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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 10	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 11	100.00 1	99.85 19

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 18	99.34 15	99.69 4	99.58 55	99.90 2	99.86 18	99.78 8	99.58 6	99.95 22	99.00 61	99.95 32	99.78 33

pmmvs6		99.67 3	99.70 3	99.60 11	99.90 4	99.27 22	99.53 7	99.76 29	99.64 15	99.84 20	99.83 3	99.50 8	99.87 100	99.36 38	99.92 55	99.64 63

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 21	99.85 17	99.11 59	99.90 1	99.78 27	99.63 17	99.78 26	99.67 25	99.48 9	99.81 177	99.30 42	99.97 19	99.77 35
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
mvs_tets		99.63 5	99.67 5	99.49 48	99.88 9	98.61 92	99.34 20	99.71 34	99.27 57	99.90 12	99.74 13	99.68 4	99.97 4	99.55 29	99.99 5	99.88 14

jajsoiax		99.58 6	99.61 8	99.48 51	99.87 12	98.61 92	99.28 37	99.66 45	99.09 81	99.89 15	99.68 20	99.53 7	99.97 4	99.50 32	99.99 5	99.87 16

test_fmvsmconf0.01_n		99.57 7	99.63 7	99.36 64	99.87 12	98.13 131	98.08 159	99.95 1	99.45 36	99.98 2	99.75 11	99.80 1	99.97 4	99.82 8	99.99 5	99.99 1

ANet_high		99.57 7	99.67 5	99.28 85	99.89 6	98.09 135	99.14 54	99.93 4	99.82 3	99.93 6	99.81 5	99.17 18	99.94 35	99.31 41	100.00 1	99.82 25

v7n		99.53 9	99.57 9	99.41 60	99.88 9	98.54 100	99.45 10	99.61 51	99.66 13	99.68 39	99.66 27	98.44 59	99.95 22	99.73 19	99.96 25	99.75 43

test_djsdf		99.52 10	99.51 11	99.53 34	99.86 15	98.74 82	99.39 17	99.56 69	99.11 71	99.70 35	99.73 15	99.00 22	99.97 4	99.26 43	99.98 12	99.89 11

anonymousdsp		99.51 11	99.47 16	99.62 6	99.88 9	99.08 63	99.34 20	99.69 37	98.93 96	99.65 45	99.72 16	98.93 26	99.95 22	99.11 52	100.00 1	99.82 25

test_fmvsmconf0.1_n		99.49 12	99.54 10	99.34 73	99.78 25	98.11 132	97.77 203	99.90 9	99.33 50	99.97 3	99.66 27	99.71 3	99.96 11	99.79 13	99.99 5	99.96 5

UA-Net		99.47 13	99.40 20	99.70 2	99.49 115	99.29 19	99.80 3	99.72 33	99.82 3	99.04 143	99.81 5	98.05 89	99.96 11	98.85 69	99.99 5	99.86 18

PS-MVSNAJss		99.46 14	99.49 12	99.35 70	99.90 4	98.15 128	99.20 45	99.65 46	99.48 32	99.92 8	99.71 17	98.07 86	99.96 11	99.53 30	100.00 1	99.93 8

test_fmvsmconf_n		99.44 15	99.48 14	99.31 83	99.64 70	98.10 134	97.68 214	99.84 18	99.29 55	99.92 8	99.57 42	99.60 5	99.96 11	99.74 18	99.98 12	99.89 11

pm-mvs1		99.44 15	99.48 14	99.33 78	99.80 22	98.63 89	99.29 33	99.63 47	99.30 54	99.65 45	99.60 39	99.16 20	99.82 164	99.07 55	99.83 92	99.56 97

TransMVSNet (Re)		99.44 15	99.47 16	99.36 64	99.80 22	98.58 95	99.27 39	99.57 62	99.39 43	99.75 30	99.62 34	99.17 18	99.83 154	99.06 56	99.62 187	99.66 58

DTE-MVSNet		99.43 18	99.35 23	99.66 4	99.71 47	99.30 17	99.31 27	99.51 85	99.64 15	99.56 53	99.46 66	98.23 71	99.97 4	98.78 72	99.93 44	99.72 45

TDRefinement		99.42 19	99.38 21	99.55 23	99.76 31	99.33 16	99.68 5	99.71 34	99.38 44	99.53 60	99.61 37	98.64 43	99.80 184	98.24 105	99.84 85	99.52 118

PEN-MVS		99.41 20	99.34 25	99.62 6	99.73 38	99.14 52	99.29 33	99.54 78	99.62 20	99.56 53	99.42 73	98.16 82	99.96 11	98.78 72	99.93 44	99.77 35

nrg030		99.40 21	99.35 23	99.54 27	99.58 77	99.13 55	98.98 71	99.48 96	99.68 11	99.46 71	99.26 100	98.62 46	99.73 238	99.17 51	99.92 55	99.76 39

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 47	99.10 60	99.29 33	99.53 81	99.53 29	99.46 71	99.41 76	98.23 71	99.95 22	98.89 68	99.95 32	99.81 28

MIMVSNet1		99.38 23	99.32 28	99.55 23	99.86 15	99.19 37	99.41 13	99.59 53	99.59 23	99.71 33	99.57 42	97.12 155	99.90 64	99.21 48	99.87 77	99.54 108

OurMVSNet-221017-0		99.37 24	99.31 30	99.53 34	99.91 3	98.98 65	99.63 6	99.58 55	99.44 38	99.78 26	99.76 10	96.39 195	99.92 50	99.44 36	99.92 55	99.68 54

Vis-MVSNet		99.34 25	99.36 22	99.27 88	99.73 38	98.26 118	99.17 50	99.78 27	99.11 71	99.27 108	99.48 64	98.82 31	99.95 22	98.94 64	99.93 44	99.59 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsm_n_1920		99.33 26	99.45 18	98.99 135	99.57 81	97.73 177	97.93 180	99.83 20	99.22 60	99.93 6	99.30 94	99.42 10	99.96 11	99.85 5	99.99 5	99.29 212

WR-MVS_H		99.33 26	99.22 40	99.65 5	99.71 47	99.24 25	99.32 23	99.55 73	99.46 35	99.50 67	99.34 87	97.30 144	99.93 40	98.90 66	99.93 44	99.77 35

VPA-MVSNet		99.30 28	99.30 32	99.28 85	99.49 115	98.36 114	99.00 68	99.45 108	99.63 17	99.52 62	99.44 71	98.25 69	99.88 83	99.09 54	99.84 85	99.62 67

sd_testset		99.28 29	99.31 30	99.19 101	99.68 58	98.06 144	99.41 13	99.30 167	99.69 9	99.63 48	99.68 20	99.25 14	99.96 11	97.25 160	99.92 55	99.57 91

Anonymous20231211		99.27 30	99.27 35	99.26 90	99.29 158	98.18 126	99.49 8	99.51 85	99.70 8	99.80 24	99.68 20	96.84 170	99.83 154	99.21 48	99.91 63	99.77 35

FC-MVSNet-test		99.27 30	99.25 38	99.34 73	99.77 28	98.37 111	99.30 32	99.57 62	99.61 22	99.40 83	99.50 59	97.12 155	99.85 120	99.02 60	99.94 40	99.80 29

test_fmvsmvis_n_1920		99.26 32	99.49 12	98.54 203	99.66 64	96.97 218	98.00 173	99.85 15	99.24 59	99.92 8	99.50 59	99.39 11	99.95 22	99.89 3	99.98 12	98.71 306

testf1		99.25 33	99.16 45	99.51 43	99.89 6	99.63 3	98.71 91	99.69 37	98.90 98	99.43 76	99.35 83	98.86 28	99.67 267	97.81 132	99.81 99	99.24 222

APD_test2		99.25 33	99.16 45	99.51 43	99.89 6	99.63 3	98.71 91	99.69 37	98.90 98	99.43 76	99.35 83	98.86 28	99.67 267	97.81 132	99.81 99	99.24 222

KD-MVS_self_test		99.25 33	99.18 42	99.44 57	99.63 74	99.06 64	98.69 93	99.54 78	99.31 52	99.62 51	99.53 54	97.36 142	99.86 108	99.24 47	99.71 154	99.39 175

ACMH	96.65 7	99.25 33	99.24 39	99.26 90	99.72 44	98.38 109	99.07 61	99.55 73	98.30 132	99.65 45	99.45 70	99.22 15	99.76 221	98.44 96	99.77 124	99.64 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvsmamba		99.24 37	99.15 50	99.49 48	99.83 19	98.85 74	99.41 13	99.55 73	99.54 27	99.40 83	99.52 57	95.86 223	99.91 59	99.32 40	99.95 32	99.70 51

SDMVSNet		99.23 38	99.32 28	98.96 139	99.68 58	97.35 196	98.84 83	99.48 96	99.69 9	99.63 48	99.68 20	99.03 21	99.96 11	97.97 123	99.92 55	99.57 91

fmvsm_l_conf0.5_n		99.21 39	99.28 34	99.02 132	99.64 70	97.28 200	97.82 196	99.76 29	98.73 106	99.82 21	99.09 139	98.81 32	99.95 22	99.86 4	99.96 25	99.83 22

CP-MVSNet		99.21 39	99.09 55	99.56 21	99.65 65	98.96 70	99.13 55	99.34 147	99.42 41	99.33 97	99.26 100	97.01 163	99.94 35	98.74 76	99.93 44	99.79 30

fmvsm_l_conf0.5_n_a		99.19 41	99.27 35	98.94 142	99.65 65	97.05 214	97.80 199	99.76 29	98.70 109	99.78 26	99.11 133	98.79 34	99.95 22	99.85 5	99.96 25	99.83 22

fmvsm_s_conf0.1_n_a		99.17 42	99.30 32	98.80 160	99.75 35	96.59 232	97.97 179	99.86 13	98.22 140	99.88 17	99.71 17	98.59 49	99.84 137	99.73 19	99.98 12	99.98 2

TranMVSNet+NR-MVSNet		99.17 42	99.07 58	99.46 56	99.37 146	98.87 73	98.39 130	99.42 121	99.42 41	99.36 92	99.06 140	98.38 62	99.95 22	98.34 101	99.90 69	99.57 91

FMVSNet1		99.17 42	99.17 43	99.17 102	99.55 93	98.24 120	99.20 45	99.44 112	99.21 62	99.43 76	99.55 48	97.82 105	99.86 108	98.42 98	99.89 73	99.41 164

fmvsm_s_conf0.1_n		99.16 45	99.33 26	98.64 181	99.71 47	96.10 245	97.87 191	99.85 15	98.56 121	99.90 12	99.68 20	98.69 41	99.85 120	99.72 21	99.98 12	99.97 3

test_vis3_rt		99.14 46	99.17 43	99.07 120	99.78 25	98.38 109	98.92 75	99.94 2	97.80 172	99.91 11	99.67 25	97.15 154	98.91 388	99.76 16	99.56 210	99.92 9

FIs		99.14 46	99.09 55	99.29 84	99.70 54	98.28 117	99.13 55	99.52 84	99.48 32	99.24 117	99.41 76	96.79 176	99.82 164	98.69 81	99.88 74	99.76 39

XXY-MVS		99.14 46	99.15 50	99.10 114	99.76 31	97.74 175	98.85 81	99.62 48	98.48 124	99.37 89	99.49 63	98.75 36	99.86 108	98.20 108	99.80 109	99.71 46

CS-MVS		99.13 49	99.10 54	99.24 95	99.06 212	99.15 47	99.36 19	99.88 11	99.36 48	98.21 245	98.46 262	98.68 42	99.93 40	99.03 59	99.85 81	98.64 315

CS-MVS-test		99.13 49	99.09 55	99.26 90	99.13 197	98.97 66	99.31 27	99.88 11	99.44 38	98.16 248	98.51 254	98.64 43	99.93 40	98.91 65	99.85 81	98.88 283

test_fmvs3		99.12 51	99.41 19	98.25 229	99.76 31	95.07 282	99.05 64	99.94 2	97.78 174	99.82 21	99.84 2	98.56 52	99.71 246	99.96 1	99.96 25	99.97 3

casdiffmvs_mvg		99.12 51	99.16 45	98.99 135	99.43 134	97.73 177	98.00 173	99.62 48	99.22 60	99.55 55	99.22 109	98.93 26	99.75 228	98.66 82	99.81 99	99.50 123
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_a		99.10 53	99.20 41	98.78 166	99.55 93	96.59 232	97.79 200	99.82 22	98.21 141	99.81 23	99.53 54	98.46 58	99.84 137	99.70 22	99.97 19	99.90 10

fmvsm_s_conf0.5_n		99.09 54	99.26 37	98.61 189	99.55 93	96.09 248	97.74 208	99.81 24	98.55 122	99.85 19	99.55 48	98.60 48	99.84 137	99.69 24	99.98 12	99.89 11

RRT_MVS		99.09 54	98.94 66	99.55 23	99.87 12	98.82 78	99.48 9	98.16 316	99.49 31	99.59 52	99.65 30	94.79 256	99.95 22	99.45 35	99.96 25	99.88 14

EC-MVSNet		99.09 54	99.05 59	99.20 99	99.28 159	98.93 71	99.24 41	99.84 18	99.08 83	98.12 253	98.37 270	98.72 38	99.90 64	99.05 57	99.77 124	98.77 300

ACMH+	96.62 9	99.08 57	99.00 62	99.33 78	99.71 47	98.83 76	98.60 101	99.58 55	99.11 71	99.53 60	99.18 116	98.81 32	99.67 267	96.71 209	99.77 124	99.50 123

GeoE		99.05 58	98.99 64	99.25 93	99.44 129	98.35 115	98.73 88	99.56 69	98.42 125	98.91 167	98.81 208	98.94 25	99.91 59	98.35 100	99.73 142	99.49 127

Gipuma		99.03 59	99.16 45	98.64 181	99.94 2	98.51 102	99.32 23	99.75 32	99.58 25	98.60 211	99.62 34	98.22 74	99.51 332	97.70 140	99.73 142	97.89 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v8		99.01 60	99.16 45	98.57 195	99.47 124	96.31 240	98.90 76	99.47 103	99.03 87	99.52 62	99.57 42	96.93 166	99.81 177	99.60 25	99.98 12	99.60 74

HPM-MVS_fast		99.01 60	98.82 77	99.57 16	99.71 47	99.35 12	99.00 68	99.50 87	97.33 216	98.94 164	98.86 197	98.75 36	99.82 164	97.53 147	99.71 154	99.56 97

APDe-MVS		98.99 62	98.79 80	99.60 11	99.21 173	99.15 47	98.87 78	99.48 96	97.57 190	99.35 94	99.24 105	97.83 102	99.89 74	97.88 129	99.70 159	99.75 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
EG-PatchMatch MVS		98.99 62	99.01 61	98.94 142	99.50 108	97.47 189	98.04 166	99.59 53	98.15 152	99.40 83	99.36 82	98.58 51	99.76 221	98.78 72	99.68 167	99.59 80

COLMAP_ROB	96.50 10	98.99 62	98.85 75	99.41 60	99.58 77	99.10 60	98.74 85	99.56 69	99.09 81	99.33 97	99.19 113	98.40 61	99.72 245	95.98 257	99.76 135	99.42 161
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		98.98 65	98.86 74	99.36 64	99.82 21	98.55 97	97.47 241	99.57 62	99.37 45	99.21 120	99.61 37	96.76 179	99.83 154	98.06 116	99.83 92	99.71 46

v10		98.97 66	99.11 52	98.55 200	99.44 129	96.21 244	98.90 76	99.55 73	98.73 106	99.48 68	99.60 39	96.63 186	99.83 154	99.70 22	99.99 5	99.61 73

DeepC-MVS	97.60 4	98.97 66	98.93 67	99.10 114	99.35 151	97.98 151	98.01 172	99.46 105	97.56 192	99.54 56	99.50 59	98.97 23	99.84 137	98.06 116	99.92 55	99.49 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		98.96 68	99.02 60	98.76 170	99.38 140	97.26 202	98.49 118	99.50 87	98.86 101	99.19 122	99.06 140	98.23 71	99.69 255	98.71 79	99.76 135	99.33 201

casdiffmvs		98.95 69	99.00 62	98.81 158	99.38 140	97.33 197	97.82 196	99.57 62	99.17 69	99.35 94	99.17 120	98.35 66	99.69 255	98.46 95	99.73 142	99.41 164
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NR-MVSNet		98.95 69	98.82 77	99.36 64	99.16 190	98.72 87	99.22 42	99.20 198	99.10 78	99.72 31	98.76 216	96.38 197	99.86 108	98.00 121	99.82 95	99.50 123

Anonymous20240529		98.93 71	98.87 71	99.12 110	99.19 180	98.22 125	99.01 66	98.99 246	99.25 58	99.54 56	99.37 79	97.04 159	99.80 184	97.89 126	99.52 222	99.35 194

DP-MVS		98.93 71	98.81 79	99.28 85	99.21 173	98.45 106	98.46 123	99.33 152	99.63 17	99.48 68	99.15 126	97.23 150	99.75 228	97.17 163	99.66 178	99.63 66

SED-MVS		98.91 73	98.72 87	99.49 48	99.49 115	99.17 39	98.10 157	99.31 159	98.03 155	99.66 42	99.02 152	98.36 63	99.88 83	96.91 185	99.62 187	99.41 164

ACMM	96.08 12	98.91 73	98.73 85	99.48 51	99.55 93	99.14 52	98.07 161	99.37 132	97.62 184	99.04 143	98.96 174	98.84 30	99.79 197	97.43 151	99.65 179	99.49 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		98.90 75	98.70 92	99.51 43	98.43 317	99.15 47	99.43 11	99.32 154	98.17 148	99.26 112	99.02 152	98.18 78	99.88 83	97.07 173	99.45 236	99.49 127

tfpnnormal		98.90 75	98.90 70	98.91 147	99.67 62	97.82 168	99.00 68	99.44 112	99.45 36	99.51 66	99.24 105	98.20 77	99.86 108	95.92 259	99.69 162	99.04 255

MTAPA		98.88 77	98.64 101	99.61 9	99.67 62	99.36 11	98.43 126	99.20 198	98.83 105	98.89 170	98.90 187	96.98 165	99.92 50	97.16 164	99.70 159	99.56 97

mvsany_test3		98.87 78	98.92 68	98.74 177	99.38 140	96.94 222	98.58 103	99.10 224	96.49 268	99.96 4	99.81 5	98.18 78	99.45 344	98.97 63	99.79 114	99.83 22

VPNet		98.87 78	98.83 76	99.01 133	99.70 54	97.62 184	98.43 126	99.35 141	99.47 34	99.28 106	99.05 147	96.72 182	99.82 164	98.09 114	99.36 247	99.59 80

UniMVSNet (Re)		98.87 78	98.71 89	99.35 70	99.24 166	98.73 85	97.73 210	99.38 128	98.93 96	99.12 128	98.73 219	96.77 177	99.86 108	98.63 85	99.80 109	99.46 146

UniMVSNet_NR-MVSNet		98.86 81	98.68 95	99.40 62	99.17 188	98.74 82	97.68 214	99.40 124	99.14 70	99.06 136	98.59 246	96.71 183	99.93 40	98.57 88	99.77 124	99.53 115

APD-MVS_3200maxsize		98.84 82	98.61 108	99.53 34	99.19 180	99.27 22	98.49 118	99.33 152	98.64 110	99.03 146	98.98 169	97.89 99	99.85 120	96.54 225	99.42 240	99.46 146

APD_test1		98.83 83	98.66 98	99.34 73	99.78 25	99.47 6	98.42 128	99.45 108	98.28 137	98.98 150	99.19 113	97.76 108	99.58 309	96.57 217	99.55 213	98.97 267

PM-MVS		98.82 84	98.72 87	99.12 110	99.64 70	98.54 100	97.98 176	99.68 42	97.62 184	99.34 96	99.18 116	97.54 127	99.77 215	97.79 134	99.74 139	99.04 255

DU-MVS		98.82 84	98.63 102	99.39 63	99.16 190	98.74 82	97.54 233	99.25 187	98.84 104	99.06 136	98.76 216	96.76 179	99.93 40	98.57 88	99.77 124	99.50 123

SR-MVS-dyc-post		98.81 86	98.55 113	99.57 16	99.20 177	99.38 8	98.48 121	99.30 167	98.64 110	98.95 157	98.96 174	97.49 136	99.86 108	96.56 221	99.39 243	99.45 150

3Dnovator	98.27 2	98.81 86	98.73 85	99.05 127	98.76 265	97.81 170	99.25 40	99.30 167	98.57 119	98.55 220	99.33 89	97.95 97	99.90 64	97.16 164	99.67 173	99.44 154

HPM-MVS		98.79 88	98.53 116	99.59 15	99.65 65	99.29 19	99.16 51	99.43 118	96.74 258	98.61 209	98.38 269	98.62 46	99.87 100	96.47 229	99.67 173	99.59 80
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 88	98.54 115	99.54 27	99.73 38	99.16 43	98.23 141	99.31 159	97.92 163	98.90 168	98.90 187	98.00 92	99.88 83	96.15 250	99.72 149	99.58 86
Skip Steuart: Steuart Systems R&D Blog.
dcpmvs_2		98.78 90	99.11 52	97.78 263	99.56 89	93.67 327	99.06 62	99.86 13	99.50 30	99.66 42	99.26 100	97.21 152	99.99 2	98.00 121	99.91 63	99.68 54

V42		98.78 90	98.78 81	98.76 170	99.44 129	97.04 215	98.27 138	99.19 202	97.87 167	99.25 116	99.16 122	96.84 170	99.78 208	99.21 48	99.84 85	99.46 146

test20.03		98.78 90	98.77 82	98.78 166	99.46 125	97.20 207	97.78 201	99.24 192	99.04 86	99.41 80	98.90 187	97.65 115	99.76 221	97.70 140	99.79 114	99.39 175

DVP-MVS		98.77 93	98.52 117	99.52 39	99.50 108	99.21 28	98.02 169	98.84 270	97.97 158	99.08 134	99.02 152	97.61 121	99.88 83	96.99 179	99.63 184	99.48 137
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
test_0402		98.76 94	98.71 89	98.93 144	99.56 89	98.14 130	98.45 125	99.34 147	99.28 56	98.95 157	98.91 184	98.34 67	99.79 197	95.63 274	99.91 63	98.86 285

ACMMP_NAP		98.75 95	98.48 125	99.57 16	99.58 77	99.29 19	97.82 196	99.25 187	96.94 247	98.78 188	99.12 132	98.02 90	99.84 137	97.13 169	99.67 173	99.59 80

SixPastTwentyTwo		98.75 95	98.62 104	99.16 105	99.83 19	97.96 155	99.28 37	98.20 313	99.37 45	99.70 35	99.65 30	92.65 297	99.93 40	99.04 58	99.84 85	99.60 74

ACMMP		98.75 95	98.50 120	99.52 39	99.56 89	99.16 43	98.87 78	99.37 132	97.16 237	98.82 185	99.01 161	97.71 111	99.87 100	96.29 240	99.69 162	99.54 108
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
XVS		98.72 98	98.45 130	99.53 34	99.46 125	99.21 28	98.65 95	99.34 147	98.62 114	97.54 293	98.63 240	97.50 133	99.83 154	96.79 198	99.53 219	99.56 97

SSC-MVS		98.71 99	98.74 83	98.62 186	99.72 44	96.08 250	98.74 85	98.64 294	99.74 6	99.67 41	99.24 105	94.57 260	99.95 22	99.11 52	99.24 267	99.82 25

SR-MVS		98.71 99	98.43 133	99.57 16	99.18 187	99.35 12	98.36 133	99.29 175	98.29 135	98.88 174	98.85 200	97.53 129	99.87 100	96.14 251	99.31 255	99.48 137

HFP-MVS		98.71 99	98.44 132	99.51 43	99.49 115	99.16 43	98.52 110	99.31 159	97.47 200	98.58 215	98.50 258	97.97 96	99.85 120	96.57 217	99.59 198	99.53 115

LPG-MVS_test		98.71 99	98.46 129	99.47 54	99.57 81	98.97 66	98.23 141	99.48 96	96.60 263	99.10 132	99.06 140	98.71 39	99.83 154	95.58 277	99.78 119	99.62 67

test_fmvs2		98.70 103	98.97 65	97.89 256	99.54 98	94.05 309	98.55 106	99.92 6	96.78 256	99.72 31	99.78 8	96.60 187	99.67 267	99.91 2	99.90 69	99.94 7

ACMMPR		98.70 103	98.42 135	99.54 27	99.52 103	99.14 52	98.52 110	99.31 159	97.47 200	98.56 218	98.54 250	97.75 109	99.88 83	96.57 217	99.59 198	99.58 86

CP-MVS		98.70 103	98.42 135	99.52 39	99.36 147	99.12 57	98.72 89	99.36 136	97.54 195	98.30 240	98.40 266	97.86 101	99.89 74	96.53 226	99.72 149	99.56 97

tt0805		98.69 106	98.62 104	98.90 150	99.75 35	99.30 17	99.15 53	96.97 346	98.86 101	98.87 178	97.62 323	98.63 45	98.96 385	99.41 37	98.29 336	98.45 326

Anonymous20240521		98.69 106	98.87 71	98.16 237	99.77 28	95.11 281	99.08 58	99.44 112	99.34 49	99.33 97	99.55 48	94.10 274	99.94 35	99.25 45	99.96 25	99.42 161

region2R		98.69 106	98.40 137	99.54 27	99.53 101	99.17 39	98.52 110	99.31 159	97.46 205	98.44 230	98.51 254	97.83 102	99.88 83	96.46 230	99.58 203	99.58 86

EI-MVSNet-UG-set		98.69 106	98.71 89	98.62 186	99.10 201	96.37 237	97.23 257	98.87 261	99.20 64	99.19 122	98.99 165	97.30 144	99.85 120	98.77 75	99.79 114	99.65 62

3Dnovator+	97.89 3	98.69 106	98.51 118	99.24 95	98.81 260	98.40 107	99.02 65	99.19 202	98.99 90	98.07 257	99.28 96	97.11 157	99.84 137	96.84 196	99.32 253	99.47 144

ZNCC-MVS		98.68 111	98.40 137	99.54 27	99.57 81	99.21 28	98.46 123	99.29 175	97.28 222	98.11 254	98.39 267	98.00 92	99.87 100	96.86 195	99.64 181	99.55 104

EI-MVSNet-Vis-set		98.68 111	98.70 92	98.63 185	99.09 204	96.40 236	97.23 257	98.86 266	99.20 64	99.18 126	98.97 171	97.29 146	99.85 120	98.72 78	99.78 119	99.64 63

CSCG		98.68 111	98.50 120	99.20 99	99.45 128	98.63 89	98.56 105	99.57 62	97.87 167	98.85 179	98.04 298	97.66 114	99.84 137	96.72 207	99.81 99	99.13 244

test_f		98.67 114	98.87 71	98.05 246	99.72 44	95.59 260	98.51 115	99.81 24	96.30 280	99.78 26	99.82 4	96.14 204	98.63 393	99.82 8	99.93 44	99.95 6

PGM-MVS		98.66 115	98.37 143	99.55 23	99.53 101	99.18 38	98.23 141	99.49 94	97.01 244	98.69 198	98.88 194	98.00 92	99.89 74	95.87 263	99.59 198	99.58 86

GBi-Net		98.65 116	98.47 127	99.17 102	98.90 240	98.24 120	99.20 45	99.44 112	98.59 116	98.95 157	99.55 48	94.14 270	99.86 108	97.77 135	99.69 162	99.41 164

test1		98.65 116	98.47 127	99.17 102	98.90 240	98.24 120	99.20 45	99.44 112	98.59 116	98.95 157	99.55 48	94.14 270	99.86 108	97.77 135	99.69 162	99.41 164

LCM-MVSNet-Re		98.64 118	98.48 125	99.11 112	98.85 251	98.51 102	98.49 118	99.83 20	98.37 126	99.69 37	99.46 66	98.21 76	99.92 50	94.13 314	99.30 258	98.91 279

mPP-MVS		98.64 118	98.34 147	99.54 27	99.54 98	99.17 39	98.63 97	99.24 192	97.47 200	98.09 256	98.68 228	97.62 120	99.89 74	96.22 245	99.62 187	99.57 91

TSAR-MVS + MP.		98.63 120	98.49 124	99.06 126	99.64 70	97.90 159	98.51 115	98.94 248	96.96 245	99.24 117	98.89 193	97.83 102	99.81 177	96.88 192	99.49 232	99.48 137
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
LS3D		98.63 120	98.38 142	99.36 64	97.25 380	99.38 8	99.12 57	99.32 154	99.21 62	98.44 230	98.88 194	97.31 143	99.80 184	96.58 215	99.34 251	98.92 276

RPSCF		98.62 122	98.36 144	99.42 58	99.65 65	99.42 7	98.55 106	99.57 62	97.72 178	98.90 168	99.26 100	96.12 206	99.52 328	95.72 270	99.71 154	99.32 203

GST-MVS		98.61 123	98.30 152	99.52 39	99.51 105	99.20 34	98.26 139	99.25 187	97.44 208	98.67 200	98.39 267	97.68 112	99.85 120	96.00 255	99.51 224	99.52 118

v1192		98.60 124	98.66 98	98.41 216	99.27 161	95.88 254	97.52 235	99.36 136	97.41 209	99.33 97	99.20 112	96.37 198	99.82 164	99.57 27	99.92 55	99.55 104

v1144		98.60 124	98.66 98	98.41 216	99.36 147	95.90 253	97.58 229	99.34 147	97.51 196	99.27 108	99.15 126	96.34 200	99.80 184	99.47 34	99.93 44	99.51 120

DPE-MVS		98.59 126	98.26 157	99.57 16	99.27 161	99.15 47	97.01 269	99.39 126	97.67 180	99.44 75	98.99 165	97.53 129	99.89 74	95.40 281	99.68 167	99.66 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		98.57 127	98.23 160	99.60 11	99.69 56	99.35 12	97.16 264	99.38 128	94.87 321	98.97 154	98.99 165	98.01 91	99.88 83	97.29 157	99.70 159	99.58 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 128	98.32 151	99.25 93	99.41 137	98.73 85	97.13 266	99.18 206	97.10 240	98.75 194	98.92 183	98.18 78	99.65 283	96.68 211	99.56 210	99.37 184
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VDD-MVS		98.56 128	98.39 140	99.07 120	99.13 197	98.07 141	98.59 102	97.01 344	99.59 23	99.11 129	99.27 98	94.82 251	99.79 197	98.34 101	99.63 184	99.34 196

v2v482		98.56 128	98.62 104	98.37 220	99.42 135	95.81 257	97.58 229	99.16 213	97.90 165	99.28 106	99.01 161	95.98 217	99.79 197	99.33 39	99.90 69	99.51 120

XVG-ACMP-BASELINE		98.56 128	98.34 147	99.22 98	99.54 98	98.59 94	97.71 211	99.46 105	97.25 225	98.98 150	98.99 165	97.54 127	99.84 137	95.88 260	99.74 139	99.23 224

v1240		98.55 132	98.62 104	98.32 223	99.22 171	95.58 262	97.51 237	99.45 108	97.16 237	99.45 74	99.24 105	96.12 206	99.85 120	99.60 25	99.88 74	99.55 104

IterMVS-LS		98.55 132	98.70 92	98.09 239	99.48 122	94.73 290	97.22 260	99.39 126	98.97 92	99.38 87	99.31 93	96.00 212	99.93 40	98.58 86	99.97 19	99.60 74
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 134	98.57 112	98.45 212	99.21 173	95.98 251	97.63 222	99.36 136	97.15 239	99.32 103	99.18 116	95.84 224	99.84 137	99.50 32	99.91 63	99.54 108

v1921920		98.54 134	98.60 109	98.38 219	99.20 177	95.76 259	97.56 231	99.36 136	97.23 231	99.38 87	99.17 120	96.02 210	99.84 137	99.57 27	99.90 69	99.54 108

SF-MVS		98.53 136	98.27 156	99.32 80	99.31 154	98.75 81	98.19 146	99.41 122	96.77 257	98.83 182	98.90 187	97.80 106	99.82 164	95.68 273	99.52 222	99.38 182

XVG-OURS		98.53 136	98.34 147	99.11 112	99.50 108	98.82 78	95.97 325	99.50 87	97.30 220	99.05 141	98.98 169	99.35 12	99.32 363	95.72 270	99.68 167	99.18 236

UGNet		98.53 136	98.45 130	98.79 163	97.94 347	96.96 220	99.08 58	98.54 298	99.10 78	96.82 331	99.47 65	96.55 189	99.84 137	98.56 91	99.94 40	99.55 104
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
WB-MVS		98.52 139	98.55 113	98.43 214	99.65 65	95.59 260	98.52 110	98.77 281	99.65 14	99.52 62	99.00 164	94.34 266	99.93 40	98.65 83	98.83 311	99.76 39

patch_mono-2		98.51 140	98.63 102	98.17 235	99.38 140	94.78 287	97.36 247	99.69 37	98.16 151	98.49 226	99.29 95	97.06 158	99.97 4	98.29 104	99.91 63	99.76 39

XVG-OURS-SEG-HR		98.49 141	98.28 154	99.14 108	99.49 115	98.83 76	96.54 293	99.48 96	97.32 218	99.11 129	98.61 244	99.33 13	99.30 366	96.23 244	98.38 332	99.28 214

FMVSNet2		98.49 141	98.40 137	98.75 173	98.90 240	97.14 213	98.61 100	99.13 220	98.59 116	99.19 122	99.28 96	94.14 270	99.82 164	97.97 123	99.80 109	99.29 212

pmmvs-eth3d		98.47 143	98.34 147	98.86 152	99.30 157	97.76 173	97.16 264	99.28 178	95.54 303	99.42 79	99.19 113	97.27 147	99.63 289	97.89 126	99.97 19	99.20 229

MP-MVS		98.46 144	98.09 175	99.54 27	99.57 81	99.22 27	98.50 117	99.19 202	97.61 187	97.58 289	98.66 233	97.40 140	99.88 83	94.72 295	99.60 194	99.54 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 145	98.60 109	98.00 249	99.44 129	94.98 283	97.44 243	99.06 229	98.30 132	99.32 103	98.97 171	96.65 185	99.62 292	98.37 99	99.85 81	99.39 175

AllTest		98.44 146	98.20 162	99.16 105	99.50 108	98.55 97	98.25 140	99.58 55	96.80 254	98.88 174	99.06 140	97.65 115	99.57 311	94.45 302	99.61 192	99.37 184

VNet		98.42 147	98.30 152	98.79 163	98.79 264	97.29 199	98.23 141	98.66 291	99.31 52	98.85 179	98.80 209	94.80 254	99.78 208	98.13 111	99.13 284	99.31 207

ab-mvs		98.41 148	98.36 144	98.59 192	99.19 180	97.23 203	99.32 23	98.81 275	97.66 181	98.62 207	99.40 78	96.82 173	99.80 184	95.88 260	99.51 224	98.75 303

ACMP	95.32 15	98.41 148	98.09 175	99.36 64	99.51 105	98.79 80	97.68 214	99.38 128	95.76 297	98.81 187	98.82 206	98.36 63	99.82 164	94.75 292	99.77 124	99.48 137
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n_1920		98.40 150	98.92 68	96.81 326	99.74 37	90.76 375	98.15 151	99.91 7	98.33 129	99.89 15	99.55 48	95.07 244	99.88 83	99.76 16	99.93 44	99.79 30

SMA-MVS		98.40 150	98.03 182	99.51 43	99.16 190	99.21 28	98.05 164	99.22 195	94.16 337	98.98 150	99.10 136	97.52 131	99.79 197	96.45 231	99.64 181	99.53 115
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
MSP-MVS		98.40 150	98.00 184	99.61 9	99.57 81	99.25 24	98.57 104	99.35 141	97.55 194	99.31 105	97.71 316	94.61 259	99.88 83	96.14 251	99.19 276	99.70 51
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
SD-MVS		98.40 150	98.68 95	97.54 287	98.96 228	97.99 148	97.88 188	99.36 136	98.20 145	99.63 48	99.04 149	98.76 35	95.33 405	96.56 221	99.74 139	99.31 207
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
EI-MVSNet		98.40 150	98.51 118	98.04 247	99.10 201	94.73 290	97.20 261	98.87 261	98.97 92	99.06 136	99.02 152	96.00 212	99.80 184	98.58 86	99.82 95	99.60 74

WR-MVS		98.40 150	98.19 164	99.03 130	99.00 221	97.65 181	96.85 279	98.94 248	98.57 119	98.89 170	98.50 258	95.60 229	99.85 120	97.54 146	99.85 81	99.59 80

new-patchmatchnet		98.35 156	98.74 83	97.18 306	99.24 166	92.23 354	96.42 301	99.48 96	98.30 132	99.69 37	99.53 54	97.44 138	99.82 164	98.84 70	99.77 124	99.49 127

canonicalmvs		98.34 157	98.26 157	98.58 193	98.46 314	97.82 168	98.96 72	99.46 105	99.19 68	97.46 300	95.46 377	98.59 49	99.46 343	98.08 115	98.71 319	98.46 324

test_cas_vis1_n_1920		98.33 158	98.68 95	97.27 303	99.69 56	92.29 352	98.03 167	99.85 15	97.62 184	99.96 4	99.62 34	93.98 275	99.74 233	99.52 31	99.86 80	99.79 30

testgi		98.32 159	98.39 140	98.13 238	99.57 81	95.54 263	97.78 201	99.49 94	97.37 213	99.19 122	97.65 320	98.96 24	99.49 335	96.50 228	98.99 300	99.34 196

DeepPCF-MVS	96.93 5	98.32 159	98.01 183	99.23 97	98.39 322	98.97 66	95.03 360	99.18 206	96.88 250	99.33 97	98.78 212	98.16 82	99.28 370	96.74 204	99.62 187	99.44 154

test_vis1_n		98.31 161	98.50 120	97.73 272	99.76 31	94.17 307	98.68 94	99.91 7	96.31 278	99.79 25	99.57 42	92.85 294	99.42 349	99.79 13	99.84 85	99.60 74

MVS_111021_LR		98.30 162	98.12 173	98.83 155	99.16 190	98.03 146	96.09 321	99.30 167	97.58 189	98.10 255	98.24 281	98.25 69	99.34 360	96.69 210	99.65 179	99.12 245

EPP-MVSNet		98.30 162	98.04 181	99.07 120	99.56 89	97.83 165	99.29 33	98.07 320	99.03 87	98.59 213	99.13 130	92.16 303	99.90 64	96.87 193	99.68 167	99.49 127

DeepC-MVS_fast	96.85 6	98.30 162	98.15 170	98.75 173	98.61 295	97.23 203	97.76 206	99.09 226	97.31 219	98.75 194	98.66 233	97.56 125	99.64 286	96.10 254	99.55 213	99.39 175
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 165	97.95 188	99.34 73	98.44 316	99.16 43	98.12 154	99.38 128	96.01 289	98.06 258	98.43 264	97.80 106	99.67 267	95.69 272	99.58 203	99.20 229

Fast-Effi-MVS+-dtu		98.27 166	98.09 175	98.81 158	98.43 317	98.11 132	97.61 225	99.50 87	98.64 110	97.39 305	97.52 328	98.12 85	99.95 22	96.90 190	98.71 319	98.38 334

DELS-MVS		98.27 166	98.20 162	98.48 209	98.86 248	96.70 230	95.60 342	99.20 198	97.73 176	98.45 229	98.71 222	97.50 133	99.82 164	98.21 107	99.59 198	98.93 275
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
Effi-MVS+-dtu		98.26 168	97.90 194	99.35 70	98.02 342	99.49 5	98.02 169	99.16 213	98.29 135	97.64 284	97.99 300	96.44 194	99.95 22	96.66 212	98.93 307	98.60 318

MVSFormer		98.26 168	98.43 133	97.77 264	98.88 246	93.89 321	99.39 17	99.56 69	99.11 71	98.16 248	98.13 288	93.81 278	99.97 4	99.26 43	99.57 207	99.43 158

MVS_111021_HR		98.25 170	98.08 178	98.75 173	99.09 204	97.46 190	95.97 325	99.27 181	97.60 188	97.99 263	98.25 280	98.15 84	99.38 355	96.87 193	99.57 207	99.42 161

TAMVS		98.24 171	98.05 180	98.80 160	99.07 208	97.18 209	97.88 188	98.81 275	96.66 262	99.17 127	99.21 110	94.81 253	99.77 215	96.96 183	99.88 74	99.44 154

MM		98.22 172	97.99 185	98.91 147	98.66 291	96.97 218	97.89 187	94.44 379	99.54 27	98.95 157	99.14 129	93.50 282	99.92 50	99.80 12	99.96 25	99.85 19

diffmvs		98.22 172	98.24 159	98.17 235	99.00 221	95.44 268	96.38 303	99.58 55	97.79 173	98.53 223	98.50 258	96.76 179	99.74 233	97.95 125	99.64 181	99.34 196
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Anonymous20231206		98.21 174	98.21 161	98.20 233	99.51 105	95.43 269	98.13 152	99.32 154	96.16 283	98.93 165	98.82 206	96.00 212	99.83 154	97.32 156	99.73 142	99.36 190

VDDNet		98.21 174	97.95 188	99.01 133	99.58 77	97.74 175	99.01 66	97.29 339	99.67 12	98.97 154	99.50 59	90.45 317	99.80 184	97.88 129	99.20 273	99.48 137

IS-MVSNet		98.19 176	97.90 194	99.08 118	99.57 81	97.97 152	99.31 27	98.32 308	99.01 89	98.98 150	99.03 151	91.59 308	99.79 197	95.49 279	99.80 109	99.48 137

MVS_Test		98.18 177	98.36 144	97.67 274	98.48 312	94.73 290	98.18 147	99.02 240	97.69 179	98.04 261	99.11 133	97.22 151	99.56 314	98.57 88	98.90 309	98.71 306

TSAR-MVS + GP.		98.18 177	97.98 186	98.77 169	98.71 274	97.88 160	96.32 307	98.66 291	96.33 276	99.23 119	98.51 254	97.48 137	99.40 351	97.16 164	99.46 234	99.02 258

CNVR-MVS		98.17 179	97.87 197	99.07 120	98.67 286	98.24 120	97.01 269	98.93 250	97.25 225	97.62 285	98.34 274	97.27 147	99.57 311	96.42 232	99.33 252	99.39 175

PVSNet_Blended_VisFu		98.17 179	98.15 170	98.22 232	99.73 38	95.15 278	97.36 247	99.68 42	94.45 331	98.99 149	99.27 98	96.87 169	99.94 35	97.13 169	99.91 63	99.57 91

MVS_0304		98.10 181	97.88 196	98.76 170	98.82 257	96.50 234	97.90 185	91.35 397	99.56 26	98.32 239	99.13 130	96.06 208	99.93 40	99.84 7	99.97 19	99.85 19

HPM-MVS++		98.10 181	97.64 214	99.48 51	99.09 204	99.13 55	97.52 235	98.75 285	97.46 205	96.90 326	97.83 311	96.01 211	99.84 137	95.82 267	99.35 249	99.46 146

APD-MVS		98.10 181	97.67 209	99.42 58	99.11 199	98.93 71	97.76 206	99.28 178	94.97 318	98.72 197	98.77 214	97.04 159	99.85 120	93.79 324	99.54 215	99.49 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_fmvs1_n		98.09 184	98.28 154	97.52 289	99.68 58	93.47 331	98.63 97	99.93 4	95.41 310	99.68 39	99.64 32	91.88 307	99.48 338	99.82 8	99.87 77	99.62 67

MVP-Stereo		98.08 185	97.92 192	98.57 195	98.96 228	96.79 226	97.90 185	99.18 206	96.41 274	98.46 228	98.95 178	95.93 220	99.60 299	96.51 227	98.98 302	99.31 207
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PMMVS2		98.07 186	98.08 178	98.04 247	99.41 137	94.59 296	94.59 375	99.40 124	97.50 197	98.82 185	98.83 203	96.83 172	99.84 137	97.50 149	99.81 99	99.71 46

ETV-MVS		98.03 187	97.86 198	98.56 199	98.69 283	98.07 141	97.51 237	99.50 87	98.10 153	97.50 297	95.51 374	98.41 60	99.88 83	96.27 241	99.24 267	97.71 370

Effi-MVS+		98.02 188	97.82 200	98.62 186	98.53 309	97.19 208	97.33 249	99.68 42	97.30 220	96.68 336	97.46 332	98.56 52	99.80 184	96.63 213	98.20 339	98.86 285

MSLP-MVS++		98.02 188	98.14 172	97.64 278	98.58 302	95.19 277	97.48 239	99.23 194	97.47 200	97.90 267	98.62 242	97.04 159	98.81 391	97.55 144	99.41 241	98.94 274

EIA-MVS		98.00 190	97.74 204	98.80 160	98.72 271	98.09 135	98.05 164	99.60 52	97.39 211	96.63 338	95.55 373	97.68 112	99.80 184	96.73 206	99.27 262	98.52 322

MCST-MVS		98.00 190	97.63 215	99.10 114	99.24 166	98.17 127	96.89 278	98.73 288	95.66 298	97.92 265	97.70 318	97.17 153	99.66 278	96.18 249	99.23 269	99.47 144

K. test v3		98.00 190	97.66 212	99.03 130	99.79 24	97.56 185	99.19 49	92.47 391	99.62 20	99.52 62	99.66 27	89.61 322	99.96 11	99.25 45	99.81 99	99.56 97

HQP_MVS		97.99 193	97.67 209	98.93 144	99.19 180	97.65 181	97.77 203	99.27 181	98.20 145	97.79 276	97.98 301	94.90 247	99.70 250	94.42 304	99.51 224	99.45 150

MDA-MVSNet-bldmvs		97.94 194	97.91 193	98.06 244	99.44 129	94.96 284	96.63 291	99.15 218	98.35 127	98.83 182	99.11 133	94.31 267	99.85 120	96.60 214	98.72 317	99.37 184

Anonymous202405211		97.90 195	97.50 223	99.08 118	98.90 240	98.25 119	98.53 109	96.16 362	98.87 100	99.11 129	98.86 197	90.40 318	99.78 208	97.36 154	99.31 255	99.19 234

LF4IMVS		97.90 195	97.69 208	98.52 205	99.17 188	97.66 180	97.19 263	99.47 103	96.31 278	97.85 272	98.20 285	96.71 183	99.52 328	94.62 296	99.72 149	98.38 334

UnsupCasMVSNet_eth		97.89 197	97.60 217	98.75 173	99.31 154	97.17 210	97.62 223	99.35 141	98.72 108	98.76 193	98.68 228	92.57 298	99.74 233	97.76 139	95.60 391	99.34 196

TinyColmap		97.89 197	97.98 186	97.60 280	98.86 248	94.35 302	96.21 313	99.44 112	97.45 207	99.06 136	98.88 194	97.99 95	99.28 370	94.38 308	99.58 203	99.18 236

OMC-MVS		97.88 199	97.49 224	99.04 129	98.89 245	98.63 89	96.94 273	99.25 187	95.02 316	98.53 223	98.51 254	97.27 147	99.47 341	93.50 332	99.51 224	99.01 259

CANet		97.87 200	97.76 202	98.19 234	97.75 355	95.51 265	96.76 284	99.05 232	97.74 175	96.93 320	98.21 284	95.59 230	99.89 74	97.86 131	99.93 44	99.19 234

xiu_mvs_v1_base_debu		97.86 201	98.17 166	96.92 319	98.98 225	93.91 318	96.45 298	99.17 210	97.85 169	98.41 233	97.14 344	98.47 55	99.92 50	98.02 118	99.05 290	96.92 382

xiu_mvs_v1_base		97.86 201	98.17 166	96.92 319	98.98 225	93.91 318	96.45 298	99.17 210	97.85 169	98.41 233	97.14 344	98.47 55	99.92 50	98.02 118	99.05 290	96.92 382

xiu_mvs_v1_base_debi		97.86 201	98.17 166	96.92 319	98.98 225	93.91 318	96.45 298	99.17 210	97.85 169	98.41 233	97.14 344	98.47 55	99.92 50	98.02 118	99.05 290	96.92 382

NCCC		97.86 201	97.47 227	99.05 127	98.61 295	98.07 141	96.98 271	98.90 256	97.63 183	97.04 316	97.93 306	95.99 216	99.66 278	95.31 282	98.82 313	99.43 158

PMVS	91.26 20	97.86 201	97.94 190	97.65 276	99.71 47	97.94 157	98.52 110	98.68 290	98.99 90	97.52 295	99.35 83	97.41 139	98.18 397	91.59 362	99.67 173	96.82 385
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
IterMVS-SCA-FT		97.85 206	98.18 165	96.87 322	99.27 161	91.16 370	95.53 344	99.25 187	99.10 78	99.41 80	99.35 83	93.10 287	99.96 11	98.65 83	99.94 40	99.49 127

D2MVS		97.84 207	97.84 199	97.83 259	99.14 195	94.74 289	96.94 273	98.88 259	95.84 295	98.89 170	98.96 174	94.40 264	99.69 255	97.55 144	99.95 32	99.05 251

CPTT-MVS		97.84 207	97.36 232	99.27 88	99.31 154	98.46 105	98.29 136	99.27 181	94.90 320	97.83 273	98.37 270	94.90 247	99.84 137	93.85 323	99.54 215	99.51 120

mvs_anonymous		97.83 209	98.16 169	96.87 322	98.18 334	91.89 356	97.31 251	98.90 256	97.37 213	98.83 182	99.46 66	96.28 201	99.79 197	98.90 66	98.16 343	98.95 270

h-mvs33		97.77 210	97.33 235	99.10 114	99.21 173	97.84 164	98.35 134	98.57 297	99.11 71	98.58 215	99.02 152	88.65 331	99.96 11	98.11 112	96.34 383	99.49 127

test_vis1_rt		97.75 211	97.72 207	97.83 259	98.81 260	96.35 238	97.30 252	99.69 37	94.61 325	97.87 269	98.05 297	96.26 202	98.32 396	98.74 76	98.18 340	98.82 288

IterMVS		97.73 212	98.11 174	96.57 331	99.24 166	90.28 378	95.52 346	99.21 196	98.86 101	99.33 97	99.33 89	93.11 286	99.94 35	98.49 94	99.94 40	99.48 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_fmvs1		97.72 213	97.94 190	97.07 313	98.66 291	92.39 349	97.68 214	99.81 24	95.20 314	99.54 56	99.44 71	91.56 309	99.41 350	99.78 15	99.77 124	99.40 173

MSDG		97.71 214	97.52 221	98.28 228	98.91 239	96.82 225	94.42 378	99.37 132	97.65 182	98.37 238	98.29 279	97.40 140	99.33 362	94.09 315	99.22 270	98.68 313

CDS-MVSNet		97.69 215	97.35 233	98.69 178	98.73 269	97.02 217	96.92 277	98.75 285	95.89 294	98.59 213	98.67 230	92.08 305	99.74 233	96.72 207	99.81 99	99.32 203
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 216	97.75 203	97.45 295	98.23 332	93.78 324	97.29 253	98.84 270	96.10 285	98.64 204	98.65 235	96.04 209	99.36 356	96.84 196	99.14 282	99.20 229

Fast-Effi-MVS+		97.67 217	97.38 230	98.57 195	98.71 274	97.43 193	97.23 257	99.45 108	94.82 322	96.13 352	96.51 354	98.52 54	99.91 59	96.19 247	98.83 311	98.37 336

EU-MVSNet		97.66 218	98.50 120	95.13 364	99.63 74	85.84 394	98.35 134	98.21 312	98.23 139	99.54 56	99.46 66	95.02 245	99.68 264	98.24 105	99.87 77	99.87 16

pmmvs5		97.64 219	97.49 224	98.08 242	99.14 195	95.12 280	96.70 288	99.05 232	93.77 344	98.62 207	98.83 203	93.23 283	99.75 228	98.33 103	99.76 135	99.36 190

N_pmnet		97.63 220	97.17 241	98.99 135	99.27 161	97.86 162	95.98 324	93.41 388	95.25 312	99.47 70	98.90 187	95.63 228	99.85 120	96.91 185	99.73 142	99.27 215

bld_raw_dy_0_64		97.62 221	97.51 222	97.96 252	97.97 344	96.28 241	98.20 145	99.82 22	96.46 271	99.37 89	97.12 347	92.42 299	99.70 250	96.27 241	99.97 19	97.90 356

mvsany_test1		97.60 222	97.54 219	97.77 264	97.72 356	95.35 271	95.36 352	97.13 342	94.13 338	99.71 33	99.33 89	97.93 98	99.30 366	97.60 143	98.94 306	98.67 314

YYNet1		97.60 222	97.67 209	97.39 299	99.04 216	93.04 338	95.27 353	98.38 307	97.25 225	98.92 166	98.95 178	95.48 235	99.73 238	96.99 179	98.74 315	99.41 164

MDA-MVSNet_test_wron		97.60 222	97.66 212	97.41 298	99.04 216	93.09 334	95.27 353	98.42 304	97.26 224	98.88 174	98.95 178	95.43 236	99.73 238	97.02 176	98.72 317	99.41 164

pmmvs4		97.58 225	97.28 236	98.51 206	98.84 252	96.93 223	95.40 351	98.52 300	93.60 346	98.61 209	98.65 235	95.10 243	99.60 299	96.97 182	99.79 114	98.99 263

PVSNet_BlendedMVS		97.55 226	97.53 220	97.60 280	98.92 236	93.77 325	96.64 290	99.43 118	94.49 327	97.62 285	99.18 116	96.82 173	99.67 267	94.73 293	99.93 44	99.36 190

ppachtmachnet_test		97.50 227	97.74 204	96.78 328	98.70 278	91.23 369	94.55 376	99.05 232	96.36 275	99.21 120	98.79 211	96.39 195	99.78 208	96.74 204	99.82 95	99.34 196

FMVSNet3		97.50 227	97.24 238	98.29 227	98.08 340	95.83 256	97.86 193	98.91 255	97.89 166	98.95 157	98.95 178	87.06 337	99.81 177	97.77 135	99.69 162	99.23 224

CHOSEN 1792x2688		97.49 229	97.14 245	98.54 203	99.68 58	96.09 248	96.50 296	99.62 48	91.58 369	98.84 181	98.97 171	92.36 300	99.88 83	96.76 202	99.95 32	99.67 57

CLD-MVS		97.49 229	97.16 242	98.48 209	99.07 208	97.03 216	94.71 368	99.21 196	94.46 329	98.06 258	97.16 342	97.57 124	99.48 338	94.46 301	99.78 119	98.95 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		97.46 231	97.07 246	98.64 181	98.73 269	97.33 197	97.45 242	97.64 332	99.11 71	98.58 215	97.98 301	88.65 331	99.79 197	98.11 112	97.39 366	98.81 292

Vis-MVSNet (Re-imp)		97.46 231	97.16 242	98.34 222	99.55 93	96.10 245	98.94 73	98.44 303	98.32 131	98.16 248	98.62 242	88.76 327	99.73 238	93.88 321	99.79 114	99.18 236

jason		97.45 233	97.35 233	97.76 267	99.24 166	93.93 317	95.86 333	98.42 304	94.24 335	98.50 225	98.13 288	94.82 251	99.91 59	97.22 161	99.73 142	99.43 158
jason: jason.
CL-MVSNet_self_test		97.44 234	97.22 239	98.08 242	98.57 304	95.78 258	94.30 381	98.79 278	96.58 265	98.60 211	98.19 286	94.74 258	99.64 286	96.41 233	98.84 310	98.82 288

DSMNet-mixed		97.42 235	97.60 217	96.87 322	99.15 194	91.46 360	98.54 108	99.12 221	92.87 357	97.58 289	99.63 33	96.21 203	99.90 64	95.74 269	99.54 215	99.27 215

USDC		97.41 236	97.40 228	97.44 296	98.94 230	93.67 327	95.17 356	99.53 81	94.03 341	98.97 154	99.10 136	95.29 238	99.34 360	95.84 266	99.73 142	99.30 210

our_test_3		97.39 237	97.73 206	96.34 336	98.70 278	89.78 380	94.61 374	98.97 247	96.50 267	99.04 143	98.85 200	95.98 217	99.84 137	97.26 159	99.67 173	99.41 164

c3_l		97.36 238	97.37 231	97.31 300	98.09 339	93.25 333	95.01 361	99.16 213	97.05 241	98.77 191	98.72 221	92.88 292	99.64 286	96.93 184	99.76 135	99.05 251

alignmvs		97.35 239	96.88 256	98.78 166	98.54 307	98.09 135	97.71 211	97.69 329	99.20 64	97.59 288	95.90 367	88.12 336	99.55 317	98.18 109	98.96 304	98.70 309

Patchmtry		97.35 239	96.97 250	98.50 208	97.31 379	96.47 235	98.18 147	98.92 253	98.95 95	98.78 188	99.37 79	85.44 352	99.85 120	95.96 258	99.83 92	99.17 240

DP-MVS Recon		97.33 241	96.92 253	98.57 195	99.09 204	97.99 148	96.79 281	99.35 141	93.18 351	97.71 280	98.07 296	95.00 246	99.31 364	93.97 317	99.13 284	98.42 331

QAPM		97.31 242	96.81 263	98.82 156	98.80 263	97.49 188	99.06 62	99.19 202	90.22 381	97.69 282	99.16 122	96.91 167	99.90 64	90.89 375	99.41 241	99.07 249

UnsupCasMVSNet_bld		97.30 243	96.92 253	98.45 212	99.28 159	96.78 229	96.20 314	99.27 181	95.42 307	98.28 242	98.30 278	93.16 285	99.71 246	94.99 287	97.37 367	98.87 284

F-COLMAP		97.30 243	96.68 270	99.14 108	99.19 180	98.39 108	97.27 256	99.30 167	92.93 355	96.62 339	98.00 299	95.73 226	99.68 264	92.62 350	98.46 331	99.35 194

1112_ss		97.29 245	96.86 257	98.58 193	99.34 153	96.32 239	96.75 285	99.58 55	93.14 352	96.89 327	97.48 330	92.11 304	99.86 108	96.91 185	99.54 215	99.57 91

CANet_DTU		97.26 246	97.06 247	97.84 258	97.57 364	94.65 294	96.19 315	98.79 278	97.23 231	95.14 373	98.24 281	93.22 284	99.84 137	97.34 155	99.84 85	99.04 255

Patchmatch-RL test		97.26 246	97.02 249	97.99 250	99.52 103	95.53 264	96.13 319	99.71 34	97.47 200	99.27 108	99.16 122	84.30 361	99.62 292	97.89 126	99.77 124	98.81 292

CDPH-MVS		97.26 246	96.66 273	99.07 120	99.00 221	98.15 128	96.03 323	99.01 243	91.21 375	97.79 276	97.85 310	96.89 168	99.69 255	92.75 347	99.38 246	99.39 175

PatchMatch-RL		97.24 249	96.78 264	98.61 189	99.03 219	97.83 165	96.36 304	99.06 229	93.49 349	97.36 307	97.78 312	95.75 225	99.49 335	93.44 333	98.77 314	98.52 322

eth_miper_zixun_eth		97.23 250	97.25 237	97.17 308	98.00 343	92.77 342	94.71 368	99.18 206	97.27 223	98.56 218	98.74 218	91.89 306	99.69 255	97.06 175	99.81 99	99.05 251

sss		97.21 251	96.93 251	98.06 244	98.83 254	95.22 276	96.75 285	98.48 302	94.49 327	97.27 308	97.90 307	92.77 295	99.80 184	96.57 217	99.32 253	99.16 243

LFMVS		97.20 252	96.72 267	98.64 181	98.72 271	96.95 221	98.93 74	94.14 385	99.74 6	98.78 188	99.01 161	84.45 358	99.73 238	97.44 150	99.27 262	99.25 219

HyFIR lowres test		97.19 253	96.60 277	98.96 139	99.62 76	97.28 200	95.17 356	99.50 87	94.21 336	99.01 147	98.32 277	86.61 340	99.99 2	97.10 171	99.84 85	99.60 74

miper_lstm_enhance		97.18 254	97.16 242	97.25 305	98.16 335	92.85 340	95.15 358	99.31 159	97.25 225	98.74 196	98.78 212	90.07 319	99.78 208	97.19 162	99.80 109	99.11 246

CNLPA		97.17 255	96.71 268	98.55 200	98.56 305	98.05 145	96.33 306	98.93 250	96.91 249	97.06 315	97.39 335	94.38 265	99.45 344	91.66 359	99.18 278	98.14 345

xiu_mvs_v2_base		97.16 256	97.49 224	96.17 345	98.54 307	92.46 347	95.45 348	98.84 270	97.25 225	97.48 299	96.49 355	98.31 68	99.90 64	96.34 237	98.68 322	96.15 393

AdaColmap		97.14 257	96.71 268	98.46 211	98.34 324	97.80 171	96.95 272	98.93 250	95.58 302	96.92 321	97.66 319	95.87 222	99.53 324	90.97 372	99.14 282	98.04 350

train_agg		97.10 258	96.45 282	99.07 120	98.71 274	98.08 139	95.96 327	99.03 237	91.64 367	95.85 358	97.53 326	96.47 192	99.76 221	93.67 326	99.16 279	99.36 190

OpenMVS	96.65 7	97.09 259	96.68 270	98.32 223	98.32 325	97.16 211	98.86 80	99.37 132	89.48 385	96.29 350	99.15 126	96.56 188	99.90 64	92.90 341	99.20 273	97.89 358

PS-MVSNAJ		97.08 260	97.39 229	96.16 347	98.56 305	92.46 347	95.24 355	98.85 269	97.25 225	97.49 298	95.99 364	98.07 86	99.90 64	96.37 234	98.67 323	96.12 394

miper_ehance_all_eth		97.06 261	97.03 248	97.16 310	97.83 352	93.06 335	94.66 371	99.09 226	95.99 290	98.69 198	98.45 263	92.73 296	99.61 298	96.79 198	99.03 294	98.82 288

lupinMVS		97.06 261	96.86 257	97.65 276	98.88 246	93.89 321	95.48 347	97.97 322	93.53 347	98.16 248	97.58 324	93.81 278	99.91 59	96.77 201	99.57 207	99.17 240

API-MVS		97.04 263	96.91 255	97.42 297	97.88 351	98.23 124	98.18 147	98.50 301	97.57 190	97.39 305	96.75 351	96.77 177	99.15 379	90.16 378	99.02 297	94.88 399

cl____		97.02 264	96.83 260	97.58 282	97.82 353	94.04 311	94.66 371	99.16 213	97.04 242	98.63 205	98.71 222	88.68 330	99.69 255	97.00 177	99.81 99	99.00 262

DIV-MVS_self_test		97.02 264	96.84 259	97.58 282	97.82 353	94.03 312	94.66 371	99.16 213	97.04 242	98.63 205	98.71 222	88.69 328	99.69 255	97.00 177	99.81 99	99.01 259

RPMNet		97.02 264	96.93 251	97.30 301	97.71 358	94.22 303	98.11 155	99.30 167	99.37 45	96.91 323	99.34 87	86.72 339	99.87 100	97.53 147	97.36 369	97.81 363

HQP-MVS		97.00 267	96.49 281	98.55 200	98.67 286	96.79 226	96.29 309	99.04 235	96.05 286	95.55 364	96.84 348	93.84 276	99.54 322	92.82 344	99.26 265	99.32 203

FA-MVS(test-final)		96.99 268	96.82 261	97.50 291	98.70 278	94.78 287	99.34 20	96.99 345	95.07 315	98.48 227	99.33 89	88.41 334	99.65 283	96.13 253	98.92 308	98.07 349

new_pmnet		96.99 268	96.76 265	97.67 274	98.72 271	94.89 285	95.95 329	98.20 313	92.62 360	98.55 220	98.54 250	94.88 250	99.52 328	93.96 318	99.44 239	98.59 320

Test_1112_low_res		96.99 268	96.55 279	98.31 225	99.35 151	95.47 267	95.84 336	99.53 81	91.51 371	96.80 332	98.48 261	91.36 311	99.83 154	96.58 215	99.53 219	99.62 67

PVSNet_Blended		96.88 271	96.68 270	97.47 294	98.92 236	93.77 325	94.71 368	99.43 118	90.98 377	97.62 285	97.36 338	96.82 173	99.67 267	94.73 293	99.56 210	98.98 264

MVSTER		96.86 272	96.55 279	97.79 262	97.91 349	94.21 305	97.56 231	98.87 261	97.49 199	99.06 136	99.05 147	80.72 374	99.80 184	98.44 96	99.82 95	99.37 184

BH-untuned		96.83 273	96.75 266	97.08 311	98.74 268	93.33 332	96.71 287	98.26 310	96.72 259	98.44 230	97.37 337	95.20 240	99.47 341	91.89 356	97.43 364	98.44 328

BH-RMVSNet		96.83 273	96.58 278	97.58 282	98.47 313	94.05 309	96.67 289	97.36 335	96.70 261	97.87 269	97.98 301	95.14 242	99.44 346	90.47 377	98.58 329	99.25 219

PAPM_NR		96.82 275	96.32 285	98.30 226	99.07 208	96.69 231	97.48 239	98.76 282	95.81 296	96.61 340	96.47 357	94.12 273	99.17 377	90.82 376	97.78 355	99.06 250

MG-MVS		96.77 276	96.61 275	97.26 304	98.31 326	93.06 335	95.93 330	98.12 319	96.45 272	97.92 265	98.73 219	93.77 280	99.39 353	91.19 370	99.04 293	99.33 201

iter_conf05_11		96.72 277	96.30 286	97.97 251	97.97 344	96.24 243	94.99 362	96.19 361	96.45 272	96.77 334	96.84 348	91.46 310	99.78 208	96.27 241	99.78 119	97.90 356

test_yl		96.69 278	96.29 287	97.90 254	98.28 327	95.24 274	97.29 253	97.36 335	98.21 141	98.17 246	97.86 308	86.27 342	99.55 317	94.87 290	98.32 333	98.89 280

DCV-MVSNet		96.69 278	96.29 287	97.90 254	98.28 327	95.24 274	97.29 253	97.36 335	98.21 141	98.17 246	97.86 308	86.27 342	99.55 317	94.87 290	98.32 333	98.89 280

WTY-MVS		96.67 280	96.27 289	97.87 257	98.81 260	94.61 295	96.77 283	97.92 324	94.94 319	97.12 311	97.74 315	91.11 313	99.82 164	93.89 320	98.15 344	99.18 236

PatchT		96.65 281	96.35 283	97.54 287	97.40 376	95.32 272	97.98 176	96.64 355	99.33 50	96.89 327	99.42 73	84.32 360	99.81 177	97.69 142	97.49 360	97.48 376

TAPA-MVS	96.21 11	96.63 282	95.95 293	98.65 180	98.93 232	98.09 135	96.93 275	99.28 178	83.58 398	98.13 252	97.78 312	96.13 205	99.40 351	93.52 330	99.29 260	98.45 326
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 283	96.25 290	97.71 273	99.04 216	94.66 293	99.16 51	96.92 350	97.23 231	97.87 269	99.10 136	86.11 346	99.65 283	91.65 360	99.21 272	98.82 288

Patchmatch-test		96.55 284	96.34 284	97.17 308	98.35 323	93.06 335	98.40 129	97.79 325	97.33 216	98.41 233	98.67 230	83.68 365	99.69 255	95.16 285	99.31 255	98.77 300

iter_conf05		96.54 285	96.07 291	97.92 253	97.90 350	94.50 297	97.87 191	99.14 219	97.73 176	98.89 170	98.95 178	75.75 391	99.87 100	98.50 93	99.92 55	99.40 173

PMMVS		96.51 286	95.98 292	98.09 239	97.53 369	95.84 255	94.92 364	98.84 270	91.58 369	96.05 356	95.58 372	95.68 227	99.66 278	95.59 276	98.09 347	98.76 302

PLC	94.65 16	96.51 286	95.73 297	98.85 153	98.75 267	97.91 158	96.42 301	99.06 229	90.94 378	95.59 361	97.38 336	94.41 263	99.59 303	90.93 373	98.04 353	99.05 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 288	95.77 295	98.69 178	99.48 122	97.43 193	97.84 195	99.55 73	81.42 400	96.51 344	98.58 247	95.53 231	99.67 267	93.41 334	99.58 203	98.98 264

test1111		96.49 289	96.82 261	95.52 358	99.42 135	87.08 391	99.22 42	87.14 404	99.11 71	99.46 71	99.58 41	88.69 328	99.86 108	98.80 71	99.95 32	99.62 67

MAR-MVS		96.47 290	95.70 298	98.79 163	97.92 348	99.12 57	98.28 137	98.60 296	92.16 365	95.54 367	96.17 362	94.77 257	99.52 328	89.62 380	98.23 337	97.72 369
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
ECVR-MVS		96.42 291	96.61 275	95.85 350	99.38 140	88.18 387	99.22 42	86.00 406	99.08 83	99.36 92	99.57 42	88.47 333	99.82 164	98.52 92	99.95 32	99.54 108

SCA		96.41 292	96.66 273	95.67 354	98.24 330	88.35 385	95.85 335	96.88 351	96.11 284	97.67 283	98.67 230	93.10 287	99.85 120	94.16 310	99.22 270	98.81 292

DPM-MVS		96.32 293	95.59 303	98.51 206	98.76 265	97.21 206	94.54 377	98.26 310	91.94 366	96.37 348	97.25 340	93.06 289	99.43 347	91.42 365	98.74 315	98.89 280

CMPMVS	75.91 23	96.29 294	95.44 309	98.84 154	96.25 399	98.69 88	97.02 268	99.12 221	88.90 388	97.83 273	98.86 197	89.51 323	98.90 389	91.92 355	99.51 224	98.92 276
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		96.28 295	95.95 293	97.28 302	97.71 358	94.22 303	98.11 155	98.92 253	92.31 363	96.91 323	99.37 79	85.44 352	99.81 177	97.39 153	97.36 369	97.81 363

CVMVSNet		96.25 296	97.21 240	93.38 382	99.10 201	80.56 409	97.20 261	98.19 315	96.94 247	99.00 148	99.02 152	89.50 324	99.80 184	96.36 236	99.59 198	99.78 33

AUN-MVS		96.24 297	95.45 308	98.60 191	98.70 278	97.22 205	97.38 245	97.65 330	95.95 292	95.53 368	97.96 305	82.11 373	99.79 197	96.31 238	97.44 363	98.80 297

EPNet		96.14 298	95.44 309	98.25 229	90.76 409	95.50 266	97.92 182	94.65 377	98.97 92	92.98 393	98.85 200	89.12 326	99.87 100	95.99 256	99.68 167	99.39 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 299	97.62 216	91.38 385	98.65 294	98.57 96	98.85 81	96.95 348	96.86 252	99.90 12	99.16 122	99.18 17	98.40 395	89.23 382	99.77 124	77.18 403

Syy-MVS		96.04 300	95.56 305	97.49 292	97.10 384	94.48 298	96.18 316	96.58 356	95.65 299	94.77 376	92.29 401	91.27 312	99.36 356	98.17 110	98.05 351	98.63 316

miper_enhance_ethall		96.01 301	95.74 296	96.81 326	96.41 397	92.27 353	93.69 390	98.89 258	91.14 376	98.30 240	97.35 339	90.58 316	99.58 309	96.31 238	99.03 294	98.60 318

FMVSNet5		96.01 301	95.20 318	98.41 216	97.53 369	96.10 245	98.74 85	99.50 87	97.22 234	98.03 262	99.04 149	69.80 395	99.88 83	97.27 158	99.71 154	99.25 219

dmvs_re		95.98 303	95.39 312	97.74 270	98.86 248	97.45 191	98.37 132	95.69 372	97.95 160	96.56 341	95.95 365	90.70 315	97.68 399	88.32 384	96.13 387	98.11 346

baseline1		95.96 304	95.44 309	97.52 289	98.51 311	93.99 315	98.39 130	96.09 364	98.21 141	98.40 237	97.76 314	86.88 338	99.63 289	95.42 280	89.27 403	98.95 270

HY-MVS	95.94 13	95.90 305	95.35 314	97.55 286	97.95 346	94.79 286	98.81 84	96.94 349	92.28 364	95.17 372	98.57 248	89.90 321	99.75 228	91.20 369	97.33 371	98.10 347

GA-MVS		95.86 306	95.32 315	97.49 292	98.60 297	94.15 308	93.83 388	97.93 323	95.49 305	96.68 336	97.42 334	83.21 366	99.30 366	96.22 245	98.55 330	99.01 259

OpenMVS_ROB	95.38 14	95.84 307	95.18 319	97.81 261	98.41 321	97.15 212	97.37 246	98.62 295	83.86 397	98.65 203	98.37 270	94.29 268	99.68 264	88.41 383	98.62 327	96.60 388

cl22		95.79 308	95.39 312	96.98 316	96.77 391	92.79 341	94.40 379	98.53 299	94.59 326	97.89 268	98.17 287	82.82 370	99.24 372	96.37 234	99.03 294	98.92 276

1314		95.74 309	95.60 302	96.17 345	97.53 369	92.75 343	98.07 161	98.31 309	91.22 374	94.25 382	96.68 352	95.53 231	99.03 381	91.64 361	97.18 373	96.74 386

WB-MVSnew		95.73 310	95.57 304	96.23 342	96.70 392	90.70 376	96.07 322	93.86 386	95.60 301	97.04 316	95.45 379	96.00 212	99.55 317	91.04 371	98.31 335	98.43 329

PVSNet	93.40 17	95.67 311	95.70 298	95.57 357	98.83 254	88.57 383	92.50 395	97.72 327	92.69 359	96.49 347	96.44 358	93.72 281	99.43 347	93.61 327	99.28 261	98.71 306

FE-MVS		95.66 312	94.95 324	97.77 264	98.53 309	95.28 273	99.40 16	96.09 364	93.11 353	97.96 264	99.26 100	79.10 383	99.77 215	92.40 353	98.71 319	98.27 340

tttt0517		95.64 313	94.98 322	97.64 278	99.36 147	93.81 323	98.72 89	90.47 399	98.08 154	98.67 200	98.34 274	73.88 393	99.92 50	97.77 135	99.51 224	99.20 229

PatchmatchNet		95.58 314	95.67 300	95.30 363	97.34 378	87.32 390	97.65 220	96.65 354	95.30 311	97.07 314	98.69 226	84.77 355	99.75 228	94.97 288	98.64 324	98.83 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 315	95.12 320	96.86 325	97.54 367	93.94 316	96.49 297	96.53 358	94.36 334	97.03 318	96.61 353	94.26 269	99.16 378	86.91 390	96.31 384	97.47 377

JIA-IIPM		95.52 316	95.03 321	97.00 314	96.85 389	94.03 312	96.93 275	95.82 368	99.20 64	94.63 380	99.71 17	83.09 367	99.60 299	94.42 304	94.64 395	97.36 379

CHOSEN 280x420		95.51 317	95.47 306	95.65 356	98.25 329	88.27 386	93.25 392	98.88 259	93.53 347	94.65 379	97.15 343	86.17 344	99.93 40	97.41 152	99.93 44	98.73 305

ADS-MVSNet2		95.43 318	94.98 322	96.76 329	98.14 336	91.74 357	97.92 182	97.76 326	90.23 379	96.51 344	98.91 184	85.61 349	99.85 120	92.88 342	96.90 376	98.69 310

PAPR		95.29 319	94.47 328	97.75 268	97.50 374	95.14 279	94.89 365	98.71 289	91.39 373	95.35 371	95.48 376	94.57 260	99.14 380	84.95 393	97.37 367	98.97 267

thisisatest0530		95.27 320	94.45 329	97.74 270	99.19 180	94.37 301	97.86 193	90.20 400	97.17 236	98.22 244	97.65 320	73.53 394	99.90 64	96.90 190	99.35 249	98.95 270

ADS-MVSNet		95.24 321	94.93 325	96.18 344	98.14 336	90.10 379	97.92 182	97.32 338	90.23 379	96.51 344	98.91 184	85.61 349	99.74 233	92.88 342	96.90 376	98.69 310

BH-w/o		95.13 322	94.89 326	95.86 349	98.20 333	91.31 364	95.65 340	97.37 334	93.64 345	96.52 343	95.70 371	93.04 290	99.02 382	88.10 385	95.82 390	97.24 380

tpmrst		95.07 323	95.46 307	93.91 375	97.11 383	84.36 402	97.62 223	96.96 347	94.98 317	96.35 349	98.80 209	85.46 351	99.59 303	95.60 275	96.23 385	97.79 366

pmmvs3		95.03 324	94.40 330	96.93 318	97.70 360	92.53 346	95.08 359	97.71 328	88.57 389	97.71 280	98.08 295	79.39 381	99.82 164	96.19 247	99.11 288	98.43 329

tpmvs		95.02 325	95.25 316	94.33 370	96.39 398	85.87 393	98.08 159	96.83 352	95.46 306	95.51 369	98.69 226	85.91 347	99.53 324	94.16 310	96.23 385	97.58 374

EPNet_dtu		94.93 326	94.78 327	95.38 362	93.58 406	87.68 389	96.78 282	95.69 372	97.35 215	89.14 402	98.09 294	88.15 335	99.49 335	94.95 289	99.30 258	98.98 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		94.79 327	94.33 333	96.15 348	96.02 402	92.36 351	92.34 397	99.26 186	85.34 396	95.08 374	94.96 385	92.96 291	98.53 394	94.41 307	98.59 328	97.56 375

tpm		94.67 328	94.34 332	95.66 355	97.68 363	88.42 384	97.88 188	94.90 375	94.46 329	96.03 357	98.56 249	78.66 384	99.79 197	95.88 260	95.01 394	98.78 299

test0.0.03 1		94.51 329	93.69 338	96.99 315	96.05 400	93.61 330	94.97 363	93.49 387	96.17 281	97.57 291	94.88 386	82.30 371	99.01 384	93.60 328	94.17 398	98.37 336

thres600view7		94.45 330	93.83 336	96.29 338	99.06 212	91.53 359	97.99 175	94.24 383	98.34 128	97.44 302	95.01 382	79.84 377	99.67 267	84.33 394	98.23 337	97.66 371

PCF-MVS	92.86 18	94.36 331	93.00 348	98.42 215	98.70 278	97.56 185	93.16 393	99.11 223	79.59 401	97.55 292	97.43 333	92.19 302	99.73 238	79.85 402	99.45 236	97.97 355
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		94.32 332	92.59 350	99.53 34	99.46 125	99.21 28	98.65 95	99.34 147	98.62 114	97.54 293	45.85 404	97.50 133	99.83 154	96.79 198	99.53 219	99.56 97

MVS-HIRNet		94.32 332	95.62 301	90.42 386	98.46 314	75.36 410	96.29 309	89.13 402	95.25 312	95.38 370	99.75 11	92.88 292	99.19 376	94.07 316	99.39 243	96.72 387

ET-MVSNet_ETH3D		94.30 334	93.21 344	97.58 282	98.14 336	94.47 299	94.78 367	93.24 390	94.72 323	89.56 401	95.87 368	78.57 386	99.81 177	96.91 185	97.11 375	98.46 324

thres100view900		94.19 335	93.67 339	95.75 353	99.06 212	91.35 363	98.03 167	94.24 383	98.33 129	97.40 304	94.98 384	79.84 377	99.62 292	83.05 396	98.08 348	96.29 389

E-PMN		94.17 336	94.37 331	93.58 379	96.86 388	85.71 396	90.11 399	97.07 343	98.17 148	97.82 275	97.19 341	84.62 357	98.94 386	89.77 379	97.68 357	96.09 395

thres400		94.14 337	93.44 341	96.24 341	98.93 232	91.44 361	97.60 226	94.29 381	97.94 161	97.10 312	94.31 390	79.67 379	99.62 292	83.05 396	98.08 348	97.66 371

thisisatest0515		94.12 338	93.16 345	96.97 317	98.60 297	92.90 339	93.77 389	90.61 398	94.10 339	96.91 323	95.87 368	74.99 392	99.80 184	94.52 299	99.12 287	98.20 342

tfpn200view9		94.03 339	93.44 341	95.78 352	98.93 232	91.44 361	97.60 226	94.29 381	97.94 161	97.10 312	94.31 390	79.67 379	99.62 292	83.05 396	98.08 348	96.29 389

CostFormer		93.97 340	93.78 337	94.51 369	97.53 369	85.83 395	97.98 176	95.96 366	89.29 387	94.99 375	98.63 240	78.63 385	99.62 292	94.54 298	96.50 381	98.09 348

test-LLR		93.90 341	93.85 335	94.04 373	96.53 394	84.62 400	94.05 385	92.39 392	96.17 281	94.12 384	95.07 380	82.30 371	99.67 267	95.87 263	98.18 340	97.82 361

EMVS		93.83 342	94.02 334	93.23 383	96.83 390	84.96 397	89.77 400	96.32 360	97.92 163	97.43 303	96.36 361	86.17 344	98.93 387	87.68 386	97.73 356	95.81 396

baseline2		93.73 343	92.83 349	96.42 335	97.70 360	91.28 366	96.84 280	89.77 401	93.96 343	92.44 396	95.93 366	79.14 382	99.77 215	92.94 340	96.76 380	98.21 341

thres200		93.72 344	93.14 346	95.46 361	98.66 291	91.29 365	96.61 292	94.63 378	97.39 211	96.83 330	93.71 393	79.88 376	99.56 314	82.40 399	98.13 345	95.54 398

EPMVS		93.72 344	93.27 343	95.09 366	96.04 401	87.76 388	98.13 152	85.01 407	94.69 324	96.92 321	98.64 238	78.47 388	99.31 364	95.04 286	96.46 382	98.20 342

testing3		93.51 346	92.09 355	97.75 268	98.60 297	94.40 300	97.32 250	95.26 374	97.56 192	96.79 333	95.50 375	53.57 411	99.77 215	95.26 283	98.97 303	99.08 247

dp		93.47 347	93.59 340	93.13 384	96.64 393	81.62 408	97.66 218	96.42 359	92.80 358	96.11 353	98.64 238	78.55 387	99.59 303	93.31 335	92.18 402	98.16 344

FPMVS		93.44 348	92.23 353	97.08 311	99.25 165	97.86 162	95.61 341	97.16 341	92.90 356	93.76 390	98.65 235	75.94 390	95.66 403	79.30 403	97.49 360	97.73 368

testing91		93.32 349	92.27 352	96.47 334	97.54 367	91.25 367	96.17 318	96.76 353	97.18 235	93.65 391	93.50 395	65.11 405	99.63 289	93.04 339	97.45 362	98.53 321

tpm cat1		93.29 350	93.13 347	93.75 377	97.39 377	84.74 398	97.39 244	97.65 330	83.39 399	94.16 383	98.41 265	82.86 369	99.39 353	91.56 363	95.35 393	97.14 381

MVS		93.19 351	92.09 355	96.50 333	96.91 387	94.03 312	98.07 161	98.06 321	68.01 402	94.56 381	96.48 356	95.96 219	99.30 366	83.84 395	96.89 378	96.17 391

tpm2		93.09 352	92.58 351	94.62 368	97.56 365	86.53 392	97.66 218	95.79 369	86.15 394	94.07 386	98.23 283	75.95 389	99.53 324	90.91 374	96.86 379	97.81 363

testing11		93.08 353	92.02 357	96.26 340	97.56 365	90.83 374	96.32 307	95.70 370	96.47 270	92.66 395	93.73 392	64.36 406	99.59 303	93.77 325	97.57 358	98.37 336

testing99		93.04 354	91.98 360	96.23 342	97.53 369	90.70 376	96.35 305	95.94 367	96.87 251	93.41 392	93.43 396	63.84 407	99.59 303	93.24 337	97.19 372	98.40 332

dmvs_testset		92.94 355	92.21 354	95.13 364	98.59 300	90.99 371	97.65 220	92.09 394	96.95 246	94.00 387	93.55 394	92.34 301	96.97 402	72.20 405	92.52 400	97.43 378

KD-MVS_2432*1600		92.87 356	91.99 358	95.51 359	91.37 407	89.27 381	94.07 383	98.14 317	95.42 307	97.25 309	96.44 358	67.86 397	99.24 372	91.28 367	96.08 388	98.02 351

miper_refine_blended		92.87 356	91.99 358	95.51 359	91.37 407	89.27 381	94.07 383	98.14 317	95.42 307	97.25 309	96.44 358	67.86 397	99.24 372	91.28 367	96.08 388	98.02 351

ETVMVS		92.60 358	91.08 367	97.18 306	97.70 360	93.65 329	96.54 293	95.70 370	96.51 266	94.68 378	92.39 400	61.80 408	99.50 333	86.97 388	97.41 365	98.40 332

MVE	83.40 22	92.50 359	91.92 361	94.25 371	98.83 254	91.64 358	92.71 394	83.52 408	95.92 293	86.46 405	95.46 377	95.20 240	95.40 404	80.51 401	98.64 324	95.73 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		92.39 360	91.89 362	93.89 376	99.38 140	82.28 406	99.32 23	66.03 412	99.08 83	98.77 191	99.57 42	66.26 402	99.84 137	98.71 79	99.95 32	99.54 108

UWE-MVS		92.38 361	91.76 364	94.21 372	97.16 382	84.65 399	95.42 350	88.45 403	95.96 291	96.17 351	95.84 370	66.36 401	99.71 246	91.87 357	98.64 324	98.28 339

gg-mvs-nofinetune		92.37 362	91.20 366	95.85 350	95.80 403	92.38 350	99.31 27	81.84 409	99.75 5	91.83 398	99.74 13	68.29 396	99.02 382	87.15 387	97.12 374	96.16 392

test-mter		92.33 363	91.76 364	94.04 373	96.53 394	84.62 400	94.05 385	92.39 392	94.00 342	94.12 384	95.07 380	65.63 404	99.67 267	95.87 263	98.18 340	97.82 361

IB-MVS	91.63 19	92.24 364	90.90 368	96.27 339	97.22 381	91.24 368	94.36 380	93.33 389	92.37 362	92.24 397	94.58 389	66.20 403	99.89 74	93.16 338	94.63 396	97.66 371
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
TESTMET0.1,1		92.19 365	91.77 363	93.46 380	96.48 396	82.80 405	94.05 385	91.52 396	94.45 331	94.00 387	94.88 386	66.65 400	99.56 314	95.78 268	98.11 346	98.02 351

testing222		91.96 366	90.37 370	96.72 330	97.47 375	92.59 344	96.11 320	94.76 376	96.83 253	92.90 394	92.87 398	57.92 409	99.55 317	86.93 389	97.52 359	98.00 354

myMVS_eth3d		91.92 367	90.45 369	96.30 337	97.10 384	90.90 372	96.18 316	96.58 356	95.65 299	94.77 376	92.29 401	53.88 410	99.36 356	89.59 381	98.05 351	98.63 316

PAPM		91.88 368	90.34 371	96.51 332	98.06 341	92.56 345	92.44 396	97.17 340	86.35 393	90.38 400	96.01 363	86.61 340	99.21 375	70.65 406	95.43 392	97.75 367

PVSNet_0	89.98 21	91.15 369	90.30 372	93.70 378	97.72 356	84.34 403	90.24 398	97.42 333	90.20 382	93.79 389	93.09 397	90.90 314	98.89 390	86.57 391	72.76 405	97.87 360

EGC-MVSNET		85.24 370	80.54 373	99.34 73	99.77 28	99.20 34	99.08 58	99.29 175	12.08 406	20.84 407	99.42 73	97.55 126	99.85 120	97.08 172	99.72 149	98.96 269

test_method		79.78 371	79.50 374	80.62 387	80.21 410	45.76 413	70.82 401	98.41 306	31.08 405	80.89 406	97.71 316	84.85 354	97.37 400	91.51 364	80.03 404	98.75 303

tmp_tt		78.77 372	78.73 375	78.90 388	58.45 411	74.76 412	94.20 382	78.26 411	39.16 404	86.71 404	92.82 399	80.50 375	75.19 407	86.16 392	92.29 401	86.74 402

cdsmvs_eth3d_5k		24.66 373	32.88 376	0.00 391	0.00 414	0.00 416	0.00 402	99.10 224	0.00 409	0.00 410	97.58 324	99.21 16	0.00 410	0.00 409	0.00 408	0.00 406

testmvs		17.12 374	20.53 377	6.87 390	12.05 412	4.20 415	93.62 391	6.73 413	4.62 408	10.41 408	24.33 405	8.28 413	3.56 409	9.69 408	15.07 406	12.86 405

test123		17.04 375	20.11 378	7.82 389	10.25 413	4.91 414	94.80 366	4.47 414	4.93 407	10.00 409	24.28 406	9.69 412	3.64 408	10.14 407	12.43 407	14.92 404

pcd_1.5k_mvsjas		8.17 376	10.90 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	98.07 86	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.12 377	10.83 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	97.48 330	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						90.90 372								91.37 366

FOURS1					99.73 38	99.67 2	99.43 11	99.54 78	99.43 40	99.26 112

MSC_two_6792asdad				99.32 80	98.43 317	98.37 111		98.86 266					99.89 74	97.14 167	99.60 194	99.71 46

PC_three_1452									93.27 350	99.40 83	98.54 250	98.22 74	97.00 401	95.17 284	99.45 236	99.49 127

No_MVS				99.32 80	98.43 317	98.37 111		98.86 266					99.89 74	97.14 167	99.60 194	99.71 46

test_one_0601					99.39 139	99.20 34		99.31 159	98.49 123	98.66 202	99.02 152	97.64 118

eth-test2					0.00 414

eth-test					0.00 414

ZD-MVS					99.01 220	98.84 75		99.07 228	94.10 339	98.05 260	98.12 290	96.36 199	99.86 108	92.70 349	99.19 276

RE-MVS-def			98.58 111		99.20 177	99.38 8	98.48 121	99.30 167	98.64 110	98.95 157	98.96 174	97.75 109		96.56 221	99.39 243	99.45 150

IU-MVS					99.49 115	99.15 47		98.87 261	92.97 354	99.41 80				96.76 202	99.62 187	99.66 58

OPU-MVS				98.82 156	98.59 300	98.30 116	98.10 157				98.52 253	98.18 78	98.75 392	94.62 296	99.48 233	99.41 164

test_241102_TWO								99.30 167	98.03 155	99.26 112	99.02 152	97.51 132	99.88 83	96.91 185	99.60 194	99.66 58

test_241102_ONE					99.49 115	99.17 39		99.31 159	97.98 157	99.66 42	98.90 187	98.36 63	99.48 338

9.14			97.78 201		99.07 208		97.53 234	99.32 154	95.53 304	98.54 222	98.70 225	97.58 123	99.76 221	94.32 309	99.46 234

save fliter					99.11 199	97.97 152	96.53 295	99.02 240	98.24 138

test_0728_THIRD									98.17 148	99.08 134	99.02 152	97.89 99	99.88 83	97.07 173	99.71 154	99.70 51

test_0728_SECOND				99.60 11	99.50 108	99.23 26	98.02 169	99.32 154					99.88 83	96.99 179	99.63 184	99.68 54

test0726					99.50 108	99.21 28	98.17 150	99.35 141	97.97 158	99.26 112	99.06 140	97.61 121

GSMVS																98.81 292

test_part2					99.36 147	99.10 60				99.05 141

sam_mvs1												84.74 356				98.81 292

sam_mvs												84.29 362

ambc				98.24 231	98.82 257	95.97 252	98.62 99	99.00 245		99.27 108	99.21 110	96.99 164	99.50 333	96.55 224	99.50 231	99.26 218

MTGPA								99.20 198

test_post1							97.59 228				20.48 408	83.07 368	99.66 278	94.16 310

test_post											21.25 407	83.86 364	99.70 250

patchmatchnet-post											98.77 214	84.37 359	99.85 120

GG-mvs-BLEND				94.76 367	94.54 405	92.13 355	99.31 27	80.47 410		88.73 403	91.01 403	67.59 399	98.16 398	82.30 400	94.53 397	93.98 400

MTMP							97.93 180	91.91 395

gm-plane-assit					94.83 404	81.97 407			88.07 391		94.99 383		99.60 299	91.76 358

test9_res														93.28 336	99.15 281	99.38 182

TEST9					98.71 274	98.08 139	95.96 327	99.03 237	91.40 372	95.85 358	97.53 326	96.52 190	99.76 221

test_8					98.67 286	98.01 147	95.91 332	99.02 240	91.64 367	95.79 360	97.50 329	96.47 192	99.76 221

agg_prior2														92.50 352	99.16 279	99.37 184

agg_prior					98.68 285	97.99 148		99.01 243		95.59 361			99.77 215

TestCases				99.16 105	99.50 108	98.55 97		99.58 55	96.80 254	98.88 174	99.06 140	97.65 115	99.57 311	94.45 302	99.61 192	99.37 184

test_prior4						97.97 152	95.86 333

test_prior2							95.74 338		96.48 269	96.11 353	97.63 322	95.92 221		94.16 310	99.20 273

test_prior				98.95 141	98.69 283	97.95 156		99.03 237					99.59 303			99.30 210

旧先验2							95.76 337		88.56 390	97.52 295			99.66 278	94.48 300

新几何2							95.93 330

新几何1				98.91 147	98.94 230	97.76 173		98.76 282	87.58 392	96.75 335	98.10 292	94.80 254	99.78 208	92.73 348	99.00 299	99.20 229

旧先验1					98.82 257	97.45 191		98.76 282			98.34 274	95.50 234			99.01 298	99.23 224

无先验							95.74 338	98.74 287	89.38 386				99.73 238	92.38 354		99.22 228

原ACMM2							95.53 344

原ACMM1				98.35 221	98.90 240	96.25 242		98.83 274	92.48 361	96.07 355	98.10 292	95.39 237	99.71 246	92.61 351	98.99 300	99.08 247

test222					98.92 236	96.93 223	95.54 343	98.78 280	85.72 395	96.86 329	98.11 291	94.43 262			99.10 289	99.23 224

testdata2													99.79 197	92.80 346

segment_acmp												97.02 162

testdata				98.09 239	98.93 232	95.40 270		98.80 277	90.08 383	97.45 301	98.37 270	95.26 239	99.70 250	93.58 329	98.95 305	99.17 240

testdata1							95.44 349		96.32 277

test12				98.93 144	98.58 302	97.83 165		98.66 291		96.53 342		95.51 233	99.69 255		99.13 284	99.27 215

plane_prior7					99.19 180	97.87 161

plane_prior6					98.99 224	97.70 179						94.90 247

plane_prior5								99.27 181					99.70 250	94.42 304	99.51 224	99.45 150

plane_prior4											97.98 301

plane_prior3						97.78 172			97.41 209	97.79 276

plane_prior2							97.77 203		98.20 145

plane_prior1					99.05 215

plane_prior						97.65 181	97.07 267		96.72 259						99.36 247

n2								0.00 415

nn								0.00 415

door-mid								99.57 62

lessismore_v0				98.97 138	99.73 38	97.53 187		86.71 405		99.37 89	99.52 57	89.93 320	99.92 50	98.99 62	99.72 149	99.44 154

LGP-MVS_train				99.47 54	99.57 81	98.97 66		99.48 96	96.60 263	99.10 132	99.06 140	98.71 39	99.83 154	95.58 277	99.78 119	99.62 67

test11								98.87 261

door								99.41 122

HQP5-MVS						96.79 226

HQP-NCC					98.67 286		96.29 309		96.05 286	95.55 364

ACMP_Plane					98.67 286		96.29 309		96.05 286	95.55 364

BP-MVS														92.82 344

HQP4-MVS										95.56 363			99.54 322			99.32 203

HQP3-MVS								99.04 235							99.26 265

HQP2-MVS												93.84 276

NP-MVS					98.84 252	97.39 195					96.84 348

MDTV_nov1_ep13_2view						74.92 411	97.69 213		90.06 384	97.75 279		85.78 348		93.52 330		98.69 310

MDTV_nov1_ep13			95.22 317		97.06 386	83.20 404	97.74 208	96.16 362	94.37 333	96.99 319	98.83 203	83.95 363	99.53 324	93.90 319	97.95 354

ACMMP++_ref															99.77 124

ACMMP++															99.68 167

Test By Simon												96.52 190

ITE_SJBPF				98.87 151	99.22 171	98.48 104		99.35 141	97.50 197	98.28 242	98.60 245	97.64 118	99.35 359	93.86 322	99.27 262	98.79 298

DeepMVS_CX				93.44 381	98.24 330	94.21 305		94.34 380	64.28 403	91.34 399	94.87 388	89.45 325	92.77 406	77.54 404	93.14 399	93.35 401

High-resolution multi-view benchmark