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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

ACMMPR		99.30 11	99.54 9	99.03 18	99.66 18	99.64 30	99.68 6	98.25 17	99.56 12	97.12 33	99.19 24	99.95 17	99.72 1	99.43 18	99.25 17	99.72 84	99.77 61

MCST-MVS		99.11 22	99.27 36	98.93 23	99.67 15	99.33 117	99.51 24	98.31 10	99.28 48	96.57 38	99.10 33	99.90 35	99.71 2	99.19 33	98.35 83	99.82 17	99.71 113

HFP-MVS		99.32 10	99.53 11	99.07 15	99.69 9	99.59 48	99.63 14	98.31 10	99.56 12	97.37 29	99.27 22	99.97 8	99.70 3	99.35 24	99.24 19	99.71 95	99.76 68

PGM-MVS		98.86 33	99.35 30	98.29 36	99.77 1	99.63 33	99.67 7	95.63 48	98.66 142	95.27 63	99.11 31	99.82 44	99.67 4	99.33 26	99.19 23	99.73 71	99.74 85

SMA-MVS		99.38 8	99.60 3	99.12 11	99.76 2	99.62 36	99.39 33	98.23 21	99.52 17	98.03 20	99.45 14	99.98 2	99.64 5	99.58 8	99.30 13	99.68 117	99.76 68
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
SD-MVS		99.25 14	99.50 14	98.96 22	98.79 55	99.55 56	99.33 36	98.29 13	99.75 2	97.96 21	99.15 27	99.95 17	99.61 6	99.17 34	99.06 30	99.81 25	99.84 26
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
DVP-MVS		99.45 4	99.54 9	99.35 3	99.72 6	99.76 6	99.63 14	98.37 2	99.63 9	99.03 6	98.95 42	99.98 2	99.60 7	99.60 7	99.05 32	99.74 57	99.79 46
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
CPTT-MVS		99.14 21	99.20 40	99.06 16	99.58 27	99.53 58	99.45 29	97.80 39	99.19 64	98.32 15	98.58 61	99.95 17	99.60 7	99.28 28	98.20 102	99.64 142	99.69 121

TSAR-MVS + MP.		99.27 12	99.57 5	98.92 24	98.78 56	99.53 58	99.72 4	98.11 31	99.73 3	97.43 28	99.15 27	99.96 12	99.59 9	99.73 1	99.07 28	99.88 4	99.82 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
X-MVS		98.93 31	99.37 26	98.42 33	99.67 15	99.62 36	99.60 18	98.15 26	99.08 89	93.81 95	98.46 68	99.95 17	99.59 9	99.49 14	99.21 22	99.68 117	99.75 76

CP-MVS		99.27 12	99.44 19	99.08 14	99.62 24	99.58 51	99.53 22	98.16 24	99.21 61	97.79 23	99.15 27	99.96 12	99.59 9	99.54 11	98.86 49	99.78 36	99.74 85

AdaColmap		99.06 26	98.98 54	99.15 8	99.60 26	99.30 121	99.38 34	98.16 24	99.02 98	98.55 10	98.71 57	99.57 58	99.58 12	99.09 40	97.84 133	99.64 142	99.36 184

ACMMP_NAP		99.05 27	99.45 16	98.58 32	99.73 5	99.60 46	99.64 10	98.28 16	99.23 55	94.57 76	99.35 19	99.97 8	99.55 13	99.63 3	98.66 61	99.70 105	99.74 85

MP-MVS		99.07 25	99.36 27	98.74 29	99.63 22	99.57 53	99.66 8	98.25 17	99.00 100	95.62 49	98.97 40	99.94 26	99.54 14	99.51 12	98.79 58	99.71 95	99.73 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APDe-MVS		99.49 3	99.64 1	99.32 4	99.74 4	99.74 12	99.75 3	98.34 4	99.56 12	98.72 9	99.57 10	99.97 8	99.53 15	99.65 2	99.25 17	99.84 12	99.77 61
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		99.51 1	99.57 5	99.44 1	99.71 7	99.65 24	99.83 1	98.29 13	99.50 20	99.61 1	99.69 5	99.94 26	99.50 16	99.50 13	99.06 30	99.71 95	99.64 142

MSLP-MVS++		99.15 20	99.24 38	99.04 17	99.52 34	99.49 66	99.09 47	98.07 32	99.37 34	98.47 11	97.79 86	99.89 37	99.50 16	98.93 53	99.45 4	99.61 152	99.76 68

CNVR-MVS		99.23 16	99.28 35	99.17 7	99.65 20	99.34 112	99.46 28	98.21 22	99.28 48	98.47 11	98.89 47	99.94 26	99.50 16	99.42 19	98.61 64	99.73 71	99.52 165

EC-MVSNet		98.22 54	99.44 19	96.79 78	95.62 139	99.56 54	99.01 53	92.22 130	99.17 66	94.51 79	99.41 16	99.62 54	99.49 19	99.16 36	99.26 16	99.91 2	99.94 1

CSCG		98.90 32	98.93 56	98.85 26	99.75 3	99.72 13	99.49 25	96.58 45	99.38 32	98.05 19	98.97 40	97.87 80	99.49 19	97.78 149	98.92 44	99.78 36	99.90 7

DeepC-MVS_fast	98.34 1	99.17 19	99.45 16	98.85 26	99.55 31	99.37 104	99.64 10	98.05 34	99.53 15	96.58 37	98.93 43	99.92 30	99.49 19	99.46 15	99.32 12	99.80 32	99.64 142
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MED-MVS		99.50 2	99.57 5	99.41 2	99.71 7	99.67 19	99.61 17	98.33 6	99.71 4	99.61 1	99.69 5	99.95 17	99.47 22	99.45 16	98.92 44	99.74 57	99.64 142

MVSMamba_PlusPlus		98.20 55	99.31 33	96.90 77	95.83 117	99.65 24	98.96 55	94.33 72	99.46 22	93.04 114	98.73 56	98.88 67	99.47 22	99.13 39	99.41 6	99.78 36	99.89 13

DeepC-MVS	97.63 4	98.33 51	98.57 65	98.04 43	98.62 59	99.65 24	99.45 29	98.15 26	99.51 18	92.80 121	95.74 154	96.44 95	99.46 24	99.37 21	99.50 2	99.78 36	99.81 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		99.05 27	99.08 45	99.02 20	99.62 24	99.38 98	99.43 32	98.21 22	99.36 38	97.66 26	97.79 86	99.90 35	99.45 25	99.17 34	98.43 76	99.77 42	99.51 170

train_agg		98.73 38	99.11 43	98.28 37	99.36 41	99.35 109	99.48 27	97.96 36	98.83 120	93.86 94	98.70 58	99.86 40	99.44 26	99.08 42	98.38 80	99.61 152	99.58 153

3Dnovator+	96.92 7	98.71 39	99.05 48	98.32 35	99.53 32	99.34 112	99.06 49	94.61 62	99.65 7	97.49 27	96.75 118	99.86 40	99.44 26	98.78 67	99.30 13	99.81 25	99.67 131

3Dnovator	96.92 7	98.67 40	99.05 48	98.23 39	99.57 28	99.45 75	99.11 45	94.66 61	99.69 5	96.80 35	96.55 130	99.61 55	99.40 28	98.87 61	99.49 3	99.85 10	99.66 135

SPE-MVS-test		98.58 45	99.42 23	97.60 54	98.52 60	99.91 1	98.60 68	94.60 64	99.37 34	94.62 75	99.40 17	99.16 63	99.39 29	99.36 22	98.85 52	99.90 3	99.92 3

TSAR-MVS + GP.		98.66 42	99.36 27	97.85 47	97.16 84	99.46 71	99.03 51	94.59 65	99.09 86	97.19 32	99.73 3	99.95 17	99.39 29	98.95 51	98.69 60	99.75 50	99.65 138

HPM-MVS++		99.10 23	99.30 34	98.86 25	99.69 9	99.48 67	99.59 19	98.34 4	99.26 52	96.55 39	99.10 33	99.96 12	99.36 31	99.25 29	98.37 82	99.64 142	99.66 135

PLC	97.93 2	99.02 30	98.94 55	99.11 12	99.46 36	99.24 127	99.06 49	97.96 36	99.31 44	99.16 4	97.90 84	99.79 47	99.36 31	98.71 75	98.12 110	99.65 136	99.52 165
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DVP-MVS++		99.41 6	99.64 1	99.14 9	99.69 9	99.75 9	99.64 10	98.33 6	99.67 6	98.10 16	99.66 7	99.99 1	99.33 33	99.62 5	98.86 49	99.74 57	99.90 7

QAPM		98.62 43	99.04 51	98.13 40	99.57 28	99.48 67	99.17 41	94.78 58	99.57 11	96.16 43	96.73 119	99.80 45	99.33 33	98.79 65	99.29 15	99.75 50	99.64 142

PHI-MVS		99.08 24	99.43 22	98.67 30	99.15 47	99.59 48	99.11 45	97.35 42	99.14 78	97.30 30	99.44 15	99.96 12	99.32 35	98.89 58	99.39 8	99.79 33	99.58 153

DPE-MVS		99.39 7	99.55 8	99.20 6	99.63 22	99.71 16	99.66 8	98.33 6	99.29 47	98.40 14	99.64 8	99.98 2	99.31 36	99.56 9	98.96 41	99.85 10	99.70 116
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNLPA		99.03 29	99.05 48	99.01 21	99.27 45	99.22 131	99.03 51	97.98 35	99.34 42	99.00 7	98.25 75	99.71 51	99.31 36	98.80 64	98.82 56	99.48 192	99.17 196

MSP-MVS		99.34 9	99.52 12	99.14 9	99.68 14	99.75 9	99.64 10	98.31 10	99.44 26	98.10 16	99.28 21	99.98 2	99.30 38	99.34 25	99.05 32	99.81 25	99.79 46
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
APD-MVS		99.25 14	99.38 25	99.09 13	99.69 9	99.58 51	99.56 21	98.32 9	98.85 115	97.87 22	98.91 45	99.92 30	99.30 38	99.45 16	99.38 9	99.79 33	99.58 153
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SED-MVS		99.44 5	99.58 4	99.28 5	99.69 9	99.76 6	99.62 16	98.35 3	99.51 18	99.05 5	99.60 9	99.98 2	99.28 40	99.61 6	98.83 54	99.70 105	99.77 61

OMC-MVS		98.84 34	99.01 53	98.65 31	99.39 38	99.23 130	99.22 38	96.70 44	99.40 30	97.77 24	97.89 85	99.80 45	99.21 41	99.02 46	98.65 62	99.57 175	99.07 203

TAPA-MVS	97.53 5	98.41 48	98.84 60	97.91 46	99.08 49	99.33 117	99.15 42	97.13 43	99.34 42	93.20 109	97.75 89	99.19 62	99.20 42	98.66 77	98.13 107	99.66 131	99.48 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS		98.56 46	99.32 31	97.68 50	98.28 65	99.89 2	98.71 65	94.53 67	99.41 29	95.43 53	99.05 38	98.66 68	99.19 43	99.21 31	99.07 28	99.93 1	99.94 1

thres600view7		96.69 121	96.43 177	97.00 74	96.28 101	99.67 19	98.41 78	93.99 78	97.85 191	94.29 86	95.96 144	85.91 206	99.19 43	98.26 107	97.63 142	99.82 17	99.73 96

LS3D		97.79 63	98.25 76	97.26 63	98.40 62	99.63 33	99.53 22	98.63 1	99.25 54	88.13 167	96.93 115	94.14 127	99.19 43	99.14 37	99.23 20	99.69 109	99.42 178

thres400		96.71 120	96.45 175	97.02 71	96.28 101	99.63 33	98.41 78	94.00 77	97.82 192	94.42 83	95.74 154	86.26 203	99.18 46	98.20 111	97.79 136	99.81 25	99.70 116

thres200		96.76 115	96.53 167	97.03 69	96.31 98	99.67 19	98.37 81	93.99 78	97.68 198	94.49 80	95.83 153	86.77 197	99.18 46	98.26 107	97.82 134	99.82 17	99.66 135

ACMMP		98.74 37	99.03 52	98.40 34	99.36 41	99.64 30	99.20 39	97.75 40	98.82 122	95.24 64	98.85 48	99.87 39	99.17 48	98.74 73	97.50 148	99.71 95	99.76 68
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
sasdasda		97.31 80	97.81 101	96.72 79	96.20 104	99.45 75	98.21 91	91.60 142	99.22 58	95.39 54	98.48 64	90.95 161	99.16 49	97.66 158	99.05 32	99.76 44	99.90 7

canonicalmvs		97.31 80	97.81 101	96.72 79	96.20 104	99.45 75	98.21 91	91.60 142	99.22 58	95.39 54	98.48 64	90.95 161	99.16 49	97.66 158	99.05 32	99.76 44	99.90 7

tfpn200view9		96.75 117	96.51 169	97.03 69	96.31 98	99.67 19	98.41 78	93.99 78	97.35 203	94.52 77	95.90 147	86.93 195	99.14 51	98.26 107	97.80 135	99.82 17	99.70 116

SteuartSystems-ACMMP		99.20 17	99.51 13	98.83 28	99.66 18	99.66 23	99.71 5	98.12 30	99.14 78	96.62 36	99.16 26	99.98 2	99.12 52	99.63 3	99.19 23	99.78 36	99.83 30
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MSDG		98.27 53	98.29 74	98.24 38	99.20 46	99.22 131	99.20 39	97.82 38	99.37 34	94.43 82	95.90 147	97.31 86	99.12 52	98.76 69	98.35 83	99.67 126	99.14 200

CDPH-MVS		98.41 48	99.10 44	97.61 53	99.32 44	99.36 106	99.49 25	96.15 47	98.82 122	91.82 146	98.41 69	99.66 53	99.10 54	98.93 53	98.97 40	99.75 50	99.58 153

OpenMVS	96.23 11	97.95 61	98.45 70	97.35 58	99.52 34	99.42 92	98.91 57	94.61 62	98.87 112	92.24 139	94.61 176	99.05 66	99.10 54	98.64 79	99.05 32	99.74 57	99.51 170

thres100view900		96.72 119	96.47 173	97.00 74	96.31 98	99.52 61	98.28 87	94.01 76	97.35 203	94.52 77	95.90 147	86.93 195	99.09 56	98.07 122	97.87 128	99.81 25	99.63 146

MGCFI-Net		97.26 86	97.79 104	96.64 86	96.17 106	99.43 87	98.14 98	91.52 147	99.23 55	95.16 66	98.48 64	90.87 163	99.07 57	97.59 164	99.02 37	99.76 44	99.91 6

TSAR-MVS + ACMM		98.77 36	99.45 16	97.98 45	99.37 39	99.46 71	99.44 31	98.13 29	99.65 7	92.30 135	98.91 45	99.95 17	99.05 58	99.42 19	98.95 42	99.58 171	99.82 31

MVS_111021_LR		98.67 40	99.41 24	97.81 48	99.37 39	99.53 58	98.51 71	95.52 51	99.27 50	94.85 71	99.56 11	99.69 52	99.04 59	99.36 22	98.88 48	99.60 160	99.58 153

HyFIR lowres test		95.99 150	96.56 165	95.32 139	97.99 70	99.65 24	96.54 176	88.86 196	98.44 153	89.77 163	84.14 253	97.05 90	99.03 60	98.55 89	98.19 103	99.73 71	99.86 22

PCF-MVS	97.50 6	98.18 57	98.35 73	97.99 44	98.65 58	99.36 106	98.94 56	98.14 28	98.59 144	93.62 101	96.61 126	99.76 50	99.03 60	97.77 150	97.45 153	99.57 175	98.89 211
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		98.59 44	99.36 27	97.68 50	99.42 37	99.61 41	98.14 98	94.81 57	99.31 44	95.00 69	99.51 12	99.79 47	99.00 62	98.94 52	98.83 54	99.69 109	99.57 158

Casviewmamba		97.31 80	97.93 96	96.58 89	95.74 125	99.47 70	98.19 93	93.31 103	99.17 66	93.45 106	96.43 134	93.34 138	98.98 63	98.82 63	98.55 66	99.82 17	99.75 76

hybridcas		97.23 87	97.70 110	96.69 81	95.70 130	99.48 67	98.27 89	93.27 108	99.23 55	94.08 88	95.30 166	92.92 142	98.98 63	98.79 65	98.41 77	99.83 15	99.75 76

EIA-MVS		97.70 68	98.78 61	96.44 96	95.72 127	99.65 24	98.14 98	93.72 86	98.30 167	92.31 134	98.63 59	97.90 79	98.97 65	98.92 55	98.30 89	99.78 36	99.80 38

casdiffmvs		96.93 104	97.43 123	96.34 101	95.70 130	99.50 65	97.75 117	93.22 113	98.98 102	92.64 123	94.97 171	91.71 155	98.93 66	98.62 81	98.52 70	99.82 17	99.72 110
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SF-MVS		99.18 18	99.32 31	99.03 18	99.65 20	99.41 95	98.87 58	98.24 20	99.14 78	98.73 8	99.11 31	99.92 30	98.92 67	99.22 30	98.84 53	99.76 44	99.56 159

baseline1		97.58 71	98.05 87	97.02 71	96.21 103	99.45 75	97.71 119	93.71 87	98.47 152	95.75 48	98.78 51	93.20 141	98.91 68	98.52 91	98.44 74	99.81 25	99.53 162

casdiffmvs_mvg		97.27 84	97.97 94	96.46 95	95.83 117	99.51 64	98.42 77	93.32 100	98.34 165	92.38 133	95.64 157	95.35 110	98.91 68	98.73 74	98.45 73	99.86 9	99.80 38
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETV-MVS		98.05 58	99.25 37	96.65 84	95.61 140	99.61 41	98.26 90	93.52 89	98.90 111	93.74 99	99.32 20	99.20 61	98.90 70	99.21 31	98.72 59	99.87 8	99.79 46

Anonymous20231211		97.10 94	97.06 144	97.14 65	96.32 97	99.52 61	98.16 96	93.76 83	98.84 119	95.98 45	90.92 212	94.58 122	98.90 70	97.72 155	98.10 113	99.71 95	99.75 76

COLMAP_ROB	96.15 12	97.78 64	98.17 82	97.32 59	98.84 52	99.45 75	99.28 37	95.43 52	99.48 21	91.80 147	94.83 174	98.36 75	98.90 70	98.09 119	97.85 132	99.68 117	99.15 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
E6new		96.66 127	97.04 147	96.21 109	95.52 154	99.46 71	97.65 128	93.22 113	98.40 159	92.26 137	95.22 168	90.02 175	98.89 73	98.06 126	98.30 89	99.74 57	99.79 46

E6		96.66 127	97.04 147	96.21 109	95.52 154	99.46 71	97.65 128	93.22 113	98.40 159	92.26 137	95.22 168	90.02 175	98.89 73	98.06 126	98.30 89	99.74 57	99.79 46

test2506		97.16 91	96.68 163	97.73 49	96.95 88	99.79 4	98.48 72	94.42 69	99.17 66	97.74 25	99.15 27	80.93 247	98.89 73	99.03 44	99.09 26	99.88 4	99.62 148

ECVR-MVS		97.27 84	97.09 141	97.48 56	96.95 88	99.79 4	98.48 72	94.42 69	99.17 66	96.28 42	93.54 187	89.39 180	98.89 73	99.03 44	99.09 26	99.88 4	99.61 151

Anonymous202405211			97.40 126		96.45 94	99.54 57	98.08 104	93.79 82	98.24 171		93.55 186	94.41 123	98.88 77	98.04 129	98.24 98	99.75 50	99.76 68

E4		96.62 131	96.98 153	96.21 109	95.53 151	99.45 75	97.68 122	93.28 107	98.43 154	92.18 141	94.78 175	90.21 171	98.86 78	98.00 133	98.19 103	99.74 57	99.75 76

MAR-MVS		97.71 67	98.04 89	97.32 59	99.35 43	98.91 145	97.65 128	91.68 140	98.00 181	97.01 34	97.72 91	94.83 116	98.85 79	98.44 96	98.86 49	99.41 203	99.52 165
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
E3new		96.98 100	97.47 120	96.40 98	95.57 148	99.44 84	97.67 124	93.32 100	98.72 138	93.30 108	96.50 131	91.42 159	98.83 80	98.28 105	98.21 99	99.73 71	99.74 85

viewmanbaseed2359cas		96.92 106	97.60 112	96.14 118	95.71 128	99.44 84	97.82 111	93.39 91	98.93 107	91.34 151	96.10 141	92.27 148	98.82 81	98.40 98	98.30 89	99.75 50	99.75 76

E3		96.98 100	97.49 115	96.39 99	95.60 143	99.44 84	97.68 122	93.32 100	98.80 125	93.19 110	96.50 131	91.49 157	98.80 82	98.28 105	98.19 103	99.73 71	99.74 85

E5new		96.68 123	97.05 145	96.24 105	95.52 154	99.45 75	97.67 124	93.33 98	98.42 156	92.41 131	95.34 164	90.30 169	98.79 83	97.94 137	98.13 107	99.74 57	99.74 85

E5		96.68 123	97.05 145	96.24 105	95.52 154	99.45 75	97.67 124	93.33 98	98.42 156	92.41 131	95.34 164	90.30 169	98.79 83	97.94 137	98.13 107	99.74 57	99.74 85

E2		97.34 79	98.05 87	96.50 93	95.61 140	99.43 87	97.83 110	93.38 94	99.15 73	93.69 100	97.79 86	93.65 133	98.79 83	98.36 100	98.28 95	99.73 71	99.73 96

casdiffseed414692147		96.17 144	96.26 181	96.06 122	95.50 158	99.38 98	97.34 144	93.13 125	98.09 177	91.89 145	93.14 196	87.49 189	98.78 86	98.12 115	97.86 129	99.75 50	99.77 61

test1111		97.09 95	96.83 158	97.39 57	96.92 90	99.81 3	98.44 76	94.45 68	99.17 66	95.85 47	92.10 204	88.97 184	98.78 86	99.02 46	99.11 25	99.88 4	99.63 146

viewcassd2359sk11		97.19 90	97.82 99	96.44 96	95.59 146	99.43 87	97.70 120	93.35 96	99.15 73	93.50 103	97.20 104	92.68 144	98.77 88	98.38 99	98.21 99	99.73 71	99.73 96

viewmacassd2359aftdt		96.50 135	97.01 150	95.91 129	95.65 137	99.45 75	97.65 128	93.31 103	98.36 163	90.30 157	94.48 179	90.82 164	98.77 88	97.91 141	98.26 96	99.76 44	99.77 61

viewdifsd2359ckpt09		97.00 99	97.68 111	96.21 109	95.54 150	99.40 96	97.73 118	93.31 103	99.17 66	92.24 139	96.62 125	92.71 143	98.76 90	98.19 112	97.95 120	99.66 131	99.71 113

Fast-Effi-MVS+		95.38 162	96.52 168	94.05 160	94.15 180	99.14 135	97.24 150	86.79 220	98.53 149	87.62 173	94.51 177	87.06 192	98.76 90	98.60 85	98.04 117	99.72 84	99.77 61

dtuplus		96.76 115	97.19 137	96.26 103	95.48 161	99.38 98	97.81 113	93.18 123	98.69 140	92.60 125	95.24 167	92.14 150	98.75 92	97.27 181	97.86 129	99.73 71	99.74 85

viewmambaseed2359dif		96.82 112	97.19 137	96.39 99	95.64 138	99.38 98	98.15 97	93.24 110	98.78 132	92.85 120	95.93 146	91.24 160	98.75 92	97.41 173	97.86 129	99.70 105	99.74 85

viewmamba		96.88 109	97.43 123	96.23 107	95.81 122	99.35 109	97.57 132	93.17 124	99.46 22	92.46 130	96.40 136	91.48 158	98.72 94	97.59 164	98.05 115	99.63 148	99.68 126

viewdifsd2359ckpt13		96.93 104	97.71 105	96.03 125	95.58 147	99.43 87	97.42 140	93.30 106	99.09 86	91.43 149	96.95 113	92.45 145	98.70 95	98.30 104	97.98 118	99.72 84	99.73 96

DPM-MVS		98.31 52	98.53 67	98.05 42	98.76 57	98.77 153	99.13 43	98.07 32	99.10 85	94.27 87	96.70 121	99.84 43	98.70 95	97.90 143	98.11 111	99.40 205	99.28 187

Effi-MVS+		95.81 153	97.31 134	94.06 159	95.09 168	99.35 109	97.24 150	88.22 207	98.54 148	85.38 189	98.52 62	88.68 185	98.70 95	98.32 102	97.93 122	99.74 57	99.84 26

viewdifsd2359ckpt11		96.47 136	96.78 159	96.10 121	95.69 132	99.24 127	97.16 154	93.19 120	99.37 34	92.90 119	95.88 151	89.35 181	98.69 98	96.32 208	97.65 140	98.99 223	99.68 126

viewmsd2359difaftdt		96.47 136	96.78 159	96.11 120	95.69 132	99.24 127	97.16 154	93.19 120	99.35 40	92.93 118	95.88 151	89.34 182	98.69 98	96.31 209	97.65 140	98.99 223	99.68 126

TSAR-MVS + COLMAP		96.79 114	96.55 166	97.06 67	97.70 73	98.46 178	99.07 48	96.23 46	99.38 32	91.32 152	98.80 49	85.61 208	98.69 98	97.64 162	96.92 165	99.37 207	99.06 204

diffmvs_AUTHOR		96.68 123	97.10 140	96.19 115	95.71 128	99.37 104	97.91 107	93.19 120	99.36 38	91.97 143	95.90 147	89.02 183	98.67 101	98.01 132	98.30 89	99.68 117	99.74 85

EPP-MVSNet		97.75 66	98.71 63	96.63 87	95.68 135	99.56 54	97.51 135	93.10 126	99.22 58	94.99 70	97.18 105	97.30 87	98.65 102	98.83 62	98.93 43	99.84 12	99.92 3

CHOSEN 280x420		97.99 60	99.24 38	96.53 90	98.34 63	99.61 41	98.36 83	89.80 178	99.27 50	95.08 68	99.81 1	98.58 71	98.64 103	99.02 46	98.92 44	98.93 225	99.48 174

ET-MVSNet_ETH3D		96.17 144	96.99 151	95.21 140	88.53 250	98.54 173	98.28 87	92.61 128	98.85 115	93.60 102	99.06 37	90.39 167	98.63 104	95.98 219	96.68 170	99.61 152	99.41 179

MVS_Test		97.30 83	98.54 66	95.87 130	95.74 125	99.28 122	98.19 93	91.40 149	99.18 65	91.59 148	98.17 77	96.18 100	98.63 104	98.61 82	98.55 66	99.66 131	99.78 54

DCV-MVSNet		97.56 72	98.36 72	96.62 88	96.44 95	98.36 187	98.37 81	91.73 139	99.11 84	94.80 72	98.36 72	96.28 98	98.60 106	98.12 115	98.44 74	99.76 44	99.87 19

GeoE		95.98 152	97.24 136	94.51 148	95.02 170	99.38 98	98.02 106	87.86 213	98.37 162	87.86 171	92.99 201	93.54 134	98.56 107	98.61 82	97.92 124	99.73 71	99.85 25

diffmvs		96.83 111	97.33 129	96.25 104	95.76 123	99.34 112	98.06 105	93.22 113	99.43 28	92.30 135	96.90 116	89.83 179	98.55 108	98.00 133	98.14 106	99.64 142	99.70 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PMMVS		97.52 73	98.39 71	96.51 92	95.82 120	98.73 160	97.80 114	93.05 127	98.76 134	94.39 85	99.07 36	97.03 91	98.55 108	98.31 103	97.61 143	99.43 200	99.21 194

viewdifsd2359ckpt07		97.07 96	97.81 101	96.22 108	95.75 124	99.42 92	98.19 93	93.27 108	99.14 78	91.92 144	95.46 163	93.66 132	98.53 110	98.75 71	98.48 71	99.65 136	99.73 96

RPSCF		97.61 70	98.16 83	96.96 76	98.10 66	99.00 138	98.84 60	93.76 83	99.45 24	94.78 73	99.39 18	99.31 60	98.53 110	96.61 196	95.43 206	97.74 239	97.93 243

IS_MVSNet		97.86 62	98.86 58	96.68 82	96.02 107	99.72 13	98.35 84	93.37 95	98.75 137	94.01 89	96.88 117	98.40 74	98.48 112	99.09 40	99.42 5	99.83 15	99.80 38

hybridnocas07		96.80 113	97.32 130	96.20 114	95.82 120	99.34 112	97.56 133	93.20 119	99.45 24	92.55 128	96.73 119	90.52 166	98.44 113	97.51 169	97.93 122	99.64 142	99.75 76

PatchMatch-RL		97.77 65	98.25 76	97.21 64	99.11 48	99.25 125	97.06 163	94.09 75	98.72 138	95.14 67	98.47 67	96.29 97	98.43 114	98.65 78	97.44 154	99.45 196	98.94 206

onestephybrid01		96.90 107	97.41 125	96.31 102	95.85 115	99.34 112	97.43 139	93.35 96	99.39 31	93.17 112	95.53 162	92.12 151	98.40 115	97.73 153	98.11 111	99.65 136	99.68 126

hybrid		96.87 110	97.45 121	96.19 115	95.83 117	99.32 120	97.44 138	93.21 118	99.44 26	92.66 122	97.41 96	90.38 168	98.39 116	97.93 139	97.94 121	99.59 166	99.70 116

CANet		98.46 47	99.16 41	97.64 52	98.48 61	99.64 30	99.35 35	94.71 60	99.53 15	95.17 65	97.63 93	99.59 56	98.38 117	98.88 60	98.99 39	99.74 57	99.86 22

CHOSEN 1792x2688		96.41 138	96.99 151	95.74 133	98.01 69	99.72 13	97.70 120	90.78 162	99.13 83	90.03 160	87.35 243	95.36 109	98.33 118	98.59 87	98.91 47	99.59 166	99.87 19

FA-MVS(training)		96.52 134	98.29 74	94.45 150	95.88 114	99.52 61	97.66 127	81.47 244	98.94 105	93.79 98	95.54 161	99.11 64	98.29 119	98.89 58	96.49 178	99.63 148	99.52 165

MGCNet		98.81 35	99.44 19	98.08 41	98.83 53	99.75 9	99.58 20	95.53 49	99.76 1	96.48 41	99.70 4	98.64 69	98.21 120	99.00 49	99.33 11	99.82 17	99.90 7

thisisatest0530		97.23 87	98.25 76	96.05 123	95.60 143	99.59 48	96.96 165	93.23 111	99.17 66	92.60 125	98.75 54	96.19 99	98.17 121	98.19 112	96.10 191	99.72 84	99.77 61

tttt0517		97.23 87	98.24 79	96.04 124	95.60 143	99.60 46	96.94 166	93.23 111	99.15 73	92.56 127	98.74 55	96.12 102	98.17 121	98.21 110	96.10 191	99.73 71	99.78 54

ACMM	96.26 9	96.67 126	96.69 162	96.66 83	97.29 81	98.46 178	96.48 179	95.09 54	99.21 61	93.19 110	98.78 51	86.73 198	98.17 121	97.84 147	96.32 183	99.74 57	99.49 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		95.77 154	96.41 178	95.03 141	93.42 194	97.86 204	97.11 159	89.89 175	98.53 149	92.00 142	89.17 228	93.23 140	98.15 124	98.07 122	98.34 85	99.61 152	99.69 121

DI_MVS_pp		96.90 107	97.49 115	96.21 109	95.61 140	99.40 96	98.72 64	92.11 131	99.14 78	92.98 117	93.08 199	95.14 112	98.13 125	98.05 128	97.91 126	99.74 57	99.73 96

dmvs_re		96.02 149	96.49 172	95.47 137	93.49 193	99.26 124	97.25 149	93.82 81	97.51 200	90.43 156	97.52 95	87.93 187	98.12 126	96.86 192	96.59 174	99.73 71	99.76 68

OPM-MVS		96.22 143	95.85 188	96.65 84	97.75 71	98.54 173	99.00 54	95.53 49	96.88 216	89.88 161	95.95 145	86.46 202	98.07 127	97.65 161	96.63 172	99.67 126	98.83 215
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PVSNet_BlendedMVS		97.51 74	97.71 105	97.28 61	98.06 67	99.61 41	97.31 145	95.02 55	99.08 89	95.51 51	98.05 79	90.11 172	98.07 127	98.91 56	98.40 78	99.72 84	99.78 54

PVSNet_Blended		97.51 74	97.71 105	97.28 61	98.06 67	99.61 41	97.31 145	95.02 55	99.08 89	95.51 51	98.05 79	90.11 172	98.07 127	98.91 56	98.40 78	99.72 84	99.78 54

CLD-MVS		96.74 118	96.51 169	97.01 73	96.71 92	98.62 167	98.73 63	94.38 71	98.94 105	94.46 81	97.33 97	87.03 193	98.07 127	97.20 184	96.87 166	99.72 84	99.54 161
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
dtuonly		94.95 169	96.84 157	92.74 188	93.54 192	98.69 163	97.08 161	89.98 172	97.82 192	78.62 232	92.78 202	94.68 119	98.05 131	97.68 157	97.05 161	99.13 219	99.20 195

baseline		97.45 76	98.70 64	95.99 128	95.89 112	99.36 106	98.29 86	91.37 150	99.21 61	92.99 116	98.40 70	96.87 92	97.96 132	98.60 85	98.60 65	99.42 202	99.86 22

DELS-MVS		98.19 56	98.77 62	97.52 55	98.29 64	99.71 16	99.12 44	94.58 66	98.80 125	95.38 56	96.24 139	98.24 77	97.92 133	99.06 43	99.52 1	99.82 17	99.79 46
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
GBi-Net		96.98 100	98.00 92	95.78 131	93.81 185	97.98 197	98.09 101	91.32 151	98.80 125	93.92 91	97.21 100	95.94 105	97.89 134	98.07 122	98.34 85	99.68 117	99.67 131

test1		96.98 100	98.00 92	95.78 131	93.81 185	97.98 197	98.09 101	91.32 151	98.80 125	93.92 91	97.21 100	95.94 105	97.89 134	98.07 122	98.34 85	99.68 117	99.67 131

FMVSNet2		96.64 129	97.50 114	95.63 136	93.81 185	97.98 197	98.09 101	90.87 158	98.99 101	93.48 104	93.17 195	95.25 111	97.89 134	98.63 80	98.80 57	99.68 117	99.67 131

MDTV_nov1_ep13		95.57 157	97.48 117	93.35 179	95.43 162	98.97 142	97.19 153	83.72 242	98.92 110	87.91 170	97.75 89	96.12 102	97.88 137	96.84 194	95.64 204	97.96 237	98.10 238

UniMVSNet_ETH3D		93.15 206	92.33 240	94.11 157	93.91 182	98.61 169	94.81 219	90.98 157	97.06 212	87.51 174	82.27 257	76.33 263	97.87 138	94.79 235	97.47 152	99.56 178	99.81 36

IterMVS-LS		96.12 147	97.48 117	94.53 147	95.19 167	97.56 222	97.15 156	89.19 190	99.08 89	88.23 166	94.97 171	94.73 118	97.84 139	97.86 146	98.26 96	99.60 160	99.88 17
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LGP-MVS_train		96.23 142	96.89 154	95.46 138	97.32 78	98.77 153	98.81 61	93.60 88	98.58 145	85.52 187	99.08 35	86.67 199	97.83 140	97.87 145	97.51 147	99.69 109	99.73 96

0.3-1-1-0.015		93.30 203	92.54 237	94.20 154	89.52 245	95.62 247	96.78 169	88.89 195	94.12 248	95.31 57	97.26 99	83.52 228	97.69 141	87.57 262	91.45 249	96.99 258	98.23 236

SCA		94.95 169	97.44 122	92.04 198	95.55 149	99.16 133	96.26 185	79.30 255	99.02 98	85.73 186	98.18 76	97.13 89	97.69 141	96.03 217	94.91 220	97.69 244	97.65 245

HQP-MVS		96.37 139	96.58 164	96.13 119	97.31 80	98.44 180	98.45 75	95.22 53	98.86 113	88.58 165	98.33 73	87.00 194	97.67 143	97.23 182	96.56 176	99.56 178	99.62 148

FMVSNet3		97.02 98	98.12 85	95.73 134	93.59 191	97.98 197	98.34 85	91.32 151	98.80 125	93.92 91	97.21 100	95.94 105	97.63 144	98.61 82	98.62 63	99.61 152	99.65 138

0.4-1-1-0.1		93.46 199	92.78 236	94.25 153	89.58 243	95.89 246	96.90 167	89.00 193	94.50 245	95.29 61	97.21 100	83.62 224	97.58 145	88.01 260	91.72 247	97.15 257	98.48 228

TPM-MVS					99.57 28	98.90 146	98.79 62			96.52 40	98.62 60	99.91 33	97.56 146			99.44 198	99.28 187
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
CostFormer		94.25 187	94.88 198	93.51 174	95.43 162	98.34 188	96.21 186	80.64 248	97.94 186	94.01 89	98.30 74	86.20 205	97.52 147	92.71 243	92.69 238	97.23 256	98.02 241

FMVSNet5		95.42 160	96.47 173	94.20 154	92.26 206	95.99 245	95.66 194	87.15 218	97.87 189	93.46 105	96.68 122	93.79 131	97.52 147	97.10 188	97.21 159	99.11 220	96.62 258

EPMVS		95.05 167	96.86 156	92.94 185	95.84 116	98.96 143	96.68 172	79.87 251	99.05 95	90.15 158	97.12 107	95.99 104	97.49 149	95.17 229	94.75 225	97.59 246	96.96 254

FC-MVSNet-train		97.04 97	97.91 97	96.03 125	96.00 109	98.41 183	96.53 178	93.42 90	99.04 97	93.02 115	98.03 81	94.32 125	97.47 150	97.93 139	97.77 137	99.75 50	99.88 17

CANet_DTU		96.64 129	99.08 45	93.81 163	97.10 85	99.42 92	98.85 59	90.01 171	99.31 44	79.98 225	99.78 2	99.10 65	97.42 151	98.35 101	98.05 115	99.47 194	99.53 162

PatchmatchNet		94.70 176	97.08 143	91.92 203	95.53 151	98.85 148	95.77 192	79.54 253	98.95 103	85.98 182	98.52 62	96.45 93	97.39 152	95.32 226	94.09 231	97.32 253	97.38 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
0.4-1-1-0.2		93.21 205	92.46 239	94.08 158	89.56 244	95.52 249	96.71 170	88.73 199	93.97 256	95.29 61	97.17 106	83.59 225	97.33 153	87.65 261	91.30 250	96.89 260	98.03 240

tpmrst		93.86 195	95.88 186	91.50 210	95.69 132	98.62 167	95.64 195	79.41 254	98.80 125	83.76 200	95.63 158	96.13 101	97.25 154	92.92 242	92.31 241	97.27 254	96.74 255

DeepPCF-MVS	97.74 3	98.34 50	99.46 15	97.04 68	98.82 54	99.33 117	96.28 184	97.47 41	99.58 10	94.70 74	98.99 39	99.85 42	97.24 155	99.55 10	99.34 10	97.73 241	99.56 159

ACMP	96.25 10	96.62 131	96.72 161	96.50 93	96.96 87	98.75 157	97.80 114	94.30 73	98.85 115	93.12 113	98.78 51	86.61 200	97.23 156	97.73 153	96.61 173	99.62 150	99.71 113
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ADS-MVSNet		94.65 178	97.04 147	91.88 206	95.68 135	98.99 140	95.89 190	79.03 258	99.15 73	85.81 185	96.96 112	98.21 78	97.10 157	94.48 237	94.24 229	97.74 239	97.21 250

tfpnnormal		93.85 196	94.12 212	93.54 173	93.22 195	98.24 191	95.45 199	91.96 136	94.61 243	83.91 196	90.74 218	81.75 244	97.04 158	97.49 170	96.16 189	99.68 117	99.84 26

ACMH	95.42 14	95.27 165	95.96 184	94.45 150	96.83 91	98.78 152	94.72 222	91.67 141	98.95 103	86.82 179	96.42 135	83.67 223	97.00 159	97.48 171	96.68 170	99.69 109	99.76 68
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	95.51 13	95.40 161	96.00 182	94.70 145	96.33 96	98.79 150	96.79 168	91.32 151	98.77 133	87.18 175	95.60 159	85.46 209	96.97 160	97.15 185	96.59 174	99.59 166	99.65 138

MIMVSNet		94.49 184	97.59 113	90.87 224	91.74 218	98.70 162	94.68 224	78.73 260	97.98 182	83.71 201	97.71 92	94.81 117	96.96 161	97.97 135	97.92 124	99.40 205	98.04 239

RPMNet		94.66 177	97.16 139	91.75 207	94.98 171	98.59 170	97.00 164	78.37 262	97.98 182	83.78 198	96.27 138	94.09 130	96.91 162	97.36 176	96.73 168	99.48 192	99.09 202

MVSTER		97.16 91	97.71 105	96.52 91	95.97 111	98.48 176	98.63 67	92.10 132	98.68 141	95.96 46	99.23 23	91.79 154	96.87 163	98.76 69	97.37 157	99.57 175	99.68 126

CR-MVSNet		94.57 183	97.34 128	91.33 214	94.90 172	98.59 170	97.15 156	79.14 256	97.98 182	80.42 221	96.59 129	93.50 136	96.85 164	98.10 117	97.49 149	99.50 190	99.15 197

PatchT		93.96 192	97.36 127	90.00 235	94.76 176	98.65 165	90.11 253	78.57 261	97.96 185	80.42 221	96.07 142	94.10 129	96.85 164	98.10 117	97.49 149	99.26 214	99.15 197

USDC		94.26 186	94.83 199	93.59 170	96.02 107	98.44 180	97.84 109	88.65 202	98.86 113	82.73 210	94.02 182	80.56 248	96.76 166	97.28 180	96.15 190	99.55 180	98.50 226

Effi-MVS+-dtu		95.74 155	98.04 89	93.06 183	93.92 181	99.16 133	97.90 108	88.16 209	99.07 94	82.02 213	98.02 82	94.32 125	96.74 167	98.53 90	97.56 145	99.61 152	99.62 148

IterMVS-SCA-FT		94.89 172	97.87 98	91.42 211	94.86 174	97.70 208	97.24 150	84.88 236	98.93 107	75.74 242	94.26 181	98.25 76	96.69 168	98.52 91	97.68 139	99.10 221	99.73 96

TinyColmap		94.00 190	94.35 208	93.60 169	95.89 112	98.26 189	97.49 136	88.82 197	98.56 147	83.21 204	91.28 211	80.48 250	96.68 169	97.34 177	96.26 186	99.53 186	98.24 235

pmmvs4		95.09 166	95.90 185	94.14 156	92.29 205	97.70 208	95.45 199	90.31 168	98.60 143	90.70 154	93.25 193	89.90 177	96.67 170	97.13 186	95.42 207	99.44 198	99.28 187

IterMVS		94.81 175	97.71 105	91.42 211	94.83 175	97.63 215	97.38 141	85.08 233	98.93 107	75.67 243	94.02 182	97.64 82	96.66 171	98.45 94	97.60 144	98.90 226	99.72 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LTVRE_ROB	93.20 16	92.84 211	94.92 196	90.43 232	92.83 196	98.63 166	97.08 161	87.87 212	97.91 187	68.42 264	93.54 187	79.46 257	96.62 172	97.55 167	97.40 156	99.74 57	99.92 3
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
UniMVSNet_NR-MVSNet		94.59 181	95.47 191	93.55 172	91.85 215	97.89 203	95.03 205	92.00 134	97.33 205	86.12 180	93.19 194	87.29 191	96.60 173	96.12 214	96.70 169	99.72 84	99.80 38

DU-MVS		93.98 191	94.44 207	93.44 175	91.66 220	97.77 205	95.03 205	91.57 144	97.17 209	86.12 180	93.13 197	81.13 246	96.60 173	95.10 231	97.01 164	99.67 126	99.80 38

tpm		92.38 226	94.79 200	89.56 239	94.30 179	97.50 225	94.24 234	78.97 259	97.72 196	74.93 247	97.97 83	82.91 236	96.60 173	93.65 240	94.81 224	98.33 233	98.98 205

SixPastTwentyTwo		93.44 201	95.32 193	91.24 216	92.11 208	98.40 184	92.77 240	88.64 203	98.09 177	77.83 235	93.51 189	85.74 207	96.52 176	96.91 191	94.89 223	99.59 166	99.73 96

PVSNet_Blended_VisFu		97.41 77	98.49 69	96.15 117	97.49 74	99.76 6	96.02 189	93.75 85	99.26 52	93.38 107	93.73 185	99.35 59	96.47 177	98.96 50	98.46 72	99.77 42	99.90 7

baseline2		96.36 140	97.82 99	94.65 146	94.60 177	99.09 136	96.45 180	89.63 180	98.36 163	91.29 153	97.60 94	94.13 128	96.37 178	98.45 94	97.70 138	99.54 184	99.41 179

Baseline_NR-MVSNet		93.87 194	93.98 217	93.75 165	91.66 220	97.02 236	95.53 197	91.52 147	97.16 211	87.77 172	87.93 241	83.69 222	96.35 179	95.10 231	97.23 158	99.68 117	99.73 96

dps		94.63 179	95.31 194	93.84 162	95.53 151	98.71 161	96.54 176	80.12 250	97.81 195	97.21 31	96.98 111	92.37 146	96.34 180	92.46 245	91.77 245	97.26 255	97.08 252

CDS-MVSNet		96.59 133	98.02 91	94.92 143	94.45 178	98.96 143	97.46 137	91.75 138	97.86 190	90.07 159	96.02 143	97.25 88	96.21 181	98.04 129	98.38 80	99.60 160	99.65 138
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		95.53 158	96.50 171	94.39 152	93.86 184	99.03 137	96.67 173	89.55 182	97.33 205	90.64 155	93.02 200	91.58 156	96.21 181	97.72 155	97.43 155	99.43 200	99.36 184

MS-PatchMatch		95.99 150	97.26 135	94.51 148	97.46 75	98.76 156	97.27 147	86.97 219	99.09 86	89.83 162	93.51 189	97.78 81	96.18 183	97.53 168	95.71 203	99.35 208	98.41 231

TranMVSNet+NR-MVSNet		93.67 197	94.14 210	93.13 182	91.28 234	97.58 220	95.60 196	91.97 135	97.06 212	84.05 194	90.64 221	82.22 241	96.17 184	94.94 234	96.78 167	99.69 109	99.78 54

CP-MVSNet		93.25 204	94.00 216	92.38 192	91.65 222	97.56 222	94.38 231	89.20 189	96.05 234	83.16 205	89.51 226	81.97 242	96.16 185	96.43 202	96.56 176	99.71 95	99.89 13

UniMVSNet (Re)		94.58 182	95.34 192	93.71 167	92.25 207	98.08 195	94.97 207	91.29 156	97.03 214	87.94 169	93.97 184	86.25 204	96.07 186	96.27 211	95.97 196	99.72 84	99.79 46

usedtu_dtu_shiyan1		94.86 173	96.31 179	93.16 181	88.71 248	98.02 196	96.17 188	91.31 155	98.43 154	87.18 175	91.68 207	93.37 137	96.06 187	97.46 172	95.83 199	99.53 186	99.40 181

tpm cat1		94.06 188	94.90 197	93.06 183	95.42 164	98.52 175	96.64 174	80.67 247	97.82 192	92.63 124	93.39 192	95.00 114	96.06 187	91.36 249	91.58 248	96.98 259	96.66 257

v2v482		92.77 215	93.52 227	91.90 205	91.59 225	97.63 215	94.57 229	90.31 168	96.80 220	79.22 228	88.74 233	81.55 245	96.04 189	95.26 227	94.97 219	99.66 131	99.69 121

testgi		95.67 156	97.48 117	93.56 171	95.07 169	99.00 138	95.33 202	88.47 204	98.80 125	86.90 178	97.30 98	92.33 147	95.97 190	97.66 158	97.91 126	99.60 160	99.38 183

usedtu_blend_shiyan5		92.28 230	91.78 241	92.86 186	82.44 258	94.55 257	96.69 171	89.26 185	93.99 252	95.31 57	97.12 107	83.52 228	95.91 191	88.61 255	85.85 260	97.57 247	98.84 213

blend_shiyan4		92.70 219	91.74 243	93.81 163	88.98 246	94.51 261	96.29 183	88.71 200	94.00 251	95.31 57	97.12 107	83.52 228	95.91 191	88.20 259	85.99 259	97.69 244	98.84 213

FE-MVSNET3		92.14 232	91.78 241	92.55 190	82.44 258	94.55 257	94.83 216	89.26 185	93.99 252	95.31 57	97.12 107	83.52 228	95.91 191	88.61 255	85.85 260	97.57 247	98.83 215

PS-CasMVS		92.72 216	93.36 228	91.98 201	91.62 224	97.52 224	94.13 235	88.98 194	95.94 237	81.51 216	87.35 243	79.95 254	95.91 191	96.37 204	96.49 178	99.70 105	99.89 13

v1192		92.43 224	93.61 223	91.05 220	91.53 226	97.43 228	94.61 227	87.99 211	96.60 224	76.72 238	87.11 245	82.74 239	95.85 195	96.35 206	95.30 210	99.60 160	99.74 85

blended_shiyan6		90.91 238	91.00 248	90.80 226	82.44 258	94.60 256	94.86 215	89.05 192	94.08 249	84.93 193	90.75 217	83.74 219	95.81 196	88.79 252	86.19 257	97.71 242	98.83 215

wanda-best-256-512		90.85 240	90.88 250	90.80 226	82.44 258	94.55 257	94.83 216	89.26 185	93.99 252	84.94 191	90.86 214	83.70 220	95.80 197	88.61 255	85.85 260	97.57 247	98.64 221

FE-blended-shiyan7		90.85 240	90.88 250	90.80 226	82.44 258	94.55 257	94.83 216	89.26 185	93.99 252	84.94 191	90.86 214	83.70 220	95.80 197	88.61 255	85.85 260	97.57 247	98.64 221

gbinet_0.2-2-1-0.02		91.19 237	91.20 246	91.18 217	83.37 255	94.62 254	95.06 204	89.43 183	94.06 250	85.87 183	91.99 205	84.54 217	95.79 199	88.81 251	85.62 264	97.56 251	98.74 220

blended_shiyan8		90.91 238	90.97 249	90.84 225	82.45 257	94.62 254	94.96 208	89.15 191	93.94 257	85.03 190	90.85 216	83.58 226	95.78 200	88.79 252	86.19 257	97.70 243	98.80 219

v1921920		92.36 228	93.57 224	90.94 222	91.39 230	97.39 230	94.70 223	87.63 215	96.60 224	76.63 239	86.98 246	82.89 237	95.75 201	96.26 212	95.14 215	99.55 180	99.73 96

test0.0.03 1		96.69 121	98.12 85	95.01 142	95.49 159	98.99 140	95.86 191	90.82 160	98.38 161	92.54 129	96.66 123	97.33 85	95.75 201	97.75 152	98.34 85	99.60 160	99.40 181

Vis-MVSNet (Re-imp)		97.40 78	98.89 57	95.66 135	95.99 110	99.62 36	97.82 111	93.22 113	98.82 122	91.40 150	96.94 114	98.56 72	95.70 203	99.14 37	99.41 6	99.79 33	99.75 76

gm-plane-assit		89.44 249	92.82 235	85.49 252	91.37 231	95.34 251	79.55 270	82.12 243	91.68 264	64.79 268	87.98 239	80.26 251	95.66 204	98.51 93	97.56 145	99.45 196	98.41 231

v10		92.79 214	94.06 214	91.31 215	91.78 217	97.29 234	94.87 213	86.10 228	96.97 215	79.82 226	88.16 237	84.56 216	95.63 205	96.33 207	95.31 209	99.65 136	99.80 38

Fast-Effi-MVS+-dtu		95.38 162	98.20 81	92.09 197	93.91 182	98.87 147	97.35 143	85.01 235	99.08 89	81.09 217	98.10 78	96.36 96	95.62 206	98.43 97	97.03 162	99.55 180	99.50 172

PEN-MVS		92.72 216	93.20 230	92.15 196	91.29 232	97.31 232	94.67 225	89.81 176	96.19 230	81.83 214	88.58 234	79.06 258	95.61 207	95.21 228	96.27 184	99.72 84	99.82 31

pmmvs5		92.71 218	94.27 209	90.90 223	91.42 229	97.74 207	93.23 237	86.66 223	95.99 236	78.96 231	91.45 209	83.44 232	95.55 208	97.30 179	95.05 217	99.58 171	98.93 207

test-LLR		95.50 159	97.32 130	93.37 177	95.49 159	98.74 158	96.44 181	90.82 160	98.18 172	82.75 208	96.60 127	94.67 120	95.54 209	98.09 119	96.00 193	99.20 216	98.93 207

TESTMET0.1,1		94.95 169	97.32 130	92.20 195	92.62 198	98.74 158	96.44 181	86.67 222	98.18 172	82.75 208	96.60 127	94.67 120	95.54 209	98.09 119	96.00 193	99.20 216	98.93 207

v8		92.87 210	93.87 221	91.72 209	92.05 209	97.50 225	94.79 220	88.20 208	96.85 218	80.11 224	90.01 223	82.86 238	95.48 211	95.15 230	94.90 221	99.66 131	99.80 38

EPNet		98.05 58	98.86 58	97.10 66	99.02 50	99.43 87	98.47 74	94.73 59	99.05 95	95.62 49	98.93 43	97.62 84	95.48 211	98.59 87	98.55 66	99.29 212	99.84 26
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-mter		94.86 173	97.32 130	92.00 200	92.41 203	98.82 149	96.18 187	86.35 226	98.05 179	82.28 211	96.48 133	94.39 124	95.46 213	98.17 114	96.20 187	99.32 210	99.13 201

v1144		92.81 212	94.03 215	91.40 213	91.68 219	97.60 219	94.73 221	88.40 205	96.71 221	78.48 233	88.14 238	84.46 218	95.45 214	96.31 209	95.22 212	99.65 136	99.76 68

V42		93.05 208	93.90 220	92.04 198	91.91 212	97.66 212	94.91 210	89.91 174	96.85 218	80.58 220	89.66 225	83.43 233	95.37 215	95.03 233	94.90 221	99.59 166	99.78 54

v144192		92.38 226	93.55 226	91.00 221	91.44 228	97.47 227	94.27 232	87.41 216	96.52 226	78.03 234	87.50 242	82.65 240	95.32 216	95.82 222	95.15 214	99.55 180	99.78 54

v1240		91.99 234	93.33 229	90.44 231	91.29 232	97.30 233	94.25 233	86.79 220	96.43 227	75.49 245	86.34 249	81.85 243	95.29 217	96.42 203	95.22 212	99.52 188	99.73 96

NR-MVSNet		94.01 189	94.51 205	93.44 175	92.56 200	97.77 205	95.67 193	91.57 144	97.17 209	85.84 184	93.13 197	80.53 249	95.29 217	97.01 189	96.17 188	99.69 109	99.75 76

anonymousdsp		93.12 207	95.86 187	89.93 237	91.09 235	98.25 190	95.12 203	85.08 233	97.44 202	73.30 253	90.89 213	90.78 165	95.25 219	97.91 141	95.96 197	99.71 95	99.82 31

gg-mvs-nofinetune		90.85 240	94.14 210	87.02 248	94.89 173	99.25 125	98.64 66	76.29 266	88.24 265	57.50 271	79.93 259	95.45 108	95.18 220	98.77 68	98.07 114	99.62 150	99.24 192

MVS-HIRNet		92.51 220	95.97 183	88.48 244	93.73 188	98.37 186	90.33 251	75.36 268	98.32 166	77.78 236	89.15 229	94.87 115	95.14 221	97.62 163	96.39 181	98.51 229	97.11 251

MDTV_nov1_ep13_2view		92.44 222	95.66 189	88.68 241	91.05 236	97.92 201	92.17 243	79.64 252	98.83 120	76.20 240	91.45 209	93.51 135	95.04 222	95.68 223	93.70 235	97.96 237	98.53 225

DTE-MVSNet		92.42 225	92.85 233	91.91 204	90.87 238	96.97 237	94.53 230	89.81 176	95.86 239	81.59 215	88.83 232	77.88 261	95.01 223	94.34 238	96.35 182	99.64 142	99.73 96

pm-mvs1		94.27 185	95.57 190	92.75 187	92.58 199	98.13 194	94.87 213	90.71 164	96.70 222	83.78 198	89.94 224	89.85 178	94.96 224	97.58 166	97.07 160	99.61 152	99.72 110

pmnet_mix02		92.44 222	94.68 202	89.83 238	92.46 202	97.65 214	89.92 255	90.49 167	98.76 134	73.05 256	91.78 206	90.08 174	94.86 225	94.53 236	91.94 244	98.21 235	98.01 242

PM-MVS		89.55 248	90.30 253	88.67 242	87.06 251	95.60 248	90.88 247	84.51 239	96.14 231	75.75 241	86.89 247	63.47 272	94.64 226	96.85 193	93.89 232	99.17 218	99.29 186

FC-MVSNet-test		96.07 148	97.94 95	93.89 161	93.60 190	98.67 164	96.62 175	90.30 170	98.76 134	88.62 164	95.57 160	97.63 83	94.48 227	97.97 135	97.48 151	99.71 95	99.52 165

WR-MVS_H		93.54 198	94.67 203	92.22 193	91.95 211	97.91 202	94.58 228	88.75 198	96.64 223	83.88 197	90.66 220	85.13 212	94.40 228	96.54 200	95.91 198	99.73 71	99.89 13

GA-MVS		93.93 193	96.31 179	91.16 219	93.61 189	98.79 150	95.39 201	90.69 165	98.25 170	73.28 254	96.15 140	88.42 186	94.39 229	97.76 151	95.35 208	99.58 171	99.45 176

TransMVSNet (Re)		93.45 200	94.08 213	92.72 189	92.83 196	97.62 218	94.94 209	91.54 146	95.65 240	83.06 206	88.93 231	83.53 227	94.25 230	97.41 173	97.03 162	99.67 126	98.40 234

UGNet		97.66 69	99.07 47	96.01 127	97.19 83	99.65 24	97.09 160	93.39 91	99.35 40	94.40 84	98.79 50	99.59 56	94.24 231	98.04 129	98.29 94	99.73 71	99.80 38
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
pmmvs6		91.90 235	92.53 238	91.17 218	91.81 216	97.63 215	93.23 237	88.37 206	93.43 260	80.61 219	77.32 262	87.47 190	94.12 232	96.58 198	95.72 202	98.88 227	99.53 162

EPNet_dtu		96.30 141	98.53 67	93.70 168	98.97 51	98.24 191	97.36 142	94.23 74	98.85 115	79.18 229	99.19 24	98.47 73	94.09 233	97.89 144	98.21 99	98.39 232	98.85 212
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net		97.13 93	99.14 42	94.78 144	97.21 82	99.38 98	97.56 133	92.04 133	98.48 151	88.03 168	98.39 71	99.91 33	94.03 234	99.33 26	99.23 20	99.81 25	99.25 191

pmmvs-eth3d		89.81 247	89.65 255	90.00 235	86.94 252	95.38 250	91.08 245	86.39 225	94.57 244	82.27 212	83.03 256	64.94 269	93.96 235	96.57 199	93.82 234	99.35 208	99.24 192

WR-MVS		93.43 202	94.48 206	92.21 194	91.52 227	97.69 210	94.66 226	89.98 172	96.86 217	83.43 202	90.12 222	85.03 213	93.94 236	96.02 218	95.82 200	99.71 95	99.82 31

CVMVSNet		95.33 164	97.09 141	93.27 180	95.23 166	98.39 185	95.49 198	92.58 129	97.71 197	83.00 207	94.44 180	93.28 139	93.92 237	97.79 148	98.54 69	99.41 203	99.45 176

N_pmnet		92.21 231	94.60 204	89.42 240	91.88 213	97.38 231	89.15 258	89.74 179	97.89 188	73.75 251	87.94 240	92.23 149	93.85 238	96.10 215	93.20 237	98.15 236	97.43 248

v7n		91.61 236	92.95 231	90.04 234	90.56 239	97.69 210	93.74 236	85.59 230	95.89 238	76.95 237	86.60 248	78.60 260	93.76 239	97.01 189	94.99 218	99.65 136	99.87 19

Vis-MVSNet		96.16 146	98.22 80	93.75 165	95.33 165	99.70 18	97.27 147	90.85 159	98.30 167	85.51 188	95.72 156	96.45 93	93.69 240	98.70 76	99.00 38	99.84 12	99.69 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		94.61 180	96.89 154	91.95 202	92.00 210	98.47 177	92.01 244	90.73 163	98.18 172	83.96 195	94.51 177	95.13 113	93.38 241	97.38 175	94.74 226	99.61 152	99.79 46

new_pmnet		90.45 246	92.84 234	87.66 245	88.96 247	96.16 243	88.71 259	84.66 237	97.56 199	71.91 260	85.60 251	86.58 201	93.28 242	96.07 216	93.54 236	98.46 230	94.39 262

pmmvs3		88.19 251	91.27 245	84.60 254	85.60 254	93.66 263	85.68 265	81.13 246	92.36 263	63.66 270	89.51 226	77.10 262	93.22 243	96.37 204	92.40 240	98.30 234	97.46 247

EG-PatchMatch MVS		92.45 221	93.92 219	90.72 229	92.56 200	98.43 182	94.88 212	84.54 238	97.18 208	79.55 227	86.12 250	83.23 234	93.15 244	97.22 183	96.00 193	99.67 126	99.27 190

v148		92.36 228	92.88 232	91.75 207	91.63 223	97.66 212	92.64 241	90.55 166	96.09 232	83.34 203	88.19 236	80.00 252	92.74 245	93.98 239	94.58 227	99.58 171	99.69 121

MDA-MVSNet-bldmvs		87.84 252	89.22 256	86.23 250	81.74 263	96.77 240	83.74 266	89.57 181	94.50 245	72.83 258	96.64 124	64.47 271	92.71 246	81.43 266	92.28 242	96.81 261	98.47 229

DeepMVS_CX						96.85 238	87.43 263	89.27 184	98.30 167	75.55 244	95.05 170	79.47 256	92.62 247	89.48 250		95.18 267	95.96 259

dtuonlycased		92.09 233	95.05 195	88.64 243	90.98 237	97.03 235	89.54 257	85.55 231	98.13 175	74.33 249	93.51 189	92.03 153	92.59 248	93.63 241	92.52 239	98.85 228	98.50 226

FE-MVSNET2		87.81 253	88.02 258	87.56 246	80.30 266	96.14 244	90.86 248	87.34 217	93.58 258	74.84 248	71.50 264	65.61 268	92.53 249	96.74 195	94.12 230	99.50 190	98.47 229

CMPMVS	70.31 18	90.74 243	91.06 247	90.36 233	97.32 78	97.43 228	92.97 239	87.82 214	93.50 259	75.34 246	83.27 255	84.90 214	92.19 250	92.64 244	91.21 251	96.50 264	94.46 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		92.80 213	94.76 201	90.51 230	91.88 213	96.74 241	92.48 242	88.69 201	96.21 229	79.00 230	91.51 208	87.82 188	91.83 251	95.87 221	96.27 184	99.21 215	98.92 210

MIMVSNet1		88.61 250	90.68 252	86.19 251	81.56 264	95.30 252	87.78 262	85.98 229	94.19 247	72.30 259	78.84 260	78.90 259	90.06 252	96.59 197	95.47 205	99.46 195	95.49 260

new-patchmatchnet		86.12 256	87.30 259	84.74 253	86.92 253	95.19 253	83.57 267	84.42 240	92.67 262	65.66 265	80.32 258	64.72 270	89.41 253	92.33 247	89.21 253	98.43 231	96.69 256

test_method		87.27 254	91.58 244	82.25 258	75.65 270	87.52 270	86.81 264	72.60 269	97.51 200	73.20 255	85.07 252	79.97 253	88.69 254	97.31 178	95.24 211	96.53 263	98.41 231

FE-MVSNET		86.50 255	88.24 257	84.47 255	76.04 268	94.06 262	87.91 261	86.26 227	92.71 261	69.03 263	77.33 261	66.72 267	88.34 255	95.57 224	93.83 233	99.27 213	97.48 246

TDRefinement		93.04 209	93.57 224	92.41 191	96.58 93	98.77 153	97.78 116	91.96 136	98.12 176	80.84 218	89.13 230	79.87 255	87.78 256	96.44 201	94.50 228	99.54 184	98.15 237

usedtu_dtu_shiyan2		84.24 257	84.83 260	83.55 256	75.12 272	92.45 264	88.33 260	81.21 245	87.18 266	73.36 252	64.78 266	73.58 265	86.68 257	88.73 254	88.30 255	96.59 262	98.82 218

Gipuma		81.40 259	81.78 262	80.96 260	83.21 256	85.61 271	79.73 269	76.25 267	97.33 205	64.21 269	55.32 268	55.55 273	86.04 258	92.43 246	92.20 243	96.32 265	93.99 263
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231206		90.70 244	93.93 218	86.92 249	90.21 242	96.79 239	90.30 252	86.61 224	96.05 234	69.25 261	88.46 235	84.86 215	85.86 259	97.11 187	96.47 180	99.30 211	97.80 244

test20.03		90.65 245	93.71 222	87.09 247	90.44 240	96.24 242	89.74 256	85.46 232	95.59 241	72.99 257	90.68 219	85.33 210	84.41 260	95.94 220	95.10 216	99.52 188	97.06 253

IB-MVS	93.96 15	95.02 168	96.44 176	93.36 178	97.05 86	99.28 122	90.43 250	93.39 91	98.02 180	96.02 44	94.92 173	92.07 152	83.52 261	95.38 225	95.82 200	99.72 84	99.59 152
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
ambc			80.99 263		80.04 267	90.84 265	90.91 246		96.09 232	74.18 250	62.81 267	30.59 278	82.44 262	96.25 213	91.77 245	95.91 266	98.56 224

FPMVS		83.82 258	84.61 261	82.90 257	90.39 241	90.71 266	90.85 249	84.10 241	95.47 242	65.15 266	83.44 254	74.46 264	75.48 263	81.63 265	79.42 267	91.42 269	87.14 267

EMVS		68.12 265	68.11 267	68.14 265	75.51 271	71.76 273	55.38 276	77.20 264	77.78 269	37.79 275	53.59 269	43.61 275	74.72 264	67.05 270	76.70 269	88.27 273	86.24 268

E-PMN		68.30 264	68.43 266	68.15 264	74.70 273	71.56 274	55.64 275	77.24 263	77.48 270	39.46 274	51.95 271	41.68 276	73.28 265	70.65 269	79.51 266	88.61 272	86.20 269

MVE	67.97 19	65.53 266	67.43 268	63.31 266	59.33 274	74.20 272	53.09 277	70.43 270	66.27 271	43.13 273	45.98 272	30.62 277	70.65 266	79.34 268	86.30 256	83.25 274	89.33 265
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		77.26 261	79.47 264	74.70 262	76.00 269	88.37 268	74.22 271	76.34 265	78.31 268	54.13 272	69.96 265	52.50 274	70.14 267	84.83 264	88.71 254	97.35 252	93.58 264

tmp_tt				82.25 258	97.73 72	88.71 267	80.18 268	68.65 271	99.15 73	86.98 177	99.47 13	85.31 211	68.35 268	87.51 263	83.81 265	91.64 268

PMVS	72.60 17	76.39 262	77.66 265	74.92 261	81.04 265	69.37 275	68.47 273	80.54 249	85.39 267	65.07 267	73.52 263	72.91 266	65.67 269	80.35 267	76.81 268	88.71 271	85.25 270
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		81.36 260	89.93 254	71.35 263	88.65 249	87.85 269	71.46 272	88.12 210	96.23 228	32.21 276	92.61 203	83.00 235	56.27 270	91.92 248	89.43 252	91.39 270	88.49 266

testmvs		31.24 267	40.15 269	20.86 268	12.61 275	17.99 276	25.16 278	13.30 272	48.42 272	24.82 277	53.07 270	30.13 279	28.47 271	42.73 271	37.65 270	20.79 275	51.04 271

test123		26.75 268	34.25 270	18.01 269	7.93 276	17.18 277	24.85 279	12.36 273	44.83 273	16.52 278	41.80 273	18.10 280	28.29 272	33.08 272	34.79 271	18.10 276	49.95 272

GG-mvs-BLEND		69.11 263	98.13 84	35.26 267	3.49 277	98.20 193	94.89 211	2.38 274	98.42 156	5.82 279	96.37 137	98.60 70	5.97 273	98.75 71	97.98 118	99.01 222	98.61 223

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

TestfortrainingZip							99.83 1	98.29 13		99.52 3						99.71 95

RE-MVS-def										69.05 262

9.14												99.79 47

SR-MVS					99.67 15			98.25 17				99.94 26

our_test_3					92.30 204	97.58 220	90.09 254

MTAPA										98.09 18		99.97 8

MTMP										98.46 13		99.96 12

Patchmatch-RL test							66.86 274

XVS					97.42 76	99.62 36	98.59 69			93.81 95		99.95 17				99.69 109

X-MVStestdata					97.42 76	99.62 36	98.59 69			93.81 95		99.95 17				99.69 109

mPP-MVS					99.53 32							99.89 37

NP-MVS									98.57 146

Patchmtry						98.59 170	97.15 156	79.14 256		80.42 221

High-resolution multi-view benchmark