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.

LTVRE_ROB	98.82 1	99.76 1	99.75 1	99.77 7	99.87 16	99.71 10	99.77 8	99.76 19	99.52 2	99.80 3	99.79 22	99.91 1	99.56 13	99.83 3	99.75 4	99.86 9	99.75 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
pmmvs6		99.74 2	99.75 1	99.73 11	99.92 5	99.67 15	99.76 10	99.84 11	99.59 1	99.52 24	99.87 11	99.91 1	99.43 27	99.87 1	99.81 2	99.89 6	99.52 12

SixPastTwentyTwo		99.70 3	99.59 4	99.82 2	99.93 3	99.80 1	99.86 2	99.87 6	98.87 12	99.79 5	99.85 14	99.33 95	99.74 5	99.85 2	99.82 1	99.74 24	99.63 5

v7n		99.68 4	99.61 3	99.76 8	99.89 12	99.74 7	99.87 1	99.82 13	99.20 6	99.71 6	99.96 1	99.73 23	99.76 3	99.58 20	99.59 16	99.52 47	99.46 17

anonymousdsp		99.64 5	99.55 6	99.74 10	99.87 16	99.56 26	99.82 3	99.73 23	98.54 30	99.71 6	99.92 4	99.84 7	99.61 9	99.70 9	99.63 9	99.69 33	99.64 3

UniMVSNet_ETH3D		99.61 6	99.59 4	99.63 13	99.96 1	99.70 11	99.53 37	99.86 8	99.28 5	99.48 32	99.44 78	99.86 5	99.01 71	99.78 4	99.76 3	99.90 2	99.33 24

WR-MVS		99.61 6	99.44 8	99.82 2	99.92 5	99.80 1	99.80 4	99.89 1	98.54 30	99.66 13	99.78 23	99.16 119	99.68 7	99.70 9	99.63 9	99.94 1	99.49 15

PEN-MVS		99.54 8	99.30 16	99.83 1	99.92 5	99.76 4	99.80 4	99.88 3	97.60 89	99.71 6	99.59 48	99.52 67	99.75 4	99.64 15	99.51 19	99.90 2	99.46 17

TDRefinement		99.54 8	99.50 7	99.60 17	99.70 84	99.35 48	99.77 8	99.58 51	99.40 4	99.28 50	99.66 35	99.41 82	99.55 15	99.74 8	99.65 8	99.70 30	99.25 29

DTE-MVSNet		99.52 10	99.27 17	99.82 2	99.93 3	99.77 3	99.79 6	99.87 6	97.89 67	99.70 11	99.55 62	99.21 109	99.77 2	99.65 13	99.43 23	99.90 2	99.36 21

PS-CasMVS		99.50 11	99.23 20	99.82 2	99.92 5	99.75 6	99.78 7	99.89 1	97.30 104	99.71 6	99.60 46	99.23 104	99.71 6	99.65 13	99.55 18	99.90 2	99.56 8

WR-MVS_H		99.48 12	99.23 20	99.76 8	99.91 9	99.76 4	99.75 12	99.88 3	97.27 107	99.58 17	99.56 58	99.24 103	99.56 13	99.60 18	99.60 15	99.88 8	99.58 7

pm-mvs1		99.47 13	99.38 9	99.57 21	99.82 29	99.49 30	99.63 24	99.65 39	98.88 11	99.31 44	99.85 14	99.02 140	99.23 47	99.60 18	99.58 17	99.80 15	99.22 36

MIMVSNet1		99.46 14	99.34 10	99.60 17	99.83 24	99.68 14	99.74 15	99.71 27	98.20 46	99.41 37	99.86 13	99.66 41	99.41 30	99.50 24	99.39 26	99.50 55	99.10 48

TransMVSNet (Re)		99.45 15	99.32 13	99.61 15	99.88 14	99.60 21	99.75 12	99.63 43	99.11 7	99.28 50	99.83 19	98.35 175	99.27 44	99.70 9	99.62 13	99.84 10	99.03 56

ACMH	97.81 6	99.44 16	99.33 11	99.56 22	99.81 33	99.42 37	99.73 16	99.58 51	99.02 8	99.10 78	99.41 83	99.69 32	99.60 10	99.45 28	99.26 37	99.55 43	99.05 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.39 17	99.04 31	99.80 6	99.91 9	99.70 11	99.75 12	99.88 3	96.82 133	99.68 12	99.32 91	98.86 149	99.68 7	99.57 21	99.47 20	99.89 6	99.52 12

COLMAP_ROB	98.29 2	99.37 18	99.25 18	99.51 31	99.74 71	99.12 98	99.56 34	99.39 92	98.96 10	99.17 66	99.44 78	99.63 49	99.58 11	99.48 26	99.27 36	99.60 40	98.81 82
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	97.88 4	99.33 19	99.15 24	99.53 30	99.73 77	99.05 109	99.49 43	99.40 90	98.42 34	99.55 21	99.71 27	99.89 3	99.49 19	99.14 44	98.81 72	99.54 44	99.02 58
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		99.32 20	99.33 11	99.31 58	99.87 16	99.65 18	99.63 24	99.75 21	97.76 72	97.29 234	99.87 11	99.63 49	99.52 16	99.66 12	99.63 9	99.77 20	99.12 44

UA-Net		99.30 21	99.22 22	99.39 45	99.94 2	99.66 17	98.91 141	99.86 8	97.74 78	98.74 122	99.00 123	99.60 56	99.17 56	99.50 24	99.39 26	99.70 30	99.64 3

ACMH+	97.53 7	99.29 22	99.20 23	99.40 44	99.81 33	99.22 74	99.59 31	99.50 72	98.64 26	98.29 173	99.21 105	99.69 32	99.57 12	99.53 23	99.33 31	99.66 34	98.81 82

FE-MVSNET2		99.25 23	99.00 33	99.55 26	99.77 50	99.40 39	99.76 10	99.54 59	98.10 51	99.50 28	99.71 27	99.81 12	99.39 33	98.44 110	99.00 53	99.36 77	98.50 114

Vis-MVSNet		99.25 23	99.32 13	99.17 73	99.65 102	99.55 28	99.63 24	99.33 112	98.16 48	99.29 47	99.65 39	99.77 20	97.56 186	99.44 30	99.14 42	99.58 41	99.51 14
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TranMVSNet+NR-MVSNet		99.23 25	98.91 45	99.61 15	99.81 33	99.45 34	99.47 46	99.68 30	97.28 106	99.39 38	99.54 64	99.08 134	99.45 22	99.09 50	98.84 68	99.83 11	99.04 54

CSCG		99.23 25	99.15 24	99.32 57	99.83 24	99.45 34	98.97 132	99.21 138	98.83 16	99.04 89	99.43 80	99.64 47	99.26 45	98.85 77	98.20 119	99.62 38	99.62 6

Gipuma		99.22 27	98.86 51	99.64 12	99.70 84	99.24 68	99.17 98	99.63 43	99.52 2	99.89 1	96.54 218	99.14 123	99.93 1	99.42 32	99.15 41	99.52 47	99.04 54
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tfpnnormal		99.19 28	98.90 46	99.54 27	99.81 33	99.55 28	99.60 29	99.54 59	98.53 32	99.23 54	98.40 147	98.23 178	99.40 31	99.29 37	99.36 29	99.63 37	98.95 68

Baseline_NR-MVSNet		99.18 29	98.87 48	99.54 27	99.74 71	99.56 26	99.36 62	99.62 48	96.53 153	99.29 47	99.85 14	98.64 167	99.40 31	99.03 61	99.63 9	99.83 11	98.86 77

thisisatest0515		99.16 30	98.94 40	99.41 39	99.75 65	99.43 36	99.36 62	99.63 43	97.68 84	99.35 40	99.31 92	98.90 146	99.09 65	98.95 66	99.20 38	99.27 91	99.11 45

SPE-MVS-test		99.16 30	98.78 58	99.60 17	99.80 39	99.72 9	99.69 17	99.73 23	95.88 177	99.51 27	98.53 142	99.54 63	99.21 50	99.24 40	99.43 23	99.66 34	99.15 43

CS-MVS		99.15 32	98.75 63	99.62 14	99.76 60	99.73 8	99.60 29	99.75 21	95.67 185	99.50 28	98.53 142	99.39 87	99.29 41	99.21 42	99.46 22	99.79 18	99.29 27

APDe-MVS		99.15 32	98.95 37	99.39 45	99.77 50	99.28 60	99.52 38	99.54 59	97.22 111	99.06 83	99.20 106	99.64 47	99.05 69	99.14 44	99.02 52	99.39 70	99.17 40
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
WB-MVS		99.14 34	99.31 15	98.95 112	99.81 33	99.61 20	98.85 150	99.51 69	99.01 9	97.37 228	99.33 89	99.56 61	98.70 97	99.44 30	99.29 33	99.45 60	98.96 67

FC-MVSNet-train		99.13 35	99.05 28	99.21 65	99.87 16	99.57 25	99.67 19	99.60 50	96.75 138	98.28 174	99.48 70	99.52 67	98.10 157	99.47 27	99.37 28	99.76 22	99.21 37

E6new		99.12 36	99.05 28	99.20 69	99.78 44	99.33 52	99.32 74	99.34 109	98.86 13	98.62 129	99.74 24	99.83 8	98.98 73	98.53 104	98.64 92	99.16 108	98.46 118

E6		99.12 36	99.05 28	99.20 69	99.78 44	99.33 52	99.32 74	99.34 109	98.86 13	98.62 129	99.74 24	99.83 8	98.98 73	98.53 104	98.64 92	99.16 108	98.46 118

NR-MVSNet		99.10 38	98.68 75	99.58 20	99.89 12	99.23 71	99.35 66	99.63 43	96.58 146	99.36 39	99.05 117	98.67 165	99.46 20	99.63 16	98.73 86	99.80 15	98.88 76

DVP-MVS++		99.09 39	99.25 18	98.90 120	99.53 140	99.37 46	99.17 98	99.48 77	98.28 44	97.95 200	99.54 64	99.88 4	98.13 156	99.08 51	98.94 57	99.15 111	99.65 2

DVP-MVS		99.09 39	99.07 27	99.12 80	99.55 131	99.40 39	99.36 62	99.44 89	97.75 75	98.23 177	99.23 102	99.80 16	98.97 75	99.08 51	98.96 54	99.19 100	99.25 29
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
UniMVSNet (Re)		99.08 41	98.69 73	99.54 27	99.75 65	99.33 52	99.29 79	99.64 42	96.75 138	99.48 32	99.30 94	98.69 160	99.26 45	98.94 68	98.76 81	99.78 19	99.02 58

casdiffseed414692147		99.06 42	98.93 44	99.21 65	99.79 40	99.26 62	99.49 43	99.35 104	98.20 46	98.46 157	99.68 30	99.82 10	98.84 86	98.72 94	98.36 112	99.34 79	98.45 122

Casviewmamba		99.05 43	99.04 31	99.07 88	99.77 50	99.38 43	99.29 79	99.21 138	98.63 27	97.91 203	99.46 73	99.69 32	98.73 94	98.76 90	98.77 79	99.52 47	98.57 105

ACMMPR		99.05 43	98.72 67	99.44 33	99.79 40	99.12 98	99.35 66	99.56 54	97.74 78	99.21 57	97.72 178	99.55 62	99.29 41	98.90 75	98.81 72	99.41 68	99.19 38

DU-MVS		99.04 45	98.59 80	99.56 22	99.74 71	99.23 71	99.29 79	99.63 43	96.58 146	99.55 21	99.05 117	98.68 162	99.36 36	99.03 61	98.60 95	99.77 20	98.97 63

usedtu_dtu_shiyan2		99.03 46	98.84 54	99.27 63	99.87 16	99.20 81	99.52 38	98.77 193	98.46 33	99.52 24	99.84 18	99.65 45	98.85 84	98.75 91	97.80 147	99.05 131	98.15 158

TSAR-MVS + MP.		99.02 47	98.95 37	99.11 84	99.23 194	98.79 147	99.51 40	98.73 197	97.50 95	98.56 139	99.03 120	99.59 57	99.16 58	99.29 37	99.17 40	99.50 55	99.24 33
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MED-MVS		99.01 48	98.98 36	99.04 97	99.67 93	99.17 88	99.00 125	99.35 104	98.30 43	98.49 149	99.59 48	99.49 76	98.38 138	98.81 84	98.74 84	99.38 72	98.74 89

v10		99.01 48	98.66 76	99.41 39	99.52 145	99.39 41	99.57 33	99.66 37	97.59 90	99.32 43	99.88 9	99.23 104	99.50 18	97.77 165	97.98 135	98.92 155	98.78 87

EG-PatchMatch MVS		99.01 48	98.77 62	99.28 62	99.64 107	98.90 138	98.81 160	99.27 123	96.55 150	99.71 6	99.31 92	99.66 41	99.17 56	99.28 39	99.11 44	99.10 114	98.57 105

hybridcas		98.99 51	98.99 35	98.98 109	99.77 50	99.34 49	99.30 77	99.15 152	98.66 25	97.64 214	99.45 76	99.70 30	98.61 105	98.61 99	98.79 76	99.41 68	98.40 127

viewmacassd2359aftdt		98.99 51	98.89 47	99.12 80	99.78 44	99.27 61	99.21 92	99.26 125	98.73 23	98.30 171	99.61 43	99.82 10	98.94 78	98.26 134	98.29 114	99.20 99	98.24 144

E4		98.98 53	98.87 48	99.11 84	99.78 44	99.26 62	99.20 94	99.27 123	98.81 17	98.57 137	99.68 30	99.81 12	98.69 99	98.08 144	98.23 116	99.15 111	98.24 144

PVSNet_Blended_VisFu		98.98 53	98.79 56	99.21 65	99.76 60	99.34 49	99.35 66	99.35 104	97.12 120	99.46 34	99.56 58	98.89 147	98.08 161	99.05 55	98.58 97	99.27 91	98.98 62

HFP-MVS		98.97 55	98.70 71	99.29 60	99.67 93	98.98 122	99.13 109	99.53 64	97.76 72	98.90 104	98.07 162	99.50 74	99.14 61	98.64 98	98.78 77	99.37 73	99.18 39

UniMVSNet_NR-MVSNet		98.97 55	98.46 93	99.56 22	99.76 60	99.34 49	99.29 79	99.61 49	96.55 150	99.55 21	99.05 117	97.96 186	99.36 36	98.84 78	98.50 104	99.81 14	98.97 63

casdiffmvs_mvg		98.96 57	98.87 48	99.07 88	99.82 29	99.36 47	99.36 62	99.22 135	98.13 50	97.74 208	99.42 81	99.46 80	98.59 108	98.39 115	98.95 56	99.71 29	98.39 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		98.96 57	98.45 96	99.56 22	99.88 14	99.70 11	99.68 18	99.78 16	94.15 226	98.97 93	98.26 154	99.21 109	99.35 38	99.30 36	99.14 42	99.73 25	99.40 20

SED-MVS		98.94 59	98.95 37	98.91 119	99.43 164	99.38 43	99.12 112	99.46 82	97.05 125	98.43 160	99.23 102	99.79 17	97.99 167	99.05 55	98.94 57	99.05 131	99.23 34

ACMMP_NAP		98.94 59	98.72 67	99.21 65	99.67 93	99.08 104	99.26 85	99.39 92	96.84 130	98.88 109	98.22 155	99.68 36	98.82 87	99.06 54	98.90 60	99.25 94	99.25 29

v1144		98.94 59	98.53 87	99.42 37	99.62 111	99.03 116	99.58 32	99.36 101	97.99 58	99.49 31	99.91 8	99.20 112	99.51 17	97.61 173	97.85 145	98.95 148	98.10 163

v8		98.94 59	98.60 78	99.35 54	99.54 137	99.39 41	99.55 35	99.67 34	97.48 96	99.13 74	99.81 20	99.10 130	99.39 33	97.86 156	97.89 143	98.81 166	98.66 98

SteuartSystems-ACMMP		98.94 59	98.52 89	99.43 36	99.79 40	99.13 96	99.33 72	99.55 56	96.17 170	99.04 89	97.53 184	99.65 45	99.46 20	99.04 60	98.76 81	99.44 63	99.35 22
Skip Steuart: Steuart Systems R&D Blog.
E5new		98.92 64	98.78 58	99.07 88	99.77 50	99.25 66	99.16 102	99.23 133	98.80 18	98.58 134	99.61 43	99.81 12	98.50 128	97.83 159	98.01 131	99.17 103	98.17 154

E5		98.92 64	98.78 58	99.07 88	99.77 50	99.25 66	99.16 102	99.23 133	98.80 18	98.58 134	99.61 43	99.81 12	98.50 128	97.83 159	98.01 131	99.17 103	98.17 154

viewdifsd2359ckpt11		98.92 64	98.94 40	98.90 120	99.71 82	99.16 90	99.16 102	98.82 188	98.78 21	98.12 187	99.68 30	99.78 18	98.52 123	98.80 85	98.11 124	99.05 131	98.25 142

viewmsd2359difaftdt		98.92 64	98.94 40	98.90 120	99.71 82	99.16 90	99.16 102	98.82 188	98.78 21	98.12 187	99.68 30	99.78 18	98.52 123	98.80 85	98.11 124	99.05 131	98.25 142

v1192		98.91 68	98.48 92	99.41 39	99.61 115	99.03 116	99.64 21	99.25 129	97.91 64	99.58 17	99.92 4	99.07 136	99.45 22	97.55 178	97.68 160	98.93 151	98.23 147

FMVSNet1		98.90 69	99.10 26	98.67 153	99.54 137	99.48 31	99.22 90	99.66 37	98.39 37	97.50 220	99.66 35	99.04 139	96.58 209	99.05 55	99.03 49	99.52 47	99.08 50

ACMM	96.66 11	98.90 69	98.44 98	99.44 33	99.74 71	98.95 128	99.47 46	99.55 56	97.66 87	99.09 79	96.43 220	99.41 82	99.35 38	98.95 66	98.67 89	99.45 60	99.03 56
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		98.89 71	98.79 56	98.99 107	99.82 29	99.41 38	99.18 97	99.31 118	96.92 127	98.54 141	98.58 140	98.84 152	97.46 189	99.45 28	99.29 33	99.65 36	99.08 50

v1921920		98.89 71	98.46 93	99.39 45	99.58 123	99.04 114	99.64 21	99.17 147	97.91 64	99.64 15	99.92 4	98.99 144	99.44 25	97.44 189	97.57 169	98.84 164	98.35 132

GeoE		98.88 73	98.43 103	99.41 39	99.83 24	99.24 68	99.51 40	99.82 13	96.55 150	99.22 56	98.76 131	99.22 108	98.96 76	98.55 102	98.15 121	99.10 114	98.56 109

v144192		98.88 73	98.46 93	99.37 52	99.56 130	99.03 116	99.61 27	99.26 125	97.79 70	99.58 17	99.88 9	99.11 128	99.43 27	97.38 194	97.61 165	98.80 167	98.43 125

aaEdge-Enhanced		98.87 75	98.78 58	98.98 109	99.67 93	99.13 96	99.34 70	98.89 179	97.44 98	98.49 149	99.59 48	99.54 63	98.49 130	98.48 108	98.52 103	99.01 140	98.74 89

SMA-MVS		98.87 75	98.73 66	99.04 97	99.72 80	99.05 109	98.64 175	99.17 147	96.31 165	98.80 116	99.07 113	99.70 30	98.67 100	98.93 71	98.82 69	99.23 97	99.23 34
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMP	96.54 13	98.87 75	98.40 107	99.41 39	99.74 71	98.88 140	99.29 79	99.50 72	96.85 129	98.96 96	97.05 200	99.66 41	99.43 27	98.98 65	98.60 95	99.52 47	98.81 82
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		98.86 78	98.57 85	99.19 71	99.86 21	99.67 15	99.39 56	99.71 27	97.53 94	98.69 126	95.85 231	98.48 170	97.75 180	99.57 21	99.41 25	99.72 26	99.48 16

v1240		98.86 78	98.41 105	99.38 50	99.59 120	99.05 109	99.65 20	99.14 153	97.68 84	99.66 13	99.93 3	98.72 159	99.45 22	97.38 194	97.72 158	98.79 168	98.35 132

CP-MVS		98.86 78	98.43 103	99.36 53	99.68 90	98.97 126	99.19 95	99.46 82	96.60 144	99.20 59	97.11 199	99.51 72	99.15 60	98.92 72	98.82 69	99.45 60	99.08 50

E3new		98.85 81	98.72 67	99.01 102	99.73 77	99.20 81	99.08 114	99.18 145	98.57 28	98.50 145	99.54 64	99.73 23	98.52 123	97.87 154	97.97 136	99.06 128	98.14 161

E3		98.85 81	98.72 67	99.01 102	99.73 77	99.20 81	99.08 114	99.18 145	98.57 28	98.51 144	99.54 64	99.73 23	98.52 123	97.86 156	97.97 136	99.06 128	98.14 161

v2v482		98.85 81	98.40 107	99.38 50	99.65 102	98.98 122	99.55 35	99.39 92	97.92 63	99.35 40	99.85 14	99.14 123	99.39 33	97.50 182	97.78 148	98.98 145	97.60 191

DPE-MVS		98.84 84	98.69 73	99.00 104	99.05 213	99.26 62	99.19 95	99.35 104	95.85 179	98.74 122	99.27 96	99.66 41	98.30 147	98.90 75	98.93 59	99.37 73	99.00 60
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		98.84 84	98.59 80	99.12 80	99.52 145	98.50 179	99.13 109	99.22 135	97.76 72	98.76 118	98.70 133	99.61 52	98.90 80	98.67 96	98.37 110	99.19 100	98.57 105
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test20.03		98.84 84	98.74 65	98.95 112	99.77 50	99.33 52	99.21 92	99.46 82	97.29 105	98.88 109	99.65 39	99.10 130	97.07 201	99.11 47	98.76 81	99.32 84	97.98 172

casdiffmvs		98.84 84	98.75 63	98.94 116	99.75 65	99.21 75	99.33 72	99.04 166	98.04 54	97.46 223	99.72 26	99.72 27	98.60 106	98.30 127	98.37 110	99.48 57	97.92 176
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LGP-MVS_train		98.84 84	98.33 114	99.44 33	99.78 44	98.98 122	99.39 56	99.55 56	95.41 190	98.90 104	97.51 185	99.68 36	99.44 25	99.03 61	98.81 72	99.57 42	98.91 72

RPSCF		98.84 84	98.81 55	98.89 125	99.37 173	98.95 128	98.51 187	98.85 185	97.73 80	98.33 168	98.97 125	99.14 123	98.95 77	99.18 43	98.68 88	99.31 85	98.99 61

ACMMP		98.82 90	98.33 114	99.39 45	99.77 50	99.14 94	99.37 59	99.54 59	96.47 157	99.03 91	96.26 224	99.52 67	99.28 43	98.92 72	98.80 75	99.37 73	99.16 41
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
FE-MVSNET		98.81 91	98.41 105	99.27 63	99.55 131	99.09 101	99.61 27	99.46 82	97.15 117	98.70 125	99.18 108	99.17 116	99.23 47	97.94 149	98.48 105	99.10 114	97.88 178

V42		98.81 91	98.49 91	99.18 72	99.52 145	98.92 133	99.50 42	99.29 120	97.43 100	98.97 93	99.81 20	99.00 143	99.30 40	97.93 150	98.01 131	98.51 197	98.34 136

viewdifsd2359ckpt07		98.79 93	98.85 53	98.72 145	99.74 71	99.14 94	98.97 132	98.91 177	98.84 15	98.32 170	99.48 70	99.73 23	98.40 135	98.29 128	98.12 122	97.96 217	98.31 138

LS3D		98.79 93	98.52 89	99.12 80	99.64 107	99.09 101	99.24 88	99.46 82	97.75 75	98.93 102	97.47 187	98.23 178	97.98 169	99.36 33	99.30 32	99.46 58	98.42 126

MP-MVS		98.78 95	98.30 116	99.34 56	99.75 65	98.95 128	99.26 85	99.46 82	95.78 183	99.17 66	96.98 204	99.72 27	99.06 68	98.84 78	98.74 84	99.33 81	99.11 45
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
viewmanbaseed2359cas		98.77 96	98.64 77	98.93 117	99.70 84	99.16 90	98.95 136	99.09 162	98.35 40	98.14 184	99.33 89	99.69 32	98.63 103	97.91 152	97.90 140	99.08 121	98.15 158

v148		98.77 96	98.45 96	99.15 76	99.68 90	98.94 132	99.49 43	99.31 118	97.95 60	98.91 103	99.65 39	99.62 51	99.18 53	97.99 147	97.64 164	98.33 202	97.38 197

test1111		98.75 98	98.14 134	99.46 32	99.86 21	99.63 19	99.47 46	99.68 30	98.34 41	98.76 118	99.66 35	90.92 238	99.23 47	99.77 5	99.71 5	99.75 23	98.95 68

viewcassd2359sk11		98.74 99	98.58 82	98.93 117	99.69 87	99.16 90	98.98 129	99.10 160	98.36 38	98.45 158	99.39 85	99.61 52	98.38 138	97.68 170	97.77 153	98.99 143	98.08 165

ECVR-MVS		98.74 99	98.15 131	99.42 37	99.83 24	99.58 23	99.37 59	99.67 34	98.02 56	98.85 113	99.59 48	91.66 236	99.10 63	99.77 5	99.70 6	99.72 26	98.73 91

SD-MVS		98.73 101	98.54 86	98.95 112	99.14 203	98.76 151	98.46 191	99.14 153	97.71 82	98.56 139	98.06 164	99.61 52	98.85 84	98.56 101	97.74 155	99.54 44	99.32 25
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
MSP-MVS		98.72 102	98.60 78	98.87 127	99.67 93	99.33 52	99.15 106	99.26 125	96.99 126	97.90 204	98.19 157	99.74 22	98.29 148	97.69 169	98.96 54	98.96 146	99.27 28
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		98.69 103	98.09 139	99.39 45	99.76 60	99.07 105	99.30 77	99.51 69	94.76 204	99.18 63	96.70 213	99.51 72	99.20 51	98.79 87	98.71 87	99.39 70	99.11 45

pmmvs-eth3d		98.68 104	98.14 134	99.29 60	99.49 150	98.45 182	99.45 51	99.38 97	97.21 112	99.50 28	99.65 39	99.21 109	99.16 58	97.11 202	97.56 170	98.79 168	97.82 181

EU-MVSNet		98.68 104	98.94 40	98.37 181	99.14 203	98.74 155	99.64 21	98.20 227	98.21 45	99.17 66	99.66 35	99.18 115	99.08 66	99.11 47	98.86 63	95.00 249	98.83 79

PMVS	92.51 17	98.66 106	98.86 51	98.43 176	99.26 189	98.98 122	98.60 181	98.59 210	97.73 80	99.45 35	99.38 86	98.54 169	95.24 229	99.62 17	99.61 14	99.42 65	98.17 154
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS_fast	97.38 8	98.65 107	98.34 113	99.02 101	99.33 177	98.29 189	98.99 126	98.71 200	97.40 101	99.31 44	98.20 156	99.40 85	98.54 121	98.33 124	98.18 120	99.23 97	98.58 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	98.16 3	98.65 107	98.35 112	99.00 104	99.59 120	98.70 159	98.90 145	99.36 101	97.97 59	99.09 79	96.55 217	99.09 132	97.97 170	98.70 95	98.65 91	99.12 113	98.81 82

TSAR-MVS + ACMM		98.64 109	98.58 82	98.72 145	99.17 201	98.63 168	98.69 170	99.10 160	97.69 83	98.30 171	99.12 111	99.38 89	98.70 97	98.45 109	97.51 173	98.35 201	99.25 29

E2		98.63 110	98.44 98	98.86 130	99.65 102	99.12 98	98.88 147	99.03 167	98.10 51	98.40 161	99.27 96	99.48 78	98.24 151	97.51 181	97.56 170	98.93 151	98.05 166

DELS-MVS		98.63 110	98.70 71	98.55 171	99.24 193	99.04 114	98.96 134	98.52 214	96.83 132	98.38 163	99.58 53	99.68 36	97.06 202	98.74 93	98.44 107	99.10 114	98.59 102
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
QAPM		98.62 112	98.40 107	98.89 125	99.57 129	98.80 145	98.63 176	99.35 104	96.82 133	98.60 132	98.85 130	99.08 134	98.09 159	98.31 125	98.21 117	99.08 121	98.72 92

EPP-MVSNet		98.61 113	98.19 128	99.11 84	99.86 21	99.60 21	99.44 52	99.53 64	97.37 102	96.85 249	98.69 134	93.75 229	99.18 53	99.22 41	99.35 30	99.82 13	99.32 25

3Dnovator+	97.85 5	98.61 113	98.14 134	99.15 76	99.62 111	98.37 186	99.10 113	99.51 69	98.04 54	98.98 92	96.07 228	98.75 158	98.55 118	98.51 106	98.40 108	99.17 103	98.82 80

viewdifsd2359ckpt13		98.60 115	98.39 110	98.85 134	99.67 93	99.05 109	98.77 165	99.05 165	97.89 67	98.19 179	99.25 99	99.54 63	98.37 140	97.55 178	97.45 176	99.04 136	97.99 169

X-MVS		98.59 116	97.99 146	99.30 59	99.75 65	99.07 105	99.17 98	99.50 72	96.62 142	98.95 98	93.95 247	99.37 90	99.11 62	98.94 68	98.86 63	99.35 78	99.09 49

MVSMamba_PlusPlus		98.58 117	98.58 82	98.57 167	99.48 152	99.17 88	98.03 221	98.59 210	96.47 157	97.74 208	98.40 147	99.07 136	98.40 135	98.84 78	98.77 79	99.17 103	99.35 22

MVS_111021_HR		98.58 117	98.26 119	98.96 111	99.32 180	98.81 143	98.48 189	98.99 172	96.81 135	99.16 69	98.07 162	99.23 104	98.89 82	98.43 112	98.27 115	98.90 157	98.24 144

MGCNet		98.57 119	98.44 98	98.71 148	99.76 60	99.31 58	99.43 53	99.24 131	97.79 70	98.35 166	98.48 144	96.64 206	96.30 218	98.91 74	98.82 69	99.18 102	99.16 41

PM-MVS		98.57 119	98.24 124	98.95 112	99.26 189	98.59 171	99.03 121	98.74 196	96.84 130	99.44 36	99.13 110	98.31 177	98.75 92	98.03 145	98.21 117	98.48 198	98.58 103

PHI-MVS		98.57 119	98.20 127	99.00 104	99.48 152	98.91 135	98.68 171	99.17 147	94.97 199	99.27 52	98.33 150	99.33 95	98.05 164	98.82 82	98.62 94	99.34 79	98.38 130

diffmvs_AUTHOR		98.56 122	98.53 87	98.60 160	99.69 87	98.90 138	99.01 124	98.86 184	98.36 38	97.21 236	99.70 29	99.67 40	98.08 161	97.61 173	97.45 176	98.77 170	98.00 168

HPM-MVS++		98.56 122	98.08 140	99.11 84	99.53 140	98.61 170	99.02 123	99.32 116	96.29 167	99.06 83	97.23 194	99.50 74	98.77 90	98.15 140	97.90 140	98.96 146	98.90 73

TSAR-MVS + GP.		98.54 124	98.29 118	98.82 138	99.28 187	98.59 171	97.73 234	99.24 131	95.93 176	98.59 133	99.07 113	99.17 116	98.86 83	98.44 110	98.10 126	99.26 93	98.72 92

viewdifsd2359ckpt09		98.53 125	98.25 121	98.86 130	99.68 90	99.09 101	98.73 168	99.12 157	97.85 69	98.38 163	99.07 113	99.28 101	98.25 150	97.06 204	97.39 179	98.99 143	98.02 167

UGNet		98.52 126	99.00 33	97.96 206	99.58 123	99.26 62	99.27 84	99.40 90	98.07 53	98.28 174	98.76 131	99.71 29	92.24 260	98.94 68	98.85 65	99.00 142	99.43 19
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
Anonymous20231206		98.50 127	98.03 143	99.05 95	99.50 148	99.01 119	99.15 106	99.26 125	96.38 162	99.12 76	99.50 69	99.12 126	98.60 106	97.68 170	97.24 188	98.66 180	97.30 201

CLD-MVS		98.48 128	98.15 131	98.86 130	99.53 140	98.35 187	98.55 184	97.83 236	96.02 175	98.97 93	99.08 112	99.75 21	99.03 70	98.10 143	97.33 184	99.28 89	98.44 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		98.47 129	98.30 116	98.67 153	99.65 102	98.87 141	98.82 155	99.01 170	96.14 171	99.29 47	98.86 128	99.01 141	96.54 210	98.36 119	98.08 128	98.72 175	98.80 86

APD-MVS		98.47 129	97.97 147	99.05 95	99.64 107	98.91 135	98.94 137	99.45 88	94.40 219	98.77 117	97.26 193	99.41 82	98.21 153	98.67 96	98.57 100	99.31 85	98.57 105
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
viewmamba		98.46 131	98.36 111	98.58 162	99.65 102	98.80 145	98.82 155	98.72 199	98.33 42	97.54 218	99.58 53	99.61 52	97.96 171	97.71 167	96.86 197	98.76 173	97.65 189

Vis-MVSNet (Re-imp)		98.46 131	98.23 125	98.73 144	99.81 33	99.29 59	98.79 162	99.50 72	96.20 169	96.03 256	98.29 152	96.98 201	98.54 121	99.11 47	99.08 45	99.70 30	98.62 100

Fast-Effi-MVS+		98.42 133	97.79 155	99.15 76	99.69 87	98.66 166	98.94 137	99.68 30	94.49 213	99.05 85	98.06 164	98.86 149	98.48 131	98.18 137	97.78 148	99.05 131	98.54 112

ETV-MVS		98.41 134	97.76 156	99.17 73	99.58 123	99.01 119	98.91 141	99.50 72	93.33 239	99.31 44	96.82 210	98.42 173	98.17 155	99.13 46	99.08 45	99.54 44	98.56 109

dtuplus		98.39 135	98.22 126	98.58 162	99.58 123	98.69 160	99.05 117	98.83 187	97.68 84	97.19 238	99.46 73	99.59 57	97.89 176	97.47 185	96.69 201	98.95 148	97.97 173

MVS_111021_LR		98.39 135	98.11 137	98.71 148	99.08 210	98.54 177	98.23 214	98.56 213	96.57 148	99.13 74	98.41 146	98.86 149	98.65 102	98.23 135	97.87 144	98.65 182	98.28 139

onestephybrid01		98.38 137	98.25 121	98.53 173	99.61 115	98.79 147	99.04 120	98.68 202	97.17 116	97.58 216	99.58 53	99.57 60	97.51 188	97.79 164	97.14 190	98.77 170	97.71 184

pmmvs5		98.37 138	97.81 154	99.03 99	99.46 155	98.97 126	99.03 121	98.96 174	95.85 179	99.05 85	99.45 76	98.66 166	98.79 89	96.02 221	97.52 172	98.87 159	98.21 150

OMC-MVS		98.35 139	98.10 138	98.64 159	98.85 221	97.99 208	98.56 183	98.21 225	97.26 109	98.87 111	98.54 141	99.27 102	98.43 133	98.34 122	97.66 161	98.92 155	97.65 189

sasdasda		98.34 140	97.92 150	98.83 135	99.45 157	99.21 75	98.37 199	99.53 64	97.06 122	97.74 208	96.95 207	95.05 219	98.36 141	98.77 88	98.85 65	99.51 53	99.53 10

canonicalmvs		98.34 140	97.92 150	98.83 135	99.45 157	99.21 75	98.37 199	99.53 64	97.06 122	97.74 208	96.95 207	95.05 219	98.36 141	98.77 88	98.85 65	99.51 53	99.53 10

hybridnocas07		98.31 142	98.25 121	98.38 180	99.61 115	98.75 153	98.81 160	98.68 202	97.57 91	97.09 242	99.58 53	99.47 79	97.38 193	97.67 172	96.91 196	98.71 176	97.82 181

CHOSEN 1792x2688		98.31 142	98.02 144	98.66 155	99.55 131	98.57 174	99.38 58	99.25 129	98.42 34	98.48 153	99.58 53	99.85 6	98.31 146	95.75 225	95.71 219	96.96 230	98.27 141

viewmambaseed2359dif		98.30 144	98.05 142	98.58 162	99.55 131	98.69 160	98.99 126	98.76 195	97.06 122	97.32 231	99.40 84	99.52 67	97.99 167	97.22 200	96.54 207	98.85 163	97.95 174

CPTT-MVS		98.28 145	97.51 170	99.16 75	99.54 137	98.78 149	98.96 134	99.36 101	96.30 166	98.89 108	93.10 251	99.30 98	99.20 51	98.35 121	97.96 138	99.03 138	98.82 80

usedtu_dtu_shiyan1		98.27 146	97.72 160	98.90 120	98.96 216	98.75 153	99.17 98	98.96 174	96.35 163	98.90 104	98.69 134	99.05 138	98.55 118	96.31 216	97.36 182	98.86 161	97.55 194

TinyColmap		98.27 146	97.62 167	99.03 99	99.29 185	97.79 217	98.92 140	98.95 176	97.48 96	99.52 24	98.65 137	97.86 188	98.90 80	98.34 122	97.27 186	98.64 183	95.97 227

diffmvs		98.26 148	98.16 129	98.39 178	99.61 115	98.78 149	98.79 162	98.61 208	97.94 61	97.11 241	99.51 68	99.52 67	97.61 184	96.55 212	96.93 195	98.61 185	97.87 179
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
USDC		98.26 148	97.57 168	99.06 92	99.42 167	97.98 210	98.83 152	98.85 185	97.57 91	99.59 16	99.15 109	98.59 168	98.99 72	97.42 190	96.08 218	98.69 179	96.23 224

SF-MVS		98.25 150	98.16 129	98.35 182	99.43 164	98.42 185	97.05 256	99.09 162	96.42 160	98.13 185	97.73 177	99.20 112	97.22 197	98.36 119	98.38 109	99.16 108	98.62 100

MCST-MVS		98.25 150	97.57 168	99.06 92	99.53 140	98.24 195	98.63 176	99.17 147	95.88 177	98.58 134	96.11 226	99.09 132	99.18 53	97.58 177	97.31 185	99.25 94	98.75 88

hybrid		98.24 152	98.15 131	98.34 183	99.60 119	98.74 155	98.76 166	98.62 207	97.54 93	97.16 240	99.55 62	99.35 94	97.39 192	97.49 183	96.72 200	98.53 193	97.66 188

MGCFI-Net		98.23 153	97.93 149	98.58 162	99.44 161	99.20 81	98.37 199	99.54 59	97.14 118	96.70 253	96.98 204	95.04 221	97.92 175	98.75 91	98.89 61	99.52 47	99.55 9

IterMVS-LS		98.23 153	97.66 163	98.90 120	99.63 110	99.38 43	99.07 116	99.48 77	97.75 75	98.81 115	99.37 87	94.57 223	97.88 177	96.54 213	97.04 192	98.53 193	98.97 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.65 12	98.23 153	97.96 148	98.55 171	98.81 223	98.16 199	98.40 196	97.94 234	96.68 140	98.49 149	98.61 138	98.89 147	98.57 116	97.45 187	97.59 167	99.09 120	98.35 132
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNVR-MVS		98.22 156	97.76 156	98.76 142	99.33 177	98.26 193	98.48 189	98.88 182	96.22 168	98.47 155	95.79 232	99.33 95	98.35 143	98.37 118	97.99 134	99.03 138	98.38 130

IS_MVSNet		98.20 157	98.00 145	98.44 175	99.82 29	99.48 31	99.25 87	99.56 54	95.58 187	93.93 268	97.56 183	96.52 207	98.27 149	99.08 51	99.20 38	99.80 15	98.56 109

DeepPCF-MVS	96.68 10	98.20 157	98.26 119	98.12 196	97.03 267	98.11 202	98.44 193	97.70 239	96.77 137	98.52 143	98.91 126	99.17 116	98.58 115	98.41 114	98.02 130	98.46 199	98.46 118

MSDG		98.20 157	97.88 153	98.56 169	99.33 177	97.74 218	98.27 211	98.10 228	97.20 114	98.06 193	98.59 139	99.16 119	98.76 91	98.39 115	97.71 159	98.86 161	96.38 221

testgi		98.18 160	98.44 98	97.89 208	99.78 44	99.23 71	98.78 164	99.21 138	97.26 109	97.41 225	97.39 190	99.36 93	92.85 256	98.82 82	98.66 90	99.31 85	98.35 132

Effi-MVS+		98.11 161	97.29 177	99.06 92	99.62 111	98.55 175	98.16 217	99.80 15	94.64 209	99.15 72	96.59 215	97.43 194	98.44 132	97.46 186	97.90 140	99.17 103	98.45 122

FA-MVS(training)		98.08 162	97.68 161	98.56 169	99.14 203	98.69 160	98.41 194	99.83 12	95.85 179	98.57 137	97.95 171	96.92 203	96.85 204	98.51 106	98.09 127	98.54 191	97.74 183

HyFIR lowres test		98.08 162	97.16 186	99.14 79	99.72 80	98.91 135	99.41 54	99.58 51	97.93 62	98.82 114	99.24 100	95.81 213	98.73 94	95.16 236	95.13 228	98.60 187	97.94 175

EIA-MVS		98.03 164	97.20 183	98.99 107	99.66 99	99.24 68	98.53 186	99.52 68	91.56 255	99.25 53	95.34 236	98.78 155	97.72 181	98.38 117	98.58 97	99.28 89	98.54 112

train_agg		97.99 165	97.26 178	98.83 135	99.43 164	98.22 197	98.91 141	99.07 164	94.43 217	97.96 199	96.42 221	99.30 98	98.81 88	97.39 192	96.62 205	98.82 165	98.47 116

MSLP-MVS++		97.99 165	97.64 166	98.40 177	98.91 219	98.47 181	97.12 254	98.78 192	96.49 155	98.48 153	93.57 249	99.12 126	98.51 127	98.31 125	98.58 97	98.58 189	98.95 68

CDPH-MVS		97.99 165	97.23 181	98.87 127	99.58 123	98.29 189	98.83 152	99.20 141	93.76 233	98.11 189	96.11 226	99.16 119	98.23 152	97.80 162	97.22 189	99.29 88	98.28 139

FMVSNet2		97.94 168	98.08 140	97.77 216	98.71 228	99.21 75	98.62 178	99.47 79	96.62 142	96.37 255	99.20 106	97.70 190	94.39 241	97.39 192	97.75 154	99.08 121	98.70 95

PVSNet_BlendedMVS		97.93 169	97.66 163	98.25 189	99.30 182	98.67 163	98.31 206	97.95 232	94.30 223	98.75 120	97.63 180	98.76 156	96.30 218	98.29 128	97.78 148	98.93 151	98.18 152

PVSNet_Blended		97.93 169	97.66 163	98.25 189	99.30 182	98.67 163	98.31 206	97.95 232	94.30 223	98.75 120	97.63 180	98.76 156	96.30 218	98.29 128	97.78 148	98.93 151	98.18 152

OpenMVS	97.26 9	97.88 171	97.17 185	98.70 150	99.50 148	98.55 175	98.34 204	99.11 158	93.92 231	98.90 104	95.04 241	98.23 178	97.38 193	98.11 142	98.12 122	98.95 148	98.23 147

pmmvs4		97.87 172	97.02 190	98.86 130	99.20 195	97.68 221	98.89 146	99.03 167	96.57 148	99.12 76	99.03 120	97.26 198	98.42 134	95.16 236	96.34 210	98.53 193	97.10 208

NCCC		97.84 173	96.96 192	98.87 127	99.39 171	98.27 192	98.46 191	99.02 169	96.78 136	98.73 124	91.12 255	98.91 145	98.57 116	97.83 159	97.49 174	99.04 136	98.33 137

Effi-MVS+-dtu		97.78 174	97.37 174	98.26 187	99.25 191	98.50 179	97.89 228	99.19 144	94.51 211	98.16 182	95.93 229	98.80 154	95.97 222	98.27 133	97.38 180	99.10 114	98.23 147

MDA-MVSNet-bldmvs		97.75 175	97.26 178	98.33 184	99.35 176	98.45 182	99.32 74	97.21 245	97.90 66	99.05 85	99.01 122	96.86 204	99.08 66	99.36 33	92.97 240	95.97 245	96.25 223

CDS-MVSNet		97.75 175	97.68 161	97.83 214	99.08 210	98.20 198	98.68 171	98.61 208	95.63 186	97.80 206	99.24 100	96.93 202	94.09 247	97.96 148	97.82 146	98.71 176	97.99 169
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		97.75 175	97.26 178	98.32 186	98.58 236	97.86 213	97.80 230	98.09 229	96.49 155	98.49 149	96.15 225	98.08 181	98.35 143	98.00 146	97.03 193	98.61 185	97.21 205

PLC	95.63 15	97.73 178	97.01 191	98.57 167	99.10 207	97.80 216	97.72 235	98.77 193	96.34 164	98.38 163	93.46 250	98.06 182	98.66 101	97.90 153	97.65 163	98.77 170	97.90 177
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		97.69 179	97.15 187	98.33 184	99.27 188	98.43 184	98.25 212	99.29 120	95.00 198	97.39 227	98.86 128	98.00 185	97.14 199	95.38 231	96.22 212	98.62 184	98.15 158

GBi-Net		97.69 179	97.75 158	97.62 217	98.71 228	99.21 75	98.62 178	99.33 112	94.09 227	95.60 258	98.17 159	95.97 210	94.39 241	99.05 55	99.03 49	99.08 121	98.70 95

test1		97.69 179	97.75 158	97.62 217	98.71 228	99.21 75	98.62 178	99.33 112	94.09 227	95.60 258	98.17 159	95.97 210	94.39 241	99.05 55	99.03 49	99.08 121	98.70 95

CANet_DTU		97.65 182	97.50 172	97.82 215	99.19 198	98.08 204	98.41 194	98.67 204	94.40 219	99.16 69	98.32 151	98.69 160	93.96 250	97.87 154	97.61 165	97.51 223	97.56 193

IterMVS-SCA-FT		97.63 183	96.86 194	98.52 174	99.48 152	98.71 158	98.84 151	98.91 177	96.44 159	99.16 69	99.56 58	95.54 215	97.95 172	95.68 228	95.07 231	96.76 236	97.03 211

TSAR-MVS + COLMAP		97.62 184	97.31 176	97.98 204	98.47 244	97.39 225	98.29 208	98.25 224	96.68 140	97.54 218	98.87 127	98.04 184	97.08 200	96.78 207	96.26 211	98.26 205	97.12 207

MS-PatchMatch		97.60 185	97.22 182	98.04 203	98.67 232	97.18 230	97.91 226	98.28 223	95.82 182	98.34 167	97.66 179	98.38 174	97.77 179	97.10 203	97.25 187	97.27 225	97.18 206

PCF-MVS	95.58 16	97.60 185	96.67 195	98.69 151	99.44 161	98.23 196	98.37 199	98.81 190	93.01 243	98.22 178	97.97 170	99.59 57	98.20 154	95.72 227	95.08 229	99.08 121	97.09 210
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		97.58 187	96.65 198	98.66 155	99.30 182	97.99 208	97.88 229	98.65 205	94.58 210	98.66 127	94.65 245	99.15 122	98.59 108	96.10 219	95.59 220	98.90 157	98.50 114

DI_MVS_pp		97.57 188	96.55 200	98.77 141	99.55 131	98.76 151	99.22 90	99.00 171	97.08 121	97.95 200	97.78 176	91.35 237	98.02 165	96.20 217	96.81 199	98.87 159	97.87 179

AdaColmap		97.57 188	96.57 199	98.74 143	99.25 191	98.01 206	98.36 203	98.98 173	94.44 216	98.47 155	92.44 252	97.91 187	98.62 104	98.19 136	97.74 155	98.73 174	97.28 202

baseline		97.50 190	97.51 170	97.50 221	99.18 199	97.38 226	98.00 222	98.00 231	96.52 154	97.49 221	99.28 95	99.43 81	95.31 228	95.27 233	96.22 212	96.99 228	98.47 116

IterMVS		97.40 191	96.67 195	98.25 189	99.45 157	98.66 166	98.87 148	98.73 197	96.40 161	98.94 101	99.56 58	95.26 217	97.58 185	95.38 231	94.70 233	95.90 246	96.72 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		97.38 192	96.15 209	98.82 138	99.39 171	98.34 188	98.65 174	98.88 182	90.80 262	98.86 112	92.35 253	95.13 218	98.09 159	98.84 78	98.88 62	99.06 128	98.71 94

CVMVSNet		97.38 192	97.39 173	97.37 225	98.58 236	97.72 219	98.70 169	97.42 243	97.21 112	95.95 257	99.46 73	93.31 232	97.38 193	97.60 175	97.78 148	96.18 242	98.66 98

new-patchmatchnet		97.26 194	96.12 210	98.58 162	99.55 131	98.63 168	99.14 108	97.04 247	98.80 18	99.19 61	99.92 4	99.19 114	98.92 79	95.51 230	87.04 257	97.66 220	93.73 247

MIMVSNet		97.24 195	97.15 187	97.36 226	99.03 214	98.52 178	98.55 184	99.73 23	94.94 202	94.94 265	97.98 169	97.37 196	93.66 251	97.60 175	97.34 183	98.23 208	96.29 222

PatchMatch-RL		97.24 195	96.45 203	98.17 193	98.70 231	97.57 224	97.31 249	98.48 217	94.42 218	98.39 162	95.74 233	96.35 209	97.88 177	97.75 166	97.48 175	98.24 207	95.87 231

dtuonlycased		97.23 197	97.32 175	97.13 231	99.59 120	98.67 163	98.87 148	97.72 238	97.34 103	92.21 272	99.35 88	99.39 87	98.07 163	95.98 223	94.57 234	96.35 239	95.94 230

thisisatest0530		97.20 198	95.95 214	98.66 155	99.46 155	98.84 142	98.29 208	99.20 141	94.51 211	98.25 176	97.42 188	85.03 253	97.68 182	98.43 112	98.56 101	99.08 121	98.89 75

tttt0517		97.18 199	95.92 215	98.65 158	99.49 150	98.92 133	98.29 208	99.20 141	94.37 221	98.17 180	97.37 191	84.72 256	97.68 182	98.55 102	98.56 101	99.10 114	98.95 68

MDTV_nov1_ep13_2view		97.12 200	96.19 208	98.22 192	99.13 206	98.05 205	99.24 88	99.47 79	97.61 88	99.15 72	99.59 48	99.01 141	98.40 135	94.87 240	90.14 243	93.91 256	94.04 246

MAR-MVS		97.12 200	96.28 206	98.11 197	98.94 217	97.22 228	97.65 239	99.38 97	90.93 261	98.15 183	95.17 238	97.13 199	96.48 213	97.71 167	97.40 178	98.06 212	98.40 127
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
Fast-Effi-MVS+-dtu		96.99 202	96.46 202	97.61 219	98.98 215	97.89 211	97.54 243	99.76 19	93.43 237	96.55 254	94.93 242	98.06 182	94.32 244	96.93 205	96.50 208	98.53 193	97.47 195

FPMVS		96.97 203	97.20 183	96.70 245	97.75 259	96.11 251	97.72 235	95.47 254	97.13 119	98.02 195	97.57 182	96.67 205	92.97 255	99.00 64	98.34 113	98.28 204	95.58 233

TAMVS		96.95 204	96.94 193	96.97 239	99.07 212	97.67 223	97.98 224	97.12 246	95.04 197	95.41 261	99.27 96	95.57 214	94.09 247	97.32 196	97.11 191	98.16 210	96.59 215

FMVSNet3		96.85 205	96.67 195	97.06 233	97.56 262	99.01 119	97.99 223	99.33 112	94.09 227	95.60 258	98.17 159	95.97 210	93.26 254	94.76 242	96.22 212	98.59 188	98.46 118

GA-MVS		96.84 206	95.86 217	97.98 204	99.16 202	98.29 189	97.91 226	98.64 206	95.14 193	97.71 212	98.04 166	88.90 241	96.50 212	96.41 215	96.61 206	97.97 216	97.60 191

CHOSEN 280x420		96.80 207	96.30 205	97.39 223	99.09 208	96.52 243	98.76 166	99.29 120	93.88 232	97.65 213	98.34 149	93.66 230	96.29 221	98.28 131	97.73 157	93.27 259	95.70 232

gg-mvs-nofinetune		96.77 208	96.52 201	97.06 233	99.66 99	97.82 215	97.54 243	99.86 8	98.69 24	98.61 131	99.94 2	89.62 239	88.37 268	97.55 178	96.67 203	98.30 203	95.35 234

dtuonly		96.75 209	96.24 207	97.34 227	99.41 169	96.98 235	98.07 220	97.70 239	95.68 184	98.07 192	99.07 113	99.23 104	96.32 217	95.14 238	93.82 237	94.49 252	93.72 248

DPM-MVS		96.73 210	95.70 220	97.95 207	98.93 218	97.26 227	97.39 248	98.44 219	95.47 189	97.62 215	90.71 256	98.47 172	97.03 203	95.02 239	95.27 225	98.26 205	97.67 186

baseline1		96.72 211	95.40 222	98.26 187	99.53 140	98.81 143	98.32 205	98.80 191	94.96 200	96.78 252	96.50 219	84.87 255	96.68 208	97.42 190	97.91 139	99.46 58	97.33 200

N_pmnet		96.68 212	95.70 220	97.84 213	99.42 167	98.00 207	99.35 66	98.21 225	98.40 36	98.13 185	99.42 81	99.30 98	97.44 191	94.00 246	88.79 244	94.47 253	91.96 256

pmnet_mix02		96.61 213	95.32 223	98.11 197	99.41 169	97.68 221	99.05 117	97.59 241	98.16 48	99.05 85	99.48 70	99.11 128	98.32 145	92.36 250	87.67 252	95.26 248	92.80 254

new_pmnet		96.59 214	96.40 204	96.81 242	98.24 255	95.46 260	97.71 237	94.75 261	96.92 127	96.80 251	99.23 102	97.81 189	96.69 206	96.58 211	95.16 227	96.69 237	93.64 249

PMMVS		96.47 215	95.81 218	97.23 228	97.38 264	95.96 255	97.31 249	96.91 248	93.21 240	97.93 202	97.14 197	97.64 192	95.70 224	95.24 234	96.18 215	98.17 209	95.33 235

EPNet		96.44 216	96.08 211	96.86 241	99.32 180	97.15 231	97.69 238	99.32 116	93.67 234	98.11 189	95.64 234	93.44 231	89.07 266	96.86 206	96.83 198	97.67 219	98.97 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres600view7		96.35 217	94.27 227	98.79 140	99.66 99	99.18 85	98.94 137	99.38 97	94.37 221	97.21 236	87.19 261	84.10 257	98.10 157	98.16 138	99.47 20	99.42 65	97.43 196

EPNet_dtu		96.31 218	95.96 213	96.72 244	99.18 199	95.39 261	97.03 257	99.13 156	93.02 242	99.35 40	97.23 194	97.07 200	90.70 265	95.74 226	95.08 229	94.94 250	98.16 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		96.30 219	95.87 216	96.80 243	97.66 261	96.48 244	97.93 225	93.80 263	93.40 238	98.54 141	98.27 153	97.50 193	97.37 196	97.49 183	93.11 239	95.52 247	94.85 239

PMMVS2		96.29 220	97.05 189	95.40 255	98.32 254	96.16 248	98.18 216	97.46 242	97.20 114	84.51 275	99.60 46	98.68 162	96.37 214	98.59 100	97.38 180	97.58 222	91.76 257

thres200		96.23 221	94.13 228	98.69 151	99.44 161	99.18 85	98.58 182	99.38 97	93.52 236	97.35 229	86.33 266	85.83 251	97.93 173	98.16 138	98.78 77	99.42 65	97.10 208

thres400		96.22 222	94.08 231	98.72 145	99.58 123	99.05 109	98.83 152	99.22 135	94.01 230	97.40 226	86.34 265	84.91 254	97.93 173	97.85 158	99.08 45	99.37 73	97.28 202

tfpn200view9		96.17 223	94.08 231	98.60 160	99.37 173	99.18 85	98.68 171	99.39 92	92.02 249	97.30 232	86.53 263	86.34 248	97.45 190	98.15 140	99.08 45	99.43 64	97.28 202

CMPMVS	74.71 19	96.17 223	96.06 212	96.30 249	97.41 263	94.52 264	94.83 269	95.46 255	91.57 254	97.26 235	94.45 246	98.33 176	94.98 231	98.28 131	97.59 167	97.86 218	97.68 185
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2506		96.12 225	93.35 243	99.35 54	99.83 24	99.58 23	99.37 59	99.67 34	98.02 56	98.44 159	97.51 185	60.03 277	99.10 63	99.77 5	99.70 6	99.72 26	98.86 77

blended_shiyan8		96.02 226	94.28 226	98.05 201	98.55 242	97.09 232	98.98 129	95.56 252	95.13 194	99.23 54	98.03 168	94.19 225	98.73 94	90.28 253	88.65 245	97.22 226	96.56 218

blended_shiyan6		96.02 226	94.29 225	98.05 201	98.56 239	97.09 232	98.99 126	95.56 252	95.11 195	99.21 57	98.04 166	94.28 224	98.74 93	90.26 254	88.64 246	97.22 226	96.57 216

gbinet_0.2-2-1-0.02		95.92 228	94.09 229	98.06 199	98.81 223	97.08 234	99.13 109	96.47 249	94.88 203	99.08 81	98.47 145	94.16 226	98.02 165	90.43 252	87.61 253	96.86 231	95.99 226

IB-MVS	95.85 14	95.87 229	94.88 224	97.02 236	99.09 208	98.25 194	97.16 251	97.38 244	91.97 252	97.77 207	83.61 271	97.29 197	92.03 263	97.16 201	97.66 161	98.66 180	98.20 151
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
test0.0.03 1		95.81 230	95.77 219	95.85 254	99.20 195	98.15 201	97.49 247	98.50 215	92.24 245	92.74 271	96.82 210	92.70 233	88.60 267	97.31 198	97.01 194	98.57 190	96.19 225

thres100view900		95.74 231	93.66 242	98.17 193	99.37 173	98.59 171	98.10 218	98.33 222	92.02 249	97.30 232	86.53 263	86.34 248	96.69 206	96.77 208	98.47 106	99.24 96	96.89 212

wanda-best-256-512		95.72 232	93.88 236	97.86 211	98.45 245	96.92 236	98.82 155	95.29 257	94.75 205	99.18 63	97.92 173	94.13 227	98.59 108	89.77 256	87.74 248	96.86 231	95.95 228

FE-blended-shiyan7		95.72 232	93.88 236	97.86 211	98.45 245	96.92 236	98.82 155	95.29 257	94.75 205	99.18 63	97.92 173	94.13 227	98.59 108	89.77 256	87.74 248	96.86 231	95.95 228

ET-MVSNet_ETH3D		95.72 232	93.85 238	97.89 208	97.30 265	98.09 203	98.19 215	98.40 220	94.46 215	98.01 198	96.71 212	77.85 273	96.76 205	96.08 220	96.39 209	98.70 178	97.36 198

baseline2		95.58 235	94.04 233	97.38 224	98.80 225	98.16 199	97.14 252	97.80 237	91.45 256	97.49 221	95.22 237	83.63 258	94.98 231	96.42 214	96.66 204	98.06 212	96.76 213

PatchT		95.49 236	93.29 244	98.06 199	98.65 233	96.20 247	98.91 141	99.73 23	92.00 251	98.50 145	96.67 214	83.25 259	96.34 215	94.40 243	95.50 221	96.21 241	95.04 237

CR-MVSNet		95.38 237	93.01 245	98.16 195	98.63 234	95.85 257	97.64 240	99.78 16	91.27 258	98.50 145	96.84 209	82.16 260	96.34 215	94.40 243	95.50 221	98.05 214	95.04 237

MVSTER		95.38 237	93.99 235	97.01 237	98.83 222	98.95 128	96.62 258	99.14 153	92.17 247	97.44 224	97.29 192	77.88 272	91.63 264	97.45 187	96.18 215	98.41 200	97.99 169

MVS-HIRNet		94.86 239	93.83 239	96.07 250	97.07 266	94.00 266	94.31 270	99.17 147	91.23 260	98.17 180	98.69 134	97.43 194	95.66 225	94.05 245	91.92 241	92.04 266	89.46 265

test-LLR		94.79 240	93.71 240	96.06 251	99.20 195	96.16 248	96.31 260	98.50 215	89.98 263	94.08 266	97.01 201	86.43 246	92.20 261	96.76 209	95.31 223	96.05 243	94.31 243

RPMNet		94.72 241	92.01 250	97.88 210	98.56 239	95.85 257	97.78 231	99.70 29	91.27 258	98.33 168	93.69 248	81.88 261	94.91 234	92.60 248	94.34 236	98.01 215	94.46 242

gm-plane-assit		94.62 242	91.39 252	98.39 178	99.90 11	99.47 33	99.40 55	99.65 39	97.44 98	99.56 20	99.68 30	59.40 278	94.23 245	96.17 218	94.77 232	97.61 221	92.79 255

test-mter		94.62 242	94.02 234	95.32 256	97.72 260	96.75 240	96.23 262	95.67 251	89.83 266	93.23 270	96.99 203	85.94 250	92.66 259	97.32 196	96.11 217	96.44 238	95.22 236

FMVSNet5		94.57 244	92.77 246	96.67 246	97.88 257	98.72 157	97.54 243	98.70 201	88.64 267	95.11 263	86.90 262	81.77 262	93.27 253	97.92 151	98.07 129	97.50 224	97.34 199

SCA		94.53 245	91.95 251	97.55 220	98.58 236	97.86 213	98.49 188	99.68 30	95.11 195	99.07 82	95.87 230	87.24 244	96.53 211	89.77 256	87.08 256	92.96 261	90.69 260

MDTV_nov1_ep13		94.47 246	92.15 248	97.17 229	98.54 243	96.42 245	98.10 218	98.89 179	94.49 213	98.02 195	97.41 189	86.49 245	95.56 226	90.85 251	87.95 247	93.91 256	91.45 259

TESTMET0.1,1		94.44 247	93.71 240	95.30 257	97.84 258	96.16 248	96.31 260	95.32 256	89.98 263	94.08 266	97.01 201	86.43 246	92.20 261	96.76 209	95.31 223	96.05 243	94.31 243

ADS-MVSNet		94.41 248	92.13 249	97.07 232	98.86 220	96.60 242	98.38 198	98.47 218	96.13 173	98.02 195	96.98 204	87.50 243	95.87 223	89.89 255	87.58 254	92.79 263	90.27 262

tpm		93.89 249	91.21 253	97.03 235	98.36 252	96.07 252	97.53 246	99.65 39	92.24 245	98.64 128	97.23 194	74.67 276	94.64 239	92.68 247	90.73 242	93.37 258	94.82 240

PatchmatchNet		93.88 250	91.08 254	97.14 230	98.75 227	96.01 254	98.25 212	99.39 92	94.95 201	98.96 96	96.32 222	85.35 252	95.50 227	88.89 261	85.89 260	91.99 267	90.15 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		93.67 251	90.82 255	96.99 238	98.62 235	96.39 246	98.40 196	99.11 158	95.54 188	97.87 205	97.14 197	81.27 264	94.97 233	88.54 263	86.80 258	92.95 262	90.06 264

FE-MVSNET3		93.58 252	90.22 256	97.50 221	98.45 245	96.92 236	98.82 155	95.29 257	94.75 205	96.98 244	86.26 267	79.50 267	98.59 108	89.77 256	87.74 248	96.86 231	96.57 216

usedtu_blend_shiyan5		93.31 253	90.20 257	96.93 240	98.45 245	96.92 236	95.44 266	95.29 257	94.75 205	96.98 244	86.26 267	79.50 267	98.59 108	89.77 256	87.74 248	96.86 231	96.46 219

MVE	82.47 18	93.12 254	94.09 229	91.99 264	90.79 271	82.50 273	93.93 271	96.30 250	96.06 174	88.81 273	98.19 157	96.38 208	97.56 186	97.24 199	95.18 226	84.58 273	93.07 251
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CostFormer		92.75 255	89.49 259	96.55 247	98.78 226	95.83 259	97.55 242	98.59 210	91.83 253	97.34 230	96.31 223	78.53 271	94.50 240	86.14 265	84.92 261	92.54 264	92.84 253

tpmrst		92.45 256	89.48 260	95.92 253	98.43 250	95.03 262	97.14 252	97.92 235	94.16 225	97.56 217	97.86 175	81.63 263	93.56 252	85.89 266	82.86 265	90.91 271	88.95 267

dps		92.35 257	88.78 262	96.52 248	98.21 256	95.94 256	97.78 231	98.38 221	89.88 265	96.81 250	95.07 240	75.31 275	94.70 237	88.62 262	86.21 259	93.21 260	90.41 261

E-PMN		92.28 258	90.12 258	94.79 259	98.56 239	90.90 270	95.16 268	93.68 264	95.36 191	95.10 264	96.56 216	89.05 240	95.24 229	95.21 235	81.84 267	90.98 269	81.94 269

EMVS		91.84 259	89.39 261	94.70 260	98.44 249	90.84 271	95.27 267	93.53 265	95.18 192	95.26 262	95.62 235	87.59 242	94.77 236	94.87 240	80.72 268	90.95 270	80.88 270

tpm cat1		91.52 260	87.70 264	95.97 252	98.33 253	94.98 263	97.06 255	98.03 230	92.11 248	98.03 194	94.77 244	77.19 274	92.71 257	83.56 267	82.24 266	91.67 268	89.04 266

blend_shiyan4		91.30 261	88.16 263	94.96 258	89.60 272	96.63 241	93.72 272	93.90 262	82.52 271	96.98 244	86.26 267	79.50 267	98.59 108	88.21 264	87.51 255	96.99 228	96.46 219

0.4-1-1-0.1		90.20 262	87.09 265	93.83 261	91.98 268	94.48 265	96.12 263	88.26 266	84.35 268	97.04 243	88.99 257	79.83 265	94.68 238	83.11 268	84.34 262	94.87 251	94.55 241

0.3-1-1-0.015		89.53 263	86.18 266	93.43 262	91.67 270	93.80 267	95.70 264	87.54 267	83.38 269	96.98 244	87.42 259	79.50 267	94.21 246	81.99 270	83.67 263	94.46 254	93.50 250

0.4-1-1-0.2		89.46 264	86.17 267	93.30 263	91.74 269	93.59 269	95.48 265	87.42 268	83.04 270	96.95 248	88.20 258	79.80 266	93.99 249	82.16 269	83.38 264	94.21 255	93.03 252

test_method		77.69 265	85.40 268	68.69 265	42.66 274	55.39 275	82.17 275	52.05 270	92.83 244	84.52 274	94.88 243	95.41 216	65.37 269	92.49 249	79.32 269	85.36 272	87.50 268

GG-mvs-BLEND		65.66 266	92.62 247	34.20 267	1.45 277	93.75 268	85.40 274	1.64 274	91.37 257	17.21 277	87.25 260	94.78 222	3.25 273	95.64 229	93.80 238	96.27 240	91.74 258

testmvs		9.73 267	13.38 269	5.48 269	3.62 275	4.12 276	6.40 278	3.19 273	14.92 272	7.68 279	22.10 272	13.89 280	6.83 271	13.47 271	10.38 271	5.14 276	14.81 271

test123		9.37 268	12.26 270	6.00 268	3.32 276	4.06 277	6.39 279	3.41 272	13.20 273	10.48 278	16.43 273	16.22 279	6.76 272	11.37 272	10.40 270	5.62 275	14.10 272

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

TestfortrainingZip							99.34 70	98.89 179		98.10 191						99.01 140

TPM-MVS					98.38 251	97.20 229	96.44 259			97.17 239	95.17 238	98.68 162	92.69 258			98.11 211	97.67 186
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										99.88 2

9.14												98.83 153

SR-MVS					99.62 111			99.47 79				99.40 85

Anonymous202405211			98.44 98		99.79 40	99.32 57	99.05 117	99.34 109	96.59 145		97.95 171	97.68 191	97.16 198	99.36 33	99.28 35	99.61 39	98.90 73

our_test_3					99.29 185	97.72 219	98.98 129

ambc			97.89 152		99.45 157	97.88 212	97.78 231		97.27 107	99.80 3	98.99 124	98.48 170	98.55 118	97.80 162	96.68 202	98.54 191	98.10 163

MTAPA										99.19 61		99.68 36

MTMP										99.20 59		99.54 63

Patchmatch-RL test							32.47 277

tmp_tt				65.28 266	82.24 273	71.50 274	70.81 276	23.21 271	96.14 171	81.70 276	85.98 270	92.44 234	49.84 270	95.81 224	94.36 235	83.86 274

XVS					99.77 50	99.07 105	99.46 49			98.95 98		99.37 90				99.33 81

X-MVStestdata					99.77 50	99.07 105	99.46 49			98.95 98		99.37 90				99.33 81

mPP-MVS					99.75 65							99.49 76

NP-MVS									93.07 241

Patchmtry						96.05 253	97.64 240	99.78 16		98.50 145

DeepMVS_CX						87.86 272	92.27 273	61.98 269	93.64 235	93.62 269	91.17 254	91.67 235	94.90 235	95.99 222		92.48 265	94.18 245

High-resolution multi-view benchmark