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 28	99.35 23	98.29 31	99.77 1	99.63 30	99.67 6	95.63 40	98.66 103	95.27 45	99.11 23	99.82 37	99.67 4	99.33 21	99.19 20	99.73 56	99.74 66

SMA-MVS		99.30 5	99.62 2	98.93 17	99.76 2	99.64 25	99.44 24	98.21 14	99.53 12	96.79 29	99.41 9	99.98 1	99.67 4	99.63 3	99.37 9	99.71 71	99.78 42

CSCG		98.90 27	98.93 46	98.85 21	99.75 3	99.72 4	99.49 18	96.58 37	99.38 20	98.05 11	98.97 29	97.87 64	99.49 18	97.78 119	98.92 31	99.78 37	99.90 3

APDe-MVS		99.49 1	99.64 1	99.32 1	99.74 4	99.74 3	99.75 1	98.34 2	99.56 9	98.72 3	99.57 4	99.97 4	99.53 15	99.65 2	99.25 14	99.84 5	99.77 50

ACMMP_Plus		99.05 22	99.45 9	98.58 27	99.73 5	99.60 42	99.64 8	98.28 10	99.23 44	94.57 58	99.35 12	99.97 4	99.55 13	99.63 3	98.66 46	99.70 80	99.74 66

zzz-MVS		99.31 3	99.44 12	99.16 4	99.73 5	99.65 20	99.63 10	98.26 11	99.27 36	98.01 12	99.27 14	99.97 4	99.60 7	99.59 6	98.58 52	99.71 71	99.73 71

ESAPD		99.23 11	99.41 16	99.01 14	99.70 7	99.69 11	99.40 27	98.31 5	98.94 76	97.70 17	99.40 10	99.97 4	99.17 42	99.54 8	98.67 45	99.78 37	99.67 107

HFP-MVS		99.32 2	99.53 5	99.07 9	99.69 8	99.59 44	99.63 10	98.31 5	99.56 9	97.37 21	99.27 14	99.97 4	99.70 3	99.35 19	99.24 16	99.71 71	99.76 54

HPM-MVS++		99.10 18	99.30 24	98.86 20	99.69 8	99.48 58	99.59 13	98.34 2	99.26 39	96.55 33	99.10 24	99.96 10	99.36 26	99.25 24	98.37 66	99.64 120	99.66 116

APD-MVS		99.25 9	99.38 18	99.09 7	99.69 8	99.58 46	99.56 14	98.32 4	98.85 83	97.87 14	98.91 36	99.92 25	99.30 31	99.45 13	99.38 8	99.79 34	99.58 132
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HSP-MVS		99.31 3	99.43 14	99.17 2	99.68 11	99.75 2	99.72 2	98.31 5	99.45 17	98.16 9	99.28 13	99.98 1	99.30 31	99.34 20	98.41 61	99.81 26	99.81 31

X-MVS		98.93 26	99.37 19	98.42 28	99.67 12	99.62 34	99.60 12	98.15 19	99.08 61	93.81 80	98.46 54	99.95 15	99.59 9	99.49 11	99.21 19	99.68 91	99.75 63

MCST-MVS		99.11 17	99.27 26	98.93 17	99.67 12	99.33 79	99.51 17	98.31 5	99.28 34	96.57 32	99.10 24	99.90 28	99.71 2	99.19 25	98.35 68	99.82 13	99.71 87

ACMMPR		99.30 5	99.54 4	99.03 12	99.66 14	99.64 25	99.68 5	98.25 12	99.56 9	97.12 25	99.19 17	99.95 15	99.72 1	99.43 14	99.25 14	99.72 61	99.77 50

SteuartSystems-ACMMP		99.20 13	99.51 6	98.83 23	99.66 14	99.66 19	99.71 4	98.12 23	99.14 54	96.62 30	99.16 19	99.98 1	99.12 50	99.63 3	99.19 20	99.78 37	99.83 24
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		99.23 11	99.28 25	99.17 2	99.65 16	99.34 77	99.46 21	98.21 14	99.28 34	98.47 5	98.89 38	99.94 23	99.50 16	99.42 15	98.61 49	99.73 56	99.52 143

MP-MVS		99.07 20	99.36 20	98.74 24	99.63 17	99.57 48	99.66 7	98.25 12	99.00 71	95.62 38	98.97 29	99.94 23	99.54 14	99.51 10	98.79 41	99.71 71	99.73 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		99.05 22	99.08 34	99.02 13	99.62 18	99.38 70	99.43 26	98.21 14	99.36 24	97.66 18	97.79 72	99.90 28	99.45 21	99.17 26	98.43 58	99.77 42	99.51 147

CP-MVS		99.27 7	99.44 12	99.08 8	99.62 18	99.58 46	99.53 15	98.16 17	99.21 47	97.79 15	99.15 20	99.96 10	99.59 9	99.54 8	98.86 36	99.78 37	99.74 66

AdaColmap		99.06 21	98.98 44	99.15 5	99.60 20	99.30 83	99.38 28	98.16 17	99.02 70	98.55 4	98.71 44	99.57 49	99.58 12	99.09 30	97.84 94	99.64 120	99.36 160

CPTT-MVS		99.14 16	99.20 29	99.06 10	99.58 21	99.53 53	99.45 22	97.80 31	99.19 50	98.32 8	98.58 48	99.95 15	99.60 7	99.28 23	98.20 78	99.64 120	99.69 95

QAPM		98.62 37	99.04 40	98.13 35	99.57 22	99.48 58	99.17 36	94.78 50	99.57 8	96.16 35	96.73 98	99.80 38	99.33 28	98.79 49	99.29 13	99.75 45	99.64 123

3Dnovator	96.92 7	98.67 34	99.05 37	98.23 34	99.57 22	99.45 62	99.11 39	94.66 53	99.69 3	96.80 28	96.55 106	99.61 46	99.40 24	98.87 45	99.49 3	99.85 4	99.66 116

DeepC-MVS_fast	98.34 1	99.17 14	99.45 9	98.85 21	99.55 24	99.37 72	99.64 8	98.05 26	99.53 12	96.58 31	98.93 31	99.92 25	99.49 18	99.46 12	99.32 11	99.80 33	99.64 123
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 25							99.89 30

3Dnovator+	96.92 7	98.71 33	99.05 37	98.32 30	99.53 25	99.34 77	99.06 43	94.61 54	99.65 4	97.49 19	96.75 96	99.86 33	99.44 22	98.78 50	99.30 12	99.81 26	99.67 107

MSLP-MVS++		99.15 15	99.24 27	99.04 11	99.52 27	99.49 57	99.09 41	98.07 25	99.37 22	98.47 5	97.79 72	99.89 30	99.50 16	98.93 39	99.45 4	99.61 135	99.76 54

OpenMVS	96.23 11	97.95 50	98.45 59	97.35 47	99.52 27	99.42 66	98.91 49	94.61 54	98.87 80	92.24 98	94.61 140	99.05 54	99.10 53	98.64 62	99.05 24	99.74 50	99.51 147

PLC	97.93 2	99.02 25	98.94 45	99.11 6	99.46 29	99.24 91	99.06 43	97.96 28	99.31 31	99.16 1	97.90 70	99.79 40	99.36 26	98.71 56	98.12 81	99.65 109	99.52 143
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		98.59 38	99.36 20	97.68 43	99.42 30	99.61 38	98.14 84	94.81 49	99.31 31	95.00 51	99.51 6	99.79 40	99.00 61	98.94 38	98.83 38	99.69 82	99.57 137

OMC-MVS		98.84 29	99.01 43	98.65 26	99.39 31	99.23 92	99.22 33	96.70 36	99.40 19	97.77 16	97.89 71	99.80 38	99.21 35	99.02 34	98.65 47	99.57 156	99.07 176

TSAR-MVS + ACMM		98.77 30	99.45 9	97.98 39	99.37 32	99.46 60	99.44 24	98.13 22	99.65 4	92.30 97	98.91 36	99.95 15	99.05 56	99.42 15	98.95 29	99.58 152	99.82 26

MVS_111021_LR		98.67 34	99.41 16	97.81 42	99.37 32	99.53 53	98.51 60	95.52 42	99.27 36	94.85 54	99.56 5	99.69 44	99.04 57	99.36 18	98.88 34	99.60 142	99.58 132

train_agg		98.73 32	99.11 32	98.28 32	99.36 34	99.35 75	99.48 20	97.96 28	98.83 86	93.86 79	98.70 45	99.86 33	99.44 22	99.08 32	98.38 64	99.61 135	99.58 132

ACMMP		98.74 31	99.03 41	98.40 29	99.36 34	99.64 25	99.20 34	97.75 32	98.82 88	95.24 46	98.85 39	99.87 32	99.17 42	98.74 55	97.50 111	99.71 71	99.76 54
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 56	98.04 77	97.32 48	99.35 36	98.91 107	97.65 101	91.68 102	98.00 130	97.01 26	97.72 76	94.83 95	98.85 63	98.44 77	98.86 36	99.41 179	99.52 143
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 36	99.33 37	99.22 93	98.79 53	94.96 48	98.52 112	97.00 27	97.30 82	99.86 33	98.76 64			99.69 82	99.41 157

CDPH-MVS		98.41 40	99.10 33	97.61 45	99.32 38	99.36 73	99.49 18	96.15 39	98.82 88	91.82 100	98.41 55	99.66 45	99.10 53	98.93 39	98.97 28	99.75 45	99.58 132

CNLPA		99.03 24	99.05 37	99.01 14	99.27 39	99.22 93	99.03 45	97.98 27	99.34 29	99.00 2	98.25 60	99.71 43	99.31 30	98.80 48	98.82 39	99.48 169	99.17 169

MSDG		98.27 44	98.29 66	98.24 33	99.20 40	99.22 93	99.20 34	97.82 30	99.37 22	94.43 65	95.90 121	97.31 70	99.12 50	98.76 52	98.35 68	99.67 98	99.14 173

PHI-MVS		99.08 19	99.43 14	98.67 25	99.15 41	99.59 44	99.11 39	97.35 34	99.14 54	97.30 22	99.44 8	99.96 10	99.32 29	98.89 43	99.39 7	99.79 34	99.58 132

PatchMatch-RL		97.77 54	98.25 67	97.21 53	99.11 42	99.25 89	97.06 123	94.09 66	98.72 101	95.14 48	98.47 53	96.29 81	98.43 76	98.65 59	97.44 116	99.45 173	98.94 179

TAPA-MVS	97.53 5	98.41 40	98.84 50	97.91 40	99.08 43	99.33 79	99.15 37	97.13 35	99.34 29	93.20 87	97.75 74	99.19 52	99.20 36	98.66 58	98.13 80	99.66 103	99.48 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		98.05 48	98.86 48	97.10 55	99.02 44	99.43 65	98.47 61	94.73 51	99.05 67	95.62 38	98.93 31	97.62 68	95.48 159	98.59 69	98.55 53	99.29 187	99.84 20
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 104	98.53 57	93.70 125	98.97 45	98.24 149	97.36 108	94.23 63	98.85 83	79.18 194	99.19 17	98.47 59	94.09 196	97.89 114	98.21 77	98.39 203	98.85 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	96.15 12	97.78 53	98.17 72	97.32 48	98.84 46	99.45 62	99.28 31	95.43 43	99.48 16	91.80 101	94.83 138	98.36 61	98.90 62	98.09 99	97.85 93	99.68 91	99.15 170
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 42	99.46 8	97.04 59	98.82 47	99.33 79	96.28 137	97.47 33	99.58 7	94.70 57	98.99 28	99.85 36	97.24 109	99.55 7	99.34 10	97.73 213	99.56 138

SD-MVS		99.25 9	99.50 7	98.96 16	98.79 48	99.55 51	99.33 30	98.29 9	99.75 1	97.96 13	99.15 20	99.95 15	99.61 6	99.17 26	99.06 23	99.81 26	99.84 20

TSAR-MVS + MP.		99.27 7	99.57 3	98.92 19	98.78 49	99.53 53	99.72 2	98.11 24	99.73 2	97.43 20	99.15 20	99.96 10	99.59 9	99.73 1	99.07 22	99.88 1	99.82 26

PCF-MVS	97.50 6	98.18 46	98.35 63	97.99 38	98.65 50	99.36 73	98.94 48	98.14 21	98.59 105	93.62 83	96.61 102	99.76 42	99.03 58	97.77 120	97.45 115	99.57 156	98.89 184
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	97.63 4	98.33 43	98.57 55	98.04 37	98.62 51	99.65 20	99.45 22	98.15 19	99.51 15	92.80 93	95.74 126	96.44 79	99.46 20	99.37 17	99.50 2	99.78 37	99.81 31
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 39	99.16 30	97.64 44	98.48 52	99.64 25	99.35 29	94.71 52	99.53 12	95.17 47	97.63 78	99.59 47	98.38 78	98.88 44	98.99 27	99.74 50	99.86 15

LS3D		97.79 52	98.25 67	97.26 52	98.40 53	99.63 30	99.53 15	98.63 1	99.25 41	88.13 123	96.93 94	94.14 107	99.19 38	99.14 28	99.23 17	99.69 82	99.42 156

CHOSEN 280x420		97.99 49	99.24 27	96.53 80	98.34 54	99.61 38	98.36 73	89.80 141	99.27 36	95.08 49	99.81 1	98.58 57	98.64 70	99.02 34	98.92 31	98.93 195	99.48 152

DELS-MVS		98.19 45	98.77 51	97.52 46	98.29 55	99.71 8	99.12 38	94.58 57	98.80 91	95.38 44	96.24 112	98.24 62	97.92 93	99.06 33	99.52 1	99.82 13	99.79 40
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 59	98.16 73	96.96 71	98.10 56	99.00 100	98.84 51	93.76 82	99.45 17	94.78 56	99.39 11	99.31 51	98.53 74	96.61 152	95.43 162	97.74 211	97.93 202

PVSNet_BlendedMVS		97.51 62	97.71 86	97.28 50	98.06 57	99.61 38	97.31 110	95.02 46	99.08 61	95.51 41	98.05 64	90.11 128	98.07 89	98.91 41	98.40 62	99.72 61	99.78 42

PVSNet_Blended		97.51 62	97.71 86	97.28 50	98.06 57	99.61 38	97.31 110	95.02 46	99.08 61	95.51 41	98.05 64	90.11 128	98.07 89	98.91 41	98.40 62	99.72 61	99.78 42

MVS_0304		98.14 47	99.03 41	97.10 55	98.05 59	99.63 30	99.27 32	94.33 59	99.63 6	93.06 90	97.32 81	99.05 54	98.09 88	98.82 47	98.87 35	99.81 26	99.89 7

CHOSEN 1792x2688		96.41 99	96.99 108	95.74 100	98.01 60	99.72 4	97.70 100	90.78 121	99.13 58	90.03 116	87.35 205	95.36 90	98.33 80	98.59 69	98.91 33	99.59 148	99.87 13

HyFIR lowres test		95.99 111	96.56 115	95.32 105	97.99 61	99.65 20	96.54 131	88.86 149	98.44 114	89.77 119	84.14 217	97.05 73	99.03 58	98.55 71	98.19 79	99.73 56	99.86 15

OPM-MVS		96.22 106	95.85 139	96.65 78	97.75 62	98.54 132	99.00 47	95.53 41	96.88 180	89.88 117	95.95 120	86.46 150	98.07 89	97.65 127	96.63 132	99.67 98	98.83 187

tmp_tt				82.25 223	97.73 63	88.71 234	80.18 229	68.65 237	99.15 52	86.98 131	99.47 7	85.31 161	68.35 234	87.51 229	83.81 230	91.64 233

TSAR-MVS + COLMAP		96.79 83	96.55 116	97.06 58	97.70 64	98.46 135	99.07 42	96.23 38	99.38 20	91.32 106	98.80 40	85.61 157	98.69 68	97.64 128	96.92 126	99.37 182	99.06 177

PVSNet_Blended_VisFu		97.41 65	98.49 58	96.15 88	97.49 65	99.76 1	96.02 140	93.75 83	99.26 39	93.38 86	93.73 146	99.35 50	96.47 132	98.96 36	98.46 57	99.77 42	99.90 3

MS-PatchMatch		95.99 111	97.26 103	94.51 112	97.46 66	98.76 117	97.27 112	86.97 171	99.09 59	89.83 118	93.51 148	97.78 65	96.18 137	97.53 132	95.71 159	99.35 183	98.41 193

XVS					97.42 67	99.62 34	98.59 58			93.81 80		99.95 15				99.69 82

X-MVStestdata					97.42 67	99.62 34	98.59 58			93.81 80		99.95 15				99.69 82

LGP-MVS_train		96.23 105	96.89 110	95.46 104	97.32 69	98.77 115	98.81 52	93.60 84	98.58 106	85.52 140	99.08 26	86.67 147	97.83 99	97.87 115	97.51 110	99.69 82	99.73 71

CMPMVS	70.31 18	90.74 207	91.06 215	90.36 197	97.32 69	97.43 192	92.97 202	87.82 165	93.50 222	75.34 212	83.27 220	84.90 165	92.19 210	92.64 217	91.21 226	96.50 228	94.46 223
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 100	96.58 114	96.13 90	97.31 71	98.44 138	98.45 62	95.22 44	98.86 81	88.58 121	98.33 58	87.00 139	97.67 100	97.23 140	96.56 135	99.56 159	99.62 126

ACMM	96.26 9	96.67 94	96.69 113	96.66 77	97.29 72	98.46 135	96.48 134	95.09 45	99.21 47	93.19 88	98.78 42	86.73 146	98.17 85	97.84 117	96.32 141	99.74 50	99.49 151
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 76	99.14 31	94.78 109	97.21 73	99.38 70	97.56 102	92.04 96	98.48 113	88.03 124	98.39 57	99.91 27	94.03 197	99.33 21	99.23 17	99.81 26	99.25 165

UGNet		97.66 58	99.07 36	96.01 94	97.19 74	99.65 20	97.09 121	93.39 87	99.35 26	94.40 67	98.79 41	99.59 47	94.24 194	98.04 108	98.29 75	99.73 56	99.80 33
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 36	99.36 20	97.85 41	97.16 75	99.46 60	99.03 45	94.59 56	99.09 59	97.19 24	99.73 3	99.95 15	99.39 25	98.95 37	98.69 44	99.75 45	99.65 119

CANet_DTU		96.64 95	99.08 34	93.81 121	97.10 76	99.42 66	98.85 50	90.01 135	99.31 31	79.98 180	99.78 2	99.10 53	97.42 106	98.35 81	98.05 84	99.47 171	99.53 141

IB-MVS	93.96 15	95.02 129	96.44 128	93.36 135	97.05 77	99.28 86	90.43 213	93.39 87	98.02 129	96.02 36	94.92 137	92.07 122	83.52 225	95.38 179	95.82 155	99.72 61	99.59 130
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	96.72 112	96.50 83	96.96 78	98.75 118	97.80 97	94.30 61	98.85 83	93.12 89	98.78 42	86.61 148	97.23 110	97.73 123	96.61 133	99.62 132	99.71 87
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH	95.42 14	95.27 126	95.96 135	94.45 113	96.83 79	98.78 114	94.72 180	91.67 103	98.95 73	86.82 133	96.42 109	83.67 174	97.00 114	97.48 134	96.68 131	99.69 82	99.76 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		96.74 87	96.51 119	97.01 66	96.71 80	98.62 127	98.73 54	94.38 58	98.94 76	94.46 64	97.33 80	87.03 138	98.07 89	97.20 142	96.87 127	99.72 61	99.54 140
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 165	93.57 190	92.41 145	96.58 81	98.77 115	97.78 99	91.96 99	98.12 125	80.84 166	89.13 182	79.87 213	87.78 216	96.44 158	94.50 201	99.54 164	98.15 197

Anonymous20240521		97.21 73	97.65 90	96.71 75	96.33 82	99.55 51	98.17 83	94.32 60	97.97 134	92.66 94	96.01 117	93.08 116	99.01 60	98.39 80	98.78 42	99.75 45	99.74 66

ACMH+	95.51 13	95.40 121	96.00 133	94.70 110	96.33 82	98.79 112	96.79 126	91.32 111	98.77 97	87.18 130	95.60 131	85.46 159	96.97 115	97.15 143	96.59 134	99.59 148	99.65 119

tfpn1000		97.60 60	98.21 70	96.89 73	96.32 84	99.60 42	97.99 92	93.85 79	99.21 47	95.03 50	98.49 51	93.69 111	98.31 81	98.50 74	98.31 74	99.86 2	99.70 89

tfpn111		96.96 81	96.91 109	97.03 60	96.31 85	99.67 13	98.41 64	93.99 69	97.35 156	94.50 61	98.65 46	86.93 140	99.14 45	98.26 86	97.80 96	99.82 13	99.70 89

tfpn_ndepth		97.71 56	98.30 65	97.02 64	96.31 85	99.56 49	98.05 89	93.94 77	98.95 73	95.59 40	98.40 56	94.79 97	98.39 77	98.40 79	98.42 59	99.86 2	99.56 138

conf200view11		96.75 85	96.51 119	97.03 60	96.31 85	99.67 13	98.41 64	93.99 69	97.35 156	94.50 61	95.90 121	86.93 140	99.14 45	98.26 86	97.80 96	99.82 13	99.70 89

thres100view900		96.72 88	96.47 123	97.00 68	96.31 85	99.52 56	98.28 78	94.01 67	97.35 156	94.52 59	95.90 121	86.93 140	99.09 55	98.07 102	97.87 92	99.81 26	99.63 125

tfpn200view9		96.75 85	96.51 119	97.03 60	96.31 85	99.67 13	98.41 64	93.99 69	97.35 156	94.52 59	95.90 121	86.93 140	99.14 45	98.26 86	97.80 96	99.82 13	99.70 89

thres200		96.76 84	96.53 117	97.03 60	96.31 85	99.67 13	98.37 72	93.99 69	97.68 151	94.49 63	95.83 125	86.77 145	99.18 40	98.26 86	97.82 95	99.82 13	99.66 116

conf0.01		96.35 101	95.71 140	97.10 55	96.30 91	99.65 20	98.41 64	94.10 65	97.35 156	94.82 55	95.44 134	81.88 201	99.14 45	98.16 95	97.80 96	99.82 13	99.69 95

conf0.002		96.31 103	95.63 142	97.11 54	96.29 92	99.64 25	98.41 64	94.11 64	97.35 156	94.86 53	95.49 133	81.06 206	99.14 45	98.14 96	98.02 86	99.82 13	99.69 95

view800		96.70 90	96.45 126	96.99 70	96.29 92	99.69 11	98.39 71	93.95 76	97.92 138	94.25 72	96.23 113	85.57 158	99.22 33	98.28 84	97.71 102	99.82 13	99.76 54

tfpn		96.22 106	95.62 143	96.93 72	96.29 92	99.72 4	98.34 75	93.94 77	97.96 135	93.94 75	96.45 108	79.09 216	99.22 33	98.28 84	98.06 83	99.83 9	99.78 42

view600		96.70 90	96.44 128	97.01 66	96.28 95	99.67 13	98.42 63	93.99 69	97.87 141	94.34 69	95.99 118	85.94 154	99.20 36	98.26 86	97.64 104	99.82 13	99.73 71

thres600view7		96.69 92	96.43 130	97.00 68	96.28 95	99.67 13	98.41 64	93.99 69	97.85 144	94.29 71	95.96 119	85.91 155	99.19 38	98.26 86	97.63 105	99.82 13	99.73 71

thres400		96.71 89	96.45 126	97.02 64	96.28 95	99.63 30	98.41 64	94.00 68	97.82 146	94.42 66	95.74 126	86.26 151	99.18 40	98.20 93	97.79 100	99.81 26	99.70 89

canonicalmvs		97.31 71	97.81 84	96.72 74	96.20 98	99.45 62	98.21 79	91.60 104	99.22 45	95.39 43	98.48 52	90.95 126	99.16 44	97.66 125	99.05 24	99.76 44	99.90 3

conf0.05thres1000		96.34 102	96.47 123	96.17 87	96.16 99	99.71 8	97.82 95	93.46 85	98.10 126	90.69 108	96.75 96	85.26 162	99.11 52	98.05 106	97.65 103	99.82 13	99.80 33

thresconf0.02		97.18 74	97.81 84	96.45 84	96.11 100	99.20 96	98.21 79	94.26 62	99.14 54	91.72 102	98.65 46	91.51 125	98.57 72	98.22 92	98.47 56	99.82 13	99.50 149

tfpn_n400		97.32 68	98.38 61	96.09 91	96.07 101	99.30 83	98.00 90	93.84 80	99.35 26	90.50 111	98.93 31	94.24 104	98.30 82	98.65 59	98.60 50	99.83 9	99.60 128

tfpnconf		97.32 68	98.38 61	96.09 91	96.07 101	99.30 83	98.00 90	93.84 80	99.35 26	90.50 111	98.93 31	94.24 104	98.30 82	98.65 59	98.60 50	99.83 9	99.60 128

tfpnview11		97.32 68	98.33 64	96.14 89	96.07 101	99.31 82	98.08 88	93.96 75	99.25 41	90.50 111	98.93 31	94.24 104	98.38 78	98.61 65	98.36 67	99.84 5	99.59 130

IS_MVSNet		97.86 51	98.86 48	96.68 76	96.02 104	99.72 4	98.35 74	93.37 89	98.75 100	94.01 73	96.88 95	98.40 60	98.48 75	99.09 30	99.42 5	99.83 9	99.80 33

USDC		94.26 142	94.83 153	93.59 127	96.02 104	98.44 138	97.84 94	88.65 153	98.86 81	82.73 158	94.02 143	80.56 207	96.76 122	97.28 139	96.15 148	99.55 160	98.50 191

FC-MVSNet-train		97.04 77	97.91 83	96.03 93	96.00 106	98.41 141	96.53 133	93.42 86	99.04 69	93.02 91	98.03 66	94.32 102	97.47 105	97.93 112	97.77 101	99.75 45	99.88 11

Vis-MVSNet (Re-imp)		97.40 66	98.89 47	95.66 102	95.99 107	99.62 34	97.82 95	93.22 90	98.82 88	91.40 105	96.94 93	98.56 58	95.70 148	99.14 28	99.41 6	99.79 34	99.75 63

MVSTER		97.16 75	97.71 86	96.52 81	95.97 108	98.48 134	98.63 57	92.10 95	98.68 102	95.96 37	99.23 16	91.79 123	96.87 119	98.76 52	97.37 119	99.57 156	99.68 102

TinyColmap		94.00 146	94.35 164	93.60 126	95.89 109	98.26 147	97.49 105	88.82 150	98.56 108	83.21 152	91.28 161	80.48 209	96.68 124	97.34 137	96.26 144	99.53 165	98.24 196

diffmvs		97.50 64	98.63 54	96.18 86	95.88 110	99.26 88	98.19 81	91.08 116	99.36 24	94.32 70	98.24 61	96.83 75	98.22 84	98.45 75	98.42 59	99.42 178	99.86 15

DWT-MVSNet_training		95.38 122	95.05 149	95.78 97	95.86 111	98.88 108	97.55 103	90.09 134	98.23 121	96.49 34	97.62 79	86.92 144	97.16 111	92.03 222	94.12 203	97.52 217	97.50 205

EPMVS		95.05 128	96.86 111	92.94 142	95.84 112	98.96 105	96.68 127	79.87 211	99.05 67	90.15 114	97.12 88	95.99 85	97.49 104	95.17 188	94.75 197	97.59 216	96.96 214

PMMVS		97.52 61	98.39 60	96.51 82	95.82 113	98.73 121	97.80 97	93.05 92	98.76 98	94.39 68	99.07 27	97.03 74	98.55 73	98.31 83	97.61 106	99.43 176	99.21 168

casdiffmvs		97.40 66	98.64 53	95.96 95	95.76 114	99.40 68	98.33 77	91.48 109	99.24 43	91.72 102	98.03 66	96.57 76	98.73 66	98.64 62	98.77 43	99.72 61	99.83 24

MVS_Test		97.30 72	98.54 56	95.87 96	95.74 115	99.28 86	98.19 81	91.40 110	99.18 51	91.59 104	98.17 62	96.18 82	98.63 71	98.61 65	98.55 53	99.66 103	99.78 42

tpmrst		93.86 151	95.88 137	91.50 169	95.69 116	98.62 127	95.64 146	79.41 216	98.80 91	83.76 148	95.63 130	96.13 83	97.25 108	92.92 213	92.31 219	97.27 222	96.74 217

ADS-MVSNet		94.65 135	97.04 107	91.88 162	95.68 117	98.99 102	95.89 141	79.03 220	99.15 52	85.81 139	96.96 92	98.21 63	97.10 112	94.48 207	94.24 202	97.74 211	97.21 210

EPP-MVSNet		97.75 55	98.71 52	96.63 79	95.68 117	99.56 49	97.51 104	93.10 91	99.22 45	94.99 52	97.18 87	97.30 71	98.65 69	98.83 46	98.93 30	99.84 5	99.92 1

DI_MVS_plusplus_trai		96.90 82	97.49 93	96.21 85	95.61 119	99.40 68	98.72 55	92.11 94	99.14 54	92.98 92	93.08 156	95.14 92	98.13 87	98.05 106	97.91 90	99.74 50	99.73 71

dps		94.63 136	95.31 148	93.84 120	95.53 120	98.71 122	96.54 131	80.12 210	97.81 148	97.21 23	96.98 91	92.37 119	96.34 134	92.46 219	91.77 223	97.26 223	97.08 212

PatchmatchNet		94.70 133	97.08 106	91.92 159	95.53 120	98.85 110	95.77 143	79.54 215	98.95 73	85.98 137	98.52 49	96.45 77	97.39 107	95.32 180	94.09 204	97.32 221	97.38 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 119	97.32 99	93.37 134	95.49 122	98.74 119	96.44 135	90.82 119	98.18 122	82.75 156	96.60 103	94.67 99	95.54 155	98.09 99	96.00 149	99.20 190	98.93 180

test0.0.03 1		96.69 92	98.12 75	95.01 107	95.49 122	98.99 102	95.86 142	90.82 119	98.38 116	92.54 96	96.66 100	97.33 69	95.75 146	97.75 122	98.34 70	99.60 142	99.40 158

CostFormer		94.25 143	94.88 152	93.51 131	95.43 124	98.34 145	96.21 138	80.64 207	97.94 137	94.01 73	98.30 59	86.20 153	97.52 102	92.71 214	92.69 214	97.23 225	98.02 201

MDTV_nov1_ep13		95.57 117	97.48 94	93.35 136	95.43 124	98.97 104	97.19 116	83.72 202	98.92 79	87.91 126	97.75 74	96.12 84	97.88 97	96.84 151	95.64 160	97.96 209	98.10 198

tpm cat1		94.06 144	94.90 151	93.06 139	95.42 126	98.52 133	96.64 129	80.67 206	97.82 146	92.63 95	93.39 150	95.00 93	96.06 141	91.36 226	91.58 225	96.98 226	96.66 219

tpmp4_e23		93.84 153	94.58 159	92.98 141	95.41 127	98.29 146	96.81 125	80.57 208	98.15 124	90.53 110	97.00 90	84.39 170	96.91 117	93.69 210	92.45 217	97.67 214	98.06 199

Vis-MVSNet		96.16 108	98.22 69	93.75 122	95.33 128	99.70 10	97.27 112	90.85 118	98.30 118	85.51 141	95.72 128	96.45 77	93.69 203	98.70 57	99.00 26	99.84 5	99.69 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 125	97.09 105	93.27 137	95.23 129	98.39 143	95.49 149	92.58 93	97.71 150	83.00 155	94.44 142	93.28 114	93.92 200	97.79 118	98.54 55	99.41 179	99.45 154

IterMVS-LS		96.12 109	97.48 94	94.53 111	95.19 130	97.56 183	97.15 117	89.19 147	99.08 61	88.23 122	94.97 136	94.73 98	97.84 98	97.86 116	98.26 76	99.60 142	99.88 11
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 113	97.31 102	94.06 117	95.09 131	99.35 75	97.24 114	88.22 158	98.54 109	85.38 142	98.52 49	88.68 132	98.70 67	98.32 82	97.93 88	99.74 50	99.84 20

testgi		95.67 116	97.48 94	93.56 128	95.07 132	99.00 100	95.33 153	88.47 155	98.80 91	86.90 132	97.30 82	92.33 120	95.97 143	97.66 125	97.91 90	99.60 142	99.38 159

RPMNet		94.66 134	97.16 104	91.75 165	94.98 133	98.59 129	97.00 124	78.37 224	97.98 131	83.78 146	96.27 111	94.09 109	96.91 117	97.36 136	96.73 129	99.48 169	99.09 175

LP		92.12 198	94.60 157	89.22 205	94.96 134	98.45 137	93.01 201	77.58 225	97.85 144	77.26 203	89.80 176	93.00 117	94.54 187	93.69 210	92.58 215	98.00 208	96.83 216

CR-MVSNet		94.57 139	97.34 98	91.33 173	94.90 135	98.59 129	97.15 117	79.14 218	97.98 131	80.42 173	96.59 105	93.50 113	96.85 120	98.10 97	97.49 112	99.50 168	99.15 170

gg-mvs-nofinetune		90.85 206	94.14 166	87.02 213	94.89 136	99.25 89	98.64 56	76.29 229	88.24 230	57.50 234	79.93 225	95.45 89	95.18 181	98.77 51	98.07 82	99.62 132	99.24 166

IterMVS		94.81 132	97.71 86	91.42 171	94.83 137	97.63 176	97.38 107	85.08 186	98.93 78	75.67 209	94.02 143	97.64 66	96.66 126	98.45 75	97.60 107	98.90 196	99.72 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 148	97.36 97	90.00 200	94.76 138	98.65 125	90.11 216	78.57 223	97.96 135	80.42 173	96.07 115	94.10 108	96.85 120	98.10 97	97.49 112	99.26 188	99.15 170

CDS-MVSNet		96.59 98	98.02 79	94.92 108	94.45 139	98.96 105	97.46 106	91.75 101	97.86 143	90.07 115	96.02 116	97.25 72	96.21 135	98.04 108	98.38 64	99.60 142	99.65 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 188	94.79 154	89.56 203	94.30 140	97.50 188	94.24 194	78.97 221	97.72 149	74.93 213	97.97 69	82.91 185	96.60 128	93.65 212	94.81 195	98.33 204	98.98 178

Fast-Effi-MVS+		95.38 122	96.52 118	94.05 118	94.15 141	99.14 98	97.24 114	86.79 172	98.53 110	87.62 128	94.51 141	87.06 137	98.76 64	98.60 68	98.04 85	99.72 61	99.77 50

Effi-MVS+-dtu		95.74 115	98.04 77	93.06 139	93.92 142	99.16 97	97.90 93	88.16 161	99.07 66	82.02 161	98.02 68	94.32 102	96.74 123	98.53 72	97.56 108	99.61 135	99.62 126

Fast-Effi-MVS+-dtu		95.38 122	98.20 71	92.09 152	93.91 143	98.87 109	97.35 109	85.01 188	99.08 61	81.09 165	98.10 63	96.36 80	95.62 152	98.43 78	97.03 123	99.55 160	99.50 149

testpf		91.80 203	94.43 163	88.74 206	93.89 144	95.30 221	92.05 207	71.77 233	97.52 153	87.24 129	94.77 139	92.68 118	91.48 212	91.75 225	92.11 222	96.02 230	96.89 215

TAMVS		95.53 118	96.50 122	94.39 114	93.86 145	99.03 99	96.67 128	89.55 144	97.33 162	90.64 109	93.02 157	91.58 124	96.21 135	97.72 124	97.43 117	99.43 176	99.36 160

GBi-Net		96.98 79	98.00 80	95.78 97	93.81 146	97.98 154	98.09 85	91.32 111	98.80 91	93.92 76	97.21 84	95.94 86	97.89 94	98.07 102	98.34 70	99.68 91	99.67 107

test1		96.98 79	98.00 80	95.78 97	93.81 146	97.98 154	98.09 85	91.32 111	98.80 91	93.92 76	97.21 84	95.94 86	97.89 94	98.07 102	98.34 70	99.68 91	99.67 107

FMVSNet2		96.64 95	97.50 92	95.63 103	93.81 146	97.98 154	98.09 85	90.87 117	98.99 72	93.48 84	93.17 153	95.25 91	97.89 94	98.63 64	98.80 40	99.68 91	99.67 107

MVS-HIRNet		92.51 182	95.97 134	88.48 209	93.73 149	98.37 144	90.33 214	75.36 232	98.32 117	77.78 200	89.15 181	94.87 94	95.14 182	97.62 129	96.39 139	98.51 199	97.11 211

GA-MVS		93.93 149	96.31 132	91.16 179	93.61 150	98.79 112	95.39 152	90.69 123	98.25 120	73.28 217	96.15 114	88.42 133	94.39 192	97.76 121	95.35 166	99.58 152	99.45 154

FC-MVSNet-test		96.07 110	97.94 82	93.89 119	93.60 151	98.67 124	96.62 130	90.30 130	98.76 98	88.62 120	95.57 132	97.63 67	94.48 190	97.97 110	97.48 114	99.71 71	99.52 143

FMVSNet3		97.02 78	98.12 75	95.73 101	93.59 152	97.98 154	98.34 75	91.32 111	98.80 91	93.92 76	97.21 84	95.94 86	97.63 101	98.61 65	98.62 48	99.61 135	99.65 119

FMVSNet1		95.77 114	96.41 131	95.03 106	93.42 153	97.86 161	97.11 120	89.89 138	98.53 110	92.00 99	89.17 180	93.23 115	98.15 86	98.07 102	98.34 70	99.61 135	99.69 95

tfpnnormal		93.85 152	94.12 168	93.54 130	93.22 154	98.24 149	95.45 150	91.96 99	94.61 218	83.91 144	90.74 163	81.75 203	97.04 113	97.49 133	96.16 147	99.68 91	99.84 20

TransMVSNet (Re)		93.45 156	94.08 170	92.72 144	92.83 155	97.62 179	94.94 159	91.54 107	95.65 214	83.06 154	88.93 183	83.53 175	94.25 193	97.41 135	97.03 123	99.67 98	98.40 195

LTVRE_ROB	93.20 16	92.84 168	94.92 150	90.43 196	92.83 155	98.63 126	97.08 122	87.87 164	97.91 139	68.42 223	93.54 147	79.46 215	96.62 127	97.55 131	97.40 118	99.74 50	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		94.95 130	97.32 99	92.20 149	92.62 157	98.74 119	96.44 135	86.67 174	98.18 122	82.75 156	96.60 103	94.67 99	95.54 155	98.09 99	96.00 149	99.20 190	98.93 180

pm-mvs1		94.27 141	95.57 144	92.75 143	92.58 158	98.13 152	94.87 165	90.71 122	96.70 186	83.78 146	89.94 175	89.85 131	94.96 185	97.58 130	97.07 122	99.61 135	99.72 83

NR-MVSNet		94.01 145	94.51 160	93.44 132	92.56 159	97.77 162	95.67 144	91.57 105	97.17 167	85.84 138	93.13 154	80.53 208	95.29 178	97.01 147	96.17 146	99.69 82	99.75 63

EG-PatchMatch MVS		92.45 183	93.92 179	90.72 191	92.56 159	98.43 140	94.88 164	84.54 192	97.18 166	79.55 188	86.12 215	83.23 179	93.15 206	97.22 141	96.00 149	99.67 98	99.27 164

test-mter		94.86 131	97.32 99	92.00 156	92.41 161	98.82 111	96.18 139	86.35 178	98.05 128	82.28 159	96.48 107	94.39 101	95.46 165	98.17 94	96.20 145	99.32 185	99.13 174

our_test_3					92.30 162	97.58 181	90.09 217

pmmvs4		95.09 127	95.90 136	94.14 116	92.29 163	97.70 168	95.45 150	90.31 128	98.60 104	90.70 107	93.25 151	89.90 130	96.67 125	97.13 144	95.42 163	99.44 175	99.28 163

FMVSNet5		95.42 120	96.47 123	94.20 115	92.26 164	95.99 208	95.66 145	87.15 168	97.87 141	93.46 85	96.68 99	93.79 110	97.52 102	97.10 146	97.21 121	99.11 193	96.62 220

UniMVSNet (Re)		94.58 138	95.34 146	93.71 124	92.25 165	98.08 153	94.97 158	91.29 115	97.03 173	87.94 125	93.97 145	86.25 152	96.07 140	96.27 167	95.97 152	99.72 61	99.79 40

v18		92.63 180	93.67 185	91.43 170	92.13 166	95.65 209	95.09 155	85.44 183	97.06 171	80.78 167	90.06 168	83.06 180	95.47 164	95.16 192	95.01 181	99.64 120	99.67 107

v16		92.66 179	93.80 182	91.32 174	92.13 166	95.62 211	94.89 161	85.12 185	97.20 165	80.66 168	89.96 174	83.93 172	95.49 158	95.17 188	95.04 176	99.63 126	99.68 102

v17		92.55 181	93.65 186	91.27 176	92.11 168	95.63 210	94.89 161	85.15 184	97.12 170	80.39 176	90.02 169	83.02 181	95.45 166	95.17 188	94.92 191	99.66 103	99.68 102

SixPastTwentyTwo		93.44 157	95.32 147	91.24 177	92.11 168	98.40 142	92.77 203	88.64 154	98.09 127	77.83 199	93.51 148	85.74 156	96.52 131	96.91 149	94.89 194	99.59 148	99.73 71

v8		92.87 167	93.87 181	91.72 167	92.05 170	97.50 188	94.79 172	88.20 159	96.85 182	80.11 179	90.01 170	82.86 187	95.48 159	95.15 196	94.90 192	99.66 103	99.80 33

v6		93.11 161	93.98 174	92.10 151	92.01 171	97.71 165	94.86 168	90.15 131	96.96 176	80.47 172	90.01 170	83.26 178	95.48 159	95.17 188	95.01 181	99.64 120	99.76 54

v1neww		93.06 162	93.94 176	92.03 154	91.99 172	97.70 168	94.79 172	90.14 132	96.93 178	80.13 177	89.97 172	83.01 182	95.48 159	95.16 192	95.01 181	99.63 126	99.76 54

v7new		93.06 162	93.94 176	92.03 154	91.99 172	97.70 168	94.79 172	90.14 132	96.93 178	80.13 177	89.97 172	83.01 182	95.48 159	95.16 192	95.01 181	99.63 126	99.76 54

WR-MVS_H		93.54 155	94.67 156	92.22 147	91.95 174	97.91 159	94.58 188	88.75 151	96.64 190	83.88 145	90.66 165	85.13 163	94.40 191	96.54 157	95.91 154	99.73 56	99.89 7

V42		93.05 164	93.90 180	92.04 153	91.91 175	97.66 174	94.91 160	89.91 137	96.85 182	80.58 170	89.66 177	83.43 177	95.37 171	95.03 202	94.90 192	99.59 148	99.78 42

EU-MVSNet		92.80 171	94.76 155	90.51 194	91.88 176	96.74 205	92.48 205	88.69 152	96.21 201	79.00 195	91.51 158	87.82 134	91.83 211	95.87 175	96.27 142	99.21 189	98.92 183

N_pmnet		92.21 195	94.60 157	89.42 204	91.88 176	97.38 195	89.15 219	89.74 142	97.89 140	73.75 215	87.94 202	92.23 121	93.85 201	96.10 171	93.20 210	98.15 207	97.43 208

UniMVSNet_NR-MVSNet		94.59 137	95.47 145	93.55 129	91.85 178	97.89 160	95.03 156	92.00 97	97.33 162	86.12 134	93.19 152	87.29 136	96.60 128	96.12 170	96.70 130	99.72 61	99.80 33

v15		92.27 193	93.33 197	91.04 181	91.83 179	95.60 212	94.79 172	84.88 189	96.66 188	79.66 186	88.72 188	82.45 194	95.40 169	95.19 187	95.00 185	99.65 109	99.67 107

v7		92.97 166	94.11 169	91.65 168	91.83 179	97.55 185	94.86 168	88.19 160	96.96 176	79.72 185	88.16 197	84.68 167	95.63 150	96.33 164	95.30 168	99.65 109	99.77 50

pmmvs6		91.90 202	92.53 212	91.17 178	91.81 181	97.63 176	93.23 199	88.37 157	93.43 223	80.61 169	77.32 227	87.47 135	94.12 195	96.58 154	95.72 158	98.88 197	99.53 141

V14		92.31 192	93.41 195	91.03 182	91.80 182	95.59 214	94.79 172	84.70 190	96.58 193	79.83 181	88.79 186	82.98 184	95.41 168	95.22 182	95.02 180	99.65 109	99.67 107

v1		92.81 169	93.57 190	91.94 158	91.79 183	97.70 168	94.80 171	90.32 126	96.52 196	79.75 183	88.47 193	82.46 193	95.32 175	95.14 198	94.96 188	99.63 126	99.73 71

v10		92.79 173	94.06 171	91.31 175	91.78 184	97.29 199	94.87 165	86.10 179	96.97 175	79.82 182	88.16 197	84.56 168	95.63 150	96.33 164	95.31 167	99.65 109	99.80 33

V9		92.24 194	93.32 199	90.98 184	91.76 185	95.58 216	94.83 170	84.50 194	96.68 187	79.73 184	88.66 189	82.39 195	95.39 170	95.22 182	95.03 178	99.65 109	99.67 107

v1141		92.79 173	93.61 187	91.84 164	91.75 186	97.71 165	94.74 178	90.33 125	96.58 193	79.21 193	88.59 190	82.53 192	95.36 172	95.16 192	94.96 188	99.63 126	99.72 83

divwei89l23v2f112		92.80 171	93.60 189	91.86 163	91.75 186	97.71 165	94.75 177	90.32 126	96.54 195	79.35 190	88.59 190	82.55 191	95.35 173	95.15 196	94.96 188	99.63 126	99.72 83

v13		92.16 197	93.28 201	90.85 189	91.75 186	95.58 216	94.65 185	84.23 199	96.49 199	79.51 189	88.40 195	82.58 190	95.31 177	95.21 185	95.03 178	99.66 103	99.68 102

MIMVSNet		94.49 140	97.59 91	90.87 188	91.74 189	98.70 123	94.68 182	78.73 222	97.98 131	83.71 149	97.71 77	94.81 96	96.96 116	97.97 110	97.92 89	99.40 181	98.04 200

v11		92.43 185	93.77 183	90.85 189	91.72 190	95.58 216	94.87 165	84.07 201	96.98 174	79.28 191	88.03 200	84.22 171	95.53 157	96.55 156	95.36 165	99.65 109	99.70 89

v12		92.18 196	93.29 200	90.88 187	91.70 191	95.59 214	94.61 186	84.36 196	96.65 189	79.59 187	88.85 184	82.03 199	95.35 173	95.22 182	95.04 176	99.65 109	99.68 102

v1144		92.81 169	94.03 172	91.40 172	91.68 192	97.60 180	94.73 179	88.40 156	96.71 185	78.48 197	88.14 199	84.46 169	95.45 166	96.31 166	95.22 170	99.65 109	99.76 54

DU-MVS		93.98 147	94.44 162	93.44 132	91.66 193	97.77 162	95.03 156	91.57 105	97.17 167	86.12 134	93.13 154	81.13 205	96.60 128	95.10 199	97.01 125	99.67 98	99.80 33

Baseline_NR-MVSNet		93.87 150	93.98 174	93.75 122	91.66 193	97.02 200	95.53 148	91.52 108	97.16 169	87.77 127	87.93 203	83.69 173	96.35 133	95.10 199	97.23 120	99.68 91	99.73 71

CP-MVSNet		93.25 159	94.00 173	92.38 146	91.65 195	97.56 183	94.38 191	89.20 146	96.05 206	83.16 153	89.51 178	81.97 200	96.16 139	96.43 159	96.56 135	99.71 71	99.89 7

v148		92.36 190	92.88 206	91.75 165	91.63 196	97.66 174	92.64 204	90.55 124	96.09 204	83.34 151	88.19 196	80.00 211	92.74 207	93.98 209	94.58 200	99.58 152	99.69 95

PS-CasMVS		92.72 176	93.36 196	91.98 157	91.62 197	97.52 186	94.13 195	88.98 148	95.94 209	81.51 164	87.35 205	79.95 212	95.91 144	96.37 161	96.49 137	99.70 80	99.89 7

v2v482		92.77 175	93.52 194	91.90 161	91.59 198	97.63 176	94.57 189	90.31 128	96.80 184	79.22 192	88.74 187	81.55 204	96.04 142	95.26 181	94.97 187	99.66 103	99.69 95

v1192		92.43 185	93.61 187	91.05 180	91.53 199	97.43 192	94.61 186	87.99 162	96.60 191	76.72 205	87.11 207	82.74 188	95.85 145	96.35 163	95.30 168	99.60 142	99.74 66

WR-MVS		93.43 158	94.48 161	92.21 148	91.52 200	97.69 172	94.66 184	89.98 136	96.86 181	83.43 150	90.12 167	85.03 164	93.94 199	96.02 173	95.82 155	99.71 71	99.82 26

v144192		92.38 188	93.55 193	91.00 183	91.44 201	97.47 191	94.27 192	87.41 167	96.52 196	78.03 198	87.50 204	82.65 189	95.32 175	95.82 176	95.15 172	99.55 160	99.78 42

pmmvs5		92.71 178	94.27 165	90.90 186	91.42 202	97.74 164	93.23 199	86.66 175	95.99 208	78.96 196	91.45 159	83.44 176	95.55 154	97.30 138	95.05 175	99.58 152	98.93 180

v1921920		92.36 190	93.57 190	90.94 185	91.39 203	97.39 194	94.70 181	87.63 166	96.60 191	76.63 206	86.98 208	82.89 186	95.75 146	96.26 168	95.14 173	99.55 160	99.73 71

gm-plane-assit		89.44 213	92.82 210	85.49 217	91.37 204	95.34 220	79.55 231	82.12 204	91.68 226	64.79 229	87.98 201	80.26 210	95.66 149	98.51 73	97.56 108	99.45 173	98.41 193

v1240		91.99 199	93.33 197	90.44 195	91.29 205	97.30 198	94.25 193	86.79 172	96.43 200	75.49 211	86.34 213	81.85 202	95.29 178	96.42 160	95.22 170	99.52 166	99.73 71

PEN-MVS		92.72 176	93.20 202	92.15 150	91.29 205	97.31 197	94.67 183	89.81 139	96.19 202	81.83 162	88.58 192	79.06 217	95.61 153	95.21 185	96.27 142	99.72 61	99.82 26

TranMVSNet+NR-MVSNet		93.67 154	94.14 166	93.13 138	91.28 207	97.58 181	95.60 147	91.97 98	97.06 171	84.05 143	90.64 166	82.22 196	96.17 138	94.94 203	96.78 128	99.69 82	99.78 42

anonymousdsp		93.12 160	95.86 138	89.93 202	91.09 208	98.25 148	95.12 154	85.08 186	97.44 154	73.30 216	90.89 162	90.78 127	95.25 180	97.91 113	95.96 153	99.71 71	99.82 26

MDTV_nov1_ep13_2view		92.44 184	95.66 141	88.68 207	91.05 209	97.92 158	92.17 206	79.64 213	98.83 86	76.20 207	91.45 159	93.51 112	95.04 183	95.68 177	93.70 207	97.96 209	98.53 190

DTE-MVSNet		92.42 187	92.85 208	91.91 160	90.87 210	96.97 201	94.53 190	89.81 139	95.86 211	81.59 163	88.83 185	77.88 220	95.01 184	94.34 208	96.35 140	99.64 120	99.73 71

V4		91.92 201	93.10 203	90.55 193	90.64 211	97.51 187	93.93 197	87.02 169	95.81 213	77.61 202	86.93 209	82.19 197	94.50 189	94.72 204	94.68 199	99.62 132	99.85 18

v52		91.94 200	93.10 203	90.57 192	90.62 212	97.50 188	93.98 196	87.02 169	95.86 211	77.67 201	86.93 209	82.16 198	94.53 188	94.71 205	94.70 198	99.61 135	99.85 18

v748		91.12 205	91.95 213	90.16 198	90.60 213	97.35 196	91.11 208	87.92 163	94.75 217	80.54 171	86.26 214	75.97 222	91.13 213	94.63 206	94.81 195	99.65 109	99.90 3

v7n		91.61 204	92.95 205	90.04 199	90.56 214	97.69 172	93.74 198	85.59 181	95.89 210	76.95 204	86.60 212	78.60 219	93.76 202	97.01 147	94.99 186	99.65 109	99.87 13

test20.03		90.65 209	93.71 184	87.09 212	90.44 215	96.24 206	89.74 218	85.46 182	95.59 215	72.99 218	90.68 164	85.33 160	84.41 223	95.94 174	95.10 174	99.52 166	97.06 213

FPMVS		83.82 221	84.61 225	82.90 222	90.39 216	90.71 229	90.85 212	84.10 200	95.47 216	65.15 227	83.44 218	74.46 224	75.48 227	81.63 231	79.42 233	91.42 234	87.14 232

Anonymous20231206		90.70 208	93.93 178	86.92 214	90.21 217	96.79 203	90.30 215	86.61 176	96.05 206	69.25 222	88.46 194	84.86 166	85.86 220	97.11 145	96.47 138	99.30 186	97.80 204

new_pmnet		90.45 210	92.84 209	87.66 210	88.96 218	96.16 207	88.71 220	84.66 191	97.56 152	71.91 221	85.60 216	86.58 149	93.28 204	96.07 172	93.54 208	98.46 201	94.39 224

testus		88.77 215	92.77 211	84.10 220	88.24 219	93.95 224	87.16 223	84.24 197	97.37 155	61.54 233	95.70 129	73.10 225	84.90 222	95.56 178	95.82 155	98.51 199	97.88 203

test2356		88.81 214	92.86 207	84.09 221	87.85 220	93.46 226	87.07 224	83.60 203	96.50 198	62.08 232	97.06 89	75.04 223	85.17 221	95.08 201	95.42 163	98.75 198	97.46 206

PM-MVS		89.55 212	90.30 217	88.67 208	87.06 221	95.60 212	90.88 211	84.51 193	96.14 203	75.75 208	86.89 211	63.47 232	94.64 186	96.85 150	93.89 205	99.17 192	99.29 162

pmmvs-eth3d		89.81 211	89.65 218	90.00 200	86.94 222	95.38 219	91.08 209	86.39 177	94.57 219	82.27 160	83.03 221	64.94 229	93.96 198	96.57 155	93.82 206	99.35 183	99.24 166

new-patchmatchnet		86.12 219	87.30 220	84.74 218	86.92 223	95.19 223	83.57 228	84.42 195	92.67 224	65.66 226	80.32 224	64.72 230	89.41 215	92.33 221	89.21 227	98.43 202	96.69 218

pmmvs3		88.19 217	91.27 214	84.60 219	85.60 224	93.66 225	85.68 226	81.13 205	92.36 225	63.66 231	89.51 178	77.10 221	93.22 205	96.37 161	92.40 218	98.30 205	97.46 206

Anonymous20231211		85.20 220	83.39 226	87.31 211	85.29 225	93.44 227	86.29 225	84.24 197	85.55 233	86.07 136	61.25 233	59.57 235	84.33 224	92.50 218	92.52 216	97.65 215	98.89 184

testmv		81.83 223	86.26 221	76.66 226	84.10 226	89.42 232	74.29 235	79.65 212	90.61 227	51.85 238	82.11 222	63.06 234	72.61 230	91.94 223	92.75 212	97.49 218	93.94 226

test1235678		81.83 223	86.26 221	76.66 226	84.10 226	89.41 233	74.29 235	79.64 213	90.60 228	51.84 239	82.11 222	63.07 233	72.61 230	91.94 223	92.75 212	97.49 218	93.94 226

Gipuma		81.40 225	81.78 227	80.96 224	83.21 228	85.61 237	79.73 230	76.25 230	97.33 162	64.21 230	55.32 234	55.55 237	86.04 219	92.43 220	92.20 221	96.32 229	93.99 225
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		80.53 226	84.80 224	75.54 228	82.31 229	88.05 236	75.99 232	79.31 217	88.53 229	53.24 237	83.30 219	56.38 236	65.16 236	90.87 227	93.10 211	97.25 224	93.34 229

1111		82.87 222	85.67 223	79.62 225	81.86 230	89.62 230	74.44 233	68.81 235	87.44 231	66.59 224	76.83 228	70.33 227	87.71 217	92.65 215	93.37 209	98.28 206	89.42 230

.test1245		69.67 229	72.22 231	66.70 233	81.86 230	89.62 230	74.44 233	68.81 235	87.44 231	66.59 224	76.83 228	70.33 227	87.71 217	92.65 215	37.65 236	20.79 240	51.04 237

MDA-MVSNet-bldmvs		87.84 218	89.22 219	86.23 215	81.74 232	96.77 204	83.74 227	89.57 143	94.50 220	72.83 219	96.64 101	64.47 231	92.71 208	81.43 232	92.28 220	96.81 227	98.47 192

MIMVSNet1		88.61 216	90.68 216	86.19 216	81.56 233	95.30 221	87.78 221	85.98 180	94.19 221	72.30 220	78.84 226	78.90 218	90.06 214	96.59 153	95.47 161	99.46 172	95.49 222

PMVS	72.60 17	76.39 228	77.66 230	74.92 229	81.04 234	69.37 242	68.47 238	80.54 209	85.39 234	65.07 228	73.52 230	72.91 226	65.67 235	80.35 233	76.81 234	88.71 236	85.25 236
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 228		80.04 235	90.84 228	90.91 210		96.09 204	74.18 214	62.81 232	30.59 243	82.44 226	96.25 169	91.77 223	95.91 231	98.56 189

PMMVS2		77.26 227	79.47 229	74.70 230	76.00 236	88.37 235	74.22 237	76.34 228	78.31 235	54.13 235	69.96 231	52.50 238	70.14 233	84.83 230	88.71 228	97.35 220	93.58 228

EMVS		68.12 232	68.11 233	68.14 232	75.51 237	71.76 240	55.38 241	77.20 227	77.78 236	37.79 242	53.59 235	43.61 239	74.72 228	67.05 237	76.70 235	88.27 238	86.24 234

E-PMN		68.30 231	68.43 232	68.15 231	74.70 238	71.56 241	55.64 240	77.24 226	77.48 237	39.46 241	51.95 237	41.68 241	73.28 229	70.65 235	79.51 232	88.61 237	86.20 235

no-one		66.79 233	67.62 234	65.81 234	73.06 239	81.79 238	51.90 243	76.20 231	61.07 239	54.05 236	51.62 238	41.72 240	49.18 237	67.26 236	82.83 231	90.47 235	87.07 233

MVE	67.97 19	65.53 234	67.43 235	63.31 235	59.33 240	74.20 239	53.09 242	70.43 234	66.27 238	43.13 240	45.98 239	30.62 242	70.65 232	79.34 234	86.30 229	83.25 239	89.33 231
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 235	40.15 236	20.86 237	12.61 241	17.99 243	25.16 244	13.30 238	48.42 240	24.82 243	53.07 236	30.13 244	28.47 238	42.73 238	37.65 236	20.79 240	51.04 237

test123		26.75 236	34.25 237	18.01 238	7.93 242	17.18 244	24.85 245	12.36 239	44.83 241	16.52 244	41.80 240	18.10 245	28.29 239	33.08 239	34.79 238	18.10 242	49.95 239

GG-mvs-BLEND		69.11 230	98.13 74	35.26 236	3.49 243	98.20 151	94.89 161	2.38 240	98.42 115	5.82 245	96.37 110	98.60 56	5.97 240	98.75 54	97.98 87	99.01 194	98.61 188

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

MTAPA										98.09 10		99.97 4

MTMP										98.46 7		99.96 10

Patchmatch-RL test							66.86 239

NP-MVS									98.57 107

Patchmtry						98.59 129	97.15 117	79.14 218		80.42 173

DeepMVS_CX						96.85 202	87.43 222	89.27 145	98.30 118	75.55 210	95.05 135	79.47 214	92.62 209	89.48 228		95.18 232	95.96 221

High-resolution multi-view benchmark