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.

APDe-MVS		99.49 1	99.64 1	99.32 1	99.74 3	99.74 3	99.75 1	98.34 2	99.56 9	98.72 3	99.57 4	99.97 3	99.53 14	99.65 2	99.25 13	99.84 5	99.77 48

HFP-MVS		99.32 2	99.53 4	99.07 9	99.69 6	99.59 39	99.63 10	98.31 5	99.56 9	97.37 20	99.27 12	99.97 3	99.70 3	99.35 17	99.24 15	99.71 64	99.76 52

HSP-MVS		99.31 3	99.43 13	99.17 2	99.68 9	99.75 2	99.72 2	98.31 5	99.45 16	98.16 9	99.28 11	99.98 1	99.30 30	99.34 18	98.41 56	99.81 22	99.81 30

MPTG		99.31 3	99.44 11	99.16 4	99.73 4	99.65 18	99.63 10	98.26 10	99.27 35	98.01 12	99.27 12	99.97 3	99.60 6	99.59 5	98.58 48	99.71 64	99.73 68

ACMMPR		99.30 5	99.54 3	99.03 12	99.66 12	99.64 22	99.68 5	98.25 11	99.56 9	97.12 24	99.19 15	99.95 13	99.72 1	99.43 12	99.25 13	99.72 55	99.77 48

TSAR-MVS + MP.		99.27 6	99.57 2	98.92 17	98.78 47	99.53 47	99.72 2	98.11 22	99.73 2	97.43 19	99.15 18	99.96 8	99.59 8	99.73 1	99.07 21	99.88 1	99.82 25

CP-MVS		99.27 6	99.44 11	99.08 8	99.62 16	99.58 41	99.53 15	98.16 15	99.21 45	97.79 15	99.15 18	99.96 8	99.59 8	99.54 7	98.86 35	99.78 33	99.74 64

SD-MVS		99.25 8	99.50 6	98.96 15	98.79 46	99.55 46	99.33 28	98.29 8	99.75 1	97.96 13	99.15 18	99.95 13	99.61 5	99.17 24	99.06 22	99.81 22	99.84 20

APD-MVS		99.25 8	99.38 16	99.09 7	99.69 6	99.58 41	99.56 14	98.32 4	98.85 79	97.87 14	98.91 34	99.92 23	99.30 30	99.45 11	99.38 8	99.79 30	99.58 125
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.23 10	99.28 23	99.17 2	99.65 14	99.34 70	99.46 21	98.21 13	99.28 33	98.47 5	98.89 36	99.94 21	99.50 15	99.42 13	98.61 45	99.73 50	99.52 136

SteuartSystems-ACMMP		99.20 11	99.51 5	98.83 21	99.66 12	99.66 17	99.71 4	98.12 21	99.14 52	96.62 28	99.16 17	99.98 1	99.12 45	99.63 3	99.19 19	99.78 33	99.83 24
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.34 1	99.17 12	99.45 8	98.85 19	99.55 22	99.37 65	99.64 8	98.05 24	99.53 12	96.58 29	98.93 29	99.92 23	99.49 17	99.46 10	99.32 10	99.80 29	99.64 116
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		99.15 13	99.24 25	99.04 11	99.52 25	99.49 51	99.09 39	98.07 23	99.37 21	98.47 5	97.79 67	99.89 28	99.50 15	98.93 37	99.45 4	99.61 127	99.76 52

CPTT-MVS		99.14 14	99.20 27	99.06 10	99.58 19	99.53 47	99.45 22	97.80 29	99.19 48	98.32 8	98.58 44	99.95 13	99.60 6	99.28 21	98.20 73	99.64 112	99.69 91

MCST-MVS		99.11 15	99.27 24	98.93 16	99.67 10	99.33 72	99.51 17	98.31 5	99.28 33	96.57 30	99.10 22	99.90 26	99.71 2	99.19 23	98.35 63	99.82 13	99.71 84

HPM-MVS++		99.10 16	99.30 22	98.86 18	99.69 6	99.48 52	99.59 13	98.34 2	99.26 38	96.55 31	99.10 22	99.96 8	99.36 25	99.25 22	98.37 61	99.64 112	99.66 109

PHI-MVS		99.08 17	99.43 13	98.67 23	99.15 39	99.59 39	99.11 37	97.35 32	99.14 52	97.30 21	99.44 8	99.96 8	99.32 28	98.89 41	99.39 7	99.79 30	99.58 125

MP-MVS		99.07 18	99.36 18	98.74 22	99.63 15	99.57 43	99.66 7	98.25 11	99.00 68	95.62 36	98.97 27	99.94 21	99.54 13	99.51 8	98.79 40	99.71 64	99.73 68

AdaColmap		99.06 19	98.98 42	99.15 5	99.60 18	99.30 76	99.38 26	98.16 15	99.02 67	98.55 4	98.71 42	99.57 47	99.58 11	99.09 28	97.84 88	99.64 112	99.36 152

ACMMP_Plus		99.05 20	99.45 8	98.58 25	99.73 4	99.60 37	99.64 8	98.28 9	99.23 42	94.57 54	99.35 10	99.97 3	99.55 12	99.63 3	98.66 42	99.70 72	99.74 64

NCCC		99.05 20	99.08 32	99.02 13	99.62 16	99.38 63	99.43 25	98.21 13	99.36 23	97.66 17	97.79 67	99.90 26	99.45 20	99.17 24	98.43 53	99.77 37	99.51 140

CNLPA		99.03 22	99.05 35	99.01 14	99.27 37	99.22 86	99.03 43	97.98 25	99.34 28	99.00 2	98.25 56	99.71 41	99.31 29	98.80 46	98.82 38	99.48 161	99.17 161

PLC	97.93 2	99.02 23	98.94 43	99.11 6	99.46 27	99.24 84	99.06 41	97.96 26	99.31 30	99.16 1	97.90 65	99.79 38	99.36 25	98.71 54	98.12 76	99.65 101	99.52 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
X-MVS		98.93 24	99.37 17	98.42 26	99.67 10	99.62 29	99.60 12	98.15 17	99.08 58	93.81 75	98.46 50	99.95 13	99.59 8	99.49 9	99.21 18	99.68 83	99.75 61

CSCG		98.90 25	98.93 44	98.85 19	99.75 2	99.72 4	99.49 18	96.58 35	99.38 19	98.05 11	98.97 27	97.87 62	99.49 17	97.78 111	98.92 30	99.78 33	99.90 3

PGM-MVS		98.86 26	99.35 21	98.29 29	99.77 1	99.63 25	99.67 6	95.63 38	98.66 99	95.27 43	99.11 21	99.82 35	99.67 4	99.33 19	99.19 19	99.73 50	99.74 64

OMC-MVS		98.84 27	99.01 41	98.65 24	99.39 29	99.23 85	99.22 31	96.70 34	99.40 18	97.77 16	97.89 66	99.80 36	99.21 34	99.02 32	98.65 43	99.57 148	99.07 168

TSAR-MVS + ACMM		98.77 28	99.45 8	97.98 37	99.37 30	99.46 54	99.44 24	98.13 20	99.65 4	92.30 91	98.91 34	99.95 13	99.05 51	99.42 13	98.95 28	99.58 144	99.82 25

ACMMP		98.74 29	99.03 39	98.40 27	99.36 32	99.64 22	99.20 32	97.75 30	98.82 84	95.24 44	98.85 37	99.87 30	99.17 41	98.74 53	97.50 103	99.71 64	99.76 52
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
train_agg		98.73 30	99.11 30	98.28 30	99.36 32	99.35 68	99.48 20	97.96 26	98.83 82	93.86 74	98.70 43	99.86 31	99.44 21	99.08 30	98.38 59	99.61 127	99.58 125

3Dnovator+	96.92 7	98.71 31	99.05 35	98.32 28	99.53 23	99.34 70	99.06 41	94.61 52	99.65 4	97.49 18	96.75 91	99.86 31	99.44 21	98.78 48	99.30 11	99.81 22	99.67 101

MVS_111021_LR		98.67 32	99.41 15	97.81 40	99.37 30	99.53 47	98.51 58	95.52 40	99.27 35	94.85 51	99.56 5	99.69 42	99.04 52	99.36 16	98.88 33	99.60 134	99.58 125

3Dnovator	96.92 7	98.67 32	99.05 35	98.23 32	99.57 20	99.45 56	99.11 37	94.66 51	99.69 3	96.80 27	96.55 101	99.61 44	99.40 23	98.87 43	99.49 3	99.85 4	99.66 109

TSAR-MVS + GP.		98.66 34	99.36 18	97.85 39	97.16 73	99.46 54	99.03 43	94.59 54	99.09 56	97.19 23	99.73 3	99.95 13	99.39 24	98.95 35	98.69 41	99.75 40	99.65 112

QAPM		98.62 35	99.04 38	98.13 33	99.57 20	99.48 52	99.17 34	94.78 48	99.57 8	96.16 33	96.73 93	99.80 36	99.33 27	98.79 47	99.29 12	99.75 40	99.64 116

MVS_111021_HR		98.59 36	99.36 18	97.68 41	99.42 28	99.61 33	98.14 76	94.81 47	99.31 30	95.00 49	99.51 6	99.79 38	99.00 55	98.94 36	98.83 37	99.69 74	99.57 130

CANet		98.46 37	99.16 28	97.64 42	98.48 50	99.64 22	99.35 27	94.71 50	99.53 12	95.17 45	97.63 73	99.59 45	98.38 70	98.88 42	98.99 26	99.74 44	99.86 15

CDPH-MVS		98.41 38	99.10 31	97.61 43	99.32 36	99.36 66	99.49 18	96.15 37	98.82 84	91.82 94	98.41 51	99.66 43	99.10 48	98.93 37	98.97 27	99.75 40	99.58 125

TAPA-MVS	97.53 5	98.41 38	98.84 48	97.91 38	99.08 41	99.33 72	99.15 35	97.13 33	99.34 28	93.20 82	97.75 69	99.19 50	99.20 35	98.66 56	98.13 75	99.66 95	99.48 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.74 3	98.34 40	99.46 7	97.04 55	98.82 45	99.33 72	96.28 129	97.47 31	99.58 7	94.70 53	98.99 26	99.85 34	97.24 101	99.55 6	99.34 9	97.73 205	99.56 131

DeepC-MVS	97.63 4	98.33 41	98.57 52	98.04 35	98.62 49	99.65 18	99.45 22	98.15 17	99.51 14	92.80 88	95.74 120	96.44 76	99.46 19	99.37 15	99.50 2	99.78 33	99.81 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		98.27 42	98.29 63	98.24 31	99.20 38	99.22 86	99.20 32	97.82 28	99.37 21	94.43 60	95.90 115	97.31 68	99.12 45	98.76 50	98.35 63	99.67 90	99.14 165

DELS-MVS		98.19 43	98.77 49	97.52 44	98.29 53	99.71 8	99.12 36	94.58 55	98.80 87	95.38 42	96.24 107	98.24 60	97.92 85	99.06 31	99.52 1	99.82 13	99.79 39
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
PCF-MVS	97.50 6	98.18 44	98.35 60	97.99 36	98.65 48	99.36 66	98.94 46	98.14 19	98.59 101	93.62 78	96.61 97	99.76 40	99.03 53	97.77 112	97.45 107	99.57 148	98.89 176
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_0304		98.14 45	99.03 39	97.10 52	98.05 57	99.63 25	99.27 30	94.33 57	99.63 6	93.06 85	97.32 76	99.05 52	98.09 80	98.82 45	98.87 34	99.81 22	99.89 7

EPNet		98.05 46	98.86 46	97.10 52	99.02 42	99.43 59	98.47 59	94.73 49	99.05 64	95.62 36	98.93 29	97.62 66	95.48 151	98.59 66	98.55 49	99.29 179	99.84 20
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		97.99 47	99.24 25	96.53 74	98.34 52	99.61 33	98.36 68	89.80 133	99.27 35	95.08 47	99.81 1	98.58 55	98.64 63	99.02 32	98.92 30	98.93 187	99.48 144

OpenMVS	96.23 11	97.95 48	98.45 56	97.35 45	99.52 25	99.42 60	98.91 47	94.61 52	98.87 76	92.24 92	94.61 132	99.05 52	99.10 48	98.64 60	99.05 23	99.74 44	99.51 140

IS_MVSNet		97.86 49	98.86 46	96.68 70	96.02 97	99.72 4	98.35 69	93.37 82	98.75 96	94.01 68	96.88 90	98.40 58	98.48 67	99.09 28	99.42 5	99.83 9	99.80 32

LS3D		97.79 50	98.25 64	97.26 50	98.40 51	99.63 25	99.53 15	98.63 1	99.25 40	88.13 115	96.93 89	94.14 104	99.19 37	99.14 26	99.23 16	99.69 74	99.42 148

COLMAP_ROB	96.15 12	97.78 51	98.17 69	97.32 46	98.84 44	99.45 56	99.28 29	95.43 41	99.48 15	91.80 95	94.83 130	98.36 59	98.90 56	98.09 91	97.85 87	99.68 83	99.15 162
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchMatch-RL		97.77 52	98.25 64	97.21 51	99.11 40	99.25 82	97.06 115	94.09 60	98.72 97	95.14 46	98.47 49	96.29 78	98.43 68	98.65 57	97.44 108	99.45 165	98.94 171

EPP-MVSNet		97.75 53	98.71 50	96.63 73	95.68 109	99.56 44	97.51 96	93.10 84	99.22 43	94.99 50	97.18 82	97.30 69	98.65 62	98.83 44	98.93 29	99.84 5	99.92 1

tfpn_ndepth		97.71 54	98.30 62	97.02 59	96.31 82	99.56 44	98.05 81	93.94 70	98.95 70	95.59 38	98.40 52	94.79 94	98.39 69	98.40 76	98.42 54	99.86 2	99.56 131

MAR-MVS		97.71 54	98.04 74	97.32 46	99.35 34	98.91 99	97.65 93	91.68 95	98.00 126	97.01 25	97.72 71	94.83 92	98.85 57	98.44 74	98.86 35	99.41 171	99.52 136
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
UGNet		97.66 56	99.07 34	96.01 87	97.19 72	99.65 18	97.09 113	93.39 80	99.35 25	94.40 62	98.79 39	99.59 45	94.24 186	98.04 100	98.29 70	99.73 50	99.80 32
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
RPSCF		97.61 57	98.16 70	96.96 66	98.10 54	99.00 92	98.84 49	93.76 75	99.45 16	94.78 52	99.39 9	99.31 49	98.53 66	96.61 144	95.43 154	97.74 203	97.93 194

tfpn1000		97.60 58	98.21 67	96.89 68	96.32 81	99.60 37	97.99 84	93.85 72	99.21 45	95.03 48	98.49 47	93.69 108	98.31 73	98.50 71	98.31 69	99.86 2	99.70 86

PMMVS		97.52 59	98.39 57	96.51 76	95.82 106	98.73 113	97.80 89	93.05 85	98.76 94	94.39 63	99.07 25	97.03 72	98.55 65	98.31 79	97.61 98	99.43 168	99.21 160

PVSNet_BlendedMVS		97.51 60	97.71 82	97.28 48	98.06 55	99.61 33	97.31 102	95.02 44	99.08 58	95.51 39	98.05 60	90.11 123	98.07 81	98.91 39	98.40 57	99.72 55	99.78 41

PVSNet_Blended		97.51 60	97.71 82	97.28 48	98.06 55	99.61 33	97.31 102	95.02 44	99.08 58	95.51 39	98.05 60	90.11 123	98.07 81	98.91 39	98.40 57	99.72 55	99.78 41

diffmvs		97.50 62	98.63 51	96.18 79	95.88 103	99.26 81	98.19 74	91.08 108	99.36 23	94.32 65	98.24 57	96.83 73	98.22 76	98.45 72	98.42 54	99.42 170	99.86 15

PVSNet_Blended_VisFu		97.41 63	98.49 55	96.15 81	97.49 63	99.76 1	96.02 132	93.75 76	99.26 38	93.38 81	93.73 138	99.35 48	96.47 124	98.96 34	98.46 52	99.77 37	99.90 3

Vis-MVSNet (Re-imp)		97.40 64	98.89 45	95.66 94	95.99 100	99.62 29	97.82 87	93.22 83	98.82 84	91.40 97	96.94 88	98.56 56	95.70 140	99.14 26	99.41 6	99.79 30	99.75 61

tfpn_n400		97.32 65	98.38 58	96.09 84	96.07 94	99.30 76	98.00 82	93.84 73	99.35 25	90.50 103	98.93 29	94.24 101	98.30 74	98.65 57	98.60 46	99.83 9	99.60 121

tfpnconf		97.32 65	98.38 58	96.09 84	96.07 94	99.30 76	98.00 82	93.84 73	99.35 25	90.50 103	98.93 29	94.24 101	98.30 74	98.65 57	98.60 46	99.83 9	99.60 121

tfpnview11		97.32 65	98.33 61	96.14 82	96.07 94	99.31 75	98.08 80	93.96 68	99.25 40	90.50 103	98.93 29	94.24 101	98.38 70	98.61 62	98.36 62	99.84 5	99.59 123

canonicalmvs		97.31 68	97.81 81	96.72 69	96.20 92	99.45 56	98.21 73	91.60 97	99.22 43	95.39 41	98.48 48	90.95 121	99.16 42	97.66 117	99.05 23	99.76 39	99.90 3

MVS_Test		97.30 69	98.54 53	95.87 88	95.74 107	99.28 79	98.19 74	91.40 102	99.18 49	91.59 96	98.17 58	96.18 79	98.63 64	98.61 62	98.55 49	99.66 95	99.78 41

MVSTER		97.16 70	97.71 82	96.52 75	95.97 101	98.48 126	98.63 55	92.10 88	98.68 98	95.96 35	99.23 14	91.79 119	96.87 111	98.76 50	97.37 111	99.57 148	99.68 96

UA-Net		97.13 71	99.14 29	94.78 101	97.21 71	99.38 63	97.56 94	92.04 89	98.48 109	88.03 116	98.39 53	99.91 25	94.03 189	99.33 19	99.23 16	99.81 22	99.25 157

FC-MVSNet-train		97.04 72	97.91 80	96.03 86	96.00 99	98.41 133	96.53 125	93.42 79	99.04 66	93.02 86	98.03 62	94.32 99	97.47 97	97.93 104	97.77 93	99.75 40	99.88 11

FMVSNet3		97.02 73	98.12 72	95.73 93	93.59 144	97.98 146	98.34 70	91.32 103	98.80 87	93.92 71	97.21 79	95.94 83	97.63 93	98.61 62	98.62 44	99.61 127	99.65 112

GBi-Net		96.98 74	98.00 77	95.78 89	93.81 138	97.98 146	98.09 77	91.32 103	98.80 87	93.92 71	97.21 79	95.94 83	97.89 86	98.07 94	98.34 65	99.68 83	99.67 101

test1		96.98 74	98.00 77	95.78 89	93.81 138	97.98 146	98.09 77	91.32 103	98.80 87	93.92 71	97.21 79	95.94 83	97.89 86	98.07 94	98.34 65	99.68 83	99.67 101

DI_MVS_plusplus_trai		96.90 76	97.49 88	96.21 78	95.61 111	99.40 62	98.72 53	92.11 87	99.14 52	92.98 87	93.08 148	95.14 89	98.13 79	98.05 98	97.91 84	99.74 44	99.73 68

TSAR-MVS + COLMAP		96.79 77	96.55 110	97.06 54	97.70 62	98.46 127	99.07 40	96.23 36	99.38 19	91.32 98	98.80 38	85.61 151	98.69 61	97.64 120	96.92 118	99.37 174	99.06 169

thres200		96.76 78	96.53 111	97.03 56	96.31 82	99.67 12	98.37 67	93.99 63	97.68 146	94.49 58	95.83 119	86.77 139	99.18 39	98.26 82	97.82 89	99.82 13	99.66 109

conf200view11		96.75 79	96.51 113	97.03 56	96.31 82	99.67 12	98.41 62	93.99 63	97.35 151	94.50 57	95.90 115	86.93 135	99.14 43	98.26 82	97.80 90	99.82 13	99.70 86

tfpn200view9		96.75 79	96.51 113	97.03 56	96.31 82	99.67 12	98.41 62	93.99 63	97.35 151	94.52 55	95.90 115	86.93 135	99.14 43	98.26 82	97.80 90	99.82 13	99.70 86

CLD-MVS		96.74 81	96.51 113	97.01 61	96.71 78	98.62 119	98.73 52	94.38 56	98.94 73	94.46 59	97.33 75	87.03 133	98.07 81	97.20 134	96.87 119	99.72 55	99.54 133
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		96.72 82	96.47 117	97.00 63	96.31 82	99.52 50	98.28 72	94.01 61	97.35 151	94.52 55	95.90 115	86.93 135	99.09 50	98.07 94	97.87 86	99.81 22	99.63 118

thres400		96.71 83	96.45 120	97.02 59	96.28 89	99.63 25	98.41 62	94.00 62	97.82 141	94.42 61	95.74 120	86.26 145	99.18 39	98.20 87	97.79 92	99.81 22	99.70 86

view600		96.70 84	96.44 122	97.01 61	96.28 89	99.67 12	98.42 61	93.99 63	97.87 136	94.34 64	95.99 112	85.94 148	99.20 35	98.26 82	97.64 96	99.82 13	99.73 68

view800		96.70 84	96.45 120	96.99 65	96.29 87	99.69 11	98.39 66	93.95 69	97.92 133	94.25 67	96.23 108	85.57 152	99.22 32	98.28 80	97.71 94	99.82 13	99.76 52

thres600view7		96.69 86	96.43 124	97.00 63	96.28 89	99.67 12	98.41 62	93.99 63	97.85 139	94.29 66	95.96 113	85.91 149	99.19 37	98.26 82	97.63 97	99.82 13	99.73 68

test0.0.03 1		96.69 86	98.12 72	95.01 99	95.49 114	98.99 94	95.86 134	90.82 111	98.38 112	92.54 90	96.66 95	97.33 67	95.75 138	97.75 114	98.34 65	99.60 134	99.40 150

ACMM	96.26 9	96.67 88	96.69 107	96.66 71	97.29 70	98.46 127	96.48 126	95.09 43	99.21 45	93.19 83	98.78 40	86.73 140	98.17 77	97.84 109	96.32 133	99.74 44	99.49 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		96.64 89	99.08 32	93.81 113	97.10 74	99.42 60	98.85 48	90.01 127	99.31 30	79.98 171	99.78 2	99.10 51	97.42 98	98.35 77	98.05 79	99.47 163	99.53 134

FMVSNet2		96.64 89	97.50 87	95.63 95	93.81 138	97.98 146	98.09 77	90.87 109	98.99 69	93.48 79	93.17 145	95.25 88	97.89 86	98.63 61	98.80 39	99.68 83	99.67 101

ACMP	96.25 10	96.62 91	96.72 106	96.50 77	96.96 76	98.75 110	97.80 89	94.30 58	98.85 79	93.12 84	98.78 40	86.61 142	97.23 102	97.73 115	96.61 125	99.62 124	99.71 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CDS-MVSNet		96.59 92	98.02 76	94.92 100	94.45 131	98.96 97	97.46 98	91.75 94	97.86 138	90.07 107	96.02 111	97.25 70	96.21 127	98.04 100	98.38 59	99.60 134	99.65 112
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 1792x2688		96.41 93	96.99 103	95.74 92	98.01 58	99.72 4	97.70 92	90.78 113	99.13 55	90.03 108	87.35 197	95.36 87	98.33 72	98.59 66	98.91 32	99.59 140	99.87 13

HQP-MVS		96.37 94	96.58 108	96.13 83	97.31 69	98.44 130	98.45 60	95.22 42	98.86 77	88.58 113	98.33 54	87.00 134	97.67 92	97.23 132	96.56 127	99.56 151	99.62 119

conf0.05thres1000		96.34 95	96.47 117	96.17 80	96.16 93	99.71 8	97.82 87	93.46 78	98.10 122	90.69 100	96.75 91	85.26 156	99.11 47	98.05 98	97.65 95	99.82 13	99.80 32

EPNet_dtu		96.30 96	98.53 54	93.70 117	98.97 43	98.24 141	97.36 100	94.23 59	98.85 79	79.18 185	99.19 15	98.47 57	94.09 188	97.89 106	98.21 72	98.39 195	98.85 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LGP-MVS_train		96.23 97	96.89 104	95.46 96	97.32 67	98.77 107	98.81 50	93.60 77	98.58 102	85.52 131	99.08 24	86.67 141	97.83 91	97.87 107	97.51 102	99.69 74	99.73 68

tfpn		96.22 98	95.62 135	96.93 67	96.29 87	99.72 4	98.34 70	93.94 70	97.96 130	93.94 70	96.45 103	79.09 208	99.22 32	98.28 80	98.06 78	99.83 9	99.78 41

OPM-MVS		96.22 98	95.85 133	96.65 72	97.75 60	98.54 124	99.00 45	95.53 39	96.88 172	89.88 109	95.95 114	86.46 144	98.07 81	97.65 119	96.63 124	99.67 90	98.83 179

Vis-MVSNet		96.16 100	98.22 66	93.75 114	95.33 120	99.70 10	97.27 104	90.85 110	98.30 114	85.51 132	95.72 122	96.45 74	93.69 195	98.70 55	99.00 25	99.84 5	99.69 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS-LS		96.12 101	97.48 89	94.53 103	95.19 122	97.56 174	97.15 109	89.19 139	99.08 58	88.23 114	94.97 128	94.73 95	97.84 90	97.86 108	98.26 71	99.60 134	99.88 11
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		96.07 102	97.94 79	93.89 111	93.60 143	98.67 116	96.62 122	90.30 122	98.76 94	88.62 112	95.57 126	97.63 65	94.48 182	97.97 102	97.48 106	99.71 64	99.52 136

MS-PatchMatch		95.99 103	97.26 98	94.51 104	97.46 64	98.76 109	97.27 104	86.97 163	99.09 56	89.83 110	93.51 140	97.78 63	96.18 129	97.53 124	95.71 151	99.35 175	98.41 185

HyFIR lowres test		95.99 103	96.56 109	95.32 97	97.99 59	99.65 18	96.54 123	88.86 141	98.44 110	89.77 111	84.14 209	97.05 71	99.03 53	98.55 68	98.19 74	99.73 50	99.86 15

Effi-MVS+		95.81 105	97.31 97	94.06 109	95.09 123	99.35 68	97.24 106	88.22 150	98.54 105	85.38 133	98.52 45	88.68 127	98.70 60	98.32 78	97.93 82	99.74 44	99.84 20

FMVSNet1		95.77 106	96.41 125	95.03 98	93.42 145	97.86 153	97.11 112	89.89 130	98.53 106	92.00 93	89.17 172	93.23 112	98.15 78	98.07 94	98.34 65	99.61 127	99.69 91

Effi-MVS+-dtu		95.74 107	98.04 74	93.06 131	93.92 134	99.16 89	97.90 85	88.16 153	99.07 63	82.02 152	98.02 63	94.32 99	96.74 115	98.53 69	97.56 100	99.61 127	99.62 119

testgi		95.67 108	97.48 89	93.56 120	95.07 124	99.00 92	95.33 145	88.47 147	98.80 87	86.90 124	97.30 77	92.33 116	95.97 135	97.66 117	97.91 84	99.60 134	99.38 151

MDTV_nov1_ep13		95.57 109	97.48 89	93.35 128	95.43 116	98.97 96	97.19 108	83.72 194	98.92 75	87.91 118	97.75 69	96.12 81	97.88 89	96.84 143	95.64 152	97.96 201	98.10 190

TAMVS		95.53 110	96.50 116	94.39 106	93.86 137	99.03 91	96.67 120	89.55 136	97.33 154	90.64 101	93.02 149	91.58 120	96.21 127	97.72 116	97.43 109	99.43 168	99.36 152

test-LLR		95.50 111	97.32 94	93.37 126	95.49 114	98.74 111	96.44 127	90.82 111	98.18 118	82.75 147	96.60 98	94.67 96	95.54 147	98.09 91	96.00 141	99.20 182	98.93 172

FMVSNet5		95.42 112	96.47 117	94.20 107	92.26 155	95.99 199	95.66 137	87.15 160	97.87 136	93.46 80	96.68 94	93.79 107	97.52 94	97.10 138	97.21 113	99.11 185	96.62 212

ACMH+	95.51 13	95.40 113	96.00 127	94.70 102	96.33 80	98.79 104	96.79 118	91.32 103	98.77 93	87.18 122	95.60 125	85.46 153	96.97 107	97.15 135	96.59 126	99.59 140	99.65 112

Fast-Effi-MVS+-dtu		95.38 114	98.20 68	92.09 144	93.91 135	98.87 101	97.35 101	85.01 180	99.08 58	81.09 156	98.10 59	96.36 77	95.62 144	98.43 75	97.03 115	99.55 152	99.50 142

Fast-Effi-MVS+		95.38 114	96.52 112	94.05 110	94.15 133	99.14 90	97.24 106	86.79 164	98.53 106	87.62 120	94.51 133	87.06 132	98.76 58	98.60 65	98.04 80	99.72 55	99.77 48

DWT-MVSNet_training		95.38 114	95.05 141	95.78 89	95.86 104	98.88 100	97.55 95	90.09 126	98.23 117	96.49 32	97.62 74	86.92 138	97.16 103	92.03 214	94.12 195	97.52 209	97.50 197

CVMVSNet		95.33 117	97.09 100	93.27 129	95.23 121	98.39 135	95.49 141	92.58 86	97.71 145	83.00 146	94.44 134	93.28 111	93.92 192	97.79 110	98.54 51	99.41 171	99.45 146

ACMH	95.42 14	95.27 118	95.96 129	94.45 105	96.83 77	98.78 106	94.72 172	91.67 96	98.95 70	86.82 125	96.42 104	83.67 168	97.00 106	97.48 126	96.68 123	99.69 74	99.76 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		95.09 119	95.90 130	94.14 108	92.29 154	97.70 160	95.45 142	90.31 120	98.60 100	90.70 99	93.25 143	89.90 125	96.67 117	97.13 136	95.42 155	99.44 167	99.28 155

EPMVS		95.05 120	96.86 105	92.94 134	95.84 105	98.96 97	96.68 119	79.87 203	99.05 64	90.15 106	97.12 83	95.99 82	97.49 96	95.17 180	94.75 189	97.59 208	96.96 206

IB-MVS	93.96 15	95.02 121	96.44 122	93.36 127	97.05 75	99.28 79	90.43 205	93.39 80	98.02 125	96.02 34	94.92 129	92.07 118	83.52 217	95.38 171	95.82 147	99.72 55	99.59 123
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
TESTMET0.1,1		94.95 122	97.32 94	92.20 141	92.62 149	98.74 111	96.44 127	86.67 166	98.18 118	82.75 147	96.60 98	94.67 96	95.54 147	98.09 91	96.00 141	99.20 182	98.93 172

test-mter		94.86 123	97.32 94	92.00 148	92.41 153	98.82 103	96.18 131	86.35 170	98.05 124	82.28 150	96.48 102	94.39 98	95.46 157	98.17 88	96.20 137	99.32 177	99.13 166

IterMVS		94.81 124	97.71 82	91.42 163	94.83 129	97.63 168	97.38 99	85.08 178	98.93 74	75.67 200	94.02 135	97.64 64	96.66 118	98.45 72	97.60 99	98.90 188	99.72 80
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		94.70 125	97.08 101	91.92 151	95.53 112	98.85 102	95.77 135	79.54 207	98.95 70	85.98 128	98.52 45	96.45 74	97.39 99	95.32 172	94.09 196	97.32 213	97.38 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RPMNet		94.66 126	97.16 99	91.75 157	94.98 125	98.59 121	97.00 116	78.37 216	97.98 127	83.78 137	96.27 106	94.09 106	96.91 109	97.36 128	96.73 121	99.48 161	99.09 167

ADS-MVSNet		94.65 127	97.04 102	91.88 154	95.68 109	98.99 94	95.89 133	79.03 212	99.15 50	85.81 130	96.96 87	98.21 61	97.10 104	94.48 199	94.24 194	97.74 203	97.21 202

dps		94.63 128	95.31 140	93.84 112	95.53 112	98.71 114	96.54 123	80.12 202	97.81 143	97.21 22	96.98 86	92.37 115	96.34 126	92.46 211	91.77 215	97.26 215	97.08 204

UniMVSNet_NR-MVSNet		94.59 129	95.47 137	93.55 121	91.85 169	97.89 152	95.03 148	92.00 90	97.33 154	86.12 126	93.19 144	87.29 131	96.60 120	96.12 162	96.70 122	99.72 55	99.80 32

UniMVSNet (Re)		94.58 130	95.34 138	93.71 116	92.25 156	98.08 145	94.97 150	91.29 107	97.03 165	87.94 117	93.97 137	86.25 146	96.07 132	96.27 159	95.97 144	99.72 55	99.79 39

CR-MVSNet		94.57 131	97.34 93	91.33 165	94.90 127	98.59 121	97.15 109	79.14 210	97.98 127	80.42 164	96.59 100	93.50 110	96.85 112	98.10 89	97.49 104	99.50 160	99.15 162

MIMVSNet		94.49 132	97.59 86	90.87 180	91.74 180	98.70 115	94.68 174	78.73 214	97.98 127	83.71 140	97.71 72	94.81 93	96.96 108	97.97 102	97.92 83	99.40 173	98.04 192

pm-mvs1		94.27 133	95.57 136	92.75 135	92.58 150	98.13 144	94.87 157	90.71 114	96.70 178	83.78 137	89.94 167	89.85 126	94.96 177	97.58 122	97.07 114	99.61 127	99.72 80

USDC		94.26 134	94.83 145	93.59 119	96.02 97	98.44 130	97.84 86	88.65 145	98.86 77	82.73 149	94.02 135	80.56 199	96.76 114	97.28 131	96.15 140	99.55 152	98.50 183

CostFormer		94.25 135	94.88 144	93.51 123	95.43 116	98.34 137	96.21 130	80.64 199	97.94 132	94.01 68	98.30 55	86.20 147	97.52 94	92.71 206	92.69 206	97.23 217	98.02 193

tpm cat1		94.06 136	94.90 143	93.06 131	95.42 118	98.52 125	96.64 121	80.67 198	97.82 141	92.63 89	93.39 142	95.00 90	96.06 133	91.36 218	91.58 217	96.98 218	96.66 211

NR-MVSNet		94.01 137	94.51 152	93.44 124	92.56 151	97.77 154	95.67 136	91.57 98	97.17 159	85.84 129	93.13 146	80.53 200	95.29 170	97.01 139	96.17 138	99.69 74	99.75 61

TinyColmap		94.00 138	94.35 156	93.60 118	95.89 102	98.26 139	97.49 97	88.82 142	98.56 104	83.21 143	91.28 153	80.48 201	96.68 116	97.34 129	96.26 136	99.53 157	98.24 188

DU-MVS		93.98 139	94.44 154	93.44 124	91.66 184	97.77 154	95.03 148	91.57 98	97.17 159	86.12 126	93.13 146	81.13 198	96.60 120	95.10 191	97.01 117	99.67 90	99.80 32

PatchT		93.96 140	97.36 92	90.00 192	94.76 130	98.65 117	90.11 208	78.57 215	97.96 130	80.42 164	96.07 110	94.10 105	96.85 112	98.10 89	97.49 104	99.26 180	99.15 162

GA-MVS		93.93 141	96.31 126	91.16 171	93.61 142	98.79 104	95.39 144	90.69 115	98.25 116	73.28 208	96.15 109	88.42 128	94.39 184	97.76 113	95.35 158	99.58 144	99.45 146

Baseline_NR-MVSNet		93.87 142	93.98 166	93.75 114	91.66 184	97.02 191	95.53 140	91.52 101	97.16 161	87.77 119	87.93 195	83.69 167	96.35 125	95.10 191	97.23 112	99.68 83	99.73 68

tpmrst		93.86 143	95.88 131	91.50 161	95.69 108	98.62 119	95.64 138	79.41 208	98.80 87	83.76 139	95.63 124	96.13 80	97.25 100	92.92 205	92.31 211	97.27 214	96.74 209

tfpnnormal		93.85 144	94.12 160	93.54 122	93.22 146	98.24 141	95.45 142	91.96 92	94.61 210	83.91 135	90.74 155	81.75 196	97.04 105	97.49 125	96.16 139	99.68 83	99.84 20

tpmp4_e23		93.84 145	94.58 151	92.98 133	95.41 119	98.29 138	96.81 117	80.57 200	98.15 120	90.53 102	97.00 85	84.39 164	96.91 109	93.69 202	92.45 209	97.67 206	98.06 191

TranMVSNet+NR-MVSNet		93.67 146	94.14 158	93.13 130	91.28 198	97.58 173	95.60 139	91.97 91	97.06 163	84.05 134	90.64 158	82.22 190	96.17 130	94.94 195	96.78 120	99.69 74	99.78 41

WR-MVS_H		93.54 147	94.67 148	92.22 139	91.95 165	97.91 151	94.58 180	88.75 143	96.64 182	83.88 136	90.66 157	85.13 157	94.40 183	96.54 149	95.91 146	99.73 50	99.89 7

TransMVSNet (Re)		93.45 148	94.08 162	92.72 136	92.83 147	97.62 171	94.94 151	91.54 100	95.65 206	83.06 145	88.93 175	83.53 169	94.25 185	97.41 127	97.03 115	99.67 90	98.40 187

SixPastTwentyTwo		93.44 149	95.32 139	91.24 169	92.11 159	98.40 134	92.77 195	88.64 146	98.09 123	77.83 190	93.51 140	85.74 150	96.52 123	96.91 141	94.89 186	99.59 140	99.73 68

WR-MVS		93.43 150	94.48 153	92.21 140	91.52 191	97.69 164	94.66 176	89.98 128	96.86 173	83.43 141	90.12 159	85.03 158	93.94 191	96.02 165	95.82 147	99.71 64	99.82 25

CP-MVSNet		93.25 151	94.00 165	92.38 138	91.65 186	97.56 174	94.38 183	89.20 138	96.05 198	83.16 144	89.51 170	81.97 194	96.16 131	96.43 151	96.56 127	99.71 64	99.89 7

anonymousdsp		93.12 152	95.86 132	89.93 194	91.09 199	98.25 140	95.12 146	85.08 178	97.44 149	73.30 207	90.89 154	90.78 122	95.25 172	97.91 105	95.96 145	99.71 64	99.82 25

v6		93.11 153	93.98 166	92.10 143	92.01 162	97.71 157	94.86 160	90.15 123	96.96 168	80.47 163	90.01 162	83.26 172	95.48 151	95.17 180	95.01 173	99.64 112	99.76 52

v1neww		93.06 154	93.94 168	92.03 146	91.99 163	97.70 160	94.79 164	90.14 124	96.93 170	80.13 168	89.97 164	83.01 176	95.48 151	95.16 184	95.01 173	99.63 118	99.76 52

v7new		93.06 154	93.94 168	92.03 146	91.99 163	97.70 160	94.79 164	90.14 124	96.93 170	80.13 168	89.97 164	83.01 176	95.48 151	95.16 184	95.01 173	99.63 118	99.76 52

V42		93.05 156	93.90 172	92.04 145	91.91 166	97.66 166	94.91 152	89.91 129	96.85 174	80.58 161	89.66 169	83.43 171	95.37 163	95.03 194	94.90 184	99.59 140	99.78 41

TDRefinement		93.04 157	93.57 182	92.41 137	96.58 79	98.77 107	97.78 91	91.96 92	98.12 121	80.84 157	89.13 174	79.87 205	87.78 208	96.44 150	94.50 193	99.54 156	98.15 189

v7		92.97 158	94.11 161	91.65 160	91.83 170	97.55 176	94.86 160	88.19 152	96.96 168	79.72 176	88.16 189	84.68 161	95.63 142	96.33 156	95.30 160	99.65 101	99.77 48

v8		92.87 159	93.87 173	91.72 159	92.05 161	97.50 179	94.79 164	88.20 151	96.85 174	80.11 170	90.01 162	82.86 181	95.48 151	95.15 188	94.90 184	99.66 95	99.80 32

LTVRE_ROB	93.20 16	92.84 160	94.92 142	90.43 188	92.83 147	98.63 118	97.08 114	87.87 156	97.91 134	68.42 215	93.54 139	79.46 207	96.62 119	97.55 123	97.40 110	99.74 44	99.92 1
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
v1144		92.81 161	94.03 164	91.40 164	91.68 183	97.60 172	94.73 171	88.40 148	96.71 177	78.48 188	88.14 191	84.46 163	95.45 158	96.31 158	95.22 162	99.65 101	99.76 52

v1		92.81 161	93.57 182	91.94 150	91.79 174	97.70 160	94.80 163	90.32 118	96.52 188	79.75 174	88.47 185	82.46 187	95.32 167	95.14 190	94.96 180	99.63 118	99.73 68

divwei89l23v2f112		92.80 163	93.60 181	91.86 155	91.75 177	97.71 157	94.75 169	90.32 118	96.54 187	79.35 181	88.59 182	82.55 185	95.35 165	95.15 188	94.96 180	99.63 118	99.72 80

EU-MVSNet		92.80 163	94.76 147	90.51 186	91.88 167	96.74 196	92.48 197	88.69 144	96.21 193	79.00 186	91.51 150	87.82 129	91.83 203	95.87 167	96.27 134	99.21 181	98.92 175

v1141		92.79 165	93.61 179	91.84 156	91.75 177	97.71 157	94.74 170	90.33 117	96.58 185	79.21 184	88.59 182	82.53 186	95.36 164	95.16 184	94.96 180	99.63 118	99.72 80

v10		92.79 165	94.06 163	91.31 167	91.78 175	97.29 190	94.87 157	86.10 171	96.97 167	79.82 173	88.16 189	84.56 162	95.63 142	96.33 156	95.31 159	99.65 101	99.80 32

v2v482		92.77 167	93.52 186	91.90 153	91.59 189	97.63 168	94.57 181	90.31 120	96.80 176	79.22 183	88.74 179	81.55 197	96.04 134	95.26 173	94.97 179	99.66 95	99.69 91

PS-CasMVS		92.72 168	93.36 188	91.98 149	91.62 188	97.52 177	94.13 187	88.98 140	95.94 201	81.51 155	87.35 197	79.95 204	95.91 136	96.37 153	96.49 129	99.70 72	99.89 7

PEN-MVS		92.72 168	93.20 194	92.15 142	91.29 196	97.31 188	94.67 175	89.81 131	96.19 194	81.83 153	88.58 184	79.06 209	95.61 145	95.21 177	96.27 134	99.72 55	99.82 25

pmmvs5		92.71 170	94.27 157	90.90 178	91.42 193	97.74 156	93.23 191	86.66 167	95.99 200	78.96 187	91.45 151	83.44 170	95.55 146	97.30 130	95.05 167	99.58 144	98.93 172

v16		92.66 171	93.80 174	91.32 166	92.13 157	95.62 202	94.89 153	85.12 177	97.20 157	80.66 159	89.96 166	83.93 166	95.49 150	95.17 180	95.04 168	99.63 118	99.68 96

v18		92.63 172	93.67 177	91.43 162	92.13 157	95.65 200	95.09 147	85.44 175	97.06 163	80.78 158	90.06 160	83.06 174	95.47 156	95.16 184	95.01 173	99.64 112	99.67 101

v17		92.55 173	93.65 178	91.27 168	92.11 159	95.63 201	94.89 153	85.15 176	97.12 162	80.39 167	90.02 161	83.02 175	95.45 158	95.17 180	94.92 183	99.66 95	99.68 96

MVS-HIRNet		92.51 174	95.97 128	88.48 201	93.73 141	98.37 136	90.33 206	75.36 224	98.32 113	77.78 191	89.15 173	94.87 91	95.14 174	97.62 121	96.39 131	98.51 191	97.11 203

EG-PatchMatch MVS		92.45 175	93.92 171	90.72 183	92.56 151	98.43 132	94.88 156	84.54 184	97.18 158	79.55 179	86.12 207	83.23 173	93.15 198	97.22 133	96.00 141	99.67 90	99.27 156

MDTV_nov1_ep13_2view		92.44 176	95.66 134	88.68 199	91.05 200	97.92 150	92.17 198	79.64 205	98.83 82	76.20 198	91.45 151	93.51 109	95.04 175	95.68 169	93.70 199	97.96 201	98.53 182

v1192		92.43 177	93.61 179	91.05 172	91.53 190	97.43 183	94.61 178	87.99 154	96.60 183	76.72 196	87.11 199	82.74 182	95.85 137	96.35 155	95.30 160	99.60 134	99.74 64

v11		92.43 177	93.77 175	90.85 181	91.72 181	95.58 207	94.87 157	84.07 193	96.98 166	79.28 182	88.03 192	84.22 165	95.53 149	96.55 148	95.36 157	99.65 101	99.70 86

DTE-MVSNet		92.42 179	92.85 200	91.91 152	90.87 201	96.97 192	94.53 182	89.81 131	95.86 203	81.59 154	88.83 177	77.88 212	95.01 176	94.34 200	96.35 132	99.64 112	99.73 68

v144192		92.38 180	93.55 185	91.00 175	91.44 192	97.47 182	94.27 184	87.41 159	96.52 188	78.03 189	87.50 196	82.65 183	95.32 167	95.82 168	95.15 164	99.55 152	99.78 41

tpm		92.38 180	94.79 146	89.56 195	94.30 132	97.50 179	94.24 186	78.97 213	97.72 144	74.93 204	97.97 64	82.91 179	96.60 120	93.65 204	94.81 187	98.33 196	98.98 170

v1921920		92.36 182	93.57 182	90.94 177	91.39 194	97.39 185	94.70 173	87.63 158	96.60 183	76.63 197	86.98 200	82.89 180	95.75 138	96.26 160	95.14 165	99.55 152	99.73 68

v148		92.36 182	92.88 198	91.75 157	91.63 187	97.66 166	92.64 196	90.55 116	96.09 196	83.34 142	88.19 188	80.00 203	92.74 199	93.98 201	94.58 192	99.58 144	99.69 91

V14		92.31 184	93.41 187	91.03 174	91.80 173	95.59 205	94.79 164	84.70 182	96.58 185	79.83 172	88.79 178	82.98 178	95.41 160	95.22 174	95.02 172	99.65 101	99.67 101

v15		92.27 185	93.33 189	91.04 173	91.83 170	95.60 203	94.79 164	84.88 181	96.66 180	79.66 177	88.72 180	82.45 188	95.40 161	95.19 179	95.00 177	99.65 101	99.67 101

V9		92.24 186	93.32 191	90.98 176	91.76 176	95.58 207	94.83 162	84.50 186	96.68 179	79.73 175	88.66 181	82.39 189	95.39 162	95.22 174	95.03 170	99.65 101	99.67 101

N_pmnet		92.21 187	94.60 149	89.42 196	91.88 167	97.38 186	89.15 210	89.74 134	97.89 135	73.75 206	87.94 194	92.23 117	93.85 193	96.10 163	93.20 202	98.15 199	97.43 200

v12		92.18 188	93.29 192	90.88 179	91.70 182	95.59 205	94.61 178	84.36 188	96.65 181	79.59 178	88.85 176	82.03 193	95.35 165	95.22 174	95.04 168	99.65 101	99.68 96

v13		92.16 189	93.28 193	90.85 181	91.75 177	95.58 207	94.65 177	84.23 191	96.49 191	79.51 180	88.40 187	82.58 184	95.31 169	95.21 177	95.03 170	99.66 95	99.68 96

LP		92.12 190	94.60 149	89.22 197	94.96 126	98.45 129	93.01 193	77.58 217	97.85 139	77.26 194	89.80 168	93.00 113	94.54 179	93.69 202	92.58 207	98.00 200	96.83 208

v1240		91.99 191	93.33 189	90.44 187	91.29 196	97.30 189	94.25 185	86.79 164	96.43 192	75.49 202	86.34 205	81.85 195	95.29 170	96.42 152	95.22 162	99.52 158	99.73 68

v52		91.94 192	93.10 195	90.57 184	90.62 203	97.50 179	93.98 188	87.02 161	95.86 203	77.67 192	86.93 201	82.16 192	94.53 180	94.71 197	94.70 190	99.61 127	99.85 18

V4		91.92 193	93.10 195	90.55 185	90.64 202	97.51 178	93.93 189	87.02 161	95.81 205	77.61 193	86.93 201	82.19 191	94.50 181	94.72 196	94.68 191	99.62 124	99.85 18

pmmvs6		91.90 194	92.53 204	91.17 170	91.81 172	97.63 168	93.23 191	88.37 149	93.43 215	80.61 160	77.32 219	87.47 130	94.12 187	96.58 146	95.72 150	98.88 189	99.53 134

testpf		91.80 195	94.43 155	88.74 198	93.89 136	95.30 212	92.05 199	71.77 225	97.52 148	87.24 121	94.77 131	92.68 114	91.48 204	91.75 217	92.11 214	96.02 222	96.89 207

v7n		91.61 196	92.95 197	90.04 191	90.56 205	97.69 164	93.74 190	85.59 173	95.89 202	76.95 195	86.60 204	78.60 211	93.76 194	97.01 139	94.99 178	99.65 101	99.87 13

v748		91.12 197	91.95 205	90.16 190	90.60 204	97.35 187	91.11 200	87.92 155	94.75 209	80.54 162	86.26 206	75.97 214	91.13 205	94.63 198	94.81 187	99.65 101	99.90 3

gg-mvs-nofinetune		90.85 198	94.14 158	87.02 204	94.89 128	99.25 82	98.64 54	76.29 221	88.24 222	57.50 226	79.93 217	95.45 86	95.18 173	98.77 49	98.07 77	99.62 124	99.24 158

CMPMVS	70.31 18	90.74 199	91.06 207	90.36 189	97.32 67	97.43 183	92.97 194	87.82 157	93.50 214	75.34 203	83.27 212	84.90 159	92.19 202	92.64 209	91.21 218	96.50 220	94.46 215
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		90.70 200	93.93 170	86.92 205	90.21 208	96.79 194	90.30 207	86.61 168	96.05 198	69.25 213	88.46 186	84.86 160	85.86 212	97.11 137	96.47 130	99.30 178	97.80 196

test20.03		90.65 201	93.71 176	87.09 203	90.44 206	96.24 197	89.74 209	85.46 174	95.59 207	72.99 209	90.68 156	85.33 154	84.41 215	95.94 166	95.10 166	99.52 158	97.06 205

new_pmnet		90.45 202	92.84 201	87.66 202	88.96 209	96.16 198	88.71 211	84.66 183	97.56 147	71.91 212	85.60 208	86.58 143	93.28 196	96.07 164	93.54 200	98.46 193	94.39 216

pmmvs-eth3d		89.81 203	89.65 210	90.00 192	86.94 213	95.38 210	91.08 201	86.39 169	94.57 211	82.27 151	83.03 213	64.94 221	93.96 190	96.57 147	93.82 198	99.35 175	99.24 158

PM-MVS		89.55 204	90.30 209	88.67 200	87.06 212	95.60 203	90.88 203	84.51 185	96.14 195	75.75 199	86.89 203	63.47 224	94.64 178	96.85 142	93.89 197	99.17 184	99.29 154

gm-plane-assit		89.44 205	92.82 202	85.49 208	91.37 195	95.34 211	79.55 222	82.12 196	91.68 218	64.79 221	87.98 193	80.26 202	95.66 141	98.51 70	97.56 100	99.45 165	98.41 185

test2356		88.81 206	92.86 199	84.09 213	87.85 211	93.46 217	87.07 215	83.60 195	96.50 190	62.08 224	97.06 84	75.04 215	85.17 213	95.08 193	95.42 155	98.75 190	97.46 198

testus		88.77 207	92.77 203	84.10 212	88.24 210	93.95 215	87.16 214	84.24 189	97.37 150	61.54 225	95.70 123	73.10 217	84.90 214	95.56 170	95.82 147	98.51 191	97.88 195

MIMVSNet1		88.61 208	90.68 208	86.19 207	81.56 224	95.30 212	87.78 212	85.98 172	94.19 213	72.30 211	78.84 218	78.90 210	90.06 206	96.59 145	95.47 153	99.46 164	95.49 214

pmmvs3		88.19 209	91.27 206	84.60 210	85.60 215	93.66 216	85.68 217	81.13 197	92.36 217	63.66 223	89.51 170	77.10 213	93.22 197	96.37 153	92.40 210	98.30 197	97.46 198

MDA-MVSNet-bldmvs		87.84 210	89.22 211	86.23 206	81.74 223	96.77 195	83.74 218	89.57 135	94.50 212	72.83 210	96.64 96	64.47 223	92.71 200	81.43 224	92.28 212	96.81 219	98.47 184

new-patchmatchnet		86.12 211	87.30 212	84.74 209	86.92 214	95.19 214	83.57 219	84.42 187	92.67 216	65.66 218	80.32 216	64.72 222	89.41 207	92.33 213	89.21 219	98.43 194	96.69 210

Anonymous20231211		83.86 212	83.39 218	84.40 211	85.29 216	93.44 218	86.29 216	84.24 189	85.55 225	68.63 214	61.25 225	59.57 227	84.33 216	92.50 210	92.52 208	97.65 207	98.89 176

FPMVS		83.82 213	84.61 217	82.90 214	90.39 207	90.71 220	90.85 204	84.10 192	95.47 208	65.15 219	83.44 210	74.46 216	75.48 219	81.63 223	79.42 225	91.42 226	87.14 224

1111		82.87 214	85.67 215	79.62 217	81.86 221	89.62 221	74.44 224	68.81 227	87.44 223	66.59 216	76.83 220	70.33 219	87.71 209	92.65 207	93.37 201	98.28 198	89.42 222

testmv		81.83 215	86.26 213	76.66 218	84.10 217	89.42 223	74.29 226	79.65 204	90.61 219	51.85 230	82.11 214	63.06 226	72.61 222	91.94 215	92.75 204	97.49 210	93.94 218

test1235678		81.83 215	86.26 213	76.66 218	84.10 217	89.41 224	74.29 226	79.64 205	90.60 220	51.84 231	82.11 214	63.07 225	72.61 222	91.94 215	92.75 204	97.49 210	93.94 218

Gipuma		81.40 217	81.78 219	80.96 216	83.21 219	85.61 228	79.73 221	76.25 222	97.33 154	64.21 222	55.32 226	55.55 229	86.04 211	92.43 212	92.20 213	96.32 221	93.99 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		80.53 218	84.80 216	75.54 220	82.31 220	88.05 227	75.99 223	79.31 209	88.53 221	53.24 229	83.30 211	56.38 228	65.16 228	90.87 219	93.10 203	97.25 216	93.34 221

PMMVS2		77.26 219	79.47 221	74.70 222	76.00 227	88.37 226	74.22 228	76.34 220	78.31 227	54.13 227	69.96 223	52.50 230	70.14 225	84.83 222	88.71 220	97.35 212	93.58 220

PMVS	72.60 17	76.39 220	77.66 222	74.92 221	81.04 225	69.37 233	68.47 229	80.54 201	85.39 226	65.07 220	73.52 222	72.91 218	65.67 227	80.35 225	76.81 226	88.71 228	85.25 228
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		69.67 221	72.22 223	66.70 225	81.86 221	89.62 221	74.44 224	68.81 227	87.44 223	66.59 216	76.83 220	70.33 219	87.71 209	92.65 207	37.65 228	20.79 232	51.04 229

GG-mvs-BLEND		69.11 222	98.13 71	35.26 228	3.49 234	98.20 143	94.89 153	2.38 232	98.42 111	5.82 237	96.37 105	98.60 54	5.97 232	98.75 52	97.98 81	99.01 186	98.61 180

E-PMN		68.30 223	68.43 224	68.15 223	74.70 229	71.56 232	55.64 231	77.24 218	77.48 229	39.46 233	51.95 229	41.68 233	73.28 221	70.65 227	79.51 224	88.61 229	86.20 227

EMVS		68.12 224	68.11 225	68.14 224	75.51 228	71.76 231	55.38 232	77.20 219	77.78 228	37.79 234	53.59 227	43.61 231	74.72 220	67.05 229	76.70 227	88.27 230	86.24 226

no-one		66.79 225	67.62 226	65.81 226	73.06 230	81.79 229	51.90 234	76.20 223	61.07 231	54.05 228	51.62 230	41.72 232	49.18 229	67.26 228	82.83 223	90.47 227	87.07 225

MVE	67.97 19	65.53 226	67.43 227	63.31 227	59.33 231	74.20 230	53.09 233	70.43 226	66.27 230	43.13 232	45.98 231	30.62 234	70.65 224	79.34 226	86.30 221	83.25 231	89.33 223
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 227	40.15 228	20.86 229	12.61 232	17.99 234	25.16 235	13.30 230	48.42 232	24.82 235	53.07 228	30.13 236	28.47 230	42.73 230	37.65 228	20.79 232	51.04 229

test123		26.75 228	34.25 229	18.01 230	7.93 233	17.18 235	24.85 236	12.36 231	44.83 233	16.52 236	41.80 232	18.10 237	28.29 231	33.08 231	34.79 230	18.10 234	49.95 231

test_full		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet-low-res		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

ambc			80.99 220		80.04 226	90.84 219	90.91 202		96.09 196	74.18 205	62.81 224	30.59 235	82.44 218	96.25 161	91.77 215	95.91 223	98.56 181

MTAPA										98.09 10		99.97 3

MTMP										98.46 7		99.96 8

Patchmatch-RL test							66.86 230

tmp_tt				82.25 215	97.73 61	88.71 225	80.18 220	68.65 229	99.15 50	86.98 123	99.47 7	85.31 155	68.35 226	87.51 221	83.81 222	91.64 225

XVS					97.42 65	99.62 29	98.59 56			93.81 75		99.95 13				99.69 74

X-MVStestdata					97.42 65	99.62 29	98.59 56			93.81 75		99.95 13				99.69 74

abl_6				98.09 34	99.33 35	99.22 86	98.79 51	94.96 46	98.52 108	97.00 26	97.30 77	99.86 31	98.76 58			99.69 74	99.41 149

mPP-MVS					99.53 23							99.89 28

NP-MVS									98.57 103

Patchmtry						98.59 121	97.15 109	79.14 210		80.42 164

DeepMVS_CX						96.85 193	87.43 213	89.27 137	98.30 114	75.55 201	95.05 127	79.47 206	92.62 201	89.48 220		95.18 224	95.96 213

High-resolution multi-view benchmark