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		99.40 1	99.81 2	98.92 3	99.62 5	99.96 7	99.76 5	96.87 9	99.95 19	97.66 4	99.57 26	100.00 1	99.63 23	99.88 8	99.28 24	100.00 1	100.00 1

MSLP-MVS++		99.39 2	99.76 7	98.95 2	99.60 11	99.99 1	99.83 1	96.82 12	99.92 28	97.58 6	99.58 25	100.00 1	99.93 1	98.98 30	99.86 7	99.96 11	100.00 1

CNVR-MVS		99.39 2	99.75 10	98.98 1	99.69 1	99.95 12	99.76 5	96.91 6	99.98 3	97.59 5	99.64 19	100.00 1	99.93 1	99.94 2	98.75 45	99.97 10	99.97 80

HSP-MVS		99.36 4	99.79 4	98.85 6	99.61 9	99.96 7	99.71 18	96.94 4	99.97 6	97.11 8	99.60 22	100.00 1	99.70 15	99.96 1	99.12 29	100.00 1	99.96 99

APD-MVS		99.33 5	99.85 1	98.73 9	99.61 9	99.92 34	99.77 4	96.91 6	99.93 23	96.31 15	99.59 23	99.95 32	99.84 7	99.73 15	99.84 8	99.95 13	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ESAPD		99.25 6	99.69 17	98.74 8	99.62 5	99.94 17	99.79 2	96.87 9	99.93 23	96.33 14	99.59 23	100.00 1	99.84 7	99.88 8	98.50 52	100.00 1	100.00 1

NCCC		99.24 7	99.75 10	98.65 10	99.63 4	99.96 7	99.76 5	96.91 6	99.97 6	95.86 18	99.67 11	100.00 1	99.75 12	99.85 10	98.80 41	99.98 9	99.97 80

CNLPA		99.24 7	99.58 28	98.85 6	99.34 27	99.95 12	99.32 30	96.65 21	99.96 15	98.44 2	98.97 50	100.00 1	99.57 26	98.66 38	99.56 14	99.76 72	99.97 80

AdaColmap		99.21 9	99.45 34	98.92 3	99.67 3	99.95 12	99.65 22	96.77 16	99.97 6	97.67 3	100.00 1	99.69 45	99.93 1	99.26 26	97.25 84	99.85 27	100.00 1

HFP-MVS		99.19 10	99.77 6	98.51 14	99.55 15	99.94 17	99.76 5	96.84 11	99.88 33	95.27 22	99.67 11	100.00 1	99.85 6	99.56 20	99.36 19	99.79 53	99.97 80

PLC	98.06 1	99.17 11	99.38 36	98.92 3	99.47 17	99.90 42	99.48 27	96.47 26	99.96 15	98.73 1	99.52 29	100.00 1	99.55 28	98.54 50	97.73 75	99.84 29	99.99 47
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 12	99.73 13	98.49 15	97.93 47	99.95 12	99.74 13	96.94 4	99.96 15	96.60 11	99.47 32	100.00 1	99.88 5	99.15 28	99.59 12	99.84 29	100.00 1

CP-MVS		99.14 13	99.67 19	98.53 13	99.45 19	99.94 17	99.63 24	96.62 23	99.82 45	95.92 17	99.65 16	100.00 1	99.71 14	99.76 13	98.56 49	99.83 34	100.00 1

MPTG		99.12 14	99.52 33	98.65 10	99.58 14	99.93 28	99.74 13	96.72 19	99.44 82	96.47 12	99.62 21	100.00 1	99.63 23	99.74 14	97.97 62	99.77 65	99.94 112

ACMMPR		99.12 14	99.76 7	98.36 16	99.45 19	99.94 17	99.75 11	96.70 20	99.93 23	94.65 26	99.65 16	99.96 30	99.84 7	99.51 22	99.35 20	99.79 53	99.96 99

MCST-MVS		99.08 16	99.72 15	98.33 17	99.59 13	99.97 3	99.78 3	96.96 2	99.95 19	93.72 30	99.67 11	100.00 1	99.90 4	99.91 5	98.55 50	100.00 1	100.00 1

CPTT-MVS		99.08 16	99.53 32	98.57 12	99.44 21	99.93 28	99.60 25	95.92 31	99.77 52	97.01 9	99.67 11	100.00 1	99.72 13	99.56 20	97.76 72	99.70 109	99.98 67

DeepC-MVS_fast	98.03 2	99.05 18	99.78 5	98.21 20	99.47 17	99.97 3	99.75 11	96.80 13	99.97 6	93.58 33	98.68 61	99.94 33	99.69 16	99.93 4	99.95 2	99.96 11	99.98 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		98.99 19	99.61 25	98.27 18	97.88 48	99.92 34	99.71 18	96.80 13	99.96 15	95.58 20	98.71 60	100.00 1	99.68 18	99.91 5	98.78 43	99.99 6	100.00 1

HPM-MVS++		98.98 20	99.62 24	98.22 19	99.62 5	99.94 17	99.74 13	96.95 3	99.87 36	93.76 29	99.49 31	100.00 1	99.39 34	99.73 15	98.35 54	99.89 22	99.96 99

SteuartSystems-ACMMP		98.95 21	99.80 3	97.95 23	99.43 22	99.96 7	99.76 5	96.45 27	99.82 45	93.63 31	99.64 19	100.00 1	98.56 71	99.90 7	99.31 22	99.84 29	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 22	99.67 19	97.89 24	98.63 43	99.93 28	98.95 41	95.20 33	99.84 43	94.94 23	99.74 10	100.00 1	99.69 16	98.40 57	99.75 10	99.93 16	99.99 47

MP-MVS		98.82 23	99.63 22	97.88 25	99.41 23	99.91 41	99.74 13	96.76 17	99.88 33	91.89 40	99.50 30	99.94 33	99.65 21	99.71 18	98.49 53	99.82 38	99.97 80

ACMMP_Plus		98.68 24	99.58 28	97.62 26	99.62 5	99.92 34	99.72 17	96.78 15	99.71 60	90.13 67	99.66 15	99.99 25	99.64 22	99.78 12	98.14 59	99.82 38	99.89 135

train_agg		98.62 25	99.76 7	97.28 28	99.03 36	99.93 28	99.65 22	96.37 28	99.98 3	89.24 77	99.53 27	99.83 38	99.59 25	99.85 10	99.19 27	99.80 49	100.00 1

X-MVS		98.62 25	99.75 10	97.29 27	99.50 16	99.94 17	99.71 18	96.55 24	99.85 40	88.58 82	99.65 16	99.98 27	99.67 19	99.60 19	99.26 25	99.77 65	99.97 80

OMC-MVS		98.59 27	99.07 38	98.03 22	99.41 23	99.90 42	99.26 33	94.33 35	99.94 21	96.03 16	96.68 86	99.72 44	99.42 31	98.86 33	98.84 38	99.72 105	99.58 173

PGM-MVS		98.47 28	99.73 13	97.00 32	99.68 2	99.94 17	99.76 5	91.74 40	99.84 43	91.17 51	100.00 1	99.69 45	99.81 10	99.38 24	99.30 23	99.82 38	99.95 108

TSAR-MVS + ACMM		98.30 29	99.64 21	96.74 35	99.08 35	99.94 17	99.67 21	96.73 18	99.97 6	86.30 95	98.30 66	99.99 25	98.78 65	99.73 15	99.57 13	99.88 25	99.98 67

CSCG		98.22 30	98.37 59	98.04 21	99.60 11	99.82 55	99.45 28	93.59 36	99.16 97	96.46 13	98.22 72	95.86 90	99.41 33	96.33 122	99.22 26	99.75 85	99.94 112

3Dnovator+	95.21 7	98.17 31	99.08 37	97.12 30	99.28 30	99.78 69	98.61 49	89.93 55	99.93 23	95.36 21	95.50 97	100.00 1	99.56 27	98.58 45	99.80 9	99.95 13	99.97 80

ACMMP		98.16 32	99.01 39	97.18 29	98.86 38	99.92 34	98.77 46	95.73 32	99.31 93	91.15 52	100.00 1	99.81 40	98.82 64	98.11 71	95.91 121	99.77 65	99.97 80
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
MVS_111021_LR		98.15 33	99.69 17	96.36 40	99.23 32	99.93 28	97.79 60	91.84 39	99.87 36	90.53 62	100.00 1	99.57 50	98.93 58	99.44 23	99.08 31	99.85 27	99.95 108

EPNet		98.11 34	99.63 22	96.34 41	98.44 45	99.88 48	98.55 50	90.25 51	99.93 23	92.60 37	100.00 1	99.73 42	98.41 72	98.87 32	99.02 32	99.82 38	99.97 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 35	99.55 31	96.32 42	94.72 73	99.92 34	99.22 34	89.98 53	99.97 6	94.77 25	99.94 9	100.00 1	99.43 30	98.52 54	98.53 51	99.79 53	100.00 1

3Dnovator	95.01 8	97.98 36	98.89 42	96.92 34	99.36 25	99.76 71	98.72 47	89.98 53	99.98 3	93.99 28	94.60 111	99.43 55	99.50 29	98.55 47	99.91 4	99.99 6	99.98 67

MVS_111021_HR		97.94 37	99.59 26	96.02 44	99.27 31	99.97 3	97.03 83	90.44 48	99.89 30	90.75 55	100.00 1	99.73 42	98.68 70	98.67 37	98.89 36	99.95 13	99.97 80

QAPM		97.90 38	98.89 42	96.74 35	99.35 26	99.80 67	98.84 43	90.20 52	99.94 21	92.85 34	94.17 114	99.78 41	99.42 31	98.71 36	99.87 6	99.79 53	99.98 67

CDPH-MVS		97.88 39	99.59 26	95.89 45	98.90 37	99.95 12	99.40 29	92.86 38	99.86 39	85.33 98	98.62 62	99.45 54	99.06 56	99.29 25	99.94 3	99.81 46	100.00 1

CANet		97.62 40	98.94 41	96.08 43	97.19 52	99.93 28	99.29 32	90.38 49	99.87 36	91.00 53	95.79 96	99.51 51	98.72 69	98.53 51	99.00 33	99.90 21	99.99 47

TAPA-MVS	96.62 5	97.60 41	98.46 57	96.60 38	98.73 41	99.90 42	99.30 31	94.96 34	99.46 81	87.57 87	96.05 95	98.53 63	99.26 45	98.04 76	97.33 83	99.77 65	99.88 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 42	99.72 15	95.07 56	98.45 44	99.96 7	93.83 133	95.93 30	100.00 1	90.79 54	98.38 65	99.85 37	95.28 123	99.94 2	99.97 1	96.15 219	99.97 80

PCF-MVS	97.20 3	97.49 43	98.20 65	96.66 37	97.62 50	99.92 34	98.93 42	96.64 22	98.53 124	88.31 85	94.04 116	99.58 49	98.94 57	97.53 90	97.79 70	99.54 134	99.97 80
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		97.29 44	97.55 80	97.00 32	98.66 42	99.71 73	99.03 39	96.15 29	99.59 68	89.67 74	92.77 128	94.86 94	98.75 66	98.22 66	97.94 63	99.72 105	99.76 158

CHOSEN 280x420		97.16 45	99.58 28	94.35 77	96.95 55	99.97 3	97.19 79	81.55 139	99.92 28	91.75 41	100.00 1	100.00 1	98.84 63	98.55 47	98.65 46	99.79 53	99.97 80

DELS-MVS		97.05 46	98.05 69	95.88 47	97.09 53	99.99 1	98.82 44	90.30 50	98.44 129	91.40 46	92.91 125	96.57 83	97.68 93	98.56 46	99.88 5	100.00 1	100.00 1
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
DeepC-MVS	96.33 6	97.05 46	97.59 79	96.42 39	97.37 51	99.92 34	99.10 37	96.54 25	99.34 92	86.64 94	91.93 132	93.15 106	99.11 54	99.11 29	99.68 11	99.73 101	99.97 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		97.04 48	98.72 50	95.08 55	96.32 59	99.90 42	99.15 35	89.61 59	99.89 30	87.22 92	95.47 98	98.22 73	98.22 77	98.63 42	98.90 35	99.93 16	100.00 1

MAR-MVS		97.03 49	98.00 71	95.89 45	99.32 28	99.74 72	96.76 89	84.89 101	99.97 6	94.86 24	98.29 67	90.58 113	99.67 19	98.02 78	99.50 15	99.82 38	99.92 119
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
MVSTER		97.00 50	98.85 45	94.83 65	92.71 98	97.43 145	99.03 39	85.52 94	99.82 45	92.74 36	99.15 41	99.94 33	99.19 48	98.66 38	96.99 98	99.79 53	99.98 67

OpenMVS	94.03 11	96.87 51	98.10 68	95.44 51	99.29 29	99.78 69	98.46 55	89.92 56	99.47 80	85.78 96	91.05 135	98.50 64	99.30 38	98.49 55	99.41 16	99.89 22	99.98 67

tfpn_ndepth		96.84 52	98.58 53	94.81 66	93.18 81	99.62 80	96.83 87	88.75 75	99.73 58	92.38 38	98.45 64	96.34 87	97.90 86	98.34 62	97.59 78	99.84 29	99.99 47

PatchMatch-RL		96.84 52	98.03 70	95.47 48	98.84 39	99.81 63	95.61 108	89.20 63	99.65 63	91.28 49	99.39 33	93.46 104	98.18 78	98.05 74	96.28 108	99.69 115	99.55 178

IS_MVSNet		96.66 54	98.62 52	94.38 74	92.41 108	99.70 74	97.19 79	87.67 88	99.05 104	91.27 50	95.09 103	98.46 68	97.95 85	98.64 40	99.37 17	99.79 53	100.00 1

tfpn1000		96.58 55	98.37 59	94.50 73	93.04 89	99.59 81	96.53 92	88.54 79	99.73 58	91.59 42	98.28 68	95.76 91	97.46 95	98.19 67	97.10 93	99.82 38	99.96 99

conf0.002		96.51 56	97.75 76	95.07 56	93.11 82	99.83 51	97.67 62	89.10 65	98.62 116	91.47 45	99.39 33	91.68 109	99.28 40	97.49 92	97.24 85	99.76 72	100.00 1

thresconf0.02		96.46 57	98.87 44	93.64 82	92.77 97	99.11 101	97.05 82	89.36 60	99.64 65	85.14 99	99.07 43	96.84 81	97.72 90	98.72 35	98.76 44	99.78 60	99.95 108

PMMVS		96.45 58	98.24 62	94.36 76	92.58 100	99.01 108	97.08 81	87.42 89	99.88 33	90.06 68	99.39 33	94.63 95	99.33 37	97.85 83	96.99 98	99.70 109	99.96 99

LS3D		96.44 59	97.31 85	95.41 52	97.06 54	99.87 49	99.51 26	97.48 1	99.57 69	79.00 121	95.39 99	89.19 119	99.81 10	98.55 47	98.84 38	99.62 123	99.78 156

diffmvs		96.35 60	98.76 49	93.54 84	92.41 108	99.55 83	97.22 78	83.75 114	99.57 69	89.64 75	96.86 82	98.33 69	98.37 73	98.42 56	98.61 47	99.88 25	99.99 47

EPP-MVSNet		96.29 61	98.34 61	93.90 79	91.77 119	99.38 93	95.45 113	87.25 91	99.38 88	91.36 48	94.86 110	98.49 66	97.83 88	98.01 79	98.23 56	99.75 85	99.99 47

DWT-MVSNet_training		96.26 62	98.44 58	93.72 81	92.58 100	99.34 95	96.15 97	83.00 122	99.76 54	93.63 31	97.89 76	99.46 52	97.23 99	94.43 153	98.19 57	99.70 109	100.00 1

conf0.01		96.20 63	97.19 89	95.05 58	93.11 82	99.83 51	97.67 62	89.06 66	98.62 116	91.38 47	99.19 40	89.09 120	99.28 40	97.48 93	96.10 112	99.76 72	100.00 1

UGNet		96.05 64	98.55 54	93.13 88	94.64 74	99.65 77	94.70 122	87.78 86	99.40 87	89.69 73	98.25 69	99.25 58	92.12 155	96.50 114	97.08 94	99.84 29	99.72 162
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
COLMAP_ROB	93.56 12	96.03 65	96.83 99	95.11 54	97.87 49	99.52 84	98.81 45	91.40 43	99.42 84	84.97 100	90.46 137	96.82 82	98.05 80	96.46 118	96.19 111	99.54 134	98.92 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PVSNet_BlendedMVS		96.01 66	96.48 109	95.46 49	96.47 57	99.89 46	95.64 105	91.23 44	99.75 56	91.59 42	96.80 83	82.44 144	98.05 80	98.53 51	97.92 67	99.80 49	100.00 1

PVSNet_Blended		96.01 66	96.48 109	95.46 49	96.47 57	99.89 46	95.64 105	91.23 44	99.75 56	91.59 42	96.80 83	82.44 144	98.05 80	98.53 51	97.92 67	99.80 49	100.00 1

tfpn		95.93 68	97.06 92	94.62 70	92.94 96	99.81 63	97.25 77	88.71 78	98.32 136	89.98 69	98.79 59	88.55 122	99.11 54	97.26 107	96.71 100	99.75 85	99.98 67

thres100view900		95.86 69	96.62 101	94.97 59	93.10 84	99.83 51	97.76 61	89.15 64	98.62 116	90.69 56	99.00 46	84.86 131	99.30 38	97.57 89	96.48 102	99.81 46	100.00 1

RPSCF		95.86 69	96.94 98	94.61 71	96.52 56	98.67 122	98.54 51	88.43 82	99.56 71	90.51 65	99.39 33	98.70 61	97.72 90	93.77 166	92.00 174	95.93 220	96.50 215

canonicalmvs		95.80 71	97.02 93	94.37 75	92.96 92	99.47 88	97.49 70	84.58 103	99.44 82	92.05 39	98.54 63	86.65 126	99.37 35	96.18 125	98.93 34	99.77 65	99.92 119

tfpn111		95.79 72	96.55 103	94.89 60	93.10 84	99.82 55	97.67 62	88.85 69	98.62 116	90.69 56	99.07 43	84.86 131	99.28 40	97.41 97	96.10 112	99.76 72	99.99 47

tfpnview11		95.78 73	98.17 67	93.01 92	92.58 100	99.04 107	96.64 90	88.72 77	99.63 67	83.08 108	98.90 51	94.24 99	97.25 98	98.35 61	97.21 86	99.77 65	99.80 155

conf200view11		95.78 73	96.54 105	94.89 60	93.10 84	99.82 55	97.67 62	88.85 69	98.62 116	90.69 56	99.00 46	84.86 131	99.28 40	97.41 97	96.10 112	99.76 72	99.99 47

tfpn200view9		95.78 73	96.54 105	94.89 60	93.10 84	99.82 55	97.67 62	88.85 69	98.62 116	90.69 56	99.00 46	84.86 131	99.28 40	97.41 97	96.10 112	99.76 72	99.99 47

thres200		95.77 76	96.55 103	94.86 63	93.09 88	99.82 55	97.63 68	88.85 69	98.49 125	90.66 60	98.99 49	84.86 131	99.20 46	97.41 97	96.28 108	99.76 72	100.00 1

tfpn_n400		95.76 77	98.21 63	92.90 94	92.57 104	99.05 105	96.42 93	88.50 80	99.49 75	83.08 108	98.90 51	94.24 99	97.07 100	98.10 72	97.93 65	99.74 89	99.76 158

tfpnconf		95.76 77	98.21 63	92.90 94	92.57 104	99.05 105	96.42 93	88.50 80	99.49 75	83.08 108	98.90 51	94.24 99	97.07 100	98.10 72	97.93 65	99.74 89	99.76 158

MVS_Test		95.74 79	98.18 66	92.90 94	92.16 112	99.49 87	97.36 76	84.30 108	99.79 49	84.94 101	96.65 87	93.63 103	98.85 62	98.61 44	99.10 30	99.81 46	100.00 1

thres400		95.72 80	96.48 109	94.84 64	93.00 91	99.83 51	97.55 69	88.93 67	98.49 125	90.61 61	98.86 54	84.63 136	99.20 46	97.45 94	96.10 112	99.77 65	99.99 47

view600		95.64 81	96.38 112	94.79 67	92.96 92	99.82 55	97.48 73	88.85 69	98.38 130	90.52 63	98.84 56	84.61 137	99.15 50	97.41 97	95.60 129	99.76 72	99.99 47

thres600view7		95.64 81	96.38 112	94.79 67	92.96 92	99.82 55	97.48 73	88.85 69	98.38 130	90.52 63	98.84 56	84.61 137	99.15 50	97.41 97	95.60 129	99.76 72	99.99 47

view800		95.62 83	96.38 112	94.73 69	92.96 92	99.81 63	97.38 75	88.75 75	98.35 135	90.43 66	98.81 58	84.54 139	99.13 53	97.35 103	95.82 124	99.76 72	99.98 67

Vis-MVSNet (Re-imp)		95.60 84	98.52 56	92.19 100	92.37 110	99.56 82	96.37 95	87.41 90	98.95 107	84.77 103	94.88 109	98.48 67	92.44 152	98.63 42	99.37 17	99.76 72	99.77 157

FMVSNet3		95.59 85	97.51 81	93.34 86	89.48 139	96.57 156	97.67 62	84.17 109	99.48 77	89.76 70	95.09 103	94.35 96	99.14 52	98.37 59	98.86 37	99.82 38	99.89 135

PVSNet_Blended_VisFu		95.37 86	97.44 83	92.95 93	95.20 66	99.80 67	92.68 140	88.41 83	99.12 99	87.64 86	88.31 145	99.10 59	94.07 138	98.27 64	97.51 80	99.73 101	100.00 1

DI_MVS_plusplus_trai		95.29 87	97.02 93	93.28 87	91.76 120	99.52 84	97.84 59	85.67 93	99.08 103	87.29 90	87.76 148	97.46 79	97.31 97	97.83 84	97.48 81	99.83 34	100.00 1

TSAR-MVS + COLMAP		95.20 88	95.03 130	95.41 52	96.17 60	98.69 121	99.11 36	93.40 37	99.97 6	84.89 102	98.23 71	75.01 163	99.34 36	97.27 106	96.37 107	99.58 128	99.64 168

GBi-Net		95.19 89	96.99 96	93.09 90	89.11 140	96.47 158	96.90 84	84.17 109	99.48 77	89.76 70	95.09 103	94.35 96	98.87 59	96.50 114	97.21 86	99.74 89	99.81 151

test1		95.19 89	96.99 96	93.09 90	89.11 140	96.47 158	96.90 84	84.17 109	99.48 77	89.76 70	95.09 103	94.35 96	98.87 59	96.50 114	97.21 86	99.74 89	99.81 151

test0.0.03 1		95.15 91	97.87 75	91.99 101	91.69 122	98.82 118	93.04 138	83.60 115	99.65 63	88.80 80	94.15 115	97.67 77	94.97 125	96.62 113	98.16 58	99.83 34	100.00 1

EPNet_dtu		95.10 92	98.81 47	90.78 106	98.38 46	98.47 124	96.54 91	89.36 60	99.78 51	65.65 188	99.31 37	98.24 72	94.79 128	98.28 63	99.35 20	99.93 16	98.27 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net		94.95 93	98.66 51	90.63 108	94.60 76	98.94 114	96.03 99	85.28 96	98.01 142	78.92 122	97.42 80	99.96 30	89.09 196	98.95 31	98.80 41	99.82 38	98.57 197

CANet_DTU		94.90 94	98.98 40	90.13 114	94.74 72	99.81 63	98.53 52	82.23 130	99.97 6	66.76 176	100.00 1	98.50 64	98.74 67	97.52 91	97.19 92	99.76 72	99.88 138

FC-MVSNet-train		94.61 95	96.27 116	92.68 98	92.35 111	97.14 148	93.45 137	87.73 87	98.93 108	87.31 89	96.42 89	89.35 117	95.67 118	96.06 131	96.01 119	99.56 131	99.98 67

CLD-MVS		94.53 96	94.45 137	94.61 71	93.85 79	98.36 126	98.12 57	89.68 57	99.35 91	89.62 76	95.19 101	77.08 153	96.66 110	95.51 136	95.67 126	99.74 89	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
conf0.05thres1000		94.50 97	95.70 124	93.11 89	92.68 99	99.67 76	96.04 98	87.81 85	97.52 147	83.71 104	96.20 93	84.52 140	98.73 68	96.39 120	95.66 127	99.71 107	99.92 119

FMVSNet2		94.48 98	95.95 121	92.77 97	89.11 140	96.47 158	96.90 84	83.38 117	99.11 100	88.64 81	87.50 153	92.26 108	98.87 59	97.91 81	98.60 48	99.74 89	99.81 151

HQP-MVS		94.48 98	95.39 128	93.42 85	95.10 67	98.35 127	98.19 56	91.41 42	99.77 52	79.79 118	99.30 38	77.08 153	96.25 113	96.93 108	96.28 108	99.76 72	99.99 47

MDTV_nov1_ep13		94.32 100	98.77 48	89.14 123	91.70 121	99.52 84	95.21 115	72.09 200	99.80 48	78.91 123	96.32 90	99.62 47	97.71 92	98.39 58	97.71 76	99.22 202	100.00 1

CDS-MVSNet		94.32 100	97.00 95	91.19 105	89.82 137	98.71 120	95.51 110	85.14 100	96.85 151	82.33 113	92.48 129	96.40 86	94.71 129	96.86 110	97.76 72	99.63 121	99.92 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 102	97.33 84	90.75 107	92.02 115	99.21 98	94.31 127	66.97 208	99.50 74	95.61 19	96.22 92	98.64 62	96.08 114	93.71 168	94.03 150	99.52 138	99.98 67

ACMM	94.44 10	94.26 103	94.62 134	93.84 80	94.86 71	97.73 140	93.48 136	90.76 47	99.27 94	87.46 88	99.04 45	76.60 156	96.76 108	96.37 121	93.76 153	99.74 89	99.55 178
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 104	94.74 133	93.56 83	94.25 77	98.32 129	96.02 100	89.35 62	98.90 111	87.28 91	99.14 42	76.41 159	94.94 126	96.07 130	94.35 147	99.49 145	99.99 47
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 105	98.54 55	88.87 125	92.51 106	99.47 88	94.18 129	66.53 209	99.68 62	82.40 112	95.24 100	99.40 56	97.86 87	98.12 70	97.99 61	99.75 85	99.88 138

test-LLR		93.71 106	97.23 87	89.60 118	91.69 122	99.10 102	94.68 124	83.60 115	99.36 89	71.94 146	93.82 118	96.51 84	95.96 116	97.42 95	94.37 144	99.74 89	99.99 47

CHOSEN 1792x2688		93.69 107	94.89 132	92.28 99	96.17 60	99.84 50	95.69 104	83.17 120	98.54 123	82.04 114	77.58 203	91.15 111	96.90 103	98.36 60	98.82 40	99.73 101	99.98 67

LGP-MVS_train		93.60 108	95.05 129	91.90 102	94.90 70	98.29 130	97.93 58	88.06 84	99.14 98	74.83 136	99.26 39	76.50 157	96.07 115	96.31 123	95.90 123	99.59 126	99.97 80

FMVSNet5		93.53 109	96.09 120	90.56 110	86.74 153	92.84 205	92.64 141	77.50 166	99.41 86	88.97 79	98.02 74	97.81 75	98.00 83	94.85 147	95.43 131	99.50 144	94.25 220

OPM-MVS		93.50 110	93.00 147	94.07 78	95.82 63	98.26 131	98.49 54	91.62 41	94.69 173	81.93 115	92.82 127	76.18 161	96.82 105	96.12 127	94.57 138	99.74 89	98.39 198

CostFormer		93.50 110	96.50 108	90.00 115	91.69 122	98.65 123	93.88 132	67.64 205	98.97 105	89.16 78	97.79 77	88.92 121	97.97 84	95.14 144	96.06 117	99.63 121	100.00 1

IterMVS-LS		93.50 110	96.22 117	90.33 113	90.93 127	95.50 187	94.83 120	80.54 143	98.92 109	79.11 120	90.64 136	93.70 102	96.79 106	96.93 108	97.85 69	99.78 60	99.99 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 113	98.84 46	87.22 142	91.93 116	99.39 92	92.55 142	66.06 213	99.71 60	75.61 133	98.24 70	99.59 48	97.35 96	97.87 82	97.64 77	99.83 34	99.43 183
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 114	95.91 123	90.61 109	95.48 64	99.31 96	95.62 107	77.23 168	99.42 84	81.88 116	88.92 142	96.06 89	93.80 140	96.45 119	93.11 162	99.65 119	98.10 204

tpm cat1		93.29 115	96.53 107	89.50 120	91.84 117	99.18 100	94.70 122	67.70 204	98.38 130	86.67 93	89.16 140	99.38 57	96.66 110	94.33 154	95.30 132	99.43 164	100.00 1

Effi-MVS+-dtu		93.13 116	97.13 90	88.47 133	88.86 146	99.19 99	96.79 88	79.08 156	99.64 65	70.01 156	97.51 79	89.38 116	96.53 112	97.60 87	96.55 101	99.57 129	100.00 1

HyFIR lowres test		93.13 116	94.48 136	91.56 103	96.12 62	99.68 75	93.52 135	79.98 147	97.24 148	81.73 117	72.66 214	95.74 92	98.29 76	98.27 64	97.79 70	99.70 109	100.00 1

Vis-MVSNet		93.08 118	96.76 100	88.78 129	91.14 126	99.63 79	94.85 119	83.34 118	97.19 149	74.78 137	91.92 133	93.15 106	88.81 199	97.59 88	98.35 54	99.78 60	99.49 182
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 119	95.94 122	89.70 117	90.82 128	99.45 90	95.71 103	78.94 158	98.72 112	74.71 138	97.92 75	80.73 148	98.35 74	97.72 85	97.05 97	99.70 109	100.00 1

tpmp4_e23		92.95 120	96.28 115	89.06 124	91.80 118	98.81 119	94.95 118	67.56 207	99.21 95	82.97 111	96.54 88	88.52 123	97.47 94	94.47 152	96.42 105	99.61 124	100.00 1

ADS-MVSNet		92.91 121	97.97 72	87.01 144	92.07 114	99.27 97	92.70 139	65.39 218	99.85 40	75.40 134	94.93 108	98.26 70	96.86 104	96.09 128	97.52 79	99.65 119	99.84 147

TESTMET0.1,1		92.87 122	97.23 87	87.79 139	86.96 152	99.10 102	94.68 124	77.46 167	99.36 89	71.94 146	93.82 118	96.51 84	95.96 116	97.42 95	94.37 144	99.74 89	99.99 47

FC-MVSNet-test		92.78 123	96.19 119	88.80 128	88.00 149	97.54 142	93.60 134	82.36 129	98.16 137	79.71 119	91.55 134	95.41 93	89.65 191	96.09 128	95.23 133	99.49 145	99.31 186

Fast-Effi-MVS+-dtu		92.73 124	97.62 78	87.02 143	88.91 144	98.83 117	95.79 101	73.98 187	99.89 30	68.62 161	97.73 78	93.30 105	95.21 124	97.67 86	95.96 120	99.59 126	100.00 1

IB-MVS	90.59 15	92.70 125	95.70 124	89.21 122	94.62 75	99.45 90	83.77 208	88.92 68	99.53 72	92.82 35	98.86 54	86.08 128	75.24 220	92.81 184	93.17 160	99.89 22	100.00 1
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
test-mter		92.67 126	97.13 90	87.47 141	86.72 154	99.07 104	94.28 128	76.90 171	99.21 95	71.53 150	93.63 120	96.32 88	95.67 118	97.32 104	94.36 146	99.74 89	99.99 47

RPMNet		92.64 127	97.88 74	86.53 149	90.79 129	98.95 112	95.13 116	64.44 222	99.09 101	72.36 142	93.58 121	99.01 60	96.74 109	98.05 74	96.45 104	99.71 107	100.00 1

FMVSNet1		92.55 128	93.66 141	91.26 104	87.91 150	96.12 165	94.75 121	81.69 138	97.67 144	85.63 97	80.56 178	87.88 125	98.15 79	96.50 114	97.21 86	99.41 182	99.71 163

tpmrst		92.52 129	97.45 82	86.77 147	92.15 113	99.36 94	92.53 143	65.95 214	99.53 72	72.50 141	92.22 130	99.83 38	97.81 89	95.18 143	96.05 118	99.69 115	100.00 1

testgi		92.47 130	95.68 126	88.73 130	90.68 130	98.35 127	91.67 150	79.50 152	98.96 106	77.12 129	95.17 102	85.84 129	93.95 139	95.75 134	96.47 103	99.45 158	99.21 189

TAMVS		92.43 131	94.21 139	90.35 112	88.68 147	98.85 116	94.15 130	81.53 140	95.58 160	83.61 106	87.05 154	86.45 127	94.71 129	96.27 124	95.91 121	99.42 170	99.38 185

CR-MVSNet		92.32 132	97.97 72	85.74 161	90.63 132	98.95 112	95.46 111	65.50 216	99.09 101	67.51 167	94.20 113	98.18 74	95.59 121	98.16 68	97.20 90	99.74 89	100.00 1

CVMVSNet		92.13 133	95.40 127	88.32 136	91.29 125	97.29 147	91.85 147	86.42 92	96.71 153	71.84 148	89.56 139	91.18 110	88.98 198	96.17 126	97.76 72	99.51 142	99.14 191

Fast-Effi-MVS+		92.11 134	94.33 138	89.52 119	89.06 143	99.00 109	95.13 116	76.72 173	98.59 122	78.21 127	89.99 138	77.35 152	98.34 75	97.97 80	97.44 82	99.67 117	99.96 99

ACMH+	92.61 13	91.80 135	93.03 145	90.37 111	93.03 90	98.17 132	94.00 131	84.13 112	98.12 139	77.39 128	91.95 131	74.62 164	94.36 135	94.62 151	93.82 152	99.32 192	99.87 142

IterMVS		91.65 136	96.62 101	85.85 158	90.27 135	95.80 177	95.32 114	74.15 183	98.91 110	60.95 205	88.79 144	97.76 76	94.69 131	98.04 76	97.07 95	99.73 101	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 137	93.02 146	89.92 116	93.97 78	97.98 136	90.10 178	84.70 102	98.46 127	76.80 130	93.38 123	71.94 178	94.39 133	95.34 140	94.04 149	99.54 134	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 138	93.05 144	89.49 121	85.85 160	96.52 157	91.70 149	82.49 124	98.14 138	83.17 107	87.57 150	81.76 147	94.39 133	95.47 137	92.62 168	99.33 191	99.29 187

testpf		91.26 139	97.28 86	84.23 184	89.52 138	97.45 144	88.08 197	56.08 231	99.76 54	78.71 124	95.06 107	98.26 70	93.44 144	94.72 149	95.69 125	99.57 129	99.99 47

PatchT		91.06 140	97.66 77	83.36 197	90.32 134	98.96 111	82.30 213	64.72 221	98.45 128	67.51 167	93.28 124	97.60 78	95.59 121	98.16 68	97.20 90	99.70 109	100.00 1

MIMVSNet		91.01 141	96.22 117	84.93 172	85.24 170	98.09 133	90.40 165	64.96 220	97.55 146	72.65 139	96.23 91	90.81 112	96.79 106	96.69 111	97.06 96	99.52 138	97.09 211

UniMVSNet_NR-MVSNet		90.50 142	92.31 149	88.38 134	85.04 175	96.34 161	90.94 152	85.32 95	95.87 159	75.69 131	87.68 149	78.49 149	93.78 141	93.21 177	94.60 137	99.53 137	99.97 80

UniMVSNet (Re)		90.41 143	91.96 151	88.59 132	85.71 161	96.73 153	90.82 155	84.11 113	95.23 166	78.54 125	88.91 143	76.41 159	92.84 149	93.40 175	93.05 163	99.55 133	100.00 1

GA-MVS		90.38 144	94.59 135	85.46 166	88.30 148	98.44 125	92.18 144	83.30 119	97.89 143	58.05 212	92.86 126	84.25 142	91.27 180	96.65 112	92.61 169	99.66 118	99.43 183

USDC		90.36 145	91.68 153	88.82 127	92.58 100	98.02 134	96.27 96	79.83 148	98.37 133	70.61 155	89.05 141	67.50 210	94.17 136	95.77 133	94.43 142	99.46 155	98.62 196

TinyColmap		89.94 146	90.88 159	88.84 126	92.43 107	97.91 138	95.59 109	80.10 146	98.12 139	71.33 152	84.56 155	67.46 211	94.15 137	95.57 135	94.27 148	99.43 164	98.26 201

pm-mvs1		89.68 147	92.00 150	86.96 145	86.23 158	96.62 155	90.36 167	83.05 121	93.97 185	72.15 145	81.77 173	82.10 146	90.69 186	95.38 139	94.50 140	99.29 196	99.65 165

tpm		89.60 148	94.93 131	83.39 195	89.94 136	97.11 149	90.09 179	65.28 219	98.67 114	60.03 209	96.79 85	84.38 141	95.66 120	91.90 189	95.65 128	99.32 192	99.98 67

NR-MVSNet		89.52 149	90.71 160	88.14 138	86.19 159	96.20 162	92.07 145	84.58 103	95.54 161	75.27 135	87.52 151	67.96 209	91.24 182	94.33 154	93.45 157	99.49 145	99.97 80

DU-MVS		89.49 150	90.60 161	88.19 137	84.71 189	96.20 162	90.94 152	84.58 103	95.54 161	75.69 131	87.52 151	68.74 208	93.78 141	91.10 207	95.13 134	99.47 152	99.97 80

Baseline_NR-MVSNet		89.13 151	89.53 175	88.66 131	84.71 189	94.43 198	91.79 148	84.49 106	95.54 161	78.28 126	78.52 200	72.46 177	93.29 146	91.10 207	94.82 136	99.42 170	99.86 145

tfpnnormal		89.09 152	89.71 168	88.38 134	87.37 151	96.78 152	91.46 151	85.20 98	90.33 215	72.35 143	83.45 159	69.30 206	94.45 132	95.29 141	92.86 165	99.44 163	99.93 115

TranMVSNet+NR-MVSNet		88.88 153	89.90 166	87.69 140	84.06 201	95.68 179	91.88 146	85.23 97	95.16 167	72.54 140	83.06 162	70.14 200	92.93 148	90.81 210	94.53 139	99.48 149	99.89 135

WR-MVS_H		88.47 154	90.55 162	86.04 152	85.13 172	96.07 170	89.86 187	79.80 149	94.37 182	72.32 144	83.12 161	74.44 167	89.60 192	93.52 172	92.40 170	99.51 142	99.96 99

SixPastTwentyTwo		88.35 155	91.51 155	84.66 176	85.39 166	96.96 150	86.57 201	79.62 151	96.57 154	63.73 196	87.86 147	75.18 162	93.43 145	94.03 158	90.37 206	99.24 201	99.58 173

TransMVSNet (Re)		88.33 156	89.55 174	86.91 146	86.65 155	95.56 184	90.48 161	84.44 107	92.02 214	71.07 154	80.13 180	72.48 176	89.41 193	95.05 146	94.44 141	99.39 184	97.14 210

LP		88.31 157	93.18 143	82.63 200	90.66 131	97.98 136	87.32 200	63.49 225	97.17 150	63.02 199	82.08 165	90.47 114	91.92 157	92.75 185	93.42 158	99.38 186	98.37 199

MVS-HIRNet		88.27 158	94.05 140	81.51 204	88.90 145	98.93 115	83.38 211	60.52 230	98.06 141	63.78 195	80.67 177	90.36 115	92.94 147	97.29 105	96.41 106	99.56 131	96.66 213

WR-MVS		88.23 159	90.15 164	86.00 154	84.39 196	95.64 180	89.96 183	81.80 135	94.46 180	71.60 149	82.10 164	74.36 168	88.76 200	92.48 186	92.20 172	99.46 155	99.83 149

CP-MVSNet		88.09 160	89.57 172	86.36 150	84.63 192	95.46 189	89.48 189	80.53 144	93.42 200	71.26 153	81.25 175	69.90 201	92.78 150	93.30 176	93.69 154	99.47 152	99.96 99

anonymousdsp		87.98 161	92.38 148	82.85 198	83.68 205	96.79 151	90.78 156	74.06 186	95.29 165	57.91 213	83.33 160	83.12 143	91.15 184	95.96 132	92.37 171	99.52 138	99.76 158

v6		87.96 162	89.58 171	86.08 151	85.34 167	96.14 164	90.44 162	82.19 131	94.56 174	67.43 171	81.90 168	71.57 183	91.62 167	91.54 194	91.43 187	99.43 164	99.92 119

v1neww		87.88 163	89.51 177	85.97 156	85.32 168	96.12 165	90.33 169	82.17 132	94.51 175	66.96 173	81.84 170	71.21 186	91.64 164	91.52 196	91.43 187	99.42 170	99.92 119

v7new		87.88 163	89.51 177	85.97 156	85.32 168	96.12 165	90.33 169	82.17 132	94.51 175	66.96 173	81.84 170	71.21 186	91.64 164	91.52 196	91.43 187	99.42 170	99.92 119

LTVRE_ROB	88.65 16	87.87 165	91.11 158	84.10 187	86.64 156	97.47 143	94.40 126	78.41 162	96.13 157	52.02 220	87.95 146	65.92 216	93.59 143	95.29 141	95.09 135	99.52 138	99.95 108
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
V42		87.84 166	89.42 179	85.99 155	85.16 171	96.01 173	90.52 158	81.78 137	94.43 181	67.59 165	81.32 174	71.87 179	91.48 171	91.25 206	91.16 201	99.43 164	99.92 119

TDRefinement		87.79 167	88.76 193	86.66 148	93.54 80	98.02 134	95.76 102	85.18 99	96.57 154	67.90 162	80.51 179	66.51 215	78.37 217	93.20 178	89.73 210	99.22 202	96.75 212

MDTV_nov1_ep13_2view		87.75 168	93.32 142	81.26 206	83.74 204	96.64 154	85.66 204	66.20 212	98.36 134	61.61 203	84.34 157	87.95 124	91.12 185	94.01 159	92.66 167	99.22 202	99.27 188

v7		87.72 169	89.75 167	85.35 168	85.01 176	95.79 178	90.43 164	78.98 157	94.50 178	66.39 179	78.87 194	73.65 171	91.85 160	93.69 169	91.86 178	99.45 158	99.92 119

v8		87.54 170	89.33 180	85.45 167	85.41 165	95.50 187	90.32 172	78.94 158	94.35 183	66.93 175	81.90 168	70.99 191	91.62 167	91.49 199	91.22 198	99.48 149	99.87 142

v1144		87.49 171	89.64 169	84.97 171	84.73 188	95.84 176	90.17 177	79.30 153	93.96 186	64.65 193	78.83 196	73.38 173	91.51 170	93.77 166	91.77 179	99.45 158	99.93 115

v1		87.48 172	88.91 188	85.81 159	84.93 179	96.07 170	90.33 169	82.45 127	93.65 195	66.39 179	79.38 191	70.40 197	91.33 177	91.58 193	91.38 193	99.42 170	99.93 115

divwei89l23v2f112		87.46 173	88.97 185	85.70 163	84.85 184	96.08 168	90.23 175	82.46 125	93.69 194	65.83 186	79.57 188	70.54 194	91.39 176	91.60 192	91.39 191	99.43 164	99.92 119

v2v482		87.46 173	88.90 189	85.78 160	84.58 193	95.95 175	89.90 186	82.43 128	94.19 184	65.65 188	79.80 184	69.12 207	92.67 151	91.88 190	91.46 185	99.45 158	99.93 115

v1141		87.45 175	88.98 184	85.67 164	84.86 183	96.08 168	90.23 175	82.46 125	93.75 190	65.64 190	79.57 188	70.52 195	91.41 175	91.63 191	91.39 191	99.42 170	99.92 119

v10		87.40 176	89.62 170	84.80 174	84.93 179	95.07 195	90.44 162	75.63 177	94.51 175	66.52 177	78.87 194	73.47 172	91.86 159	93.69 169	91.87 177	99.45 158	99.86 145

pmmvs5		87.33 177	90.01 165	84.20 185	84.31 198	96.04 172	87.63 198	76.59 174	93.17 205	65.35 192	84.30 158	71.68 180	91.91 158	95.41 138	91.37 194	99.39 184	98.13 202

N_pmnet		87.31 178	91.51 155	82.41 203	85.13 172	95.57 183	80.59 215	81.79 136	96.20 156	58.52 211	78.62 198	85.66 130	89.36 194	94.64 150	92.14 173	99.08 207	97.72 209

PS-CasMVS		87.24 179	88.52 196	85.73 162	84.58 193	95.35 191	89.03 192	80.17 145	93.11 206	68.86 160	77.71 202	66.89 212	92.30 153	93.13 180	93.50 156	99.46 155	99.96 99

EU-MVSNet		87.20 180	90.47 163	83.38 196	85.11 174	93.85 203	86.10 203	79.76 150	93.30 204	65.39 191	84.41 156	78.43 150	85.04 210	92.20 188	93.03 164	98.86 209	98.05 205

PEN-MVS		87.20 180	88.22 200	86.01 153	84.01 203	94.93 197	90.00 181	81.52 142	93.46 199	69.29 158	79.69 186	65.51 217	91.72 161	91.01 209	93.12 161	99.49 145	99.84 147

v16		87.15 182	89.13 181	84.83 173	85.55 163	91.94 209	90.50 159	74.13 185	95.06 168	67.72 164	81.84 170	72.55 175	91.65 163	91.50 198	91.42 190	99.42 170	99.60 170

v18		87.14 183	88.96 186	85.01 170	85.57 162	92.03 207	90.89 154	74.62 181	94.80 172	67.90 162	82.02 166	71.28 185	91.63 166	91.53 195	91.44 186	99.47 152	99.60 170

v17		86.99 184	88.90 189	84.76 175	85.52 164	91.96 208	90.50 159	74.17 182	94.88 170	67.33 172	81.94 167	71.21 186	91.57 169	91.49 199	91.20 199	99.48 149	99.60 170

EG-PatchMatch MVS		86.96 185	89.56 173	83.93 191	86.29 157	97.61 141	90.75 157	73.31 192	95.43 164	66.08 184	75.88 211	71.31 184	87.55 205	94.79 148	92.74 166	99.61 124	99.13 192

v1192		86.93 186	89.01 182	84.50 177	84.46 195	95.51 186	89.93 185	78.65 160	93.75 190	62.29 201	77.19 204	70.88 192	92.28 154	93.84 163	91.96 175	99.38 186	99.90 131

v1921920		86.81 187	88.93 187	84.33 182	84.23 199	95.41 190	90.09 179	78.10 163	93.74 192	62.17 202	76.98 206	71.14 189	92.05 156	93.69 169	91.69 182	99.32 192	99.88 138

v144192		86.80 188	88.90 189	84.35 179	84.33 197	95.56 184	89.34 190	77.74 165	93.60 196	64.03 194	77.82 201	70.76 193	91.28 179	92.91 183	91.74 181	99.37 188	99.90 131

v11		86.74 189	89.01 182	84.09 189	84.79 186	91.79 214	90.39 166	72.53 199	94.47 179	65.75 187	78.64 197	72.96 174	91.66 162	93.92 161	91.69 182	99.42 170	99.61 169

DTE-MVSNet		86.70 190	87.66 208	85.58 165	83.30 206	94.29 199	89.74 188	81.53 140	92.77 208	68.93 159	80.13 180	64.00 220	90.62 187	89.45 214	93.34 159	99.32 192	99.67 164

gg-mvs-nofinetune		86.69 191	91.30 157	81.30 205	90.42 133	99.64 78	98.50 53	61.68 227	79.23 228	40.35 231	66.58 222	97.14 80	96.92 102	98.64 40	97.94 63	99.91 20	99.97 80

v148		86.63 192	87.79 204	85.28 169	84.65 191	95.97 174	86.46 202	82.84 123	92.91 207	71.52 151	78.99 193	66.74 214	86.83 207	89.28 215	90.69 204	99.41 182	99.94 112

V14		86.54 193	88.41 197	84.35 179	84.94 178	91.83 211	90.28 174	73.48 190	93.73 193	66.50 178	79.89 183	71.12 190	91.46 172	91.48 201	91.25 196	99.42 170	99.58 173

v15		86.50 194	88.32 198	84.37 178	85.00 177	91.86 210	90.30 173	73.76 188	93.90 188	66.28 182	79.78 185	70.37 198	91.45 173	91.48 201	91.27 195	99.43 164	99.58 173

V9		86.42 195	88.26 199	84.27 183	84.88 181	91.80 212	90.34 168	73.18 194	93.92 187	66.37 181	79.68 187	70.25 199	91.42 174	91.43 203	91.23 197	99.42 170	99.55 178

v12		86.32 196	88.22 200	84.10 187	84.76 187	91.80 212	89.94 184	72.97 196	93.85 189	66.18 183	79.98 182	69.72 205	91.33 177	91.40 204	91.20 199	99.42 170	99.56 177

v13		86.27 197	88.16 202	84.06 190	84.85 184	91.77 215	90.00 181	72.77 198	93.56 197	66.06 185	79.25 192	70.50 196	91.25 181	91.35 205	91.15 202	99.42 170	99.55 178

v1240		86.24 198	88.56 195	83.54 192	84.05 202	95.21 194	89.27 191	76.76 172	93.42 200	60.68 208	75.99 210	69.80 203	91.21 183	93.83 165	91.76 180	99.29 196	99.91 130

v52		85.80 199	87.74 205	83.53 193	82.87 209	95.31 193	88.71 193	77.04 170	92.23 211	63.53 197	76.91 207	69.80 203	89.78 189	90.05 212	90.07 208	99.26 200	99.82 150

V4		85.78 200	87.74 205	83.50 194	82.90 208	95.33 192	88.62 194	77.05 169	92.14 213	63.45 198	76.91 207	69.85 202	89.72 190	90.07 211	90.05 209	99.27 199	99.81 151

pmmvs6		85.75 201	86.97 209	84.34 181	84.88 181	95.59 182	87.41 199	79.19 155	87.81 221	67.56 166	63.05 225	77.76 151	89.15 195	93.45 174	91.90 176	97.83 216	99.21 189

v7n		85.39 202	87.70 207	82.70 199	82.77 211	95.64 180	88.27 196	74.83 179	92.30 210	62.58 200	76.37 209	64.80 219	88.38 202	94.29 156	90.61 205	99.34 189	99.87 142

gm-plane-assit		84.93 203	91.61 154	77.14 213	84.14 200	91.29 217	66.18 228	69.70 202	85.22 224	47.95 226	78.58 199	89.24 118	94.90 127	98.82 34	98.12 60	99.99 6	100.00 1

CMPMVS	65.66 17	84.62 204	85.02 212	84.15 186	95.40 65	97.79 139	88.35 195	79.22 154	89.66 219	60.71 207	72.20 215	73.94 169	87.32 206	86.73 219	84.55 224	93.90 226	90.31 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		84.47 205	86.06 210	82.62 201	82.85 210	95.02 196	83.73 209	78.48 161	90.20 217	67.45 170	75.86 212	61.27 222	83.84 211	89.87 213	90.28 207	99.34 189	99.90 131

Anonymous20231206		84.28 206	89.53 175	78.17 210	82.31 213	94.16 201	82.57 212	76.51 175	93.38 203	52.98 218	79.47 190	73.74 170	75.45 219	95.07 145	94.41 143	99.18 205	96.46 216

new_pmnet		84.12 207	87.89 203	79.72 208	80.43 214	94.14 202	80.26 216	74.14 184	96.01 158	56.30 217	74.94 213	76.45 158	88.59 201	93.11 181	89.31 211	98.59 212	91.27 223

test20.03		83.86 208	88.73 194	78.16 211	82.60 212	93.00 204	81.61 214	74.68 180	92.36 209	57.50 214	83.01 163	74.48 166	73.30 223	92.40 187	91.14 203	99.29 196	94.75 219

test2356		83.84 209	91.77 152	74.59 217	78.71 216	89.10 221	78.24 220	72.07 201	96.78 152	45.18 229	96.19 94	76.77 155	74.87 221	93.17 179	94.01 151	98.44 213	96.38 217

pmmvs-eth3d		82.92 210	83.31 215	82.47 202	76.97 218	91.76 216	83.79 207	76.10 176	90.33 215	69.95 157	71.04 218	48.09 226	89.02 197	93.85 162	89.14 212	99.02 208	98.96 194

PM-MVS		82.79 211	84.51 213	80.77 207	77.22 217	92.13 206	83.61 210	73.31 192	93.50 198	61.06 204	77.15 205	46.52 229	90.55 188	94.14 157	89.05 214	98.85 210	99.12 193

testus		82.22 212	88.82 192	74.52 218	79.14 215	89.37 220	78.38 218	72.99 195	97.57 145	44.54 230	93.44 122	58.13 224	74.20 222	92.96 182	93.67 155	97.89 215	96.58 214

pmmvs3		80.91 213	85.62 211	75.42 215	75.01 220	89.09 222	75.31 222	68.70 203	86.99 222	46.74 228	81.18 176	62.91 221	87.95 203	93.84 163	89.06 213	98.80 211	96.23 218

MIMVSNet1		80.64 214	83.97 214	76.76 214	68.91 229	91.15 219	78.32 219	75.47 178	89.58 220	56.64 216	65.10 223	65.17 218	82.14 212	93.51 173	91.64 184	99.10 206	91.66 222

MDA-MVSNet-bldmvs		80.30 215	82.83 216	77.34 212	69.16 228	94.29 199	72.16 223	81.97 134	90.14 218	57.32 215	94.01 117	47.97 227	86.81 208	68.74 230	86.82 220	96.63 218	97.86 207

new-patchmatchnet		78.17 216	80.82 217	75.07 216	76.93 219	91.20 218	71.90 224	73.32 191	86.59 223	48.91 223	67.11 221	47.85 228	81.19 213	88.18 216	87.02 219	98.19 214	97.79 208

Anonymous20231211		74.10 217	74.22 225	73.97 219	74.36 221	87.76 223	75.92 221	72.78 197	74.83 233	52.25 219	44.18 232	42.42 232	73.07 224	86.16 220	86.24 222	95.44 224	97.94 206

FPMVS		73.80 218	74.62 223	72.84 220	83.09 207	84.44 225	83.89 206	73.64 189	92.20 212	48.50 224	72.19 216	59.51 223	63.16 226	69.13 229	66.26 234	84.74 231	78.59 234

1111		73.79 219	78.62 219	68.16 222	69.34 226	81.48 227	59.42 232	52.46 233	78.55 229	50.42 221	62.43 226	71.67 181	80.43 215	86.79 217	88.22 215	96.87 217	81.17 233

testmv		71.50 220	77.62 220	64.36 223	72.64 222	81.28 229	59.32 234	66.24 210	83.91 225	35.02 235	69.74 219	46.18 230	57.12 229	85.60 222	87.48 217	95.84 221	89.16 226

test1235678		71.50 220	77.61 221	64.36 223	72.64 222	81.26 230	59.31 235	66.22 211	83.90 226	35.02 235	69.74 219	46.18 230	57.12 229	85.60 222	87.47 218	95.84 221	89.15 227

Gipuma		71.02 222	72.60 227	69.19 221	71.31 224	75.11 233	66.36 227	61.65 228	94.93 169	47.29 227	38.74 233	38.52 234	75.52 218	86.09 221	85.92 223	93.01 227	88.87 228
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		70.78 223	79.90 218	60.13 228	69.34 226	81.48 227	59.42 232	52.46 233	78.55 229	50.42 221	62.43 226	71.67 181	80.43 215	86.79 217	78.71 226	48.74 237	99.65 165

test12356		69.94 224	75.85 222	63.04 225	70.04 225	79.32 232	61.62 230	65.84 215	80.56 227	36.30 234	71.45 217	39.38 233	48.79 235	83.64 224	88.02 216	95.64 223	88.56 229

GG-mvs-BLEND		69.85 225	99.39 35	35.39 235	3.67 239	99.94 17	99.10 37	1.69 237	99.85 40	3.19 242	98.13 73	99.46 52	4.92 237	99.23 27	99.14 28	99.80 49	100.00 1

PMMVS2		65.18 226	68.25 228	61.59 226	61.37 232	79.72 231	59.18 236	61.80 226	64.72 234	37.33 232	53.82 229	35.59 235	54.46 233	73.94 228	80.52 225	95.40 225	89.43 225

PMVS	60.14 18	62.67 227	64.05 229	61.06 227	68.32 230	53.27 240	52.23 237	67.63 206	75.07 232	48.30 225	58.27 228	57.43 225	49.99 234	67.20 231	62.42 235	79.87 235	74.68 236
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 228	72.90 226	48.76 232	21.21 237	68.61 235	66.11 229	37.38 235	94.83 171	33.06 237	64.31 224	29.72 236	86.08 209	74.44 227	78.71 226	48.74 237	99.65 165

E-PMN		55.33 229	55.79 231	54.81 230	59.81 234	57.23 238	38.83 238	63.59 223	64.06 236	24.66 239	35.33 235	26.40 238	58.69 228	55.41 233	70.54 231	83.26 232	81.56 232

EMVS		55.14 230	55.29 232	54.97 229	60.87 233	57.52 237	38.58 239	63.57 224	64.54 235	23.36 240	36.96 234	27.99 237	60.69 227	51.17 234	66.61 233	82.73 234	82.25 231

no-one		52.34 231	53.36 234	51.14 231	57.63 235	69.39 234	35.07 241	61.58 229	44.14 238	37.06 233	34.80 236	26.36 239	32.65 236	50.68 235	70.83 230	82.88 233	77.30 235

MVE	58.81 19	52.07 232	55.15 233	48.48 233	42.45 236	62.35 236	36.41 240	54.70 232	49.88 237	27.65 238	29.98 237	18.08 240	54.87 232	65.93 232	77.26 228	74.79 236	82.59 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 233	58.11 230	36.51 234	8.71 238	56.81 239	59.55 231	24.08 236	77.50 231	14.41 241	49.20 230	11.94 242	80.98 214	41.62 236	69.81 232	31.32 239	99.90 131

sosnet-low-res		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

sosnet		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

ambc			74.33 224		66.84 231	84.26 226	84.17 205		93.39 202	58.99 210	45.93 231	18.06 241	70.61 225	93.94 160	86.62 221	92.61 229	98.13 202

MTAPA										96.61 10		100.00 1

MTMP										97.42 7		100.00 1

Patchmatch-RL test							68.01 226

tmp_tt				78.81 209	98.80 40	85.73 224	70.08 225	77.87 164	98.68 113	83.71 104	99.53 27	74.55 165	54.97 231	78.28 226	72.43 229	87.45 230

XVS					95.09 68	99.94 17	97.49 70			88.58 82		99.98 27				99.78 60

X-MVStestdata					95.09 68	99.94 17	97.49 70			88.58 82		99.98 27				99.78 60

abl_6				97.06 31	99.17 34	99.82 55	98.68 48	90.86 46	100.00 1	94.53 27	97.40 81	100.00 1	99.17 49			99.93 16	99.99 47

mPP-MVS					99.23 32							99.87 36

NP-MVS									99.79 49

Patchmtry						99.00 109	95.46 111	65.50 216		67.51 167

DeepMVS_CX						97.31 146	79.48 217	89.65 58	98.66 115	60.89 206	94.40 112	66.89 212	87.65 204	81.69 225		92.76 228	94.24 221

High-resolution multi-view benchmark