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 4	99.74 3	99.75 1	98.34 2	99.56 9	98.72 3	99.57 4	99.97 4	99.53 15	99.65 2	99.25 14	99.84 5	99.77 50

SMA-MVS		99.38 2	99.60 2	99.12 6	99.76 2	99.62 33	99.39 27	98.23 14	99.52 14	98.03 12	99.45 8	99.98 1	99.64 5	99.58 6	99.30 11	99.68 92	99.76 54

HFP-MVS		99.32 3	99.53 5	99.07 10	99.69 8	99.59 44	99.63 10	98.31 5	99.56 9	97.37 22	99.27 14	99.97 4	99.70 3	99.35 19	99.24 16	99.71 72	99.76 54

HSP-MVS		99.31 4	99.43 14	99.17 2	99.68 11	99.75 2	99.72 2	98.31 5	99.45 17	98.16 9	99.28 13	99.98 1	99.30 31	99.34 20	98.41 60	99.81 26	99.81 31

zzz-MVS		99.31 4	99.44 12	99.16 4	99.73 5	99.65 20	99.63 10	98.26 11	99.27 35	98.01 13	99.27 14	99.97 4	99.60 7	99.59 5	98.58 51	99.71 72	99.73 73

ACMMPR		99.30 6	99.54 4	99.03 13	99.66 14	99.64 25	99.68 5	98.25 12	99.56 9	97.12 26	99.19 17	99.95 15	99.72 1	99.43 14	99.25 14	99.72 62	99.77 50

TSAR-MVS + MP.		99.27 7	99.57 3	98.92 19	98.78 49	99.53 53	99.72 2	98.11 24	99.73 2	97.43 21	99.15 20	99.96 10	99.59 9	99.73 1	99.07 22	99.88 1	99.82 26

CP-MVS		99.27 7	99.44 12	99.08 9	99.62 18	99.58 46	99.53 15	98.16 17	99.21 46	97.79 16	99.15 20	99.96 10	99.59 9	99.54 8	98.86 36	99.78 37	99.74 69

SD-MVS		99.25 9	99.50 7	98.96 17	98.79 48	99.55 51	99.33 30	98.29 9	99.75 1	97.96 14	99.15 20	99.95 15	99.61 6	99.17 26	99.06 23	99.81 26	99.84 20

APD-MVS		99.25 9	99.38 18	99.09 8	99.69 8	99.58 46	99.56 14	98.32 4	98.85 84	97.87 15	98.91 36	99.92 25	99.30 31	99.45 13	99.38 8	99.79 34	99.58 134
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ESAPD		99.23 11	99.41 16	99.01 15	99.70 7	99.69 11	99.40 26	98.31 5	98.94 77	97.70 18	99.40 10	99.97 4	99.17 42	99.54 8	98.67 44	99.78 37	99.67 109

CNVR-MVS		99.23 11	99.28 25	99.17 2	99.65 16	99.34 78	99.46 21	98.21 15	99.28 33	98.47 5	98.89 38	99.94 23	99.50 16	99.42 15	98.61 48	99.73 57	99.52 145

SteuartSystems-ACMMP		99.20 13	99.51 6	98.83 23	99.66 14	99.66 19	99.71 4	98.12 23	99.14 53	96.62 30	99.16 19	99.98 1	99.12 50	99.63 3	99.19 20	99.78 37	99.83 24
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.34 1	99.17 14	99.45 9	98.85 21	99.55 24	99.37 73	99.64 8	98.05 26	99.53 12	96.58 31	98.93 31	99.92 25	99.49 18	99.46 12	99.32 10	99.80 33	99.64 125
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 15	99.24 27	99.04 12	99.52 27	99.49 58	99.09 41	98.07 25	99.37 22	98.47 5	97.79 73	99.89 30	99.50 16	98.93 39	99.45 4	99.61 137	99.76 54

CPTT-MVS		99.14 16	99.20 29	99.06 11	99.58 21	99.53 53	99.45 22	97.80 31	99.19 49	98.32 8	98.58 48	99.95 15	99.60 7	99.28 23	98.20 78	99.64 122	99.69 97

MCST-MVS		99.11 17	99.27 26	98.93 18	99.67 12	99.33 80	99.51 17	98.31 5	99.28 33	96.57 32	99.10 24	99.90 28	99.71 2	99.19 25	98.35 67	99.82 13	99.71 89

HPM-MVS++		99.10 18	99.30 24	98.86 20	99.69 8	99.48 59	99.59 13	98.34 2	99.26 38	96.55 33	99.10 24	99.96 10	99.36 26	99.25 24	98.37 65	99.64 122	99.66 118

PHI-MVS		99.08 19	99.43 14	98.67 25	99.15 41	99.59 44	99.11 39	97.35 34	99.14 53	97.30 23	99.44 9	99.96 10	99.32 29	98.89 43	99.39 7	99.79 34	99.58 134

MP-MVS		99.07 20	99.36 20	98.74 24	99.63 17	99.57 48	99.66 7	98.25 12	99.00 72	95.62 39	98.97 29	99.94 23	99.54 14	99.51 10	98.79 41	99.71 72	99.73 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
AdaColmap		99.06 21	98.98 44	99.15 5	99.60 20	99.30 84	99.38 28	98.16 17	99.02 71	98.55 4	98.71 44	99.57 49	99.58 12	99.09 30	97.84 95	99.64 122	99.36 162

ACMMP_Plus		99.05 22	99.45 9	98.58 27	99.73 5	99.60 42	99.64 8	98.28 10	99.23 43	94.57 60	99.35 12	99.97 4	99.55 13	99.63 3	98.66 45	99.70 81	99.74 69

NCCC		99.05 22	99.08 34	99.02 14	99.62 18	99.38 71	99.43 25	98.21 15	99.36 24	97.66 19	97.79 73	99.90 28	99.45 21	99.17 26	98.43 58	99.77 42	99.51 149

CNLPA		99.03 24	99.05 37	99.01 15	99.27 39	99.22 94	99.03 45	97.98 27	99.34 28	99.00 2	98.25 61	99.71 43	99.31 30	98.80 48	98.82 39	99.48 171	99.17 171

PLC	97.93 2	99.02 25	98.94 45	99.11 7	99.46 29	99.24 92	99.06 43	97.96 28	99.31 30	99.16 1	97.90 71	99.79 40	99.36 26	98.71 56	98.12 81	99.65 111	99.52 145
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
X-MVS		98.93 26	99.37 19	98.42 28	99.67 12	99.62 33	99.60 12	98.15 19	99.08 61	93.81 81	98.46 54	99.95 15	99.59 9	99.49 11	99.21 19	99.68 92	99.75 65

CSCG		98.90 27	98.93 46	98.85 21	99.75 3	99.72 4	99.49 18	96.58 37	99.38 20	98.05 11	98.97 29	97.87 64	99.49 18	97.78 120	98.92 31	99.78 37	99.90 3

PGM-MVS		98.86 28	99.35 23	98.29 31	99.77 1	99.63 29	99.67 6	95.63 40	98.66 105	95.27 46	99.11 23	99.82 37	99.67 4	99.33 21	99.19 20	99.73 57	99.74 69

OMC-MVS		98.84 29	99.01 43	98.65 26	99.39 31	99.23 93	99.22 33	96.70 36	99.40 19	97.77 17	97.89 72	99.80 38	99.21 35	99.02 34	98.65 46	99.57 158	99.07 178

TSAR-MVS + ACMM		98.77 30	99.45 9	97.98 39	99.37 32	99.46 61	99.44 24	98.13 22	99.65 4	92.30 98	98.91 36	99.95 15	99.05 56	99.42 15	98.95 29	99.58 154	99.82 26

ACMMP		98.74 31	99.03 41	98.40 29	99.36 34	99.64 25	99.20 34	97.75 32	98.82 90	95.24 47	98.85 39	99.87 32	99.17 42	98.74 55	97.50 113	99.71 72	99.76 54
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 32	99.11 32	98.28 32	99.36 34	99.35 76	99.48 20	97.96 28	98.83 88	93.86 80	98.70 45	99.86 33	99.44 22	99.08 32	98.38 63	99.61 137	99.58 134

3Dnovator+	96.92 7	98.71 33	99.05 37	98.32 30	99.53 25	99.34 78	99.06 43	94.61 54	99.65 4	97.49 20	96.75 97	99.86 33	99.44 22	98.78 50	99.30 11	99.81 26	99.67 109

MVS_111021_LR		98.67 34	99.41 16	97.81 42	99.37 32	99.53 53	98.51 60	95.52 42	99.27 35	94.85 55	99.56 5	99.69 44	99.04 57	99.36 18	98.88 34	99.60 144	99.58 134

3Dnovator	96.92 7	98.67 34	99.05 37	98.23 34	99.57 22	99.45 63	99.11 39	94.66 53	99.69 3	96.80 29	96.55 107	99.61 46	99.40 24	98.87 45	99.49 3	99.85 4	99.66 118

TSAR-MVS + GP.		98.66 36	99.36 20	97.85 41	97.16 75	99.46 61	99.03 45	94.59 56	99.09 59	97.19 25	99.73 3	99.95 15	99.39 25	98.95 37	98.69 43	99.75 46	99.65 121

QAPM		98.62 37	99.04 40	98.13 35	99.57 22	99.48 59	99.17 36	94.78 50	99.57 8	96.16 35	96.73 99	99.80 38	99.33 28	98.79 49	99.29 13	99.75 46	99.64 125

MVS_111021_HR		98.59 38	99.36 20	97.68 43	99.42 30	99.61 38	98.14 84	94.81 49	99.31 30	95.00 52	99.51 6	99.79 40	99.00 60	98.94 38	98.83 38	99.69 83	99.57 139

CANet		98.46 39	99.16 30	97.64 44	98.48 52	99.64 25	99.35 29	94.71 52	99.53 12	95.17 48	97.63 79	99.59 47	98.38 80	98.88 44	98.99 27	99.74 51	99.86 16

CDPH-MVS		98.41 40	99.10 33	97.61 45	99.32 38	99.36 74	99.49 18	96.15 39	98.82 90	91.82 101	98.41 55	99.66 45	99.10 53	98.93 39	98.97 28	99.75 46	99.58 134

TAPA-MVS	97.53 5	98.41 40	98.84 50	97.91 40	99.08 43	99.33 80	99.15 37	97.13 35	99.34 28	93.20 88	97.75 75	99.19 52	99.20 36	98.66 58	98.13 80	99.66 105	99.48 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.74 3	98.34 42	99.46 8	97.04 60	98.82 47	99.33 80	96.28 139	97.47 33	99.58 7	94.70 59	98.99 28	99.85 36	97.24 111	99.55 7	99.34 9	97.73 215	99.56 140

DeepC-MVS	97.63 4	98.33 43	98.57 54	98.04 37	98.62 51	99.65 20	99.45 22	98.15 19	99.51 15	92.80 94	95.74 126	96.44 78	99.46 20	99.37 17	99.50 2	99.78 37	99.81 31
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 44	98.29 66	98.24 33	99.20 40	99.22 94	99.20 34	97.82 30	99.37 22	94.43 67	95.90 121	97.31 70	99.12 50	98.76 52	98.35 67	99.67 100	99.14 175

DELS-MVS		98.19 45	98.77 51	97.52 46	98.29 55	99.71 8	99.12 38	94.58 57	98.80 93	95.38 45	96.24 113	98.24 62	97.92 95	99.06 33	99.52 1	99.82 13	99.79 41
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 46	98.35 63	97.99 38	98.65 50	99.36 74	98.94 48	98.14 21	98.59 107	93.62 84	96.61 103	99.76 42	99.03 58	97.77 121	97.45 117	99.57 158	98.89 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_0304		98.14 47	99.03 41	97.10 56	98.05 59	99.63 29	99.27 32	94.33 59	99.63 6	93.06 91	97.32 82	99.05 54	98.09 90	98.82 47	98.87 35	99.81 26	99.89 7

EPNet		98.05 48	98.86 48	97.10 56	99.02 44	99.43 66	98.47 61	94.73 51	99.05 68	95.62 39	98.93 31	97.62 68	95.48 161	98.59 69	98.55 52	99.29 189	99.84 20
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		97.99 49	99.24 27	96.53 81	98.34 54	99.61 38	98.36 74	89.80 143	99.27 35	95.08 50	99.81 1	98.58 57	98.64 71	99.02 34	98.92 31	98.93 197	99.48 154

OpenMVS	96.23 11	97.95 50	98.45 58	97.35 47	99.52 27	99.42 67	98.91 49	94.61 54	98.87 81	92.24 99	94.61 140	99.05 54	99.10 53	98.64 62	99.05 24	99.74 51	99.51 149

IS_MVSNet		97.86 51	98.86 48	96.68 76	96.02 106	99.72 4	98.35 75	93.37 90	98.75 102	94.01 74	96.88 96	98.40 60	98.48 77	99.09 30	99.42 5	99.83 9	99.80 34

LS3D		97.79 52	98.25 67	97.26 52	98.40 53	99.63 29	99.53 15	98.63 1	99.25 40	88.13 124	96.93 95	94.14 109	99.19 38	99.14 28	99.23 17	99.69 83	99.42 158

COLMAP_ROB	96.15 12	97.78 53	98.17 72	97.32 48	98.84 46	99.45 63	99.28 31	95.43 43	99.48 16	91.80 102	94.83 138	98.36 61	98.90 61	98.09 99	97.85 94	99.68 92	99.15 172
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 54	98.25 67	97.21 53	99.11 42	99.25 90	97.06 125	94.09 65	98.72 103	95.14 49	98.47 53	96.29 80	98.43 78	98.65 59	97.44 118	99.45 175	98.94 181

EPP-MVSNet		97.75 55	98.71 52	96.63 79	95.68 119	99.56 49	97.51 106	93.10 92	99.22 44	94.99 53	97.18 88	97.30 71	98.65 70	98.83 46	98.93 30	99.84 5	99.92 1

tfpn_ndepth		97.71 56	98.30 65	97.02 65	96.31 87	99.56 49	98.05 90	93.94 76	98.95 74	95.59 41	98.40 56	94.79 97	98.39 79	98.40 78	98.42 59	99.86 2	99.56 140

MAR-MVS		97.71 56	98.04 77	97.32 48	99.35 36	98.91 108	97.65 103	91.68 104	98.00 133	97.01 27	97.72 77	94.83 95	98.85 64	98.44 76	98.86 36	99.41 181	99.52 145
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 58	99.07 36	96.01 94	97.19 74	99.65 20	97.09 123	93.39 88	99.35 25	94.40 69	98.79 41	99.59 47	94.24 196	98.04 108	98.29 74	99.73 57	99.80 34
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 59	98.16 73	96.96 72	98.10 56	99.00 101	98.84 51	93.76 82	99.45 17	94.78 58	99.39 11	99.31 51	98.53 76	96.61 154	95.43 164	97.74 213	97.93 203

tfpn1000		97.60 60	98.21 70	96.89 74	96.32 85	99.60 42	97.99 93	93.85 78	99.21 46	95.03 51	98.49 51	93.69 113	98.31 83	98.50 74	98.31 73	99.86 2	99.70 91

Anonymous20240521		97.56 61	98.36 62	96.62 80	96.44 83	98.36 146	98.37 72	91.73 103	99.11 58	94.80 57	98.36 58	96.28 81	98.60 73	98.12 95	98.44 57	99.76 44	99.87 13

PMMVS		97.52 62	98.39 59	96.51 83	95.82 114	98.73 122	97.80 99	93.05 93	98.76 100	94.39 70	99.07 27	97.03 74	98.55 75	98.31 81	97.61 108	99.43 179	99.21 170

PVSNet_BlendedMVS		97.51 63	97.71 87	97.28 50	98.06 57	99.61 38	97.31 112	95.02 46	99.08 61	95.51 42	98.05 65	90.11 130	98.07 91	98.91 41	98.40 61	99.72 62	99.78 43

PVSNet_Blended		97.51 63	97.71 87	97.28 50	98.06 57	99.61 38	97.31 112	95.02 46	99.08 61	95.51 42	98.05 65	90.11 130	98.07 91	98.91 41	98.40 61	99.72 62	99.78 43

PVSNet_Blended_VisFu		97.41 65	98.49 57	96.15 88	97.49 65	99.76 1	96.02 142	93.75 84	99.26 38	93.38 87	93.73 146	99.35 50	96.47 134	98.96 36	98.46 56	99.77 42	99.90 3

casdiffmvs		97.40 66	98.64 53	95.96 95	95.76 115	99.40 69	98.33 78	91.48 111	99.24 42	91.72 103	98.03 67	96.57 75	98.73 67	98.64 62	98.77 42	99.72 62	99.83 24

Vis-MVSNet (Re-imp)		97.40 66	98.89 47	95.66 103	95.99 109	99.62 33	97.82 97	93.22 91	98.82 90	91.40 106	96.94 94	98.56 58	95.70 150	99.14 28	99.41 6	99.79 34	99.75 65

tfpn_n400		97.32 68	98.38 60	96.09 91	96.07 103	99.30 84	98.00 91	93.84 79	99.35 25	90.50 112	98.93 31	94.24 106	98.30 84	98.65 59	98.60 49	99.83 9	99.60 130

tfpnconf		97.32 68	98.38 60	96.09 91	96.07 103	99.30 84	98.00 91	93.84 79	99.35 25	90.50 112	98.93 31	94.24 106	98.30 84	98.65 59	98.60 49	99.83 9	99.60 130

tfpnview11		97.32 68	98.33 64	96.14 89	96.07 103	99.31 83	98.08 88	93.96 74	99.25 40	90.50 112	98.93 31	94.24 106	98.38 80	98.61 65	98.36 66	99.84 5	99.59 132

canonicalmvs		97.31 71	97.81 85	96.72 75	96.20 100	99.45 63	98.21 80	91.60 106	99.22 44	95.39 44	98.48 52	90.95 128	99.16 44	97.66 127	99.05 24	99.76 44	99.90 3

MVS_Test		97.30 72	98.54 55	95.87 96	95.74 116	99.28 87	98.19 82	91.40 112	99.18 50	91.59 105	98.17 62	96.18 82	98.63 72	98.61 65	98.55 52	99.66 105	99.78 43

thresconf0.02		97.18 73	97.81 85	96.45 85	96.11 102	99.20 97	98.21 80	94.26 61	99.14 53	91.72 103	98.65 46	91.51 127	98.57 74	98.22 90	98.47 55	99.82 13	99.50 151

MVSTER		97.16 74	97.71 87	96.52 82	95.97 110	98.48 135	98.63 57	92.10 96	98.68 104	95.96 38	99.23 16	91.79 125	96.87 121	98.76 52	97.37 121	99.57 158	99.68 104

UA-Net		97.13 75	99.14 31	94.78 110	97.21 73	99.38 71	97.56 104	92.04 97	98.48 115	88.03 125	98.39 57	99.91 27	94.03 199	99.33 21	99.23 17	99.81 26	99.25 167

Anonymous20231211		97.10 76	97.06 108	97.14 54	96.32 85	99.52 56	98.16 83	93.76 82	98.84 87	95.98 37	90.92 163	94.58 101	98.90 61	97.72 125	98.10 82	99.71 72	99.75 65

FC-MVSNet-train		97.04 77	97.91 83	96.03 93	96.00 108	98.41 142	96.53 135	93.42 87	99.04 70	93.02 92	98.03 67	94.32 104	97.47 107	97.93 113	97.77 103	99.75 46	99.88 11

FMVSNet3		97.02 78	98.12 75	95.73 102	93.59 154	97.98 156	98.34 76	91.32 113	98.80 93	93.92 77	97.21 85	95.94 86	97.63 103	98.61 65	98.62 47	99.61 137	99.65 121

GBi-Net		96.98 79	98.00 80	95.78 98	93.81 148	97.98 156	98.09 85	91.32 113	98.80 93	93.92 77	97.21 85	95.94 86	97.89 96	98.07 102	98.34 69	99.68 92	99.67 109

test1		96.98 79	98.00 80	95.78 98	93.81 148	97.98 156	98.09 85	91.32 113	98.80 93	93.92 77	97.21 85	95.94 86	97.89 96	98.07 102	98.34 69	99.68 92	99.67 109

tfpn111		96.96 81	96.91 111	97.03 61	96.31 87	99.67 13	98.41 64	93.99 68	97.35 158	94.50 63	98.65 46	86.93 142	99.14 45	98.26 84	97.80 98	99.82 13	99.70 91

diffmvs		96.92 82	97.86 84	95.82 97	95.70 117	99.28 87	97.98 94	91.13 118	99.08 61	92.48 97	98.09 64	92.81 119	98.18 86	98.11 96	97.83 96	99.44 177	99.81 31

DI_MVS_plusplus_trai		96.90 83	97.49 93	96.21 86	95.61 121	99.40 69	98.72 55	92.11 95	99.14 53	92.98 93	93.08 157	95.14 92	98.13 89	98.05 106	97.91 91	99.74 51	99.73 73

TSAR-MVS + COLMAP		96.79 84	96.55 118	97.06 59	97.70 64	98.46 136	99.07 42	96.23 38	99.38 20	91.32 107	98.80 40	85.61 159	98.69 69	97.64 130	96.92 128	99.37 184	99.06 179

thres200		96.76 85	96.53 119	97.03 61	96.31 87	99.67 13	98.37 72	93.99 68	97.68 153	94.49 65	95.83 125	86.77 147	99.18 40	98.26 84	97.82 97	99.82 13	99.66 118

conf200view11		96.75 86	96.51 121	97.03 61	96.31 87	99.67 13	98.41 64	93.99 68	97.35 158	94.50 63	95.90 121	86.93 142	99.14 45	98.26 84	97.80 98	99.82 13	99.70 91

tfpn200view9		96.75 86	96.51 121	97.03 61	96.31 87	99.67 13	98.41 64	93.99 68	97.35 158	94.52 61	95.90 121	86.93 142	99.14 45	98.26 84	97.80 98	99.82 13	99.70 91

CLD-MVS		96.74 88	96.51 121	97.01 67	96.71 80	98.62 128	98.73 54	94.38 58	98.94 77	94.46 66	97.33 81	87.03 140	98.07 91	97.20 144	96.87 129	99.72 62	99.54 142
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 89	96.47 125	97.00 69	96.31 87	99.52 56	98.28 79	94.01 66	97.35 158	94.52 61	95.90 121	86.93 142	99.09 55	98.07 102	97.87 93	99.81 26	99.63 127

thres400		96.71 90	96.45 128	97.02 65	96.28 97	99.63 29	98.41 64	94.00 67	97.82 148	94.42 68	95.74 126	86.26 153	99.18 40	98.20 91	97.79 102	99.81 26	99.70 91

view600		96.70 91	96.44 130	97.01 67	96.28 97	99.67 13	98.42 63	93.99 68	97.87 143	94.34 71	95.99 118	85.94 156	99.20 36	98.26 84	97.64 106	99.82 13	99.73 73

view800		96.70 91	96.45 128	96.99 71	96.29 94	99.69 11	98.39 71	93.95 75	97.92 140	94.25 73	96.23 114	85.57 160	99.22 33	98.28 82	97.71 104	99.82 13	99.76 54

thres600view7		96.69 93	96.43 132	97.00 69	96.28 97	99.67 13	98.41 64	93.99 68	97.85 146	94.29 72	95.96 119	85.91 157	99.19 38	98.26 84	97.63 107	99.82 13	99.73 73

test0.0.03 1		96.69 93	98.12 75	95.01 108	95.49 124	98.99 103	95.86 144	90.82 121	98.38 118	92.54 96	96.66 101	97.33 69	95.75 148	97.75 123	98.34 69	99.60 144	99.40 160

ACMM	96.26 9	96.67 95	96.69 115	96.66 77	97.29 72	98.46 136	96.48 136	95.09 45	99.21 46	93.19 89	98.78 42	86.73 148	98.17 87	97.84 118	96.32 143	99.74 51	99.49 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		96.64 96	99.08 34	93.81 122	97.10 76	99.42 67	98.85 50	90.01 137	99.31 30	79.98 180	99.78 2	99.10 53	97.42 108	98.35 79	98.05 85	99.47 173	99.53 143

FMVSNet2		96.64 96	97.50 92	95.63 104	93.81 148	97.98 156	98.09 85	90.87 119	98.99 73	93.48 85	93.17 154	95.25 91	97.89 96	98.63 64	98.80 40	99.68 92	99.67 109

ACMP	96.25 10	96.62 98	96.72 114	96.50 84	96.96 78	98.75 119	97.80 99	94.30 60	98.85 84	93.12 90	98.78 42	86.61 150	97.23 112	97.73 124	96.61 135	99.62 134	99.71 89
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CDS-MVSNet		96.59 99	98.02 79	94.92 109	94.45 141	98.96 106	97.46 108	91.75 102	97.86 145	90.07 116	96.02 117	97.25 72	96.21 137	98.04 108	98.38 63	99.60 144	99.65 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 1792x2688		96.41 100	96.99 110	95.74 101	98.01 60	99.72 4	97.70 102	90.78 123	99.13 57	90.03 117	87.35 207	95.36 90	98.33 82	98.59 69	98.91 33	99.59 150	99.87 13

HQP-MVS		96.37 101	96.58 116	96.13 90	97.31 71	98.44 139	98.45 62	95.22 44	98.86 82	88.58 122	98.33 59	87.00 141	97.67 102	97.23 142	96.56 137	99.56 161	99.62 128

conf0.01		96.35 102	95.71 142	97.10 56	96.30 93	99.65 20	98.41 64	94.10 64	97.35 158	94.82 56	95.44 134	81.88 203	99.14 45	98.16 93	97.80 98	99.82 13	99.69 97

conf0.05thres1000		96.34 103	96.47 125	96.17 87	96.16 101	99.71 8	97.82 97	93.46 86	98.10 129	90.69 109	96.75 97	85.26 164	99.11 52	98.05 106	97.65 105	99.82 13	99.80 34

conf0.002		96.31 104	95.63 144	97.11 55	96.29 94	99.64 25	98.41 64	94.11 63	97.35 158	94.86 54	95.49 133	81.06 208	99.14 45	98.14 94	98.02 87	99.82 13	99.69 97

EPNet_dtu		96.30 105	98.53 56	93.70 126	98.97 45	98.24 151	97.36 110	94.23 62	98.85 84	79.18 194	99.19 17	98.47 59	94.09 198	97.89 115	98.21 77	98.39 205	98.85 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LGP-MVS_train		96.23 106	96.89 112	95.46 105	97.32 69	98.77 116	98.81 52	93.60 85	98.58 108	85.52 140	99.08 26	86.67 149	97.83 101	97.87 116	97.51 112	99.69 83	99.73 73

tfpn		96.22 107	95.62 145	96.93 73	96.29 94	99.72 4	98.34 76	93.94 76	97.96 137	93.94 76	96.45 109	79.09 218	99.22 33	98.28 82	98.06 84	99.83 9	99.78 43

OPM-MVS		96.22 107	95.85 141	96.65 78	97.75 62	98.54 133	99.00 47	95.53 41	96.88 182	89.88 118	95.95 120	86.46 152	98.07 91	97.65 129	96.63 134	99.67 100	98.83 188

Vis-MVSNet		96.16 109	98.22 69	93.75 123	95.33 130	99.70 10	97.27 114	90.85 120	98.30 120	85.51 141	95.72 128	96.45 76	93.69 205	98.70 57	99.00 26	99.84 5	99.69 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS-LS		96.12 110	97.48 94	94.53 112	95.19 132	97.56 185	97.15 119	89.19 149	99.08 61	88.23 123	94.97 136	94.73 98	97.84 100	97.86 117	98.26 75	99.60 144	99.88 11
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		96.07 111	97.94 82	93.89 120	93.60 153	98.67 125	96.62 132	90.30 132	98.76 100	88.62 121	95.57 132	97.63 67	94.48 192	97.97 111	97.48 116	99.71 72	99.52 145

MS-PatchMatch		95.99 112	97.26 104	94.51 113	97.46 66	98.76 118	97.27 114	86.97 173	99.09 59	89.83 119	93.51 149	97.78 65	96.18 139	97.53 134	95.71 161	99.35 185	98.41 194

HyFIR lowres test		95.99 112	96.56 117	95.32 106	97.99 61	99.65 20	96.54 133	88.86 151	98.44 116	89.77 120	84.14 219	97.05 73	99.03 58	98.55 71	98.19 79	99.73 57	99.86 16

Effi-MVS+		95.81 114	97.31 103	94.06 118	95.09 133	99.35 76	97.24 116	88.22 160	98.54 111	85.38 142	98.52 49	88.68 134	98.70 68	98.32 80	97.93 89	99.74 51	99.84 20

FMVSNet1		95.77 115	96.41 133	95.03 107	93.42 155	97.86 163	97.11 122	89.89 140	98.53 112	92.00 100	89.17 182	93.23 117	98.15 88	98.07 102	98.34 69	99.61 137	99.69 97

Effi-MVS+-dtu		95.74 116	98.04 77	93.06 140	93.92 144	99.16 98	97.90 95	88.16 163	99.07 67	82.02 161	98.02 69	94.32 104	96.74 125	98.53 72	97.56 110	99.61 137	99.62 128

testgi		95.67 117	97.48 94	93.56 129	95.07 134	99.00 101	95.33 155	88.47 157	98.80 93	86.90 133	97.30 83	92.33 122	95.97 145	97.66 127	97.91 91	99.60 144	99.38 161

MDTV_nov1_ep13		95.57 118	97.48 94	93.35 137	95.43 126	98.97 105	97.19 118	83.72 203	98.92 80	87.91 127	97.75 75	96.12 84	97.88 99	96.84 153	95.64 162	97.96 211	98.10 199

TAMVS		95.53 119	96.50 124	94.39 115	93.86 147	99.03 100	96.67 130	89.55 146	97.33 164	90.64 110	93.02 158	91.58 126	96.21 137	97.72 125	97.43 119	99.43 179	99.36 162

test-LLR		95.50 120	97.32 100	93.37 135	95.49 124	98.74 120	96.44 137	90.82 121	98.18 125	82.75 156	96.60 104	94.67 99	95.54 157	98.09 99	96.00 151	99.20 192	98.93 182

FMVSNet5		95.42 121	96.47 125	94.20 116	92.26 166	95.99 210	95.66 147	87.15 170	97.87 143	93.46 86	96.68 100	93.79 112	97.52 104	97.10 148	97.21 123	99.11 195	96.62 221

ACMH+	95.51 13	95.40 122	96.00 135	94.70 111	96.33 84	98.79 113	96.79 128	91.32 113	98.77 99	87.18 131	95.60 131	85.46 161	96.97 117	97.15 145	96.59 136	99.59 150	99.65 121

Fast-Effi-MVS+-dtu		95.38 123	98.20 71	92.09 153	93.91 145	98.87 110	97.35 111	85.01 190	99.08 61	81.09 165	98.10 63	96.36 79	95.62 154	98.43 77	97.03 125	99.55 162	99.50 151

Fast-Effi-MVS+		95.38 123	96.52 120	94.05 119	94.15 143	99.14 99	97.24 116	86.79 174	98.53 112	87.62 129	94.51 141	87.06 139	98.76 65	98.60 68	98.04 86	99.72 62	99.77 50

DWT-MVSNet_training		95.38 123	95.05 151	95.78 98	95.86 112	98.88 109	97.55 105	90.09 136	98.23 124	96.49 34	97.62 80	86.92 146	97.16 113	92.03 223	94.12 205	97.52 218	97.50 206

CVMVSNet		95.33 126	97.09 106	93.27 138	95.23 131	98.39 144	95.49 151	92.58 94	97.71 152	83.00 155	94.44 142	93.28 116	93.92 202	97.79 119	98.54 54	99.41 181	99.45 156

ACMH	95.42 14	95.27 127	95.96 137	94.45 114	96.83 79	98.78 115	94.72 182	91.67 105	98.95 74	86.82 134	96.42 110	83.67 176	97.00 116	97.48 136	96.68 133	99.69 83	99.76 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		95.09 128	95.90 138	94.14 117	92.29 165	97.70 170	95.45 152	90.31 130	98.60 106	90.70 108	93.25 152	89.90 132	96.67 127	97.13 146	95.42 165	99.44 177	99.28 165

EPMVS		95.05 129	96.86 113	92.94 143	95.84 113	98.96 106	96.68 129	79.87 212	99.05 68	90.15 115	97.12 89	95.99 85	97.49 106	95.17 190	94.75 199	97.59 217	96.96 215

IB-MVS	93.96 15	95.02 130	96.44 130	93.36 136	97.05 77	99.28 87	90.43 215	93.39 88	98.02 132	96.02 36	94.92 137	92.07 124	83.52 226	95.38 181	95.82 157	99.72 62	99.59 132
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		94.95 131	97.32 100	92.20 150	92.62 159	98.74 120	96.44 137	86.67 176	98.18 125	82.75 156	96.60 104	94.67 99	95.54 157	98.09 99	96.00 151	99.20 192	98.93 182

test-mter		94.86 132	97.32 100	92.00 157	92.41 163	98.82 112	96.18 141	86.35 180	98.05 131	82.28 159	96.48 108	94.39 103	95.46 167	98.17 92	96.20 147	99.32 187	99.13 176

IterMVS		94.81 133	97.71 87	91.42 172	94.83 139	97.63 178	97.38 109	85.08 188	98.93 79	75.67 209	94.02 143	97.64 66	96.66 128	98.45 75	97.60 109	98.90 198	99.72 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		94.70 134	97.08 107	91.92 160	95.53 122	98.85 111	95.77 145	79.54 216	98.95 74	85.98 137	98.52 49	96.45 76	97.39 109	95.32 182	94.09 206	97.32 222	97.38 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RPMNet		94.66 135	97.16 105	91.75 166	94.98 135	98.59 130	97.00 126	78.37 225	97.98 134	83.78 146	96.27 112	94.09 111	96.91 119	97.36 138	96.73 131	99.48 171	99.09 177

ADS-MVSNet		94.65 136	97.04 109	91.88 163	95.68 119	98.99 103	95.89 143	79.03 221	99.15 51	85.81 139	96.96 93	98.21 63	97.10 114	94.48 209	94.24 204	97.74 213	97.21 211

dps		94.63 137	95.31 150	93.84 121	95.53 122	98.71 123	96.54 133	80.12 211	97.81 150	97.21 24	96.98 92	92.37 121	96.34 136	92.46 220	91.77 224	97.26 224	97.08 213

UniMVSNet_NR-MVSNet		94.59 138	95.47 147	93.55 130	91.85 180	97.89 162	95.03 158	92.00 98	97.33 164	86.12 135	93.19 153	87.29 138	96.60 130	96.12 172	96.70 132	99.72 62	99.80 34

UniMVSNet (Re)		94.58 139	95.34 148	93.71 125	92.25 167	98.08 155	94.97 160	91.29 117	97.03 175	87.94 126	93.97 145	86.25 154	96.07 142	96.27 169	95.97 154	99.72 62	99.79 41

CR-MVSNet		94.57 140	97.34 99	91.33 174	94.90 137	98.59 130	97.15 119	79.14 219	97.98 134	80.42 173	96.59 106	93.50 115	96.85 122	98.10 97	97.49 114	99.50 170	99.15 172

MIMVSNet		94.49 141	97.59 91	90.87 189	91.74 191	98.70 124	94.68 184	78.73 223	97.98 134	83.71 149	97.71 78	94.81 96	96.96 118	97.97 111	97.92 90	99.40 183	98.04 201

pm-mvs1		94.27 142	95.57 146	92.75 144	92.58 160	98.13 154	94.87 167	90.71 124	96.70 188	83.78 146	89.94 177	89.85 133	94.96 187	97.58 132	97.07 124	99.61 137	99.72 85

USDC		94.26 143	94.83 155	93.59 128	96.02 106	98.44 139	97.84 96	88.65 155	98.86 82	82.73 158	94.02 143	80.56 209	96.76 124	97.28 141	96.15 150	99.55 162	98.50 192

CostFormer		94.25 144	94.88 154	93.51 132	95.43 126	98.34 147	96.21 140	80.64 208	97.94 139	94.01 74	98.30 60	86.20 155	97.52 104	92.71 216	92.69 216	97.23 226	98.02 202

tpm cat1		94.06 145	94.90 153	93.06 140	95.42 128	98.52 134	96.64 131	80.67 207	97.82 148	92.63 95	93.39 151	95.00 93	96.06 143	91.36 227	91.58 226	96.98 227	96.66 220

NR-MVSNet		94.01 146	94.51 162	93.44 133	92.56 161	97.77 164	95.67 146	91.57 107	97.17 169	85.84 138	93.13 155	80.53 210	95.29 180	97.01 149	96.17 148	99.69 83	99.75 65

TinyColmap		94.00 147	94.35 166	93.60 127	95.89 111	98.26 149	97.49 107	88.82 152	98.56 110	83.21 152	91.28 162	80.48 211	96.68 126	97.34 139	96.26 146	99.53 167	98.24 197

DU-MVS		93.98 148	94.44 164	93.44 133	91.66 195	97.77 164	95.03 158	91.57 107	97.17 169	86.12 135	93.13 155	81.13 207	96.60 130	95.10 201	97.01 127	99.67 100	99.80 34

PatchT		93.96 149	97.36 98	90.00 201	94.76 140	98.65 126	90.11 218	78.57 224	97.96 137	80.42 173	96.07 116	94.10 110	96.85 122	98.10 97	97.49 114	99.26 190	99.15 172

GA-MVS		93.93 150	96.31 134	91.16 180	93.61 152	98.79 113	95.39 154	90.69 125	98.25 122	73.28 217	96.15 115	88.42 135	94.39 194	97.76 122	95.35 168	99.58 154	99.45 156

Baseline_NR-MVSNet		93.87 151	93.98 176	93.75 123	91.66 195	97.02 202	95.53 150	91.52 110	97.16 171	87.77 128	87.93 205	83.69 175	96.35 135	95.10 201	97.23 122	99.68 92	99.73 73

tpmrst		93.86 152	95.88 139	91.50 170	95.69 118	98.62 128	95.64 148	79.41 217	98.80 93	83.76 148	95.63 130	96.13 83	97.25 110	92.92 215	92.31 220	97.27 223	96.74 218

tfpnnormal		93.85 153	94.12 170	93.54 131	93.22 156	98.24 151	95.45 152	91.96 100	94.61 220	83.91 144	90.74 165	81.75 205	97.04 115	97.49 135	96.16 149	99.68 92	99.84 20

tpmp4_e23		93.84 154	94.58 161	92.98 142	95.41 129	98.29 148	96.81 127	80.57 209	98.15 127	90.53 111	97.00 91	84.39 172	96.91 119	93.69 212	92.45 218	97.67 216	98.06 200

TranMVSNet+NR-MVSNet		93.67 155	94.14 168	93.13 139	91.28 209	97.58 183	95.60 149	91.97 99	97.06 173	84.05 143	90.64 168	82.22 198	96.17 140	94.94 205	96.78 130	99.69 83	99.78 43

WR-MVS_H		93.54 156	94.67 158	92.22 148	91.95 176	97.91 161	94.58 190	88.75 153	96.64 192	83.88 145	90.66 167	85.13 165	94.40 193	96.54 159	95.91 156	99.73 57	99.89 7

TransMVSNet (Re)		93.45 157	94.08 172	92.72 145	92.83 157	97.62 181	94.94 161	91.54 109	95.65 216	83.06 154	88.93 185	83.53 177	94.25 195	97.41 137	97.03 125	99.67 100	98.40 196

SixPastTwentyTwo		93.44 158	95.32 149	91.24 178	92.11 170	98.40 143	92.77 205	88.64 156	98.09 130	77.83 199	93.51 149	85.74 158	96.52 133	96.91 151	94.89 196	99.59 150	99.73 73

WR-MVS		93.43 159	94.48 163	92.21 149	91.52 202	97.69 174	94.66 186	89.98 138	96.86 183	83.43 150	90.12 169	85.03 166	93.94 201	96.02 175	95.82 157	99.71 72	99.82 26

CP-MVSNet		93.25 160	94.00 175	92.38 147	91.65 197	97.56 185	94.38 193	89.20 148	96.05 208	83.16 153	89.51 180	81.97 202	96.16 141	96.43 161	96.56 137	99.71 72	99.89 7

anonymousdsp		93.12 161	95.86 140	89.93 203	91.09 210	98.25 150	95.12 156	85.08 188	97.44 156	73.30 216	90.89 164	90.78 129	95.25 182	97.91 114	95.96 155	99.71 72	99.82 26

v6		93.11 162	93.98 176	92.10 152	92.01 173	97.71 167	94.86 170	90.15 133	96.96 178	80.47 172	90.01 172	83.26 180	95.48 161	95.17 190	95.01 183	99.64 122	99.76 54

v1neww		93.06 163	93.94 178	92.03 155	91.99 174	97.70 170	94.79 174	90.14 134	96.93 180	80.13 177	89.97 174	83.01 184	95.48 161	95.16 194	95.01 183	99.63 128	99.76 54

v7new		93.06 163	93.94 178	92.03 155	91.99 174	97.70 170	94.79 174	90.14 134	96.93 180	80.13 177	89.97 174	83.01 184	95.48 161	95.16 194	95.01 183	99.63 128	99.76 54

V42		93.05 165	93.90 182	92.04 154	91.91 177	97.66 176	94.91 162	89.91 139	96.85 184	80.58 170	89.66 179	83.43 179	95.37 173	95.03 204	94.90 194	99.59 150	99.78 43

TDRefinement		93.04 166	93.57 192	92.41 146	96.58 81	98.77 116	97.78 101	91.96 100	98.12 128	80.84 166	89.13 184	79.87 215	87.78 218	96.44 160	94.50 203	99.54 166	98.15 198

v7		92.97 167	94.11 171	91.65 169	91.83 181	97.55 187	94.86 170	88.19 162	96.96 178	79.72 185	88.16 199	84.68 169	95.63 152	96.33 166	95.30 170	99.65 111	99.77 50

v8		92.87 168	93.87 183	91.72 168	92.05 172	97.50 190	94.79 174	88.20 161	96.85 184	80.11 179	90.01 172	82.86 189	95.48 161	95.15 198	94.90 194	99.66 105	99.80 34

LTVRE_ROB	93.20 16	92.84 169	94.92 152	90.43 197	92.83 157	98.63 127	97.08 124	87.87 166	97.91 141	68.42 223	93.54 148	79.46 217	96.62 129	97.55 133	97.40 120	99.74 51	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		92.81 170	94.03 174	91.40 173	91.68 194	97.60 182	94.73 181	88.40 158	96.71 187	78.48 197	88.14 201	84.46 171	95.45 168	96.31 168	95.22 172	99.65 111	99.76 54

v1		92.81 170	93.57 192	91.94 159	91.79 185	97.70 170	94.80 173	90.32 128	96.52 198	79.75 183	88.47 195	82.46 195	95.32 177	95.14 200	94.96 190	99.63 128	99.73 73

divwei89l23v2f112		92.80 172	93.60 191	91.86 164	91.75 188	97.71 167	94.75 179	90.32 128	96.54 197	79.35 190	88.59 192	82.55 193	95.35 175	95.15 198	94.96 190	99.63 128	99.72 85

EU-MVSNet		92.80 172	94.76 157	90.51 195	91.88 178	96.74 207	92.48 207	88.69 154	96.21 203	79.00 195	91.51 159	87.82 136	91.83 213	95.87 177	96.27 144	99.21 191	98.92 185

v1141		92.79 174	93.61 189	91.84 165	91.75 188	97.71 167	94.74 180	90.33 127	96.58 195	79.21 193	88.59 192	82.53 194	95.36 174	95.16 194	94.96 190	99.63 128	99.72 85

v10		92.79 174	94.06 173	91.31 176	91.78 186	97.29 201	94.87 167	86.10 181	96.97 177	79.82 182	88.16 199	84.56 170	95.63 152	96.33 166	95.31 169	99.65 111	99.80 34

v2v482		92.77 176	93.52 196	91.90 162	91.59 200	97.63 178	94.57 191	90.31 130	96.80 186	79.22 192	88.74 189	81.55 206	96.04 144	95.26 183	94.97 189	99.66 105	99.69 97

PS-CasMVS		92.72 177	93.36 198	91.98 158	91.62 199	97.52 188	94.13 197	88.98 150	95.94 211	81.51 164	87.35 207	79.95 214	95.91 146	96.37 163	96.49 139	99.70 81	99.89 7

PEN-MVS		92.72 177	93.20 204	92.15 151	91.29 207	97.31 199	94.67 185	89.81 141	96.19 204	81.83 162	88.58 194	79.06 219	95.61 155	95.21 187	96.27 144	99.72 62	99.82 26

pmmvs5		92.71 179	94.27 167	90.90 187	91.42 204	97.74 166	93.23 201	86.66 177	95.99 210	78.96 196	91.45 160	83.44 178	95.55 156	97.30 140	95.05 177	99.58 154	98.93 182

v16		92.66 180	93.80 184	91.32 175	92.13 168	95.62 213	94.89 163	85.12 187	97.20 167	80.66 168	89.96 176	83.93 174	95.49 160	95.17 190	95.04 178	99.63 128	99.68 104

v18		92.63 181	93.67 187	91.43 171	92.13 168	95.65 211	95.09 157	85.44 185	97.06 173	80.78 167	90.06 170	83.06 182	95.47 166	95.16 194	95.01 183	99.64 122	99.67 109

v17		92.55 182	93.65 188	91.27 177	92.11 170	95.63 212	94.89 163	85.15 186	97.12 172	80.39 176	90.02 171	83.02 183	95.45 168	95.17 190	94.92 193	99.66 105	99.68 104

MVS-HIRNet		92.51 183	95.97 136	88.48 210	93.73 151	98.37 145	90.33 216	75.36 233	98.32 119	77.78 200	89.15 183	94.87 94	95.14 184	97.62 131	96.39 141	98.51 201	97.11 212

EG-PatchMatch MVS		92.45 184	93.92 181	90.72 192	92.56 161	98.43 141	94.88 166	84.54 194	97.18 168	79.55 188	86.12 217	83.23 181	93.15 208	97.22 143	96.00 151	99.67 100	99.27 166

MDTV_nov1_ep13_2view		92.44 185	95.66 143	88.68 208	91.05 211	97.92 160	92.17 208	79.64 214	98.83 88	76.20 207	91.45 160	93.51 114	95.04 185	95.68 179	93.70 209	97.96 211	98.53 191

v1192		92.43 186	93.61 189	91.05 181	91.53 201	97.43 194	94.61 188	87.99 164	96.60 193	76.72 205	87.11 209	82.74 190	95.85 147	96.35 165	95.30 170	99.60 144	99.74 69

v11		92.43 186	93.77 185	90.85 190	91.72 192	95.58 218	94.87 167	84.07 202	96.98 176	79.28 191	88.03 202	84.22 173	95.53 159	96.55 158	95.36 167	99.65 111	99.70 91

DTE-MVSNet		92.42 188	92.85 210	91.91 161	90.87 212	96.97 203	94.53 192	89.81 141	95.86 213	81.59 163	88.83 187	77.88 222	95.01 186	94.34 210	96.35 142	99.64 122	99.73 73

v144192		92.38 189	93.55 195	91.00 184	91.44 203	97.47 193	94.27 194	87.41 169	96.52 198	78.03 198	87.50 206	82.65 191	95.32 177	95.82 178	95.15 174	99.55 162	99.78 43

tpm		92.38 189	94.79 156	89.56 204	94.30 142	97.50 190	94.24 196	78.97 222	97.72 151	74.93 213	97.97 70	82.91 187	96.60 130	93.65 214	94.81 197	98.33 206	98.98 180

v1921920		92.36 191	93.57 192	90.94 186	91.39 205	97.39 196	94.70 183	87.63 168	96.60 193	76.63 206	86.98 210	82.89 188	95.75 148	96.26 170	95.14 175	99.55 162	99.73 73

v148		92.36 191	92.88 208	91.75 166	91.63 198	97.66 176	92.64 206	90.55 126	96.09 206	83.34 151	88.19 198	80.00 213	92.74 209	93.98 211	94.58 202	99.58 154	99.69 97

V14		92.31 193	93.41 197	91.03 183	91.80 184	95.59 216	94.79 174	84.70 192	96.58 195	79.83 181	88.79 188	82.98 186	95.41 170	95.22 184	95.02 182	99.65 111	99.67 109

v15		92.27 194	93.33 199	91.04 182	91.83 181	95.60 214	94.79 174	84.88 191	96.66 190	79.66 186	88.72 190	82.45 196	95.40 171	95.19 189	95.00 187	99.65 111	99.67 109

V9		92.24 195	93.32 201	90.98 185	91.76 187	95.58 218	94.83 172	84.50 196	96.68 189	79.73 184	88.66 191	82.39 197	95.39 172	95.22 184	95.03 180	99.65 111	99.67 109

N_pmnet		92.21 196	94.60 159	89.42 205	91.88 178	97.38 197	89.15 221	89.74 144	97.89 142	73.75 215	87.94 204	92.23 123	93.85 203	96.10 173	93.20 212	98.15 209	97.43 209

v12		92.18 197	93.29 202	90.88 188	91.70 193	95.59 216	94.61 188	84.36 198	96.65 191	79.59 187	88.85 186	82.03 201	95.35 175	95.22 184	95.04 178	99.65 111	99.68 104

v13		92.16 198	93.28 203	90.85 190	91.75 188	95.58 218	94.65 187	84.23 200	96.49 201	79.51 189	88.40 197	82.58 192	95.31 179	95.21 187	95.03 180	99.66 105	99.68 104

LP		92.12 199	94.60 159	89.22 206	94.96 136	98.45 138	93.01 203	77.58 226	97.85 146	77.26 203	89.80 178	93.00 118	94.54 189	93.69 212	92.58 217	98.00 210	96.83 217

v1240		91.99 200	93.33 199	90.44 196	91.29 207	97.30 200	94.25 195	86.79 174	96.43 202	75.49 211	86.34 215	81.85 204	95.29 180	96.42 162	95.22 172	99.52 168	99.73 73

v52		91.94 201	93.10 205	90.57 193	90.62 214	97.50 190	93.98 198	87.02 171	95.86 213	77.67 201	86.93 211	82.16 200	94.53 190	94.71 207	94.70 200	99.61 137	99.85 18

V4		91.92 202	93.10 205	90.55 194	90.64 213	97.51 189	93.93 199	87.02 171	95.81 215	77.61 202	86.93 211	82.19 199	94.50 191	94.72 206	94.68 201	99.62 134	99.85 18

pmmvs6		91.90 203	92.53 214	91.17 179	91.81 183	97.63 178	93.23 201	88.37 159	93.43 225	80.61 169	77.32 229	87.47 137	94.12 197	96.58 156	95.72 160	98.88 199	99.53 143

testpf		91.80 204	94.43 165	88.74 207	93.89 146	95.30 223	92.05 209	71.77 234	97.52 155	87.24 130	94.77 139	92.68 120	91.48 214	91.75 226	92.11 223	96.02 231	96.89 216

v7n		91.61 205	92.95 207	90.04 200	90.56 216	97.69 174	93.74 200	85.59 183	95.89 212	76.95 204	86.60 214	78.60 221	93.76 204	97.01 149	94.99 188	99.65 111	99.87 13

v748		91.12 206	91.95 215	90.16 199	90.60 215	97.35 198	91.11 210	87.92 165	94.75 219	80.54 171	86.26 216	75.97 224	91.13 215	94.63 208	94.81 197	99.65 111	99.90 3

gg-mvs-nofinetune		90.85 207	94.14 168	87.02 213	94.89 138	99.25 90	98.64 56	76.29 230	88.24 232	57.50 234	79.93 227	95.45 89	95.18 183	98.77 51	98.07 83	99.62 134	99.24 168

CMPMVS	70.31 18	90.74 208	91.06 217	90.36 198	97.32 69	97.43 194	92.97 204	87.82 167	93.50 224	75.34 212	83.27 222	84.90 167	92.19 212	92.64 219	91.21 227	96.50 229	94.46 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		90.70 209	93.93 180	86.92 214	90.21 219	96.79 205	90.30 217	86.61 178	96.05 208	69.25 222	88.46 196	84.86 168	85.86 222	97.11 147	96.47 140	99.30 188	97.80 205

test20.03		90.65 210	93.71 186	87.09 212	90.44 217	96.24 208	89.74 220	85.46 184	95.59 217	72.99 218	90.68 166	85.33 162	84.41 225	95.94 176	95.10 176	99.52 168	97.06 214

new_pmnet		90.45 211	92.84 211	87.66 211	88.96 220	96.16 209	88.71 222	84.66 193	97.56 154	71.91 221	85.60 218	86.58 151	93.28 206	96.07 174	93.54 210	98.46 203	94.39 225

pmmvs-eth3d		89.81 212	89.65 220	90.00 201	86.94 224	95.38 221	91.08 211	86.39 179	94.57 221	82.27 160	83.03 223	64.94 231	93.96 200	96.57 157	93.82 208	99.35 185	99.24 168

PM-MVS		89.55 213	90.30 219	88.67 209	87.06 223	95.60 214	90.88 213	84.51 195	96.14 205	75.75 208	86.89 213	63.47 234	94.64 188	96.85 152	93.89 207	99.17 194	99.29 164

gm-plane-assit		89.44 214	92.82 212	85.49 217	91.37 206	95.34 222	79.55 232	82.12 205	91.68 228	64.79 229	87.98 203	80.26 212	95.66 151	98.51 73	97.56 110	99.45 175	98.41 194

test2356		88.81 215	92.86 209	84.09 221	87.85 222	93.46 228	87.07 226	83.60 204	96.50 200	62.08 232	97.06 90	75.04 225	85.17 223	95.08 203	95.42 165	98.75 200	97.46 207

testus		88.77 216	92.77 213	84.10 220	88.24 221	93.95 226	87.16 225	84.24 199	97.37 157	61.54 233	95.70 129	73.10 227	84.90 224	95.56 180	95.82 157	98.51 201	97.88 204

MIMVSNet1		88.61 217	90.68 218	86.19 216	81.56 234	95.30 223	87.78 223	85.98 182	94.19 223	72.30 220	78.84 228	78.90 220	90.06 216	96.59 155	95.47 163	99.46 174	95.49 223

pmmvs3		88.19 218	91.27 216	84.60 219	85.60 226	93.66 227	85.68 227	81.13 206	92.36 227	63.66 231	89.51 180	77.10 223	93.22 207	96.37 163	92.40 219	98.30 207	97.46 207

MDA-MVSNet-bldmvs		87.84 219	89.22 221	86.23 215	81.74 233	96.77 206	83.74 228	89.57 145	94.50 222	72.83 219	96.64 102	64.47 233	92.71 210	81.43 233	92.28 221	96.81 228	98.47 193

new-patchmatchnet		86.12 220	87.30 222	84.74 218	86.92 225	95.19 225	83.57 229	84.42 197	92.67 226	65.66 226	80.32 226	64.72 232	89.41 217	92.33 222	89.21 228	98.43 204	96.69 219

FPMVS		83.82 221	84.61 227	82.90 222	90.39 218	90.71 230	90.85 214	84.10 201	95.47 218	65.15 227	83.44 220	74.46 226	75.48 228	81.63 232	79.42 234	91.42 235	87.14 233

1111		82.87 222	85.67 225	79.62 225	81.86 231	89.62 231	74.44 234	68.81 236	87.44 233	66.59 224	76.83 230	70.33 229	87.71 219	92.65 217	93.37 211	98.28 208	89.42 231

testmv		81.83 223	86.26 223	76.66 226	84.10 227	89.42 233	74.29 236	79.65 213	90.61 229	51.85 238	82.11 224	63.06 236	72.61 231	91.94 224	92.75 214	97.49 219	93.94 227

test1235678		81.83 223	86.26 223	76.66 226	84.10 227	89.41 234	74.29 236	79.64 214	90.60 230	51.84 239	82.11 224	63.07 235	72.61 231	91.94 224	92.75 214	97.49 219	93.94 227

Gipuma		81.40 225	81.78 228	80.96 224	83.21 229	85.61 238	79.73 231	76.25 231	97.33 164	64.21 230	55.32 235	55.55 238	86.04 221	92.43 221	92.20 222	96.32 230	93.99 226
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		80.53 226	84.80 226	75.54 228	82.31 230	88.05 237	75.99 233	79.31 218	88.53 231	53.24 237	83.30 221	56.38 237	65.16 237	90.87 228	93.10 213	97.25 225	93.34 230

PMMVS2		77.26 227	79.47 230	74.70 230	76.00 237	88.37 236	74.22 238	76.34 229	78.31 236	54.13 235	69.96 233	52.50 239	70.14 234	84.83 231	88.71 229	97.35 221	93.58 229

PMVS	72.60 17	76.39 228	77.66 231	74.92 229	81.04 235	69.37 243	68.47 239	80.54 210	85.39 235	65.07 228	73.52 232	72.91 228	65.67 236	80.35 234	76.81 235	88.71 237	85.25 237
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		69.67 229	72.22 232	66.70 233	81.86 231	89.62 231	74.44 234	68.81 236	87.44 233	66.59 224	76.83 230	70.33 229	87.71 219	92.65 217	37.65 237	20.79 241	51.04 238

GG-mvs-BLEND		69.11 230	98.13 74	35.26 236	3.49 244	98.20 153	94.89 163	2.38 241	98.42 117	5.82 245	96.37 111	98.60 56	5.97 241	98.75 54	97.98 88	99.01 196	98.61 189

E-PMN		68.30 231	68.43 233	68.15 231	74.70 239	71.56 242	55.64 241	77.24 227	77.48 238	39.46 241	51.95 238	41.68 242	73.28 230	70.65 236	79.51 233	88.61 238	86.20 236

EMVS		68.12 232	68.11 234	68.14 232	75.51 238	71.76 241	55.38 242	77.20 228	77.78 237	37.79 242	53.59 236	43.61 240	74.72 229	67.05 238	76.70 236	88.27 239	86.24 235

no-one		66.79 233	67.62 235	65.81 234	73.06 240	81.79 239	51.90 244	76.20 232	61.07 240	54.05 236	51.62 239	41.72 241	49.18 238	67.26 237	82.83 232	90.47 236	87.07 234

MVE	67.97 19	65.53 234	67.43 236	63.31 235	59.33 241	74.20 240	53.09 243	70.43 235	66.27 239	43.13 240	45.98 240	30.62 243	70.65 233	79.34 235	86.30 230	83.25 240	89.33 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 235	40.15 237	20.86 237	12.61 242	17.99 244	25.16 245	13.30 239	48.42 241	24.82 243	53.07 237	30.13 245	28.47 239	42.73 239	37.65 237	20.79 241	51.04 238

test123		26.75 236	34.25 238	18.01 238	7.93 243	17.18 245	24.85 246	12.36 240	44.83 242	16.52 244	41.80 241	18.10 246	28.29 240	33.08 240	34.79 239	18.10 243	49.95 240

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

sosnet		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

Anonymous202405211			97.40 97		96.45 82	99.54 52	98.08 88	93.79 81	98.24 123		93.55 147	94.41 102	98.88 63	98.04 108	98.24 76	99.75 46	99.76 54

our_test_3					92.30 164	97.58 183	90.09 219

ambc			80.99 229		80.04 236	90.84 229	90.91 212		96.09 206	74.18 214	62.81 234	30.59 244	82.44 227	96.25 171	91.77 224	95.91 232	98.56 190

MTAPA										98.09 10		99.97 4

MTMP										98.46 7		99.96 10

Patchmatch-RL test							66.86 240

tmp_tt				82.25 223	97.73 63	88.71 235	80.18 230	68.65 238	99.15 51	86.98 132	99.47 7	85.31 163	68.35 235	87.51 230	83.81 231	91.64 234

XVS					97.42 67	99.62 33	98.59 58			93.81 81		99.95 15				99.69 83

X-MVStestdata					97.42 67	99.62 33	98.59 58			93.81 81		99.95 15				99.69 83

abl_6				98.09 36	99.33 37	99.22 94	98.79 53	94.96 48	98.52 114	97.00 28	97.30 83	99.86 33	98.76 65			99.69 83	99.41 159

mPP-MVS					99.53 25							99.89 30

NP-MVS									98.57 109

Patchmtry						98.59 130	97.15 119	79.14 219		80.42 173

DeepMVS_CX						96.85 204	87.43 224	89.27 147	98.30 120	75.55 210	95.05 135	79.47 216	92.62 211	89.48 229		95.18 233	95.96 222

High-resolution multi-view benchmark