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 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 67

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 56

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

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 51

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 67

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 71

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 51

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 56

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	99.66 101

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

SR-MVS					99.67 13			98.25 14				99.94 25

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 63

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 85

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 51

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.
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	86.38 160	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

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

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 87

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 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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 67

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

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 91

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	99.64 108

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	99.66 101

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	99.64 108
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 30							99.89 34

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 97

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 56

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	92.24 103	94.61 136	99.05 61	99.10 47	98.64 67	99.05 24	99.74 44	99.51 132

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
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

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

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	92.30 101	98.91 39	99.95 17	99.05 50	99.42 17	98.95 32	99.58 134	99.82 23

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

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

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 56
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 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
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

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	91.82 105	98.41 60	99.66 51	99.10 47	98.93 45	98.97 30	99.75 39	99.58 115

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

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

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

PatchMatch-RL		97.77 61	98.25 70	97.21 58	99.11 47	99.25 88	97.06 122	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

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
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	95.48 159	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
EPNet_dtu		96.30 105	98.53 62	93.70 126	98.97 50	98.24 151	97.36 106	94.23 67	98.85 90	79.18 177	99.19 19	98.47 67	94.09 180	97.89 115	98.21 80	98.39 187	98.85 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
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	91.80 106	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
DeepPCF-MVS	97.74 3	98.34 45	99.46 12	97.04 63	98.82 52	99.33 82	96.28 138	97.47 39	99.58 8	94.70 59	98.99 33	99.85 40	97.24 112	99.55 10	99.34 9	97.73 195	99.56 121

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
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

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
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 94	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
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

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	88.13 122	96.93 97	94.14 119	99.19 38	99.14 33	99.23 17	99.69 79	99.42 140

CHOSEN 280x420		97.99 56	99.24 31	96.53 80	98.34 60	99.61 34	98.36 74	89.80 138	99.27 37	95.08 53	99.81 1	98.58 65	98.64 72	99.02 40	98.92 34	98.93 181	99.48 136

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 94	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
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	95.43 165	97.74 193	97.93 187

PVSNet_BlendedMVS		97.51 70	97.71 92	97.28 55	98.06 63	99.61 34	97.31 108	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	97.31 108	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

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

CHOSEN 1792x2688		96.41 102	96.99 119	95.74 98	98.01 66	99.72 6	97.70 99	90.78 124	99.13 60	90.03 115	87.35 188	95.36 103	98.33 82	98.59 74	98.91 36	99.59 130	99.87 12

HyFIR lowres test		95.99 112	96.56 127	95.32 103	97.99 67	99.65 17	96.54 131	88.86 147	98.44 124	89.77 118	84.14 197	97.05 84	99.03 52	98.55 76	98.19 82	99.73 51	99.86 15

OPM-MVS		96.22 107	95.85 147	96.65 76	97.75 68	98.54 133	99.00 50	95.53 47	96.88 173	89.88 116	95.95 120	86.46 159	98.07 89	97.65 129	96.63 134	99.67 96	98.83 173

tmp_tt				82.25 202	97.73 69	88.71 210	80.18 209	68.65 212	99.15 52	86.98 130	99.47 9	85.31 169	68.35 210	87.51 204	83.81 206	91.64 209

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	91.32 109	98.80 43	85.61 166	98.69 70	97.64 130	96.92 127	99.37 167	99.06 164

PVSNet_Blended_VisFu		97.41 73	98.49 64	96.15 88	97.49 71	99.76 1	96.02 142	93.75 77	99.26 40	93.38 86	93.73 144	99.35 56	96.47 134	98.96 42	98.46 61	99.77 33	99.90 3

MS-PatchMatch		95.99 112	97.26 113	94.51 112	97.46 72	98.76 117	97.27 110	86.97 165	99.09 63	89.83 117	93.51 147	97.78 75	96.18 140	97.53 134	95.71 162	99.35 168	98.41 179

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

LGP-MVS_train		96.23 106	96.89 121	95.46 102	97.32 75	98.77 114	98.81 58	93.60 80	98.58 115	85.52 139	99.08 30	86.67 156	97.83 101	97.87 116	97.51 110	99.69 79	99.73 71

CMPMVS	70.31 18	90.74 189	91.06 196	90.36 182	97.32 75	97.43 186	92.97 186	87.82 161	93.50 203	75.34 193	83.27 199	84.90 172	92.19 195	92.64 198	91.21 202	96.50 205	94.46 204
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
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	88.58 120	98.33 64	87.00 151	97.67 103	97.23 143	96.56 137	99.56 141	99.62 111

ACMM	96.26 9	96.67 97	96.69 125	96.66 75	97.29 78	98.46 138	96.48 134	95.09 51	99.21 46	93.19 88	98.78 45	86.73 155	98.17 83	97.84 118	96.32 143	99.74 44	99.49 135
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 80	99.14 35	94.78 108	97.21 79	99.38 71	97.56 101	92.04 98	98.48 122	88.03 123	98.39 62	99.91 31	94.03 181	99.33 23	99.23 17	99.81 16	99.25 152

UGNet		97.66 65	99.07 41	96.01 93	97.19 80	99.65 17	97.09 120	93.39 83	99.35 28	94.40 67	98.79 44	99.59 53	94.24 178	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
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	99.65 104

CANet_DTU		96.64 98	99.08 39	93.81 122	97.10 82	99.42 66	98.85 56	90.01 132	99.31 31	79.98 173	99.78 2	99.10 60	97.42 109	98.35 85	98.05 89	99.47 155	99.53 125

IB-MVS	93.96 15	95.02 129	96.44 137	93.36 136	97.05 83	99.28 86	90.43 196	93.39 83	98.02 140	96.02 39	94.92 134	92.07 132	83.52 203	95.38 181	95.82 159	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
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	86.61 157	97.23 113	97.73 124	96.61 135	99.62 114	99.71 85
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH	95.42 14	95.27 126	95.96 143	94.45 113	96.83 85	98.78 113	94.72 169	91.67 106	98.95 79	86.82 132	96.42 112	83.67 177	97.00 116	97.48 136	96.68 132	99.69 79	99.76 56
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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	87.03 150	98.07 89	97.20 145	96.87 128	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
TDRefinement		93.04 165	93.57 181	92.41 145	96.58 87	98.77 114	97.78 96	91.96 101	98.12 137	80.84 166	89.13 175	79.87 199	87.78 199	96.44 160	94.50 186	99.54 147	98.15 183

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 56

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

ACMH+	95.51 13	95.40 122	96.00 141	94.70 109	96.33 90	98.79 111	96.79 126	91.32 114	98.77 106	87.18 129	95.60 129	85.46 167	96.97 117	97.15 146	96.59 136	99.59 130	99.65 104

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 63

thres100view900		96.72 93	96.47 134	97.00 69	96.31 92	99.52 53	98.28 78	94.01 69	97.35 160	94.52 61	95.90 121	86.93 152	99.09 49	98.07 101	97.87 96	99.81 16	99.63 110

tfpn200view9		96.75 91	96.51 131	97.03 64	96.31 92	99.67 12	98.41 69	93.99 71	97.35 160	94.52 61	95.90 121	86.93 152	99.14 44	98.26 88	97.80 100	99.82 10	99.70 87

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	86.77 154	99.18 40	98.26 88	97.82 99	99.82 10	99.66 101

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	85.91 164	99.19 38	98.26 88	97.63 105	99.82 10	99.73 71

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	86.26 161	99.18 40	98.20 92	97.79 101	99.81 16	99.70 87

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

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

IS_MVSNet		97.86 58	98.86 53	96.68 74	96.02 99	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

USDC		94.26 145	94.83 157	93.59 128	96.02 99	98.44 140	97.84 92	88.65 151	98.86 88	82.73 158	94.02 141	80.56 193	96.76 123	97.28 142	96.15 150	99.55 143	98.50 177

FC-MVSNet-train		97.04 82	97.91 88	96.03 92	96.00 101	98.41 143	96.53 133	93.42 82	99.04 73	93.02 91	98.03 72	94.32 117	97.47 108	97.93 112	97.77 102	99.75 39	99.88 10

Vis-MVSNet (Re-imp)		97.40 74	98.89 52	95.66 100	95.99 102	99.62 29	97.82 93	93.22 88	98.82 97	91.40 108	96.94 96	98.56 66	95.70 151	99.14 33	99.41 6	99.79 25	99.75 63

MVSTER		97.16 79	97.71 92	96.52 81	95.97 103	98.48 136	98.63 63	92.10 97	98.68 111	95.96 41	99.23 18	91.79 133	96.87 120	98.76 59	97.37 120	99.57 138	99.68 96

baseline		97.45 72	98.70 59	95.99 94	95.89 104	99.36 74	98.29 77	91.37 113	99.21 46	92.99 92	98.40 61	96.87 86	97.96 93	98.60 72	98.60 55	99.42 162	99.86 15

TinyColmap		94.00 149	94.35 165	93.60 127	95.89 104	98.26 149	97.49 103	88.82 148	98.56 117	83.21 152	91.28 160	80.48 195	96.68 126	97.34 140	96.26 146	99.53 149	98.24 182

EPMVS		95.05 128	96.86 123	92.94 142	95.84 106	98.96 105	96.68 127	79.87 193	99.05 71	90.15 113	97.12 93	95.99 98	97.49 107	95.17 185	94.75 183	97.59 197	96.96 197

CS-MVS		98.06 53	99.12 36	96.82 72	95.83 107	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

PMMVS		97.52 69	98.39 66	96.51 82	95.82 108	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

diffmvs		96.83 88	97.33 108	96.25 86	95.76 109	99.34 79	98.06 90	93.22 88	99.43 21	92.30 101	96.90 98	89.83 143	98.55 76	98.00 109	98.14 83	99.64 109	99.70 87

MVS_Test		97.30 76	98.54 61	95.87 95	95.74 110	99.28 86	98.19 82	91.40 112	99.18 50	91.59 107	98.17 68	96.18 94	98.63 73	98.61 70	98.55 57	99.66 101	99.78 44

EIA-MVS		97.70 64	98.78 56	96.44 84	95.72 111	99.65 17	98.14 84	93.72 78	98.30 129	92.31 100	98.63 52	97.90 73	98.97 55	98.92 47	98.30 76	99.78 28	99.80 30

casdiffmvs		96.93 86	97.43 103	96.34 85	95.70 112	99.50 55	97.75 97	93.22 88	98.98 78	92.64 95	94.97 132	91.71 134	98.93 57	98.62 69	98.52 60	99.82 10	99.72 82

tpmrst		93.86 154	95.88 145	91.50 164	95.69 113	98.62 127	95.64 148	79.41 196	98.80 100	83.76 148	95.63 128	96.13 95	97.25 111	92.92 196	92.31 196	97.27 200	96.74 198

ADS-MVSNet		94.65 137	97.04 118	91.88 160	95.68 114	98.99 102	95.89 143	79.03 200	99.15 52	85.81 137	96.96 95	98.21 72	97.10 114	94.48 192	94.24 187	97.74 193	97.21 193

EPP-MVSNet		97.75 62	98.71 58	96.63 78	95.68 114	99.56 47	97.51 102	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

ETV-MVS		98.05 54	99.25 30	96.65 76	95.61 116	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

DI_MVS_plusplus_trai		96.90 87	97.49 98	96.21 87	95.61 116	99.40 70	98.72 61	92.11 96	99.14 55	92.98 93	93.08 155	95.14 105	98.13 87	98.05 105	97.91 94	99.74 44	99.73 71

thisisatest0530		97.23 77	98.25 70	96.05 90	95.60 118	99.59 41	96.96 124	93.23 86	99.17 51	92.60 97	98.75 48	96.19 93	98.17 83	98.19 93	96.10 151	99.72 57	99.77 51

tttt0517		97.23 77	98.24 73	96.04 91	95.60 118	99.60 39	96.94 125	93.23 86	99.15 52	92.56 98	98.74 49	96.12 96	98.17 83	98.21 91	96.10 151	99.73 51	99.78 44

SCA		94.95 130	97.44 102	92.04 152	95.55 120	99.16 95	96.26 139	79.30 197	99.02 74	85.73 138	98.18 67	97.13 83	97.69 102	96.03 174	94.91 178	97.69 196	97.65 189

dps		94.63 138	95.31 153	93.84 121	95.53 121	98.71 122	96.54 131	80.12 192	97.81 154	97.21 28	96.98 94	92.37 129	96.34 137	92.46 199	91.77 199	97.26 201	97.08 195

PatchmatchNet		94.70 135	97.08 116	91.92 157	95.53 121	98.85 109	95.77 145	79.54 195	98.95 79	85.98 135	98.52 55	96.45 87	97.39 110	95.32 182	94.09 188	97.32 199	97.38 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 120	97.32 109	93.37 135	95.49 123	98.74 119	96.44 136	90.82 122	98.18 134	82.75 156	96.60 107	94.67 112	95.54 157	98.09 98	96.00 153	99.20 175	98.93 167

test0.0.03 1		96.69 95	98.12 79	95.01 106	95.49 123	98.99 102	95.86 144	90.82 122	98.38 126	92.54 99	96.66 104	97.33 79	95.75 149	97.75 123	98.34 72	99.60 124	99.40 144

CostFormer		94.25 146	94.88 156	93.51 132	95.43 125	98.34 148	96.21 140	80.64 190	97.94 146	94.01 72	98.30 65	86.20 163	97.52 105	92.71 197	92.69 194	97.23 202	98.02 186

MDTV_nov1_ep13		95.57 118	97.48 99	93.35 137	95.43 125	98.97 104	97.19 115	83.72 186	98.92 85	87.91 125	97.75 79	96.12 96	97.88 98	96.84 154	95.64 163	97.96 191	98.10 184

tpm cat1		94.06 147	94.90 155	93.06 140	95.42 127	98.52 135	96.64 129	80.67 189	97.82 152	92.63 96	93.39 149	95.00 107	96.06 144	91.36 202	91.58 201	96.98 203	96.66 200

Vis-MVSNet		96.16 109	98.22 74	93.75 123	95.33 128	99.70 11	97.27 110	90.85 121	98.30 129	85.51 140	95.72 127	96.45 87	93.69 187	98.70 64	99.00 28	99.84 5	99.69 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 125	97.09 115	93.27 138	95.23 129	98.39 145	95.49 151	92.58 95	97.71 156	83.00 155	94.44 139	93.28 126	93.92 184	97.79 119	98.54 59	99.41 163	99.45 138

IterMVS-LS		96.12 110	97.48 99	94.53 111	95.19 130	97.56 180	97.15 116	89.19 145	99.08 65	88.23 121	94.97 132	94.73 111	97.84 100	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.
Effi-MVS+		95.81 114	97.31 112	94.06 118	95.09 131	99.35 77	97.24 112	88.22 156	98.54 118	85.38 141	98.52 55	88.68 144	98.70 68	98.32 86	97.93 92	99.74 44	99.84 18

testgi		95.67 117	97.48 99	93.56 129	95.07 132	99.00 100	95.33 155	88.47 153	98.80 100	86.90 131	97.30 87	92.33 130	95.97 146	97.66 127	97.91 94	99.60 124	99.38 145

RPMNet		94.66 136	97.16 114	91.75 161	94.98 133	98.59 130	97.00 123	78.37 204	97.98 142	83.78 146	96.27 114	94.09 122	96.91 119	97.36 139	96.73 130	99.48 153	99.09 162

CR-MVSNet		94.57 142	97.34 107	91.33 168	94.90 134	98.59 130	97.15 116	79.14 198	97.98 142	80.42 169	96.59 109	93.50 125	96.85 121	98.10 96	97.49 112	99.50 152	99.15 157

gg-mvs-nofinetune		90.85 188	94.14 167	87.02 194	94.89 135	99.25 88	98.64 62	76.29 208	88.24 208	57.50 211	79.93 203	95.45 102	95.18 168	98.77 58	98.07 88	99.62 114	99.24 153

IterMVS-SCA-FT		94.89 132	97.87 89	91.42 165	94.86 136	97.70 167	97.24 112	84.88 180	98.93 83	75.74 189	94.26 140	98.25 70	96.69 125	98.52 78	97.68 104	99.10 179	99.73 71

IterMVS		94.81 134	97.71 92	91.42 165	94.83 137	97.63 173	97.38 105	85.08 177	98.93 83	75.67 190	94.02 141	97.64 76	96.66 128	98.45 81	97.60 107	98.90 182	99.72 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 151	97.36 106	90.00 184	94.76 138	98.65 125	90.11 199	78.57 203	97.96 145	80.42 169	96.07 117	94.10 121	96.85 121	98.10 96	97.49 112	99.26 173	99.15 157

baseline2		96.36 104	97.82 90	94.65 110	94.60 139	99.09 98	96.45 135	89.63 140	98.36 127	91.29 110	97.60 84	94.13 120	96.37 135	98.45 81	97.70 103	99.54 147	99.41 141

CDS-MVSNet		96.59 101	98.02 84	94.92 107	94.45 140	98.96 105	97.46 104	91.75 103	97.86 150	90.07 114	96.02 118	97.25 82	96.21 138	98.04 106	98.38 67	99.60 124	99.65 104
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 180	94.79 158	89.56 187	94.30 141	97.50 183	94.24 181	78.97 201	97.72 155	74.93 194	97.97 74	82.91 182	96.60 130	93.65 195	94.81 182	98.33 188	98.98 165

Fast-Effi-MVS+		95.38 123	96.52 130	94.05 119	94.15 142	99.14 97	97.24 112	86.79 166	98.53 119	87.62 127	94.51 137	87.06 149	98.76 66	98.60 72	98.04 90	99.72 57	99.77 51

Effi-MVS+-dtu		95.74 116	98.04 82	93.06 140	93.92 143	99.16 95	97.90 91	88.16 158	99.07 70	82.02 161	98.02 73	94.32 117	96.74 124	98.53 77	97.56 108	99.61 116	99.62 111

UniMVSNet_ETH3D		93.15 162	92.33 194	94.11 117	93.91 144	98.61 129	94.81 166	90.98 119	97.06 169	87.51 128	82.27 201	76.33 207	97.87 99	94.79 191	97.47 115	99.56 141	99.81 28

Fast-Effi-MVS+-dtu		95.38 123	98.20 75	92.09 151	93.91 144	98.87 108	97.35 107	85.01 179	99.08 65	81.09 165	98.10 69	96.36 90	95.62 154	98.43 84	97.03 124	99.55 143	99.50 134

TAMVS		95.53 119	96.50 133	94.39 114	93.86 146	99.03 99	96.67 128	89.55 142	97.33 162	90.64 112	93.02 156	91.58 135	96.21 138	97.72 125	97.43 118	99.43 160	99.36 146

GBi-Net		96.98 84	98.00 85	95.78 96	93.81 147	97.98 156	98.09 86	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.89 95	98.07 101	98.34 72	99.68 88	99.67 97

test1		96.98 84	98.00 85	95.78 96	93.81 147	97.98 156	98.09 86	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.89 95	98.07 101	98.34 72	99.68 88	99.67 97

FMVSNet2		96.64 98	97.50 97	95.63 101	93.81 147	97.98 156	98.09 86	90.87 120	98.99 77	93.48 84	93.17 152	95.25 104	97.89 95	98.63 68	98.80 46	99.68 88	99.67 97

MVS-HIRNet		92.51 175	95.97 142	88.48 191	93.73 150	98.37 146	90.33 197	75.36 210	98.32 128	77.78 183	89.15 174	94.87 108	95.14 169	97.62 131	96.39 141	98.51 184	97.11 194

GA-MVS		93.93 152	96.31 140	91.16 172	93.61 151	98.79 111	95.39 154	90.69 127	98.25 132	73.28 198	96.15 116	88.42 145	94.39 176	97.76 122	95.35 167	99.58 134	99.45 138

FC-MVSNet-test		96.07 111	97.94 87	93.89 120	93.60 152	98.67 124	96.62 130	90.30 131	98.76 107	88.62 119	95.57 130	97.63 77	94.48 174	97.97 110	97.48 114	99.71 67	99.52 128

FMVSNet3		97.02 83	98.12 79	95.73 99	93.59 153	97.98 156	98.34 76	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.63 104	98.61 70	98.62 53	99.61 116	99.65 104

FMVSNet1		95.77 115	96.41 139	95.03 105	93.42 154	97.86 163	97.11 119	89.89 135	98.53 119	92.00 104	89.17 173	93.23 127	98.15 86	98.07 101	98.34 72	99.61 116	99.69 91

tfpnnormal		93.85 155	94.12 169	93.54 131	93.22 155	98.24 151	95.45 152	91.96 101	94.61 199	83.91 144	90.74 163	81.75 190	97.04 115	97.49 135	96.16 149	99.68 88	99.84 18

TransMVSNet (Re)		93.45 158	94.08 170	92.72 144	92.83 156	97.62 176	94.94 160	91.54 110	95.65 196	83.06 154	88.93 176	83.53 178	94.25 177	97.41 137	97.03 124	99.67 96	98.40 181

LTVRE_ROB	93.20 16	92.84 167	94.92 154	90.43 181	92.83 156	98.63 126	97.08 121	87.87 160	97.91 147	68.42 204	93.54 146	79.46 201	96.62 129	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
TESTMET0.1,1		94.95 130	97.32 109	92.20 149	92.62 158	98.74 119	96.44 136	86.67 168	98.18 134	82.75 156	96.60 107	94.67 112	95.54 157	98.09 98	96.00 153	99.20 175	98.93 167

pm-mvs1		94.27 144	95.57 149	92.75 143	92.58 159	98.13 154	94.87 164	90.71 126	96.70 179	83.78 146	89.94 169	89.85 142	94.96 172	97.58 132	97.07 123	99.61 116	99.72 82

NR-MVSNet		94.01 148	94.51 162	93.44 133	92.56 160	97.77 164	95.67 146	91.57 108	97.17 166	85.84 136	93.13 153	80.53 194	95.29 165	97.01 150	96.17 148	99.69 79	99.75 63

EG-PatchMatch MVS		92.45 176	93.92 176	90.72 178	92.56 160	98.43 142	94.88 163	84.54 182	97.18 165	79.55 175	86.12 195	83.23 181	93.15 191	97.22 144	96.00 153	99.67 96	99.27 151

test-mter		94.86 133	97.32 109	92.00 154	92.41 162	98.82 110	96.18 141	86.35 172	98.05 139	82.28 159	96.48 111	94.39 116	95.46 161	98.17 94	96.20 147	99.32 170	99.13 161

our_test_3					92.30 163	97.58 178	90.09 200

pmmvs4		95.09 127	95.90 144	94.14 116	92.29 164	97.70 167	95.45 152	90.31 129	98.60 113	90.70 111	93.25 150	89.90 141	96.67 127	97.13 147	95.42 166	99.44 159	99.28 149

FMVSNet5		95.42 121	96.47 134	94.20 115	92.26 165	95.99 201	95.66 147	87.15 164	97.87 149	93.46 85	96.68 103	93.79 123	97.52 105	97.10 149	97.21 122	99.11 178	96.62 201

UniMVSNet (Re)		94.58 141	95.34 151	93.71 125	92.25 166	98.08 155	94.97 159	91.29 118	97.03 171	87.94 124	93.97 143	86.25 162	96.07 143	96.27 168	95.97 156	99.72 57	99.79 38

SixPastTwentyTwo		93.44 159	95.32 152	91.24 170	92.11 167	98.40 144	92.77 187	88.64 152	98.09 138	77.83 182	93.51 147	85.74 165	96.52 133	96.91 152	94.89 181	99.59 130	99.73 71

v8		92.87 166	93.87 178	91.72 163	92.05 168	97.50 183	94.79 167	88.20 157	96.85 175	80.11 172	90.01 168	82.86 184	95.48 159	95.15 186	94.90 179	99.66 101	99.80 30

thisisatest0515		94.61 139	96.89 121	91.95 156	92.00 169	98.47 137	92.01 191	90.73 125	98.18 134	83.96 143	94.51 137	95.13 106	93.38 188	97.38 138	94.74 184	99.61 116	99.79 38

WR-MVS_H		93.54 157	94.67 160	92.22 147	91.95 170	97.91 161	94.58 175	88.75 149	96.64 180	83.88 145	90.66 165	85.13 170	94.40 175	96.54 159	95.91 158	99.73 51	99.89 6

V42		93.05 164	93.90 177	92.04 152	91.91 171	97.66 171	94.91 161	89.91 134	96.85 175	80.58 168	89.66 170	83.43 180	95.37 163	95.03 189	94.90 179	99.59 130	99.78 44

EU-MVSNet		92.80 169	94.76 159	90.51 179	91.88 172	96.74 198	92.48 189	88.69 150	96.21 185	79.00 178	91.51 157	87.82 146	91.83 196	95.87 178	96.27 144	99.21 174	98.92 170

N_pmnet		92.21 184	94.60 161	89.42 188	91.88 172	97.38 189	89.15 202	89.74 139	97.89 148	73.75 196	87.94 185	92.23 131	93.85 185	96.10 172	93.20 193	98.15 190	97.43 191

UniMVSNet_NR-MVSNet		94.59 140	95.47 150	93.55 130	91.85 174	97.89 162	95.03 157	92.00 99	97.33 162	86.12 133	93.19 151	87.29 148	96.60 130	96.12 171	96.70 131	99.72 57	99.80 30

pmmvs6		91.90 186	92.53 193	91.17 171	91.81 175	97.63 173	93.23 184	88.37 155	93.43 204	80.61 167	77.32 205	87.47 147	94.12 179	96.58 157	95.72 161	98.88 183	99.53 125

v10		92.79 170	94.06 171	91.31 169	91.78 176	97.29 192	94.87 164	86.10 173	96.97 172	79.82 174	88.16 182	84.56 174	95.63 153	96.33 166	95.31 168	99.65 105	99.80 30

MIMVSNet		94.49 143	97.59 96	90.87 177	91.74 177	98.70 123	94.68 171	78.73 202	97.98 142	83.71 149	97.71 82	94.81 110	96.96 118	97.97 110	97.92 93	99.40 165	98.04 185

v1144		92.81 168	94.03 172	91.40 167	91.68 178	97.60 177	94.73 168	88.40 154	96.71 178	78.48 180	88.14 183	84.46 175	95.45 162	96.31 167	95.22 170	99.65 105	99.76 56

DU-MVS		93.98 150	94.44 164	93.44 133	91.66 179	97.77 164	95.03 157	91.57 108	97.17 166	86.12 133	93.13 153	81.13 192	96.60 130	95.10 187	97.01 126	99.67 96	99.80 30

Baseline_NR-MVSNet		93.87 153	93.98 174	93.75 123	91.66 179	97.02 193	95.53 150	91.52 111	97.16 168	87.77 126	87.93 186	83.69 176	96.35 136	95.10 187	97.23 121	99.68 88	99.73 71

CP-MVSNet		93.25 161	94.00 173	92.38 146	91.65 181	97.56 180	94.38 178	89.20 144	96.05 190	83.16 153	89.51 171	81.97 188	96.16 142	96.43 161	96.56 137	99.71 67	99.89 6

v148		92.36 182	92.88 189	91.75 161	91.63 182	97.66 171	92.64 188	90.55 128	96.09 188	83.34 151	88.19 181	80.00 197	92.74 192	93.98 194	94.58 185	99.58 134	99.69 91

PS-CasMVS		92.72 172	93.36 185	91.98 155	91.62 183	97.52 182	94.13 182	88.98 146	95.94 193	81.51 164	87.35 188	79.95 198	95.91 147	96.37 163	96.49 139	99.70 76	99.89 6

v2v482		92.77 171	93.52 184	91.90 159	91.59 184	97.63 173	94.57 176	90.31 129	96.80 177	79.22 176	88.74 178	81.55 191	96.04 145	95.26 183	94.97 177	99.66 101	99.69 91

v1192		92.43 178	93.61 180	91.05 173	91.53 185	97.43 186	94.61 174	87.99 159	96.60 181	76.72 185	87.11 190	82.74 185	95.85 148	96.35 165	95.30 169	99.60 124	99.74 67

WR-MVS		93.43 160	94.48 163	92.21 148	91.52 186	97.69 169	94.66 173	89.98 133	96.86 174	83.43 150	90.12 167	85.03 171	93.94 183	96.02 175	95.82 159	99.71 67	99.82 23

v144192		92.38 180	93.55 183	91.00 174	91.44 187	97.47 185	94.27 179	87.41 163	96.52 183	78.03 181	87.50 187	82.65 186	95.32 164	95.82 179	95.15 172	99.55 143	99.78 44

pmmvs5		92.71 174	94.27 166	90.90 176	91.42 188	97.74 166	93.23 184	86.66 169	95.99 192	78.96 179	91.45 158	83.44 179	95.55 156	97.30 141	95.05 175	99.58 134	98.93 167

v1921920		92.36 182	93.57 181	90.94 175	91.39 189	97.39 188	94.70 170	87.63 162	96.60 181	76.63 186	86.98 191	82.89 183	95.75 149	96.26 169	95.14 173	99.55 143	99.73 71

gm-plane-assit		89.44 195	92.82 192	85.49 198	91.37 190	95.34 204	79.55 211	82.12 187	91.68 207	64.79 208	87.98 184	80.26 196	95.66 152	98.51 80	97.56 108	99.45 157	98.41 179

v1240		91.99 185	93.33 186	90.44 180	91.29 191	97.30 191	94.25 180	86.79 166	96.43 184	75.49 192	86.34 194	81.85 189	95.29 165	96.42 162	95.22 170	99.52 150	99.73 71

PEN-MVS		92.72 172	93.20 187	92.15 150	91.29 191	97.31 190	94.67 172	89.81 136	96.19 186	81.83 162	88.58 179	79.06 202	95.61 155	95.21 184	96.27 144	99.72 57	99.82 23

TranMVSNet+NR-MVSNet		93.67 156	94.14 167	93.13 139	91.28 193	97.58 178	95.60 149	91.97 100	97.06 169	84.05 142	90.64 166	82.22 187	96.17 141	94.94 190	96.78 129	99.69 79	99.78 44

anonymousdsp		93.12 163	95.86 146	89.93 186	91.09 194	98.25 150	95.12 156	85.08 177	97.44 159	73.30 197	90.89 162	90.78 137	95.25 167	97.91 113	95.96 157	99.71 67	99.82 23

MDTV_nov1_ep13_2view		92.44 177	95.66 148	88.68 189	91.05 195	97.92 160	92.17 190	79.64 194	98.83 95	76.20 187	91.45 158	93.51 124	95.04 170	95.68 180	93.70 191	97.96 191	98.53 176

DTE-MVSNet		92.42 179	92.85 190	91.91 158	90.87 196	96.97 194	94.53 177	89.81 136	95.86 195	81.59 163	88.83 177	77.88 205	95.01 171	94.34 193	96.35 142	99.64 109	99.73 71

v7n		91.61 187	92.95 188	90.04 183	90.56 197	97.69 169	93.74 183	85.59 175	95.89 194	76.95 184	86.60 193	78.60 204	93.76 186	97.01 150	94.99 176	99.65 105	99.87 12

test20.03		90.65 191	93.71 179	87.09 193	90.44 198	96.24 199	89.74 201	85.46 176	95.59 197	72.99 199	90.68 164	85.33 168	84.41 202	95.94 177	95.10 174	99.52 150	97.06 196

FPMVS		83.82 200	84.61 202	82.90 201	90.39 199	90.71 209	90.85 195	84.10 185	95.47 198	65.15 206	83.44 198	74.46 208	75.48 205	81.63 206	79.42 208	91.42 210	87.14 209

Anonymous20231206		90.70 190	93.93 175	86.92 195	90.21 200	96.79 196	90.30 198	86.61 170	96.05 190	69.25 203	88.46 180	84.86 173	85.86 201	97.11 148	96.47 140	99.30 171	97.80 188

new_pmnet		90.45 192	92.84 191	87.66 192	88.96 201	96.16 200	88.71 203	84.66 181	97.56 158	71.91 202	85.60 196	86.58 158	93.28 189	96.07 173	93.54 192	98.46 185	94.39 205

ET-MVSNet_ETH3D		96.17 108	96.99 119	95.21 104	88.53 202	98.54 133	98.28 78	92.61 94	98.85 90	93.60 83	99.06 32	90.39 138	98.63 73	95.98 176	96.68 132	99.61 116	99.41 141

PM-MVS		89.55 194	90.30 198	88.67 190	87.06 203	95.60 202	90.88 194	84.51 183	96.14 187	75.75 188	86.89 192	63.47 213	94.64 173	96.85 153	93.89 189	99.17 177	99.29 148

pmmvs-eth3d		89.81 193	89.65 199	90.00 184	86.94 204	95.38 203	91.08 192	86.39 171	94.57 200	82.27 160	83.03 200	64.94 210	93.96 182	96.57 158	93.82 190	99.35 168	99.24 153

new-patchmatchnet		86.12 199	87.30 201	84.74 199	86.92 205	95.19 206	83.57 208	84.42 184	92.67 205	65.66 205	80.32 202	64.72 211	89.41 198	92.33 201	89.21 203	98.43 186	96.69 199

pmmvs3		88.19 197	91.27 195	84.60 200	85.60 206	93.66 207	85.68 206	81.13 188	92.36 206	63.66 210	89.51 171	77.10 206	93.22 190	96.37 163	92.40 195	98.30 189	97.46 190

Gipuma		81.40 201	81.78 203	80.96 203	83.21 207	85.61 212	79.73 210	76.25 209	97.33 162	64.21 209	55.32 209	55.55 214	86.04 200	92.43 200	92.20 198	96.32 206	93.99 206
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 198	89.22 200	86.23 196	81.74 208	96.77 197	83.74 207	89.57 141	94.50 201	72.83 200	96.64 105	64.47 212	92.71 193	81.43 207	92.28 197	96.81 204	98.47 178

MIMVSNet1		88.61 196	90.68 197	86.19 197	81.56 209	95.30 205	87.78 204	85.98 174	94.19 202	72.30 201	78.84 204	78.90 203	90.06 197	96.59 156	95.47 164	99.46 156	95.49 203

PMVS	72.60 17	76.39 203	77.66 206	74.92 204	81.04 210	69.37 216	68.47 213	80.54 191	85.39 209	65.07 207	73.52 206	72.91 209	65.67 211	80.35 208	76.81 209	88.71 211	85.25 212
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 204		80.04 211	90.84 208	90.91 193		96.09 188	74.18 195	62.81 208	30.59 219	82.44 204	96.25 170	91.77 199	95.91 207	98.56 175

PMMVS2		77.26 202	79.47 205	74.70 205	76.00 212	88.37 211	74.22 212	76.34 207	78.31 210	54.13 212	69.96 207	52.50 215	70.14 209	84.83 205	88.71 204	97.35 198	93.58 207

EMVS		68.12 206	68.11 208	68.14 207	75.51 213	71.76 214	55.38 216	77.20 206	77.78 211	37.79 215	53.59 210	43.61 216	74.72 206	67.05 211	76.70 210	88.27 213	86.24 210

E-PMN		68.30 205	68.43 207	68.15 206	74.70 214	71.56 215	55.64 215	77.24 205	77.48 212	39.46 214	51.95 212	41.68 217	73.28 207	70.65 210	79.51 207	88.61 212	86.20 211

MVE	67.97 19	65.53 207	67.43 209	63.31 208	59.33 215	74.20 213	53.09 217	70.43 211	66.27 213	43.13 213	45.98 213	30.62 218	70.65 208	79.34 209	86.30 205	83.25 214	89.33 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 208	40.15 210	20.86 210	12.61 216	17.99 217	25.16 218	13.30 213	48.42 214	24.82 216	53.07 211	30.13 220	28.47 212	42.73 212	37.65 211	20.79 215	51.04 213

test123		26.75 209	34.25 211	18.01 211	7.93 217	17.18 218	24.85 219	12.36 214	44.83 215	16.52 217	41.80 214	18.10 221	28.29 213	33.08 213	34.79 212	18.10 216	49.95 214

GG-mvs-BLEND		69.11 204	98.13 78	35.26 209	3.49 218	98.20 153	94.89 162	2.38 215	98.42 125	5.82 218	96.37 113	98.60 64	5.97 214	98.75 61	97.98 91	99.01 180	98.61 174

uanet_test		0.00 210	0.00 212	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.00 216	0.00 219	0.00 215	0.00 222	0.00 215	0.00 214	0.00 213	0.00 217	0.00 215

sosnet-low-res		0.00 210	0.00 212	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.00 216	0.00 219	0.00 215	0.00 222	0.00 215	0.00 214	0.00 213	0.00 217	0.00 215

sosnet		0.00 210	0.00 212	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.00 216	0.00 219	0.00 215	0.00 222	0.00 215	0.00 214	0.00 213	0.00 217	0.00 215

9.14												99.79 45

test_part1																	99.62 111

MTAPA										98.09 15		99.97 7

MTMP										98.46 11		99.96 12

Patchmatch-RL test							66.86 214

NP-MVS									98.57 116

Patchmtry						98.59 130	97.15 116	79.14 198		80.42 169

DeepMVS_CX						96.85 195	87.43 205	89.27 143	98.30 129	75.55 191	95.05 131	79.47 200	92.62 194	89.48 203		95.18 208	95.96 202

High-resolution multi-view benchmark