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.

PGM-MVS		98.86 29	99.35 24	98.29 32	99.77 1	99.63 24	99.67 5	95.63 43	98.66 109	95.27 47	99.11 23	99.82 40	99.67 4	99.33 22	99.19 20	99.73 49	99.74 66

SMA-MVS		99.38 4	99.60 2	99.12 8	99.76 2	99.62 28	99.39 28	98.23 16	99.52 15	98.03 14	99.45 9	99.98 1	99.64 5	99.58 7	99.30 11	99.68 85	99.76 55

CSCG		98.90 28	98.93 49	98.85 22	99.75 3	99.72 5	99.49 20	96.58 40	99.38 23	98.05 13	98.97 30	97.87 71	99.49 19	97.78 117	98.92 34	99.78 28	99.90 3

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 4	99.75 1	98.34 3	99.56 10	98.72 4	99.57 5	99.97 6	99.53 16	99.65 2	99.25 14	99.84 5	99.77 51

ACMMP_NAP		99.05 23	99.45 12	98.58 28	99.73 5	99.60 38	99.64 8	98.28 11	99.23 43	94.57 58	99.35 12	99.97 6	99.55 14	99.63 3	98.66 48	99.70 74	99.74 66

zzz-MVS		99.31 7	99.44 15	99.16 5	99.73 5	99.65 16	99.63 11	98.26 12	99.27 37	98.01 15	99.27 14	99.97 6	99.60 7	99.59 6	98.58 53	99.71 65	99.73 70

DVP-MVS		99.45 2	99.54 5	99.35 1	99.72 7	99.76 1	99.63 11	98.37 2	99.63 6	99.03 2	98.95 32	99.98 1	99.60 7	99.60 5	99.05 24	99.74 42	99.79 39

HFP-MVS		99.32 6	99.53 7	99.07 12	99.69 8	99.59 40	99.63 11	98.31 7	99.56 10	97.37 23	99.27 14	99.97 6	99.70 3	99.35 20	99.24 16	99.71 65	99.76 55

HPM-MVS++		99.10 19	99.30 25	98.86 21	99.69 8	99.48 56	99.59 15	98.34 3	99.26 40	96.55 34	99.10 24	99.96 11	99.36 27	99.25 25	98.37 66	99.64 106	99.66 100

APD-MVS		99.25 11	99.38 19	99.09 10	99.69 8	99.58 43	99.56 16	98.32 6	98.85 87	97.87 17	98.91 35	99.92 27	99.30 33	99.45 14	99.38 8	99.79 25	99.58 114
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSP-MVS		99.34 5	99.52 8	99.14 7	99.68 11	99.75 3	99.64 8	98.31 7	99.44 19	98.10 11	99.28 13	99.98 1	99.30 33	99.34 21	99.05 24	99.81 16	99.79 39

SR-MVS					99.67 12			98.25 13				99.94 24

X-MVS		98.93 27	99.37 20	98.42 29	99.67 12	99.62 28	99.60 14	98.15 21	99.08 63	93.81 77	98.46 56	99.95 16	99.59 10	99.49 12	99.21 19	99.68 85	99.75 62

MCST-MVS		99.11 18	99.27 27	98.93 19	99.67 12	99.33 79	99.51 19	98.31 7	99.28 35	96.57 33	99.10 24	99.90 30	99.71 2	99.19 26	98.35 67	99.82 10	99.71 84

ACMMPR		99.30 8	99.54 5	99.03 15	99.66 15	99.64 21	99.68 4	98.25 13	99.56 10	97.12 27	99.19 17	99.95 16	99.72 1	99.43 15	99.25 14	99.72 55	99.77 51

SteuartSystems-ACMMP		99.20 14	99.51 9	98.83 24	99.66 15	99.66 14	99.71 3	98.12 25	99.14 55	96.62 31	99.16 19	99.98 1	99.12 44	99.63 3	99.19 20	99.78 28	99.83 22
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		99.23 13	99.28 26	99.17 4	99.65 17	99.34 76	99.46 23	98.21 17	99.28 35	98.47 6	98.89 37	99.94 24	99.50 17	99.42 16	98.61 51	99.73 49	99.52 125

DPE-MVS		99.39 3	99.55 4	99.20 3	99.63 18	99.71 8	99.66 6	98.33 5	99.29 34	98.40 9	99.64 4	99.98 1	99.31 31	99.56 8	98.96 31	99.85 3	99.70 86

MP-MVS		99.07 21	99.36 21	98.74 25	99.63 18	99.57 45	99.66 6	98.25 13	99.00 74	95.62 41	98.97 30	99.94 24	99.54 15	99.51 11	98.79 45	99.71 65	99.73 70
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		99.05 23	99.08 37	99.02 16	99.62 20	99.38 68	99.43 27	98.21 17	99.36 27	97.66 20	97.79 74	99.90 30	99.45 22	99.17 27	98.43 61	99.77 33	99.51 129

CP-MVS		99.27 9	99.44 15	99.08 11	99.62 20	99.58 43	99.53 17	98.16 19	99.21 46	97.79 18	99.15 20	99.96 11	99.59 10	99.54 10	98.86 39	99.78 28	99.74 66

AdaColmap		99.06 22	98.98 47	99.15 6	99.60 22	99.30 82	99.38 29	98.16 19	99.02 72	98.55 5	98.71 47	99.57 52	99.58 13	99.09 32	97.84 95	99.64 106	99.36 143

CPTT-MVS		99.14 17	99.20 31	99.06 13	99.58 23	99.53 49	99.45 24	97.80 34	99.19 49	98.32 10	98.58 50	99.95 16	99.60 7	99.28 24	98.20 78	99.64 106	99.69 90

QAPM		98.62 38	99.04 43	98.13 36	99.57 24	99.48 56	99.17 37	94.78 53	99.57 9	96.16 36	96.73 99	99.80 41	99.33 29	98.79 53	99.29 13	99.75 37	99.64 107

3Dnovator	96.92 7	98.67 35	99.05 40	98.23 35	99.57 24	99.45 60	99.11 41	94.66 56	99.69 3	96.80 30	96.55 108	99.61 49	99.40 25	98.87 49	99.49 3	99.85 3	99.66 100

DeepC-MVS_fast	98.34 1	99.17 15	99.45 12	98.85 22	99.55 26	99.37 70	99.64 8	98.05 29	99.53 13	96.58 32	98.93 33	99.92 27	99.49 19	99.46 13	99.32 10	99.80 24	99.64 107
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS					99.53 27							99.89 32

3Dnovator+	96.92 7	98.71 34	99.05 40	98.32 31	99.53 27	99.34 76	99.06 45	94.61 57	99.65 4	97.49 21	96.75 98	99.86 35	99.44 23	98.78 54	99.30 11	99.81 16	99.67 96

MSLP-MVS++		99.15 16	99.24 29	99.04 14	99.52 29	99.49 55	99.09 43	98.07 27	99.37 25	98.47 6	97.79 74	99.89 32	99.50 17	98.93 42	99.45 4	99.61 113	99.76 55

OpenMVS	96.23 11	97.95 54	98.45 63	97.35 49	99.52 29	99.42 65	98.91 52	94.61 57	98.87 84	92.24 101	94.61 134	99.05 58	99.10 46	98.64 64	99.05 24	99.74 42	99.51 129

PLC	97.93 2	99.02 26	98.94 48	99.11 9	99.46 31	99.24 87	99.06 45	97.96 31	99.31 31	99.16 1	97.90 72	99.79 43	99.36 27	98.71 60	98.12 82	99.65 102	99.52 125
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		98.59 39	99.36 21	97.68 45	99.42 32	99.61 34	98.14 81	94.81 52	99.31 31	95.00 52	99.51 7	99.79 43	99.00 53	98.94 41	98.83 41	99.69 76	99.57 119

OMC-MVS		98.84 30	99.01 46	98.65 27	99.39 33	99.23 88	99.22 34	96.70 39	99.40 22	97.77 19	97.89 73	99.80 41	99.21 35	99.02 37	98.65 49	99.57 135	99.07 160

TSAR-MVS + ACMM		98.77 31	99.45 12	97.98 41	99.37 34	99.46 58	99.44 26	98.13 24	99.65 4	92.30 99	98.91 35	99.95 16	99.05 49	99.42 16	98.95 32	99.58 131	99.82 23

MVS_111021_LR		98.67 35	99.41 18	97.81 44	99.37 34	99.53 49	98.51 63	95.52 45	99.27 37	94.85 54	99.56 6	99.69 47	99.04 50	99.36 19	98.88 37	99.60 121	99.58 114

train_agg		98.73 33	99.11 35	98.28 33	99.36 36	99.35 74	99.48 22	97.96 31	98.83 92	93.86 76	98.70 48	99.86 35	99.44 23	99.08 34	98.38 64	99.61 113	99.58 114

ACMMP		98.74 32	99.03 44	98.40 30	99.36 36	99.64 21	99.20 35	97.75 35	98.82 94	95.24 48	98.85 38	99.87 34	99.17 41	98.74 59	97.50 108	99.71 65	99.76 55
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
MAR-MVS		97.71 60	98.04 80	97.32 50	99.35 38	98.91 104	97.65 97	91.68 102	98.00 139	97.01 28	97.72 78	94.83 106	98.85 62	98.44 80	98.86 39	99.41 160	99.52 125
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
abl_6				98.09 37	99.33 39	99.22 89	98.79 56	94.96 51	98.52 118	97.00 29	97.30 85	99.86 35	98.76 63			99.69 76	99.41 138

CDPH-MVS		98.41 41	99.10 36	97.61 47	99.32 40	99.36 71	99.49 20	96.15 42	98.82 94	91.82 103	98.41 57	99.66 48	99.10 46	98.93 42	98.97 30	99.75 37	99.58 114

CNLPA		99.03 25	99.05 40	99.01 17	99.27 41	99.22 89	99.03 47	97.98 30	99.34 29	99.00 3	98.25 63	99.71 46	99.31 31	98.80 52	98.82 42	99.48 150	99.17 153

MSDG		98.27 46	98.29 67	98.24 34	99.20 42	99.22 89	99.20 35	97.82 33	99.37 25	94.43 63	95.90 119	97.31 77	99.12 44	98.76 56	98.35 67	99.67 93	99.14 157

PHI-MVS		99.08 20	99.43 17	98.67 26	99.15 43	99.59 40	99.11 41	97.35 37	99.14 55	97.30 24	99.44 10	99.96 11	99.32 30	98.89 47	99.39 7	99.79 25	99.58 114

PatchMatch-RL		97.77 58	98.25 68	97.21 55	99.11 44	99.25 85	97.06 120	94.09 65	98.72 107	95.14 50	98.47 55	96.29 88	98.43 77	98.65 63	97.44 114	99.45 154	98.94 163

TAPA-MVS	97.53 5	98.41 41	98.84 53	97.91 42	99.08 45	99.33 79	99.15 38	97.13 38	99.34 29	93.20 85	97.75 76	99.19 55	99.20 36	98.66 62	98.13 81	99.66 98	99.48 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		98.05 52	98.86 51	97.10 57	99.02 46	99.43 64	98.47 64	94.73 54	99.05 69	95.62 41	98.93 33	97.62 75	95.48 157	98.59 71	98.55 54	99.29 169	99.84 18
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 102	98.53 60	93.70 124	98.97 47	98.24 149	97.36 104	94.23 64	98.85 87	79.18 175	99.19 17	98.47 64	94.09 178	97.89 112	98.21 77	98.39 184	98.85 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	96.15 12	97.78 57	98.17 74	97.32 50	98.84 48	99.45 60	99.28 32	95.43 46	99.48 17	91.80 104	94.83 133	98.36 66	98.90 59	98.09 95	97.85 94	99.68 85	99.15 154
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepPCF-MVS	97.74 3	98.34 43	99.46 11	97.04 60	98.82 49	99.33 79	96.28 136	97.47 36	99.58 8	94.70 57	98.99 29	99.85 38	97.24 109	99.55 9	99.34 9	97.73 192	99.56 120

SD-MVS		99.25 11	99.50 10	98.96 18	98.79 50	99.55 47	99.33 31	98.29 10	99.75 1	97.96 16	99.15 20	99.95 16	99.61 6	99.17 27	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
TSAR-MVS + MP.		99.27 9	99.57 3	98.92 20	98.78 51	99.53 49	99.72 2	98.11 26	99.73 2	97.43 22	99.15 20	99.96 11	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 45	98.53 60	98.05 38	98.76 52	98.77 112	99.13 39	98.07 27	99.10 60	94.27 69	96.70 100	99.84 39	98.70 65	97.90 111	98.11 83	99.40 162	99.28 146

PCF-MVS	97.50 6	98.18 48	98.35 66	97.99 40	98.65 53	99.36 71	98.94 50	98.14 23	98.59 111	93.62 80	96.61 104	99.76 45	99.03 51	97.77 118	97.45 113	99.57 135	98.89 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	97.63 4	98.33 44	98.57 58	98.04 39	98.62 54	99.65 16	99.45 24	98.15 21	99.51 16	92.80 92	95.74 123	96.44 86	99.46 21	99.37 18	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
CANet		98.46 40	99.16 32	97.64 46	98.48 55	99.64 21	99.35 30	94.71 55	99.53 13	95.17 49	97.63 80	99.59 50	98.38 78	98.88 48	98.99 29	99.74 42	99.86 15

LS3D		97.79 56	98.25 68	97.26 54	98.40 56	99.63 24	99.53 17	98.63 1	99.25 42	88.13 120	96.93 95	94.14 116	99.19 37	99.14 29	99.23 17	99.69 76	99.42 137

CHOSEN 280x420		97.99 53	99.24 29	96.53 77	98.34 57	99.61 34	98.36 72	89.80 136	99.27 37	95.08 51	99.81 1	98.58 62	98.64 69	99.02 37	98.92 34	98.93 178	99.48 133

DELS-MVS		98.19 47	98.77 55	97.52 48	98.29 58	99.71 8	99.12 40	94.58 60	98.80 97	95.38 46	96.24 113	98.24 68	97.92 91	99.06 35	99.52 1	99.82 10	99.79 39
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
RPSCF		97.61 63	98.16 75	96.96 68	98.10 59	99.00 97	98.84 54	93.76 73	99.45 18	94.78 56	99.39 11	99.31 54	98.53 75	96.61 152	95.43 162	97.74 190	97.93 184

PVSNet_BlendedMVS		97.51 67	97.71 90	97.28 52	98.06 60	99.61 34	97.31 106	95.02 49	99.08 63	95.51 43	98.05 67	90.11 136	98.07 86	98.91 45	98.40 62	99.72 55	99.78 44

PVSNet_Blended		97.51 67	97.71 90	97.28 52	98.06 60	99.61 34	97.31 106	95.02 49	99.08 63	95.51 43	98.05 67	90.11 136	98.07 86	98.91 45	98.40 62	99.72 55	99.78 44

MVS_0304		98.14 49	99.03 44	97.10 57	98.05 62	99.63 24	99.27 33	94.33 62	99.63 6	93.06 88	97.32 84	99.05 58	98.09 85	98.82 51	98.87 38	99.81 16	99.89 6

CHOSEN 1792x2688		96.41 99	96.99 116	95.74 96	98.01 63	99.72 5	97.70 96	90.78 121	99.13 58	90.03 113	87.35 186	95.36 100	98.33 79	98.59 71	98.91 36	99.59 127	99.87 12

HyFIR lowres test		95.99 109	96.56 124	95.32 101	97.99 64	99.65 16	96.54 129	88.86 145	98.44 121	89.77 116	84.14 195	97.05 81	99.03 51	98.55 73	98.19 79	99.73 49	99.86 15

OPM-MVS		96.22 104	95.85 144	96.65 74	97.75 65	98.54 131	99.00 49	95.53 44	96.88 171	89.88 114	95.95 118	86.46 157	98.07 86	97.65 126	96.63 131	99.67 93	98.83 170

tmp_tt				82.25 200	97.73 66	88.71 208	80.18 207	68.65 210	99.15 52	86.98 128	99.47 8	85.31 166	68.35 208	87.51 202	83.81 204	91.64 207

TSAR-MVS + COLMAP		96.79 86	96.55 125	97.06 59	97.70 67	98.46 136	99.07 44	96.23 41	99.38 23	91.32 107	98.80 40	85.61 163	98.69 67	97.64 127	96.92 124	99.37 164	99.06 161

PVSNet_Blended_VisFu		97.41 70	98.49 62	96.15 85	97.49 68	99.76 1	96.02 140	93.75 75	99.26 40	93.38 84	93.73 142	99.35 53	96.47 132	98.96 39	98.46 58	99.77 33	99.90 3

MS-PatchMatch		95.99 109	97.26 110	94.51 110	97.46 69	98.76 115	97.27 108	86.97 163	99.09 61	89.83 115	93.51 145	97.78 72	96.18 138	97.53 131	95.71 159	99.35 165	98.41 176

XVS					97.42 70	99.62 28	98.59 61			93.81 77		99.95 16				99.69 76

X-MVStestdata					97.42 70	99.62 28	98.59 61			93.81 77		99.95 16				99.69 76

LGP-MVS_train		96.23 103	96.89 118	95.46 100	97.32 72	98.77 112	98.81 55	93.60 78	98.58 112	85.52 137	99.08 26	86.67 154	97.83 98	97.87 113	97.51 107	99.69 76	99.73 70

CMPMVS	70.31 18	90.74 187	91.06 194	90.36 180	97.32 72	97.43 184	92.97 184	87.82 159	93.50 201	75.34 191	83.27 197	84.90 169	92.19 193	92.64 195	91.21 200	96.50 203	94.46 202
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 100	96.58 123	96.13 86	97.31 74	98.44 138	98.45 65	95.22 47	98.86 85	88.58 118	98.33 61	87.00 148	97.67 100	97.23 140	96.56 134	99.56 138	99.62 110

ACMM	96.26 9	96.67 94	96.69 122	96.66 73	97.29 75	98.46 136	96.48 132	95.09 48	99.21 46	93.19 86	98.78 42	86.73 153	98.17 80	97.84 115	96.32 140	99.74 42	99.49 132
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 77	99.14 33	94.78 106	97.21 76	99.38 68	97.56 98	92.04 95	98.48 119	88.03 121	98.39 59	99.91 29	94.03 179	99.33 22	99.23 17	99.81 16	99.25 149

UGNet		97.66 62	99.07 39	96.01 90	97.19 77	99.65 16	97.09 118	93.39 80	99.35 28	94.40 65	98.79 41	99.59 50	94.24 176	98.04 103	98.29 74	99.73 49	99.80 31
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
TSAR-MVS + GP.		98.66 37	99.36 21	97.85 43	97.16 78	99.46 58	99.03 47	94.59 59	99.09 61	97.19 26	99.73 3	99.95 16	99.39 26	98.95 40	98.69 47	99.75 37	99.65 103

CANet_DTU		96.64 95	99.08 37	93.81 120	97.10 79	99.42 65	98.85 53	90.01 130	99.31 31	79.98 171	99.78 2	99.10 57	97.42 106	98.35 82	98.05 86	99.47 152	99.53 122

IB-MVS	93.96 15	95.02 127	96.44 134	93.36 134	97.05 80	99.28 83	90.43 194	93.39 80	98.02 138	96.02 37	94.92 132	92.07 129	83.52 201	95.38 178	95.82 156	99.72 55	99.59 113
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
ACMP	96.25 10	96.62 97	96.72 121	96.50 80	96.96 81	98.75 116	97.80 91	94.30 63	98.85 87	93.12 87	98.78 42	86.61 155	97.23 110	97.73 121	96.61 132	99.62 111	99.71 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH	95.42 14	95.27 124	95.96 140	94.45 111	96.83 82	98.78 111	94.72 167	91.67 103	98.95 77	86.82 130	96.42 110	83.67 174	97.00 114	97.48 133	96.68 129	99.69 76	99.76 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		96.74 89	96.51 128	97.01 65	96.71 83	98.62 125	98.73 57	94.38 61	98.94 80	94.46 62	97.33 83	87.03 147	98.07 86	97.20 142	96.87 125	99.72 55	99.54 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 163	93.57 179	92.41 143	96.58 84	98.77 112	97.78 93	91.96 98	98.12 135	80.84 164	89.13 173	79.87 196	87.78 197	96.44 157	94.50 183	99.54 144	98.15 180

Anonymous202405211			97.40 102		96.45 85	99.54 48	98.08 86	93.79 72	98.24 130		93.55 143	94.41 112	98.88 61	98.04 103	98.24 76	99.75 37	99.76 55

DCV-MVSNet		97.56 65	98.36 65	96.62 76	96.44 86	98.36 145	98.37 70	91.73 101	99.11 59	94.80 55	98.36 60	96.28 89	98.60 72	98.12 92	98.44 59	99.76 35	99.87 12

ACMH+	95.51 13	95.40 119	96.00 138	94.70 107	96.33 87	98.79 109	96.79 124	91.32 111	98.77 103	87.18 127	95.60 127	85.46 164	96.97 115	97.15 143	96.59 133	99.59 127	99.65 103

Anonymous20231211		97.10 78	97.06 114	97.14 56	96.32 88	99.52 52	98.16 80	93.76 73	98.84 91	95.98 38	90.92 159	94.58 111	98.90 59	97.72 122	98.10 84	99.71 65	99.75 62

thres100view900		96.72 90	96.47 131	97.00 66	96.31 89	99.52 52	98.28 76	94.01 66	97.35 158	94.52 59	95.90 119	86.93 149	99.09 48	98.07 98	97.87 93	99.81 16	99.63 109

tfpn200view9		96.75 88	96.51 128	97.03 61	96.31 89	99.67 11	98.41 66	93.99 68	97.35 158	94.52 59	95.90 119	86.93 149	99.14 43	98.26 85	97.80 97	99.82 10	99.70 86

thres200		96.76 87	96.53 126	97.03 61	96.31 89	99.67 11	98.37 70	93.99 68	97.68 155	94.49 61	95.83 122	86.77 152	99.18 39	98.26 85	97.82 96	99.82 10	99.66 100

thres600view7		96.69 92	96.43 135	97.00 66	96.28 92	99.67 11	98.41 66	93.99 68	97.85 149	94.29 67	95.96 117	85.91 161	99.19 37	98.26 85	97.63 102	99.82 10	99.73 70

thres400		96.71 91	96.45 133	97.02 63	96.28 92	99.63 24	98.41 66	94.00 67	97.82 150	94.42 64	95.74 123	86.26 158	99.18 39	98.20 89	97.79 98	99.81 16	99.70 86

baseline1		97.58 64	98.05 79	97.02 63	96.21 94	99.45 60	97.71 95	93.71 77	98.47 120	95.75 40	98.78 42	93.20 125	98.91 58	98.52 75	98.44 59	99.81 16	99.53 122

canonicalmvs		97.31 72	97.81 89	96.72 71	96.20 95	99.45 60	98.21 78	91.60 104	99.22 44	95.39 45	98.48 54	90.95 133	99.16 42	97.66 124	99.05 24	99.76 35	99.90 3

IS_MVSNet		97.86 55	98.86 51	96.68 72	96.02 96	99.72 5	98.35 73	93.37 82	98.75 106	94.01 70	96.88 97	98.40 65	98.48 76	99.09 32	99.42 5	99.83 8	99.80 31

USDC		94.26 143	94.83 155	93.59 126	96.02 96	98.44 138	97.84 89	88.65 149	98.86 85	82.73 156	94.02 139	80.56 190	96.76 121	97.28 139	96.15 147	99.55 140	98.50 174

FC-MVSNet-train		97.04 79	97.91 86	96.03 89	96.00 98	98.41 141	96.53 131	93.42 79	99.04 71	93.02 89	98.03 69	94.32 114	97.47 105	97.93 109	97.77 99	99.75 37	99.88 10

Vis-MVSNet (Re-imp)		97.40 71	98.89 50	95.66 98	95.99 99	99.62 28	97.82 90	93.22 85	98.82 94	91.40 106	96.94 94	98.56 63	95.70 149	99.14 29	99.41 6	99.79 25	99.75 62

MVSTER		97.16 76	97.71 90	96.52 78	95.97 100	98.48 134	98.63 60	92.10 94	98.68 108	95.96 39	99.23 16	91.79 130	96.87 118	98.76 56	97.37 117	99.57 135	99.68 95

baseline		97.45 69	98.70 57	95.99 91	95.89 101	99.36 71	98.29 75	91.37 110	99.21 46	92.99 90	98.40 58	96.87 83	97.96 90	98.60 69	98.60 52	99.42 159	99.86 15

TinyColmap		94.00 147	94.35 163	93.60 125	95.89 101	98.26 147	97.49 101	88.82 146	98.56 114	83.21 150	91.28 158	80.48 192	96.68 124	97.34 137	96.26 143	99.53 146	98.24 179

DWT-MVSNet_training		95.38 120	95.05 151	95.78 93	95.86 103	98.88 105	97.55 99	90.09 129	98.23 131	96.49 35	97.62 81	86.92 151	97.16 111	92.03 199	94.12 185	97.52 195	97.50 187

EPMVS		95.05 126	96.86 120	92.94 140	95.84 104	98.96 102	96.68 125	79.87 191	99.05 69	90.15 111	97.12 91	95.99 95	97.49 104	95.17 182	94.75 180	97.59 194	96.96 195

CS-MVS		98.06 51	99.12 34	96.82 69	95.83 105	99.66 14	98.93 51	93.12 88	98.95 77	94.29 67	98.55 51	99.05 58	98.94 55	99.05 36	98.78 46	99.83 8	99.80 31

PMMVS		97.52 66	98.39 64	96.51 79	95.82 106	98.73 119	97.80 91	93.05 90	98.76 104	94.39 66	99.07 27	97.03 82	98.55 73	98.31 84	97.61 103	99.43 157	99.21 152

diffmvs		96.83 85	97.33 105	96.25 83	95.76 107	99.34 76	98.06 87	93.22 85	99.43 20	92.30 99	96.90 96	89.83 140	98.55 73	98.00 106	98.14 80	99.64 106	99.70 86

MVS_Test		97.30 73	98.54 59	95.87 92	95.74 108	99.28 83	98.19 79	91.40 109	99.18 50	91.59 105	98.17 65	96.18 91	98.63 70	98.61 67	98.55 54	99.66 98	99.78 44

EIA-MVS		97.70 61	98.78 54	96.44 81	95.72 109	99.65 16	98.14 81	93.72 76	98.30 126	92.31 98	98.63 49	97.90 70	98.97 54	98.92 44	98.30 73	99.78 28	99.80 31

casdiffmvs		96.93 83	97.43 101	96.34 82	95.70 110	99.50 54	97.75 94	93.22 85	98.98 76	92.64 93	94.97 130	91.71 131	98.93 56	98.62 66	98.52 57	99.82 10	99.72 81

tpmrst		93.86 152	95.88 142	91.50 162	95.69 111	98.62 125	95.64 146	79.41 194	98.80 97	83.76 146	95.63 126	96.13 92	97.25 108	92.92 193	92.31 194	97.27 198	96.74 196

ADS-MVSNet		94.65 135	97.04 115	91.88 158	95.68 112	98.99 99	95.89 141	79.03 198	99.15 52	85.81 135	96.96 93	98.21 69	97.10 112	94.48 189	94.24 184	97.74 190	97.21 191

EPP-MVSNet		97.75 59	98.71 56	96.63 75	95.68 112	99.56 46	97.51 100	93.10 89	99.22 44	94.99 53	97.18 90	97.30 78	98.65 68	98.83 50	98.93 33	99.84 5	99.92 1

ETV-MVS		98.11 50	99.25 28	96.77 70	95.66 114	99.62 28	98.38 69	93.91 71	99.41 21	93.87 75	98.85 38	99.17 56	98.92 57	99.12 31	98.80 43	99.86 2	99.81 28

DI_MVS_plusplus_trai		96.90 84	97.49 96	96.21 84	95.61 115	99.40 67	98.72 58	92.11 93	99.14 55	92.98 91	93.08 153	95.14 102	98.13 84	98.05 102	97.91 91	99.74 42	99.73 70

thisisatest0530		97.23 74	98.25 68	96.05 87	95.60 116	99.59 40	96.96 122	93.23 83	99.17 51	92.60 95	98.75 45	96.19 90	98.17 80	98.19 90	96.10 148	99.72 55	99.77 51

tttt0517		97.23 74	98.24 71	96.04 88	95.60 116	99.60 38	96.94 123	93.23 83	99.15 52	92.56 96	98.74 46	96.12 93	98.17 80	98.21 88	96.10 148	99.73 49	99.78 44

SCA		94.95 128	97.44 100	92.04 150	95.55 118	99.16 92	96.26 137	79.30 195	99.02 72	85.73 136	98.18 64	97.13 80	97.69 99	96.03 171	94.91 175	97.69 193	97.65 186

dps		94.63 136	95.31 150	93.84 119	95.53 119	98.71 120	96.54 129	80.12 190	97.81 152	97.21 25	96.98 92	92.37 126	96.34 135	92.46 196	91.77 197	97.26 199	97.08 193

PatchmatchNet		94.70 133	97.08 113	91.92 155	95.53 119	98.85 107	95.77 143	79.54 193	98.95 77	85.98 133	98.52 52	96.45 84	97.39 107	95.32 179	94.09 186	97.32 197	97.38 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 117	97.32 106	93.37 133	95.49 121	98.74 117	96.44 134	90.82 119	98.18 132	82.75 154	96.60 105	94.67 109	95.54 155	98.09 95	96.00 150	99.20 172	98.93 164

test0.0.03 1		96.69 92	98.12 77	95.01 104	95.49 121	98.99 99	95.86 142	90.82 119	98.38 123	92.54 97	96.66 102	97.33 76	95.75 147	97.75 120	98.34 69	99.60 121	99.40 141

CostFormer		94.25 144	94.88 154	93.51 130	95.43 123	98.34 146	96.21 138	80.64 188	97.94 144	94.01 70	98.30 62	86.20 160	97.52 102	92.71 194	92.69 192	97.23 200	98.02 183

MDTV_nov1_ep13		95.57 115	97.48 97	93.35 135	95.43 123	98.97 101	97.19 113	83.72 184	98.92 83	87.91 123	97.75 76	96.12 93	97.88 95	96.84 151	95.64 160	97.96 188	98.10 181

tpm cat1		94.06 145	94.90 153	93.06 138	95.42 125	98.52 133	96.64 127	80.67 187	97.82 150	92.63 94	93.39 147	95.00 104	96.06 142	91.36 200	91.58 199	96.98 201	96.66 198

Vis-MVSNet		96.16 106	98.22 72	93.75 121	95.33 126	99.70 10	97.27 108	90.85 118	98.30 126	85.51 138	95.72 125	96.45 84	93.69 185	98.70 61	99.00 28	99.84 5	99.69 90
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 123	97.09 112	93.27 136	95.23 127	98.39 143	95.49 149	92.58 92	97.71 154	83.00 153	94.44 137	93.28 123	93.92 182	97.79 116	98.54 56	99.41 160	99.45 135

IterMVS-LS		96.12 107	97.48 97	94.53 109	95.19 128	97.56 178	97.15 114	89.19 143	99.08 63	88.23 119	94.97 130	94.73 108	97.84 97	97.86 114	98.26 75	99.60 121	99.88 10
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 111	97.31 109	94.06 116	95.09 129	99.35 74	97.24 110	88.22 154	98.54 115	85.38 139	98.52 52	88.68 141	98.70 65	98.32 83	97.93 89	99.74 42	99.84 18

testgi		95.67 114	97.48 97	93.56 127	95.07 130	99.00 97	95.33 153	88.47 151	98.80 97	86.90 129	97.30 85	92.33 127	95.97 144	97.66 124	97.91 91	99.60 121	99.38 142

RPMNet		94.66 134	97.16 111	91.75 159	94.98 131	98.59 128	97.00 121	78.37 202	97.98 140	83.78 144	96.27 112	94.09 119	96.91 117	97.36 136	96.73 127	99.48 150	99.09 159

CR-MVSNet		94.57 140	97.34 104	91.33 166	94.90 132	98.59 128	97.15 114	79.14 196	97.98 140	80.42 167	96.59 107	93.50 122	96.85 119	98.10 93	97.49 109	99.50 149	99.15 154

gg-mvs-nofinetune		90.85 186	94.14 165	87.02 192	94.89 133	99.25 85	98.64 59	76.29 206	88.24 206	57.50 209	79.93 201	95.45 99	95.18 166	98.77 55	98.07 85	99.62 111	99.24 150

IterMVS-SCA-FT		94.89 130	97.87 87	91.42 163	94.86 134	97.70 165	97.24 110	84.88 178	98.93 81	75.74 187	94.26 138	98.25 67	96.69 123	98.52 75	97.68 101	99.10 176	99.73 70

IterMVS		94.81 132	97.71 90	91.42 163	94.83 135	97.63 171	97.38 103	85.08 175	98.93 81	75.67 188	94.02 139	97.64 73	96.66 126	98.45 78	97.60 104	98.90 179	99.72 81
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 149	97.36 103	90.00 182	94.76 136	98.65 123	90.11 197	78.57 201	97.96 143	80.42 167	96.07 115	94.10 118	96.85 119	98.10 93	97.49 109	99.26 170	99.15 154

baseline2		96.36 101	97.82 88	94.65 108	94.60 137	99.09 95	96.45 133	89.63 138	98.36 124	91.29 108	97.60 82	94.13 117	96.37 133	98.45 78	97.70 100	99.54 144	99.41 138

CDS-MVSNet		96.59 98	98.02 82	94.92 105	94.45 138	98.96 102	97.46 102	91.75 100	97.86 148	90.07 112	96.02 116	97.25 79	96.21 136	98.04 103	98.38 64	99.60 121	99.65 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 178	94.79 156	89.56 185	94.30 139	97.50 181	94.24 179	78.97 199	97.72 153	74.93 192	97.97 71	82.91 179	96.60 128	93.65 192	94.81 179	98.33 185	98.98 162

Fast-Effi-MVS+		95.38 120	96.52 127	94.05 117	94.15 140	99.14 94	97.24 110	86.79 164	98.53 116	87.62 125	94.51 135	87.06 146	98.76 63	98.60 69	98.04 87	99.72 55	99.77 51

Effi-MVS+-dtu		95.74 113	98.04 80	93.06 138	93.92 141	99.16 92	97.90 88	88.16 156	99.07 68	82.02 159	98.02 70	94.32 114	96.74 122	98.53 74	97.56 105	99.61 113	99.62 110

UniMVSNet_ETH3D		93.15 160	92.33 192	94.11 115	93.91 142	98.61 127	94.81 164	90.98 116	97.06 167	87.51 126	82.27 199	76.33 204	97.87 96	94.79 188	97.47 112	99.56 138	99.81 28

Fast-Effi-MVS+-dtu		95.38 120	98.20 73	92.09 149	93.91 142	98.87 106	97.35 105	85.01 177	99.08 63	81.09 163	98.10 66	96.36 87	95.62 152	98.43 81	97.03 121	99.55 140	99.50 131

TAMVS		95.53 116	96.50 130	94.39 112	93.86 144	99.03 96	96.67 126	89.55 140	97.33 160	90.64 110	93.02 154	91.58 132	96.21 136	97.72 122	97.43 115	99.43 157	99.36 143

GBi-Net		96.98 81	98.00 83	95.78 93	93.81 145	97.98 154	98.09 83	91.32 111	98.80 97	93.92 72	97.21 87	95.94 96	97.89 92	98.07 98	98.34 69	99.68 85	99.67 96

test1		96.98 81	98.00 83	95.78 93	93.81 145	97.98 154	98.09 83	91.32 111	98.80 97	93.92 72	97.21 87	95.94 96	97.89 92	98.07 98	98.34 69	99.68 85	99.67 96

FMVSNet2		96.64 95	97.50 95	95.63 99	93.81 145	97.98 154	98.09 83	90.87 117	98.99 75	93.48 82	93.17 150	95.25 101	97.89 92	98.63 65	98.80 43	99.68 85	99.67 96

MVS-HIRNet		92.51 173	95.97 139	88.48 189	93.73 148	98.37 144	90.33 195	75.36 208	98.32 125	77.78 181	89.15 172	94.87 105	95.14 167	97.62 128	96.39 138	98.51 181	97.11 192

GA-MVS		93.93 150	96.31 137	91.16 170	93.61 149	98.79 109	95.39 152	90.69 124	98.25 129	73.28 196	96.15 114	88.42 142	94.39 174	97.76 119	95.35 164	99.58 131	99.45 135

FC-MVSNet-test		96.07 108	97.94 85	93.89 118	93.60 150	98.67 122	96.62 128	90.30 128	98.76 104	88.62 117	95.57 128	97.63 74	94.48 172	97.97 107	97.48 111	99.71 65	99.52 125

FMVSNet3		97.02 80	98.12 77	95.73 97	93.59 151	97.98 154	98.34 74	91.32 111	98.80 97	93.92 72	97.21 87	95.94 96	97.63 101	98.61 67	98.62 50	99.61 113	99.65 103

FMVSNet1		95.77 112	96.41 136	95.03 103	93.42 152	97.86 161	97.11 117	89.89 133	98.53 116	92.00 102	89.17 171	93.23 124	98.15 83	98.07 98	98.34 69	99.61 113	99.69 90

tfpnnormal		93.85 153	94.12 167	93.54 129	93.22 153	98.24 149	95.45 150	91.96 98	94.61 197	83.91 142	90.74 161	81.75 187	97.04 113	97.49 132	96.16 146	99.68 85	99.84 18

TransMVSNet (Re)		93.45 156	94.08 168	92.72 142	92.83 154	97.62 174	94.94 158	91.54 107	95.65 194	83.06 152	88.93 174	83.53 175	94.25 175	97.41 134	97.03 121	99.67 93	98.40 178

LTVRE_ROB	93.20 16	92.84 165	94.92 152	90.43 179	92.83 154	98.63 124	97.08 119	87.87 158	97.91 145	68.42 202	93.54 144	79.46 198	96.62 127	97.55 130	97.40 116	99.74 42	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
TESTMET0.1,1		94.95 128	97.32 106	92.20 147	92.62 156	98.74 117	96.44 134	86.67 166	98.18 132	82.75 154	96.60 105	94.67 109	95.54 155	98.09 95	96.00 150	99.20 172	98.93 164

pm-mvs1		94.27 142	95.57 146	92.75 141	92.58 157	98.13 152	94.87 162	90.71 123	96.70 177	83.78 144	89.94 167	89.85 139	94.96 170	97.58 129	97.07 120	99.61 113	99.72 81

NR-MVSNet		94.01 146	94.51 160	93.44 131	92.56 158	97.77 162	95.67 144	91.57 105	97.17 164	85.84 134	93.13 151	80.53 191	95.29 163	97.01 147	96.17 145	99.69 76	99.75 62

EG-PatchMatch MVS		92.45 174	93.92 174	90.72 176	92.56 158	98.43 140	94.88 161	84.54 180	97.18 163	79.55 173	86.12 193	83.23 178	93.15 189	97.22 141	96.00 150	99.67 93	99.27 148

test-mter		94.86 131	97.32 106	92.00 152	92.41 160	98.82 108	96.18 139	86.35 170	98.05 137	82.28 157	96.48 109	94.39 113	95.46 159	98.17 91	96.20 144	99.32 167	99.13 158

our_test_3					92.30 161	97.58 176	90.09 198

pmmvs4		95.09 125	95.90 141	94.14 114	92.29 162	97.70 165	95.45 150	90.31 126	98.60 110	90.70 109	93.25 148	89.90 138	96.67 125	97.13 144	95.42 163	99.44 156	99.28 146

FMVSNet5		95.42 118	96.47 131	94.20 113	92.26 163	95.99 199	95.66 145	87.15 162	97.87 147	93.46 83	96.68 101	93.79 120	97.52 102	97.10 146	97.21 119	99.11 175	96.62 199

UniMVSNet (Re)		94.58 139	95.34 148	93.71 123	92.25 164	98.08 153	94.97 157	91.29 115	97.03 169	87.94 122	93.97 141	86.25 159	96.07 141	96.27 165	95.97 153	99.72 55	99.79 39

SixPastTwentyTwo		93.44 157	95.32 149	91.24 168	92.11 165	98.40 142	92.77 185	88.64 150	98.09 136	77.83 180	93.51 145	85.74 162	96.52 131	96.91 149	94.89 178	99.59 127	99.73 70

v8		92.87 164	93.87 176	91.72 161	92.05 166	97.50 181	94.79 165	88.20 155	96.85 173	80.11 170	90.01 166	82.86 181	95.48 157	95.15 183	94.90 176	99.66 98	99.80 31

thisisatest0515		94.61 137	96.89 118	91.95 154	92.00 167	98.47 135	92.01 189	90.73 122	98.18 132	83.96 141	94.51 135	95.13 103	93.38 186	97.38 135	94.74 181	99.61 113	99.79 39

WR-MVS_H		93.54 155	94.67 158	92.22 145	91.95 168	97.91 159	94.58 173	88.75 147	96.64 178	83.88 143	90.66 163	85.13 167	94.40 173	96.54 156	95.91 155	99.73 49	99.89 6

V42		93.05 162	93.90 175	92.04 150	91.91 169	97.66 169	94.91 159	89.91 132	96.85 173	80.58 166	89.66 168	83.43 177	95.37 161	95.03 186	94.90 176	99.59 127	99.78 44

EU-MVSNet		92.80 167	94.76 157	90.51 177	91.88 170	96.74 196	92.48 187	88.69 148	96.21 183	79.00 176	91.51 155	87.82 143	91.83 194	95.87 175	96.27 141	99.21 171	98.92 167

N_pmnet		92.21 182	94.60 159	89.42 186	91.88 170	97.38 187	89.15 200	89.74 137	97.89 146	73.75 194	87.94 183	92.23 128	93.85 183	96.10 169	93.20 191	98.15 187	97.43 189

UniMVSNet_NR-MVSNet		94.59 138	95.47 147	93.55 128	91.85 172	97.89 160	95.03 155	92.00 96	97.33 160	86.12 131	93.19 149	87.29 145	96.60 128	96.12 168	96.70 128	99.72 55	99.80 31

pmmvs6		91.90 184	92.53 191	91.17 169	91.81 173	97.63 171	93.23 182	88.37 153	93.43 202	80.61 165	77.32 203	87.47 144	94.12 177	96.58 154	95.72 158	98.88 180	99.53 122

v10		92.79 168	94.06 169	91.31 167	91.78 174	97.29 190	94.87 162	86.10 171	96.97 170	79.82 172	88.16 180	84.56 171	95.63 151	96.33 163	95.31 165	99.65 102	99.80 31

MIMVSNet		94.49 141	97.59 94	90.87 175	91.74 175	98.70 121	94.68 169	78.73 200	97.98 140	83.71 147	97.71 79	94.81 107	96.96 116	97.97 107	97.92 90	99.40 162	98.04 182

v1144		92.81 166	94.03 170	91.40 165	91.68 176	97.60 175	94.73 166	88.40 152	96.71 176	78.48 178	88.14 181	84.46 172	95.45 160	96.31 164	95.22 167	99.65 102	99.76 55

DU-MVS		93.98 148	94.44 162	93.44 131	91.66 177	97.77 162	95.03 155	91.57 105	97.17 164	86.12 131	93.13 151	81.13 189	96.60 128	95.10 184	97.01 123	99.67 93	99.80 31

Baseline_NR-MVSNet		93.87 151	93.98 172	93.75 121	91.66 177	97.02 191	95.53 148	91.52 108	97.16 166	87.77 124	87.93 184	83.69 173	96.35 134	95.10 184	97.23 118	99.68 85	99.73 70

CP-MVSNet		93.25 159	94.00 171	92.38 144	91.65 179	97.56 178	94.38 176	89.20 142	96.05 188	83.16 151	89.51 169	81.97 185	96.16 140	96.43 158	96.56 134	99.71 65	99.89 6

v148		92.36 180	92.88 187	91.75 159	91.63 180	97.66 169	92.64 186	90.55 125	96.09 186	83.34 149	88.19 179	80.00 194	92.74 190	93.98 191	94.58 182	99.58 131	99.69 90

PS-CasMVS		92.72 170	93.36 183	91.98 153	91.62 181	97.52 180	94.13 180	88.98 144	95.94 191	81.51 162	87.35 186	79.95 195	95.91 145	96.37 160	96.49 136	99.70 74	99.89 6

v2v482		92.77 169	93.52 182	91.90 157	91.59 182	97.63 171	94.57 174	90.31 126	96.80 175	79.22 174	88.74 176	81.55 188	96.04 143	95.26 180	94.97 174	99.66 98	99.69 90

v1192		92.43 176	93.61 178	91.05 171	91.53 183	97.43 184	94.61 172	87.99 157	96.60 179	76.72 183	87.11 188	82.74 182	95.85 146	96.35 162	95.30 166	99.60 121	99.74 66

WR-MVS		93.43 158	94.48 161	92.21 146	91.52 184	97.69 167	94.66 171	89.98 131	96.86 172	83.43 148	90.12 165	85.03 168	93.94 181	96.02 172	95.82 156	99.71 65	99.82 23

v144192		92.38 178	93.55 181	91.00 172	91.44 185	97.47 183	94.27 177	87.41 161	96.52 181	78.03 179	87.50 185	82.65 183	95.32 162	95.82 176	95.15 169	99.55 140	99.78 44

pmmvs5		92.71 172	94.27 164	90.90 174	91.42 186	97.74 164	93.23 182	86.66 167	95.99 190	78.96 177	91.45 156	83.44 176	95.55 154	97.30 138	95.05 172	99.58 131	98.93 164

v1921920		92.36 180	93.57 179	90.94 173	91.39 187	97.39 186	94.70 168	87.63 160	96.60 179	76.63 184	86.98 189	82.89 180	95.75 147	96.26 166	95.14 170	99.55 140	99.73 70

gm-plane-assit		89.44 193	92.82 190	85.49 196	91.37 188	95.34 202	79.55 209	82.12 185	91.68 205	64.79 206	87.98 182	80.26 193	95.66 150	98.51 77	97.56 105	99.45 154	98.41 176

v1240		91.99 183	93.33 184	90.44 178	91.29 189	97.30 189	94.25 178	86.79 164	96.43 182	75.49 190	86.34 192	81.85 186	95.29 163	96.42 159	95.22 167	99.52 147	99.73 70

PEN-MVS		92.72 170	93.20 185	92.15 148	91.29 189	97.31 188	94.67 170	89.81 134	96.19 184	81.83 160	88.58 177	79.06 199	95.61 153	95.21 181	96.27 141	99.72 55	99.82 23

TranMVSNet+NR-MVSNet		93.67 154	94.14 165	93.13 137	91.28 191	97.58 176	95.60 147	91.97 97	97.06 167	84.05 140	90.64 164	82.22 184	96.17 139	94.94 187	96.78 126	99.69 76	99.78 44

anonymousdsp		93.12 161	95.86 143	89.93 184	91.09 192	98.25 148	95.12 154	85.08 175	97.44 157	73.30 195	90.89 160	90.78 134	95.25 165	97.91 110	95.96 154	99.71 65	99.82 23

MDTV_nov1_ep13_2view		92.44 175	95.66 145	88.68 187	91.05 193	97.92 158	92.17 188	79.64 192	98.83 92	76.20 185	91.45 156	93.51 121	95.04 168	95.68 177	93.70 189	97.96 188	98.53 173

DTE-MVSNet		92.42 177	92.85 188	91.91 156	90.87 194	96.97 192	94.53 175	89.81 134	95.86 193	81.59 161	88.83 175	77.88 202	95.01 169	94.34 190	96.35 139	99.64 106	99.73 70

v7n		91.61 185	92.95 186	90.04 181	90.56 195	97.69 167	93.74 181	85.59 173	95.89 192	76.95 182	86.60 191	78.60 201	93.76 184	97.01 147	94.99 173	99.65 102	99.87 12

test20.03		90.65 189	93.71 177	87.09 191	90.44 196	96.24 197	89.74 199	85.46 174	95.59 195	72.99 197	90.68 162	85.33 165	84.41 200	95.94 174	95.10 171	99.52 147	97.06 194

FPMVS		83.82 198	84.61 200	82.90 199	90.39 197	90.71 207	90.85 193	84.10 183	95.47 196	65.15 204	83.44 196	74.46 205	75.48 203	81.63 204	79.42 206	91.42 208	87.14 207

Anonymous20231206		90.70 188	93.93 173	86.92 193	90.21 198	96.79 194	90.30 196	86.61 168	96.05 188	69.25 201	88.46 178	84.86 170	85.86 199	97.11 145	96.47 137	99.30 168	97.80 185

new_pmnet		90.45 190	92.84 189	87.66 190	88.96 199	96.16 198	88.71 201	84.66 179	97.56 156	71.91 200	85.60 194	86.58 156	93.28 187	96.07 170	93.54 190	98.46 182	94.39 203

ET-MVSNet_ETH3D		96.17 105	96.99 116	95.21 102	88.53 200	98.54 131	98.28 76	92.61 91	98.85 87	93.60 81	99.06 28	90.39 135	98.63 70	95.98 173	96.68 129	99.61 113	99.41 138

PM-MVS		89.55 192	90.30 196	88.67 188	87.06 201	95.60 200	90.88 192	84.51 181	96.14 185	75.75 186	86.89 190	63.47 210	94.64 171	96.85 150	93.89 187	99.17 174	99.29 145

pmmvs-eth3d		89.81 191	89.65 197	90.00 182	86.94 202	95.38 201	91.08 190	86.39 169	94.57 198	82.27 158	83.03 198	64.94 207	93.96 180	96.57 155	93.82 188	99.35 165	99.24 150

new-patchmatchnet		86.12 197	87.30 199	84.74 197	86.92 203	95.19 204	83.57 206	84.42 182	92.67 203	65.66 203	80.32 200	64.72 208	89.41 196	92.33 198	89.21 201	98.43 183	96.69 197

pmmvs3		88.19 195	91.27 193	84.60 198	85.60 204	93.66 205	85.68 204	81.13 186	92.36 204	63.66 208	89.51 169	77.10 203	93.22 188	96.37 160	92.40 193	98.30 186	97.46 188

Gipuma		81.40 199	81.78 201	80.96 201	83.21 205	85.61 210	79.73 208	76.25 207	97.33 160	64.21 207	55.32 207	55.55 211	86.04 198	92.43 197	92.20 196	96.32 204	93.99 204
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 196	89.22 198	86.23 194	81.74 206	96.77 195	83.74 205	89.57 139	94.50 199	72.83 198	96.64 103	64.47 209	92.71 191	81.43 205	92.28 195	96.81 202	98.47 175

MIMVSNet1		88.61 194	90.68 195	86.19 195	81.56 207	95.30 203	87.78 202	85.98 172	94.19 200	72.30 199	78.84 202	78.90 200	90.06 195	96.59 153	95.47 161	99.46 153	95.49 201

PMVS	72.60 17	76.39 201	77.66 204	74.92 202	81.04 208	69.37 214	68.47 211	80.54 189	85.39 207	65.07 205	73.52 204	72.91 206	65.67 209	80.35 206	76.81 207	88.71 209	85.25 210
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 202		80.04 209	90.84 206	90.91 191		96.09 186	74.18 193	62.81 206	30.59 216	82.44 202	96.25 167	91.77 197	95.91 205	98.56 172

PMMVS2		77.26 200	79.47 203	74.70 203	76.00 210	88.37 209	74.22 210	76.34 205	78.31 208	54.13 210	69.96 205	52.50 212	70.14 207	84.83 203	88.71 202	97.35 196	93.58 205

EMVS		68.12 204	68.11 206	68.14 205	75.51 211	71.76 212	55.38 214	77.20 204	77.78 209	37.79 213	53.59 208	43.61 213	74.72 204	67.05 209	76.70 208	88.27 211	86.24 208

E-PMN		68.30 203	68.43 205	68.15 204	74.70 212	71.56 213	55.64 213	77.24 203	77.48 210	39.46 212	51.95 210	41.68 214	73.28 205	70.65 208	79.51 205	88.61 210	86.20 209

MVE	67.97 19	65.53 205	67.43 207	63.31 206	59.33 213	74.20 211	53.09 215	70.43 209	66.27 211	43.13 211	45.98 211	30.62 215	70.65 206	79.34 207	86.30 203	83.25 212	89.33 206
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 206	40.15 208	20.86 208	12.61 214	17.99 215	25.16 216	13.30 211	48.42 212	24.82 214	53.07 209	30.13 217	28.47 210	42.73 210	37.65 209	20.79 213	51.04 211

test123		26.75 207	34.25 209	18.01 209	7.93 215	17.18 216	24.85 217	12.36 212	44.83 213	16.52 215	41.80 212	18.10 218	28.29 211	33.08 211	34.79 210	18.10 214	49.95 212

GG-mvs-BLEND		69.11 202	98.13 76	35.26 207	3.49 216	98.20 151	94.89 160	2.38 213	98.42 122	5.82 216	96.37 111	98.60 61	5.97 212	98.75 58	97.98 88	99.01 177	98.61 171

sosnet-low-res		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

sosnet		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

test_part1																	99.62 110

MTAPA										98.09 12		99.97 6

MTMP										98.46 8		99.96 11

Patchmatch-RL test							66.86 212

NP-MVS									98.57 113

Patchmtry						98.59 128	97.15 114	79.14 196		80.42 167

DeepMVS_CX						96.85 193	87.43 203	89.27 141	98.30 126	75.55 189	95.05 129	79.47 197	92.62 192	89.48 201		95.18 206	95.96 200

High-resolution multi-view benchmark