41 340	76.14 259	53.99 278	65.98 248	83.51 216	49.48 151	76.24 342	48.60 303	73.46 257	84.14 209

PEN-MVS		66.60 259	66.45 239	67.04 308	77.11 234	36.56 419	77.03 195	80.42 164	62.95 55	62.51 315	84.03 200	46.69 195	79.07 279	44.22 346	63.08 387	85.51 154

TAPA-MVS	59.36 10	66.60 259	65.20 268	70.81 247	76.63 249	48.75 284	76.52 212	80.04 169	50.64 335	65.24 267	84.93 175	39.15 291	78.54 291	36.77 405	76.88 205	85.14 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 261	65.07 269	71.17 238	79.18 149	49.63 267	73.48 282	75.20 281	52.95 295	67.90 203	80.33 290	39.81 282	83.68 154	43.20 361	73.56 254	80.20 318

CP-MVSNet		66.49 262	66.41 243	66.72 310	77.67 204	36.33 422	76.83 205	79.52 178	62.45 68	62.54 313	83.47 218	46.32 198	78.37 292	45.47 340	63.43 384	85.45 159

PS-CasMVS		66.42 263	66.32 247	66.70 312	77.60 212	36.30 424	76.94 199	79.61 176	62.36 70	62.43 318	83.66 210	45.69 202	78.37 292	45.35 342	63.26 385	85.42 162

icg_test_0407_2		66.41 264	66.75 234	65.37 342	77.06 235	49.73 259	63.79 403	78.60 200	52.70 299	66.19 243	82.58 232	45.17 216	63.65 417	59.20 213	75.46 227	82.74 257

VortexMVS		66.41 264	65.50 261	69.16 281	73.75 315	48.14 294	73.41 284	78.28 218	53.73 286	64.98 276	78.33 327	40.62 274	79.07 279	58.88 217	67.50 350	80.26 317

FMVSNet3		66.32 266	65.61 259	68.46 289	76.48 253	42.34 360	74.98 250	77.15 239	55.83 229	65.04 272	81.16 272	39.91 279	80.14 255	47.18 318	72.76 270	82.90 254

ACMH+	57.40 11	66.12 267	64.06 276	72.30 199	77.79 198	52.83 192	80.39 105	78.03 221	57.30 193	57.47 376	82.55 237	27.68 418	84.17 143	45.54 336	69.78 320	79.90 324

cascas		65.98 268	63.42 289	73.64 158	77.26 221	52.58 199	72.26 310	77.21 238	48.56 361	61.21 332	74.60 390	32.57 376	85.82 108	50.38 288	76.75 208	82.52 265

FE-MVS		65.91 269	63.33 291	73.63 159	77.36 218	51.95 215	72.62 302	75.81 264	53.70 287	65.31 261	78.96 317	28.81 407	86.39 89	43.93 351	73.48 256	82.55 262

thisisatest0515		65.83 270	63.50 287	72.82 184	73.75 315	49.50 268	71.32 322	73.12 318	49.39 350	63.82 290	76.50 367	34.95 338	84.84 134	53.20 266	75.49 226	84.13 210

DP-MVS		65.68 271	63.66 284	71.75 211	84.93 59	56.87 109	80.74 103	73.16 316	53.06 294	59.09 357	82.35 243	36.79 323	85.94 105	32.82 429	69.96 316	72.45 414

HyFIR lowres test		65.67 272	63.01 296	73.67 155	79.97 131	55.65 129	69.07 357	75.52 271	42.68 426	63.53 293	77.95 333	40.43 276	81.64 212	46.01 330	71.91 284	83.73 228

DTE-MVSNet		65.58 273	65.34 265	66.31 320	76.06 259	34.79 432	76.43 213	79.38 181	62.55 66	61.66 327	83.83 205	45.60 204	79.15 275	41.64 376	60.88 406	85.00 179

GA-MVS		65.53 274	63.70 283	71.02 244	70.87 373	48.10 295	70.48 338	74.40 293	56.69 204	64.70 279	76.77 357	33.66 355	81.10 228	55.42 247	70.32 308	83.87 220

CNLPA		65.43 275	64.02 277	69.68 269	78.73 162	58.07 87	77.82 167	70.71 336	51.49 321	61.57 329	83.58 215	38.23 305	70.82 373	43.90 352	70.10 313	80.16 319

MVP-Stereo		65.41 276	63.80 281	70.22 257	77.62 210	55.53 134	76.30 215	78.53 206	50.59 336	56.47 388	78.65 322	39.84 281	82.68 191	44.10 350	72.12 283	72.44 415
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 277	62.73 300	73.40 170	74.89 283	52.78 193	73.09 295	75.13 282	55.69 233	58.48 366	73.73 398	32.86 364	86.32 92	50.63 286	70.11 312	81.10 296
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
test2506		65.33 278	64.61 272	67.50 301	79.46 140	34.19 440	74.43 264	51.92 451	58.72 160	66.75 232	88.05 80	25.99 432	80.92 236	51.94 275	84.25 78	87.39 74

pm-mvs1		65.24 279	64.97 270	66.04 328	72.38 344	39.40 391	72.62 302	75.63 267	55.53 238	62.35 320	83.18 223	47.45 182	76.47 339	49.06 300	66.54 358	82.24 271

ACMH	55.70 15	65.20 280	63.57 285	70.07 261	78.07 189	52.01 213	79.48 125	79.69 173	55.75 232	56.59 385	80.98 277	27.12 423	80.94 234	42.90 365	71.58 289	77.25 363
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 281	63.21 294	70.72 250	81.04 110	54.87 146	78.57 139	77.47 230	48.51 363	55.71 393	81.89 258	33.71 353	79.71 258	41.66 374	70.37 305	77.58 356
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 282	62.84 298	71.82 208	81.49 100	56.26 115	66.32 377	74.20 300	40.53 438	63.16 299	78.65 322	41.30 265	77.80 305	45.80 332	74.09 241	81.40 286

SSM_04072		64.98 283	65.42 262	63.68 357	78.65 164	53.46 171	50.83 461	79.09 185	53.75 284	68.14 195	83.83 205	41.79 257	53.03 462	56.58 232	76.11 214	84.54 193

TransMVSNet (Re)		64.72 284	64.33 274	65.87 333	75.22 276	38.56 397	74.66 258	75.08 286	58.90 158	61.79 324	82.63 230	51.18 130	78.07 297	43.63 357	55.87 430	80.99 300

EG-PatchMatch MVS		64.71 285	62.87 297	70.22 257	77.68 203	53.48 170	77.99 160	78.82 192	53.37 291	56.03 392	77.41 348	24.75 440	84.04 146	46.37 326	73.42 259	73.14 405

LS3D		64.71 285	62.50 302	71.34 233	79.72 135	55.71 127	79.82 116	74.72 289	48.50 364	56.62 384	84.62 184	33.59 356	82.34 201	29.65 451	75.23 231	75.97 375

IMVS_0404		64.63 287	64.22 275	65.88 332	77.06 235	49.73 259	64.40 397	78.60 200	52.70 299	53.16 423	82.58 232	34.82 339	65.16 411	59.20 213	75.46 227	82.74 257

1314		64.61 288	63.21 294	68.80 285	71.87 354	47.46 306	73.95 272	78.39 216	42.88 425	59.97 344	76.60 364	38.11 306	79.39 267	54.84 250	72.32 278	79.55 331

HY-MVS	56.14 13	64.55 289	63.89 278	66.55 316	74.73 291	41.02 374	69.96 346	74.43 292	49.29 352	61.66 327	80.92 279	47.43 183	76.68 335	44.91 344	71.69 287	81.94 276

testing91		64.46 290	63.80 281	66.47 317	78.43 173	40.06 383	67.63 367	69.59 346	59.06 154	63.18 298	78.05 331	34.05 347	76.99 325	48.30 306	75.87 220	82.37 269

sd_testset		64.46 290	64.45 273	64.51 350	77.13 230	42.25 362	62.67 410	72.11 326	58.02 177	65.08 270	82.55 237	41.22 270	69.88 381	47.32 316	73.92 244	81.41 284

XVG-ACMP-BASELINE		64.36 292	62.23 306	70.74 249	72.35 345	52.45 204	70.80 334	78.45 211	53.84 283	59.87 346	81.10 274	16.24 459	79.32 268	55.64 245	71.76 285	80.47 308

FE-MVSNET3		64.34 293	63.57 285	66.66 314	72.44 343	40.74 380	69.60 351	76.80 249	53.21 293	61.73 326	77.92 335	41.92 253	77.68 309	46.23 327	72.25 281	81.57 281

MonoMVSNet		64.15 294	63.31 292	66.69 313	70.51 378	44.12 342	74.47 262	74.21 299	57.81 185	63.03 301	76.62 361	38.33 302	77.31 316	54.22 256	60.59 412	78.64 342

testing99		64.05 295	63.29 293	66.34 319	78.17 186	39.76 387	67.33 372	68.00 360	58.60 165	63.03 301	78.10 330	32.57 376	76.94 327	48.22 307	75.58 224	82.34 270

CostFormer		64.04 296	62.51 301	68.61 288	71.88 353	45.77 320	71.30 323	70.60 337	47.55 379	64.31 284	76.61 363	41.63 260	79.62 261	49.74 292	69.00 335	80.42 310

1112_ss		64.00 297	63.36 290	65.93 330	79.28 144	42.58 359	71.35 321	72.36 324	46.41 393	60.55 338	77.89 338	46.27 200	73.28 356	46.18 328	69.97 315	81.92 277

baseline1		63.81 298	63.87 280	63.62 358	76.29 255	36.36 420	71.78 318	67.29 365	56.05 226	64.23 287	82.95 225	47.11 188	74.41 351	47.30 317	61.85 400	80.10 321

pmmvs6		63.69 299	62.82 299	66.27 322	70.63 375	39.27 392	73.13 294	75.47 274	52.69 304	59.75 350	82.30 245	39.71 283	77.03 322	47.40 314	64.35 376	82.53 263

Vis-MVSNet (Re-imp)		63.69 299	63.88 279	63.14 363	74.75 290	31.04 458	71.16 326	63.64 399	56.32 219	59.80 348	84.99 174	44.51 223	75.46 346	39.12 390	80.62 121	82.92 252

baseline2		63.42 301	61.26 320	69.89 267	72.55 338	47.62 304	71.54 319	68.38 357	50.11 340	54.82 405	75.55 380	43.06 239	80.96 233	48.13 308	67.16 354	81.11 295

thres400		63.31 302	62.18 307	66.72 310	76.85 244	39.62 388	71.96 315	69.44 349	56.63 207	62.61 311	79.83 298	37.18 315	79.17 272	31.84 435	73.25 262	81.36 287

thres600view7		63.30 303	62.27 305	66.41 318	77.18 223	38.87 394	72.35 307	69.11 353	56.98 201	62.37 319	80.96 278	37.01 321	79.00 285	31.43 442	73.05 266	81.36 287

thres100view900		63.28 304	62.41 303	65.89 331	77.31 220	38.66 396	72.65 300	69.11 353	57.07 198	62.45 316	81.03 276	37.01 321	79.17 272	31.84 435	73.25 262	79.83 327

test_0402		63.25 305	61.01 325	69.96 262	80.00 130	54.37 152	76.86 203	72.02 327	54.58 269	58.71 360	80.79 284	35.00 337	84.36 141	26.41 463	64.71 371	71.15 433

tfpn200view9		63.18 306	62.18 307	66.21 323	76.85 244	39.62 388	71.96 315	69.44 349	56.63 207	62.61 311	79.83 298	37.18 315	79.17 272	31.84 435	73.25 262	79.83 327

LTVRE_ROB	55.42 16	63.15 307	61.23 321	68.92 284	76.57 251	47.80 300	59.92 426	76.39 254	54.35 273	58.67 362	82.46 242	29.44 401	81.49 217	42.12 369	71.14 293	77.46 357
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
SD_0403		63.07 308	63.49 288	61.82 371	75.16 279	31.14 457	71.89 317	73.47 308	53.34 292	58.22 368	81.81 261	45.17 216	73.86 354	37.43 399	74.87 234	80.45 309

F-COLMAP		63.05 309	60.87 329	69.58 273	76.99 243	53.63 166	78.12 155	76.16 257	47.97 373	52.41 426	81.61 265	27.87 415	78.11 296	40.07 381	66.66 357	77.00 366

testing11		62.81 310	61.90 310	65.54 336	78.38 174	40.76 379	67.59 369	66.78 371	55.48 239	60.13 340	77.11 351	31.67 383	76.79 330	45.53 337	74.45 237	79.06 337

IterMVS		62.79 311	61.27 319	67.35 306	69.37 399	52.04 212	71.17 325	68.24 359	52.63 305	59.82 347	76.91 355	37.32 314	72.36 361	52.80 268	63.19 386	77.66 355
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
usedtu_blend_shiyan5		62.63 312	60.77 330	68.20 293	68.53 410	44.64 334	73.47 283	77.00 243	51.91 312	57.10 380	69.95 429	38.83 296	79.61 262	47.44 311	62.67 389	80.37 313

reproduce_monomvs		62.56 313	61.20 322	66.62 315	70.62 376	44.30 339	70.13 344	73.13 317	54.78 263	61.13 333	76.37 368	25.63 435	75.63 345	58.75 220	60.29 413	79.93 323

IterMVS-SCA-FT		62.49 314	61.52 314	65.40 341	71.99 352	50.80 231	71.15 327	69.63 345	45.71 401	60.61 337	77.93 334	37.45 311	65.99 407	55.67 243	63.50 383	79.42 333

tfpnnormal		62.47 315	61.63 313	64.99 347	74.81 288	39.01 393	71.22 324	73.72 306	55.22 247	60.21 339	80.09 296	41.26 268	76.98 326	30.02 449	68.09 345	78.97 340

blended_shiyan6		62.46 316	60.71 331	67.71 299	69.14 403	43.42 348	70.82 332	76.52 252	51.50 320	57.64 373	71.37 417	39.38 286	79.08 278	47.36 315	62.67 389	80.65 306

MS-PatchMatch		62.42 317	61.46 315	65.31 344	75.21 277	52.10 209	72.05 312	74.05 301	46.41 393	57.42 378	74.36 391	34.35 345	77.57 311	45.62 335	73.67 249	66.26 452

Test_1112_low_res		62.32 318	61.77 311	64.00 355	79.08 153	39.53 390	68.17 363	70.17 339	43.25 421	59.03 358	79.90 297	44.08 227	71.24 371	43.79 354	68.42 342	81.25 291

D2MVS		62.30 319	60.29 334	68.34 292	66.46 427	48.42 291	65.70 381	73.42 309	47.71 377	58.16 369	75.02 386	30.51 387	77.71 308	53.96 259	71.68 288	78.90 341

testing222		62.29 320	61.31 318	65.25 345	77.87 195	38.53 398	68.34 361	66.31 375	56.37 218	63.15 300	77.58 346	28.47 409	76.18 344	37.04 403	76.65 210	81.05 299

thres200		62.20 321	61.16 323	65.34 343	75.38 274	39.99 384	69.60 351	69.29 351	55.64 236	61.87 323	76.99 353	37.07 320	78.96 286	31.28 443	73.28 261	77.06 364

tpm2		62.07 322	60.10 336	67.99 296	72.79 333	43.86 344	71.05 330	66.85 370	43.14 423	62.77 306	75.39 384	38.32 303	80.80 239	41.69 373	68.88 336	79.32 334

testing3-2		62.06 323	62.36 304	61.17 379	79.29 142	30.31 460	64.09 402	63.49 400	63.50 44	62.84 304	82.22 248	32.35 380	69.02 385	40.01 384	73.43 258	84.17 208

miper_lstm_enhance		62.03 324	60.88 327	65.49 339	66.71 424	46.25 315	56.29 445	75.70 266	50.68 333	61.27 331	75.48 382	40.21 277	68.03 391	56.31 236	65.25 367	82.18 272

FE-MVSNET2		62.01 325	60.88 327	65.42 340	68.74 407	38.43 400	72.92 297	77.39 233	54.74 266	55.40 398	76.71 358	35.46 332	76.72 333	44.25 345	62.31 396	81.10 296

FE-blended-shiyan7		62.00 326	60.17 335	67.49 302	68.53 410	43.07 354	69.65 349	76.38 255	51.26 326	57.10 380	69.95 429	38.83 296	79.04 281	47.14 321	62.67 389	80.37 313

EPNet_dtu		61.90 327	61.97 309	61.68 372	72.89 332	39.78 386	75.85 230	65.62 380	55.09 250	54.56 409	79.36 312	37.59 310	67.02 400	39.80 386	76.95 204	78.25 345
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 328	61.35 317	63.46 359	74.58 297	31.48 456	61.42 417	58.14 429	58.71 162	53.02 424	79.55 307	43.07 238	76.80 329	45.69 333	77.96 185	82.11 275

MSDG		61.81 329	59.23 341	69.55 274	72.64 335	52.63 198	70.45 339	75.81 264	51.38 323	53.70 416	76.11 370	29.52 399	81.08 230	37.70 397	65.79 364	74.93 390

SixPastTwentyTwo		61.65 330	58.80 348	70.20 259	75.80 261	47.22 308	75.59 234	69.68 344	54.61 267	54.11 413	79.26 314	27.07 424	82.96 173	43.27 359	49.79 452	80.41 311

CL-MVSNet_self_test		61.53 331	60.94 326	63.30 361	68.95 404	36.93 416	67.60 368	72.80 320	55.67 234	59.95 345	76.63 360	45.01 219	72.22 365	39.74 387	62.09 399	80.74 305

RPMNet		61.53 331	58.42 351	70.86 246	69.96 389	52.07 210	65.31 390	81.36 136	43.20 422	59.36 353	70.15 427	35.37 333	85.47 118	36.42 412	64.65 372	75.06 386

pmmvs4		61.48 333	59.39 340	67.76 298	71.57 358	53.86 159	71.42 320	65.34 382	44.20 412	59.46 352	77.92 335	35.90 328	74.71 349	43.87 353	64.87 370	74.71 395

blend_shiyan4		61.38 334	59.10 344	68.20 293	68.94 405	44.64 334	70.81 333	76.52 252	51.63 315	57.56 375	69.94 431	28.30 411	79.61 262	47.44 311	60.78 408	80.36 315

OurMVSNet-221017-0		61.37 335	58.63 350	69.61 270	72.05 350	48.06 297	73.93 274	72.51 321	47.23 385	54.74 406	80.92 279	21.49 450	81.24 224	48.57 304	56.22 429	79.53 332

COLMAP_ROB	52.97 17	61.27 336	58.81 346	68.64 287	74.63 294	52.51 201	78.42 142	73.30 312	49.92 344	50.96 431	81.51 268	23.06 443	79.40 266	31.63 439	65.85 362	74.01 402
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 337	61.67 312	57.70 406	70.43 380	38.45 399	64.19 399	66.47 372	48.05 372	63.22 296	80.86 281	49.28 156	60.47 427	45.25 343	67.28 353	74.19 400

myMVS_eth3d28		60.66 338	61.04 324	59.51 386	77.32 219	31.58 455	63.11 407	63.87 396	59.00 155	60.90 336	78.26 328	32.69 371	66.15 406	36.10 414	78.13 182	80.81 303

SSC-MVS3.2		60.57 339	61.39 316	58.12 402	74.29 306	32.63 450	59.52 427	65.53 381	59.90 135	62.45 316	79.75 302	41.96 250	63.90 416	39.47 388	69.65 326	77.84 353

WBMVS		60.54 340	60.61 332	60.34 383	78.00 192	35.95 427	64.55 396	64.89 385	49.63 346	63.39 295	78.70 319	33.85 352	67.65 394	42.10 370	70.35 307	77.43 358

SCA		60.49 341	58.38 352	66.80 309	74.14 311	48.06 297	63.35 406	63.23 403	49.13 354	59.33 356	72.10 409	37.45 311	74.27 352	44.17 347	62.57 393	78.05 348

K. test v3		60.47 342	57.11 361	70.56 253	73.74 317	48.22 293	75.10 247	62.55 408	58.27 172	53.62 419	76.31 369	27.81 416	81.59 214	47.42 313	39.18 467	81.88 278

mmtdpeth		60.40 343	59.12 343	64.27 353	69.59 395	48.99 279	70.67 335	70.06 341	54.96 260	62.78 305	73.26 403	27.00 425	67.66 393	58.44 223	45.29 459	76.16 374

UWE-MVS		60.18 344	59.78 337	61.39 377	77.67 204	33.92 443	69.04 358	63.82 397	48.56 361	64.27 285	77.64 345	27.20 422	70.40 378	33.56 426	76.24 212	79.83 327

OpenMVS_ROB	52.78 18	60.03 345	58.14 355	65.69 335	70.47 379	44.82 330	75.33 238	70.86 335	45.04 404	56.06 391	76.00 372	26.89 427	79.65 259	35.36 418	67.29 352	72.60 410

CR-MVSNet		59.91 346	57.90 358	65.96 329	69.96 389	52.07 210	65.31 390	63.15 404	42.48 427	59.36 353	74.84 387	35.83 329	70.75 374	45.50 338	64.65 372	75.06 386

PatchmatchNet		59.84 347	58.24 353	64.65 349	73.05 329	46.70 312	69.42 354	62.18 414	47.55 379	58.88 359	71.96 411	34.49 343	69.16 383	42.99 363	63.60 381	78.07 347
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 348	57.84 359	65.54 336	74.87 285	42.95 357	69.61 350	64.16 394	48.90 357	58.68 361	77.12 350	28.19 413	72.35 362	43.75 356	55.28 432	81.31 290

WTY-MVS		59.75 349	60.39 333	57.85 404	72.32 346	37.83 405	61.05 422	64.18 392	45.95 400	61.91 322	79.11 316	47.01 192	60.88 426	42.50 367	69.49 327	74.83 391

WB-MVSnew		59.66 350	59.69 338	59.56 385	75.19 278	35.78 429	69.34 355	64.28 391	46.88 389	61.76 325	75.79 376	40.61 275	65.20 410	32.16 431	71.21 292	77.70 354

CVMVSNet		59.63 351	59.14 342	61.08 381	74.47 299	38.84 395	75.20 243	68.74 355	31.15 458	58.24 367	76.51 365	32.39 378	68.58 387	49.77 291	65.84 363	75.81 377

UBG		59.62 352	59.53 339	59.89 384	78.12 187	35.92 428	64.11 401	60.81 421	49.45 349	61.34 330	75.55 380	33.05 360	67.39 398	38.68 392	74.62 235	76.35 373

ETVMVS		59.51 353	58.81 346	61.58 374	77.46 215	34.87 431	64.94 394	59.35 424	54.06 277	61.08 334	76.67 359	29.54 398	71.87 367	32.16 431	74.07 242	78.01 352

tpm cat1		59.25 354	56.95 364	66.15 325	72.19 348	46.96 310	68.09 364	65.76 378	40.03 442	57.81 372	70.56 422	38.32 303	74.51 350	38.26 395	61.50 403	77.00 366

test_vis1_n_1920		58.86 355	59.06 345	58.25 398	63.76 440	43.14 353	67.49 370	66.36 374	40.22 440	65.89 252	71.95 412	31.04 384	59.75 432	59.94 204	64.90 369	71.85 423

pmmvs-eth3d		58.81 356	56.31 373	66.30 321	67.61 417	52.42 205	72.30 308	64.76 387	43.55 418	54.94 404	74.19 393	28.95 404	72.60 359	43.31 358	57.21 424	73.88 403

tt0320		58.59 357	56.81 367	63.92 356	75.46 271	41.32 372	68.63 360	64.06 395	47.05 387	56.19 390	74.19 393	30.34 389	71.36 369	39.92 385	55.45 431	79.09 336

tpmvs		58.47 358	56.95 364	63.03 365	70.20 384	41.21 373	67.90 366	67.23 366	49.62 347	54.73 407	70.84 420	34.14 346	76.24 342	36.64 409	61.29 404	71.64 425

PVSNet	50.76 19	58.40 359	57.39 360	61.42 375	75.53 269	44.04 343	61.43 416	63.45 401	47.04 388	56.91 382	73.61 399	27.00 425	64.76 412	39.12 390	72.40 276	75.47 382

tt0320-xc		58.33 360	56.41 372	64.08 354	75.79 262	41.34 371	68.30 362	62.72 407	47.90 374	56.29 389	74.16 395	28.53 408	71.04 372	41.50 377	52.50 444	79.88 325

tpmrst		58.24 361	58.70 349	56.84 408	66.97 421	34.32 438	69.57 353	61.14 419	47.17 386	58.58 365	71.60 414	41.28 267	60.41 428	49.20 298	62.84 388	75.78 378

Patchmatch-RL test		58.16 362	55.49 379	66.15 325	67.92 416	48.89 283	60.66 424	51.07 455	47.86 376	59.36 353	62.71 460	34.02 349	72.27 364	56.41 235	59.40 416	77.30 360

test-LLR		58.15 363	58.13 356	58.22 399	68.57 408	44.80 331	65.46 386	57.92 430	50.08 341	55.44 396	69.82 432	32.62 373	57.44 444	49.66 294	73.62 251	72.41 416

ppachtmachnet_test		58.06 364	55.38 380	66.10 327	69.51 396	48.99 279	68.01 365	66.13 377	44.50 409	54.05 414	70.74 421	32.09 381	72.34 363	36.68 408	56.71 428	76.99 368

gg-mvs-nofinetune		57.86 365	56.43 371	62.18 369	72.62 336	35.35 430	66.57 374	56.33 439	50.65 334	57.64 373	57.10 466	30.65 386	76.36 340	37.38 400	78.88 163	74.82 392

CMPMVS	42.80 21	57.81 366	55.97 375	63.32 360	60.98 456	47.38 307	64.66 395	69.50 348	32.06 456	46.83 449	77.80 340	29.50 400	71.36 369	48.68 302	73.75 247	71.21 432
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 367	57.07 362	58.22 399	74.21 308	37.18 411	62.46 411	60.88 420	48.88 358	55.29 400	75.99 374	31.68 382	62.04 423	31.87 434	72.35 277	75.43 383

tpm		57.34 368	58.16 354	54.86 418	71.80 355	34.77 433	67.47 371	56.04 442	48.20 369	60.10 341	76.92 354	37.17 317	53.41 461	40.76 379	65.01 368	76.40 372

Patchmtry		57.16 369	56.47 370	59.23 390	69.17 402	34.58 436	62.98 408	63.15 404	44.53 408	56.83 383	74.84 387	35.83 329	68.71 386	40.03 382	60.91 405	74.39 398

AllTest		57.08 370	54.65 384	64.39 351	71.44 362	49.03 276	69.92 347	67.30 363	45.97 398	47.16 447	79.77 300	17.47 453	67.56 396	33.65 423	59.16 417	76.57 370

test_cas_vis1_n_1920		56.91 371	56.71 368	57.51 407	59.13 462	45.40 327	63.58 404	61.29 418	36.24 450	67.14 225	71.85 413	29.89 396	56.69 448	57.65 226	63.58 382	70.46 437

mamv4		56.85 372	58.00 357	53.43 428	72.46 342	54.47 149	57.56 440	54.74 443	38.81 446	57.42 378	79.45 310	47.57 179	38.70 481	60.88 196	53.07 441	67.11 451

dmvs_re		56.77 373	56.83 366	56.61 409	69.23 400	41.02 374	58.37 432	64.18 392	50.59 336	57.45 377	71.42 415	35.54 331	58.94 437	37.23 401	67.45 351	69.87 442

testing3		56.54 374	55.92 376	58.41 397	77.52 213	27.93 468	69.72 348	56.36 438	54.75 265	58.63 364	77.80 340	20.88 451	71.75 368	25.31 465	62.25 397	75.53 381

our_test_3		56.49 375	54.42 387	62.68 367	69.51 396	45.48 326	66.08 378	61.49 417	44.11 415	50.73 435	69.60 435	33.05 360	68.15 388	38.38 394	56.86 425	74.40 397

pmmvs5		56.47 376	55.68 378	58.86 394	61.41 452	36.71 418	66.37 376	62.75 406	40.38 439	53.70 416	76.62 361	34.56 341	67.05 399	40.02 383	65.27 366	72.83 408

test-mter		56.42 377	55.82 377	58.22 399	68.57 408	44.80 331	65.46 386	57.92 430	39.94 443	55.44 396	69.82 432	21.92 446	57.44 444	49.66 294	73.62 251	72.41 416

USDC		56.35 378	54.24 391	62.69 366	64.74 436	40.31 381	65.05 392	73.83 305	43.93 416	47.58 445	77.71 344	15.36 462	75.05 348	38.19 396	61.81 401	72.70 409

PatchMatch-RL		56.25 379	54.55 386	61.32 378	77.06 235	56.07 119	65.57 383	54.10 448	44.13 414	53.49 422	71.27 419	25.20 437	66.78 401	36.52 411	63.66 380	61.12 456

sss		56.17 380	56.57 369	54.96 417	66.93 422	36.32 423	57.94 435	61.69 416	41.67 430	58.64 363	75.32 385	38.72 298	56.25 451	42.04 371	66.19 361	72.31 419

Syy-MVS		56.00 381	56.23 374	55.32 415	74.69 292	26.44 474	65.52 384	57.49 433	50.97 331	56.52 386	72.18 407	39.89 280	68.09 389	24.20 466	64.59 374	71.44 429

FMVSNet5		55.86 382	54.93 382	58.66 396	71.05 371	36.35 421	64.18 400	62.48 409	46.76 391	50.66 436	74.73 389	25.80 433	64.04 414	33.11 427	65.57 365	75.59 380

RPSCF		55.80 383	54.22 392	60.53 382	65.13 435	42.91 358	64.30 398	57.62 432	36.84 449	58.05 371	82.28 246	28.01 414	56.24 452	37.14 402	58.61 419	82.44 268

mvs5depth		55.64 384	53.81 395	61.11 380	59.39 461	40.98 378	65.89 379	68.28 358	50.21 339	58.11 370	75.42 383	17.03 455	67.63 395	43.79 354	46.21 456	74.73 394

EU-MVSNet		55.61 385	54.41 388	59.19 392	65.41 433	33.42 445	72.44 306	71.91 328	28.81 460	51.27 429	73.87 397	24.76 439	69.08 384	43.04 362	58.20 420	75.06 386

Anonymous20240521		55.30 386	54.41 388	57.96 403	60.92 458	41.73 367	71.09 329	71.06 334	41.18 433	48.65 443	73.31 401	16.93 456	59.25 434	42.54 366	64.01 377	72.90 407

TESTMET0.1,1		55.28 387	54.90 383	56.42 410	66.56 425	43.67 346	65.46 386	56.27 440	39.18 445	53.83 415	67.44 444	24.21 441	55.46 455	48.04 309	73.11 265	70.13 440

KD-MVS_self_test		55.22 388	53.89 394	59.21 391	57.80 465	27.47 470	57.75 438	74.32 294	47.38 381	50.90 432	70.00 428	28.45 410	70.30 379	40.44 380	57.92 421	79.87 326

MIMVSNet1		55.17 389	54.31 390	57.77 405	70.03 388	32.01 453	65.68 382	64.81 386	49.19 353	46.75 450	76.00 372	25.53 436	64.04 414	28.65 454	62.13 398	77.26 362

FE-MVSNET		55.16 390	53.75 396	59.41 387	65.29 434	33.20 447	67.21 373	66.21 376	48.39 367	49.56 441	73.53 400	29.03 403	72.51 360	30.38 447	54.10 438	72.52 412

Anonymous20231206		55.10 391	55.30 381	54.48 420	69.81 394	33.94 442	62.91 409	62.13 415	41.08 434	55.18 401	75.65 378	32.75 368	56.59 450	30.32 448	67.86 346	72.91 406

myMVS_eth3d		54.86 392	54.61 385	55.61 414	74.69 292	27.31 471	65.52 384	57.49 433	50.97 331	56.52 386	72.18 407	21.87 449	68.09 389	27.70 457	64.59 374	71.44 429

TinyColmap		54.14 393	51.72 405	61.40 376	66.84 423	41.97 364	66.52 375	68.51 356	44.81 405	42.69 461	75.77 377	11.66 469	72.94 357	31.96 433	56.77 427	69.27 446

EPMVS		53.96 394	53.69 397	54.79 419	66.12 430	31.96 454	62.34 413	49.05 459	44.42 411	55.54 394	71.33 418	30.22 391	56.70 447	41.65 375	62.54 394	75.71 379

PMMVS		53.96 394	53.26 400	56.04 411	62.60 447	50.92 228	61.17 420	56.09 441	32.81 455	53.51 421	66.84 449	34.04 348	59.93 431	44.14 349	68.18 344	57.27 464

test20.03		53.87 396	54.02 393	53.41 429	61.47 451	28.11 467	61.30 418	59.21 425	51.34 325	52.09 427	77.43 347	33.29 359	58.55 439	29.76 450	60.27 414	73.58 404

MDA-MVSNet-bldmvs		53.87 396	50.81 409	63.05 364	66.25 428	48.58 289	56.93 443	63.82 397	48.09 371	41.22 462	70.48 425	30.34 389	68.00 392	34.24 421	45.92 458	72.57 411

KD-MVS_2432*1600		53.45 398	51.50 407	59.30 388	62.82 444	37.14 412	55.33 446	71.79 329	47.34 383	55.09 402	70.52 423	21.91 447	70.45 376	35.72 416	42.97 462	70.31 438

miper_refine_blended		53.45 398	51.50 407	59.30 388	62.82 444	37.14 412	55.33 446	71.79 329	47.34 383	55.09 402	70.52 423	21.91 447	70.45 376	35.72 416	42.97 462	70.31 438

TDRefinement		53.44 400	50.72 410	61.60 373	64.31 439	46.96 310	70.89 331	65.27 384	41.78 428	44.61 456	77.98 332	11.52 471	66.36 404	28.57 455	51.59 446	71.49 428

test0.0.03 1		53.32 401	53.59 398	52.50 435	62.81 446	29.45 462	59.51 428	54.11 447	50.08 341	54.40 411	74.31 392	32.62 373	55.92 453	30.50 446	63.95 379	72.15 421

PatchT		53.17 402	53.44 399	52.33 436	68.29 414	25.34 478	58.21 433	54.41 446	44.46 410	54.56 409	69.05 438	33.32 358	60.94 425	36.93 404	61.76 402	70.73 436

UnsupCasMVSNet_eth		53.16 403	52.47 401	55.23 416	59.45 460	33.39 446	59.43 429	69.13 352	45.98 397	50.35 438	72.32 406	29.30 402	58.26 441	42.02 372	44.30 460	74.05 401

PM-MVS		52.33 404	50.19 413	58.75 395	62.10 449	45.14 329	65.75 380	40.38 477	43.60 417	53.52 420	72.65 404	9.16 477	65.87 408	50.41 287	54.18 437	65.24 454

UWE-MVS-28		52.25 405	52.35 403	51.93 439	66.99 420	22.79 482	63.48 405	48.31 463	46.78 390	52.73 425	76.11 370	27.78 417	57.82 443	20.58 472	68.41 343	75.17 384

testgi		51.90 406	52.37 402	50.51 442	60.39 459	23.55 481	58.42 431	58.15 428	49.03 355	51.83 428	79.21 315	22.39 444	55.59 454	29.24 453	62.64 392	72.40 418

dp		51.89 407	51.60 406	52.77 433	68.44 413	32.45 452	62.36 412	54.57 445	44.16 413	49.31 442	67.91 440	28.87 406	56.61 449	33.89 422	54.89 434	69.24 447

JIA-IIPM		51.56 408	47.68 422	63.21 362	64.61 437	50.73 236	47.71 467	58.77 427	42.90 424	48.46 444	51.72 470	24.97 438	70.24 380	36.06 415	53.89 439	68.64 448

test_fmvs1_n		51.37 409	50.35 412	54.42 422	52.85 469	37.71 407	61.16 421	51.93 450	28.15 462	63.81 291	69.73 434	13.72 463	53.95 459	51.16 282	60.65 410	71.59 426

ADS-MVSNet2		51.33 410	48.76 417	59.07 393	66.02 431	44.60 336	50.90 459	59.76 423	36.90 447	50.74 433	66.18 452	26.38 428	63.11 419	27.17 459	54.76 435	69.50 444

test_fmvs1		51.32 411	50.48 411	53.81 424	53.57 467	37.51 409	60.63 425	51.16 453	28.02 464	63.62 292	69.23 437	16.41 458	53.93 460	51.01 283	60.70 409	69.99 441

YYNet1		50.73 412	48.96 414	56.03 412	61.10 454	41.78 366	51.94 456	56.44 437	40.94 436	44.84 454	67.80 442	30.08 394	55.08 457	36.77 405	50.71 448	71.22 431

MDA-MVSNet_test_wron		50.71 413	48.95 415	56.00 413	61.17 453	41.84 365	51.90 457	56.45 436	40.96 435	44.79 455	67.84 441	30.04 395	55.07 458	36.71 407	50.69 449	71.11 434

dmvs_testset		50.16 414	51.90 404	44.94 450	66.49 426	11.78 490	61.01 423	51.50 452	51.17 329	50.30 439	67.44 444	39.28 288	60.29 429	22.38 469	57.49 423	62.76 455

UnsupCasMVSNet_bld		50.07 415	48.87 416	53.66 425	60.97 457	33.67 444	57.62 439	64.56 389	39.47 444	47.38 446	64.02 458	27.47 419	59.32 433	34.69 420	43.68 461	67.98 450

test_vis1_n		49.89 416	48.69 418	53.50 427	53.97 466	37.38 410	61.53 415	47.33 467	28.54 461	59.62 351	67.10 448	13.52 464	52.27 465	49.07 299	57.52 422	70.84 435

Patchmatch-test		49.08 417	48.28 419	51.50 440	64.40 438	30.85 459	45.68 471	48.46 462	35.60 451	46.10 453	72.10 409	34.47 344	46.37 473	27.08 461	60.65 410	77.27 361

test_fmvs2		48.69 418	47.49 423	52.29 437	48.63 476	33.06 449	57.76 437	48.05 465	25.71 468	59.76 349	69.60 435	11.57 470	52.23 466	49.45 297	56.86 425	71.58 427

ADS-MVSNet		48.48 419	47.77 420	50.63 441	66.02 431	29.92 461	50.90 459	50.87 457	36.90 447	50.74 433	66.18 452	26.38 428	52.47 464	27.17 459	54.76 435	69.50 444

CHOSEN 280x420		47.83 420	46.36 424	52.24 438	67.37 419	49.78 258	38.91 479	43.11 475	35.00 452	43.27 460	63.30 459	28.95 404	49.19 469	36.53 410	60.80 407	57.76 463

new-patchmatchnet		47.56 421	47.73 421	47.06 445	58.81 463	9.37 493	48.78 465	59.21 425	43.28 420	44.22 457	68.66 439	25.67 434	57.20 446	31.57 441	49.35 453	74.62 396

PVSNet_0	43.31 20	47.46 422	45.64 425	52.92 432	67.60 418	44.65 333	54.06 451	54.64 444	41.59 431	46.15 452	58.75 463	30.99 385	58.66 438	32.18 430	24.81 478	55.46 466

ttmdpeth		45.56 423	42.95 428	53.39 430	52.33 472	29.15 463	57.77 436	48.20 464	31.81 457	49.86 440	77.21 349	8.69 478	59.16 435	27.31 458	33.40 474	71.84 424

MVS-HIRNet		45.52 424	44.48 426	48.65 444	68.49 412	34.05 441	59.41 430	44.50 472	27.03 465	37.96 472	50.47 474	26.16 431	64.10 413	26.74 462	59.52 415	47.82 473

pmmvs3		44.92 425	41.95 432	53.86 423	52.58 471	43.55 347	62.11 414	46.90 469	26.05 467	40.63 463	60.19 462	11.08 474	57.91 442	31.83 438	46.15 457	60.11 457

test_fmvs3		44.30 426	42.55 429	49.55 443	42.83 481	27.15 473	53.03 453	44.93 471	22.03 476	53.69 418	64.94 455	4.21 485	49.63 468	47.47 310	49.82 451	71.88 422

WB-MVS		43.26 427	43.41 427	42.83 454	63.32 443	10.32 492	58.17 434	45.20 470	45.42 402	40.44 465	67.26 447	34.01 350	58.98 436	11.96 483	24.88 477	59.20 458

LF4IMVS		42.95 428	42.26 430	45.04 448	48.30 477	32.50 451	54.80 448	48.49 461	28.03 463	40.51 464	70.16 426	9.24 476	43.89 476	31.63 439	49.18 454	58.72 460

MVStest1		42.65 429	39.29 436	52.71 434	47.26 479	34.58 436	54.41 450	50.84 458	23.35 470	39.31 470	74.08 396	12.57 466	55.09 456	23.32 467	28.47 476	68.47 449

EGC-MVSNET		42.47 430	38.48 438	54.46 421	74.33 304	48.73 285	70.33 342	51.10 454	0.03 491	0.18 492	67.78 443	13.28 465	66.49 403	18.91 474	50.36 450	48.15 471

FPMVS		42.18 431	41.11 433	45.39 447	58.03 464	41.01 376	49.50 463	53.81 449	30.07 459	33.71 474	64.03 456	11.69 468	52.08 467	14.01 478	55.11 433	43.09 475

SSC-MVS		41.96 432	41.99 431	41.90 455	62.46 448	9.28 494	57.41 441	44.32 473	43.38 419	38.30 471	66.45 450	32.67 372	58.42 440	10.98 484	21.91 480	57.99 462

ANet_high		41.38 433	37.47 440	53.11 431	39.73 487	24.45 479	56.94 442	69.69 343	47.65 378	26.04 479	52.32 469	12.44 467	62.38 422	21.80 470	10.61 488	72.49 413

test_vis1_rt		41.35 434	39.45 435	47.03 446	46.65 480	37.86 404	47.76 466	38.65 478	23.10 472	44.21 458	51.22 472	11.20 473	44.08 475	39.27 389	53.02 442	59.14 459

LCM-MVSNet		40.30 435	35.88 441	53.57 426	42.24 482	29.15 463	45.21 473	60.53 422	22.23 475	28.02 477	50.98 473	3.72 487	61.78 424	31.22 444	38.76 468	69.78 443

mvsany_test1		39.38 436	38.16 439	43.02 453	49.05 474	34.28 439	44.16 475	25.94 488	22.74 474	46.57 451	62.21 461	23.85 442	41.16 480	33.01 428	35.91 470	53.63 467

N_pmnet		39.35 437	40.28 434	36.54 461	63.76 440	1.62 498	49.37 464	0.76 497	34.62 453	43.61 459	66.38 451	26.25 430	42.57 477	26.02 464	51.77 445	65.44 453

DSMNet-mixed		39.30 438	38.72 437	41.03 456	51.22 473	19.66 485	45.53 472	31.35 484	15.83 483	39.80 467	67.42 446	22.19 445	45.13 474	22.43 468	52.69 443	58.31 461

APD_test1		37.39 439	34.94 442	44.72 451	48.88 475	33.19 448	52.95 454	44.00 474	19.49 477	27.28 478	58.59 464	3.18 489	52.84 463	18.92 473	41.17 465	48.14 472

PMVS	28.69 22	36.22 440	33.29 445	45.02 449	36.82 489	35.98 426	54.68 449	48.74 460	26.31 466	21.02 482	51.61 471	2.88 490	60.10 430	9.99 487	47.58 455	38.99 480
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 441	31.91 446	43.33 452	62.05 450	37.87 403	20.39 484	67.03 368	23.23 471	18.41 484	25.84 484	4.24 484	62.73 420	14.71 477	51.32 447	29.38 482
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 442	34.94 442	33.26 464	61.06 455	16.00 489	52.79 455	23.78 490	40.71 437	39.33 469	48.65 478	16.91 457	48.34 470	12.18 482	19.05 482	35.44 481

new_pmnet		34.13 443	34.29 444	33.64 463	52.63 470	18.23 487	44.43 474	33.90 483	22.81 473	30.89 476	53.18 468	10.48 475	35.72 485	20.77 471	39.51 466	46.98 474

mvsany_test3		32.62 444	30.57 449	38.77 459	36.16 490	24.20 480	38.10 480	20.63 492	19.14 478	40.36 466	57.43 465	5.06 482	36.63 484	29.59 452	28.66 475	55.49 465

test_vis3_rt		32.09 445	30.20 450	37.76 460	35.36 491	27.48 469	40.60 478	28.29 487	16.69 481	32.52 475	40.53 480	1.96 491	37.40 483	33.64 425	42.21 464	48.39 470

test_f		31.86 446	31.05 447	34.28 462	32.33 493	21.86 483	32.34 481	30.46 485	16.02 482	39.78 468	55.45 467	4.80 483	32.36 487	30.61 445	37.66 469	48.64 469

testf1		31.46 447	28.89 451	39.16 457	41.99 484	28.78 465	46.45 469	37.56 479	14.28 484	21.10 480	48.96 475	1.48 493	47.11 471	13.63 479	34.56 471	41.60 476

APD_test2		31.46 447	28.89 451	39.16 457	41.99 484	28.78 465	46.45 469	37.56 479	14.28 484	21.10 480	48.96 475	1.48 493	47.11 471	13.63 479	34.56 471	41.60 476

kuosan		29.62 449	30.82 448	26.02 469	52.99 468	16.22 488	51.09 458	22.71 491	33.91 454	33.99 473	40.85 479	15.89 460	33.11 486	7.59 490	18.37 483	28.72 483

PMMVS2		27.40 450	25.91 453	31.87 466	39.46 488	6.57 495	31.17 482	28.52 486	23.96 469	20.45 483	48.94 477	4.20 486	37.94 482	16.51 475	19.97 481	51.09 468

E-PMN		23.77 451	22.73 455	26.90 467	42.02 483	20.67 484	42.66 476	35.70 481	17.43 479	10.28 489	25.05 485	6.42 480	42.39 478	10.28 486	14.71 485	17.63 484

EMVS		22.97 452	21.84 456	26.36 468	40.20 486	19.53 486	41.95 477	34.64 482	17.09 480	9.73 490	22.83 486	7.29 479	42.22 479	9.18 488	13.66 486	17.32 485

MVE	17.77 23	21.41 453	17.77 458	32.34 465	34.34 492	25.44 477	16.11 485	24.11 489	11.19 486	13.22 486	31.92 482	1.58 492	30.95 488	10.47 485	17.03 484	40.62 479
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 454	18.10 457	24.41 470	13.68 495	3.11 497	12.06 487	42.37 476	2.00 489	11.97 487	36.38 481	5.77 481	29.35 489	15.06 476	23.65 479	40.76 478

cdsmvs_eth3d_5k		17.50 455	23.34 454	0.00 476	0.00 499	0.00 500	0.00 488	78.63 199	0.00 494	0.00 495	82.18 249	49.25 157	0.00 493	0.00 494	0.00 491	0.00 491

wuyk23d		13.32 456	12.52 459	15.71 471	47.54 478	26.27 475	31.06 483	1.98 496	4.93 488	5.18 491	1.94 491	0.45 495	18.54 490	6.81 491	12.83 487	2.33 488

tmp_tt		9.43 457	11.14 460	4.30 473	2.38 496	4.40 496	13.62 486	16.08 494	0.39 490	15.89 485	13.06 487	15.80 461	5.54 492	12.63 481	10.46 489	2.95 487

ab-mvs-re		6.49 458	8.65 461	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	77.89 338	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

test123		4.73 459	6.30 462	0.02 474	0.01 497	0.01 499	56.36 444	0.00 498	0.01 492	0.04 493	0.21 493	0.01 496	0.00 493	0.03 493	0.00 491	0.04 489

testmvs		4.52 460	6.03 463	0.01 475	0.01 497	0.00 500	53.86 452	0.00 498	0.01 492	0.04 493	0.27 492	0.00 497	0.00 493	0.04 492	0.00 491	0.03 490

pcd_1.5k_mvsjas		3.92 461	5.23 464	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	47.05 189	0.00 493	0.00 494	0.00 491	0.00 491

mmdepth		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

monomultidepth		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

test_blank		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

uanet_test		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

DCPMVS		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

sosnet-low-res		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

sosnet		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

uncertanet		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

Regformer		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

uanet		0.00 462	0.00 465	0.00 476	0.00 499	0.00 500	0.00 488	0.00 498	0.00 494	0.00 495	0.00 494	0.00 497	0.00 493	0.00 494	0.00 491	0.00 491

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 102	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 42

TestfortrainingZip							86.84 11

WAC-MVS						27.31 471								27.77 456

FOURS1					86.12 37	60.82 37	88.18 1	83.61 81	60.87 105	81.50 20

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 52

PC_three_1452									55.09 250	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 29

No_MVS				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 52

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

eth-test2					0.00 499

eth-test					0.00 499

ZD-MVS					86.64 21	60.38 45		82.70 113	57.95 181	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

RE-MVS-def			73.71 84		83.49 72	59.87 54	84.29 48	81.36 136	58.07 175	73.14 107	90.07 43	43.06 239		68.20 104	81.76 108	84.03 211

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 280	84.64 3				79.07 13	90.87 5	88.37 31

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 37

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 61

test_241102_ONE					87.77 4	58.90 77		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

9.14			78.75 18		83.10 77		84.15 54	88.26 1	59.90 135	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

save fliter					86.17 34	61.30 28	83.98 58	79.66 175	59.00 155

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 12	90.75 8	79.48 7	90.63 10	88.09 45

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 63

test0726					87.75 7	59.07 72	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

GSMVS																78.05 348

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 340				78.05 348

sam_mvs												33.43 357

ambc				65.13 346	63.72 442	37.07 414	47.66 468	78.78 195		54.37 412	71.42 415	11.24 472	80.94 234	45.64 334	53.85 440	77.38 359

MTGPA								80.97 154

test_post1							68.67 359				3.64 489	32.39 378	69.49 382	44.17 347

test_post											3.55 490	33.90 351	66.52 402

patchmatchnet-post											64.03 456	34.50 342	74.27 352

GG-mvs-BLEND				62.34 368	71.36 366	37.04 415	69.20 356	57.33 435		54.73 407	65.48 454	30.37 388	77.82 304	34.82 419	74.93 233	72.17 420

MTMP							86.03 23	17.08 493

gm-plane-assit					71.40 365	41.72 369			48.85 359		73.31 401		82.48 199	48.90 301

test9_res														75.28 54	88.31 36	83.81 222

TEST9					85.58 44	61.59 24	81.62 91	81.26 143	55.65 235	74.93 63	88.81 67	53.70 87	84.68 136

test_8					85.40 47	60.96 34	81.54 94	81.18 147	55.86 227	74.81 68	88.80 69	53.70 87	84.45 140

agg_prior2														73.09 72	87.93 44	84.33 200

agg_prior					85.04 54	59.96 50		81.04 152		74.68 72			84.04 146

TestCases				64.39 351	71.44 362	49.03 276		67.30 363	45.97 398	47.16 447	79.77 300	17.47 453	67.56 396	33.65 423	59.16 417	76.57 370

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 121	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 111

旧先验2							76.08 222		45.32 403	76.55 47			65.56 409	58.75 220

新几何2							76.12 220

新几何1				70.76 248	85.66 42	61.13 30		66.43 373	44.68 407	70.29 153	86.64 124	41.29 266	75.23 347	49.72 293	81.75 110	75.93 376

旧先验1					83.04 78	53.15 181		67.52 362			87.85 86	44.08 227			80.76 119	78.03 351

无先验							79.66 121	74.30 296	48.40 366				80.78 240	53.62 261		79.03 339

原ACMM2							79.02 128

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 87	51.47 322	70.27 154	86.61 128	48.61 165	86.51 86	53.85 260	87.96 43	78.16 346

test222					83.14 76	58.68 81	72.57 304	63.45 401	41.78 428	67.56 216	86.12 146	37.13 318			78.73 169	74.98 389

testdata2													72.18 366	46.95 323

segment_acmp												54.23 74

testdata				64.66 348	81.52 98	52.93 186		65.29 383	46.09 396	73.88 89	87.46 93	38.08 307	66.26 405	53.31 265	78.48 176	74.78 393

testdata1							72.65 300		60.50 115

test12				77.76 50	84.52 62	58.41 83		83.36 90		72.93 115		54.61 71	88.05 43		88.12 38	86.81 94

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 214

plane_prior5								84.01 57					87.21 63	68.16 108	80.58 123	84.65 191

plane_prior4											86.10 147

plane_prior3						56.09 118			63.92 38	69.27 174

plane_prior2							84.22 51		64.52 27

plane_prior1					81.27 106

plane_prior						56.31 112	83.58 64		63.19 51						80.48 126

n2								0.00 498

nn								0.00 498

door-mid								47.19 468

lessismore_v0				69.91 265	71.42 364	47.80 300		50.90 456		50.39 437	75.56 379	27.43 421	81.33 221	45.91 331	34.10 473	80.59 307

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 88	61.32 93	66.67 235	87.33 99	39.15 291	86.59 79	67.70 117	77.30 199	83.19 245

test11								83.47 85

door								47.60 466

HQP5-MVS						54.94 143

HQP-NCC					80.66 115		82.31 82		62.10 78	67.85 205

ACMP_Plane					80.66 115		82.31 82		62.10 78	67.85 205

BP-MVS														67.04 127

HQP4-MVS										67.85 205			86.93 71			84.32 201

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 208

NP-MVS					80.98 111	56.05 120					85.54 168

MDTV_nov1_ep13_2view						25.89 476	61.22 419		40.10 441	51.10 430		32.97 363		38.49 393		78.61 343

MDTV_nov1_ep13			57.00 363		72.73 334	38.26 401	65.02 393	64.73 388	44.74 406	55.46 395	72.48 405	32.61 375	70.47 375	37.47 398	67.75 348

ACMMP++_ref															74.07 242

ACMMP++															72.16 282

Test By Simon												48.33 168

ITE_SJBPF				62.09 370	66.16 429	44.55 338		64.32 390	47.36 382	55.31 399	80.34 289	19.27 452	62.68 421	36.29 413	62.39 395	79.04 338

DeepMVS_CX				12.03 472	17.97 494	10.91 491		10.60 495	7.46 487	11.07 488	28.36 483	3.28 488	11.29 491	8.01 489	9.74 490	13.89 486

High-resolution multi-view benchmark