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	99.39 1	99.90 1	99.87 1	99.93 1	99.97 2	99.82 8	99.91 3	99.92 38	99.75 4	99.93 8	99.89 34	100.00 1	99.87 2	99.93 3	99.82 10	99.96 3	99.90 2
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
v7n		99.89 2	99.86 3	99.93 1	99.97 2	99.83 4	99.93 1	99.96 12	99.77 3	99.89 21	99.99 1	99.86 103	99.84 5	99.89 11	99.81 11	99.97 1	99.88 7

SixPastTwentyTwo		99.89 2	99.85 5	99.93 1	99.97 2	99.88 1	99.92 2	99.97 1	99.66 22	99.94 6	99.94 11	99.74 141	99.81 7	99.97 1	99.89 1	99.96 3	99.89 5

pmmvs6		99.88 4	99.87 1	99.89 9	99.97 2	99.76 22	99.89 5	99.96 12	99.82 2	99.90 18	99.92 18	99.95 38	99.68 34	99.93 3	99.88 3	99.95 7	99.86 13

anonymousdsp		99.87 5	99.86 3	99.88 13	99.95 10	99.75 28	99.90 4	99.96 12	99.69 13	99.83 59	99.96 4	99.99 5	99.74 22	99.95 2	99.83 7	99.91 25	99.88 7

FC-MVSNet-test		99.84 6	99.80 6	99.89 9	99.96 7	99.83 4	99.84 17	99.95 23	99.37 78	99.77 81	99.95 6	99.96 25	99.85 3	99.93 3	99.83 7	99.95 7	99.72 44

WB-MVS		99.82 7	99.76 9	99.89 9	99.94 23	99.82 8	99.79 31	99.93 27	99.67 17	99.97 2	99.83 63	99.78 137	99.79 12	99.72 39	99.70 23	99.95 7	99.78 30

UniMVSNet_ETH3D		99.81 8	99.79 7	99.85 20	99.98 1	99.76 22	99.73 54	99.96 12	99.68 15	99.87 37	99.59 119	99.91 79	99.58 54	99.90 10	99.85 6	99.96 3	99.81 22

TDRefinement		99.81 8	99.76 9	99.86 16	99.83 115	99.53 78	99.89 5	99.91 44	99.73 5	99.88 30	99.83 63	99.96 25	99.76 17	99.91 9	99.81 11	99.86 44	99.59 81

WR-MVS		99.79 10	99.68 15	99.91 5	99.95 10	99.83 4	99.87 9	99.96 12	99.39 76	99.93 8	99.87 43	99.29 188	99.77 15	99.83 22	99.72 21	99.97 1	99.82 18

MIMVSNet1		99.79 10	99.75 11	99.84 23	99.89 50	99.83 4	99.84 17	99.89 55	99.31 83	99.93 8	99.92 18	99.97 18	99.68 34	99.89 11	99.64 29	99.82 60	99.66 58

pm-mvs1		99.77 12	99.69 14	99.86 16	99.94 23	99.68 38	99.84 17	99.93 27	99.59 38	99.87 37	99.92 18	99.21 191	99.65 40	99.88 15	99.77 16	99.93 21	99.78 30

PEN-MVS		99.77 12	99.65 21	99.91 5	99.95 10	99.80 16	99.86 11	99.97 1	99.08 114	99.89 21	99.69 102	99.68 152	99.84 5	99.81 27	99.64 29	99.95 7	99.81 22

FE-MVSNET2		99.76 14	99.67 16	99.86 16	99.94 23	99.68 38	99.87 9	99.90 53	99.50 59	99.94 6	99.78 79	100.00 1	99.69 32	99.71 43	99.43 55	99.85 47	99.58 90

EU-MVSNet		99.76 14	99.74 13	99.78 44	99.82 125	99.81 13	99.88 7	99.87 61	99.31 83	99.75 90	99.91 27	99.76 139	99.78 13	99.84 21	99.74 19	99.56 167	99.81 22

Vis-MVSNet		99.76 14	99.78 8	99.75 55	99.92 33	99.77 21	99.83 20	99.85 73	99.43 69	99.85 50	99.84 59	100.00 1	99.13 149	99.83 22	99.66 26	99.90 29	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		99.75 17	99.66 20	99.85 20	99.87 70	99.86 2	99.83 20	99.91 44	98.84 155	99.92 12	99.57 121	99.85 109	99.60 49	99.82 25	99.79 13	99.94 16	99.87 11

SPE-MVS-test		99.75 17	99.67 16	99.84 23	99.91 37	99.85 3	99.85 14	99.92 38	98.75 165	99.89 21	99.64 109	99.95 38	99.55 57	99.89 11	99.79 13	99.92 22	99.83 16

DTE-MVSNet		99.75 17	99.61 34	99.92 4	99.95 10	99.81 13	99.86 11	99.96 12	99.18 102	99.92 12	99.66 105	99.45 173	99.85 3	99.80 28	99.56 35	99.96 3	99.79 29

tfpnnormal		99.74 20	99.63 28	99.86 16	99.93 30	99.75 28	99.80 30	99.89 55	99.31 83	99.88 30	99.43 145	99.66 156	99.77 15	99.80 28	99.71 22	99.92 22	99.76 35

DeepC-MVS	99.05 5	99.74 20	99.64 24	99.84 23	99.90 43	99.39 127	99.79 31	99.81 103	99.69 13	99.90 18	99.87 43	99.98 11	99.81 7	99.62 57	99.32 68	99.83 56	99.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thisisatest0515		99.73 22	99.67 16	99.81 33	99.93 30	99.74 30	99.68 65	99.91 44	99.59 38	99.88 30	99.73 89	99.81 123	99.55 57	99.59 58	99.53 40	99.89 34	99.70 52

PS-CasMVS		99.73 22	99.59 40	99.90 8	99.95 10	99.80 16	99.85 14	99.97 1	98.95 139	99.86 43	99.73 89	99.36 180	99.81 7	99.83 22	99.67 25	99.95 7	99.83 16

WR-MVS_H		99.73 22	99.61 34	99.88 13	99.95 10	99.82 8	99.83 20	99.96 12	99.01 128	99.84 54	99.71 99	99.41 179	99.74 22	99.77 33	99.70 23	99.95 7	99.82 18

TransMVSNet (Re)		99.72 25	99.59 40	99.88 13	99.95 10	99.76 22	99.88 7	99.94 24	99.58 40	99.92 12	99.90 31	98.55 208	99.65 40	99.89 11	99.76 17	99.95 7	99.70 52

ACMH	99.11 4	99.72 25	99.63 28	99.84 23	99.87 70	99.59 56	99.83 20	99.88 60	99.46 64	99.87 37	99.66 105	99.95 38	99.76 17	99.73 38	99.47 49	99.84 51	99.52 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		99.70 27	99.67 16	99.74 61	99.94 23	99.71 34	99.82 26	99.91 44	99.14 110	99.53 168	99.70 100	99.88 94	99.33 101	99.88 15	99.61 34	99.94 16	99.77 33

EC-MVSNet		99.70 27	99.57 44	99.85 20	99.95 10	99.81 13	99.85 14	99.93 27	98.39 203	99.76 84	99.48 141	99.94 50	99.70 31	99.85 19	99.66 26	99.91 25	99.87 11

COLMAP_ROB	99.18 2	99.70 27	99.60 38	99.81 33	99.84 107	99.37 137	99.76 39	99.84 82	99.54 50	99.82 62	99.64 109	99.95 38	99.75 19	99.79 30	99.56 35	99.83 56	99.37 161
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
casdiffseed414692147		99.69 30	99.62 32	99.76 50	99.91 37	99.55 68	99.73 54	99.82 94	99.63 31	99.78 76	99.88 40	100.00 1	99.47 87	99.49 68	99.19 80	99.83 56	99.63 67

ACMH+	98.94 6	99.69 30	99.59 40	99.81 33	99.88 63	99.41 122	99.75 43	99.86 66	99.43 69	99.80 67	99.54 125	99.97 18	99.73 25	99.82 25	99.52 42	99.85 47	99.43 144

E6new		99.68 32	99.65 21	99.72 65	99.89 50	99.59 56	99.58 90	99.80 111	99.71 7	99.78 76	99.89 34	99.99 5	99.48 82	99.42 83	99.31 69	99.82 60	99.63 67

E6		99.68 32	99.65 21	99.72 65	99.89 50	99.59 56	99.58 90	99.80 111	99.71 7	99.78 76	99.89 34	99.99 5	99.48 82	99.42 83	99.31 69	99.82 60	99.63 67

test20.03		99.68 32	99.60 38	99.76 50	99.91 37	99.70 37	99.68 65	99.87 61	99.05 123	99.88 30	99.92 18	99.88 94	99.50 74	99.77 33	99.42 58	99.75 90	99.49 124

CP-MVSNet		99.68 32	99.51 56	99.89 9	99.95 10	99.76 22	99.83 20	99.96 12	98.83 159	99.84 54	99.65 108	99.09 194	99.80 10	99.78 31	99.62 33	99.95 7	99.82 18

casdiffmvs_mvg		99.67 36	99.61 34	99.74 61	99.94 23	99.60 50	99.62 79	99.77 129	99.54 50	99.67 136	99.82 70	99.80 129	99.52 67	99.40 87	99.51 43	99.91 25	99.59 81
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Casviewmamba		99.66 37	99.63 28	99.69 74	99.87 70	99.60 50	99.54 106	99.70 159	99.58 40	99.73 106	99.86 53	99.93 60	99.42 92	99.40 87	99.37 62	99.90 29	99.66 58

hybridcas		99.66 37	99.63 28	99.68 76	99.88 63	99.60 50	99.58 90	99.67 176	99.61 35	99.67 136	99.87 43	99.95 38	99.38 93	99.40 87	99.37 62	99.90 29	99.64 65

viewdifsd2359ckpt11		99.66 37	99.64 24	99.68 76	99.90 43	99.67 41	99.56 96	99.72 152	99.67 17	99.69 125	99.87 43	99.93 60	99.53 61	99.51 65	99.23 75	99.69 118	99.60 77

viewmsd2359difaftdt		99.66 37	99.64 24	99.68 76	99.90 43	99.67 41	99.56 96	99.72 152	99.67 17	99.69 125	99.87 43	99.93 60	99.53 61	99.51 65	99.23 75	99.69 118	99.60 77

PVSNet_Blended_VisFu		99.66 37	99.64 24	99.67 80	99.91 37	99.71 34	99.61 80	99.79 116	99.41 71	99.91 16	99.85 56	99.61 160	99.00 165	99.67 47	99.42 58	99.81 65	99.81 22

v10		99.65 42	99.51 56	99.81 33	99.83 115	99.61 49	99.75 43	99.94 24	99.56 45	99.76 84	99.94 11	99.60 162	99.73 25	99.11 158	99.01 121	99.85 47	99.74 39

CHOSEN 1792x2688		99.65 42	99.55 49	99.77 49	99.93 30	99.60 50	99.79 31	99.92 38	99.73 5	99.74 97	99.93 16	99.98 11	99.80 10	98.83 204	99.01 121	99.45 190	99.76 35

RA-MVS		99.64 44	99.75 11	99.51 132	99.89 50	99.72 33	99.04 212	99.74 147	99.68 15	99.70 121	99.85 56	99.98 11	99.08 153	99.54 63	99.73 20	99.61 148	99.78 30

UA-Net		99.64 44	99.62 32	99.66 84	99.97 2	99.82 8	99.14 201	99.96 12	98.95 139	99.52 174	99.38 155	99.86 103	99.55 57	99.72 39	99.66 26	99.80 70	99.94 1

viewmacassd2359aftdt		99.63 46	99.56 47	99.71 68	99.89 50	99.56 66	99.55 101	99.77 129	99.65 23	99.72 110	99.84 59	99.99 5	99.53 61	99.25 123	99.09 105	99.81 65	99.57 97

GeoE		99.63 46	99.51 56	99.78 44	99.91 37	99.57 62	99.78 34	99.97 1	99.23 93	99.72 110	99.72 95	99.80 129	99.50 74	99.45 81	99.10 103	99.79 75	99.71 50

Baseline_NR-MVSNet		99.62 48	99.48 65	99.78 44	99.85 101	99.76 22	99.59 86	99.82 94	98.84 155	99.88 30	99.91 27	99.04 195	99.61 47	99.46 74	99.78 15	99.94 16	99.60 77

E4		99.61 49	99.56 47	99.67 80	99.89 50	99.56 66	99.52 112	99.76 139	99.70 9	99.76 84	99.87 43	99.99 5	99.31 108	99.21 132	99.06 109	99.79 75	99.55 104

pmmvs-eth3d		99.61 49	99.48 65	99.75 55	99.87 70	99.30 154	99.75 43	99.89 55	99.23 93	99.85 50	99.88 40	99.97 18	99.49 79	99.46 74	99.01 121	99.68 121	99.52 116

v1144		99.61 49	99.43 77	99.82 28	99.88 63	99.41 122	99.76 39	99.86 66	99.64 26	99.84 54	99.95 6	99.49 171	99.74 22	99.00 173	98.93 133	99.84 51	99.58 90

v8		99.61 49	99.45 73	99.79 43	99.80 131	99.59 56	99.73 54	99.93 27	99.48 62	99.77 81	99.90 31	99.48 172	99.67 37	99.11 158	98.89 142	99.84 51	99.73 41

casdiffmvs		99.61 49	99.55 49	99.68 76	99.89 50	99.53 78	99.64 73	99.68 171	99.51 56	99.62 148	99.90 31	99.96 25	99.37 95	99.28 116	99.25 74	99.88 36	99.44 141
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CSCG		99.61 49	99.52 54	99.71 68	99.89 50	99.62 47	99.52 112	99.76 139	99.61 35	99.69 125	99.73 89	99.96 25	99.57 55	99.27 119	98.62 176	99.81 65	99.85 15

v1192		99.60 55	99.41 81	99.82 28	99.89 50	99.43 112	99.81 28	99.84 82	99.63 31	99.85 50	99.95 6	99.35 183	99.72 27	99.01 169	98.90 141	99.82 60	99.58 90

APDe-MVS		99.60 55	99.48 65	99.73 64	99.85 101	99.51 93	99.75 43	99.85 73	99.17 103	99.81 65	99.56 123	99.94 50	99.44 89	99.42 83	99.22 77	99.67 123	99.54 108
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
FE-MVSNET		99.59 57	99.41 81	99.80 38	99.80 131	99.53 78	99.83 20	99.87 61	99.06 119	99.88 30	99.47 142	99.94 50	99.71 30	99.58 60	99.06 109	99.73 101	99.26 180

v1921920		99.59 57	99.40 85	99.82 28	99.88 63	99.45 106	99.81 28	99.83 87	99.65 23	99.86 43	99.95 6	99.29 188	99.75 19	98.98 176	98.86 146	99.78 78	99.59 81

TranMVSNet+NR-MVSNet		99.59 57	99.42 80	99.80 38	99.87 70	99.55 68	99.64 73	99.86 66	99.05 123	99.88 30	99.72 95	99.33 186	99.64 44	99.47 73	99.14 89	99.91 25	99.67 57

EG-PatchMatch MVS		99.59 57	99.49 64	99.70 72	99.82 125	99.26 161	99.39 153	99.83 87	98.99 131	99.93 8	99.54 125	99.92 71	99.51 70	99.78 31	99.50 44	99.73 101	99.41 149

viewdifsd2359ckpt07		99.58 61	99.59 40	99.56 118	99.86 90	99.53 78	99.31 169	99.65 183	99.62 34	99.71 118	99.78 79	99.94 50	99.29 111	99.35 97	99.29 72	99.57 162	99.62 73

pmmvs5		99.58 61	99.47 68	99.70 72	99.84 107	99.50 94	99.58 90	99.80 111	98.98 134	99.73 106	99.92 18	99.81 123	99.49 79	99.28 116	99.05 114	99.77 82	99.73 41

v144192		99.58 61	99.39 87	99.80 38	99.87 70	99.44 108	99.77 35	99.84 82	99.64 26	99.86 43	99.93 16	99.35 183	99.72 27	98.92 182	98.82 151	99.74 96	99.66 58

v148		99.58 61	99.43 77	99.76 50	99.87 70	99.40 125	99.76 39	99.85 73	99.48 62	99.83 59	99.82 70	99.83 117	99.51 70	99.20 136	98.82 151	99.75 90	99.45 138

v1240		99.58 61	99.38 91	99.82 28	99.89 50	99.49 96	99.82 26	99.83 87	99.63 31	99.86 43	99.96 4	98.92 201	99.75 19	99.15 148	98.96 130	99.76 84	99.56 99

E5new		99.57 66	99.51 56	99.64 91	99.89 50	99.55 68	99.49 127	99.74 147	99.70 9	99.75 90	99.83 63	99.98 11	99.17 133	99.06 164	98.92 134	99.80 70	99.51 119

E5		99.57 66	99.51 56	99.64 91	99.89 50	99.55 68	99.49 127	99.74 147	99.70 9	99.75 90	99.83 63	99.98 11	99.17 133	99.06 164	98.92 134	99.80 70	99.51 119

V42		99.57 66	99.41 81	99.75 55	99.84 107	99.37 137	99.73 54	99.83 87	99.41 71	99.75 90	99.89 34	99.42 177	99.60 49	99.15 148	98.96 130	99.76 84	99.65 62

E3		99.56 69	99.50 62	99.62 98	99.87 70	99.52 87	99.43 143	99.72 152	99.64 26	99.74 97	99.83 63	99.97 18	99.18 131	99.13 154	98.92 134	99.76 84	99.51 119

TSAR-MVS + MP.		99.56 69	99.54 52	99.58 105	99.69 181	99.14 183	99.73 54	99.45 222	99.50 59	99.35 210	99.60 117	99.93 60	99.50 74	99.56 61	99.37 62	99.77 82	99.64 65
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v2v482		99.56 69	99.35 95	99.81 33	99.87 70	99.35 143	99.75 43	99.85 73	99.56 45	99.87 37	99.95 6	99.44 175	99.66 38	98.91 185	98.76 158	99.86 44	99.45 138

E3new		99.55 72	99.50 62	99.61 100	99.87 70	99.52 87	99.43 143	99.71 157	99.64 26	99.74 97	99.83 63	99.97 18	99.18 131	99.13 154	98.92 134	99.76 84	99.51 119

Gipuma		99.55 72	99.23 121	99.91 5	99.87 70	99.52 87	99.86 11	99.93 27	99.87 1	99.96 3	96.72 255	99.55 167	99.97 1	99.77 33	99.46 51	99.87 42	99.74 39
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
viewmanbaseed2359cas		99.53 74	99.46 71	99.61 100	99.85 101	99.49 96	99.37 156	99.69 163	99.54 50	99.68 134	99.73 89	99.96 25	99.32 104	99.14 151	98.86 146	99.76 84	99.52 116

DVP-MVS		99.53 74	99.51 56	99.55 119	99.82 125	99.58 60	99.54 106	99.78 121	99.28 89	99.21 222	99.70 100	99.97 18	99.32 104	99.32 104	99.14 89	99.64 136	99.58 90
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
diffmvs_AUTHOR		99.52 76	99.47 68	99.57 111	99.90 43	99.47 103	99.45 136	99.70 159	99.70 9	99.57 162	99.92 18	99.95 38	99.20 126	98.88 190	98.92 134	99.63 138	99.48 127

NR-MVSNet		99.52 76	99.29 107	99.80 38	99.96 7	99.38 133	99.55 101	99.81 103	98.86 152	99.87 37	99.51 136	98.81 203	99.72 27	99.86 18	99.04 116	99.89 34	99.54 108

usedtu_dtu_shiyan2		99.51 78	99.38 91	99.67 80	99.94 23	99.48 99	99.77 35	99.32 236	99.13 112	99.96 3	99.92 18	99.96 25	99.52 67	99.40 87	98.35 201	99.52 177	99.39 157

viewcassd2359sk11		99.51 78	99.45 73	99.57 111	99.84 107	99.50 94	99.37 156	99.67 176	99.58 40	99.72 110	99.79 77	99.92 71	99.08 153	99.07 163	98.81 154	99.73 101	99.48 127

ACMMPR		99.51 78	99.32 102	99.72 65	99.87 70	99.33 147	99.61 80	99.85 73	99.19 100	99.73 106	98.73 208	99.95 38	99.61 47	99.35 97	99.14 89	99.66 126	99.58 90

UniMVSNet (Re)		99.50 81	99.29 107	99.75 55	99.86 90	99.47 103	99.51 116	99.82 94	98.90 147	99.89 21	99.64 109	99.00 196	99.54 60	99.32 104	99.08 107	99.90 29	99.59 81

FMVSNet1		99.50 81	99.57 44	99.42 152	99.67 190	99.65 44	99.60 84	99.91 44	99.40 74	99.39 202	99.83 63	99.27 190	98.14 214	99.68 44	99.50 44	99.81 65	99.68 54

HyFIR lowres test		99.50 81	99.26 115	99.80 38	99.95 10	99.62 47	99.76 39	99.97 1	99.67 17	99.56 163	99.94 11	98.40 211	99.78 13	98.84 202	98.59 181	99.76 84	99.72 44

PM-MVS		99.49 84	99.43 77	99.57 111	99.76 157	99.34 146	99.53 108	99.77 129	98.93 143	99.75 90	99.46 143	99.83 117	99.11 151	99.72 39	99.29 72	99.49 184	99.46 137

MED-MVS		99.48 85	99.44 76	99.53 123	99.79 136	99.39 127	99.49 127	99.78 121	99.44 67	99.40 201	99.77 83	99.91 79	99.02 163	99.26 121	99.03 118	99.70 116	99.27 175

Anonymous20231206		99.48 85	99.31 104	99.69 74	99.79 136	99.57 62	99.63 77	99.79 116	98.88 149	99.91 16	99.72 95	99.93 60	99.59 51	99.24 124	98.63 174	99.43 194	99.18 184

DU-MVS		99.48 85	99.26 115	99.75 55	99.85 101	99.38 133	99.50 120	99.81 103	98.86 152	99.89 21	99.51 136	98.98 197	99.59 51	99.46 74	98.97 128	99.87 42	99.63 67

RPSCF		99.48 85	99.45 73	99.52 130	99.73 174	99.33 147	99.13 202	99.77 129	99.33 81	99.47 188	99.39 154	99.92 71	99.36 96	99.63 54	99.13 97	99.63 138	99.41 149

ACMMP_NAP		99.47 89	99.33 99	99.63 94	99.85 101	99.28 159	99.56 96	99.83 87	98.75 165	99.48 184	99.03 195	99.95 38	99.47 87	99.48 70	99.19 80	99.57 162	99.59 81

Anonymous20231211		99.47 89	99.39 87	99.57 111	99.89 50	99.60 50	99.50 120	99.69 163	98.91 146	99.62 148	99.17 181	99.35 183	98.86 184	99.63 54	99.46 51	99.84 51	99.62 73

SteuartSystems-ACMMP		99.47 89	99.22 124	99.76 50	99.88 63	99.36 139	99.65 72	99.84 82	98.47 190	99.80 67	98.68 211	99.96 25	99.68 34	99.37 94	99.06 109	99.72 109	99.66 58
Skip Steuart: Steuart Systems R&D Blog.
ACMM	98.37 12	99.47 89	99.23 121	99.74 61	99.86 90	99.19 177	99.68 65	99.86 66	99.16 107	99.71 118	98.52 221	99.95 38	99.62 46	99.35 97	99.02 119	99.74 96	99.42 147
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
E2		99.46 93	99.40 85	99.53 123	99.83 115	99.48 99	99.30 175	99.63 187	99.52 54	99.70 121	99.75 85	99.85 109	98.99 168	99.01 169	98.71 165	99.71 113	99.47 133

DVP-MVS++		99.46 93	99.57 44	99.33 174	99.75 161	99.57 62	99.44 139	99.81 103	99.38 77	98.56 263	99.81 74	99.99 5	98.79 190	99.33 102	99.13 97	99.62 146	99.81 22

HFP-MVS		99.46 93	99.30 105	99.65 86	99.82 125	99.25 165	99.50 120	99.82 94	99.23 93	99.58 159	98.86 199	99.94 50	99.56 56	99.14 151	99.12 101	99.63 138	99.56 99

LGP-MVS_train		99.46 93	99.18 135	99.78 44	99.87 70	99.25 165	99.71 62	99.87 61	98.02 222	99.79 72	98.90 198	99.96 25	99.66 38	99.49 68	99.17 85	99.79 75	99.49 124

dtuplus		99.45 97	99.35 95	99.58 105	99.83 115	99.43 112	99.60 84	99.72 152	99.41 71	99.50 178	99.80 75	99.91 79	99.08 153	98.84 202	98.54 183	99.73 101	99.48 127

viewdifsd2359ckpt13		99.45 97	99.39 87	99.53 123	99.83 115	99.44 108	99.17 196	99.66 181	99.51 56	99.66 141	99.75 85	99.92 71	99.14 145	99.01 169	98.62 176	99.72 109	99.47 133

MVSMamba_PlusPlus		99.45 97	99.52 54	99.36 171	99.79 136	99.54 74	98.88 234	99.26 239	98.97 135	99.22 220	99.51 136	99.80 129	99.29 111	99.65 51	99.37 62	99.73 101	99.82 18

SED-MVS		99.45 97	99.46 71	99.42 152	99.77 152	99.57 62	99.42 145	99.80 111	99.06 119	99.38 203	99.66 105	99.96 25	98.65 200	99.31 106	99.14 89	99.53 175	99.55 104

ETV-MVS		99.45 97	99.32 102	99.60 102	99.79 136	99.60 50	99.40 150	99.78 121	97.88 228	99.83 59	99.33 159	99.70 150	98.97 169	99.74 36	99.43 55	99.84 51	99.58 90

ACMP	98.32 13	99.44 102	99.18 135	99.75 55	99.83 115	99.18 178	99.64 73	99.83 87	98.81 161	99.79 72	98.42 230	99.96 25	99.64 44	99.46 74	98.98 127	99.74 96	99.44 141
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
viewmamba		99.43 103	99.36 93	99.50 135	99.87 70	99.40 125	99.29 179	99.62 190	99.64 26	99.56 163	99.87 43	99.94 50	99.16 138	98.78 209	98.50 188	99.54 173	99.37 161

DCV-MVSNet		99.43 103	99.23 121	99.67 80	99.92 33	99.76 22	99.64 73	99.93 27	99.06 119	99.68 134	97.77 242	98.97 198	98.97 169	99.72 39	99.54 39	99.88 36	99.81 22

SMA-MVS		99.43 103	99.41 81	99.45 147	99.82 125	99.31 152	99.02 218	99.59 198	99.06 119	99.34 213	99.53 131	99.96 25	99.38 93	99.29 111	99.13 97	99.53 175	99.59 81
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
testgi		99.43 103	99.47 68	99.38 161	99.90 43	99.67 41	99.30 175	99.73 151	98.64 178	99.53 168	99.52 133	99.90 84	98.08 217	99.65 51	99.40 61	99.75 90	99.55 104

DELS-MVS		99.42 107	99.53 53	99.29 178	99.52 218	99.43 112	99.42 145	99.28 238	99.16 107	99.72 110	99.82 70	99.97 18	98.17 211	99.56 61	99.16 86	99.65 128	99.59 81
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
3Dnovator	99.16 3	99.42 107	99.22 124	99.65 86	99.78 143	99.13 187	99.50 120	99.85 73	99.40 74	99.80 67	98.59 217	99.79 134	99.30 110	99.20 136	99.06 109	99.71 113	99.35 165

onestephybrid01		99.41 109	99.35 95	99.49 137	99.88 63	99.41 122	99.45 136	99.61 191	99.44 67	99.59 155	99.88 40	99.90 84	98.88 182	98.83 204	98.60 180	99.54 173	99.35 165

viewmambaseed2359dif		99.41 109	99.27 113	99.58 105	99.83 115	99.42 117	99.56 96	99.68 171	99.27 90	99.58 159	99.80 75	99.85 109	99.14 145	98.70 216	98.41 196	99.67 123	99.47 133

DPE-MVS		99.41 109	99.36 93	99.47 141	99.66 191	99.48 99	99.46 135	99.75 145	98.65 174	99.41 198	99.67 103	99.95 38	98.82 185	99.21 132	99.14 89	99.72 109	99.40 154
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UniMVSNet_NR-MVSNet		99.41 109	99.12 147	99.76 50	99.86 90	99.48 99	99.50 120	99.81 103	98.84 155	99.89 21	99.45 144	98.32 214	99.59 51	99.22 128	98.89 142	99.90 29	99.63 67

CP-MVS		99.41 109	99.20 130	99.65 86	99.80 131	99.23 172	99.44 139	99.75 145	98.60 183	99.74 97	98.66 212	99.93 60	99.48 82	99.33 102	99.16 86	99.73 101	99.48 127

QAPM		99.41 109	99.21 129	99.64 91	99.78 143	99.16 180	99.51 116	99.85 73	99.20 97	99.72 110	99.43 145	99.81 123	99.25 119	98.87 192	98.71 165	99.71 113	99.30 171

aaEdge-Enhanced		99.40 115	99.34 98	99.48 139	99.78 143	99.36 139	99.75 43	99.46 220	99.08 114	99.38 203	99.77 83	99.89 87	99.07 157	99.16 147	98.84 149	99.41 198	99.27 175

UGNet		99.40 115	99.61 34	99.16 200	99.88 63	99.64 45	99.61 80	99.77 129	99.31 83	99.63 147	99.33 159	99.93 60	96.46 252	99.63 54	99.53 40	99.63 138	99.89 5
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
Vis-MVSNet (Re-imp)		99.40 115	99.28 110	99.55 119	99.92 33	99.68 38	99.31 169	99.87 61	98.69 171	99.16 225	99.08 190	98.64 207	99.20 126	99.65 51	99.46 51	99.83 56	99.72 44

hybridnocas07		99.39 118	99.33 99	99.47 141	99.86 90	99.39 127	99.35 162	99.63 187	99.55 47	99.48 184	99.87 43	99.83 117	98.90 181	98.71 215	98.44 194	99.56 167	99.50 123

OPM-MVS		99.39 118	99.22 124	99.59 103	99.76 157	98.82 213	99.51 116	99.79 116	99.17 103	99.53 168	99.31 164	99.95 38	99.35 97	99.22 128	98.79 157	99.60 153	99.27 175
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Fast-Effi-MVS+		99.39 118	99.18 135	99.63 94	99.86 90	99.28 159	99.45 136	99.91 44	98.47 190	99.61 151	99.50 139	99.57 164	99.17 133	99.24 124	98.66 171	99.78 78	99.59 81

LS3D		99.39 118	99.28 110	99.52 130	99.77 152	99.39 127	99.55 101	99.82 94	98.93 143	99.64 145	98.52 221	99.67 154	98.58 204	99.74 36	99.63 31	99.75 90	99.06 201

diffmvs		99.38 122	99.33 99	99.45 147	99.87 70	99.39 127	99.28 184	99.58 202	99.55 47	99.50 178	99.85 56	99.85 109	98.94 175	98.58 222	98.68 169	99.51 181	99.39 157
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt09		99.37 123	99.29 107	99.46 144	99.83 115	99.42 117	99.12 205	99.63 187	99.52 54	99.67 136	99.73 89	99.67 154	98.91 177	98.81 207	98.47 189	99.61 148	99.42 147

usedtu_dtu_shiyan1		99.36 124	99.20 130	99.55 119	99.40 246	99.35 143	99.56 96	99.69 163	98.96 137	99.81 65	99.52 133	99.66 156	99.24 120	99.14 151	98.63 174	99.60 153	99.18 184

CANet		99.36 124	99.39 87	99.34 173	99.80 131	99.35 143	99.41 149	99.47 217	99.20 97	99.74 97	99.54 125	99.68 152	98.05 219	99.23 126	98.97 128	99.57 162	99.73 41

ACMMP		99.36 124	99.06 155	99.71 68	99.86 90	99.36 139	99.63 77	99.85 73	98.33 205	99.72 110	97.73 244	99.94 50	99.53 61	99.37 94	99.13 97	99.65 128	99.56 99
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
hybrid		99.35 127	99.28 110	99.44 149	99.86 90	99.39 127	99.32 166	99.61 191	99.51 56	99.49 181	99.87 43	99.72 145	98.92 176	98.65 219	98.40 197	99.47 187	99.40 154

SD-MVS		99.35 127	99.26 115	99.46 144	99.66 191	99.15 182	98.92 229	99.67 176	99.55 47	99.35 210	98.83 201	99.91 79	99.35 97	99.19 139	98.53 185	99.78 78	99.68 54
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
MP-MVS		99.35 127	99.09 153	99.65 86	99.84 107	99.22 173	99.59 86	99.78 121	98.13 214	99.67 136	98.44 226	99.93 60	99.43 91	99.31 106	99.09 105	99.60 153	99.49 124
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
pmmvs4		99.34 130	99.15 142	99.57 111	99.77 152	98.90 206	99.51 116	99.77 129	99.07 117	99.73 106	99.72 95	99.84 115	99.07 157	98.85 197	98.39 199	99.55 171	99.27 175

EPP-MVSNet		99.34 130	99.10 151	99.62 98	99.94 23	99.74 30	99.66 71	99.80 111	99.07 117	98.93 244	99.61 114	96.13 230	99.49 79	99.67 47	99.63 31	99.92 22	99.86 13

TSAR-MVS + GP.		99.33 132	99.17 139	99.51 132	99.71 179	99.00 200	98.84 238	99.71 157	98.23 211	99.74 97	99.53 131	99.90 84	99.35 97	99.38 93	98.85 148	99.72 109	99.31 169

PHI-MVS		99.33 132	99.19 133	99.49 137	99.69 181	99.25 165	99.27 185	99.59 198	98.44 194	99.78 76	99.15 182	99.92 71	98.95 174	99.39 91	99.04 116	99.64 136	99.18 184

MSP-MVS		99.32 134	99.26 115	99.38 161	99.76 157	99.54 74	99.42 145	99.72 152	98.92 145	98.84 252	98.96 197	99.96 25	98.91 177	98.72 214	99.14 89	99.63 138	99.58 90
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		99.32 134	98.99 164	99.71 68	99.86 90	99.31 152	99.59 86	99.86 66	97.51 239	99.75 90	98.23 234	99.94 50	99.53 61	99.29 111	99.08 107	99.65 128	99.54 108

DeepC-MVS_fast	98.69 9	99.32 134	99.13 145	99.53 123	99.63 197	98.78 216	99.53 108	99.33 235	99.08 114	99.77 81	99.18 180	99.89 87	99.29 111	99.00 173	98.70 167	99.65 128	99.30 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		99.32 134	99.09 153	99.58 105	99.75 161	98.74 220	99.36 159	99.54 206	99.14 110	99.72 110	99.24 170	99.89 87	99.51 70	99.30 108	98.76 158	99.62 146	98.54 222

TSAR-MVS + ACMM		99.31 138	99.26 115	99.37 167	99.66 191	98.97 203	99.20 193	99.56 204	99.33 81	99.19 224	99.54 125	99.91 79	99.32 104	99.12 156	98.34 203	99.29 210	99.65 62

3Dnovator+	98.92 7	99.31 138	99.03 159	99.63 94	99.77 152	98.90 206	99.52 112	99.81 103	99.37 78	99.72 110	98.03 239	99.73 144	99.32 104	98.99 175	98.81 154	99.67 123	99.36 163

X-MVS		99.30 140	98.99 164	99.66 84	99.85 101	99.30 154	99.49 127	99.82 94	98.32 206	99.69 125	97.31 253	99.93 60	99.50 74	99.37 94	99.16 86	99.60 153	99.53 111

MVS_111021_HR		99.30 140	99.14 143	99.48 139	99.58 214	99.25 165	99.27 185	99.61 191	98.74 167	99.66 141	99.02 196	99.84 115	99.33 101	99.20 136	98.76 158	99.44 191	99.18 184

TAPA-MVS	98.54 10	99.30 140	99.24 120	99.36 171	99.44 236	98.77 218	99.00 220	99.41 226	99.23 93	99.60 153	99.50 139	99.86 103	99.15 143	99.29 111	98.95 132	99.56 167	99.08 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 140	99.01 163	99.63 94	99.75 161	98.89 209	99.35 162	99.60 195	98.53 188	99.86 43	99.57 121	99.94 50	99.52 67	98.96 177	98.10 216	99.70 116	99.08 197
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 144	98.98 166	99.65 86	99.72 176	98.87 211	99.47 132	99.66 181	99.35 80	99.87 37	99.58 120	99.87 101	99.51 70	98.85 197	97.93 222	99.65 128	98.38 226

PMVS	94.32 17	99.27 145	99.55 49	98.94 217	99.60 206	99.43 112	99.39 153	99.54 206	98.99 131	99.69 125	99.60 117	99.81 123	95.68 259	99.88 15	99.83 7	99.73 101	99.31 169
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dtuonlycased		99.26 146	99.27 113	99.24 186	99.84 107	99.49 96	99.47 132	99.22 241	99.27 90	99.21 222	99.94 11	99.76 139	99.11 151	99.12 156	98.54 183	98.62 236	98.76 216

FA-MVS(training)		99.26 146	99.12 147	99.44 149	99.60 206	99.26 161	99.24 190	99.97 1	98.84 155	99.76 84	99.43 145	98.74 204	98.47 207	99.39 91	99.10 103	99.57 162	99.07 200

MVS_111021_LR		99.25 148	99.13 145	99.39 157	99.50 226	99.14 183	99.23 191	99.50 214	98.67 172	99.61 151	99.12 186	99.81 123	99.16 138	99.28 116	98.67 170	99.35 206	99.21 183

ECVR-MVS		99.24 149	98.74 190	99.82 28	99.95 10	99.78 18	99.67 69	99.93 27	99.45 65	99.80 67	99.86 53	92.58 256	99.65 40	99.93 3	99.88 3	99.94 16	99.71 50

baseline		99.24 149	99.30 105	99.17 199	99.78 143	99.14 183	99.10 207	99.69 163	98.97 135	99.49 181	99.84 59	99.88 94	97.99 224	98.85 197	98.73 163	98.98 225	99.72 44

EIA-MVS		99.23 151	99.03 159	99.47 141	99.83 115	99.64 45	99.16 198	99.81 103	97.11 252	99.65 144	98.44 226	99.78 137	98.61 203	99.46 74	99.22 77	99.75 90	99.59 81

HPM-MVS++		99.23 151	98.98 166	99.53 123	99.75 161	99.02 198	99.44 139	99.77 129	98.65 174	99.52 174	98.72 209	99.92 71	99.33 101	98.77 212	98.40 197	99.40 200	99.36 163

PMMVS2		99.23 151	99.22 124	99.24 186	99.80 131	99.14 183	99.50 120	99.82 94	99.12 113	98.41 269	99.91 27	99.98 11	98.51 205	99.48 70	98.76 158	99.38 202	98.14 234

MGCNet		99.22 154	99.22 124	99.23 188	99.87 70	99.58 60	99.70 63	99.59 198	99.58 40	98.98 240	99.40 152	97.31 227	97.53 233	99.41 86	99.43 55	99.69 118	99.81 22

test1111		99.21 155	98.67 196	99.84 23	99.96 7	99.82 8	99.72 59	99.94 24	99.54 50	99.78 76	99.89 34	91.89 259	99.69 32	99.93 3	99.89 1	99.95 7	99.75 37

CPTT-MVS		99.21 155	98.89 176	99.58 105	99.72 176	99.12 190	99.30 175	99.76 139	98.62 179	99.66 141	97.51 249	99.89 87	99.48 82	99.01 169	98.64 173	99.58 161	99.40 154

TinyColmap		99.21 155	98.89 176	99.59 103	99.61 202	98.61 228	99.47 132	99.67 176	99.02 127	99.82 62	99.15 182	99.74 141	99.35 97	99.17 145	98.33 204	99.63 138	98.22 232

Effi-MVS+		99.20 158	98.93 171	99.50 135	99.79 136	99.26 161	98.82 241	99.96 12	98.37 204	99.60 153	99.12 186	98.36 212	99.05 161	98.93 180	98.82 151	99.78 78	99.68 54

PVSNet_BlendedMVS		99.20 158	99.17 139	99.23 188	99.69 181	99.33 147	99.04 212	99.13 243	98.41 199	99.79 72	99.33 159	99.36 180	98.10 215	99.29 111	98.87 144	99.65 128	99.56 99

PVSNet_Blended		99.20 158	99.17 139	99.23 188	99.69 181	99.33 147	99.04 212	99.13 243	98.41 199	99.79 72	99.33 159	99.36 180	98.10 215	99.29 111	98.87 144	99.65 128	99.56 99

MCST-MVS		99.17 161	98.82 185	99.57 111	99.75 161	98.70 224	99.25 189	99.69 163	98.62 179	99.59 155	98.54 219	99.79 134	99.53 61	98.48 226	98.15 212	99.63 138	99.43 144

APD-MVS		99.17 161	98.92 172	99.46 144	99.78 143	99.24 170	99.34 164	99.78 121	97.79 231	99.48 184	98.25 233	99.88 94	98.77 192	99.18 142	98.92 134	99.63 138	99.18 184
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 161	98.85 180	99.53 123	99.75 161	99.06 196	99.36 159	99.82 94	98.28 208	99.76 84	98.47 223	99.61 160	98.91 177	98.80 208	98.70 167	99.60 153	99.04 205

IterMVS-LS		99.16 164	98.82 185	99.57 111	99.87 70	99.71 34	99.58 90	99.92 38	99.24 92	99.71 118	99.73 89	95.79 231	98.91 177	98.82 206	98.66 171	99.43 194	99.77 33
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 164	99.20 130	99.12 204	99.20 259	98.71 223	98.85 237	99.06 246	99.17 103	98.96 243	99.61 114	99.86 103	99.29 111	99.17 145	98.72 164	99.36 204	99.15 193

IterMVS-SCA-FT		99.15 166	98.96 168	99.38 161	99.87 70	99.54 74	99.53 108	99.79 116	98.94 141	99.82 62	99.92 18	97.65 221	98.82 185	98.95 179	98.26 206	98.45 238	99.47 133

CDS-MVSNet		99.15 166	99.10 151	99.21 195	99.59 211	99.22 173	99.48 131	99.47 217	98.89 148	99.41 198	99.84 59	98.11 217	97.76 227	99.26 121	99.01 121	99.57 162	99.38 159
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 166	99.12 147	99.19 197	99.92 33	99.73 32	99.55 101	99.86 66	98.45 193	96.91 275	98.74 207	98.33 213	99.02 163	99.54 63	99.47 49	99.88 36	99.61 76

dmvs_re		99.14 169	98.76 188	99.58 105	99.75 161	99.38 133	99.30 175	99.68 171	96.94 257	99.74 97	97.70 245	99.20 192	99.29 111	99.22 128	99.35 66	99.73 101	99.55 104

MDA-MVSNet-bldmvs		99.11 170	99.11 150	99.12 204	99.91 37	99.38 133	99.77 35	98.72 250	99.31 83	99.85 50	99.43 145	98.26 215	99.48 82	99.85 19	98.47 189	96.99 258	99.08 197

OMC-MVS		99.11 170	98.95 169	99.29 178	99.37 248	98.57 230	99.19 194	99.20 242	98.87 151	99.58 159	99.13 184	99.88 94	99.00 165	99.19 139	98.46 191	99.43 194	98.57 221

MVS_Test		99.09 172	98.92 172	99.29 178	99.61 202	99.07 195	99.04 212	99.81 103	98.58 185	99.37 207	99.74 87	98.87 202	98.41 209	98.61 221	98.01 220	99.50 183	99.57 97

CNVR-MVS		99.08 173	98.83 182	99.37 167	99.61 202	98.74 220	99.15 199	99.54 206	98.59 184	99.37 207	98.15 236	99.88 94	99.08 153	98.91 185	98.46 191	99.48 185	99.06 201

IterMVS		99.08 173	98.90 175	99.29 178	99.87 70	99.53 78	99.52 112	99.77 129	98.94 141	99.75 90	99.91 27	97.52 225	98.72 196	98.86 195	98.14 213	98.09 241	99.43 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 175	99.19 133	98.93 219	99.02 264	99.53 78	99.31 169	99.84 82	98.86 152	98.88 247	99.64 109	98.44 210	96.92 245	99.35 97	99.00 125	99.61 148	99.53 111

CVMVSNet		99.06 176	98.88 179	99.28 182	99.52 218	99.53 78	99.42 145	99.69 163	98.74 167	98.27 271	99.89 34	95.48 237	99.44 89	99.46 74	99.33 67	99.32 209	99.75 37

CDPH-MVS		99.05 177	98.63 197	99.54 122	99.75 161	98.78 216	99.59 86	99.68 171	97.79 231	99.37 207	98.20 235	99.86 103	99.14 145	98.58 222	98.01 220	99.68 121	99.16 191

TAMVS		99.05 177	99.02 162	99.08 209	99.69 181	99.22 173	99.33 165	99.32 236	99.16 107	98.97 242	99.87 43	97.36 226	97.76 227	99.21 132	99.00 125	99.44 191	99.33 167

dtuonly		99.03 179	98.84 181	99.25 185	99.90 43	98.95 204	99.44 139	99.47 217	99.05 123	99.30 215	99.94 11	99.72 145	98.81 187	98.29 229	97.35 233	98.60 237	98.59 220

CANet_DTU		99.03 179	99.18 135	98.87 222	99.58 214	99.03 197	99.18 195	99.41 226	98.65 174	99.74 97	99.55 124	99.71 147	96.13 257	99.19 139	98.92 134	99.17 219	99.18 184

Effi-MVS+-dtu		99.01 181	99.05 156	98.98 213	99.60 206	99.13 187	99.03 217	99.61 191	98.52 189	99.01 237	98.53 220	99.83 117	96.95 244	99.48 70	98.59 181	99.66 126	99.25 182

sasdasda		99.00 182	98.68 194	99.37 167	99.68 187	99.42 117	98.94 227	99.89 55	99.00 129	98.99 238	98.43 228	95.69 233	98.96 172	99.18 142	99.18 82	99.74 96	99.88 7

canonicalmvs		99.00 182	98.68 194	99.37 167	99.68 187	99.42 117	98.94 227	99.89 55	99.00 129	98.99 238	98.43 228	95.69 233	98.96 172	99.18 142	99.18 82	99.74 96	99.88 7

MIMVSNet		99.00 182	99.03 159	98.97 216	99.32 254	99.32 151	99.39 153	99.91 44	98.41 199	98.76 255	99.24 170	99.17 193	97.13 238	99.30 108	98.80 156	99.29 210	99.01 206

CHOSEN 280x420		98.99 185	98.91 174	99.07 210	99.77 152	99.26 161	99.55 101	99.92 38	98.62 179	98.67 260	99.62 113	97.20 228	98.44 208	99.50 67	99.18 82	98.08 242	98.99 209

MGCFI-Net		98.98 186	98.69 193	99.33 174	99.68 187	99.42 117	98.95 225	99.90 53	99.04 126	98.88 247	98.45 225	95.64 235	98.81 187	99.15 148	99.21 79	99.75 90	99.90 2

SF-MVS		98.96 187	98.95 169	98.98 213	99.64 196	98.89 209	98.00 268	99.58 202	98.42 197	99.08 230	98.63 214	99.83 117	98.04 221	99.02 168	98.76 158	99.52 177	99.13 194

GBi-Net		98.96 187	99.05 156	98.85 223	99.02 264	99.53 78	99.31 169	99.78 121	98.13 214	98.48 265	99.43 145	97.58 222	96.92 245	99.68 44	99.50 44	99.61 148	99.53 111

test1		98.96 187	99.05 156	98.85 223	99.02 264	99.53 78	99.31 169	99.78 121	98.13 214	98.48 265	99.43 145	97.58 222	96.92 245	99.68 44	99.50 44	99.61 148	99.53 111

PCF-MVS	97.86 15	98.95 190	98.53 202	99.44 149	99.70 180	98.80 215	98.96 222	99.69 163	98.65 174	99.59 155	99.33 159	99.94 50	99.12 150	98.01 237	97.11 234	99.59 160	97.83 243
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 191	98.71 192	99.21 195	99.52 218	98.22 246	98.97 221	99.53 211	98.76 163	99.50 178	98.59 217	99.56 166	98.68 197	98.63 220	98.45 193	99.05 222	98.73 217

AdaColmap		98.93 192	98.53 202	99.39 157	99.52 218	98.65 227	99.11 206	99.59 198	98.08 218	99.44 191	97.46 251	99.45 173	99.24 120	98.92 182	98.44 194	99.44 191	98.73 217

MSLP-MVS++		98.92 193	98.73 191	99.14 201	99.44 236	99.00 200	98.36 257	99.35 232	98.82 160	99.38 203	96.06 259	99.79 134	99.07 157	98.88 190	99.05 114	99.27 212	99.53 111

new_pmnet		98.91 194	98.89 176	98.94 217	99.51 224	98.27 242	99.15 199	98.66 251	99.17 103	99.48 184	99.79 77	99.80 129	98.49 206	99.23 126	98.20 210	98.34 239	97.74 247

train_agg		98.89 195	98.48 207	99.38 161	99.69 181	98.76 219	99.31 169	99.60 195	97.71 233	98.98 240	97.89 240	99.89 87	99.29 111	98.32 227	97.59 229	99.42 197	99.16 191

NCCC		98.88 196	98.42 208	99.42 152	99.62 198	98.81 214	99.10 207	99.54 206	98.76 163	99.53 168	95.97 260	99.80 129	99.16 138	98.49 225	98.06 219	99.55 171	99.05 203

PLC	97.83 16	98.88 196	98.52 204	99.30 177	99.45 234	98.60 229	98.65 247	99.49 215	98.66 173	99.59 155	96.33 256	99.59 163	99.17 133	98.87 192	98.53 185	99.46 188	99.05 203
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 198	98.60 198	99.13 202	99.66 191	98.72 222	99.37 156	99.06 246	98.43 196	99.76 84	99.74 87	99.55 167	99.15 143	99.04 166	96.00 242	97.80 246	98.72 219

Fast-Effi-MVS+-dtu		98.82 199	98.80 187	98.84 225	99.51 224	98.90 206	98.96 222	99.91 44	98.29 207	99.11 229	98.47 223	99.63 159	96.03 258	99.21 132	98.12 214	99.52 177	99.01 206

CNLPA		98.82 199	98.52 204	99.18 198	99.21 258	98.50 234	98.73 245	99.34 234	98.73 169	99.56 163	97.55 248	99.42 177	99.06 160	98.93 180	98.10 216	99.21 218	98.38 226

PatchMatch-RL		98.80 201	98.52 204	99.12 204	99.38 247	98.70 224	98.56 250	99.55 205	97.81 230	99.34 213	97.57 247	99.31 187	98.67 198	99.27 119	98.62 176	99.22 217	98.35 228

thisisatest0530		98.78 202	98.26 211	99.39 157	99.78 143	99.43 112	99.07 209	99.64 185	98.44 194	99.42 196	99.22 174	92.68 255	98.63 201	99.30 108	99.14 89	99.80 70	99.60 77

tttt0517		98.77 203	98.25 213	99.38 161	99.79 136	99.46 105	99.07 209	99.64 185	98.40 202	99.38 203	99.21 176	92.54 257	98.63 201	99.34 101	99.14 89	99.80 70	99.62 73

DI_MVS_pp		98.74 204	98.08 221	99.51 132	99.79 136	99.29 158	99.61 80	99.60 195	99.20 97	99.46 189	99.09 189	92.93 249	98.97 169	98.27 231	98.35 201	99.65 128	99.45 138

TSAR-MVS + COLMAP		98.74 204	98.58 200	98.93 219	99.29 255	98.23 243	99.04 212	99.24 240	98.79 162	98.80 254	99.37 156	99.71 147	98.06 218	98.02 236	97.46 231	99.16 220	98.48 224

MDTV_nov1_ep13_2view		98.73 206	98.31 210	99.22 192	99.75 161	99.24 170	99.75 43	99.93 27	99.31 83	99.84 54	99.86 53	99.81 123	99.31 108	97.40 246	94.77 244	96.73 260	97.81 244

PMMVS		98.71 207	98.55 201	98.90 221	99.28 256	98.45 236	98.53 253	99.45 222	97.67 235	99.15 227	98.76 205	99.54 169	97.79 226	98.77 212	98.23 208	99.16 220	98.46 225

HQP-MVS		98.70 208	98.19 217	99.28 182	99.61 202	98.52 232	98.71 246	99.35 232	97.97 225	99.53 168	97.38 252	99.85 109	99.14 145	97.53 241	96.85 238	99.36 204	99.26 180

N_pmnet		98.64 209	98.23 216	99.11 207	99.78 143	99.25 165	99.75 43	99.39 230	99.65 23	99.70 121	99.78 79	99.89 87	98.81 187	97.60 240	94.28 246	97.24 256	97.15 255

CMPMVS	76.62 19	98.64 209	98.60 198	98.68 235	99.33 252	97.07 268	98.11 266	98.50 252	97.69 234	99.26 217	98.35 232	99.66 156	97.62 230	99.43 82	99.02 119	99.24 215	99.01 206
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 211	98.75 189	98.49 242	98.10 270	99.44 108	99.02 218	99.78 121	98.13 214	98.48 265	99.43 145	97.58 222	96.16 256	98.85 197	98.39 199	99.40 200	99.41 149

GA-MVS		98.59 212	98.15 218	99.09 208	99.59 211	99.13 187	98.84 238	99.52 213	98.61 182	99.35 210	99.67 103	93.03 248	97.73 229	98.90 189	98.26 206	99.51 181	99.48 127

MAR-MVS		98.54 213	98.15 218	98.98 213	99.37 248	98.09 249	98.56 250	99.65 183	96.11 267	99.27 216	97.16 254	99.50 170	98.03 222	98.87 192	98.23 208	99.01 223	99.13 194
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
new-patchmatchnet		98.49 214	97.60 223	99.53 123	99.90 43	99.55 68	99.77 35	99.48 216	99.67 17	99.86 43	99.98 3	99.98 11	99.50 74	96.90 248	91.52 252	98.67 233	95.62 264

FPMVS		98.48 215	98.83 182	98.07 253	99.09 262	97.98 253	99.07 209	98.04 258	98.99 131	99.22 220	98.85 200	99.43 176	93.79 268	99.66 49	99.11 102	99.24 215	97.76 245

MVS-HIRNet		98.45 216	98.25 213	98.69 234	99.12 260	97.81 259	98.55 252	99.85 73	98.58 185	99.67 136	99.61 114	99.86 103	97.46 234	97.95 238	96.37 240	97.49 253	97.56 250

test0.0.03 1		98.41 217	98.41 209	98.40 246	99.62 198	99.16 180	98.87 235	99.41 226	97.15 250	96.60 277	99.31 164	97.00 229	96.55 251	98.91 185	98.51 187	99.37 203	98.82 213

gg-mvs-nofinetune		98.40 218	98.26 211	98.57 239	99.83 115	98.86 212	98.77 244	99.97 1	99.57 44	99.99 1	99.99 1	93.81 245	93.50 269	98.91 185	98.20 210	99.33 208	98.52 223

baseline1		98.39 219	97.59 224	99.31 176	99.78 143	99.45 106	99.13 202	99.53 211	98.06 220	98.87 249	98.63 214	90.04 263	98.76 193	98.85 197	98.84 149	99.81 65	99.28 173

pmnet_mix02		98.28 220	97.48 226	99.22 192	99.78 143	99.12 190	99.68 65	99.39 230	99.49 61	99.86 43	99.82 70	99.89 87	99.23 122	95.54 251	92.36 249	97.38 254	96.14 262

PatchT		98.11 221	97.12 232	99.26 184	99.65 195	98.34 240	99.57 95	99.97 1	97.48 241	99.43 193	99.04 194	90.84 261	98.15 212	98.04 234	97.78 223	98.82 230	98.30 229

DPM-MVS		98.10 222	97.32 230	99.01 212	99.52 218	97.92 254	98.47 255	99.45 222	98.25 209	98.91 245	93.99 269	99.69 151	98.73 195	96.29 250	96.32 241	99.00 224	98.77 214

EPNet_dtu		98.09 223	98.25 213	97.91 255	99.58 214	98.02 252	98.19 262	99.67 176	97.94 226	99.74 97	99.07 192	98.71 206	93.40 270	97.50 242	97.09 235	96.89 259	99.44 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 224	98.11 220	98.00 254	99.60 206	98.99 202	98.38 256	99.68 171	98.18 213	98.85 251	97.89 240	95.60 236	92.72 271	98.30 228	98.10 216	98.76 231	99.72 44
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 225	96.80 235	99.22 192	99.60 206	98.23 243	98.91 230	99.97 1	96.89 260	99.43 193	99.10 188	89.24 266	98.15 212	98.04 234	97.78 223	99.26 213	98.30 229

thres200		97.87 226	96.56 237	99.39 157	99.76 157	99.52 87	99.13 202	99.76 139	96.88 262	98.66 261	92.87 273	88.77 269	99.16 138	99.11 158	99.42 58	99.88 36	99.33 167

baseline2		97.87 226	97.18 231	98.67 236	99.34 251	99.17 179	98.48 254	98.82 249	97.08 253	98.83 253	98.75 206	89.47 265	97.03 243	98.67 218	98.27 205	99.52 177	98.83 212

thres600view7		97.86 228	96.53 240	99.41 155	99.84 107	99.52 87	99.36 159	99.76 139	97.32 248	98.38 270	93.24 270	87.25 271	99.23 122	99.11 158	99.75 18	99.88 36	99.48 127

tfpn200view9		97.85 229	96.54 238	99.38 161	99.74 172	99.52 87	99.17 196	99.76 139	96.10 268	98.70 258	92.99 271	89.10 267	99.00 165	99.11 158	99.56 35	99.88 36	99.41 149

thres400		97.82 230	96.47 241	99.40 156	99.81 130	99.44 108	99.29 179	99.69 163	97.15 250	98.57 262	92.82 274	87.96 270	99.16 138	98.96 177	99.55 38	99.86 44	99.41 149

IB-MVS	98.10 14	97.76 231	97.40 229	98.18 249	99.62 198	99.11 192	98.24 260	98.35 254	96.56 264	99.44 191	91.28 275	98.96 200	93.84 267	98.09 233	98.62 176	99.56 167	99.18 184
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
test-LLR		97.74 232	97.46 227	98.08 251	99.62 198	98.37 238	98.26 258	99.41 226	97.03 254	97.38 273	99.54 125	92.89 250	95.12 264	98.78 209	97.68 227	98.65 234	97.90 241

RPMNet		97.70 233	96.54 238	99.06 211	99.57 217	98.23 243	98.95 225	99.97 1	96.89 260	99.49 181	99.13 184	89.63 264	97.09 240	96.68 249	97.02 236	99.26 213	98.19 233

thres100view900		97.69 234	96.37 242	99.23 188	99.74 172	99.21 176	98.81 242	99.43 225	96.10 268	98.70 258	92.99 271	89.10 267	98.88 182	98.58 222	99.31 69	99.82 60	99.27 175

FMVSNet5		97.69 234	96.98 233	98.53 241	98.53 268	99.36 139	98.90 233	99.54 206	96.38 265	98.44 268	95.38 266	90.08 262	97.05 242	99.46 74	99.06 109	98.73 232	99.12 196

MVE	91.08 18	97.68 236	97.65 222	97.71 261	98.46 269	91.62 277	97.92 269	98.86 248	98.73 169	97.99 272	98.64 213	99.96 25	99.17 133	99.59 58	97.75 225	93.87 277	97.27 252
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 237	97.57 225	97.75 259	98.90 267	98.56 231	98.15 264	98.45 253	96.92 259	96.84 276	99.52 133	92.53 258	95.24 263	99.04 166	98.12 214	98.90 227	98.29 231

TESTMET0.1,1		97.62 238	97.46 227	97.81 257	99.07 263	98.37 238	98.26 258	98.35 254	97.03 254	97.38 273	99.54 125	92.89 250	95.12 264	98.78 209	97.68 227	98.65 234	97.90 241

test2506		97.57 239	95.67 250	99.78 44	99.95 10	99.78 18	99.67 69	99.93 27	99.45 65	99.55 167	99.20 177	71.73 285	99.65 40	99.93 3	99.88 3	99.94 16	99.72 44

MVSTER		97.55 240	96.75 236	98.48 243	99.46 232	99.54 74	98.24 260	99.77 129	97.56 238	99.41 198	99.31 164	84.86 279	94.66 266	98.86 195	97.75 225	99.34 207	99.38 159

ET-MVSNet_ETH3D		97.44 241	96.29 243	98.78 228	97.93 271	98.95 204	98.91 230	99.09 245	98.00 223	99.24 218	98.83 201	84.62 280	98.02 223	97.43 245	97.38 232	99.48 185	98.84 211

MDTV_nov1_ep13		97.41 242	96.26 244	98.76 230	99.47 229	98.43 237	99.26 188	99.82 94	98.06 220	99.23 219	99.22 174	92.86 252	98.05 219	95.33 253	93.66 248	96.73 260	96.26 260

ADS-MVSNet		97.29 243	96.17 245	98.59 238	99.59 211	98.70 224	99.32 166	99.86 66	98.47 190	99.56 163	99.08 190	98.16 216	97.34 236	92.92 256	91.17 253	95.91 266	94.72 267

SCA		97.25 244	96.05 246	98.64 237	99.36 250	99.02 198	99.27 185	99.96 12	98.25 209	99.69 125	98.71 210	94.66 244	97.95 225	93.95 254	92.35 250	95.64 267	95.40 266

blended_shiyan6		97.14 245	95.70 248	98.81 226	99.47 229	97.70 261	99.40 150	96.81 260	97.62 236	99.89 21	99.26 168	95.11 239	99.28 117	92.23 261	90.01 258	98.03 243	97.96 238

blended_shiyan8		97.13 246	95.69 249	98.81 226	99.46 232	97.71 260	99.40 150	96.81 260	97.60 237	99.90 18	99.25 169	95.03 241	99.27 118	92.25 260	90.02 257	98.03 243	97.96 238

gbinet_0.2-2-1-0.02		97.02 247	95.51 251	98.78 228	99.43 242	97.67 262	99.53 108	97.49 259	97.49 240	99.80 67	99.37 156	95.13 238	98.67 198	92.47 258	88.93 266	97.76 247	97.53 251

wanda-best-256-512		96.92 248	95.40 254	98.70 232	99.44 236	97.57 263	99.29 179	96.63 262	97.37 242	99.89 21	99.24 170	95.00 242	99.21 124	91.82 263	89.19 262	97.76 247	97.57 248

FE-blended-shiyan7		96.92 248	95.39 255	98.70 232	99.44 236	97.57 263	99.29 179	96.63 262	97.37 242	99.89 21	99.24 170	95.00 242	99.21 124	91.82 263	89.19 262	97.76 247	97.57 248

gm-plane-assit		96.82 250	94.84 258	99.13 202	99.95 10	99.78 18	99.69 64	99.92 38	99.19 100	99.84 54	99.92 18	72.93 284	96.44 254	98.21 232	97.01 237	98.92 226	96.87 258

PatchmatchNet		96.81 251	95.41 253	98.43 245	99.43 242	98.30 241	99.23 191	99.93 27	98.19 212	99.64 145	98.81 204	93.50 247	97.43 235	92.89 257	90.78 255	94.94 272	95.41 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 252	95.30 257	98.46 244	99.42 244	98.47 235	99.32 166	99.91 44	98.42 197	99.51 176	99.07 192	92.81 253	97.12 239	92.39 259	91.71 251	95.51 268	94.20 269

E-PMN		96.72 253	95.78 247	97.81 257	99.45 234	95.46 272	98.14 265	98.33 256	97.99 224	98.73 256	98.09 237	98.97 198	97.54 232	97.45 244	91.09 254	94.70 274	91.40 273

tpm		96.56 254	94.68 259	98.74 231	99.12 260	97.90 255	98.79 243	99.93 27	96.79 263	99.69 125	99.19 179	81.48 282	97.56 231	95.46 252	93.97 247	97.37 255	97.99 235

EMVS		96.47 255	95.38 256	97.74 260	99.42 244	95.37 273	98.07 267	98.27 257	97.85 229	98.90 246	97.48 250	98.73 205	97.20 237	97.21 247	90.39 256	94.59 276	90.65 274

tpmrst		96.18 256	94.47 260	98.18 249	99.52 218	97.89 256	98.96 222	99.79 116	98.07 219	99.16 225	99.30 167	92.69 254	96.69 249	90.76 269	88.85 268	94.96 271	93.69 270

FE-MVSNET3		95.98 257	93.76 261	98.56 240	99.44 236	97.57 263	99.29 179	96.63 262	97.37 242	99.06 232	95.50 263	86.90 274	99.19 128	91.82 263	89.19 262	97.76 247	97.96 238

usedtu_blend_shiyan5		95.81 258	93.76 261	98.20 248	99.44 236	97.57 263	97.14 275	96.63 262	97.37 242	99.06 232	95.50 263	86.90 274	99.19 128	91.82 263	89.19 262	97.76 247	97.97 236

CostFormer		95.61 259	93.35 265	98.24 247	99.48 228	98.03 251	98.65 247	99.83 87	96.93 258	99.42 196	98.83 201	83.65 281	97.08 241	90.39 270	89.54 260	94.94 272	96.11 263

dps		95.59 260	93.46 264	98.08 251	99.33 252	98.22 246	98.87 235	99.70 159	96.17 266	98.87 249	97.75 243	86.85 278	96.60 250	91.24 267	89.62 259	95.10 270	94.34 268

tpm cat1		95.52 261	93.49 263	97.88 256	99.28 256	97.87 257	98.65 247	99.77 129	97.27 249	99.46 189	98.04 238	90.99 260	95.46 260	88.57 271	88.14 269	94.64 275	93.54 271

blend_shiyan4		94.55 262	92.63 266	96.78 262	92.84 276	97.35 267	96.16 276	95.49 266	90.66 272	99.06 232	95.50 263	86.90 274	99.19 128	90.80 268	89.27 261	97.96 245	97.97 236

0.4-1-1-0.1		93.74 263	91.90 267	95.88 263	94.52 273	95.84 271	97.60 271	90.78 267	91.61 270	99.07 231	96.32 257	87.13 272	96.82 248	87.50 272	87.82 270	96.48 262	97.11 256

0.3-1-1-0.015		93.30 264	91.34 268	95.58 264	94.35 275	95.28 274	97.33 272	90.14 268	90.90 271	99.06 232	95.88 261	86.90 274	96.46 252	86.55 274	87.27 271	96.15 264	96.61 259

0.4-1-1-0.2		93.22 265	91.27 269	95.51 265	94.46 274	95.09 275	97.17 273	90.11 269	90.61 273	99.06 232	96.14 258	87.05 273	96.30 255	86.75 273	87.00 272	95.95 265	96.22 261

test_method		91.96 266	95.51 251	87.82 267	70.84 280	82.79 279	92.13 278	87.74 271	98.88 149	95.40 278	99.20 177	98.04 218	85.65 273	97.71 239	94.95 243	95.13 269	97.00 257

VLMVS_CLIP		79.71 267	89.12 270	68.73 268	87.10 277	83.02 278	68.48 280	62.62 272	85.86 275	55.42 280	94.79 267	95.73 232	69.45 275	91.99 262	85.95 273	69.31 278	86.50 275

MVS_clip		72.01 268	88.45 271	52.83 270	72.73 279	69.46 281	60.04 282	23.84 275	89.07 274	26.54 283	91.03 276	95.05 240	73.26 274	93.36 255	88.91 267	60.59 279	92.27 272

GG-mvs-BLEND		70.44 269	96.91 234	39.57 271	3.32 284	96.51 269	91.01 279	4.05 278	97.03 254	33.20 282	94.67 268	97.75 220	7.59 280	98.28 230	96.85 238	98.24 240	97.26 253

VLMVS		68.79 270	81.89 272	53.50 269	73.23 278	71.71 280	49.28 283	45.32 274	76.63 276	43.34 281	82.92 277	93.75 246	61.37 276	81.04 275	84.50 274	48.48 280	82.68 276

MVS_baseline		40.44 271	64.23 273	12.69 274	22.83 281	30.63 282	7.48 286	0.00 279	58.33 277	0.00 286	68.10 278	80.56 283	45.13 277	61.60 276	68.12 275	0.70 283	82.28 277

testmvs		22.33 272	29.66 274	13.79 272	8.97 282	10.35 283	15.53 285	8.09 277	32.51 278	19.87 284	45.18 279	30.56 287	17.05 279	29.96 277	24.74 276	13.21 281	34.30 278

test123		21.52 273	28.47 275	13.42 273	7.29 283	10.12 284	15.70 284	8.31 276	31.54 279	19.34 285	36.33 280	37.40 286	17.14 278	27.45 278	23.17 277	12.73 282	33.30 279

uanet_test		0.00 274	0.00 276	0.00 275	0.00 285	0.00 285	0.00 287	0.00 279	0.00 280	0.00 286	0.00 281	0.00 288	0.00 281	0.00 279	0.00 278	0.00 284	0.00 280

sosnet-low-res		0.00 274	0.00 276	0.00 275	0.00 285	0.00 285	0.00 287	0.00 279	0.00 280	0.00 286	0.00 281	0.00 288	0.00 281	0.00 279	0.00 278	0.00 284	0.00 280

sosnet		0.00 274	0.00 276	0.00 275	0.00 285	0.00 285	0.00 287	0.00 279	0.00 280	0.00 286	0.00 281	0.00 288	0.00 281	0.00 279	0.00 278	0.00 284	0.00 280

ACM-MVS					99.50 226	98.08 250	98.16 263		97.35 247	98.73 256	98.36 231	99.85 109	95.40 261			98.85 229	99.28 173

PatchmatchNet2					99.75 161	99.11 192	99.74 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1												99.87 101	98.79 190	97.50 242	94.35 245	97.24 256	97.22 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										99.70 121	99.78 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							99.75 43	99.46 220		99.15 227						99.41 198

TPM-MVS					99.47 229	97.86 258	97.79 270			98.49 264	97.62 246	99.83 117	95.33 262			98.90 227	98.77 214
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										99.96 3

9.14												99.57 164

SR-MVS					99.73 174			99.74 147				99.88 94

Anonymous202405211			99.14 143		99.87 70	99.55 68	99.50 120	99.70 159	98.55 187		98.61 216	98.46 209	98.76 193	99.66 49	99.50 44	99.85 47	99.63 67

our_test_3					99.75 161	99.11 192	99.74 52

ambc			98.83 182		99.72 176	98.52 232	98.84 238		98.96 137	99.92 12	99.34 158	99.74 141	99.04 162	98.68 217	97.57 230	99.46 188	98.99 209

MTAPA										99.62 148		99.95 38

MTMP										99.53 168		99.92 71

Patchmatch-RL test							65.75 281

tmp_tt				88.14 266	96.68 272	91.91 276	93.70 277	61.38 273	99.61 35	90.51 279	99.40 152	99.71 147	90.32 272	99.22 128	99.44 54	96.25 263

XVS					99.86 90	99.30 154	99.72 59			99.69 125		99.93 60				99.60 153

X-MVStestdata					99.86 90	99.30 154	99.72 59			99.69 125		99.93 60				99.60 153

mPP-MVS					99.84 107							99.92 71

NP-MVS									97.37 242

Patchmtry						98.19 248	98.91 230	99.97 1		99.43 193

DeepMVS_CX						96.39 270	97.15 274	88.89 270	97.94 226	99.51 176	95.71 262	97.88 219	98.19 210	98.92 182		97.73 252	97.75 246

High-resolution multi-view benchmark