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.

CNVR-MVS		99.39 2	99.75 11	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 47	99.97 10	99.97 80

MSLP-MVS++		99.39 2	99.76 8	98.95 2	99.60 12	99.99 1	99.83 1	96.82 13	99.92 29	97.58 6	99.58 25	100.00 1	99.93 1	98.98 31	99.86 7	99.96 11	100.00 1

APDe-MVS		99.40 1	99.81 2	98.92 3	99.62 6	99.96 7	99.76 5	96.87 9	99.95 20	97.66 4	99.57 26	100.00 1	99.63 24	99.88 8	99.28 25	100.00 1	100.00 1

AdaColmap		99.21 9	99.45 35	98.92 3	99.67 4	99.95 12	99.65 23	96.77 17	99.97 6	97.67 3	100.00 1	99.69 46	99.93 1	99.26 27	97.25 87	99.85 26	100.00 1

PLC	98.06 1	99.17 11	99.38 37	98.92 3	99.47 18	99.90 43	99.48 28	96.47 27	99.96 16	98.73 1	99.52 29	100.00 1	99.55 29	98.54 52	97.73 77	99.84 28	99.99 48
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HSP-MVS		99.36 4	99.79 4	98.85 6	99.61 10	99.96 7	99.71 19	96.94 4	99.97 6	97.11 8	99.60 22	100.00 1	99.70 16	99.96 1	99.12 31	100.00 1	99.96 99

CNLPA		99.24 7	99.58 29	98.85 6	99.34 28	99.95 12	99.32 31	96.65 22	99.96 16	98.44 2	98.97 51	100.00 1	99.57 27	98.66 39	99.56 15	99.76 73	99.97 80

ESAPD		99.25 6	99.69 18	98.74 8	99.62 6	99.94 17	99.79 2	96.87 9	99.93 24	96.33 14	99.59 23	100.00 1	99.84 8	99.88 8	98.50 53	100.00 1	100.00 1

APD-MVS		99.33 5	99.85 1	98.73 9	99.61 10	99.92 35	99.77 4	96.91 6	99.93 24	96.31 15	99.59 23	99.95 33	99.84 8	99.73 16	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
zzz-MVS		99.12 15	99.52 34	98.65 10	99.58 15	99.93 29	99.74 13	96.72 20	99.44 83	96.47 12	99.62 21	100.00 1	99.63 24	99.74 15	97.97 64	99.77 66	99.94 114

NCCC		99.24 7	99.75 11	98.65 10	99.63 5	99.96 7	99.76 5	96.91 6	99.97 6	95.86 18	99.67 11	100.00 1	99.75 13	99.85 10	98.80 43	99.98 9	99.97 80

CPTT-MVS		99.08 17	99.53 33	98.57 12	99.44 22	99.93 29	99.60 26	95.92 32	99.77 53	97.01 9	99.67 11	100.00 1	99.72 14	99.56 21	97.76 74	99.70 111	99.98 67

CP-MVS		99.14 13	99.67 20	98.53 13	99.45 20	99.94 17	99.63 25	96.62 24	99.82 46	95.92 17	99.65 16	100.00 1	99.71 15	99.76 14	98.56 50	99.83 33	100.00 1

HFP-MVS		99.19 10	99.77 7	98.51 14	99.55 16	99.94 17	99.76 5	96.84 12	99.88 34	95.27 22	99.67 11	100.00 1	99.85 7	99.56 21	99.36 20	99.79 54	99.97 80

SD-MVS		99.16 12	99.73 14	98.49 15	97.93 48	99.95 12	99.74 13	96.94 4	99.96 16	96.60 11	99.47 32	100.00 1	99.88 6	99.15 29	99.59 12	99.84 28	100.00 1

SMA-MVS		99.14 13	99.79 4	98.39 16	99.68 2	99.94 17	99.74 13	96.86 11	99.97 6	94.36 28	99.22 40	100.00 1	99.89 5	99.84 12	99.58 13	99.83 33	99.95 108

ACMMPR		99.12 15	99.76 8	98.36 17	99.45 20	99.94 17	99.75 11	96.70 21	99.93 24	94.65 26	99.65 16	99.96 31	99.84 8	99.51 23	99.35 21	99.79 54	99.96 99

MCST-MVS		99.08 17	99.72 16	98.33 18	99.59 14	99.97 3	99.78 3	96.96 2	99.95 20	93.72 31	99.67 11	100.00 1	99.90 4	99.91 5	98.55 51	100.00 1	100.00 1

TSAR-MVS + MP.		98.99 20	99.61 26	98.27 19	97.88 49	99.92 35	99.71 19	96.80 14	99.96 16	95.58 20	98.71 61	100.00 1	99.68 19	99.91 5	98.78 45	99.99 6	100.00 1

HPM-MVS++		98.98 21	99.62 25	98.22 20	99.62 6	99.94 17	99.74 13	96.95 3	99.87 37	93.76 30	99.49 31	100.00 1	99.39 35	99.73 16	98.35 55	99.89 22	99.96 99

DeepC-MVS_fast	98.03 2	99.05 19	99.78 6	98.21 21	99.47 18	99.97 3	99.75 11	96.80 14	99.97 6	93.58 34	98.68 62	99.94 34	99.69 17	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
CSCG		98.22 31	98.37 59	98.04 22	99.60 12	99.82 56	99.45 29	93.59 37	99.16 98	96.46 13	98.22 73	95.86 90	99.41 34	96.33 125	99.22 27	99.75 86	99.94 114

OMC-MVS		98.59 28	99.07 39	98.03 23	99.41 24	99.90 43	99.26 34	94.33 36	99.94 22	96.03 16	96.68 87	99.72 45	99.42 32	98.86 34	98.84 40	99.72 106	99.58 176

SteuartSystems-ACMMP		98.95 22	99.80 3	97.95 24	99.43 23	99.96 7	99.76 5	96.45 28	99.82 46	93.63 32	99.64 19	100.00 1	98.56 74	99.90 7	99.31 23	99.84 28	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 23	99.67 20	97.89 25	98.63 44	99.93 29	98.95 42	95.20 34	99.84 44	94.94 23	99.74 10	100.00 1	99.69 17	98.40 58	99.75 10	99.93 16	99.99 48

MP-MVS		98.82 24	99.63 23	97.88 26	99.41 24	99.91 42	99.74 13	96.76 18	99.88 34	91.89 42	99.50 30	99.94 34	99.65 22	99.71 19	98.49 54	99.82 38	99.97 80
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP_Plus		98.68 25	99.58 29	97.62 27	99.62 6	99.92 35	99.72 18	96.78 16	99.71 61	90.13 69	99.66 15	99.99 26	99.64 23	99.78 13	98.14 61	99.82 38	99.89 138

X-MVS		98.62 26	99.75 11	97.29 28	99.50 17	99.94 17	99.71 19	96.55 25	99.85 41	88.58 83	99.65 16	99.98 28	99.67 20	99.60 20	99.26 26	99.77 66	99.97 80

train_agg		98.62 26	99.76 8	97.28 29	99.03 37	99.93 29	99.65 23	96.37 29	99.98 3	89.24 78	99.53 27	99.83 39	99.59 26	99.85 10	99.19 29	99.80 50	100.00 1

ACMMP		98.16 33	99.01 40	97.18 30	98.86 39	99.92 35	98.77 47	95.73 33	99.31 94	91.15 54	100.00 1	99.81 41	98.82 66	98.11 72	95.91 124	99.77 66	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
3Dnovator+	95.21 7	98.17 32	99.08 38	97.12 31	99.28 31	99.78 70	98.61 50	89.93 56	99.93 24	95.36 21	95.50 99	100.00 1	99.56 28	98.58 46	99.80 9	99.95 13	99.97 80

abl_6				97.06 32	99.17 35	99.82 56	98.68 49	90.86 47	100.00 1	94.53 27	97.40 83	100.00 1	99.17 50			99.93 16	99.99 48

PGM-MVS		98.47 29	99.73 14	97.00 33	99.68 2	99.94 17	99.76 5	91.74 41	99.84 44	91.17 53	100.00 1	99.69 46	99.81 11	99.38 25	99.30 24	99.82 38	99.95 108

MSDG		97.29 45	97.55 81	97.00 33	98.66 43	99.71 74	99.03 40	96.15 30	99.59 70	89.67 76	92.77 131	94.86 94	98.75 69	98.22 67	97.94 65	99.72 106	99.76 161

3Dnovator	95.01 8	97.98 37	98.89 43	96.92 35	99.36 26	99.76 72	98.72 48	89.98 54	99.98 3	93.99 29	94.60 113	99.43 56	99.50 30	98.55 49	99.91 4	99.99 6	99.98 67

TSAR-MVS + ACMM		98.30 30	99.64 22	96.74 36	99.08 36	99.94 17	99.67 22	96.73 19	99.97 6	86.30 96	98.30 67	99.99 26	98.78 68	99.73 16	99.57 14	99.88 25	99.98 67

QAPM		97.90 39	98.89 43	96.74 36	99.35 27	99.80 68	98.84 44	90.20 53	99.94 22	92.85 35	94.17 116	99.78 42	99.42 32	98.71 37	99.87 6	99.79 54	99.98 67

PCF-MVS	97.20 3	97.49 44	98.20 65	96.66 38	97.62 51	99.92 35	98.93 43	96.64 23	98.53 126	88.31 86	94.04 118	99.58 50	98.94 59	97.53 92	97.79 72	99.54 137	99.97 80
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	96.62 5	97.60 42	98.46 57	96.60 39	98.73 42	99.90 43	99.30 32	94.96 35	99.46 82	87.57 88	96.05 97	98.53 64	99.26 46	98.04 78	97.33 86	99.77 66	99.88 141
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	96.33 6	97.05 47	97.59 80	96.42 40	97.37 52	99.92 35	99.10 38	96.54 26	99.34 93	86.64 95	91.93 135	93.15 107	99.11 56	99.11 30	99.68 11	99.73 102	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_111021_LR		98.15 34	99.69 18	96.36 41	99.23 33	99.93 29	97.79 61	91.84 40	99.87 37	90.53 64	100.00 1	99.57 51	98.93 60	99.44 24	99.08 33	99.85 26	99.95 108

EPNet		98.11 35	99.63 23	96.34 42	98.44 46	99.88 49	98.55 51	90.25 52	99.93 24	92.60 39	100.00 1	99.73 43	98.41 76	98.87 33	99.02 34	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 36	99.55 32	96.32 43	94.72 74	99.92 35	99.22 35	89.98 54	99.97 6	94.77 25	99.94 9	100.00 1	99.43 31	98.52 56	98.53 52	99.79 54	100.00 1

CANet		97.62 41	98.94 42	96.08 44	97.19 53	99.93 29	99.29 33	90.38 50	99.87 37	91.00 55	95.79 98	99.51 52	98.72 72	98.53 53	99.00 35	99.90 21	99.99 48

MVS_111021_HR		97.94 38	99.59 27	96.02 45	99.27 32	99.97 3	97.03 84	90.44 49	99.89 31	90.75 57	100.00 1	99.73 43	98.68 73	98.67 38	98.89 38	99.95 13	99.97 80

CDPH-MVS		97.88 40	99.59 27	95.89 46	98.90 38	99.95 12	99.40 30	92.86 39	99.86 40	85.33 99	98.62 63	99.45 55	99.06 58	99.29 26	99.94 3	99.81 47	100.00 1

MAR-MVS		97.03 50	98.00 72	95.89 46	99.32 29	99.74 73	96.76 91	84.89 103	99.97 6	94.86 24	98.29 68	90.58 114	99.67 20	98.02 80	99.50 16	99.82 38	99.92 121
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
DELS-MVS		97.05 47	98.05 70	95.88 48	97.09 54	99.99 1	98.82 45	90.30 51	98.44 131	91.40 48	92.91 128	96.57 83	97.68 96	98.56 48	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
PatchMatch-RL		96.84 53	98.03 71	95.47 49	98.84 40	99.81 64	95.61 111	89.20 65	99.65 64	91.28 51	99.39 33	93.46 105	98.18 81	98.05 76	96.28 111	99.69 117	99.55 181

PVSNet_BlendedMVS		96.01 66	96.48 111	95.46 50	96.47 58	99.89 47	95.64 108	91.23 45	99.75 57	91.59 44	96.80 84	82.44 147	98.05 83	98.53 53	97.92 69	99.80 50	100.00 1

PVSNet_Blended		96.01 66	96.48 111	95.46 50	96.47 58	99.89 47	95.64 108	91.23 45	99.75 57	91.59 44	96.80 84	82.44 147	98.05 83	98.53 53	97.92 69	99.80 50	100.00 1

OpenMVS	94.03 11	96.87 52	98.10 69	95.44 52	99.29 30	99.78 70	98.46 56	89.92 57	99.47 81	85.78 97	91.05 138	98.50 65	99.30 39	98.49 57	99.41 17	99.89 22	99.98 67

TSAR-MVS + COLMAP		95.20 90	95.03 133	95.41 53	96.17 61	98.69 124	99.11 37	93.40 38	99.97 6	84.89 104	98.23 72	75.01 166	99.34 37	97.27 108	96.37 110	99.58 131	99.64 171

LS3D		96.44 60	97.31 86	95.41 53	97.06 55	99.87 50	99.51 27	97.48 1	99.57 71	79.00 124	95.39 101	89.19 120	99.81 11	98.55 49	98.84 40	99.62 126	99.78 159

Anonymous20240521		95.82 71	96.38 114	95.18 55	93.09 89	99.66 78	97.00 85	89.49 61	97.70 146	92.61 38	93.55 124	88.28 125	99.16 51	97.26 109	98.24 57	99.72 106	99.95 108

COLMAP_ROB	93.56 12	96.03 65	96.83 101	95.11 56	97.87 50	99.52 85	98.81 46	91.40 44	99.42 85	84.97 102	90.46 140	96.82 82	98.05 83	96.46 121	96.19 114	99.54 137	98.92 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVS_0304		97.04 49	98.72 50	95.08 57	96.32 60	99.90 43	99.15 36	89.61 60	99.89 31	87.22 93	95.47 100	98.22 73	98.22 80	98.63 43	98.90 37	99.93 16	100.00 1

conf0.002		96.51 57	97.75 77	95.07 58	93.11 83	99.83 52	97.67 63	89.10 67	98.62 118	91.47 47	99.39 33	91.68 110	99.28 41	97.49 94	97.24 88	99.76 73	100.00 1

DeepPCF-MVS	97.16 4	97.58 43	99.72 16	95.07 58	98.45 45	99.96 7	93.83 136	95.93 31	100.00 1	90.79 56	98.38 66	99.85 38	95.28 126	99.94 2	99.97 1	96.15 222	99.97 80

conf0.01		96.20 63	97.19 90	95.05 60	93.11 83	99.83 52	97.67 63	89.06 68	98.62 118	91.38 49	99.19 41	89.09 121	99.28 41	97.48 95	96.10 115	99.76 73	100.00 1

thres100view900		95.86 69	96.62 103	94.97 61	93.10 85	99.83 52	97.76 62	89.15 66	98.62 118	90.69 58	99.00 47	84.86 134	99.30 39	97.57 91	96.48 105	99.81 47	100.00 1

tfpn111		95.79 73	96.55 105	94.89 62	93.10 85	99.82 56	97.67 63	88.85 71	98.62 118	90.69 58	99.07 44	84.86 134	99.28 41	97.41 99	96.10 115	99.76 73	99.99 48

conf200view11		95.78 74	96.54 107	94.89 62	93.10 85	99.82 56	97.67 63	88.85 71	98.62 118	90.69 58	99.00 47	84.86 134	99.28 41	97.41 99	96.10 115	99.76 73	99.99 48

tfpn200view9		95.78 74	96.54 107	94.89 62	93.10 85	99.82 56	97.67 63	88.85 71	98.62 118	90.69 58	99.00 47	84.86 134	99.28 41	97.41 99	96.10 115	99.76 73	99.99 48

thres200		95.77 77	96.55 105	94.86 65	93.09 89	99.82 56	97.63 69	88.85 71	98.49 127	90.66 62	98.99 50	84.86 134	99.20 47	97.41 99	96.28 111	99.76 73	100.00 1

thres400		95.72 82	96.48 111	94.84 66	93.00 93	99.83 52	97.55 70	88.93 69	98.49 127	90.61 63	98.86 55	84.63 139	99.20 47	97.45 96	96.10 115	99.77 66	99.99 48

MVSTER		97.00 51	98.85 46	94.83 67	92.71 100	97.43 148	99.03 40	85.52 96	99.82 46	92.74 37	99.15 42	99.94 34	99.19 49	98.66 39	96.99 101	99.79 54	99.98 67

tfpn_ndepth		96.84 53	98.58 53	94.81 68	93.18 82	99.62 82	96.83 89	88.75 77	99.73 59	92.38 40	98.45 65	96.34 87	97.90 89	98.34 63	97.59 80	99.84 28	99.99 48

view600		95.64 83	96.38 114	94.79 69	92.96 94	99.82 56	97.48 74	88.85 71	98.38 132	90.52 65	98.84 57	84.61 140	99.15 52	97.41 99	95.60 132	99.76 73	99.99 48

thres600view7		95.64 83	96.38 114	94.79 69	92.96 94	99.82 56	97.48 74	88.85 71	98.38 132	90.52 65	98.84 57	84.61 140	99.15 52	97.41 99	95.60 132	99.76 73	99.99 48

view800		95.62 85	96.38 114	94.73 71	92.96 94	99.81 64	97.38 77	88.75 77	98.35 137	90.43 68	98.81 59	84.54 142	99.13 55	97.35 105	95.82 127	99.76 73	99.98 67

tfpn		95.93 68	97.06 93	94.62 72	92.94 98	99.81 64	97.25 79	88.71 80	98.32 138	89.98 71	98.79 60	88.55 123	99.11 56	97.26 109	96.71 103	99.75 86	99.98 67

RPSCF		95.86 69	96.94 99	94.61 73	96.52 57	98.67 125	98.54 52	88.43 84	99.56 72	90.51 67	99.39 33	98.70 62	97.72 93	93.77 169	92.00 177	95.93 223	96.50 218

CLD-MVS		94.53 99	94.45 140	94.61 73	93.85 80	98.36 129	98.12 58	89.68 58	99.35 92	89.62 77	95.19 103	77.08 156	96.66 113	95.51 139	95.67 129	99.74 90	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
tfpn1000		96.58 56	98.37 59	94.50 75	93.04 91	99.59 83	96.53 94	88.54 81	99.73 59	91.59 44	98.28 69	95.76 91	97.46 98	98.19 68	97.10 96	99.82 38	99.96 99

IS_MVSNet		96.66 55	98.62 52	94.38 76	92.41 110	99.70 75	97.19 80	87.67 90	99.05 106	91.27 52	95.09 105	98.46 69	97.95 88	98.64 41	99.37 18	99.79 54	100.00 1

canonicalmvs		95.80 72	97.02 94	94.37 77	92.96 94	99.47 90	97.49 71	84.58 105	99.44 83	92.05 41	98.54 64	86.65 129	99.37 36	96.18 128	98.93 36	99.77 66	99.92 121

PMMVS		96.45 59	98.24 62	94.36 78	92.58 102	99.01 111	97.08 82	87.42 91	99.88 34	90.06 70	99.39 33	94.63 95	99.33 38	97.85 85	96.99 101	99.70 111	99.96 99

CHOSEN 280x420		97.16 46	99.58 29	94.35 79	96.95 56	99.97 3	97.19 80	81.55 142	99.92 29	91.75 43	100.00 1	100.00 1	98.84 65	98.55 49	98.65 48	99.79 54	99.97 80

OPM-MVS		93.50 113	93.00 150	94.07 80	95.82 64	98.26 134	98.49 55	91.62 42	94.69 176	81.93 118	92.82 130	76.18 164	96.82 108	96.12 130	94.57 141	99.74 90	98.39 201

EPP-MVSNet		96.29 61	98.34 61	93.90 81	91.77 121	99.38 96	95.45 116	87.25 93	99.38 89	91.36 50	94.86 112	98.49 67	97.83 91	98.01 81	98.23 58	99.75 86	99.99 48

ACMM	94.44 10	94.26 106	94.62 137	93.84 82	94.86 72	97.73 143	93.48 139	90.76 48	99.27 95	87.46 89	99.04 46	76.60 159	96.76 111	96.37 124	93.76 156	99.74 90	99.55 181
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DWT-MVSNet_training		96.26 62	98.44 58	93.72 83	92.58 102	99.34 98	96.15 100	83.00 125	99.76 55	93.63 32	97.89 77	99.46 53	97.23 102	94.43 156	98.19 59	99.70 111	100.00 1

thresconf0.02		96.46 58	98.87 45	93.64 84	92.77 99	99.11 104	97.05 83	89.36 62	99.64 66	85.14 100	99.07 44	96.84 81	97.72 93	98.72 36	98.76 46	99.78 61	99.95 108

ACMP	94.49 9	94.19 107	94.74 136	93.56 85	94.25 78	98.32 132	96.02 103	89.35 64	98.90 113	87.28 92	99.14 43	76.41 162	94.94 129	96.07 133	94.35 150	99.49 148	99.99 48
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP-MVS		94.48 101	95.39 131	93.42 86	95.10 68	98.35 130	98.19 57	91.41 43	99.77 53	79.79 121	99.30 38	77.08 156	96.25 116	96.93 111	96.28 111	99.76 73	99.99 48

FMVSNet3		95.59 87	97.51 82	93.34 87	89.48 142	96.57 159	97.67 63	84.17 112	99.48 78	89.76 72	95.09 105	94.35 96	99.14 54	98.37 60	98.86 39	99.82 38	99.89 138

DI_MVS_plusplus_trai		95.29 89	97.02 94	93.28 88	91.76 122	99.52 85	97.84 60	85.67 95	99.08 104	87.29 91	87.76 151	97.46 79	97.31 100	97.83 86	97.48 83	99.83 33	100.00 1

UGNet		96.05 64	98.55 54	93.13 89	94.64 75	99.65 79	94.70 125	87.78 88	99.40 88	89.69 75	98.25 70	99.25 59	92.12 158	96.50 117	97.08 97	99.84 28	99.72 165
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
conf0.05thres1000		94.50 100	95.70 127	93.11 90	92.68 101	99.67 77	96.04 101	87.81 87	97.52 150	83.71 106	96.20 95	84.52 143	98.73 71	96.39 123	95.66 130	99.71 109	99.92 121

GBi-Net		95.19 91	96.99 97	93.09 91	89.11 143	96.47 161	96.90 86	84.17 112	99.48 78	89.76 72	95.09 105	94.35 96	98.87 61	96.50 117	97.21 89	99.74 90	99.81 154

test1		95.19 91	96.99 97	93.09 91	89.11 143	96.47 161	96.90 86	84.17 112	99.48 78	89.76 72	95.09 105	94.35 96	98.87 61	96.50 117	97.21 89	99.74 90	99.81 154

tfpnview11		95.78 74	98.17 67	93.01 93	92.58 102	99.04 110	96.64 92	88.72 79	99.63 69	83.08 111	98.90 52	94.24 99	97.25 101	98.35 62	97.21 89	99.77 66	99.80 158

casdiffmvs		95.75 80	98.15 68	92.96 94	92.18 113	99.51 88	97.47 76	84.42 110	99.64 66	85.04 101	96.50 90	93.62 104	98.81 67	98.57 47	99.21 28	99.82 38	100.00 1

PVSNet_Blended_VisFu		95.37 88	97.44 84	92.95 95	95.20 67	99.80 68	92.68 143	88.41 85	99.12 100	87.64 87	88.31 148	99.10 60	94.07 141	98.27 65	97.51 82	99.73 102	100.00 1

tfpn_n400		95.76 78	98.21 63	92.90 96	92.57 106	99.05 108	96.42 95	88.50 82	99.49 76	83.08 111	98.90 52	94.24 99	97.07 103	98.10 73	97.93 67	99.74 90	99.76 161

tfpnconf		95.76 78	98.21 63	92.90 96	92.57 106	99.05 108	96.42 95	88.50 82	99.49 76	83.08 111	98.90 52	94.24 99	97.07 103	98.10 73	97.93 67	99.74 90	99.76 161

MVS_Test		95.74 81	98.18 66	92.90 96	92.16 114	99.49 89	97.36 78	84.30 111	99.79 50	84.94 103	96.65 88	93.63 103	98.85 64	98.61 45	99.10 32	99.81 47	100.00 1

FMVSNet2		94.48 101	95.95 124	92.77 99	89.11 143	96.47 161	96.90 86	83.38 120	99.11 101	88.64 82	87.50 156	92.26 109	98.87 61	97.91 83	98.60 49	99.74 90	99.81 154

FC-MVSNet-train		94.61 98	96.27 119	92.68 100	92.35 112	97.14 151	93.45 140	87.73 89	98.93 110	87.31 90	96.42 91	89.35 118	95.67 121	96.06 134	96.01 122	99.56 134	99.98 67

CHOSEN 1792x2688		93.69 110	94.89 135	92.28 101	96.17 61	99.84 51	95.69 107	83.17 123	98.54 125	82.04 117	77.58 206	91.15 112	96.90 106	98.36 61	98.82 42	99.73 102	99.98 67

diffmvs		94.78 97	96.94 99	92.27 102	91.76 122	99.43 94	96.30 98	83.71 117	99.06 105	83.65 108	97.57 80	87.14 128	98.56 74	98.09 75	97.37 85	99.63 123	99.90 133

Vis-MVSNet (Re-imp)		95.60 86	98.52 56	92.19 103	92.37 111	99.56 84	96.37 97	87.41 92	98.95 109	84.77 105	94.88 111	98.48 68	92.44 155	98.63 43	99.37 18	99.76 73	99.77 160

test0.0.03 1		95.15 93	97.87 76	91.99 104	91.69 125	98.82 121	93.04 141	83.60 118	99.65 64	88.80 81	94.15 117	97.67 77	94.97 128	96.62 116	98.16 60	99.83 33	100.00 1

LGP-MVS_train		93.60 111	95.05 132	91.90 105	94.90 71	98.29 133	97.93 59	88.06 86	99.14 99	74.83 140	99.26 39	76.50 160	96.07 118	96.31 126	95.90 126	99.59 129	99.97 80

HyFIR lowres test		93.13 119	94.48 139	91.56 106	96.12 63	99.68 76	93.52 138	79.98 150	97.24 151	81.73 120	72.66 217	95.74 92	98.29 79	98.27 65	97.79 72	99.70 111	100.00 1

FMVSNet1		92.55 131	93.66 144	91.26 107	87.91 153	96.12 168	94.75 124	81.69 141	97.67 147	85.63 98	80.56 181	87.88 127	98.15 82	96.50 117	97.21 89	99.41 185	99.71 166

CDS-MVSNet		94.32 103	97.00 96	91.19 108	89.82 140	98.71 123	95.51 113	85.14 102	96.85 154	82.33 116	92.48 132	96.40 86	94.71 132	96.86 113	97.76 74	99.63 123	99.92 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPNet_dtu		95.10 94	98.81 48	90.78 109	98.38 47	98.47 127	96.54 93	89.36 62	99.78 52	65.65 192	99.31 37	98.24 72	94.79 131	98.28 64	99.35 21	99.93 16	98.27 203
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dps		94.29 105	97.33 85	90.75 110	92.02 117	99.21 101	94.31 130	66.97 211	99.50 75	95.61 19	96.22 94	98.64 63	96.08 117	93.71 171	94.03 153	99.52 141	99.98 67

UA-Net		94.95 95	98.66 51	90.63 111	94.60 77	98.94 117	96.03 102	85.28 98	98.01 144	78.92 125	97.42 82	99.96 31	89.09 199	98.95 32	98.80 43	99.82 38	98.57 200

MS-PatchMatch		93.46 117	95.91 126	90.61 112	95.48 65	99.31 99	95.62 110	77.23 171	99.42 85	81.88 119	88.92 145	96.06 89	93.80 143	96.45 122	93.11 165	99.65 121	98.10 207

FMVSNet5		93.53 112	96.09 123	90.56 113	86.74 156	92.84 209	92.64 144	77.50 169	99.41 87	88.97 80	98.02 75	97.81 75	98.00 86	94.85 150	95.43 134	99.50 147	94.25 223

ACMH+	92.61 13	91.80 138	93.03 148	90.37 114	93.03 92	98.17 135	94.00 134	84.13 115	98.12 141	77.39 131	91.95 134	74.62 167	94.36 138	94.62 154	93.82 155	99.32 195	99.87 145

TAMVS		92.43 134	94.21 142	90.35 115	88.68 150	98.85 119	94.15 133	81.53 143	95.58 163	83.61 109	87.05 157	86.45 130	94.71 132	96.27 127	95.91 124	99.42 173	99.38 188

IterMVS-LS		93.50 113	96.22 120	90.33 116	90.93 130	95.50 191	94.83 123	80.54 146	98.92 111	79.11 123	90.64 139	93.70 102	96.79 109	96.93 111	97.85 71	99.78 61	99.99 48
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet_DTU		94.90 96	98.98 41	90.13 117	94.74 73	99.81 64	98.53 53	82.23 133	99.97 6	66.76 180	100.00 1	98.50 65	98.74 70	97.52 93	97.19 95	99.76 73	99.88 141

CostFormer		93.50 113	96.50 110	90.00 118	91.69 125	98.65 126	93.88 135	67.64 208	98.97 107	89.16 79	97.79 78	88.92 122	97.97 87	95.14 147	96.06 120	99.63 123	100.00 1

ACMH	92.34 14	91.59 140	93.02 149	89.92 119	93.97 79	97.98 139	90.10 181	84.70 104	98.46 129	76.80 133	93.38 126	71.94 181	94.39 136	95.34 143	94.04 152	99.54 137	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+		93.06 122	95.94 125	89.70 120	90.82 131	99.45 92	95.71 106	78.94 161	98.72 114	74.71 142	97.92 76	80.73 151	98.35 77	97.72 87	97.05 100	99.70 111	100.00 1

test-LLR		93.71 109	97.23 88	89.60 121	91.69 125	99.10 105	94.68 127	83.60 118	99.36 90	71.94 150	93.82 120	96.51 84	95.96 119	97.42 97	94.37 147	99.74 90	99.99 48

Fast-Effi-MVS+		92.11 137	94.33 141	89.52 122	89.06 146	99.00 112	95.13 119	76.72 176	98.59 124	78.21 130	89.99 141	77.35 155	98.34 78	97.97 82	97.44 84	99.67 119	99.96 99

tpm cat1		93.29 118	96.53 109	89.50 123	91.84 119	99.18 103	94.70 125	67.70 207	98.38 132	86.67 94	89.16 143	99.38 58	96.66 113	94.33 157	95.30 135	99.43 167	100.00 1

pmmvs4		91.41 141	93.05 147	89.49 124	85.85 164	96.52 160	91.70 152	82.49 127	98.14 140	83.17 110	87.57 153	81.76 150	94.39 136	95.47 140	92.62 171	99.33 194	99.29 190

IB-MVS	90.59 15	92.70 128	95.70 127	89.21 125	94.62 76	99.45 92	83.77 211	88.92 70	99.53 73	92.82 36	98.86 55	86.08 131	75.24 223	92.81 187	93.17 163	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
MDTV_nov1_ep13		94.32 103	98.77 49	89.14 126	91.70 124	99.52 85	95.21 118	72.09 203	99.80 49	78.91 126	96.32 92	99.62 48	97.71 95	98.39 59	97.71 78	99.22 205	100.00 1

tpmp4_e23		92.95 123	96.28 118	89.06 127	91.80 120	98.81 122	94.95 121	67.56 210	99.21 96	82.97 114	96.54 89	88.52 124	97.47 97	94.47 155	96.42 108	99.61 127	100.00 1

EPMVS		94.08 108	98.54 55	88.87 128	92.51 108	99.47 90	94.18 132	66.53 212	99.68 63	82.40 115	95.24 102	99.40 57	97.86 90	98.12 71	97.99 63	99.75 86	99.88 141

TinyColmap		89.94 149	90.88 162	88.84 129	92.43 109	97.91 141	95.59 112	80.10 149	98.12 141	71.33 156	84.56 158	67.46 214	94.15 140	95.57 138	94.27 151	99.43 167	98.26 204

USDC		90.36 148	91.68 156	88.82 130	92.58 102	98.02 137	96.27 99	79.83 151	98.37 135	70.61 159	89.05 144	67.50 213	94.17 139	95.77 136	94.43 145	99.46 158	98.62 199

FC-MVSNet-test		92.78 126	96.19 122	88.80 131	88.00 152	97.54 145	93.60 137	82.36 132	98.16 139	79.71 122	91.55 137	95.41 93	89.65 194	96.09 131	95.23 136	99.49 148	99.31 189

Vis-MVSNet		93.08 121	96.76 102	88.78 132	91.14 129	99.63 81	94.85 122	83.34 121	97.19 152	74.78 141	91.92 136	93.15 107	88.81 202	97.59 90	98.35 55	99.78 61	99.49 185
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testgi		92.47 133	95.68 129	88.73 133	90.68 133	98.35 130	91.67 153	79.50 155	98.96 108	77.12 132	95.17 104	85.84 132	93.95 142	95.75 137	96.47 106	99.45 161	99.21 192

Baseline_NR-MVSNet		89.13 154	89.53 178	88.66 134	84.71 193	94.43 202	91.79 151	84.49 108	95.54 164	78.28 129	78.52 203	72.46 180	93.29 149	91.10 210	94.82 139	99.42 173	99.86 148

UniMVSNet (Re)		90.41 146	91.96 154	88.59 135	85.71 165	96.73 156	90.82 158	84.11 116	95.23 169	78.54 128	88.91 146	76.41 162	92.84 152	93.40 178	93.05 166	99.55 136	100.00 1

Effi-MVS+-dtu		93.13 119	97.13 91	88.47 136	88.86 149	99.19 102	96.79 90	79.08 159	99.64 66	70.01 160	97.51 81	89.38 117	96.53 115	97.60 89	96.55 104	99.57 132	100.00 1

tfpnnormal		89.09 155	89.71 171	88.38 137	87.37 154	96.78 155	91.46 154	85.20 100	90.33 218	72.35 147	83.45 162	69.30 209	94.45 135	95.29 144	92.86 168	99.44 166	99.93 117

UniMVSNet_NR-MVSNet		90.50 145	92.31 152	88.38 137	85.04 179	96.34 164	90.94 155	85.32 97	95.87 162	75.69 134	87.68 152	78.49 152	93.78 144	93.21 180	94.60 140	99.53 140	99.97 80

CVMVSNet		92.13 136	95.40 130	88.32 139	91.29 128	97.29 150	91.85 150	86.42 94	96.71 156	71.84 152	89.56 142	91.18 111	88.98 201	96.17 129	97.76 74	99.51 145	99.14 194

DU-MVS		89.49 153	90.60 164	88.19 140	84.71 193	96.20 165	90.94 155	84.58 105	95.54 164	75.69 134	87.52 154	68.74 211	93.78 144	91.10 210	95.13 137	99.47 155	99.97 80

NR-MVSNet		89.52 152	90.71 163	88.14 141	86.19 162	96.20 165	92.07 148	84.58 105	95.54 164	75.27 139	87.52 154	67.96 212	91.24 185	94.33 157	93.45 160	99.49 148	99.97 80

TESTMET0.1,1		92.87 125	97.23 88	87.79 142	86.96 155	99.10 105	94.68 127	77.46 170	99.36 90	71.94 150	93.82 120	96.51 84	95.96 119	97.42 97	94.37 147	99.74 90	99.99 48

TranMVSNet+NR-MVSNet		88.88 156	89.90 169	87.69 143	84.06 205	95.68 183	91.88 149	85.23 99	95.16 170	72.54 144	83.06 165	70.14 203	92.93 151	90.81 213	94.53 142	99.48 152	99.89 138

test-mter		92.67 129	97.13 91	87.47 144	86.72 157	99.07 107	94.28 131	76.90 174	99.21 96	71.53 154	93.63 122	96.32 88	95.67 121	97.32 106	94.36 149	99.74 90	99.99 48

PatchmatchNet		93.48 116	98.84 47	87.22 145	91.93 118	99.39 95	92.55 145	66.06 216	99.71 61	75.61 137	98.24 71	99.59 49	97.35 99	97.87 84	97.64 79	99.83 33	99.43 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		92.73 127	97.62 79	87.02 146	88.91 147	98.83 120	95.79 104	73.98 190	99.89 31	68.62 165	97.73 79	93.30 106	95.21 127	97.67 88	95.96 123	99.59 129	100.00 1

ADS-MVSNet		92.91 124	97.97 73	87.01 147	92.07 116	99.27 100	92.70 142	65.39 221	99.85 41	75.40 138	94.93 110	98.26 70	96.86 107	96.09 131	97.52 81	99.65 121	99.84 150

pm-mvs1		89.68 150	92.00 153	86.96 148	86.23 161	96.62 158	90.36 170	83.05 124	93.97 188	72.15 149	81.77 176	82.10 149	90.69 189	95.38 142	94.50 143	99.29 199	99.65 168

TransMVSNet (Re)		88.33 159	89.55 177	86.91 149	86.65 158	95.56 188	90.48 164	84.44 109	92.02 217	71.07 158	80.13 183	72.48 179	89.41 196	95.05 149	94.44 144	99.39 187	97.14 213

tpmrst		92.52 132	97.45 83	86.77 150	92.15 115	99.36 97	92.53 146	65.95 217	99.53 73	72.50 145	92.22 133	99.83 39	97.81 92	95.18 146	96.05 121	99.69 117	100.00 1

TDRefinement		87.79 170	88.76 196	86.66 151	93.54 81	98.02 137	95.76 105	85.18 101	96.57 157	67.90 166	80.51 182	66.51 218	78.37 220	93.20 181	89.73 213	99.22 205	96.75 215

RPMNet		92.64 130	97.88 75	86.53 152	90.79 132	98.95 115	95.13 119	64.44 225	99.09 102	72.36 146	93.58 123	99.01 61	96.74 112	98.05 76	96.45 107	99.71 109	100.00 1

CP-MVSNet		88.09 163	89.57 175	86.36 153	84.63 196	95.46 193	89.48 192	80.53 147	93.42 203	71.26 157	81.25 178	69.90 204	92.78 153	93.30 179	93.69 157	99.47 155	99.96 99

v6		87.96 165	89.58 174	86.08 154	85.34 171	96.14 167	90.44 165	82.19 134	94.56 177	67.43 175	81.90 171	71.57 186	91.62 170	91.54 197	91.43 190	99.43 167	99.92 121

WR-MVS_H		88.47 157	90.55 165	86.04 155	85.13 176	96.07 174	89.86 190	79.80 152	94.37 185	72.32 148	83.12 164	74.44 170	89.60 195	93.52 175	92.40 173	99.51 145	99.96 99

PEN-MVS		87.20 183	88.22 203	86.01 156	84.01 207	94.93 201	90.00 184	81.52 145	93.46 202	69.29 162	79.69 189	65.51 220	91.72 164	91.01 212	93.12 164	99.49 148	99.84 150

WR-MVS		88.23 162	90.15 167	86.00 157	84.39 200	95.64 184	89.96 186	81.80 138	94.46 183	71.60 153	82.10 167	74.36 171	88.76 203	92.48 189	92.20 175	99.46 158	99.83 152

V42		87.84 169	89.42 182	85.99 158	85.16 175	96.01 177	90.52 161	81.78 140	94.43 184	67.59 169	81.32 177	71.87 182	91.48 174	91.25 209	91.16 204	99.43 167	99.92 121

v1neww		87.88 166	89.51 180	85.97 159	85.32 172	96.12 168	90.33 172	82.17 135	94.51 178	66.96 177	81.84 173	71.21 189	91.64 167	91.52 199	91.43 190	99.42 173	99.92 121

v7new		87.88 166	89.51 180	85.97 159	85.32 172	96.12 168	90.33 172	82.17 135	94.51 178	66.96 177	81.84 173	71.21 189	91.64 167	91.52 199	91.43 190	99.42 173	99.92 121

IterMVS		91.65 139	96.62 103	85.85 161	90.27 138	95.80 181	95.32 117	74.15 186	98.91 112	60.95 209	88.79 147	97.76 76	94.69 134	98.04 78	97.07 98	99.73 102	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1		87.48 175	88.91 191	85.81 162	84.93 183	96.07 174	90.33 172	82.45 130	93.65 198	66.39 183	79.38 194	70.40 200	91.33 180	91.58 196	91.38 196	99.42 173	99.93 117

v2v482		87.46 176	88.90 192	85.78 163	84.58 197	95.95 179	89.90 189	82.43 131	94.19 187	65.65 192	79.80 187	69.12 210	92.67 154	91.88 193	91.46 188	99.45 161	99.93 117

CR-MVSNet		92.32 135	97.97 73	85.74 164	90.63 135	98.95 115	95.46 114	65.50 219	99.09 102	67.51 171	94.20 115	98.18 74	95.59 124	98.16 69	97.20 93	99.74 90	100.00 1

PS-CasMVS		87.24 182	88.52 199	85.73 165	84.58 197	95.35 195	89.03 195	80.17 148	93.11 209	68.86 164	77.71 205	66.89 215	92.30 156	93.13 183	93.50 159	99.46 158	99.96 99

divwei89l23v2f112		87.46 176	88.97 188	85.70 166	84.85 188	96.08 172	90.23 178	82.46 128	93.69 197	65.83 190	79.57 191	70.54 197	91.39 179	91.60 195	91.39 194	99.43 167	99.92 121

v1141		87.45 178	88.98 187	85.67 167	84.86 187	96.08 172	90.23 178	82.46 128	93.75 193	65.64 194	79.57 191	70.52 198	91.41 178	91.63 194	91.39 194	99.42 173	99.92 121

DTE-MVSNet		86.70 193	87.66 211	85.58 168	83.30 210	94.29 203	89.74 191	81.53 143	92.77 211	68.93 163	80.13 183	64.00 223	90.62 190	89.45 217	93.34 162	99.32 195	99.67 167

GA-MVS		90.38 147	94.59 138	85.46 169	88.30 151	98.44 128	92.18 147	83.30 122	97.89 145	58.05 216	92.86 129	84.25 145	91.27 183	96.65 115	92.61 172	99.66 120	99.43 186

v8		87.54 173	89.33 183	85.45 170	85.41 169	95.50 191	90.32 175	78.94 161	94.35 186	66.93 179	81.90 171	70.99 194	91.62 170	91.49 202	91.22 201	99.48 152	99.87 145

v7		87.72 172	89.75 170	85.35 171	85.01 180	95.79 182	90.43 167	78.98 160	94.50 181	66.39 183	78.87 197	73.65 174	91.85 163	93.69 172	91.86 181	99.45 161	99.92 121

v148		86.63 195	87.79 207	85.28 172	84.65 195	95.97 178	86.46 205	82.84 126	92.91 210	71.52 155	78.99 196	66.74 217	86.83 210	89.28 218	90.69 207	99.41 185	99.94 114

v18		87.14 186	88.96 189	85.01 173	85.57 166	92.03 211	90.89 157	74.62 184	94.80 175	67.90 166	82.02 169	71.28 188	91.63 169	91.53 198	91.44 189	99.47 155	99.60 173

v1144		87.49 174	89.64 172	84.97 174	84.73 192	95.84 180	90.17 180	79.30 156	93.96 189	64.65 197	78.83 199	73.38 176	91.51 173	93.77 169	91.77 182	99.45 161	99.93 117

MIMVSNet		91.01 144	96.22 120	84.93 175	85.24 174	98.09 136	90.40 168	64.96 223	97.55 149	72.65 143	96.23 93	90.81 113	96.79 109	96.69 114	97.06 99	99.52 141	97.09 214

v16		87.15 185	89.13 184	84.83 176	85.55 167	91.94 213	90.50 162	74.13 188	95.06 171	67.72 168	81.84 173	72.55 178	91.65 166	91.50 201	91.42 193	99.42 173	99.60 173

v10		87.40 179	89.62 173	84.80 177	84.93 183	95.07 199	90.44 165	75.63 180	94.51 178	66.52 181	78.87 197	73.47 175	91.86 162	93.69 172	91.87 180	99.45 161	99.86 148

v17		86.99 187	88.90 192	84.76 178	85.52 168	91.96 212	90.50 162	74.17 185	94.88 173	67.33 176	81.94 170	71.21 189	91.57 172	91.49 202	91.20 202	99.48 152	99.60 173

SixPastTwentyTwo		88.35 158	91.51 158	84.66 179	85.39 170	96.96 153	86.57 204	79.62 154	96.57 157	63.73 200	87.86 150	75.18 165	93.43 148	94.03 161	90.37 209	99.24 204	99.58 176

v1192		86.93 189	89.01 185	84.50 180	84.46 199	95.51 190	89.93 188	78.65 163	93.75 193	62.29 205	77.19 207	70.88 195	92.28 157	93.84 166	91.96 178	99.38 189	99.90 133

v15		86.50 197	88.32 201	84.37 181	85.00 181	91.86 214	90.30 176	73.76 191	93.90 191	66.28 186	79.78 188	70.37 201	91.45 176	91.48 204	91.27 198	99.43 167	99.58 176

v144192		86.80 191	88.90 192	84.35 182	84.33 201	95.56 188	89.34 193	77.74 168	93.60 199	64.03 198	77.82 204	70.76 196	91.28 182	92.91 186	91.74 184	99.37 191	99.90 133

V14		86.54 196	88.41 200	84.35 182	84.94 182	91.83 215	90.28 177	73.48 193	93.73 196	66.50 182	79.89 186	71.12 193	91.46 175	91.48 204	91.25 199	99.42 173	99.58 176

pmmvs6		85.75 204	86.97 212	84.34 184	84.88 185	95.59 186	87.41 202	79.19 158	87.81 224	67.56 170	63.05 228	77.76 154	89.15 198	93.45 177	91.90 179	97.83 219	99.21 192

v1921920		86.81 190	88.93 190	84.33 185	84.23 203	95.41 194	90.09 182	78.10 166	93.74 195	62.17 206	76.98 209	71.14 192	92.05 159	93.69 172	91.69 185	99.32 195	99.88 141

V9		86.42 198	88.26 202	84.27 186	84.88 185	91.80 216	90.34 171	73.18 197	93.92 190	66.37 185	79.68 190	70.25 202	91.42 177	91.43 206	91.23 200	99.42 173	99.55 181

testpf		91.26 142	97.28 87	84.23 187	89.52 141	97.45 147	88.08 200	56.08 234	99.76 55	78.71 127	95.06 109	98.26 70	93.44 147	94.72 152	95.69 128	99.57 132	99.99 48

pmmvs5		87.33 180	90.01 168	84.20 188	84.31 202	96.04 176	87.63 201	76.59 177	93.17 208	65.35 196	84.30 161	71.68 183	91.91 161	95.41 141	91.37 197	99.39 187	98.13 205

CMPMVS	65.66 17	84.62 207	85.02 215	84.15 189	95.40 66	97.79 142	88.35 198	79.22 157	89.66 222	60.71 211	72.20 218	73.94 172	87.32 209	86.73 222	84.55 227	93.90 229	90.31 227
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v12		86.32 199	88.22 203	84.10 190	84.76 191	91.80 216	89.94 187	72.97 199	93.85 192	66.18 187	79.98 185	69.72 208	91.33 180	91.40 207	91.20 202	99.42 173	99.56 180

LTVRE_ROB	88.65 16	87.87 168	91.11 161	84.10 190	86.64 159	97.47 146	94.40 129	78.41 165	96.13 160	52.02 223	87.95 149	65.92 219	93.59 146	95.29 144	95.09 138	99.52 141	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
v11		86.74 192	89.01 185	84.09 192	84.79 190	91.79 218	90.39 169	72.53 202	94.47 182	65.75 191	78.64 200	72.96 177	91.66 165	93.92 164	91.69 185	99.42 173	99.61 172

v13		86.27 200	88.16 205	84.06 193	84.85 188	91.77 219	90.00 184	72.77 201	93.56 200	66.06 189	79.25 195	70.50 199	91.25 184	91.35 208	91.15 205	99.42 173	99.55 181

EG-PatchMatch MVS		86.96 188	89.56 176	83.93 194	86.29 160	97.61 144	90.75 160	73.31 195	95.43 167	66.08 188	75.88 214	71.31 187	87.55 208	94.79 151	92.74 169	99.61 127	99.13 195

v1240		86.24 201	88.56 198	83.54 195	84.05 206	95.21 198	89.27 194	76.76 175	93.42 203	60.68 212	75.99 213	69.80 206	91.21 186	93.83 168	91.76 183	99.29 199	99.91 132

v52		85.80 202	87.74 208	83.53 196	82.87 213	95.31 197	88.71 196	77.04 173	92.23 214	63.53 201	76.91 210	69.80 206	89.78 192	90.05 215	90.07 211	99.26 203	99.82 153

V4		85.78 203	87.74 208	83.50 197	82.90 212	95.33 196	88.62 197	77.05 172	92.14 216	63.45 202	76.91 210	69.85 205	89.72 193	90.07 214	90.05 212	99.27 202	99.81 154

tpm		89.60 151	94.93 134	83.39 198	89.94 139	97.11 152	90.09 182	65.28 222	98.67 116	60.03 213	96.79 86	84.38 144	95.66 123	91.90 192	95.65 131	99.32 195	99.98 67

EU-MVSNet		87.20 183	90.47 166	83.38 199	85.11 178	93.85 207	86.10 206	79.76 153	93.30 207	65.39 195	84.41 159	78.43 153	85.04 213	92.20 191	93.03 167	98.86 212	98.05 208

PatchT		91.06 143	97.66 78	83.36 200	90.32 137	98.96 114	82.30 216	64.72 224	98.45 130	67.51 171	93.28 127	97.60 78	95.59 124	98.16 69	97.20 93	99.70 111	100.00 1

anonymousdsp		87.98 164	92.38 151	82.85 201	83.68 209	96.79 154	90.78 159	74.06 189	95.29 168	57.91 217	83.33 163	83.12 146	91.15 187	95.96 135	92.37 174	99.52 141	99.76 161

v7n		85.39 205	87.70 210	82.70 202	82.77 215	95.64 184	88.27 199	74.83 182	92.30 213	62.58 204	76.37 212	64.80 222	88.38 205	94.29 159	90.61 208	99.34 192	99.87 145

LP		88.31 160	93.18 146	82.63 203	90.66 134	97.98 139	87.32 203	63.49 228	97.17 153	63.02 203	82.08 168	90.47 115	91.92 160	92.75 188	93.42 161	99.38 189	98.37 202

v748		84.47 208	86.06 213	82.62 204	82.85 214	95.02 200	83.73 212	78.48 164	90.20 220	67.45 174	75.86 215	61.27 225	83.84 214	89.87 216	90.28 210	99.34 192	99.90 133

pmmvs-eth3d		82.92 213	83.31 218	82.47 205	76.97 222	91.76 220	83.79 210	76.10 179	90.33 218	69.95 161	71.04 221	48.09 229	89.02 200	93.85 165	89.14 215	99.02 211	98.96 197

N_pmnet		87.31 181	91.51 158	82.41 206	85.13 176	95.57 187	80.59 219	81.79 139	96.20 159	58.52 215	78.62 201	85.66 133	89.36 197	94.64 153	92.14 176	99.08 210	97.72 212

MVS-HIRNet		88.27 161	94.05 143	81.51 207	88.90 148	98.93 118	83.38 214	60.52 233	98.06 143	63.78 199	80.67 180	90.36 116	92.94 150	97.29 107	96.41 109	99.56 134	96.66 216

gg-mvs-nofinetune		86.69 194	91.30 160	81.30 208	90.42 136	99.64 80	98.50 54	61.68 230	79.23 231	40.35 234	66.58 225	97.14 80	96.92 105	98.64 41	97.94 65	99.91 20	99.97 80

MDTV_nov1_ep13_2view		87.75 171	93.32 145	81.26 209	83.74 208	96.64 157	85.66 207	66.20 215	98.36 136	61.61 207	84.34 160	87.95 126	91.12 188	94.01 162	92.66 170	99.22 205	99.27 191

PM-MVS		82.79 214	84.51 216	80.77 210	77.22 221	92.13 210	83.61 213	73.31 195	93.50 201	61.06 208	77.15 208	46.52 232	90.55 191	94.14 160	89.05 217	98.85 213	99.12 196

new_pmnet		84.12 210	87.89 206	79.72 211	80.43 218	94.14 206	80.26 220	74.14 187	96.01 161	56.30 221	74.94 216	76.45 161	88.59 204	93.11 184	89.31 214	98.59 215	91.27 226

tmp_tt				78.81 212	98.80 41	85.73 228	70.08 229	77.87 167	98.68 115	83.71 106	99.53 27	74.55 168	54.97 234	78.28 229	72.43 232	87.45 233

Anonymous20231206		84.28 209	89.53 178	78.17 213	82.31 217	94.16 205	82.57 215	76.51 178	93.38 206	52.98 222	79.47 193	73.74 173	75.45 222	95.07 148	94.41 146	99.18 208	96.46 219

test20.03		83.86 211	88.73 197	78.16 214	82.60 216	93.00 208	81.61 218	74.68 183	92.36 212	57.50 218	83.01 166	74.48 169	73.30 226	92.40 190	91.14 206	99.29 199	94.75 222

Anonymous20231211		75.90 220	74.22 228	77.87 215	74.36 225	87.76 227	75.92 225	72.78 200	74.83 236	75.62 136	44.18 235	42.42 235	73.07 227	86.16 223	86.24 225	95.44 227	97.94 209

MDA-MVSNet-bldmvs		80.30 218	82.83 219	77.34 216	69.16 232	94.29 203	72.16 227	81.97 137	90.14 221	57.32 219	94.01 119	47.97 230	86.81 211	68.74 233	86.82 223	96.63 221	97.86 210

gm-plane-assit		84.93 206	91.61 157	77.14 217	84.14 204	91.29 221	66.18 232	69.70 205	85.22 227	47.95 229	78.58 202	89.24 119	94.90 130	98.82 35	98.12 62	99.99 6	100.00 1

MIMVSNet1		80.64 217	83.97 217	76.76 218	68.91 233	91.15 223	78.32 223	75.47 181	89.58 223	56.64 220	65.10 226	65.17 221	82.14 215	93.51 176	91.64 187	99.10 209	91.66 225

pmmvs3		80.91 216	85.62 214	75.42 219	75.01 224	89.09 226	75.31 226	68.70 206	86.99 225	46.74 231	81.18 179	62.91 224	87.95 206	93.84 166	89.06 216	98.80 214	96.23 221

new-patchmatchnet		78.17 219	80.82 220	75.07 220	76.93 223	91.20 222	71.90 228	73.32 194	86.59 226	48.91 226	67.11 224	47.85 231	81.19 216	88.18 219	87.02 222	98.19 217	97.79 211

test2356		83.84 212	91.77 155	74.59 221	78.71 220	89.10 225	78.24 224	72.07 204	96.78 155	45.18 232	96.19 96	76.77 158	74.87 224	93.17 182	94.01 154	98.44 216	96.38 220

testus		82.22 215	88.82 195	74.52 222	79.14 219	89.37 224	78.38 222	72.99 198	97.57 148	44.54 233	93.44 125	58.13 227	74.20 225	92.96 185	93.67 158	97.89 218	96.58 217

FPMVS		73.80 221	74.62 226	72.84 223	83.09 211	84.44 229	83.89 209	73.64 192	92.20 215	48.50 227	72.19 219	59.51 226	63.16 229	69.13 232	66.26 237	84.74 234	78.59 237

Gipuma		71.02 225	72.60 230	69.19 224	71.31 228	75.11 237	66.36 231	61.65 231	94.93 172	47.29 230	38.74 236	38.52 237	75.52 221	86.09 224	85.92 226	93.01 230	88.87 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
1111		73.79 222	78.62 222	68.16 225	69.34 230	81.48 231	59.42 236	52.46 236	78.55 232	50.42 224	62.43 229	71.67 184	80.43 218	86.79 220	88.22 218	96.87 220	81.17 236

testmv		71.50 223	77.62 223	64.36 226	72.64 226	81.28 233	59.32 238	66.24 213	83.91 228	35.02 238	69.74 222	46.18 233	57.12 232	85.60 225	87.48 220	95.84 224	89.16 229

test1235678		71.50 223	77.61 224	64.36 226	72.64 226	81.26 234	59.31 239	66.22 214	83.90 229	35.02 238	69.74 222	46.18 233	57.12 232	85.60 225	87.47 221	95.84 224	89.15 230

test12356		69.94 227	75.85 225	63.04 228	70.04 229	79.32 236	61.62 234	65.84 218	80.56 230	36.30 237	71.45 220	39.38 236	48.79 238	83.64 227	88.02 219	95.64 226	88.56 232

PMMVS2		65.18 229	68.25 231	61.59 229	61.37 236	79.72 235	59.18 240	61.80 229	64.72 237	37.33 235	53.82 232	35.59 238	54.46 236	73.94 231	80.52 228	95.40 228	89.43 228

PMVS	60.14 18	62.67 230	64.05 232	61.06 230	68.32 234	53.27 244	52.23 241	67.63 209	75.07 235	48.30 228	58.27 231	57.43 228	49.99 237	67.20 234	62.42 238	79.87 238	74.68 239
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		70.78 226	79.90 221	60.13 231	69.34 230	81.48 231	59.42 236	52.46 236	78.55 232	50.42 224	62.43 229	71.67 184	80.43 218	86.79 220	78.71 229	48.74 240	99.65 168

EMVS		55.14 233	55.29 235	54.97 232	60.87 237	57.52 241	38.58 243	63.57 227	64.54 238	23.36 243	36.96 237	27.99 240	60.69 230	51.17 237	66.61 236	82.73 237	82.25 234

E-PMN		55.33 232	55.79 234	54.81 233	59.81 238	57.23 242	38.83 242	63.59 226	64.06 239	24.66 242	35.33 238	26.40 241	58.69 231	55.41 236	70.54 234	83.26 235	81.56 235

no-one		52.34 234	53.36 237	51.14 234	57.63 239	69.39 238	35.07 245	61.58 232	44.14 241	37.06 236	34.80 239	26.36 242	32.65 239	50.68 238	70.83 233	82.88 236	77.30 238

testmvs		61.76 231	72.90 229	48.76 235	21.21 241	68.61 239	66.11 233	37.38 238	94.83 174	33.06 240	64.31 227	29.72 239	86.08 212	74.44 230	78.71 229	48.74 240	99.65 168

MVE	58.81 19	52.07 235	55.15 236	48.48 236	42.45 240	62.35 240	36.41 244	54.70 235	49.88 240	27.65 241	29.98 240	18.08 243	54.87 235	65.93 235	77.26 231	74.79 239	82.59 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 236	58.11 233	36.51 237	8.71 242	56.81 243	59.55 235	24.08 239	77.50 234	14.41 244	49.20 233	11.94 245	80.98 217	41.62 239	69.81 235	31.32 242	99.90 133

GG-mvs-BLEND		69.85 228	99.39 36	35.39 238	3.67 243	99.94 17	99.10 38	1.69 240	99.85 41	3.19 245	98.13 74	99.46 53	4.92 240	99.23 28	99.14 30	99.80 50	100.00 1

sosnet-low-res		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 246	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

sosnet		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 246	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

our_test_3					85.89 163	96.09 171	82.15 217

ambc			74.33 227		66.84 235	84.26 230	84.17 208		93.39 205	58.99 214	45.93 234	18.06 244	70.61 228	93.94 163	86.62 224	92.61 232	98.13 205

MTAPA										96.61 10		100.00 1

MTMP										97.42 7		100.00 1

Patchmatch-RL test							68.01 230

XVS					95.09 69	99.94 17	97.49 71			88.58 83		99.98 28				99.78 61

X-MVStestdata					95.09 69	99.94 17	97.49 71			88.58 83		99.98 28				99.78 61

mPP-MVS					99.23 33							99.87 37

NP-MVS									99.79 50

Patchmtry						99.00 112	95.46 114	65.50 219		67.51 171

DeepMVS_CX						97.31 149	79.48 221	89.65 59	98.66 117	60.89 210	94.40 114	66.89 215	87.65 207	81.69 228		92.76 231	94.24 224

High-resolution multi-view benchmark