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.

LS3D		97.79 59	98.25 70	97.26 57	98.40 59	99.63 25	99.53 18	98.63 1	99.25 42	88.13 123	96.93 97	94.14 119	99.19 38	99.14 33	99.23 17	99.69 79	99.42 140

DVP-MVS		99.45 2	99.54 6	99.35 1	99.72 7	99.76 1	99.63 11	98.37 2	99.63 6	99.03 3	98.95 36	99.98 1	99.60 7	99.60 6	99.05 24	99.74 44	99.79 38

SED-MVS		99.44 3	99.58 3	99.28 3	99.69 8	99.76 1	99.62 14	98.35 3	99.51 16	99.05 2	99.60 5	99.98 1	99.28 35	99.61 5	98.83 43	99.70 76	99.77 52

HPM-MVS++		99.10 21	99.30 27	98.86 24	99.69 8	99.48 57	99.59 16	98.34 4	99.26 40	96.55 37	99.10 28	99.96 12	99.36 27	99.25 26	98.37 69	99.64 109	99.66 102

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 5	99.75 1	98.34 4	99.56 10	98.72 7	99.57 6	99.97 7	99.53 16	99.65 2	99.25 14	99.84 5	99.77 52

DPE-MVS		99.39 4	99.55 5	99.20 4	99.63 21	99.71 9	99.66 6	98.33 6	99.29 34	98.40 12	99.64 4	99.98 1	99.31 31	99.56 9	98.96 31	99.85 3	99.70 88

APD-MVS		99.25 12	99.38 20	99.09 11	99.69 8	99.58 44	99.56 17	98.32 7	98.85 90	97.87 20	98.91 39	99.92 28	99.30 33	99.45 15	99.38 8	99.79 25	99.58 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSP-MVS		99.34 6	99.52 9	99.14 8	99.68 12	99.75 4	99.64 8	98.31 8	99.44 20	98.10 14	99.28 15	99.98 1	99.30 33	99.34 22	99.05 24	99.81 16	99.79 38

HFP-MVS		99.32 7	99.53 8	99.07 13	99.69 8	99.59 41	99.63 11	98.31 8	99.56 10	97.37 26	99.27 16	99.97 7	99.70 3	99.35 21	99.24 16	99.71 67	99.76 57

MCST-MVS		99.11 20	99.27 29	98.93 22	99.67 13	99.33 82	99.51 20	98.31 8	99.28 35	96.57 36	99.10 28	99.90 32	99.71 2	99.19 30	98.35 70	99.82 10	99.71 86

SD-MVS		99.25 12	99.50 11	98.96 21	98.79 53	99.55 48	99.33 32	98.29 11	99.75 1	97.96 19	99.15 22	99.95 17	99.61 6	99.17 31	99.06 23	99.81 16	99.84 18
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMMP_NAP		99.05 25	99.45 13	98.58 31	99.73 5	99.60 39	99.64 8	98.28 12	99.23 43	94.57 60	99.35 13	99.97 7	99.55 14	99.63 3	98.66 51	99.70 76	99.74 68

zzz-MVS		99.31 8	99.44 16	99.16 6	99.73 5	99.65 17	99.63 11	98.26 13	99.27 37	98.01 18	99.27 16	99.97 7	99.60 7	99.59 7	98.58 56	99.71 67	99.73 72

SR-MVS					99.67 13			98.25 14				99.94 25

ACMMPR		99.30 9	99.54 6	99.03 16	99.66 16	99.64 22	99.68 4	98.25 14	99.56 10	97.12 30	99.19 19	99.95 17	99.72 1	99.43 16	99.25 14	99.72 57	99.77 52

MP-MVS		99.07 23	99.36 22	98.74 28	99.63 21	99.57 46	99.66 6	98.25 14	99.00 76	95.62 43	98.97 34	99.94 25	99.54 15	99.51 12	98.79 47	99.71 67	99.73 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
xxxxxxxxxxxxxcwj		98.14 51	97.38 105	99.03 16	99.65 18	99.41 68	98.87 54	98.24 17	99.14 55	98.73 5	99.11 25	86.38 161	98.92 58	99.22 27	98.84 41	99.76 35	99.56 121

SF-MVS		99.18 16	99.32 26	99.03 16	99.65 18	99.41 68	98.87 54	98.24 17	99.14 55	98.73 5	99.11 25	99.92 28	98.92 58	99.22 27	98.84 41	99.76 35	99.56 121

SMA-MVS		99.38 5	99.60 2	99.12 9	99.76 2	99.62 29	99.39 29	98.23 19	99.52 15	98.03 17	99.45 10	99.98 1	99.64 5	99.58 8	99.30 11	99.68 88	99.76 57
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CNVR-MVS		99.23 14	99.28 28	99.17 5	99.65 18	99.34 79	99.46 24	98.21 20	99.28 35	98.47 9	98.89 41	99.94 25	99.50 17	99.42 17	98.61 54	99.73 51	99.52 128

NCCC		99.05 25	99.08 39	99.02 19	99.62 23	99.38 71	99.43 28	98.21 20	99.36 27	97.66 23	97.79 77	99.90 32	99.45 22	99.17 31	98.43 64	99.77 33	99.51 132

CP-MVS		99.27 10	99.44 16	99.08 12	99.62 23	99.58 44	99.53 18	98.16 22	99.21 46	97.79 21	99.15 22	99.96 12	99.59 10	99.54 11	98.86 39	99.78 28	99.74 68

AdaColmap		99.06 24	98.98 49	99.15 7	99.60 25	99.30 85	99.38 30	98.16 22	99.02 74	98.55 8	98.71 50	99.57 55	99.58 13	99.09 35	97.84 98	99.64 109	99.36 146

X-MVS		98.93 29	99.37 21	98.42 32	99.67 13	99.62 29	99.60 15	98.15 24	99.08 65	93.81 78	98.46 59	99.95 17	99.59 10	99.49 13	99.21 19	99.68 88	99.75 64

DeepC-MVS	97.63 4	98.33 46	98.57 60	98.04 42	98.62 57	99.65 17	99.45 25	98.15 24	99.51 16	92.80 95	95.74 125	96.44 89	99.46 21	99.37 19	99.50 2	99.78 28	99.81 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	97.50 6	98.18 50	98.35 68	97.99 43	98.65 56	99.36 74	98.94 51	98.14 26	98.59 114	93.62 82	96.61 106	99.76 48	99.03 52	97.77 121	97.45 116	99.57 138	98.89 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + ACMM		98.77 33	99.45 13	97.98 44	99.37 37	99.46 59	99.44 27	98.13 27	99.65 4	92.30 102	98.91 39	99.95 17	99.05 50	99.42 17	98.95 32	99.58 134	99.82 23

SteuartSystems-ACMMP		99.20 15	99.51 10	98.83 27	99.66 16	99.66 15	99.71 3	98.12 28	99.14 55	96.62 34	99.16 21	99.98 1	99.12 45	99.63 3	99.19 20	99.78 28	99.83 22
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		99.27 10	99.57 4	98.92 23	98.78 54	99.53 50	99.72 2	98.11 29	99.73 2	97.43 25	99.15 22	99.96 12	99.59 10	99.73 1	99.07 22	99.88 1	99.82 23
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		98.31 47	98.53 62	98.05 41	98.76 55	98.77 114	99.13 40	98.07 30	99.10 62	94.27 71	96.70 102	99.84 41	98.70 68	97.90 114	98.11 86	99.40 165	99.28 149

MSLP-MVS++		99.15 18	99.24 31	99.04 15	99.52 32	99.49 56	99.09 44	98.07 30	99.37 25	98.47 9	97.79 77	99.89 34	99.50 17	98.93 45	99.45 4	99.61 116	99.76 57

DeepC-MVS_fast	98.34 1	99.17 17	99.45 13	98.85 25	99.55 29	99.37 73	99.64 8	98.05 32	99.53 13	96.58 35	98.93 37	99.92 28	99.49 19	99.46 14	99.32 10	99.80 24	99.64 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		99.03 27	99.05 42	99.01 20	99.27 44	99.22 92	99.03 48	97.98 33	99.34 29	99.00 4	98.25 66	99.71 49	99.31 31	98.80 55	98.82 45	99.48 153	99.17 156

train_agg		98.73 35	99.11 37	98.28 36	99.36 39	99.35 77	99.48 23	97.96 34	98.83 95	93.86 77	98.70 51	99.86 37	99.44 23	99.08 37	98.38 67	99.61 116	99.58 115

PLC	97.93 2	99.02 28	98.94 50	99.11 10	99.46 34	99.24 90	99.06 46	97.96 34	99.31 31	99.16 1	97.90 75	99.79 45	99.36 27	98.71 63	98.12 85	99.65 105	99.52 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		98.27 48	98.29 69	98.24 37	99.20 45	99.22 92	99.20 36	97.82 36	99.37 25	94.43 65	95.90 121	97.31 80	99.12 45	98.76 59	98.35 70	99.67 96	99.14 160

CPTT-MVS		99.14 19	99.20 33	99.06 14	99.58 26	99.53 50	99.45 25	97.80 37	99.19 49	98.32 13	98.58 53	99.95 17	99.60 7	99.28 25	98.20 81	99.64 109	99.69 92

ACMMP		98.74 34	99.03 46	98.40 33	99.36 39	99.64 22	99.20 36	97.75 38	98.82 97	95.24 50	98.85 42	99.87 36	99.17 42	98.74 62	97.50 111	99.71 67	99.76 57
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
DeepPCF-MVS	97.74 3	98.34 45	99.46 12	97.04 63	98.82 52	99.33 82	96.28 139	97.47 39	99.58 8	94.70 59	98.99 33	99.85 40	97.24 113	99.55 10	99.34 9	97.73 196	99.56 121

PHI-MVS		99.08 22	99.43 18	98.67 29	99.15 46	99.59 41	99.11 42	97.35 40	99.14 55	97.30 27	99.44 11	99.96 12	99.32 30	98.89 50	99.39 7	99.79 25	99.58 115

TAPA-MVS	97.53 5	98.41 43	98.84 55	97.91 45	99.08 48	99.33 82	99.15 39	97.13 41	99.34 29	93.20 87	97.75 79	99.19 59	99.20 37	98.66 65	98.13 84	99.66 101	99.48 136
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		98.84 32	99.01 48	98.65 30	99.39 36	99.23 91	99.22 35	96.70 42	99.40 22	97.77 22	97.89 76	99.80 43	99.21 36	99.02 40	98.65 52	99.57 138	99.07 163

CSCG		98.90 30	98.93 51	98.85 25	99.75 3	99.72 6	99.49 21	96.58 43	99.38 23	98.05 16	98.97 34	97.87 74	99.49 19	97.78 120	98.92 34	99.78 28	99.90 3

TSAR-MVS + COLMAP		96.79 89	96.55 128	97.06 62	97.70 70	98.46 138	99.07 45	96.23 44	99.38 23	91.32 110	98.80 43	85.61 167	98.69 70	97.64 130	96.92 128	99.37 167	99.06 164

CDPH-MVS		98.41 43	99.10 38	97.61 50	99.32 43	99.36 74	99.49 21	96.15 45	98.82 97	91.82 106	98.41 60	99.66 51	99.10 47	98.93 45	98.97 30	99.75 39	99.58 115

PGM-MVS		98.86 31	99.35 25	98.29 35	99.77 1	99.63 25	99.67 5	95.63 46	98.66 112	95.27 49	99.11 25	99.82 42	99.67 4	99.33 23	99.19 20	99.73 51	99.74 68

OPM-MVS		96.22 107	95.85 147	96.65 76	97.75 68	98.54 133	99.00 50	95.53 47	96.88 174	89.88 117	95.95 120	86.46 160	98.07 89	97.65 129	96.63 135	99.67 96	98.83 173

MVS_111021_LR		98.67 37	99.41 19	97.81 47	99.37 37	99.53 50	98.51 66	95.52 48	99.27 37	94.85 56	99.56 7	99.69 50	99.04 51	99.36 20	98.88 37	99.60 124	99.58 115

COLMAP_ROB	96.15 12	97.78 60	98.17 76	97.32 53	98.84 51	99.45 61	99.28 33	95.43 49	99.48 18	91.80 107	94.83 135	98.36 69	98.90 61	98.09 98	97.85 97	99.68 88	99.15 157
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HQP-MVS		96.37 103	96.58 126	96.13 89	97.31 77	98.44 140	98.45 68	95.22 50	98.86 88	88.58 121	98.33 64	87.00 152	97.67 104	97.23 143	96.56 138	99.56 141	99.62 112

ACMM	96.26 9	96.67 97	96.69 125	96.66 75	97.29 78	98.46 138	96.48 135	95.09 51	99.21 46	93.19 88	98.78 45	86.73 156	98.17 83	97.84 118	96.32 144	99.74 44	99.49 135
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PVSNet_BlendedMVS		97.51 70	97.71 92	97.28 55	98.06 63	99.61 34	97.31 109	95.02 52	99.08 65	95.51 45	98.05 70	90.11 139	98.07 89	98.91 48	98.40 65	99.72 57	99.78 44

PVSNet_Blended		97.51 70	97.71 92	97.28 55	98.06 63	99.61 34	97.31 109	95.02 52	99.08 65	95.51 45	98.05 70	90.11 139	98.07 89	98.91 48	98.40 65	99.72 57	99.78 44

abl_6				98.09 40	99.33 42	99.22 92	98.79 59	94.96 54	98.52 121	97.00 32	97.30 87	99.86 37	98.76 66			99.69 79	99.41 141

MVS_111021_HR		98.59 41	99.36 22	97.68 48	99.42 35	99.61 34	98.14 84	94.81 55	99.31 31	95.00 54	99.51 8	99.79 45	99.00 54	98.94 44	98.83 43	99.69 79	99.57 120

QAPM		98.62 40	99.04 45	98.13 39	99.57 27	99.48 57	99.17 38	94.78 56	99.57 9	96.16 38	96.73 101	99.80 43	99.33 29	98.79 56	99.29 13	99.75 39	99.64 109

EPNet		98.05 54	98.86 53	97.10 60	99.02 49	99.43 65	98.47 67	94.73 57	99.05 71	95.62 43	98.93 37	97.62 78	95.48 160	98.59 74	98.55 57	99.29 172	99.84 18
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		98.46 42	99.16 34	97.64 49	98.48 58	99.64 22	99.35 31	94.71 58	99.53 13	95.17 51	97.63 83	99.59 53	98.38 81	98.88 51	98.99 29	99.74 44	99.86 15

3Dnovator	96.92 7	98.67 37	99.05 42	98.23 38	99.57 27	99.45 61	99.11 42	94.66 59	99.69 3	96.80 33	96.55 110	99.61 52	99.40 25	98.87 52	99.49 3	99.85 3	99.66 102

3Dnovator+	96.92 7	98.71 36	99.05 42	98.32 34	99.53 30	99.34 79	99.06 46	94.61 60	99.65 4	97.49 24	96.75 100	99.86 37	99.44 23	98.78 57	99.30 11	99.81 16	99.67 98

OpenMVS	96.23 11	97.95 57	98.45 65	97.35 52	99.52 32	99.42 66	98.91 53	94.61 60	98.87 87	92.24 104	94.61 136	99.05 61	99.10 47	98.64 67	99.05 24	99.74 44	99.51 132

TSAR-MVS + GP.		98.66 39	99.36 22	97.85 46	97.16 81	99.46 59	99.03 48	94.59 62	99.09 63	97.19 29	99.73 3	99.95 17	99.39 26	98.95 43	98.69 50	99.75 39	99.65 105

DELS-MVS		98.19 49	98.77 57	97.52 51	98.29 61	99.71 9	99.12 41	94.58 63	98.80 100	95.38 48	96.24 115	98.24 71	97.92 95	99.06 38	99.52 1	99.82 10	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
CLD-MVS		96.74 92	96.51 131	97.01 68	96.71 86	98.62 127	98.73 60	94.38 64	98.94 82	94.46 64	97.33 85	87.03 151	98.07 89	97.20 145	96.87 129	99.72 57	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
MVS_0304		98.14 51	99.03 46	97.10 60	98.05 65	99.63 25	99.27 34	94.33 65	99.63 6	93.06 90	97.32 86	99.05 61	98.09 88	98.82 54	98.87 38	99.81 16	99.89 6

ACMP	96.25 10	96.62 100	96.72 124	96.50 83	96.96 84	98.75 118	97.80 94	94.30 66	98.85 90	93.12 89	98.78 45	86.61 158	97.23 114	97.73 124	96.61 136	99.62 114	99.71 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPNet_dtu		96.30 105	98.53 62	93.70 127	98.97 50	98.24 151	97.36 107	94.23 67	98.85 90	79.18 178	99.19 19	98.47 67	94.09 181	97.89 115	98.21 80	98.39 188	98.85 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchMatch-RL		97.77 61	98.25 70	97.21 58	99.11 47	99.25 88	97.06 123	94.09 68	98.72 110	95.14 52	98.47 58	96.29 91	98.43 80	98.65 66	97.44 117	99.45 157	98.94 166

thres100view900		96.72 93	96.47 134	97.00 69	96.31 92	99.52 53	98.28 78	94.01 69	97.35 161	94.52 61	95.90 121	86.93 153	99.09 49	98.07 101	97.87 96	99.81 16	99.63 111

thres400		96.71 94	96.45 136	97.02 66	96.28 95	99.63 25	98.41 69	94.00 70	97.82 152	94.42 66	95.74 125	86.26 162	99.18 40	98.20 92	97.79 101	99.81 16	99.70 88

tfpn200view9		96.75 91	96.51 131	97.03 64	96.31 92	99.67 12	98.41 69	93.99 71	97.35 161	94.52 61	95.90 121	86.93 153	99.14 44	98.26 88	97.80 100	99.82 10	99.70 88

thres600view7		96.69 95	96.43 138	97.00 69	96.28 95	99.67 12	98.41 69	93.99 71	97.85 151	94.29 69	95.96 119	85.91 165	99.19 38	98.26 88	97.63 105	99.82 10	99.73 72

thres200		96.76 90	96.53 129	97.03 64	96.31 92	99.67 12	98.37 72	93.99 71	97.68 157	94.49 63	95.83 124	86.77 155	99.18 40	98.26 88	97.82 99	99.82 10	99.66 102

Anonymous202405211			97.40 104		96.45 88	99.54 49	98.08 89	93.79 74	98.24 133		93.55 145	94.41 115	98.88 64	98.04 106	98.24 79	99.75 39	99.76 57

Anonymous20231211		97.10 81	97.06 117	97.14 59	96.32 91	99.52 53	98.16 83	93.76 75	98.84 94	95.98 40	90.92 161	94.58 114	98.90 61	97.72 125	98.10 87	99.71 67	99.75 64

RPSCF		97.61 66	98.16 77	96.96 71	98.10 62	99.00 100	98.84 57	93.76 75	99.45 19	94.78 58	99.39 12	99.31 57	98.53 78	96.61 155	95.43 166	97.74 194	97.93 187

PVSNet_Blended_VisFu		97.41 73	98.49 64	96.15 88	97.49 71	99.76 1	96.02 143	93.75 77	99.26 40	93.38 86	93.73 144	99.35 56	96.47 135	98.96 42	98.46 61	99.77 33	99.90 3

EIA-MVS		97.70 64	98.78 56	96.44 84	95.72 112	99.65 17	98.14 84	93.72 78	98.30 129	92.31 101	98.63 52	97.90 73	98.97 55	98.92 47	98.30 76	99.78 28	99.80 30

baseline1		97.58 67	98.05 81	97.02 66	96.21 97	99.45 61	97.71 98	93.71 79	98.47 123	95.75 42	98.78 45	93.20 128	98.91 60	98.52 78	98.44 62	99.81 16	99.53 125

LGP-MVS_train		96.23 106	96.89 121	95.46 103	97.32 75	98.77 114	98.81 58	93.60 80	98.58 115	85.52 140	99.08 30	86.67 157	97.83 102	97.87 116	97.51 110	99.69 79	99.73 72

ETV-MVS		98.05 54	99.25 30	96.65 76	95.61 117	99.61 34	98.26 80	93.52 81	98.90 86	93.74 81	99.32 14	99.20 58	98.90 61	99.21 29	98.72 49	99.87 2	99.79 38

FC-MVSNet-train		97.04 82	97.91 88	96.03 92	96.00 102	98.41 143	96.53 134	93.42 82	99.04 73	93.02 92	98.03 72	94.32 117	97.47 109	97.93 112	97.77 102	99.75 39	99.88 10

UGNet		97.66 65	99.07 41	96.01 93	97.19 80	99.65 17	97.09 121	93.39 83	99.35 28	94.40 67	98.79 44	99.59 53	94.24 179	98.04 106	98.29 77	99.73 51	99.80 30
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
IB-MVS	93.96 15	95.02 130	96.44 137	93.36 137	97.05 83	99.28 86	90.43 197	93.39 83	98.02 140	96.02 39	94.92 134	92.07 132	83.52 204	95.38 182	95.82 160	99.72 57	99.59 114
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
IS_MVSNet		97.86 58	98.86 53	96.68 74	96.02 100	99.72 6	98.35 75	93.37 85	98.75 109	94.01 72	96.88 99	98.40 68	98.48 79	99.09 35	99.42 5	99.83 8	99.80 30

thisisatest0530		97.23 77	98.25 70	96.05 90	95.60 119	99.59 41	96.96 125	93.23 86	99.17 51	92.60 98	98.75 48	96.19 93	98.17 83	98.19 93	96.10 152	99.72 57	99.77 52

tttt0517		97.23 77	98.24 73	96.04 91	95.60 119	99.60 39	96.94 126	93.23 86	99.15 52	92.56 99	98.74 49	96.12 96	98.17 83	98.21 91	96.10 152	99.73 51	99.78 44

casdiffmvs		96.93 86	97.43 103	96.34 85	95.70 113	99.50 55	97.75 97	93.22 88	98.98 78	92.64 96	94.97 132	91.71 134	98.93 57	98.62 69	98.52 60	99.82 10	99.72 83

diffmvs		96.83 88	97.33 108	96.25 86	95.76 110	99.34 79	98.06 90	93.22 88	99.43 21	92.30 102	96.90 98	89.83 143	98.55 76	98.00 109	98.14 83	99.64 109	99.70 88

Vis-MVSNet (Re-imp)		97.40 74	98.89 52	95.66 101	95.99 103	99.62 29	97.82 93	93.22 88	98.82 97	91.40 109	96.94 96	98.56 66	95.70 152	99.14 33	99.41 6	99.79 25	99.75 64

CS-MVS		98.06 53	99.12 36	96.82 72	95.83 108	99.66 15	98.93 52	93.12 91	98.95 79	94.29 69	98.55 54	99.05 61	98.94 56	99.05 39	98.78 48	99.83 8	99.80 30

EPP-MVSNet		97.75 62	98.71 58	96.63 78	95.68 115	99.56 47	97.51 103	93.10 92	99.22 44	94.99 55	97.18 92	97.30 81	98.65 71	98.83 53	98.93 33	99.84 5	99.92 1

PMMVS		97.52 69	98.39 66	96.51 82	95.82 109	98.73 121	97.80 94	93.05 93	98.76 107	94.39 68	99.07 31	97.03 85	98.55 76	98.31 87	97.61 106	99.43 160	99.21 155

ET-MVSNet_ETH3D		96.17 108	96.99 119	95.21 105	88.53 203	98.54 133	98.28 78	92.61 94	98.85 90	93.60 83	99.06 32	90.39 138	98.63 73	95.98 177	96.68 133	99.61 116	99.41 141

CVMVSNet		95.33 126	97.09 115	93.27 139	95.23 130	98.39 145	95.49 152	92.58 95	97.71 156	83.00 156	94.44 139	93.28 126	93.92 185	97.79 119	98.54 59	99.41 163	99.45 138

DI_MVS_plusplus_trai		96.90 87	97.49 98	96.21 87	95.61 117	99.40 70	98.72 61	92.11 96	99.14 55	92.98 94	93.08 155	95.14 105	98.13 87	98.05 105	97.91 94	99.74 44	99.73 72

MVSTER		97.16 79	97.71 92	96.52 81	95.97 104	98.48 136	98.63 63	92.10 97	98.68 111	95.96 41	99.23 18	91.79 133	96.87 121	98.76 59	97.37 121	99.57 138	99.68 97

UA-Net		97.13 80	99.14 35	94.78 109	97.21 79	99.38 71	97.56 102	92.04 98	98.48 122	88.03 124	98.39 62	99.91 31	94.03 182	99.33 23	99.23 17	99.81 16	99.25 152

UniMVSNet_NR-MVSNet		94.59 141	95.47 150	93.55 131	91.85 175	97.89 163	95.03 158	92.00 99	97.33 163	86.12 134	93.19 151	87.29 149	96.60 131	96.12 172	96.70 132	99.72 57	99.80 30

TranMVSNet+NR-MVSNet		93.67 157	94.14 168	93.13 140	91.28 194	97.58 179	95.60 150	91.97 100	97.06 170	84.05 143	90.64 166	82.22 188	96.17 142	94.94 191	96.78 130	99.69 79	99.78 44

tfpnnormal		93.85 156	94.12 170	93.54 132	93.22 156	98.24 151	95.45 153	91.96 101	94.61 200	83.91 145	90.74 163	81.75 191	97.04 116	97.49 135	96.16 150	99.68 88	99.84 18

TDRefinement		93.04 166	93.57 182	92.41 146	96.58 87	98.77 114	97.78 96	91.96 101	98.12 137	80.84 167	89.13 175	79.87 200	87.78 200	96.44 160	94.50 187	99.54 147	98.15 183

CDS-MVSNet		96.59 101	98.02 84	94.92 108	94.45 141	98.96 105	97.46 105	91.75 103	97.86 150	90.07 115	96.02 118	97.25 82	96.21 139	98.04 106	98.38 67	99.60 124	99.65 105
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DCV-MVSNet		97.56 68	98.36 67	96.62 79	96.44 89	98.36 147	98.37 72	91.73 104	99.11 61	94.80 57	98.36 63	96.28 92	98.60 75	98.12 95	98.44 62	99.76 35	99.87 12

MAR-MVS		97.71 63	98.04 82	97.32 53	99.35 41	98.91 107	97.65 100	91.68 105	98.00 141	97.01 31	97.72 81	94.83 109	98.85 65	98.44 83	98.86 39	99.41 163	99.52 128
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
ACMH	95.42 14	95.27 127	95.96 143	94.45 114	96.83 85	98.78 113	94.72 170	91.67 106	98.95 79	86.82 133	96.42 112	83.67 178	97.00 117	97.48 136	96.68 133	99.69 79	99.76 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
canonicalmvs		97.31 75	97.81 91	96.72 73	96.20 98	99.45 61	98.21 81	91.60 107	99.22 44	95.39 47	98.48 57	90.95 136	99.16 43	97.66 127	99.05 24	99.76 35	99.90 3

DU-MVS		93.98 151	94.44 165	93.44 134	91.66 180	97.77 165	95.03 158	91.57 108	97.17 167	86.12 134	93.13 153	81.13 193	96.60 131	95.10 188	97.01 127	99.67 96	99.80 30

NR-MVSNet		94.01 149	94.51 163	93.44 134	92.56 161	97.77 165	95.67 147	91.57 108	97.17 167	85.84 137	93.13 153	80.53 195	95.29 166	97.01 150	96.17 149	99.69 79	99.75 64

TransMVSNet (Re)		93.45 159	94.08 171	92.72 145	92.83 157	97.62 177	94.94 161	91.54 110	95.65 197	83.06 155	88.93 177	83.53 179	94.25 178	97.41 137	97.03 125	99.67 96	98.40 181

Baseline_NR-MVSNet		93.87 154	93.98 175	93.75 124	91.66 180	97.02 194	95.53 151	91.52 111	97.16 169	87.77 127	87.93 187	83.69 177	96.35 137	95.10 188	97.23 122	99.68 88	99.73 72

MVS_Test		97.30 76	98.54 61	95.87 95	95.74 111	99.28 86	98.19 82	91.40 112	99.18 50	91.59 108	98.17 68	96.18 94	98.63 73	98.61 70	98.55 57	99.66 101	99.78 44

baseline		97.45 72	98.70 59	95.99 94	95.89 105	99.36 74	98.29 77	91.37 113	99.21 46	92.99 93	98.40 61	96.87 86	97.96 93	98.60 72	98.60 55	99.42 162	99.86 15

GBi-Net		96.98 84	98.00 85	95.78 96	93.81 148	97.98 157	98.09 86	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.89 96	98.07 101	98.34 72	99.68 88	99.67 98

test1		96.98 84	98.00 85	95.78 96	93.81 148	97.98 157	98.09 86	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.89 96	98.07 101	98.34 72	99.68 88	99.67 98

FMVSNet3		97.02 83	98.12 79	95.73 100	93.59 154	97.98 157	98.34 76	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.63 105	98.61 70	98.62 53	99.61 116	99.65 105

ACMH+	95.51 13	95.40 123	96.00 141	94.70 110	96.33 90	98.79 111	96.79 127	91.32 114	98.77 106	87.18 130	95.60 129	85.46 168	96.97 118	97.15 146	96.59 137	99.59 130	99.65 105

UniMVSNet (Re)		94.58 142	95.34 152	93.71 126	92.25 167	98.08 156	94.97 160	91.29 118	97.03 172	87.94 125	93.97 143	86.25 163	96.07 144	96.27 169	95.97 157	99.72 57	99.79 38

UniMVSNet_ETH3D		93.15 163	92.33 195	94.11 118	93.91 145	98.61 129	94.81 167	90.98 119	97.06 170	87.51 129	82.27 202	76.33 208	97.87 100	94.79 192	97.47 115	99.56 141	99.81 28

FMVSNet2		96.64 98	97.50 97	95.63 102	93.81 148	97.98 157	98.09 86	90.87 120	98.99 77	93.48 84	93.17 152	95.25 104	97.89 96	98.63 68	98.80 46	99.68 88	99.67 98

Vis-MVSNet		96.16 109	98.22 74	93.75 124	95.33 129	99.70 11	97.27 111	90.85 121	98.30 129	85.51 141	95.72 127	96.45 87	93.69 188	98.70 64	99.00 28	99.84 5	99.69 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-LLR		95.50 121	97.32 109	93.37 136	95.49 124	98.74 119	96.44 137	90.82 122	98.18 134	82.75 157	96.60 107	94.67 112	95.54 158	98.09 98	96.00 154	99.20 176	98.93 167

test0.0.03 1		96.69 95	98.12 79	95.01 107	95.49 124	98.99 102	95.86 145	90.82 122	98.38 126	92.54 100	96.66 104	97.33 79	95.75 150	97.75 123	98.34 72	99.60 124	99.40 144

test_part1		95.56 119	95.38 151	95.78 96	96.07 99	98.16 154	97.57 101	90.78 124	97.43 160	93.04 91	89.12 176	89.41 144	97.93 94	96.38 163	97.38 120	99.29 172	99.78 44

CHOSEN 1792x2688		96.41 102	96.99 119	95.74 99	98.01 66	99.72 6	97.70 99	90.78 124	99.13 60	90.03 116	87.35 189	95.36 103	98.33 82	98.59 74	98.91 36	99.59 130	99.87 12

thisisatest0515		94.61 140	96.89 121	91.95 157	92.00 170	98.47 137	92.01 192	90.73 126	98.18 134	83.96 144	94.51 137	95.13 106	93.38 189	97.38 138	94.74 185	99.61 116	99.79 38

pm-mvs1		94.27 145	95.57 149	92.75 144	92.58 160	98.13 155	94.87 165	90.71 127	96.70 180	83.78 147	89.94 169	89.85 142	94.96 173	97.58 132	97.07 124	99.61 116	99.72 83

GA-MVS		93.93 153	96.31 140	91.16 173	93.61 152	98.79 111	95.39 155	90.69 128	98.25 132	73.28 199	96.15 116	88.42 146	94.39 177	97.76 122	95.35 168	99.58 134	99.45 138

v148		92.36 183	92.88 190	91.75 162	91.63 183	97.66 172	92.64 189	90.55 129	96.09 189	83.34 152	88.19 182	80.00 198	92.74 193	93.98 195	94.58 186	99.58 134	99.69 92

v2v482		92.77 172	93.52 185	91.90 160	91.59 185	97.63 174	94.57 177	90.31 130	96.80 178	79.22 177	88.74 179	81.55 192	96.04 146	95.26 184	94.97 178	99.66 101	99.69 92

pmmvs4		95.09 128	95.90 144	94.14 117	92.29 165	97.70 168	95.45 153	90.31 130	98.60 113	90.70 112	93.25 150	89.90 141	96.67 128	97.13 147	95.42 167	99.44 159	99.28 149

FC-MVSNet-test		96.07 111	97.94 87	93.89 121	93.60 153	98.67 124	96.62 131	90.30 132	98.76 107	88.62 120	95.57 130	97.63 77	94.48 175	97.97 110	97.48 114	99.71 67	99.52 128

CANet_DTU		96.64 98	99.08 39	93.81 123	97.10 82	99.42 66	98.85 56	90.01 133	99.31 31	79.98 174	99.78 2	99.10 60	97.42 110	98.35 85	98.05 89	99.47 155	99.53 125

WR-MVS		93.43 161	94.48 164	92.21 149	91.52 187	97.69 170	94.66 174	89.98 134	96.86 175	83.43 151	90.12 167	85.03 172	93.94 184	96.02 176	95.82 160	99.71 67	99.82 23

V42		93.05 165	93.90 178	92.04 153	91.91 172	97.66 172	94.91 162	89.91 135	96.85 176	80.58 169	89.66 170	83.43 181	95.37 164	95.03 190	94.90 180	99.59 130	99.78 44

FMVSNet1		95.77 115	96.41 139	95.03 106	93.42 155	97.86 164	97.11 120	89.89 136	98.53 119	92.00 105	89.17 173	93.23 127	98.15 86	98.07 101	98.34 72	99.61 116	99.69 92

PEN-MVS		92.72 173	93.20 188	92.15 151	91.29 192	97.31 191	94.67 173	89.81 137	96.19 187	81.83 163	88.58 180	79.06 203	95.61 156	95.21 185	96.27 145	99.72 57	99.82 23

DTE-MVSNet		92.42 180	92.85 191	91.91 159	90.87 197	96.97 195	94.53 178	89.81 137	95.86 196	81.59 164	88.83 178	77.88 206	95.01 172	94.34 194	96.35 143	99.64 109	99.73 72

CHOSEN 280x420		97.99 56	99.24 31	96.53 80	98.34 60	99.61 34	98.36 74	89.80 139	99.27 37	95.08 53	99.81 1	98.58 65	98.64 72	99.02 40	98.92 34	98.93 182	99.48 136

N_pmnet		92.21 185	94.60 162	89.42 189	91.88 173	97.38 190	89.15 203	89.74 140	97.89 148	73.75 197	87.94 186	92.23 131	93.85 186	96.10 173	93.20 194	98.15 191	97.43 191

baseline2		96.36 104	97.82 90	94.65 111	94.60 140	99.09 98	96.45 136	89.63 141	98.36 127	91.29 111	97.60 84	94.13 120	96.37 136	98.45 81	97.70 103	99.54 147	99.41 141

MDA-MVSNet-bldmvs		87.84 199	89.22 201	86.23 197	81.74 209	96.77 198	83.74 208	89.57 142	94.50 202	72.83 201	96.64 105	64.47 213	92.71 194	81.43 208	92.28 198	96.81 205	98.47 178

TAMVS		95.53 120	96.50 133	94.39 115	93.86 147	99.03 99	96.67 129	89.55 143	97.33 163	90.64 113	93.02 156	91.58 135	96.21 139	97.72 125	97.43 118	99.43 160	99.36 146

DeepMVS_CX						96.85 196	87.43 206	89.27 144	98.30 129	75.55 192	95.05 131	79.47 201	92.62 195	89.48 204		95.18 209	95.96 202

CP-MVSNet		93.25 162	94.00 174	92.38 147	91.65 182	97.56 181	94.38 179	89.20 145	96.05 191	83.16 154	89.51 171	81.97 189	96.16 143	96.43 161	96.56 138	99.71 67	99.89 6

IterMVS-LS		96.12 110	97.48 99	94.53 112	95.19 131	97.56 181	97.15 117	89.19 146	99.08 65	88.23 122	94.97 132	94.73 111	97.84 101	97.86 117	98.26 78	99.60 124	99.88 10
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-CasMVS		92.72 173	93.36 186	91.98 156	91.62 184	97.52 183	94.13 183	88.98 147	95.94 194	81.51 165	87.35 189	79.95 199	95.91 148	96.37 164	96.49 140	99.70 76	99.89 6

HyFIR lowres test		95.99 112	96.56 127	95.32 104	97.99 67	99.65 17	96.54 132	88.86 148	98.44 124	89.77 119	84.14 198	97.05 84	99.03 52	98.55 76	98.19 82	99.73 51	99.86 15

TinyColmap		94.00 150	94.35 166	93.60 128	95.89 105	98.26 149	97.49 104	88.82 149	98.56 117	83.21 153	91.28 160	80.48 196	96.68 127	97.34 140	96.26 147	99.53 149	98.24 182

WR-MVS_H		93.54 158	94.67 161	92.22 148	91.95 171	97.91 162	94.58 176	88.75 150	96.64 181	83.88 146	90.66 165	85.13 171	94.40 176	96.54 159	95.91 159	99.73 51	99.89 6

EU-MVSNet		92.80 170	94.76 160	90.51 180	91.88 173	96.74 199	92.48 190	88.69 151	96.21 186	79.00 179	91.51 157	87.82 147	91.83 197	95.87 179	96.27 145	99.21 175	98.92 170

USDC		94.26 146	94.83 158	93.59 129	96.02 100	98.44 140	97.84 92	88.65 152	98.86 88	82.73 159	94.02 141	80.56 194	96.76 124	97.28 142	96.15 151	99.55 143	98.50 177

SixPastTwentyTwo		93.44 160	95.32 153	91.24 171	92.11 168	98.40 144	92.77 188	88.64 153	98.09 138	77.83 183	93.51 147	85.74 166	96.52 134	96.91 152	94.89 182	99.59 130	99.73 72

testgi		95.67 117	97.48 99	93.56 130	95.07 133	99.00 100	95.33 156	88.47 154	98.80 100	86.90 132	97.30 87	92.33 130	95.97 147	97.66 127	97.91 94	99.60 124	99.38 145

v1144		92.81 169	94.03 173	91.40 168	91.68 179	97.60 178	94.73 169	88.40 155	96.71 179	78.48 181	88.14 184	84.46 176	95.45 163	96.31 168	95.22 171	99.65 105	99.76 57

pmmvs6		91.90 187	92.53 194	91.17 172	91.81 176	97.63 174	93.23 185	88.37 156	93.43 205	80.61 168	77.32 206	87.47 148	94.12 180	96.58 157	95.72 162	98.88 184	99.53 125

Effi-MVS+		95.81 114	97.31 112	94.06 119	95.09 132	99.35 77	97.24 113	88.22 157	98.54 118	85.38 142	98.52 55	88.68 145	98.70 68	98.32 86	97.93 92	99.74 44	99.84 18

v8		92.87 167	93.87 179	91.72 164	92.05 169	97.50 184	94.79 168	88.20 158	96.85 176	80.11 173	90.01 168	82.86 185	95.48 160	95.15 187	94.90 180	99.66 101	99.80 30

Effi-MVS+-dtu		95.74 116	98.04 82	93.06 141	93.92 144	99.16 95	97.90 91	88.16 159	99.07 70	82.02 162	98.02 73	94.32 117	96.74 125	98.53 77	97.56 108	99.61 116	99.62 112

v1192		92.43 179	93.61 181	91.05 174	91.53 186	97.43 187	94.61 175	87.99 160	96.60 182	76.72 186	87.11 191	82.74 186	95.85 149	96.35 166	95.30 170	99.60 124	99.74 68

LTVRE_ROB	93.20 16	92.84 168	94.92 155	90.43 182	92.83 157	98.63 126	97.08 122	87.87 161	97.91 147	68.42 206	93.54 146	79.46 202	96.62 130	97.55 133	97.40 119	99.74 44	99.92 1
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
CMPMVS	70.31 18	90.74 190	91.06 197	90.36 183	97.32 75	97.43 187	92.97 187	87.82 162	93.50 204	75.34 194	83.27 200	84.90 173	92.19 196	92.64 199	91.21 203	96.50 206	94.46 204
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v1921920		92.36 183	93.57 182	90.94 176	91.39 190	97.39 189	94.70 171	87.63 163	96.60 182	76.63 187	86.98 192	82.89 184	95.75 150	96.26 170	95.14 174	99.55 143	99.73 72

v144192		92.38 181	93.55 184	91.00 175	91.44 188	97.47 186	94.27 180	87.41 164	96.52 184	78.03 182	87.50 188	82.65 187	95.32 165	95.82 180	95.15 173	99.55 143	99.78 44

FMVSNet5		95.42 122	96.47 134	94.20 116	92.26 166	95.99 202	95.66 148	87.15 165	97.87 149	93.46 85	96.68 103	93.79 123	97.52 106	97.10 149	97.21 123	99.11 179	96.62 201

MS-PatchMatch		95.99 112	97.26 113	94.51 113	97.46 72	98.76 117	97.27 111	86.97 166	99.09 63	89.83 118	93.51 147	97.78 75	96.18 141	97.53 134	95.71 163	99.35 168	98.41 179

Fast-Effi-MVS+		95.38 124	96.52 130	94.05 120	94.15 143	99.14 97	97.24 113	86.79 167	98.53 119	87.62 128	94.51 137	87.06 150	98.76 66	98.60 72	98.04 90	99.72 57	99.77 52

v1240		91.99 186	93.33 187	90.44 181	91.29 192	97.30 192	94.25 181	86.79 167	96.43 185	75.49 193	86.34 195	81.85 190	95.29 166	96.42 162	95.22 171	99.52 150	99.73 72

TESTMET0.1,1		94.95 131	97.32 109	92.20 150	92.62 159	98.74 119	96.44 137	86.67 169	98.18 134	82.75 157	96.60 107	94.67 112	95.54 158	98.09 98	96.00 154	99.20 176	98.93 167

pmmvs5		92.71 175	94.27 167	90.90 177	91.42 189	97.74 167	93.23 185	86.66 170	95.99 193	78.96 180	91.45 158	83.44 180	95.55 157	97.30 141	95.05 176	99.58 134	98.93 167

Anonymous20231206		90.70 191	93.93 176	86.92 196	90.21 201	96.79 197	90.30 199	86.61 171	96.05 191	69.25 204	88.46 181	84.86 174	85.86 202	97.11 148	96.47 141	99.30 171	97.80 188

pmmvs-eth3d		89.81 194	89.65 200	90.00 185	86.94 205	95.38 204	91.08 193	86.39 172	94.57 201	82.27 161	83.03 201	64.94 211	93.96 183	96.57 158	93.82 191	99.35 168	99.24 153

test-mter		94.86 134	97.32 109	92.00 155	92.41 163	98.82 110	96.18 142	86.35 173	98.05 139	82.28 160	96.48 111	94.39 116	95.46 162	98.17 94	96.20 148	99.32 170	99.13 161

v10		92.79 171	94.06 172	91.31 170	91.78 177	97.29 193	94.87 165	86.10 174	96.97 173	79.82 175	88.16 183	84.56 175	95.63 154	96.33 167	95.31 169	99.65 105	99.80 30

MIMVSNet1		88.61 197	90.68 198	86.19 198	81.56 210	95.30 206	87.78 205	85.98 175	94.19 203	72.30 202	78.84 205	78.90 204	90.06 198	96.59 156	95.47 165	99.46 156	95.49 203

v7n		91.61 188	92.95 189	90.04 184	90.56 198	97.69 170	93.74 184	85.59 176	95.89 195	76.95 185	86.60 194	78.60 205	93.76 187	97.01 150	94.99 177	99.65 105	99.87 12

test20.03		90.65 192	93.71 180	87.09 194	90.44 199	96.24 200	89.74 202	85.46 177	95.59 198	72.99 200	90.68 164	85.33 169	84.41 203	95.94 178	95.10 175	99.52 150	97.06 196

anonymousdsp		93.12 164	95.86 146	89.93 187	91.09 195	98.25 150	95.12 157	85.08 178	97.44 159	73.30 198	90.89 162	90.78 137	95.25 168	97.91 113	95.96 158	99.71 67	99.82 23

IterMVS		94.81 135	97.71 92	91.42 166	94.83 138	97.63 174	97.38 106	85.08 178	98.93 83	75.67 191	94.02 141	97.64 76	96.66 129	98.45 81	97.60 107	98.90 183	99.72 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		95.38 124	98.20 75	92.09 152	93.91 145	98.87 108	97.35 108	85.01 180	99.08 65	81.09 166	98.10 69	96.36 90	95.62 155	98.43 84	97.03 125	99.55 143	99.50 134

IterMVS-SCA-FT		94.89 133	97.87 89	91.42 166	94.86 137	97.70 168	97.24 113	84.88 181	98.93 83	75.74 190	94.26 140	98.25 70	96.69 126	98.52 78	97.68 104	99.10 180	99.73 72

new_pmnet		90.45 193	92.84 192	87.66 193	88.96 202	96.16 201	88.71 204	84.66 182	97.56 158	71.91 203	85.60 197	86.58 159	93.28 190	96.07 174	93.54 193	98.46 186	94.39 205

EG-PatchMatch MVS		92.45 177	93.92 177	90.72 179	92.56 161	98.43 142	94.88 164	84.54 183	97.18 166	79.55 176	86.12 196	83.23 182	93.15 192	97.22 144	96.00 154	99.67 96	99.27 151

PM-MVS		89.55 195	90.30 199	88.67 191	87.06 204	95.60 203	90.88 195	84.51 184	96.14 188	75.75 189	86.89 193	63.47 214	94.64 174	96.85 153	93.89 190	99.17 178	99.29 148

new-patchmatchnet		86.12 200	87.30 202	84.74 200	86.92 206	95.19 207	83.57 209	84.42 185	92.67 206	65.66 207	80.32 203	64.72 212	89.41 199	92.33 202	89.21 204	98.43 187	96.69 199

FPMVS		83.82 201	84.61 203	82.90 202	90.39 200	90.71 210	90.85 196	84.10 186	95.47 199	65.15 208	83.44 199	74.46 209	75.48 206	81.63 207	79.42 209	91.42 211	87.14 209

MDTV_nov1_ep13		95.57 118	97.48 99	93.35 138	95.43 126	98.97 104	97.19 116	83.72 187	98.92 85	87.91 126	97.75 79	96.12 96	97.88 99	96.84 154	95.64 164	97.96 192	98.10 184

gm-plane-assit		89.44 196	92.82 193	85.49 199	91.37 191	95.34 205	79.55 212	82.12 188	91.68 208	64.79 210	87.98 185	80.26 197	95.66 153	98.51 80	97.56 108	99.45 157	98.41 179

pmmvs3		88.19 198	91.27 196	84.60 201	85.60 207	93.66 208	85.68 207	81.13 189	92.36 207	63.66 212	89.51 171	77.10 207	93.22 191	96.37 164	92.40 196	98.30 190	97.46 190

tpm cat1		94.06 148	94.90 156	93.06 141	95.42 128	98.52 135	96.64 130	80.67 190	97.82 152	92.63 97	93.39 149	95.00 107	96.06 145	91.36 203	91.58 202	96.98 204	96.66 200

CostFormer		94.25 147	94.88 157	93.51 133	95.43 126	98.34 148	96.21 141	80.64 191	97.94 146	94.01 72	98.30 65	86.20 164	97.52 106	92.71 198	92.69 195	97.23 203	98.02 186

PMVS	72.60 17	76.39 204	77.66 207	74.92 205	81.04 211	69.37 217	68.47 214	80.54 192	85.39 210	65.07 209	73.52 207	72.91 210	65.67 212	80.35 209	76.81 210	88.71 212	85.25 212
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dps		94.63 139	95.31 154	93.84 122	95.53 122	98.71 122	96.54 132	80.12 193	97.81 154	97.21 28	96.98 94	92.37 129	96.34 138	92.46 200	91.77 200	97.26 202	97.08 195

EPMVS		95.05 129	96.86 123	92.94 143	95.84 107	98.96 105	96.68 128	79.87 194	99.05 71	90.15 114	97.12 93	95.99 98	97.49 108	95.17 186	94.75 184	97.59 198	96.96 197

MDTV_nov1_ep13_2view		92.44 178	95.66 148	88.68 190	91.05 196	97.92 161	92.17 191	79.64 195	98.83 95	76.20 188	91.45 158	93.51 124	95.04 171	95.68 181	93.70 192	97.96 192	98.53 176

PatchmatchNet		94.70 136	97.08 116	91.92 158	95.53 122	98.85 109	95.77 146	79.54 196	98.95 79	85.98 136	98.52 55	96.45 87	97.39 111	95.32 183	94.09 189	97.32 200	97.38 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		93.86 155	95.88 145	91.50 165	95.69 114	98.62 127	95.64 149	79.41 197	98.80 100	83.76 149	95.63 128	96.13 95	97.25 112	92.92 197	92.31 197	97.27 201	96.74 198

SCA		94.95 131	97.44 102	92.04 153	95.55 121	99.16 95	96.26 140	79.30 198	99.02 74	85.73 139	98.18 67	97.13 83	97.69 103	96.03 175	94.91 179	97.69 197	97.65 189

CR-MVSNet		94.57 143	97.34 107	91.33 169	94.90 135	98.59 130	97.15 117	79.14 199	97.98 142	80.42 170	96.59 109	93.50 125	96.85 122	98.10 96	97.49 112	99.50 152	99.15 157

Patchmtry						98.59 130	97.15 117	79.14 199		80.42 170

ADS-MVSNet		94.65 138	97.04 118	91.88 161	95.68 115	98.99 102	95.89 144	79.03 201	99.15 52	85.81 138	96.96 95	98.21 72	97.10 115	94.48 193	94.24 188	97.74 194	97.21 193

tpm		92.38 181	94.79 159	89.56 188	94.30 142	97.50 184	94.24 182	78.97 202	97.72 155	74.93 195	97.97 74	82.91 183	96.60 131	93.65 196	94.81 183	98.33 189	98.98 165

MIMVSNet		94.49 144	97.59 96	90.87 178	91.74 178	98.70 123	94.68 172	78.73 203	97.98 142	83.71 150	97.71 82	94.81 110	96.96 119	97.97 110	97.92 93	99.40 165	98.04 185

PatchT		93.96 152	97.36 106	90.00 185	94.76 139	98.65 125	90.11 200	78.57 204	97.96 145	80.42 170	96.07 117	94.10 121	96.85 122	98.10 96	97.49 112	99.26 174	99.15 157

RPMNet		94.66 137	97.16 114	91.75 162	94.98 134	98.59 130	97.00 124	78.37 205	97.98 142	83.78 147	96.27 114	94.09 122	96.91 120	97.36 139	96.73 131	99.48 153	99.09 162

E-PMN		68.30 206	68.43 208	68.15 207	74.70 215	71.56 216	55.64 216	77.24 206	77.48 213	39.46 216	51.95 213	41.68 218	73.28 208	70.65 211	79.51 208	88.61 213	86.20 211

EMVS		68.12 207	68.11 209	68.14 208	75.51 214	71.76 215	55.38 217	77.20 207	77.78 212	37.79 217	53.59 211	43.61 217	74.72 207	67.05 212	76.70 211	88.27 214	86.24 210

PMMVS2		77.26 203	79.47 206	74.70 206	76.00 213	88.37 212	74.22 213	76.34 208	78.31 211	54.13 214	69.96 208	52.50 216	70.14 210	84.83 206	88.71 205	97.35 199	93.58 207

gg-mvs-nofinetune		90.85 189	94.14 168	87.02 195	94.89 136	99.25 88	98.64 62	76.29 209	88.24 209	57.50 213	79.93 204	95.45 102	95.18 169	98.77 58	98.07 88	99.62 114	99.24 153

Gipuma		81.40 202	81.78 204	80.96 204	83.21 208	85.61 213	79.73 211	76.25 210	97.33 163	64.21 211	55.32 210	55.55 215	86.04 201	92.43 201	92.20 199	96.32 207	93.99 206
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS-HIRNet		92.51 176	95.97 142	88.48 192	93.73 151	98.37 146	90.33 198	75.36 211	98.32 128	77.78 184	89.15 174	94.87 108	95.14 170	97.62 131	96.39 142	98.51 185	97.11 194

MVE	67.97 19	65.53 208	67.43 210	63.31 209	59.33 216	74.20 214	53.09 218	70.43 212	66.27 214	43.13 215	45.98 214	30.62 219	70.65 209	79.34 210	86.30 206	83.25 215	89.33 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				82.25 203	97.73 69	88.71 211	80.18 210	68.65 213	99.15 52	86.98 131	99.47 9	85.31 170	68.35 211	87.51 205	83.81 207	91.64 210

testmvs		31.24 209	40.15 211	20.86 211	12.61 217	17.99 218	25.16 219	13.30 214	48.42 215	24.82 218	53.07 212	30.13 221	28.47 213	42.73 213	37.65 212	20.79 216	51.04 213

test123		26.75 210	34.25 212	18.01 212	7.93 218	17.18 219	24.85 220	12.36 215	44.83 216	16.52 219	41.80 215	18.10 222	28.29 214	33.08 214	34.79 213	18.10 217	49.95 214

GG-mvs-BLEND		69.11 205	98.13 78	35.26 210	3.49 219	98.20 153	94.89 163	2.38 216	98.42 125	5.82 220	96.37 113	98.60 64	5.97 215	98.75 61	97.98 91	99.01 181	98.61 174

uanet_test		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet-low-res		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

RE-MVS-def										69.05 205

9.14												99.79 45

our_test_3					92.30 164	97.58 179	90.09 201

ambc			80.99 205		80.04 212	90.84 209	90.91 194		96.09 189	74.18 196	62.81 209	30.59 220	82.44 205	96.25 171	91.77 200	95.91 208	98.56 175

MTAPA										98.09 15		99.97 7

MTMP										98.46 11		99.96 12

Patchmatch-RL test							66.86 215

XVS					97.42 73	99.62 29	98.59 64			93.81 78		99.95 17				99.69 79

X-MVStestdata					97.42 73	99.62 29	98.59 64			93.81 78		99.95 17				99.69 79

mPP-MVS					99.53 30							99.89 34

NP-MVS									98.57 116

High-resolution multi-view benchmark