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 13	99.65 2	99.25 13	99.84 3	99.77 47

HSP-MVS		99.31 2	99.43 12	99.17 2	99.68 8	99.75 2	99.72 2	98.31 5	99.45 15	98.16 9	99.28 11	99.98 1	99.30 29	99.34 17	98.41 52	99.81 16	99.81 29

MPTG		99.31 2	99.44 10	99.16 4	99.73 4	99.65 17	99.63 10	98.26 9	99.27 32	98.01 12	99.27 12	99.97 3	99.60 5	99.59 5	98.58 45	99.71 57	99.73 66

ACMMPR		99.30 4	99.54 3	99.03 11	99.66 11	99.64 21	99.68 5	98.25 10	99.56 9	97.12 23	99.19 14	99.95 12	99.72 1	99.43 12	99.25 13	99.72 48	99.77 47

TSAR-MVS + MP.		99.27 5	99.57 2	98.92 16	98.78 46	99.53 43	99.72 2	98.11 21	99.73 2	97.43 19	99.15 17	99.96 7	99.59 7	99.73 1	99.07 20	99.88 1	99.82 24

CP-MVS		99.27 5	99.44 10	99.08 8	99.62 15	99.58 38	99.53 14	98.16 14	99.21 41	97.79 15	99.15 17	99.96 7	99.59 7	99.54 7	98.86 34	99.78 26	99.74 62

SD-MVS		99.25 7	99.50 5	98.96 14	98.79 45	99.55 42	99.33 27	98.29 7	99.75 1	97.96 13	99.15 17	99.95 12	99.61 4	99.17 23	99.06 21	99.81 16	99.84 20

APD-MVS		99.25 7	99.38 15	99.09 7	99.69 6	99.58 38	99.56 13	98.32 4	98.85 73	97.87 14	98.91 30	99.92 22	99.30 29	99.45 11	99.38 8	99.79 23	99.58 117
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.23 9	99.28 22	99.17 2	99.65 13	99.34 65	99.46 20	98.21 12	99.28 30	98.47 5	98.89 32	99.94 20	99.50 14	99.42 13	98.61 44	99.73 43	99.52 127

SteuartSystems-ACMMP		99.20 10	99.51 4	98.83 20	99.66 11	99.66 16	99.71 4	98.12 20	99.14 47	96.62 27	99.16 16	99.98 1	99.12 43	99.63 3	99.19 18	99.78 26	99.83 23
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.34 1	99.17 11	99.45 7	98.85 18	99.55 21	99.37 60	99.64 8	98.05 23	99.53 11	96.58 28	98.93 28	99.92 22	99.49 16	99.46 10	99.32 10	99.80 22	99.64 112
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 12	99.24 24	99.04 10	99.52 24	99.49 46	99.09 38	98.07 22	99.37 20	98.47 5	97.79 61	99.89 27	99.50 14	98.93 36	99.45 4	99.61 118	99.76 51

CPTT-MVS		99.14 13	99.20 26	99.06 9	99.58 18	99.53 43	99.45 21	97.80 28	99.19 43	98.32 8	98.58 40	99.95 12	99.60 5	99.28 20	98.20 67	99.64 103	99.69 87

MCST-MVS		99.11 14	99.27 23	98.93 15	99.67 9	99.33 67	99.51 16	98.31 5	99.28 30	96.57 29	99.10 21	99.90 25	99.71 2	99.19 22	98.35 58	99.82 8	99.71 82

HPM-MVS++		99.10 15	99.30 21	98.86 17	99.69 6	99.48 47	99.59 12	98.34 2	99.26 35	96.55 30	99.10 21	99.96 7	99.36 24	99.25 21	98.37 57	99.64 103	99.66 105

PHI-MVS		99.08 16	99.43 12	98.67 22	99.15 38	99.59 37	99.11 36	97.35 31	99.14 47	97.30 20	99.44 8	99.96 7	99.32 27	98.89 40	99.39 7	99.79 23	99.58 117

MP-MVS		99.07 17	99.36 17	98.74 21	99.63 14	99.57 40	99.66 7	98.25 10	99.00 63	95.62 35	98.97 26	99.94 20	99.54 12	99.51 8	98.79 39	99.71 57	99.73 66

AdaColmap		99.06 18	98.98 41	99.15 5	99.60 17	99.30 70	99.38 25	98.16 14	99.02 62	98.55 4	98.71 38	99.57 46	99.58 10	99.09 27	97.84 81	99.64 103	99.36 143

ACMMP_Plus		99.05 19	99.45 7	98.58 24	99.73 4	99.60 36	99.64 8	98.28 8	99.23 38	94.57 51	99.35 10	99.97 3	99.55 11	99.63 3	98.66 41	99.70 64	99.74 62

NCCC		99.05 19	99.08 31	99.02 12	99.62 15	99.38 58	99.43 24	98.21 12	99.36 22	97.66 17	97.79 61	99.90 25	99.45 19	99.17 23	98.43 50	99.77 30	99.51 131

CNLPA		99.03 21	99.05 34	99.01 13	99.27 36	99.22 78	99.03 42	97.98 24	99.34 25	99.00 2	98.25 50	99.71 40	99.31 28	98.80 45	98.82 37	99.48 152	99.17 152

PLC	97.93 2	99.02 22	98.94 42	99.11 6	99.46 26	99.24 76	99.06 40	97.96 25	99.31 27	99.16 1	97.90 59	99.79 37	99.36 24	98.71 53	98.12 70	99.65 92	99.52 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
X-MVS		98.93 23	99.37 16	98.42 25	99.67 9	99.62 28	99.60 11	98.15 16	99.08 53	93.81 70	98.46 45	99.95 12	99.59 7	99.49 9	99.21 17	99.68 75	99.75 59

CSCG		98.90 24	98.93 43	98.85 18	99.75 2	99.72 4	99.49 17	96.58 34	99.38 18	98.05 11	98.97 26	97.87 61	99.49 16	97.78 103	98.92 29	99.78 26	99.90 3

PGM-MVS		98.86 25	99.35 20	98.29 28	99.77 1	99.63 24	99.67 6	95.63 37	98.66 93	95.27 41	99.11 20	99.82 34	99.67 3	99.33 18	99.19 18	99.73 43	99.74 62

OMC-MVS		98.84 26	99.01 40	98.65 23	99.39 28	99.23 77	99.22 30	96.70 33	99.40 17	97.77 16	97.89 60	99.80 35	99.21 33	99.02 31	98.65 42	99.57 139	99.07 159

TSAR-MVS + ACMM		98.77 27	99.45 7	97.98 36	99.37 29	99.46 49	99.44 23	98.13 19	99.65 4	92.30 86	98.91 30	99.95 12	99.05 48	99.42 13	98.95 27	99.58 135	99.82 24

ACMMP		98.74 28	99.03 38	98.40 26	99.36 31	99.64 21	99.20 31	97.75 29	98.82 78	95.24 42	98.85 33	99.87 29	99.17 40	98.74 52	97.50 95	99.71 57	99.76 51
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 29	99.11 29	98.28 29	99.36 31	99.35 63	99.48 19	97.96 25	98.83 76	93.86 69	98.70 39	99.86 30	99.44 20	99.08 29	98.38 55	99.61 118	99.58 117

3Dnovator+	96.92 7	98.71 30	99.05 34	98.32 27	99.53 22	99.34 65	99.06 40	94.61 51	99.65 4	97.49 18	96.75 85	99.86 30	99.44 20	98.78 47	99.30 11	99.81 16	99.67 97

MVS_111021_LR		98.67 31	99.41 14	97.81 39	99.37 29	99.53 43	98.51 57	95.52 39	99.27 32	94.85 48	99.56 5	99.69 41	99.04 49	99.36 16	98.88 32	99.60 125	99.58 117

3Dnovator	96.92 7	98.67 31	99.05 34	98.23 31	99.57 19	99.45 51	99.11 36	94.66 50	99.69 3	96.80 26	96.55 95	99.61 43	99.40 22	98.87 42	99.49 3	99.85 2	99.66 105

TSAR-MVS + GP.		98.66 33	99.36 17	97.85 38	97.16 72	99.46 49	99.03 42	94.59 53	99.09 51	97.19 22	99.73 3	99.95 12	99.39 23	98.95 34	98.69 40	99.75 33	99.65 108

QAPM		98.62 34	99.04 37	98.13 32	99.57 19	99.48 47	99.17 33	94.78 47	99.57 8	96.16 32	96.73 87	99.80 35	99.33 26	98.79 46	99.29 12	99.75 33	99.64 112

MVS_111021_HR		98.59 35	99.36 17	97.68 40	99.42 27	99.61 32	98.14 73	94.81 46	99.31 27	95.00 46	99.51 6	99.79 37	99.00 52	98.94 35	98.83 36	99.69 66	99.57 122

CANet		98.46 36	99.16 27	97.64 41	98.48 49	99.64 21	99.35 26	94.71 49	99.53 11	95.17 43	97.63 67	99.59 44	98.38 66	98.88 41	98.99 25	99.74 37	99.86 15

CDPH-MVS		98.41 37	99.10 30	97.61 42	99.32 35	99.36 61	99.49 17	96.15 36	98.82 78	91.82 89	98.41 46	99.66 42	99.10 46	98.93 36	98.97 26	99.75 33	99.58 117

TAPA-MVS	97.53 5	98.41 37	98.84 47	97.91 37	99.08 40	99.33 67	99.15 34	97.13 32	99.34 25	93.20 77	97.75 63	99.19 49	99.20 34	98.66 55	98.13 69	99.66 86	99.48 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.74 3	98.34 39	99.46 6	97.04 54	98.82 44	99.33 67	96.28 121	97.47 30	99.58 7	94.70 50	98.99 25	99.85 33	97.24 93	99.55 6	99.34 9	97.73 196	99.56 123

DeepC-MVS	97.63 4	98.33 40	98.57 51	98.04 34	98.62 48	99.65 17	99.45 21	98.15 16	99.51 13	92.80 83	95.74 112	96.44 75	99.46 18	99.37 15	99.50 2	99.78 26	99.81 29
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 41	98.29 58	98.24 30	99.20 37	99.22 78	99.20 31	97.82 27	99.37 20	94.43 55	95.90 109	97.31 67	99.12 43	98.76 49	98.35 58	99.67 81	99.14 156

DELS-MVS		98.19 42	98.77 48	97.52 43	98.29 52	99.71 8	99.12 35	94.58 54	98.80 81	95.38 40	96.24 101	98.24 59	97.92 77	99.06 30	99.52 1	99.82 8	99.79 38
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 43	98.35 57	97.99 35	98.65 47	99.36 61	98.94 45	98.14 18	98.59 95	93.62 73	96.61 91	99.76 39	99.03 50	97.77 104	97.45 99	99.57 139	98.89 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_0304		98.14 44	99.03 38	97.10 51	98.05 56	99.63 24	99.27 29	94.33 56	99.63 6	93.06 80	97.32 70	99.05 51	98.09 72	98.82 44	98.87 33	99.81 16	99.89 7

EPNet		98.05 45	98.86 45	97.10 51	99.02 41	99.43 54	98.47 58	94.73 48	99.05 59	95.62 35	98.93 28	97.62 65	95.48 142	98.59 62	98.55 46	99.29 170	99.84 20
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		97.99 46	99.24 24	96.53 69	98.34 51	99.61 32	98.36 66	89.80 124	99.27 32	95.08 45	99.81 1	98.58 54	98.64 60	99.02 31	98.92 29	98.93 178	99.48 135

OpenMVS	96.23 11	97.95 47	98.45 55	97.35 44	99.52 24	99.42 55	98.91 46	94.61 51	98.87 70	92.24 87	94.61 124	99.05 51	99.10 46	98.64 57	99.05 22	99.74 37	99.51 131

IS_MVSNet		97.86 48	98.86 45	96.68 65	96.02 89	99.72 4	98.35 67	93.37 74	98.75 90	94.01 63	96.88 84	98.40 57	98.48 64	99.09 27	99.42 5	99.83 6	99.80 31

LS3D		97.79 49	98.25 59	97.26 49	98.40 50	99.63 24	99.53 14	98.63 1	99.25 37	88.13 107	96.93 83	94.14 99	99.19 36	99.14 25	99.23 15	99.69 66	99.42 139

COLMAP_ROB	96.15 12	97.78 50	98.17 63	97.32 45	98.84 43	99.45 51	99.28 28	95.43 40	99.48 14	91.80 90	94.83 122	98.36 58	98.90 53	98.09 84	97.85 80	99.68 75	99.15 153
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 51	98.25 59	97.21 50	99.11 39	99.25 74	97.06 107	94.09 59	98.72 91	95.14 44	98.47 44	96.29 77	98.43 65	98.65 56	97.44 100	99.45 156	98.94 162

EPP-MVSNet		97.75 52	98.71 49	96.63 68	95.68 101	99.56 41	97.51 88	93.10 76	99.22 39	94.99 47	97.18 76	97.30 68	98.65 59	98.83 43	98.93 28	99.84 3	99.92 1

MAR-MVS		97.71 53	98.04 68	97.32 45	99.35 33	98.91 91	97.65 85	91.68 86	98.00 120	97.01 24	97.72 65	94.83 91	98.85 54	98.44 69	98.86 34	99.41 162	99.52 127
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 54	99.07 33	96.01 79	97.19 71	99.65 17	97.09 105	93.39 72	99.35 24	94.40 57	98.79 35	99.59 44	94.24 177	98.04 92	98.29 64	99.73 43	99.80 31
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 55	98.16 64	96.96 62	98.10 53	99.00 84	98.84 48	93.76 67	99.45 15	94.78 49	99.39 9	99.31 48	98.53 63	96.61 135	95.43 145	97.74 194	97.93 185

PMMVS		97.52 56	98.39 56	96.51 71	95.82 98	98.73 105	97.80 81	93.05 77	98.76 88	94.39 58	99.07 24	97.03 71	98.55 62	98.31 73	97.61 90	99.43 159	99.21 151

PVSNet_BlendedMVS		97.51 57	97.71 76	97.28 47	98.06 54	99.61 32	97.31 94	95.02 43	99.08 53	95.51 37	98.05 54	90.11 117	98.07 73	98.91 38	98.40 53	99.72 48	99.78 40

PVSNet_Blended		97.51 57	97.71 76	97.28 47	98.06 54	99.61 32	97.31 94	95.02 43	99.08 53	95.51 37	98.05 54	90.11 117	98.07 73	98.91 38	98.40 53	99.72 48	99.78 40

diffmvs		97.50 59	98.63 50	96.18 74	95.88 95	99.26 73	98.19 71	91.08 99	99.36 22	94.32 60	98.24 51	96.83 72	98.22 68	98.45 67	98.42 51	99.42 161	99.86 15

PVSNet_Blended_VisFu		97.41 60	98.49 54	96.15 76	97.49 62	99.76 1	96.02 124	93.75 68	99.26 35	93.38 76	93.73 130	99.35 47	96.47 115	98.96 33	98.46 49	99.77 30	99.90 3

Vis-MVSNet (Re-imp)		97.40 61	98.89 44	95.66 86	95.99 92	99.62 28	97.82 79	93.22 75	98.82 78	91.40 92	96.94 82	98.56 55	95.70 131	99.14 25	99.41 6	99.79 23	99.75 59

canonicalmvs		97.31 62	97.81 75	96.72 64	96.20 87	99.45 51	98.21 70	91.60 88	99.22 39	95.39 39	98.48 43	90.95 115	99.16 41	97.66 109	99.05 22	99.76 32	99.90 3

MVS_Test		97.30 63	98.54 52	95.87 80	95.74 99	99.28 71	98.19 71	91.40 93	99.18 44	91.59 91	98.17 52	96.18 78	98.63 61	98.61 59	98.55 46	99.66 86	99.78 40

MVSTER		97.16 64	97.71 76	96.52 70	95.97 93	98.48 118	98.63 54	92.10 80	98.68 92	95.96 34	99.23 13	91.79 113	96.87 102	98.76 49	97.37 103	99.57 139	99.68 92

UA-Net		97.13 65	99.14 28	94.78 93	97.21 70	99.38 58	97.56 86	92.04 81	98.48 103	88.03 108	98.39 47	99.91 24	94.03 180	99.33 18	99.23 15	99.81 16	99.25 148

FC-MVSNet-train		97.04 66	97.91 74	96.03 78	96.00 91	98.41 125	96.53 117	93.42 71	99.04 61	93.02 81	98.03 56	94.32 97	97.47 89	97.93 96	97.77 85	99.75 33	99.88 11

FMVSNet3		97.02 67	98.12 66	95.73 85	93.59 136	97.98 137	98.34 68	91.32 94	98.80 81	93.92 66	97.21 73	95.94 82	97.63 85	98.61 59	98.62 43	99.61 118	99.65 108

GBi-Net		96.98 68	98.00 71	95.78 81	93.81 130	97.98 137	98.09 74	91.32 94	98.80 81	93.92 66	97.21 73	95.94 82	97.89 78	98.07 87	98.34 60	99.68 75	99.67 97

test1		96.98 68	98.00 71	95.78 81	93.81 130	97.98 137	98.09 74	91.32 94	98.80 81	93.92 66	97.21 73	95.94 82	97.89 78	98.07 87	98.34 60	99.68 75	99.67 97

DI_MVS_plusplus_trai		96.90 70	97.49 82	96.21 73	95.61 103	99.40 57	98.72 52	92.11 79	99.14 47	92.98 82	93.08 140	95.14 88	98.13 71	98.05 90	97.91 78	99.74 37	99.73 66

TSAR-MVS + COLMAP		96.79 71	96.55 104	97.06 53	97.70 61	98.46 119	99.07 39	96.23 35	99.38 18	91.32 93	98.80 34	85.61 143	98.69 58	97.64 112	96.92 110	99.37 165	99.06 160

thres200		96.76 72	96.53 105	97.03 55	96.31 80	99.67 12	98.37 65	93.99 61	97.68 140	94.49 53	95.83 111	86.77 131	99.18 38	98.26 76	97.82 82	99.82 8	99.66 105

tfpn200view9		96.75 73	96.51 107	97.03 55	96.31 80	99.67 12	98.41 61	93.99 61	97.35 145	94.52 52	95.90 109	86.93 129	99.14 42	98.26 76	97.80 83	99.82 8	99.70 84

CLD-MVS		96.74 74	96.51 107	97.01 58	96.71 77	98.62 111	98.73 51	94.38 55	98.94 67	94.46 54	97.33 69	87.03 127	98.07 73	97.20 125	96.87 111	99.72 48	99.54 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres400		96.71 75	96.45 112	97.02 57	96.28 84	99.63 24	98.41 61	94.00 60	97.82 135	94.42 56	95.74 112	86.26 137	99.18 38	98.20 80	97.79 84	99.81 16	99.70 84

view600		96.70 76	96.44 114	97.01 58	96.28 84	99.67 12	98.42 60	93.99 61	97.87 130	94.34 59	95.99 106	85.94 140	99.20 34	98.26 76	97.64 88	99.82 8	99.73 66

view800		96.70 76	96.45 112	96.99 61	96.29 82	99.69 11	98.39 64	93.95 65	97.92 127	94.25 62	96.23 102	85.57 144	99.22 31	98.28 74	97.71 86	99.82 8	99.76 51

thres600view7		96.69 78	96.43 116	97.00 60	96.28 84	99.67 12	98.41 61	93.99 61	97.85 133	94.29 61	95.96 107	85.91 141	99.19 36	98.26 76	97.63 89	99.82 8	99.73 66

test0.0.03 1		96.69 78	98.12 66	95.01 91	95.49 106	98.99 86	95.86 126	90.82 102	98.38 106	92.54 85	96.66 89	97.33 66	95.75 129	97.75 106	98.34 60	99.60 125	99.40 141

ACMM	96.26 9	96.67 80	96.69 101	96.66 66	97.29 69	98.46 119	96.48 118	95.09 42	99.21 41	93.19 78	98.78 36	86.73 132	98.17 69	97.84 101	96.32 125	99.74 37	99.49 134
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		96.64 81	99.08 31	93.81 105	97.10 73	99.42 55	98.85 47	90.01 118	99.31 27	79.98 162	99.78 2	99.10 50	97.42 90	98.35 71	98.05 73	99.47 154	99.53 125

FMVSNet2		96.64 81	97.50 81	95.63 87	93.81 130	97.98 137	98.09 74	90.87 100	98.99 64	93.48 74	93.17 137	95.25 87	97.89 78	98.63 58	98.80 38	99.68 75	99.67 97

ACMP	96.25 10	96.62 83	96.72 100	96.50 72	96.96 75	98.75 102	97.80 81	94.30 57	98.85 73	93.12 79	98.78 36	86.61 134	97.23 94	97.73 107	96.61 117	99.62 115	99.71 82
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CDS-MVSNet		96.59 84	98.02 70	94.92 92	94.45 123	98.96 89	97.46 90	91.75 85	97.86 132	90.07 99	96.02 105	97.25 69	96.21 118	98.04 92	98.38 55	99.60 125	99.65 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 1792x2688		96.41 85	96.99 97	95.74 84	98.01 57	99.72 4	97.70 84	90.78 104	99.13 50	90.03 100	87.35 188	95.36 86	98.33 67	98.59 62	98.91 31	99.59 131	99.87 13

HQP-MVS		96.37 86	96.58 102	96.13 77	97.31 68	98.44 122	98.45 59	95.22 41	98.86 71	88.58 105	98.33 48	87.00 128	97.67 84	97.23 123	96.56 119	99.56 142	99.62 114

conf0.05thres1000		96.34 87	96.47 110	96.17 75	96.16 88	99.71 8	97.82 79	93.46 70	98.10 116	90.69 95	96.75 85	85.26 148	99.11 45	98.05 90	97.65 87	99.82 8	99.80 31

EPNet_dtu		96.30 88	98.53 53	93.70 109	98.97 42	98.24 133	97.36 92	94.23 58	98.85 73	79.18 176	99.19 14	98.47 56	94.09 179	97.89 98	98.21 66	98.39 186	98.85 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LGP-MVS_train		96.23 89	96.89 98	95.46 88	97.32 66	98.77 99	98.81 49	93.60 69	98.58 96	85.52 123	99.08 23	86.67 133	97.83 83	97.87 99	97.51 94	99.69 66	99.73 66

tfpn		96.22 90	95.62 127	96.93 63	96.29 82	99.72 4	98.34 68	93.94 66	97.96 124	93.94 65	96.45 97	79.09 199	99.22 31	98.28 74	98.06 72	99.83 6	99.78 40

OPM-MVS		96.22 90	95.85 125	96.65 67	97.75 59	98.54 116	99.00 44	95.53 38	96.88 164	89.88 101	95.95 108	86.46 136	98.07 73	97.65 111	96.63 116	99.67 81	98.83 170

Vis-MVSNet		96.16 92	98.22 61	93.75 106	95.33 112	99.70 10	97.27 96	90.85 101	98.30 108	85.51 124	95.72 114	96.45 73	93.69 186	98.70 54	99.00 24	99.84 3	99.69 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS-LS		96.12 93	97.48 83	94.53 95	95.19 114	97.56 165	97.15 101	89.19 130	99.08 53	88.23 106	94.97 120	94.73 93	97.84 82	97.86 100	98.26 65	99.60 125	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 94	97.94 73	93.89 103	93.60 135	98.67 108	96.62 114	90.30 113	98.76 88	88.62 104	95.57 118	97.63 64	94.48 173	97.97 94	97.48 98	99.71 57	99.52 127

MS-PatchMatch		95.99 95	97.26 92	94.51 96	97.46 63	98.76 101	97.27 96	86.97 154	99.09 51	89.83 102	93.51 132	97.78 62	96.18 120	97.53 116	95.71 142	99.35 166	98.41 176

HyFIR lowres test		95.99 95	96.56 103	95.32 89	97.99 58	99.65 17	96.54 115	88.86 132	98.44 104	89.77 103	84.14 200	97.05 70	99.03 50	98.55 64	98.19 68	99.73 43	99.86 15

Effi-MVS+		95.81 97	97.31 91	94.06 101	95.09 115	99.35 63	97.24 98	88.22 141	98.54 99	85.38 125	98.52 41	88.68 121	98.70 57	98.32 72	97.93 76	99.74 37	99.84 20

FMVSNet1		95.77 98	96.41 117	95.03 90	93.42 137	97.86 144	97.11 104	89.89 121	98.53 100	92.00 88	89.17 163	93.23 106	98.15 70	98.07 87	98.34 60	99.61 118	99.69 87

Effi-MVS+-dtu		95.74 99	98.04 68	93.06 122	93.92 126	99.16 81	97.90 77	88.16 144	99.07 58	82.02 143	98.02 57	94.32 97	96.74 106	98.53 65	97.56 92	99.61 118	99.62 114

testgi		95.67 100	97.48 83	93.56 112	95.07 116	99.00 84	95.33 136	88.47 138	98.80 81	86.90 116	97.30 71	92.33 110	95.97 126	97.66 109	97.91 78	99.60 125	99.38 142

MDTV_nov1_ep13		95.57 101	97.48 83	93.35 119	95.43 108	98.97 88	97.19 100	83.72 185	98.92 69	87.91 110	97.75 63	96.12 80	97.88 81	96.84 134	95.64 143	97.96 192	98.10 181

TAMVS		95.53 102	96.50 109	94.39 98	93.86 129	99.03 83	96.67 112	89.55 127	97.33 146	90.64 96	93.02 141	91.58 114	96.21 118	97.72 108	97.43 101	99.43 159	99.36 143

test-LLR		95.50 103	97.32 88	93.37 117	95.49 106	98.74 103	96.44 119	90.82 102	98.18 112	82.75 138	96.60 92	94.67 94	95.54 138	98.09 84	96.00 132	99.20 173	98.93 163

FMVSNet5		95.42 104	96.47 110	94.20 99	92.26 146	95.99 190	95.66 129	87.15 151	97.87 130	93.46 75	96.68 88	93.79 102	97.52 86	97.10 129	97.21 105	99.11 176	96.62 203

ACMH+	95.51 13	95.40 105	96.00 119	94.70 94	96.33 79	98.79 96	96.79 110	91.32 94	98.77 87	87.18 114	95.60 117	85.46 145	96.97 98	97.15 126	96.59 118	99.59 131	99.65 108

Fast-Effi-MVS+-dtu		95.38 106	98.20 62	92.09 135	93.91 127	98.87 93	97.35 93	85.01 171	99.08 53	81.09 147	98.10 53	96.36 76	95.62 135	98.43 70	97.03 107	99.55 143	99.50 133

Fast-Effi-MVS+		95.38 106	96.52 106	94.05 102	94.15 125	99.14 82	97.24 98	86.79 155	98.53 100	87.62 112	94.51 125	87.06 126	98.76 55	98.60 61	98.04 74	99.72 48	99.77 47

DWT-MVSNet_training		95.38 106	95.05 133	95.78 81	95.86 96	98.88 92	97.55 87	90.09 117	98.23 111	96.49 31	97.62 68	86.92 130	97.16 95	92.03 205	94.12 186	97.52 200	97.50 188

CVMVSNet		95.33 109	97.09 94	93.27 120	95.23 113	98.39 127	95.49 133	92.58 78	97.71 139	83.00 137	94.44 126	93.28 105	93.92 183	97.79 102	98.54 48	99.41 162	99.45 137

ACMH	95.42 14	95.27 110	95.96 121	94.45 97	96.83 76	98.78 98	94.72 163	91.67 87	98.95 65	86.82 117	96.42 98	83.67 160	97.00 97	97.48 117	96.68 115	99.69 66	99.76 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		95.09 111	95.90 122	94.14 100	92.29 145	97.70 151	95.45 134	90.31 111	98.60 94	90.70 94	93.25 135	89.90 119	96.67 108	97.13 127	95.42 146	99.44 158	99.28 146

EPMVS		95.05 112	96.86 99	92.94 125	95.84 97	98.96 89	96.68 111	79.87 194	99.05 59	90.15 98	97.12 77	95.99 81	97.49 88	95.17 171	94.75 180	97.59 199	96.96 197

IB-MVS	93.96 15	95.02 113	96.44 114	93.36 118	97.05 74	99.28 71	90.43 196	93.39 72	98.02 119	96.02 33	94.92 121	92.07 112	83.52 208	95.38 162	95.82 138	99.72 48	99.59 116
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		94.95 114	97.32 88	92.20 132	92.62 140	98.74 103	96.44 119	86.67 157	98.18 112	82.75 138	96.60 92	94.67 94	95.54 138	98.09 84	96.00 132	99.20 173	98.93 163

test-mter		94.86 115	97.32 88	92.00 139	92.41 144	98.82 95	96.18 123	86.35 161	98.05 118	82.28 141	96.48 96	94.39 96	95.46 148	98.17 81	96.20 129	99.32 168	99.13 157

IterMVS		94.81 116	97.71 76	91.42 154	94.83 121	97.63 159	97.38 91	85.08 169	98.93 68	75.67 191	94.02 127	97.64 63	96.66 109	98.45 67	97.60 91	98.90 179	99.72 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		94.70 117	97.08 95	91.92 142	95.53 104	98.85 94	95.77 127	79.54 198	98.95 65	85.98 120	98.52 41	96.45 73	97.39 91	95.32 163	94.09 187	97.32 204	97.38 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RPMNet		94.66 118	97.16 93	91.75 148	94.98 117	98.59 113	97.00 108	78.37 207	97.98 121	83.78 128	96.27 100	94.09 101	96.91 100	97.36 119	96.73 113	99.48 152	99.09 158

ADS-MVSNet		94.65 119	97.04 96	91.88 145	95.68 101	98.99 86	95.89 125	79.03 203	99.15 45	85.81 122	96.96 81	98.21 60	97.10 96	94.48 190	94.24 185	97.74 194	97.21 193

dps		94.63 120	95.31 132	93.84 104	95.53 104	98.71 106	96.54 115	80.12 193	97.81 137	97.21 21	96.98 80	92.37 109	96.34 117	92.46 202	91.77 206	97.26 206	97.08 195

UniMVSNet_NR-MVSNet		94.59 121	95.47 129	93.55 113	91.85 160	97.89 143	95.03 139	92.00 82	97.33 146	86.12 118	93.19 136	87.29 125	96.60 111	96.12 153	96.70 114	99.72 48	99.80 31

UniMVSNet (Re)		94.58 122	95.34 130	93.71 108	92.25 147	98.08 136	94.97 141	91.29 98	97.03 157	87.94 109	93.97 129	86.25 138	96.07 123	96.27 150	95.97 135	99.72 48	99.79 38

CR-MVSNet		94.57 123	97.34 87	91.33 156	94.90 119	98.59 113	97.15 101	79.14 201	97.98 121	80.42 155	96.59 94	93.50 104	96.85 103	98.10 82	97.49 96	99.50 151	99.15 153

MIMVSNet		94.49 124	97.59 80	90.87 171	91.74 171	98.70 107	94.68 165	78.73 205	97.98 121	83.71 131	97.71 66	94.81 92	96.96 99	97.97 94	97.92 77	99.40 164	98.04 183

pm-mvs1		94.27 125	95.57 128	92.75 126	92.58 141	98.13 135	94.87 148	90.71 105	96.70 170	83.78 128	89.94 158	89.85 120	94.96 168	97.58 114	97.07 106	99.61 118	99.72 78

USDC		94.26 126	94.83 137	93.59 111	96.02 89	98.44 122	97.84 78	88.65 136	98.86 71	82.73 140	94.02 127	80.56 190	96.76 105	97.28 122	96.15 131	99.55 143	98.50 174

CostFormer		94.25 127	94.88 136	93.51 114	95.43 108	98.34 129	96.21 122	80.64 190	97.94 126	94.01 63	98.30 49	86.20 139	97.52 86	92.71 197	92.69 197	97.23 208	98.02 184

tpm cat1		94.06 128	94.90 135	93.06 122	95.42 110	98.52 117	96.64 113	80.67 189	97.82 135	92.63 84	93.39 134	95.00 89	96.06 124	91.36 209	91.58 208	96.98 209	96.66 202

NR-MVSNet		94.01 129	94.51 144	93.44 115	92.56 142	97.77 145	95.67 128	91.57 89	97.17 151	85.84 121	93.13 138	80.53 191	95.29 161	97.01 130	96.17 130	99.69 66	99.75 59

TinyColmap		94.00 130	94.35 148	93.60 110	95.89 94	98.26 131	97.49 89	88.82 133	98.56 98	83.21 134	91.28 145	80.48 192	96.68 107	97.34 120	96.26 128	99.53 148	98.24 179

DU-MVS		93.98 131	94.44 146	93.44 115	91.66 175	97.77 145	95.03 139	91.57 89	97.17 151	86.12 118	93.13 138	81.13 189	96.60 111	95.10 182	97.01 109	99.67 81	99.80 31

PatchT		93.96 132	97.36 86	90.00 183	94.76 122	98.65 109	90.11 199	78.57 206	97.96 124	80.42 155	96.07 104	94.10 100	96.85 103	98.10 82	97.49 96	99.26 171	99.15 153

GA-MVS		93.93 133	96.31 118	91.16 162	93.61 134	98.79 96	95.39 135	90.69 106	98.25 110	73.28 199	96.15 103	88.42 122	94.39 175	97.76 105	95.35 149	99.58 135	99.45 137

Baseline_NR-MVSNet		93.87 134	93.98 157	93.75 106	91.66 175	97.02 182	95.53 132	91.52 92	97.16 153	87.77 111	87.93 186	83.69 159	96.35 116	95.10 182	97.23 104	99.68 75	99.73 66

tpmrst		93.86 135	95.88 123	91.50 152	95.69 100	98.62 111	95.64 130	79.41 199	98.80 81	83.76 130	95.63 116	96.13 79	97.25 92	92.92 196	92.31 202	97.27 205	96.74 200

tpmp4_e23		93.84 136	94.58 143	92.98 124	95.41 111	98.29 130	96.81 109	80.57 191	98.15 114	90.53 97	97.00 79	84.39 156	96.91 100	93.69 193	92.45 200	97.67 197	98.06 182

TranMVSNet+NR-MVSNet		93.67 137	94.14 150	93.13 121	91.28 189	97.58 164	95.60 131	91.97 83	97.06 155	84.05 126	90.64 149	82.22 182	96.17 121	94.94 186	96.78 112	99.69 66	99.78 40

WR-MVS_H		93.54 138	94.67 140	92.22 130	91.95 156	97.91 142	94.58 171	88.75 134	96.64 174	83.88 127	90.66 148	85.13 149	94.40 174	96.54 140	95.91 137	99.73 43	99.89 7

TransMVSNet (Re)		93.45 139	94.08 153	92.72 127	92.83 138	97.62 162	94.94 142	91.54 91	95.65 198	83.06 136	88.93 166	83.53 161	94.25 176	97.41 118	97.03 107	99.67 81	98.40 178

SixPastTwentyTwo		93.44 140	95.32 131	91.24 160	92.11 150	98.40 126	92.77 186	88.64 137	98.09 117	77.83 181	93.51 132	85.74 142	96.52 114	96.91 132	94.89 177	99.59 131	99.73 66

WR-MVS		93.43 141	94.48 145	92.21 131	91.52 182	97.69 155	94.66 167	89.98 119	96.86 165	83.43 132	90.12 150	85.03 150	93.94 182	96.02 156	95.82 138	99.71 57	99.82 24

CP-MVSNet		93.25 142	94.00 156	92.38 129	91.65 177	97.56 165	94.38 174	89.20 129	96.05 190	83.16 135	89.51 161	81.97 186	96.16 122	96.43 142	96.56 119	99.71 57	99.89 7

anonymousdsp		93.12 143	95.86 124	89.93 185	91.09 190	98.25 132	95.12 137	85.08 169	97.44 143	73.30 198	90.89 146	90.78 116	95.25 163	97.91 97	95.96 136	99.71 57	99.82 24

v6		93.11 144	93.98 157	92.10 134	92.01 153	97.71 148	94.86 151	90.15 114	96.96 160	80.47 154	90.01 153	83.26 164	95.48 142	95.17 171	95.01 164	99.64 103	99.76 51

v1neww		93.06 145	93.94 159	92.03 137	91.99 154	97.70 151	94.79 155	90.14 115	96.93 162	80.13 159	89.97 155	83.01 168	95.48 142	95.16 175	95.01 164	99.63 109	99.76 51

v7new		93.06 145	93.94 159	92.03 137	91.99 154	97.70 151	94.79 155	90.14 115	96.93 162	80.13 159	89.97 155	83.01 168	95.48 142	95.16 175	95.01 164	99.63 109	99.76 51

V42		93.05 147	93.90 163	92.04 136	91.91 157	97.66 157	94.91 143	89.91 120	96.85 166	80.58 152	89.66 160	83.43 163	95.37 154	95.03 185	94.90 175	99.59 131	99.78 40

TDRefinement		93.04 148	93.57 173	92.41 128	96.58 78	98.77 99	97.78 83	91.96 84	98.12 115	80.84 148	89.13 165	79.87 196	87.78 199	96.44 141	94.50 184	99.54 147	98.15 180

v7		92.97 149	94.11 152	91.65 151	91.83 161	97.55 167	94.86 151	88.19 143	96.96 160	79.72 167	88.16 180	84.68 153	95.63 133	96.33 147	95.30 151	99.65 92	99.77 47

v8		92.87 150	93.87 164	91.72 150	92.05 152	97.50 170	94.79 155	88.20 142	96.85 166	80.11 161	90.01 153	82.86 173	95.48 142	95.15 179	94.90 175	99.66 86	99.80 31

LTVRE_ROB	93.20 16	92.84 151	94.92 134	90.43 179	92.83 138	98.63 110	97.08 106	87.87 147	97.91 128	68.42 206	93.54 131	79.46 198	96.62 110	97.55 115	97.40 102	99.74 37	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		92.81 152	94.03 155	91.40 155	91.68 174	97.60 163	94.73 162	88.40 139	96.71 169	78.48 179	88.14 182	84.46 155	95.45 149	96.31 149	95.22 153	99.65 92	99.76 51

v1		92.81 152	93.57 173	91.94 141	91.79 165	97.70 151	94.80 154	90.32 109	96.52 180	79.75 165	88.47 176	82.46 179	95.32 158	95.14 181	94.96 171	99.63 109	99.73 66

divwei89l23v2f112		92.80 154	93.60 172	91.86 146	91.75 168	97.71 148	94.75 160	90.32 109	96.54 179	79.35 172	88.59 173	82.55 177	95.35 156	95.15 179	94.96 171	99.63 109	99.72 78

EU-MVSNet		92.80 154	94.76 139	90.51 177	91.88 158	96.74 187	92.48 188	88.69 135	96.21 185	79.00 177	91.51 142	87.82 123	91.83 194	95.87 158	96.27 126	99.21 172	98.92 166

v1141		92.79 156	93.61 170	91.84 147	91.75 168	97.71 148	94.74 161	90.33 108	96.58 177	79.21 175	88.59 173	82.53 178	95.36 155	95.16 175	94.96 171	99.63 109	99.72 78

v10		92.79 156	94.06 154	91.31 158	91.78 166	97.29 181	94.87 148	86.10 162	96.97 159	79.82 164	88.16 180	84.56 154	95.63 133	96.33 147	95.31 150	99.65 92	99.80 31

v2v482		92.77 158	93.52 177	91.90 144	91.59 180	97.63 159	94.57 172	90.31 111	96.80 168	79.22 174	88.74 170	81.55 188	96.04 125	95.26 164	94.97 170	99.66 86	99.69 87

PS-CasMVS		92.72 159	93.36 179	91.98 140	91.62 179	97.52 168	94.13 178	88.98 131	95.94 193	81.51 146	87.35 188	79.95 195	95.91 127	96.37 144	96.49 121	99.70 64	99.89 7

PEN-MVS		92.72 159	93.20 185	92.15 133	91.29 187	97.31 179	94.67 166	89.81 122	96.19 186	81.83 144	88.58 175	79.06 200	95.61 136	95.21 168	96.27 126	99.72 48	99.82 24

pmmvs5		92.71 161	94.27 149	90.90 169	91.42 184	97.74 147	93.23 182	86.66 158	95.99 192	78.96 178	91.45 143	83.44 162	95.55 137	97.30 121	95.05 158	99.58 135	98.93 163

v16		92.66 162	93.80 165	91.32 157	92.13 148	95.62 193	94.89 144	85.12 168	97.20 149	80.66 150	89.96 157	83.93 158	95.49 141	95.17 171	95.04 159	99.63 109	99.68 92

v18		92.63 163	93.67 168	91.43 153	92.13 148	95.65 191	95.09 138	85.44 166	97.06 155	80.78 149	90.06 151	83.06 166	95.47 147	95.16 175	95.01 164	99.64 103	99.67 97

v17		92.55 164	93.65 169	91.27 159	92.11 150	95.63 192	94.89 144	85.15 167	97.12 154	80.39 158	90.02 152	83.02 167	95.45 149	95.17 171	94.92 174	99.66 86	99.68 92

MVS-HIRNet		92.51 165	95.97 120	88.48 192	93.73 133	98.37 128	90.33 197	75.36 215	98.32 107	77.78 182	89.15 164	94.87 90	95.14 165	97.62 113	96.39 123	98.51 182	97.11 194

EG-PatchMatch MVS		92.45 166	93.92 162	90.72 174	92.56 142	98.43 124	94.88 147	84.54 175	97.18 150	79.55 170	86.12 198	83.23 165	93.15 189	97.22 124	96.00 132	99.67 81	99.27 147

MDTV_nov1_ep13_2view		92.44 167	95.66 126	88.68 190	91.05 191	97.92 141	92.17 189	79.64 196	98.83 76	76.20 189	91.45 143	93.51 103	95.04 166	95.68 160	93.70 190	97.96 192	98.53 173

v1192		92.43 168	93.61 170	91.05 163	91.53 181	97.43 174	94.61 169	87.99 145	96.60 175	76.72 187	87.11 190	82.74 174	95.85 128	96.35 146	95.30 151	99.60 125	99.74 62

v11		92.43 168	93.77 166	90.85 172	91.72 172	95.58 198	94.87 148	84.07 184	96.98 158	79.28 173	88.03 183	84.22 157	95.53 140	96.55 139	95.36 148	99.65 92	99.70 84

DTE-MVSNet		92.42 170	92.85 191	91.91 143	90.87 192	96.97 183	94.53 173	89.81 122	95.86 195	81.59 145	88.83 168	77.88 203	95.01 167	94.34 191	96.35 124	99.64 103	99.73 66

v144192		92.38 171	93.55 176	91.00 166	91.44 183	97.47 173	94.27 175	87.41 150	96.52 180	78.03 180	87.50 187	82.65 175	95.32 158	95.82 159	95.15 155	99.55 143	99.78 40

tpm		92.38 171	94.79 138	89.56 186	94.30 124	97.50 170	94.24 177	78.97 204	97.72 138	74.93 195	97.97 58	82.91 171	96.60 111	93.65 195	94.81 178	98.33 187	98.98 161

v1921920		92.36 173	93.57 173	90.94 168	91.39 185	97.39 176	94.70 164	87.63 149	96.60 175	76.63 188	86.98 191	82.89 172	95.75 129	96.26 151	95.14 156	99.55 143	99.73 66

v148		92.36 173	92.88 189	91.75 148	91.63 178	97.66 157	92.64 187	90.55 107	96.09 188	83.34 133	88.19 179	80.00 194	92.74 190	93.98 192	94.58 183	99.58 135	99.69 87

V14		92.31 175	93.41 178	91.03 165	91.80 164	95.59 196	94.79 155	84.70 173	96.58 177	79.83 163	88.79 169	82.98 170	95.41 151	95.22 165	95.02 163	99.65 92	99.67 97

v15		92.27 176	93.33 180	91.04 164	91.83 161	95.60 194	94.79 155	84.88 172	96.66 172	79.66 168	88.72 171	82.45 180	95.40 152	95.19 170	95.00 168	99.65 92	99.67 97

V9		92.24 177	93.32 182	90.98 167	91.76 167	95.58 198	94.83 153	84.50 177	96.68 171	79.73 166	88.66 172	82.39 181	95.39 153	95.22 165	95.03 161	99.65 92	99.67 97

N_pmnet		92.21 178	94.60 141	89.42 187	91.88 158	97.38 177	89.15 201	89.74 125	97.89 129	73.75 197	87.94 185	92.23 111	93.85 184	96.10 154	93.20 193	98.15 190	97.43 191

v12		92.18 179	93.29 183	90.88 170	91.70 173	95.59 196	94.61 169	84.36 179	96.65 173	79.59 169	88.85 167	82.03 185	95.35 156	95.22 165	95.04 159	99.65 92	99.68 92

v13		92.16 180	93.28 184	90.85 172	91.75 168	95.58 198	94.65 168	84.23 182	96.49 183	79.51 171	88.40 178	82.58 176	95.31 160	95.21 168	95.03 161	99.66 86	99.68 92

LP		92.12 181	94.60 141	89.22 188	94.96 118	98.45 121	93.01 184	77.58 208	97.85 133	77.26 185	89.80 159	93.00 107	94.54 170	93.69 193	92.58 198	98.00 191	96.83 199

v1240		91.99 182	93.33 180	90.44 178	91.29 187	97.30 180	94.25 176	86.79 155	96.43 184	75.49 193	86.34 196	81.85 187	95.29 161	96.42 143	95.22 153	99.52 149	99.73 66

v52		91.94 183	93.10 186	90.57 175	90.62 194	97.50 170	93.98 179	87.02 152	95.86 195	77.67 183	86.93 192	82.16 184	94.53 171	94.71 188	94.70 181	99.61 118	99.85 18

V4		91.92 184	93.10 186	90.55 176	90.64 193	97.51 169	93.93 180	87.02 152	95.81 197	77.61 184	86.93 192	82.19 183	94.50 172	94.72 187	94.68 182	99.62 115	99.85 18

pmmvs6		91.90 185	92.53 195	91.17 161	91.81 163	97.63 159	93.23 182	88.37 140	93.43 206	80.61 151	77.32 210	87.47 124	94.12 178	96.58 137	95.72 141	98.88 180	99.53 125

testpf		91.80 186	94.43 147	88.74 189	93.89 128	95.30 203	92.05 190	71.77 216	97.52 142	87.24 113	94.77 123	92.68 108	91.48 195	91.75 208	92.11 205	96.02 213	96.89 198

v7n		91.61 187	92.95 188	90.04 182	90.56 196	97.69 155	93.74 181	85.59 164	95.89 194	76.95 186	86.60 195	78.60 202	93.76 185	97.01 130	94.99 169	99.65 92	99.87 13

v748		91.12 188	91.95 196	90.16 181	90.60 195	97.35 178	91.11 191	87.92 146	94.75 201	80.54 153	86.26 197	75.97 205	91.13 196	94.63 189	94.81 178	99.65 92	99.90 3

gg-mvs-nofinetune		90.85 189	94.14 150	87.02 195	94.89 120	99.25 74	98.64 53	76.29 212	88.24 213	57.50 217	79.93 208	95.45 85	95.18 164	98.77 48	98.07 71	99.62 115	99.24 149

CMPMVS	70.31 18	90.74 190	91.06 198	90.36 180	97.32 66	97.43 174	92.97 185	87.82 148	93.50 205	75.34 194	83.27 203	84.90 151	92.19 193	92.64 200	91.21 209	96.50 211	94.46 206
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		90.70 191	93.93 161	86.92 196	90.21 199	96.79 185	90.30 198	86.61 159	96.05 190	69.25 204	88.46 177	84.86 152	85.86 203	97.11 128	96.47 122	99.30 169	97.80 187

test20.03		90.65 192	93.71 167	87.09 194	90.44 197	96.24 188	89.74 200	85.46 165	95.59 199	72.99 200	90.68 147	85.33 146	84.41 206	95.94 157	95.10 157	99.52 149	97.06 196

new_pmnet		90.45 193	92.84 192	87.66 193	88.96 200	96.16 189	88.71 202	84.66 174	97.56 141	71.91 203	85.60 199	86.58 135	93.28 187	96.07 155	93.54 191	98.46 184	94.39 207

pmmvs-eth3d		89.81 194	89.65 201	90.00 183	86.94 204	95.38 201	91.08 192	86.39 160	94.57 202	82.27 142	83.03 204	64.94 212	93.96 181	96.57 138	93.82 189	99.35 166	99.24 149

PM-MVS		89.55 195	90.30 200	88.67 191	87.06 203	95.60 194	90.88 194	84.51 176	96.14 187	75.75 190	86.89 194	63.47 215	94.64 169	96.85 133	93.89 188	99.17 175	99.29 145

gm-plane-assit		89.44 196	92.82 193	85.49 199	91.37 186	95.34 202	79.55 213	82.12 187	91.68 209	64.79 212	87.98 184	80.26 193	95.66 132	98.51 66	97.56 92	99.45 156	98.41 176

test2356		88.81 197	92.86 190	84.09 204	87.85 202	93.46 208	87.07 206	83.60 186	96.50 182	62.08 215	97.06 78	75.04 206	85.17 204	95.08 184	95.42 146	98.75 181	97.46 189

testus		88.77 198	92.77 194	84.10 203	88.24 201	93.95 206	87.16 205	84.24 180	97.37 144	61.54 216	95.70 115	73.10 208	84.90 205	95.56 161	95.82 138	98.51 182	97.88 186

MIMVSNet1		88.61 199	90.68 199	86.19 198	81.56 215	95.30 203	87.78 203	85.98 163	94.19 204	72.30 202	78.84 209	78.90 201	90.06 197	96.59 136	95.47 144	99.46 155	95.49 205

pmmvs3		88.19 200	91.27 197	84.60 201	85.60 206	93.66 207	85.68 208	81.13 188	92.36 208	63.66 214	89.51 161	77.10 204	93.22 188	96.37 144	92.40 201	98.30 188	97.46 189

MDA-MVSNet-bldmvs		87.84 201	89.22 202	86.23 197	81.74 214	96.77 186	83.74 209	89.57 126	94.50 203	72.83 201	96.64 90	64.47 214	92.71 191	81.43 215	92.28 203	96.81 210	98.47 175

new-patchmatchnet		86.12 202	87.30 203	84.74 200	86.92 205	95.19 205	83.57 210	84.42 178	92.67 207	65.66 209	80.32 207	64.72 213	89.41 198	92.33 204	89.21 210	98.43 185	96.69 201

Anonymous20231211		83.86 203	83.39 209	84.40 202	85.29 207	93.44 209	86.29 207	84.24 180	85.55 216	68.63 205	61.25 216	59.57 218	84.33 207	92.50 201	92.52 199	97.65 198	98.89 167

FPMVS		83.82 204	84.61 208	82.90 205	90.39 198	90.71 211	90.85 195	84.10 183	95.47 200	65.15 210	83.44 201	74.46 207	75.48 210	81.63 214	79.42 216	91.42 217	87.14 215

1111		82.87 205	85.67 206	79.62 208	81.86 212	89.62 212	74.44 215	68.81 218	87.44 214	66.59 207	76.83 211	70.33 210	87.71 200	92.65 198	93.37 192	98.28 189	89.42 213

testmv		81.83 206	86.26 204	76.66 209	84.10 208	89.42 214	74.29 217	79.65 195	90.61 210	51.85 221	82.11 205	63.06 217	72.61 213	91.94 206	92.75 195	97.49 201	93.94 209

test1235678		81.83 206	86.26 204	76.66 209	84.10 208	89.41 215	74.29 217	79.64 196	90.60 211	51.84 222	82.11 205	63.07 216	72.61 213	91.94 206	92.75 195	97.49 201	93.94 209

Gipuma		81.40 208	81.78 210	80.96 207	83.21 210	85.61 219	79.73 212	76.25 213	97.33 146	64.21 213	55.32 217	55.55 220	86.04 202	92.43 203	92.20 204	96.32 212	93.99 208
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		80.53 209	84.80 207	75.54 211	82.31 211	88.05 218	75.99 214	79.31 200	88.53 212	53.24 220	83.30 202	56.38 219	65.16 219	90.87 210	93.10 194	97.25 207	93.34 212

PMMVS2		77.26 210	79.47 212	74.70 213	76.00 218	88.37 217	74.22 219	76.34 211	78.31 218	54.13 218	69.96 214	52.50 221	70.14 216	84.83 213	88.71 211	97.35 203	93.58 211

PMVS	72.60 17	76.39 211	77.66 213	74.92 212	81.04 216	69.37 224	68.47 220	80.54 192	85.39 217	65.07 211	73.52 213	72.91 209	65.67 218	80.35 216	76.81 217	88.71 219	85.25 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		69.67 212	72.22 214	66.70 216	81.86 212	89.62 212	74.44 215	68.81 218	87.44 214	66.59 207	76.83 211	70.33 210	87.71 200	92.65 198	37.65 219	20.79 223	51.04 220

GG-mvs-BLEND		69.11 213	98.13 65	35.26 219	3.49 225	98.20 134	94.89 144	2.38 223	98.42 105	5.82 228	96.37 99	98.60 53	5.97 223	98.75 51	97.98 75	99.01 177	98.61 171

E-PMN		68.30 214	68.43 215	68.15 214	74.70 220	71.56 223	55.64 222	77.24 209	77.48 220	39.46 224	51.95 220	41.68 224	73.28 212	70.65 218	79.51 215	88.61 220	86.20 218

EMVS		68.12 215	68.11 216	68.14 215	75.51 219	71.76 222	55.38 223	77.20 210	77.78 219	37.79 225	53.59 218	43.61 222	74.72 211	67.05 220	76.70 218	88.27 221	86.24 217

no-one		66.79 216	67.62 217	65.81 217	73.06 221	81.79 220	51.90 225	76.20 214	61.07 222	54.05 219	51.62 221	41.72 223	49.18 220	67.26 219	82.83 214	90.47 218	87.07 216

MVE	67.97 19	65.53 217	67.43 218	63.31 218	59.33 222	74.20 221	53.09 224	70.43 217	66.27 221	43.13 223	45.98 222	30.62 225	70.65 215	79.34 217	86.30 212	83.25 222	89.33 214
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 218	40.15 219	20.86 220	12.61 223	17.99 225	25.16 226	13.30 221	48.42 223	24.82 226	53.07 219	30.13 227	28.47 221	42.73 221	37.65 219	20.79 223	51.04 220

test123		26.75 219	34.25 220	18.01 221	7.93 224	17.18 226	24.85 227	12.36 222	44.83 224	16.52 227	41.80 223	18.10 228	28.29 222	33.08 222	34.79 221	18.10 225	49.95 222

ESAPD		0.00 220	0.00 221	0.00 222	0.00 226	0.00 227	0.00 228	0.00 224	0.00 225	0.00 229	0.00 224	0.00 229	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet-low-res		0.00 220	0.00 221	0.00 222	0.00 226	0.00 227	0.00 228	0.00 224	0.00 225	0.00 229	0.00 224	0.00 229	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet		0.00 220	0.00 221	0.00 222	0.00 226	0.00 227	0.00 228	0.00 224	0.00 225	0.00 229	0.00 224	0.00 229	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

ambc			80.99 211		80.04 217	90.84 210	90.91 193		96.09 188	74.18 196	62.81 215	30.59 226	82.44 209	96.25 152	91.77 206	95.91 214	98.56 172

MTAPA										98.09 10		99.97 3

MTMP										98.46 7		99.96 7

Patchmatch-RL test							66.86 221

tmp_tt				82.25 206	97.73 60	88.71 216	80.18 211	68.65 220	99.15 45	86.98 115	99.47 7	85.31 147	68.35 217	87.51 212	83.81 213	91.64 216

XVS					97.42 64	99.62 28	98.59 55			93.81 70		99.95 12				99.69 66

X-MVStestdata					97.42 64	99.62 28	98.59 55			93.81 70		99.95 12				99.69 66

abl_6				98.09 33	99.33 34	99.22 78	98.79 50	94.96 45	98.52 102	97.00 25	97.30 71	99.86 30	98.76 55			99.69 66	99.41 140

mPP-MVS					99.53 22							99.89 27

NP-MVS									98.57 97

Patchmtry						98.59 113	97.15 101	79.14 201		80.42 155

DeepMVS_CX						96.85 184	87.43 204	89.27 128	98.30 108	75.55 192	95.05 119	79.47 197	92.62 192	89.48 211		95.18 215	95.96 204

High-resolution multi-view benchmark