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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 3	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 6

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 41	99.71 10	96.99 48	99.69 2	99.57 21	99.02 22	99.62 16	99.36 26	98.53 11	99.52 216	98.58 41	99.95 5	99.66 36
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
UniMVSNet_ETH3D		99.12 3	99.28 5	98.65 46	99.77 5	96.34 70	99.18 6	99.20 50	99.67 3	99.73 7	99.65 8	99.15 3	99.86 28	97.22 93	99.92 15	99.77 15

tt0320-xc		99.10 4	99.31 3	98.49 58	99.57 20	96.09 80	98.91 11	99.55 24	99.67 3	99.78 3	99.69 4	98.63 10	99.77 70	98.02 57	99.93 11	99.60 44

sc_t1		99.09 5	99.28 5	98.53 55	99.72 8	96.21 74	98.87 12	99.19 52	99.71 2	99.76 5	99.65 8	98.64 9	99.79 54	98.07 55	99.90 25	99.58 48

tt0320		99.07 6	99.29 4	98.43 63	99.55 24	95.92 87	98.97 10	99.53 26	99.67 3	99.79 2	99.71 3	98.33 14	99.78 59	98.11 51	99.92 15	99.57 56

pmmvs6		99.07 6	99.24 7	98.56 52	99.81 2	96.38 66	98.87 12	99.30 40	99.01 23	99.63 15	99.66 6	99.27 2	99.68 143	97.75 72	99.89 26	99.62 43

mamv4		99.05 8	98.91 11	99.46 2	98.94 127	99.62 2	97.98 67	99.70 8	99.49 6	99.78 3	99.22 39	95.92 141	99.95 3	99.31 8	99.83 52	98.83 256

mvs_tets		98.90 9	98.94 9	98.75 35	99.69 11	96.48 64	98.54 26	99.22 47	96.23 149	99.71 8	99.48 15	98.77 7	99.93 4	98.89 29	99.95 5	99.84 8

TDRefinement		98.90 9	98.86 12	99.02 10	99.54 28	98.06 9	99.34 5	99.44 31	98.85 28	99.00 61	99.20 41	97.42 47	99.59 192	97.21 94	99.76 69	99.40 125

UA-Net		98.88 11	98.76 17	99.22 3	99.11 100	97.89 17	99.47 3	99.32 38	99.08 17	97.87 196	99.67 5	96.47 117	99.92 6	97.88 63	99.98 2	99.85 6

DTE-MVSNet		98.79 12	98.86 12	98.59 50	99.55 24	96.12 78	98.48 33	99.10 73	99.36 8	99.29 39	99.06 61	97.27 53	99.93 4	97.71 74	99.91 19	99.70 31

jajsoiax		98.77 13	98.79 16	98.74 38	99.66 13	96.48 64	98.45 34	99.12 69	95.83 181	99.67 11	99.37 24	98.25 17	99.92 6	98.77 32	99.94 8	99.82 9

PEN-MVS		98.75 14	98.85 14	98.44 62	99.58 19	95.67 97	98.45 34	99.15 63	99.33 9	99.30 38	99.00 67	97.27 53	99.92 6	97.64 78	99.92 15	99.75 24

v7n		98.73 15	98.99 8	97.95 106	99.64 14	94.20 164	98.67 18	99.14 66	99.08 17	99.42 29	99.23 38	96.53 112	99.91 14	99.27 10	99.93 11	99.73 26

PS-CasMVS		98.73 15	98.85 14	98.39 67	99.55 24	95.47 110	98.49 31	99.13 68	99.22 13	99.22 44	98.96 73	97.35 49	99.92 6	97.79 69	99.93 11	99.79 13

test_djsdf		98.73 15	98.74 20	98.69 43	99.63 15	96.30 72	98.67 18	99.02 105	96.50 134	99.32 37	99.44 19	97.43 46	99.92 6	98.73 35	99.95 5	99.86 5

anonymousdsp		98.72 18	98.63 24	98.99 14	99.62 16	97.29 41	98.65 22	99.19 52	95.62 190	99.35 36	99.37 24	97.38 48	99.90 18	98.59 40	99.91 19	99.77 15

WR-MVS_H		98.65 19	98.62 26	98.75 35	99.51 31	96.61 60	98.55 25	99.17 56	99.05 20	99.17 46	98.79 89	95.47 166	99.89 21	97.95 61	99.91 19	99.75 24

OurMVSNet-221017-0		98.61 20	98.61 28	98.63 48	99.77 5	96.35 69	99.17 7	99.05 93	98.05 61	99.61 17	99.52 12	93.72 229	99.88 23	98.72 37	99.88 28	99.65 39

lecture		98.59 21	98.60 29	98.55 53	99.48 36	96.38 66	98.08 61	99.09 78	98.46 42	98.68 97	98.73 98	97.88 27	99.80 51	97.43 86	99.59 129	99.48 99

test_fmvsmconf0.01_n		98.57 22	98.74 20	98.06 96	99.39 49	94.63 144	96.70 165	99.82 1	95.44 202	99.64 14	99.52 12	98.96 4	99.74 93	99.38 6	99.86 35	99.81 10

testf1		98.57 22	98.45 37	98.93 22	99.79 3	98.78 3	97.69 91	99.42 33	97.69 75	98.92 70	98.77 93	97.80 30	99.25 314	96.27 136	99.69 95	98.76 271

APD_test2		98.57 22	98.45 37	98.93 22	99.79 3	98.78 3	97.69 91	99.42 33	97.69 75	98.92 70	98.77 93	97.80 30	99.25 314	96.27 136	99.69 95	98.76 271

Anonymous20231211		98.55 25	98.76 17	97.94 107	98.79 154	94.37 156	98.84 14	99.15 63	99.37 7	99.67 11	99.43 20	95.61 161	99.72 105	98.12 50	99.86 35	99.73 26

reproduce_model		98.54 26	98.33 47	99.15 4	99.06 108	98.04 12	97.04 136	99.09 78	98.42 44	99.03 56	98.71 102	96.93 82	99.83 36	97.09 101	99.63 110	99.56 64

nrg030		98.54 26	98.62 26	98.32 71	99.22 74	95.66 98	97.90 75	99.08 82	98.31 48	99.02 58	98.74 97	97.68 35	99.61 187	97.77 71	99.85 45	99.70 31

PS-MVSNAJss		98.53 28	98.63 24	98.21 85	99.68 12	94.82 137	98.10 59	99.21 48	96.91 113	99.75 6	99.45 18	95.82 148	99.92 6	98.80 31	99.96 4	99.89 4

MIMVSNet1		98.51 29	98.45 37	98.67 44	99.72 8	96.71 54	98.76 16	98.89 140	98.49 41	99.38 32	99.14 53	95.44 168	99.84 34	96.47 122	99.80 61	99.47 103

reproduce-ours		98.48 30	98.27 53	99.12 5	98.99 119	98.02 13	96.81 151	99.02 105	98.29 51	98.97 65	98.61 114	97.27 53	99.82 39	96.86 112	99.61 119	99.51 82

our_new_method		98.48 30	98.27 53	99.12 5	98.99 119	98.02 13	96.81 151	99.02 105	98.29 51	98.97 65	98.61 114	97.27 53	99.82 39	96.86 112	99.61 119	99.51 82

pm-mvs1		98.47 32	98.67 22	97.86 111	99.52 30	94.58 147	98.28 45	99.00 116	97.57 79	99.27 40	99.22 39	98.32 15	99.50 221	97.09 101	99.75 78	99.50 85

ACMH	93.61 9	98.44 33	98.76 17	97.51 139	99.43 42	93.54 189	98.23 49	99.05 93	97.40 93	99.37 33	99.08 60	98.79 6	99.47 232	97.74 73	99.71 89	99.50 85
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		98.42 34	98.46 34	98.30 74	99.46 39	95.22 126	98.27 47	98.84 160	99.05 20	99.01 59	98.65 111	95.37 171	99.90 18	97.57 80	99.91 19	99.77 15

test_fmvsmconf0.1_n		98.41 35	98.54 31	98.03 101	99.16 88	94.61 145	96.18 199	99.73 5	95.05 221	99.60 18	99.34 29	98.68 8	99.72 105	99.21 12	99.85 45	99.76 21

TransMVSNet (Re)		98.38 36	98.67 22	97.51 139	99.51 31	93.39 198	98.20 54	98.87 149	98.23 54	99.48 22	99.27 34	98.47 13	99.55 207	96.52 120	99.53 156	99.60 44

mmtdpeth		98.33 37	98.53 32	97.71 121	99.07 106	93.44 194	98.80 15	99.78 4	99.10 16	96.61 284	99.63 10	95.42 169	99.73 99	98.53 42	99.86 35	99.95 2

TranMVSNet+NR-MVSNet		98.33 37	98.30 51	98.43 63	99.07 106	95.87 89	96.73 163	99.05 93	98.67 31	98.84 78	98.45 136	97.58 43	99.88 23	96.45 125	99.86 35	99.54 70

HPM-MVS_fast		98.32 39	98.13 57	98.88 27	99.54 28	97.48 34	98.35 38	99.03 102	95.88 177	97.88 193	98.22 179	98.15 20	99.74 93	96.50 121	99.62 113	99.42 122

ANet_high		98.31 40	98.94 9	96.41 240	99.33 56	89.64 298	97.92 73	99.56 23	99.27 11	99.66 13	99.50 14	97.67 36	99.83 36	97.55 81	99.98 2	99.77 15

test_fmvsmconf_n		98.30 41	98.41 40	97.99 104	98.94 127	94.60 146	96.00 218	99.64 16	94.99 224	99.43 28	99.18 46	98.51 12	99.71 121	99.13 20	99.84 48	99.67 34

fmvsm_l_conf0.5_n_3		98.29 42	98.46 34	97.79 115	98.90 139	94.05 169	96.06 211	99.63 17	96.07 159	99.37 33	98.93 77	98.29 16	99.68 143	99.11 22	99.79 63	99.65 39

VPA-MVSNet		98.27 43	98.46 34	97.70 123	99.06 108	93.80 178	97.76 85	99.00 116	98.40 45	99.07 55	98.98 70	96.89 88	99.75 84	97.19 97	99.79 63	99.55 68

Vis-MVSNet		98.27 43	98.34 46	98.07 94	99.33 56	95.21 128	98.04 63	99.46 29	97.32 98	97.82 200	99.11 55	96.75 98	99.86 28	97.84 66	99.36 216	99.15 185
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
COLMAP_ROB	94.48 6	98.25 45	98.11 60	98.64 47	99.21 81	97.35 39	97.96 68	99.16 57	98.34 47	98.78 83	98.52 127	97.32 50	99.45 240	94.08 266	99.67 102	99.13 192
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	93.58 10	98.23 46	98.31 49	97.98 105	99.39 49	95.22 126	97.55 103	99.20 50	98.21 55	99.25 42	98.51 129	98.21 18	99.40 259	94.79 237	99.72 86	99.32 145

Elysia		98.19 47	98.37 41	97.66 127	99.28 60	93.52 190	97.35 117	98.90 136	98.63 33	99.45 25	98.32 156	94.31 211	99.91 14	99.19 14	99.88 28	99.54 70

StellarMVS		98.19 47	98.37 41	97.66 127	99.28 60	93.52 190	97.35 117	98.90 136	98.63 33	99.45 25	98.32 156	94.31 211	99.91 14	99.19 14	99.88 28	99.54 70

FC-MVSNet-test		98.16 49	98.37 41	97.56 134	99.49 35	93.10 205	98.35 38	99.21 48	98.43 43	98.89 73	98.83 88	94.30 213	99.81 44	97.87 64	99.91 19	99.77 15

SR-MVS-dyc-post		98.14 50	97.84 86	99.02 10	98.81 148	98.05 10	97.55 103	98.86 152	97.77 67	98.20 153	98.07 199	96.60 108	99.76 76	95.49 180	99.20 249	99.26 163

MTAPA		98.14 50	97.84 86	99.06 7	99.44 41	97.90 16	97.25 122	98.73 193	97.69 75	97.90 191	97.96 216	95.81 152	99.82 39	96.13 141	99.61 119	99.45 109

APDe-MVS		98.14 50	98.03 68	98.47 61	98.72 166	96.04 82	98.07 62	99.10 73	95.96 169	98.59 104	98.69 105	96.94 80	99.81 44	96.64 115	99.58 134	99.57 56
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		98.13 53	97.90 79	98.79 33	98.79 154	97.31 40	97.55 103	98.92 134	97.72 72	98.25 149	98.13 188	97.10 64	99.75 84	95.44 188	99.24 247	99.32 145

HPM-MVS		98.11 54	97.83 89	98.92 25	99.42 44	97.46 35	98.57 23	99.05 93	95.43 204	97.41 223	97.50 264	97.98 23	99.79 54	95.58 178	99.57 137	99.50 85
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		98.09 55	98.01 71	98.32 71	98.45 216	96.69 56	98.52 29	99.69 9	98.07 60	96.07 316	97.19 289	96.88 90	99.86 28	97.50 83	99.73 81	98.41 307

test_fmvsmvis_n_1920		98.08 56	98.47 33	96.93 194	99.03 116	93.29 200	96.32 187	99.65 13	95.59 192	99.71 8	99.01 66	97.66 38	99.60 190	99.44 4	99.83 52	97.90 362

test_fmvsm_n_1920		98.08 56	98.29 52	97.43 152	98.88 141	93.95 173	96.17 203	99.57 21	95.66 187	99.52 21	98.71 102	97.04 73	99.64 169	99.21 12	99.87 33	98.69 281

Gipuma		98.07 58	98.31 49	97.36 158	99.76 7	96.28 73	98.51 30	99.10 73	98.76 30	96.79 267	99.34 29	96.61 106	98.82 372	96.38 129	99.50 170	96.98 404
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mvs5depth		98.06 59	98.58 30	96.51 229	98.97 123	89.65 297	99.43 4	99.81 2	99.30 10	98.36 131	99.86 2	93.15 242	99.88 23	98.50 43	99.84 48	99.99 1

ACMMP		98.05 60	97.75 102	98.93 22	99.23 71	97.60 26	98.09 60	98.96 127	95.75 185	97.91 190	98.06 204	96.89 88	99.76 76	95.32 196	99.57 137	99.43 120
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
ACMM	93.33 11	98.05 60	97.79 94	98.85 28	99.15 91	97.55 30	96.68 166	98.83 167	95.21 211	98.36 131	98.13 188	98.13 22	99.62 179	96.04 145	99.54 152	99.39 130
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SteuartSystems-ACMMP		98.02 62	97.76 100	98.79 33	99.43 42	97.21 45	97.15 128	98.90 136	96.58 129	98.08 169	97.87 227	97.02 75	99.76 76	95.25 199	99.59 129	99.40 125
Skip Steuart: Steuart Systems R&D Blog.
SR-MVS		98.00 63	97.66 111	99.01 12	98.77 160	97.93 15	97.38 116	98.83 167	97.32 98	98.06 172	97.85 228	96.65 103	99.77 70	95.00 223	99.11 264	99.32 145

fmvsm_s_conf0.5_n_9		97.98 64	98.32 48	96.96 191	98.92 134	91.45 255	95.87 232	99.53 26	97.44 86	99.56 19	99.05 62	95.34 172	99.67 152	99.52 2	99.70 93	99.77 15

SDMVSNet		97.97 65	98.26 55	97.11 177	99.41 45	92.21 230	96.92 142	98.60 219	98.58 37	98.78 83	99.39 21	97.80 30	99.62 179	94.98 230	99.86 35	99.52 78

sd_testset		97.97 65	98.12 58	97.51 139	99.41 45	93.44 194	97.96 68	98.25 260	98.58 37	98.78 83	99.39 21	98.21 18	99.56 203	92.65 304	99.86 35	99.52 78

DVP-MVS++		97.96 67	97.90 79	98.12 92	97.75 311	95.40 111	99.03 8	98.89 140	96.62 123	98.62 100	98.30 162	96.97 78	99.75 84	95.70 164	99.25 244	99.21 173

Anonymous20240529		97.96 67	98.04 67	97.71 121	98.69 173	94.28 162	97.86 77	98.31 257	98.79 29	99.23 43	98.86 87	95.76 154	99.61 187	95.49 180	99.36 216	99.23 171

XVS		97.96 67	97.63 117	98.94 19	99.15 91	97.66 23	97.77 83	98.83 167	97.42 88	96.32 300	97.64 252	96.49 115	99.72 105	95.66 169	99.37 212	99.45 109

NR-MVSNet		97.96 67	97.86 85	98.26 77	98.73 163	95.54 103	98.14 57	98.73 193	97.79 66	99.42 29	97.83 231	94.40 209	99.78 59	95.91 156	99.76 69	99.46 105

APD_test1		97.95 71	97.68 108	98.75 35	99.60 17	98.60 6	97.21 126	99.08 82	96.57 132	98.07 171	98.38 146	96.22 133	99.14 332	94.71 244	99.31 234	98.52 298

ACMMPR		97.95 71	97.62 119	98.94 19	99.20 83	97.56 29	97.59 100	98.83 167	96.05 161	97.46 220	97.63 253	96.77 97	99.76 76	95.61 175	99.46 182	99.49 93

FMVSNet1		97.95 71	98.08 62	97.56 134	99.14 98	93.67 183	98.23 49	98.66 211	97.41 92	99.00 61	99.19 42	95.47 166	99.73 99	95.83 161	99.76 69	99.30 150

SED-MVS		97.94 74	97.90 79	98.07 94	99.22 74	95.35 116	96.79 155	98.83 167	96.11 155	99.08 53	98.24 174	97.87 28	99.72 105	95.44 188	99.51 166	99.14 190

HFP-MVS		97.94 74	97.64 115	98.83 29	99.15 91	97.50 33	97.59 100	98.84 160	96.05 161	97.49 214	97.54 260	97.07 68	99.70 130	95.61 175	99.46 182	99.30 150

LPG-MVS_test		97.94 74	97.67 109	98.74 38	99.15 91	97.02 46	97.09 133	99.02 105	95.15 215	98.34 135	98.23 176	97.91 25	99.70 130	94.41 252	99.73 81	99.50 85

FIs		97.93 77	98.07 63	97.48 147	99.38 51	92.95 209	98.03 65	99.11 70	98.04 62	98.62 100	98.66 107	93.75 228	99.78 59	97.23 92	99.84 48	99.73 26

fmvsm_l_conf0.5_n_9		97.92 78	98.37 41	96.57 223	98.94 127	90.54 279	95.39 270	99.58 19	96.82 116	99.56 19	98.77 93	97.23 60	99.61 187	99.17 17	99.86 35	99.57 56

ZNCC-MVS		97.92 78	97.62 119	98.83 29	99.32 58	97.24 43	97.45 111	98.84 160	95.76 183	96.93 259	97.43 268	97.26 57	99.79 54	96.06 142	99.53 156	99.45 109

region2R		97.92 78	97.59 124	98.92 25	99.22 74	97.55 30	97.60 98	98.84 160	96.00 166	97.22 231	97.62 254	96.87 92	99.76 76	95.48 184	99.43 198	99.46 105

CP-MVS		97.92 78	97.56 127	98.99 14	98.99 119	97.82 19	97.93 72	98.96 127	96.11 155	96.89 262	97.45 266	96.85 93	99.78 59	95.19 204	99.63 110	99.38 132

SPE-MVS-test		97.91 82	97.84 86	98.14 90	98.52 200	96.03 84	98.38 37	99.67 10	98.11 58	95.50 341	96.92 314	96.81 96	99.87 26	96.87 111	99.76 69	98.51 299

mPP-MVS		97.91 82	97.53 130	99.04 8	99.22 74	97.87 18	97.74 88	98.78 185	96.04 163	97.10 242	97.73 246	96.53 112	99.78 59	95.16 209	99.50 170	99.46 105

EC-MVSNet		97.90 84	97.94 78	97.79 115	98.66 176	95.14 129	98.31 42	99.66 12	97.57 79	95.95 320	97.01 308	96.99 77	99.82 39	97.66 77	99.64 108	98.39 310

ACMMP_NAP		97.89 85	97.63 117	98.67 44	99.35 54	96.84 51	96.36 184	98.79 181	95.07 219	97.88 193	98.35 150	97.24 59	99.72 105	96.05 144	99.58 134	99.45 109

fmvsm_s_conf0.5_n_3		97.88 86	98.37 41	96.41 240	98.73 163	89.82 292	95.94 227	99.49 28	96.81 117	99.09 52	99.03 65	97.09 66	99.65 163	99.37 7	99.76 69	99.76 21

PGM-MVS		97.88 86	97.52 131	98.96 17	99.20 83	97.62 25	97.09 133	99.06 87	95.45 200	97.55 209	97.94 219	97.11 63	99.78 59	94.77 240	99.46 182	99.48 99

DP-MVS		97.87 88	97.89 82	97.81 114	98.62 185	94.82 137	97.13 131	98.79 181	98.98 24	98.74 90	98.49 130	95.80 153	99.49 227	95.04 218	99.44 188	99.11 203

RPSCF		97.87 88	97.51 133	98.95 18	99.15 91	98.43 7	97.56 102	99.06 87	96.19 152	98.48 115	98.70 104	94.72 193	99.24 318	94.37 255	99.33 229	99.17 181

KD-MVS_self_test		97.86 90	98.07 63	97.25 168	99.22 74	92.81 212	97.55 103	98.94 132	97.10 105	98.85 76	98.88 85	95.03 185	99.67 152	97.39 88	99.65 106	99.26 163

test_0402		97.84 91	97.97 75	97.47 148	99.19 85	94.07 167	96.71 164	98.73 193	98.66 32	98.56 107	98.41 142	96.84 94	99.69 137	94.82 235	99.81 57	98.64 285

UniMVSNet_NR-MVSNet		97.83 92	97.65 112	98.37 68	98.72 166	95.78 91	95.66 248	99.02 105	98.11 58	98.31 141	97.69 249	94.65 199	99.85 31	97.02 106	99.71 89	99.48 99

UniMVSNet (Re)		97.83 92	97.65 112	98.35 70	98.80 151	95.86 90	95.92 229	99.04 101	97.51 83	98.22 152	97.81 236	94.68 197	99.78 59	97.14 99	99.75 78	99.41 124

casdiffmvs_mvg		97.83 92	98.11 60	97.00 190	98.57 193	92.10 238	95.97 223	99.18 54	97.67 78	99.00 61	98.48 134	97.64 39	99.50 221	96.96 108	99.54 152	99.40 125
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
KinetiMVS		97.82 95	98.02 69	97.24 170	99.24 68	92.32 226	96.92 142	98.38 246	98.56 40	99.03 56	98.33 153	93.22 240	99.83 36	98.74 34	99.71 89	99.57 56

GST-MVS		97.82 95	97.49 137	98.81 31	99.23 71	97.25 42	97.16 127	98.79 181	95.96 169	97.53 210	97.40 270	96.93 82	99.77 70	95.04 218	99.35 221	99.42 122

DeepC-MVS	95.41 4	97.82 95	97.70 104	98.16 87	98.78 158	95.72 93	96.23 197	99.02 105	93.92 271	98.62 100	98.99 69	97.69 34	99.62 179	96.18 140	99.87 33	99.15 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n_a		97.80 98	98.01 71	97.18 172	99.17 87	92.51 220	96.57 169	99.15 63	93.68 278	98.89 73	99.30 32	96.42 122	99.37 273	99.03 25	99.83 52	99.66 36

DU-MVS		97.79 99	97.60 123	98.36 69	98.73 163	95.78 91	95.65 250	98.87 149	97.57 79	98.31 141	97.83 231	94.69 195	99.85 31	97.02 106	99.71 89	99.46 105

DVP-MVS		97.78 100	97.65 112	98.16 87	99.24 68	95.51 105	96.74 159	98.23 263	95.92 174	98.40 125	98.28 167	97.06 69	99.71 121	95.48 184	99.52 161	99.26 163
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
LS3D		97.77 101	97.50 135	98.57 51	96.24 389	97.58 28	98.45 34	98.85 156	98.58 37	97.51 212	97.94 219	95.74 155	99.63 174	95.19 204	98.97 279	98.51 299

GeoE		97.75 102	97.70 104	97.89 109	98.88 141	94.53 148	97.10 132	98.98 123	95.75 185	97.62 205	97.59 256	97.61 42	99.77 70	96.34 132	99.44 188	99.36 139

fmvsm_s_conf0.1_n		97.73 103	98.02 69	96.85 202	99.09 103	91.43 257	96.37 183	99.11 70	94.19 261	99.01 59	99.25 35	96.30 128	99.38 268	99.00 26	99.88 28	99.73 26

3Dnovator+	96.13 3	97.73 103	97.59 124	98.15 89	98.11 261	95.60 99	98.04 63	98.70 202	98.13 57	96.93 259	98.45 136	95.30 175	99.62 179	95.64 171	98.96 280	99.24 169

tfpnnormal		97.72 105	97.97 75	96.94 193	99.26 64	92.23 229	97.83 80	98.45 234	98.25 53	99.13 49	98.66 107	96.65 103	99.69 137	93.92 277	99.62 113	98.91 243

Baseline_NR-MVSNet		97.72 105	97.79 94	97.50 143	99.56 22	93.29 200	95.44 264	98.86 152	98.20 56	98.37 128	99.24 36	94.69 195	99.55 207	95.98 151	99.79 63	99.65 39

MP-MVS-pluss		97.69 107	97.36 144	98.70 42	99.50 34	96.84 51	95.38 272	98.99 120	92.45 324	98.11 164	98.31 158	97.25 58	99.77 70	96.60 117	99.62 113	99.48 99
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EG-PatchMatch MVS		97.69 107	97.79 94	97.40 156	99.06 108	93.52 190	95.96 225	98.97 126	94.55 243	98.82 80	98.76 96	97.31 51	99.29 303	97.20 96	99.44 188	99.38 132

fmvsm_s_conf0.1_n_2		97.68 109	98.18 56	96.20 256	99.06 108	89.08 314	95.51 260	99.72 6	96.06 160	99.48 22	99.24 36	95.18 179	99.60 190	99.45 3	99.88 28	99.94 3

fmvsm_l_conf0.5_n		97.68 109	97.81 92	97.27 165	98.92 134	92.71 217	95.89 231	99.41 36	93.36 289	99.00 61	98.44 138	96.46 119	99.65 163	99.09 23	99.76 69	99.45 109

fmvsm_s_conf0.5_n_8		97.66 111	98.12 58	96.27 250	98.79 154	89.43 304	95.76 240	99.42 33	97.49 84	99.16 47	99.04 63	94.56 204	99.69 137	99.18 16	99.73 81	99.70 31

fmvsm_s_conf0.5_n_a		97.65 112	97.83 89	97.13 176	98.80 151	92.51 220	96.25 194	99.06 87	93.67 279	98.64 98	99.00 67	96.23 132	99.36 277	98.99 27	99.80 61	99.53 75

DPE-MVS		97.64 113	97.35 145	98.50 57	98.85 145	96.18 75	95.21 288	98.99 120	95.84 180	98.78 83	98.08 197	96.84 94	99.81 44	93.98 274	99.57 137	99.52 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS		97.64 113	97.18 157	99.00 13	99.32 58	97.77 21	97.49 109	98.73 193	96.27 144	95.59 337	97.75 243	96.30 128	99.78 59	93.70 285	99.48 177	99.45 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_5		97.63 115	97.83 89	97.04 186	98.77 160	92.33 224	95.63 255	99.58 19	93.53 282	99.10 51	98.66 107	96.44 120	99.65 163	99.12 21	99.68 99	99.12 198

fmvsm_s_conf0.5_n		97.62 116	97.89 82	96.80 206	98.79 154	91.44 256	96.14 205	99.06 87	94.19 261	98.82 80	98.98 70	96.22 133	99.38 268	98.98 28	99.86 35	99.58 48

3Dnovator	96.53 2	97.61 117	97.64 115	97.50 143	97.74 314	93.65 187	98.49 31	98.88 147	96.86 115	97.11 241	98.55 124	95.82 148	99.73 99	95.94 153	99.42 201	99.13 192

fmvsm_l_conf0.5_n_a		97.60 118	97.76 100	97.11 177	98.92 134	92.28 227	95.83 235	99.32 38	93.22 295	98.91 72	98.49 130	96.31 127	99.64 169	99.07 24	99.76 69	99.40 125

SF-MVS		97.60 118	97.39 140	98.22 82	98.93 132	95.69 95	97.05 135	99.10 73	95.32 208	97.83 199	97.88 224	96.44 120	99.72 105	94.59 249	99.39 210	99.25 168

v8		97.60 118	98.06 66	96.23 253	98.71 169	89.44 303	97.43 114	98.82 175	97.29 100	98.74 90	99.10 56	93.86 223	99.68 143	98.61 39	99.94 8	99.56 64

fmvsm_s_conf0.5_n_2		97.59 121	98.07 63	96.17 260	98.78 158	89.10 313	95.33 278	99.55 24	95.96 169	99.41 31	99.10 56	95.18 179	99.59 192	99.43 5	99.86 35	99.81 10

XVG-ACMP-BASELINE		97.58 122	97.28 150	98.49 58	99.16 88	96.90 50	96.39 179	98.98 123	95.05 221	98.06 172	98.02 209	95.86 144	99.56 203	94.37 255	99.64 108	99.00 220

v10		97.55 123	97.97 75	96.31 248	98.60 187	89.64 298	97.44 112	99.02 105	96.60 125	98.72 92	99.16 50	93.48 235	99.72 105	98.76 33	99.92 15	99.58 48

OPM-MVS		97.54 124	97.25 151	98.41 65	99.11 100	96.61 60	95.24 286	98.46 233	94.58 242	98.10 166	98.07 199	97.09 66	99.39 265	95.16 209	99.44 188	99.21 173
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XXY-MVS		97.54 124	97.70 104	97.07 183	99.46 39	92.21 230	97.22 125	99.00 116	94.93 227	98.58 105	98.92 79	97.31 51	99.41 257	94.44 250	99.43 198	99.59 47

casdiffmvs		97.50 126	97.81 92	96.56 225	98.51 202	91.04 264	95.83 235	99.09 78	97.23 101	98.33 138	98.30 162	97.03 74	99.37 273	96.58 119	99.38 211	99.28 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SixPastTwentyTwo		97.49 127	97.57 126	97.26 167	99.56 22	92.33 224	98.28 45	96.97 341	98.30 50	99.45 25	99.35 28	88.43 328	99.89 21	98.01 58	99.76 69	99.54 70

SMA-MVS		97.48 128	97.11 159	98.60 49	98.83 146	96.67 57	96.74 159	98.73 193	91.61 339	98.48 115	98.36 148	96.53 112	99.68 143	95.17 207	99.54 152	99.45 109
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
SSM_0404		97.47 129	97.75 102	96.64 216	98.81 148	91.26 260	96.57 169	99.16 57	96.95 109	98.44 121	98.09 195	97.05 71	99.72 105	95.21 202	99.44 188	98.95 232

ACMP	92.54 13	97.47 129	97.10 160	98.55 53	99.04 115	96.70 55	96.24 196	98.89 140	93.71 275	97.97 184	97.75 243	97.44 45	99.63 174	93.22 297	99.70 93	99.32 145
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_6		97.45 131	97.79 94	96.44 233	98.58 191	90.31 283	95.77 239	99.33 37	94.52 244	98.85 76	98.44 138	95.68 157	99.62 179	99.15 19	99.81 57	99.38 132

MSP-MVS		97.45 131	96.92 175	99.03 9	99.26 64	97.70 22	97.66 94	98.89 140	95.65 188	98.51 110	96.46 342	92.15 273	99.81 44	95.14 212	98.58 328	99.58 48
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
tt0805		97.44 133	97.56 127	97.11 177	99.55 24	96.36 68	98.66 21	95.66 369	98.31 48	97.09 247	95.45 382	97.17 62	98.50 407	98.67 38	97.45 388	96.48 425

baseline		97.44 133	97.78 98	96.43 235	98.52 200	90.75 274	96.84 148	99.03 102	96.51 133	97.86 197	98.02 209	96.67 100	99.36 277	97.09 101	99.47 179	99.19 177

fmvsm_s_conf0.5_n_4		97.43 135	97.77 99	96.39 244	98.48 211	89.89 290	95.65 250	99.26 44	94.73 233	98.72 92	98.58 119	95.58 163	99.57 201	99.28 9	99.67 102	99.73 26

MVSMamba_PlusPlus		97.43 135	97.98 74	95.78 280	98.88 141	89.70 294	98.03 65	98.85 156	99.18 14	96.84 266	99.12 54	93.04 246	99.91 14	98.38 46	99.55 146	97.73 376

TSAR-MVS + MP.		97.42 137	97.23 153	98.00 103	99.38 51	95.00 133	97.63 97	98.20 267	93.00 308	98.16 159	98.06 204	95.89 143	99.72 105	95.67 168	99.10 267	99.28 158
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		97.40 138	97.30 147	97.69 125	98.95 124	94.83 136	97.28 121	98.99 120	96.35 143	98.13 163	95.95 368	95.99 139	99.66 160	94.36 257	99.73 81	98.59 291

SSM_0407		97.39 139	97.67 109	96.54 228	98.51 202	90.96 267	96.40 177	99.16 57	96.95 109	98.27 145	98.09 195	97.05 71	99.67 152	95.21 202	99.40 206	98.98 226

test_fmvs3		97.38 140	97.56 127	96.84 204	98.63 183	92.81 212	97.60 98	99.61 18	90.87 353	98.76 88	99.66 6	94.03 219	97.90 433	99.24 11	99.68 99	99.81 10

XVG-OURS-SEG-HR		97.38 140	97.07 163	98.30 74	99.01 118	97.41 38	94.66 321	99.02 105	95.20 212	98.15 161	97.52 262	98.83 5	98.43 412	94.87 233	96.41 415	99.07 210

VDD-MVS		97.37 142	97.25 151	97.74 119	98.69 173	94.50 151	97.04 136	95.61 373	98.59 36	98.51 110	98.72 99	92.54 264	99.58 195	96.02 147	99.49 173	99.12 198

SD-MVS		97.37 142	97.70 104	96.35 245	98.14 257	95.13 130	96.54 172	98.92 134	95.94 172	99.19 45	98.08 197	97.74 33	95.06 457	95.24 200	99.54 152	98.87 253
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
PM-MVS		97.36 144	97.10 160	98.14 90	98.91 137	96.77 53	96.20 198	98.63 217	93.82 272	98.54 108	98.33 153	93.98 220	99.05 348	95.99 150	99.45 185	98.61 290

LCM-MVSNet-Re		97.33 145	97.33 146	97.32 161	98.13 260	93.79 179	96.99 139	99.65 13	96.74 120	99.47 24	98.93 77	96.91 86	99.84 34	90.11 362	99.06 274	98.32 319

EI-MVSNet-UG-set		97.32 146	97.40 139	97.09 181	97.34 355	92.01 241	95.33 278	97.65 314	97.74 70	98.30 143	98.14 186	95.04 184	99.69 137	97.55 81	99.52 161	99.58 48

EI-MVSNet-Vis-set		97.32 146	97.39 140	97.11 177	97.36 352	92.08 239	95.34 277	97.65 314	97.74 70	98.29 144	98.11 193	95.05 183	99.68 143	97.50 83	99.50 170	99.56 64

VPNet		97.26 148	97.49 137	96.59 220	99.47 38	90.58 276	96.27 190	98.53 227	97.77 67	98.46 118	98.41 142	94.59 201	99.68 143	94.61 245	99.29 237	99.52 78

viewmacassd2359aftdt		97.25 149	97.52 131	96.43 235	98.83 146	90.49 282	95.45 263	99.18 54	95.44 202	97.98 183	98.47 135	96.90 87	99.37 273	95.93 154	99.55 146	99.43 120

sasdasda		97.23 150	97.21 155	97.30 162	97.65 326	94.39 153	97.84 78	99.05 93	97.42 88	96.68 276	93.85 409	97.63 40	99.33 286	96.29 134	98.47 335	98.18 336

canonicalmvs		97.23 150	97.21 155	97.30 162	97.65 326	94.39 153	97.84 78	99.05 93	97.42 88	96.68 276	93.85 409	97.63 40	99.33 286	96.29 134	98.47 335	98.18 336

MGCFI-Net		97.20 152	97.23 153	97.08 182	97.68 319	93.71 182	97.79 81	99.09 78	97.40 93	96.59 285	93.96 407	97.67 36	99.35 281	96.43 127	98.50 334	98.17 338

AllTest		97.20 152	96.92 175	98.06 96	99.08 104	96.16 76	97.14 130	99.16 57	94.35 255	97.78 201	98.07 199	95.84 145	99.12 336	91.41 325	99.42 201	98.91 243

mamba_0408		97.17 154	97.38 142	96.55 227	98.51 202	90.96 267	95.19 289	99.06 87	96.60 125	98.27 145	97.78 238	96.58 109	99.72 105	95.04 218	99.40 206	98.98 226

SSM_04072		97.14 155	97.38 142	96.42 237	98.51 202	90.96 267	95.19 289	99.06 87	96.60 125	98.27 145	97.78 238	96.58 109	99.31 295	95.04 218	99.40 206	98.98 226

viewdifsd2359ckpt11		97.13 156	97.62 119	95.67 286	98.64 177	88.36 328	94.84 312	98.95 129	96.24 147	98.70 94	98.61 114	96.66 101	99.29 303	96.46 123	99.45 185	99.36 139

viewmsd2359difaftdt		97.13 156	97.62 119	95.67 286	98.64 177	88.36 328	94.84 312	98.95 129	96.24 147	98.70 94	98.61 114	96.66 101	99.29 303	96.46 123	99.45 185	99.36 139

fmvsm_s_conf0.5_n_7		97.13 156	97.50 135	96.04 265	98.43 218	89.03 315	94.92 307	99.00 116	94.51 245	98.42 122	98.96 73	94.97 189	99.54 210	98.42 45	99.85 45	99.56 64

dcpmvs_2		97.12 159	97.99 73	94.51 350	99.11 100	84.00 411	97.75 86	99.65 13	97.38 95	99.14 48	98.42 140	95.16 181	99.96 2	95.52 179	99.78 67	99.58 48

XVG-OURS		97.12 159	96.74 188	98.26 77	98.99 119	97.45 36	93.82 356	99.05 93	95.19 213	98.32 139	97.70 248	95.22 178	98.41 413	94.27 259	98.13 352	98.93 239

Anonymous20240521		97.07 161	97.51 133	95.76 281	99.35 54	88.18 335	97.78 82	98.40 243	97.11 104	98.34 135	99.04 63	89.58 313	99.79 54	98.09 53	99.93 11	99.30 150

test_vis3_rt		97.04 162	96.98 168	97.23 171	98.44 217	95.88 88	96.82 150	99.67 10	90.30 362	99.27 40	99.33 31	94.04 218	96.03 454	97.14 99	97.83 365	99.78 14

V42		97.04 162	97.16 158	96.68 215	98.59 189	91.05 263	96.33 186	98.36 249	94.60 239	97.99 179	98.30 162	93.32 237	99.62 179	97.40 87	99.53 156	99.38 132

APD-MVS		97.00 164	96.53 207	98.41 65	98.55 196	96.31 71	96.32 187	98.77 186	92.96 313	97.44 222	97.58 258	95.84 145	99.74 93	91.96 314	99.35 221	99.19 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		96.99 165	96.38 218	98.81 31	98.64 177	97.59 27	95.97 223	98.20 267	95.51 197	95.06 350	96.53 338	94.10 217	99.70 130	94.29 258	99.15 257	99.13 192

GBi-Net		96.99 165	96.80 184	97.56 134	97.96 274	93.67 183	98.23 49	98.66 211	95.59 192	97.99 179	99.19 42	89.51 317	99.73 99	94.60 246	99.44 188	99.30 150

test1		96.99 165	96.80 184	97.56 134	97.96 274	93.67 183	98.23 49	98.66 211	95.59 192	97.99 179	99.19 42	89.51 317	99.73 99	94.60 246	99.44 188	99.30 150

VDDNet		96.98 168	96.84 180	97.41 155	99.40 48	93.26 202	97.94 71	95.31 381	99.26 12	98.39 127	99.18 46	87.85 338	99.62 179	95.13 214	99.09 268	99.35 143

PHI-MVS		96.96 169	96.53 207	98.25 80	97.48 342	96.50 63	96.76 157	98.85 156	93.52 283	96.19 312	96.85 317	95.94 140	99.42 247	93.79 281	99.43 198	98.83 256

IS-MVSNet		96.93 170	96.68 191	97.70 123	99.25 67	94.00 171	98.57 23	96.74 350	98.36 46	98.14 162	97.98 215	88.23 331	99.71 121	93.10 300	99.72 86	99.38 132

CNVR-MVS		96.92 171	96.55 204	98.03 101	98.00 272	95.54 103	94.87 310	98.17 273	94.60 239	96.38 297	97.05 303	95.67 159	99.36 277	95.12 215	99.08 269	99.19 177

IterMVS-LS		96.92 171	97.29 148	95.79 279	98.51 202	88.13 338	95.10 295	98.66 211	96.99 106	98.46 118	98.68 106	92.55 262	99.74 93	96.91 109	99.79 63	99.50 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS		96.90 173	96.81 182	97.16 173	98.56 195	92.20 233	94.33 329	98.12 282	97.34 97	98.20 153	97.33 281	92.81 252	99.75 84	94.79 237	99.81 57	99.54 70

DeepPCF-MVS	94.58 5	96.90 173	96.43 213	98.31 73	97.48 342	97.23 44	92.56 394	98.60 219	92.84 316	98.54 108	97.40 270	96.64 105	98.78 376	94.40 254	99.41 205	98.93 239

balanced_conf03		96.88 175	97.29 148	95.63 289	97.66 324	89.47 302	97.95 70	98.89 140	95.94 172	97.77 203	98.55 124	92.23 271	99.68 143	97.05 105	99.61 119	97.73 376

NormalMVS		96.87 176	96.39 216	98.30 74	99.48 36	95.57 100	96.87 146	98.90 136	96.94 111	96.85 264	97.88 224	85.36 361	99.76 76	95.63 172	99.59 129	99.57 56

MM		96.87 176	96.62 194	97.62 131	97.72 316	93.30 199	96.39 179	92.61 418	97.90 65	96.76 272	98.64 112	90.46 300	99.81 44	99.16 18	99.94 8	99.76 21

v1144		96.84 178	97.08 162	96.13 263	98.42 220	89.28 307	95.41 268	98.67 208	94.21 259	97.97 184	98.31 158	93.06 245	99.65 163	98.06 56	99.62 113	99.45 109

VNet		96.84 178	96.83 181	96.88 200	98.06 263	92.02 240	96.35 185	97.57 320	97.70 74	97.88 193	97.80 237	92.40 269	99.54 210	94.73 242	98.96 280	99.08 208

EPP-MVSNet		96.84 178	96.58 198	97.65 129	99.18 86	93.78 180	98.68 17	96.34 355	97.91 64	97.30 226	98.06 204	88.46 327	99.85 31	93.85 279	99.40 206	99.32 145

v1192		96.83 181	97.06 164	96.15 262	98.28 233	89.29 306	95.36 273	98.77 186	93.73 274	98.11 164	98.34 152	93.02 250	99.67 152	98.35 47	99.58 134	99.50 85

MVS_111021_LR		96.82 182	96.55 204	97.62 131	98.27 235	95.34 118	93.81 358	98.33 253	94.59 241	96.56 288	96.63 333	96.61 106	98.73 382	94.80 236	99.34 224	98.78 263

Effi-MVS+-dtu		96.81 183	96.09 231	98.99 14	96.90 375	98.69 5	96.42 176	98.09 284	95.86 179	95.15 348	95.54 379	94.26 214	99.81 44	94.06 267	98.51 333	98.47 304

UGNet		96.81 183	96.56 201	97.58 133	96.64 379	93.84 177	97.75 86	97.12 334	96.47 138	93.62 390	98.88 85	93.22 240	99.53 213	95.61 175	99.69 95	99.36 139
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
v2v482		96.78 185	97.06 164	95.95 272	98.57 193	88.77 322	95.36 273	98.26 259	95.18 214	97.85 198	98.23 176	92.58 260	99.63 174	97.80 68	99.69 95	99.45 109

viewmanbaseed2359cas		96.77 186	96.94 172	96.27 250	98.41 222	90.24 284	95.11 294	99.03 102	94.28 258	97.45 221	97.85 228	95.92 141	99.32 294	95.18 206	99.19 253	99.24 169

LuminaMVS		96.76 187	96.58 198	97.30 162	98.94 127	92.96 208	96.17 203	96.15 357	95.54 196	98.96 67	98.18 184	87.73 339	99.80 51	97.98 59	99.61 119	99.15 185

v1240		96.74 188	97.02 167	95.91 275	98.18 248	88.52 324	95.39 270	98.88 147	93.15 304	98.46 118	98.40 145	92.80 253	99.71 121	98.45 44	99.49 173	99.49 93

DeepC-MVS_fast	94.34 7	96.74 188	96.51 209	97.44 151	97.69 318	94.15 165	96.02 216	98.43 237	93.17 303	97.30 226	97.38 276	95.48 165	99.28 307	93.74 282	99.34 224	98.88 251
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
viewcassd2359sk11		96.73 190	96.89 178	96.24 252	98.46 215	90.20 285	94.94 306	99.07 86	94.43 252	97.33 225	98.05 207	95.69 156	99.40 259	94.98 230	99.11 264	99.12 198

MVS_111021_HR		96.73 190	96.54 206	97.27 165	98.35 226	93.66 186	93.42 371	98.36 249	94.74 231	96.58 286	96.76 326	96.54 111	98.99 356	94.87 233	99.27 240	99.15 185

v1921920		96.72 192	96.96 171	95.99 267	98.21 242	88.79 321	95.42 266	98.79 181	93.22 295	98.19 157	98.26 172	92.68 256	99.70 130	98.34 48	99.55 146	99.49 93

FMVSNet2		96.72 192	96.67 192	96.87 201	97.96 274	91.88 244	97.15 128	98.06 290	95.59 192	98.50 112	98.62 113	89.51 317	99.65 163	94.99 229	99.60 126	99.07 210

PMVS	89.60 17	96.71 194	96.97 169	95.95 272	99.51 31	97.81 20	97.42 115	97.49 321	97.93 63	95.95 320	98.58 119	96.88 90	96.91 446	89.59 371	99.36 216	93.12 455
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v144192		96.69 195	96.90 177	96.03 266	98.25 238	88.92 316	95.49 261	98.77 186	93.05 306	98.09 167	98.29 166	92.51 267	99.70 130	98.11 51	99.56 140	99.47 103

CPTT-MVS		96.69 195	96.08 232	98.49 58	98.89 140	96.64 59	97.25 122	98.77 186	92.89 315	96.01 319	97.13 296	92.23 271	99.67 152	92.24 311	99.34 224	99.17 181

HQP_MVS		96.66 197	96.33 221	97.68 126	98.70 171	94.29 159	96.50 173	98.75 190	96.36 141	96.16 313	96.77 324	91.91 283	99.46 235	92.59 306	99.20 249	99.28 158

EI-MVSNet		96.63 198	96.93 173	95.74 282	97.26 360	88.13 338	95.29 284	97.65 314	96.99 106	97.94 188	98.19 181	92.55 262	99.58 195	96.91 109	99.56 140	99.50 85

FE-MVSNET		96.59 199	96.65 193	96.41 240	98.94 127	90.51 281	96.07 209	99.05 93	92.94 314	98.03 176	98.00 213	93.08 244	99.42 247	94.04 270	99.74 80	99.30 150

patch_mono-2		96.59 199	96.93 173	95.55 296	98.88 141	87.12 361	94.47 326	99.30 40	94.12 264	96.65 282	98.41 142	94.98 188	99.87 26	95.81 163	99.78 67	99.66 36

ab-mvs		96.59 199	96.59 197	96.60 219	98.64 177	92.21 230	98.35 38	97.67 310	94.45 251	96.99 253	98.79 89	94.96 190	99.49 227	90.39 359	99.07 271	98.08 342

v148		96.58 202	96.97 169	95.42 302	98.63 183	87.57 351	95.09 296	97.90 296	95.91 176	98.24 150	97.96 216	93.42 236	99.39 265	96.04 145	99.52 161	99.29 157

test20.03		96.58 202	96.61 196	96.48 232	98.49 209	91.72 248	95.68 246	97.69 309	96.81 117	98.27 145	97.92 222	94.18 216	98.71 385	90.78 343	99.66 105	99.00 220

NCCC		96.52 204	95.99 237	98.10 93	97.81 295	95.68 96	95.00 304	98.20 267	95.39 205	95.40 344	96.36 349	93.81 225	99.45 240	93.55 288	98.42 340	99.17 181

diffmvs_AUTHOR		96.50 205	96.81 182	95.57 292	98.03 264	88.26 332	93.73 360	99.14 66	94.92 228	97.24 230	97.84 230	94.62 200	99.33 286	96.44 126	99.37 212	99.13 192

pmmvs-eth3d		96.49 206	96.18 228	97.42 154	98.25 238	94.29 159	94.77 317	98.07 289	89.81 369	97.97 184	98.33 153	93.11 243	99.08 345	95.46 187	99.84 48	98.89 247

OMC-MVS		96.48 207	96.00 236	97.91 108	98.30 230	96.01 85	94.86 311	98.60 219	91.88 334	97.18 236	97.21 288	96.11 135	99.04 350	90.49 358	99.34 224	98.69 281

viewdifsd2359ckpt13		96.47 208	96.42 214	96.61 218	98.35 226	91.50 253	95.31 281	98.84 160	93.21 297	96.73 273	97.58 258	95.28 176	99.26 311	94.02 272	98.45 337	99.07 210

TSAR-MVS + GP.		96.47 208	96.12 229	97.49 146	97.74 314	95.23 123	94.15 340	96.90 343	93.26 293	98.04 175	96.70 329	94.41 208	98.89 366	94.77 240	99.14 258	98.37 312

Fast-Effi-MVS+-dtu		96.44 210	96.12 229	97.39 157	97.18 363	94.39 153	95.46 262	98.73 193	96.03 165	94.72 358	94.92 392	96.28 131	99.69 137	93.81 280	97.98 357	98.09 341

K. test v3		96.44 210	96.28 223	96.95 192	99.41 45	91.53 251	97.65 95	90.31 444	98.89 27	98.93 69	99.36 26	84.57 369	99.92 6	97.81 67	99.56 140	99.39 130

SymmetryMVS		96.43 212	95.85 246	98.17 86	98.58 191	95.57 100	96.87 146	95.29 382	96.94 111	96.85 264	97.88 224	85.36 361	99.76 76	95.63 172	99.27 240	99.19 177

MSLP-MVS++		96.42 213	96.71 189	95.57 292	97.82 294	90.56 278	95.71 242	98.84 160	94.72 234	96.71 275	97.39 274	94.91 191	98.10 430	95.28 197	99.02 276	98.05 351

AstraMVS		96.41 214	96.48 211	96.20 256	98.91 137	89.69 295	96.28 189	93.29 408	96.11 155	98.70 94	98.36 148	89.41 320	99.66 160	97.60 79	99.63 110	99.26 163

test_fmvs2		96.38 215	96.45 212	96.16 261	97.85 281	91.30 258	96.81 151	99.45 30	89.24 375	98.49 113	99.38 23	88.68 325	97.62 438	98.83 30	99.32 231	99.57 56

IMVS_0407		96.35 216	96.88 179	94.74 337	97.83 290	86.11 377	96.25 194	98.82 175	94.48 246	97.57 207	97.14 292	96.08 136	99.33 286	95.00 223	98.78 302	98.78 263

Anonymous202405211		96.34 217	95.98 238	97.43 152	98.25 238	93.85 176	96.74 159	94.41 394	97.72 72	98.37 128	98.03 208	87.15 345	99.53 213	94.06 267	99.07 271	98.92 242

h-mvs33		96.29 218	95.63 256	98.26 77	98.50 208	96.11 79	96.90 144	97.09 335	96.58 129	97.21 233	98.19 181	84.14 371	99.78 59	95.89 157	96.17 423	98.89 247

IMVS_0403		96.27 219	96.77 187	94.76 335	97.83 290	86.11 377	96.00 218	98.82 175	94.48 246	97.49 214	97.14 292	95.38 170	99.40 259	95.00 223	98.78 302	98.78 263

MVS_Test		96.27 219	96.79 186	94.73 338	96.94 373	86.63 369	96.18 199	98.33 253	94.94 225	96.07 316	98.28 167	95.25 177	99.26 311	97.21 94	97.90 362	98.30 323

MCST-MVS		96.24 221	95.80 249	97.56 134	98.75 162	94.13 166	94.66 321	98.17 273	90.17 365	96.21 310	96.10 362	95.14 182	99.43 245	94.13 265	98.85 295	99.13 192

guyue		96.21 222	96.29 222	95.98 269	98.80 151	89.14 311	96.40 177	94.34 396	95.99 168	98.58 105	98.13 188	87.42 343	99.64 169	97.39 88	99.55 146	99.16 184

mvsany_test3		96.21 222	95.93 242	97.05 184	97.40 350	94.33 158	95.76 240	94.20 397	89.10 376	99.36 35	99.60 11	93.97 221	97.85 434	95.40 195	98.63 323	98.99 223

Effi-MVS+		96.19 224	96.01 235	96.71 212	97.43 348	92.19 234	96.12 206	99.10 73	95.45 200	93.33 402	94.71 395	97.23 60	99.56 203	93.21 298	97.54 382	98.37 312

DELS-MVS		96.17 225	96.23 225	95.99 267	97.55 337	90.04 287	92.38 403	98.52 228	94.13 263	96.55 290	97.06 302	94.99 187	99.58 195	95.62 174	99.28 238	98.37 312
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
MVSFormer		96.14 226	96.36 219	95.49 299	97.68 319	87.81 347	98.67 18	99.02 105	96.50 134	94.48 365	96.15 357	86.90 347	99.92 6	98.73 35	99.13 260	98.74 273

ETV-MVS		96.13 227	95.90 243	96.82 205	97.76 309	93.89 174	95.40 269	98.95 129	95.87 178	95.58 338	91.00 445	96.36 126	99.72 105	93.36 291	98.83 298	96.85 411

testgi		96.07 228	96.50 210	94.80 332	99.26 64	87.69 350	95.96 225	98.58 223	95.08 218	98.02 178	96.25 353	97.92 24	97.60 439	88.68 385	98.74 310	99.11 203

LF4IMVS		96.07 228	95.63 256	97.36 158	98.19 245	95.55 102	95.44 264	98.82 175	92.29 327	95.70 334	96.55 336	92.63 259	98.69 388	91.75 323	99.33 229	97.85 366

VortexMVS		96.04 230	96.56 201	94.49 352	97.60 333	84.36 406	96.05 212	98.67 208	94.74 231	98.95 68	98.78 92	87.13 346	99.50 221	97.37 90	99.76 69	99.60 44

EIA-MVS		96.04 230	95.77 251	96.85 202	97.80 299	92.98 207	96.12 206	99.16 57	94.65 237	93.77 384	91.69 439	95.68 157	99.67 152	94.18 262	98.85 295	97.91 361

diffmvs		96.04 230	96.23 225	95.46 301	97.35 353	88.03 341	93.42 371	99.08 82	94.09 267	96.66 280	96.93 312	93.85 224	99.29 303	96.01 149	98.67 318	99.06 213
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
alignmvs		96.01 233	95.52 259	97.50 143	97.77 308	94.71 139	96.07 209	96.84 344	97.48 85	96.78 271	94.28 404	85.50 360	99.40 259	96.22 138	98.73 313	98.40 308

TinyColmap		96.00 234	96.34 220	94.96 323	97.90 279	87.91 343	94.13 343	98.49 231	94.41 253	98.16 159	97.76 240	96.29 130	98.68 391	90.52 355	99.42 201	98.30 323

PVSNet_Blended_VisFu		95.95 235	95.80 249	96.42 237	99.28 60	90.62 275	95.31 281	99.08 82	88.40 388	96.97 257	98.17 185	92.11 275	99.78 59	93.64 286	99.21 248	98.86 254

SSC-MVS		95.92 236	97.03 166	92.58 406	99.28 60	78.39 443	96.68 166	95.12 385	98.90 26	99.11 50	98.66 107	91.36 288	99.68 143	95.00 223	99.16 256	99.67 34

UnsupCasMVSNet_eth		95.91 237	95.73 252	96.44 233	98.48 211	91.52 252	95.31 281	98.45 234	95.76 183	97.48 217	97.54 260	89.53 316	98.69 388	94.43 251	94.61 441	99.13 192

icg_test_0407_2		95.88 238	96.39 216	94.36 356	97.83 290	86.11 377	91.82 414	98.82 175	94.48 246	97.57 207	97.14 292	96.08 136	98.20 428	95.00 223	98.78 302	98.78 263

QAPM		95.88 238	95.57 258	96.80 206	97.90 279	91.84 246	98.18 56	98.73 193	88.41 387	96.42 295	98.13 188	94.73 192	99.75 84	88.72 383	98.94 283	98.81 259

CANet		95.86 240	95.65 255	96.49 231	96.41 386	90.82 271	94.36 328	98.41 241	94.94 225	92.62 419	96.73 327	92.68 256	99.71 121	95.12 215	99.60 126	98.94 235

IterMVS-SCA-FT		95.86 240	96.19 227	94.85 329	97.68 319	85.53 385	92.42 400	97.63 318	96.99 106	98.36 131	98.54 126	87.94 333	99.75 84	97.07 104	99.08 269	99.27 162

test_f		95.82 242	95.88 245	95.66 288	97.61 331	93.21 204	95.61 256	98.17 273	86.98 404	98.42 122	99.47 16	90.46 300	94.74 459	97.71 74	98.45 337	99.03 216

RRT-MVS		95.78 243	96.25 224	94.35 358	96.68 378	84.47 404	97.72 90	99.11 70	97.23 101	97.27 228	98.72 99	86.39 351	99.79 54	95.49 180	97.67 376	98.80 260

test_vis1_n_1920		95.77 244	96.41 215	93.85 369	98.55 196	84.86 399	95.91 230	99.71 7	92.72 319	97.67 204	98.90 83	87.44 342	98.73 382	97.96 60	98.85 295	97.96 358

hse-mvs2		95.77 244	95.09 269	97.79 115	97.84 287	95.51 105	95.66 248	95.43 378	96.58 129	97.21 233	96.16 356	84.14 371	99.54 210	95.89 157	96.92 397	98.32 319

SSC-MVS3.2		95.75 246	96.56 201	93.34 380	98.69 173	80.75 435	91.60 417	97.43 325	97.37 96	96.99 253	97.02 305	93.69 230	99.71 121	96.32 133	99.89 26	99.55 68

MVS_0304		95.71 247	95.18 265	97.33 160	94.85 434	92.82 210	95.36 273	90.89 436	95.51 197	95.61 336	97.82 234	88.39 329	99.78 59	98.23 49	99.91 19	99.40 125

MVP-Stereo		95.69 248	95.28 261	96.92 195	98.15 255	93.03 206	95.64 254	98.20 267	90.39 361	96.63 283	97.73 246	91.63 285	99.10 343	91.84 319	97.31 392	98.63 287
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet-bldmvs		95.69 248	95.67 253	95.74 282	98.48 211	88.76 323	92.84 384	97.25 327	96.00 166	97.59 206	97.95 218	91.38 287	99.46 235	93.16 299	96.35 418	98.99 223

viewmambaseed2359dif		95.68 250	95.85 246	95.17 311	97.51 339	87.41 355	93.61 366	98.58 223	91.06 351	96.68 276	97.66 251	94.71 194	99.11 339	93.93 276	98.94 283	98.99 223

test_vis1_n		95.67 251	95.89 244	95.03 318	98.18 248	89.89 290	96.94 141	99.28 42	88.25 391	98.20 153	98.92 79	86.69 350	97.19 441	97.70 76	98.82 299	98.00 356

new-patchmatchnet		95.67 251	96.58 198	92.94 397	97.48 342	80.21 438	92.96 382	98.19 272	94.83 229	98.82 80	98.79 89	93.31 238	99.51 220	95.83 161	99.04 275	99.12 198

IMVS_0404		95.66 253	96.03 234	94.55 347	97.83 290	86.11 377	93.24 377	98.82 175	94.48 246	95.51 340	97.14 292	93.49 234	98.78 376	95.00 223	98.78 302	98.78 263

xiu_mvs_v1_base_debu		95.62 254	95.96 239	94.60 343	98.01 268	88.42 325	93.99 348	98.21 264	92.98 309	95.91 322	94.53 398	96.39 123	99.72 105	95.43 191	98.19 349	95.64 437

xiu_mvs_v1_base		95.62 254	95.96 239	94.60 343	98.01 268	88.42 325	93.99 348	98.21 264	92.98 309	95.91 322	94.53 398	96.39 123	99.72 105	95.43 191	98.19 349	95.64 437

xiu_mvs_v1_base_debi		95.62 254	95.96 239	94.60 343	98.01 268	88.42 325	93.99 348	98.21 264	92.98 309	95.91 322	94.53 398	96.39 123	99.72 105	95.43 191	98.19 349	95.64 437

DP-MVS Recon		95.55 257	95.13 267	96.80 206	98.51 202	93.99 172	94.60 323	98.69 203	90.20 364	95.78 330	96.21 355	92.73 255	98.98 358	90.58 354	98.86 294	97.42 393

WB-MVS		95.50 258	96.62 194	92.11 416	99.21 81	77.26 453	96.12 206	95.40 379	98.62 35	98.84 78	98.26 172	91.08 291	99.50 221	93.37 290	98.70 316	99.58 48

Fast-Effi-MVS+		95.49 259	95.07 270	96.75 210	97.67 323	92.82 210	94.22 336	98.60 219	91.61 339	93.42 400	92.90 420	96.73 99	99.70 130	92.60 305	97.89 363	97.74 375

TAMVS		95.49 259	94.94 274	97.16 173	98.31 229	93.41 197	95.07 299	96.82 346	91.09 350	97.51 212	97.82 234	89.96 309	99.42 247	88.42 388	99.44 188	98.64 285

OpenMVS	94.22 8	95.48 261	95.20 263	96.32 247	97.16 364	91.96 242	97.74 88	98.84 160	87.26 399	94.36 367	98.01 211	93.95 222	99.67 152	90.70 350	98.75 309	97.35 396

CLD-MVS		95.47 262	95.07 270	96.69 214	98.27 235	92.53 219	91.36 422	98.67 208	91.22 349	95.78 330	94.12 405	95.65 160	98.98 358	90.81 341	99.72 86	98.57 292
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg		95.46 263	94.66 292	97.88 110	97.84 287	95.23 123	93.62 364	98.39 244	87.04 402	93.78 382	95.99 364	94.58 202	99.52 216	91.76 322	98.90 288	98.89 247

CDPH-MVS		95.45 264	94.65 293	97.84 113	98.28 233	94.96 134	93.73 360	98.33 253	85.03 425	95.44 342	96.60 334	95.31 174	99.44 243	90.01 364	99.13 260	99.11 203

IterMVS		95.42 265	95.83 248	94.20 364	97.52 338	83.78 413	92.41 401	97.47 323	95.49 199	98.06 172	98.49 130	87.94 333	99.58 195	96.02 147	99.02 276	99.23 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GDP-MVS		95.39 266	94.89 279	96.90 198	98.26 237	91.91 243	96.48 175	99.28 42	95.06 220	96.54 291	97.12 298	74.83 421	99.82 39	97.19 97	99.27 240	98.96 230

BP-MVS1		95.36 267	94.86 282	96.89 199	98.35 226	91.72 248	96.76 157	95.21 383	96.48 137	96.23 308	97.19 289	75.97 417	99.80 51	97.91 62	99.60 126	99.15 185

mvs_anonymous		95.36 267	96.07 233	93.21 387	96.29 388	81.56 428	94.60 323	97.66 312	93.30 292	96.95 258	98.91 82	93.03 249	99.38 268	96.60 117	97.30 393	98.69 281

test_cas_vis1_n_1920		95.34 269	95.67 253	94.35 358	98.21 242	86.83 367	95.61 256	99.26 44	90.45 360	98.17 158	98.96 73	84.43 370	98.31 421	96.74 114	99.17 255	97.90 362

MSDG		95.33 270	95.13 267	95.94 274	97.40 350	91.85 245	91.02 433	98.37 248	95.30 209	96.31 303	95.99 364	94.51 206	98.38 416	89.59 371	97.65 379	97.60 385

LFMVS		95.32 271	94.88 281	96.62 217	98.03 264	91.47 254	97.65 95	90.72 439	99.11 15	97.89 192	98.31 158	79.20 397	99.48 230	93.91 278	99.12 263	98.93 239

F-COLMAP		95.30 272	94.38 311	98.05 100	98.64 177	96.04 82	95.61 256	98.66 211	89.00 379	93.22 403	96.40 347	92.90 251	99.35 281	87.45 403	97.53 383	98.77 270

Anonymous20231206		95.27 273	95.06 272	95.88 276	98.72 166	89.37 305	95.70 243	97.85 299	88.00 394	96.98 256	97.62 254	91.95 280	99.34 284	89.21 376	99.53 156	98.94 235

FMVSNet3		95.26 274	94.94 274	96.22 255	96.53 382	90.06 286	95.99 221	97.66 312	94.11 265	97.99 179	97.91 223	80.22 395	99.63 174	94.60 246	99.44 188	98.96 230

test_fmvs1_n		95.21 275	95.28 261	94.99 321	98.15 255	89.13 312	96.81 151	99.43 32	86.97 405	97.21 233	98.92 79	83.00 381	97.13 442	98.09 53	98.94 283	98.72 276

c3_l		95.20 276	95.32 260	94.83 331	96.19 393	86.43 372	91.83 413	98.35 252	93.47 286	97.36 224	97.26 285	88.69 324	99.28 307	95.41 194	99.36 216	98.78 263

D2MVS		95.18 277	95.17 266	95.21 308	97.76 309	87.76 349	94.15 340	97.94 293	89.77 370	96.99 253	97.68 250	87.45 341	99.14 332	95.03 222	99.81 57	98.74 273

N_pmnet		95.18 277	94.23 314	98.06 96	97.85 281	96.55 62	92.49 395	91.63 427	89.34 373	98.09 167	97.41 269	90.33 303	99.06 347	91.58 324	99.31 234	98.56 293

HQP-MVS		95.17 279	94.58 301	96.92 195	97.85 281	92.47 222	94.26 330	98.43 237	93.18 300	92.86 410	95.08 386	90.33 303	99.23 320	90.51 356	98.74 310	99.05 215

Vis-MVSNet (Re-imp)		95.11 280	94.85 283	95.87 277	99.12 99	89.17 308	97.54 108	94.92 389	96.50 134	96.58 286	97.27 284	83.64 376	99.48 230	88.42 388	99.67 102	98.97 229

AdaColmap		95.11 280	94.62 297	96.58 221	97.33 357	94.45 152	94.92 307	98.08 285	93.15 304	93.98 380	95.53 380	94.34 210	99.10 343	85.69 415	98.61 325	96.20 430

API-MVS		95.09 282	95.01 273	95.31 305	96.61 380	94.02 170	96.83 149	97.18 331	95.60 191	95.79 328	94.33 403	94.54 205	98.37 418	85.70 414	98.52 330	93.52 452

CL-MVSNet_self_test		95.04 283	94.79 289	95.82 278	97.51 339	89.79 293	91.14 430	96.82 346	93.05 306	96.72 274	96.40 347	90.82 295	99.16 330	91.95 315	98.66 320	98.50 302

CNLPA		95.04 283	94.47 306	96.75 210	97.81 295	95.25 122	94.12 344	97.89 297	94.41 253	94.57 361	95.69 373	90.30 306	98.35 419	86.72 410	98.76 308	96.64 419

Patchmtry		95.03 285	94.59 300	96.33 246	94.83 436	90.82 271	96.38 182	97.20 329	96.59 128	97.49 214	98.57 121	77.67 404	99.38 268	92.95 303	99.62 113	98.80 260

PVSNet_BlendedMVS		95.02 286	94.93 276	95.27 306	97.79 304	87.40 356	94.14 342	98.68 205	88.94 380	94.51 363	98.01 211	93.04 246	99.30 299	89.77 369	99.49 173	99.11 203

TAPA-MVS	93.32 12	94.93 287	94.23 314	97.04 186	98.18 248	94.51 149	95.22 287	98.73 193	81.22 444	96.25 307	95.95 368	93.80 226	98.98 358	89.89 367	98.87 292	97.62 383
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FA-MVS(test-final)		94.91 288	94.89 279	94.99 321	97.51 339	88.11 340	98.27 47	95.20 384	92.40 326	96.68 276	98.60 118	83.44 377	99.28 307	93.34 292	98.53 329	97.59 386

mvsmamba		94.91 288	94.41 310	96.40 243	97.65 326	91.30 258	97.92 73	95.32 380	91.50 342	95.54 339	98.38 146	83.06 380	99.68 143	92.46 309	97.84 364	98.23 330

eth_miper_zixun_eth		94.89 290	94.93 276	94.75 336	95.99 402	86.12 376	91.35 423	98.49 231	93.40 287	97.12 240	97.25 286	86.87 349	99.35 281	95.08 217	98.82 299	98.78 263

CDS-MVSNet		94.88 291	94.12 320	97.14 175	97.64 329	93.57 188	93.96 352	97.06 337	90.05 366	96.30 304	96.55 336	86.10 353	99.47 232	90.10 363	99.31 234	98.40 308
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		94.83 292	94.91 278	94.57 346	96.81 376	87.10 362	94.23 335	97.34 326	88.74 383	97.14 238	97.11 299	91.94 281	98.23 425	92.99 301	97.92 360	98.37 312

pmmvs4		94.82 293	94.19 317	96.70 213	97.42 349	92.75 216	92.09 409	96.76 348	86.80 407	95.73 333	97.22 287	89.28 321	98.89 366	93.28 295	99.14 258	98.46 306

miper_lstm_enhance		94.81 294	94.80 288	94.85 329	96.16 395	86.45 371	91.14 430	98.20 267	93.49 285	97.03 250	97.37 278	84.97 366	99.26 311	95.28 197	99.56 140	98.83 256

cl____		94.73 295	94.64 294	95.01 319	95.85 409	87.00 363	91.33 424	98.08 285	93.34 290	97.10 242	97.33 281	84.01 375	99.30 299	95.14 212	99.56 140	98.71 280

DIV-MVS_self_test		94.73 295	94.64 294	95.01 319	95.86 408	87.00 363	91.33 424	98.08 285	93.34 290	97.10 242	97.34 280	84.02 374	99.31 295	95.15 211	99.55 146	98.72 276

YYNet1		94.73 295	94.84 284	94.41 355	97.47 346	85.09 395	90.29 440	95.85 367	92.52 321	97.53 210	97.76 240	91.97 279	99.18 325	93.31 294	96.86 400	98.95 232

MDA-MVSNet_test_wron		94.73 295	94.83 286	94.42 354	97.48 342	85.15 393	90.28 441	95.87 366	92.52 321	97.48 217	97.76 240	91.92 282	99.17 329	93.32 293	96.80 405	98.94 235

UnsupCasMVSNet_bld		94.72 299	94.26 313	96.08 264	98.62 185	90.54 279	93.38 373	98.05 291	90.30 362	97.02 251	96.80 323	89.54 314	99.16 330	88.44 387	96.18 422	98.56 293

miper_ehance_all_eth		94.69 300	94.70 291	94.64 339	95.77 415	86.22 375	91.32 426	98.24 262	91.67 336	97.05 249	96.65 332	88.39 329	99.22 322	94.88 232	98.34 343	98.49 303

BH-untuned		94.69 300	94.75 290	94.52 349	97.95 277	87.53 352	94.07 345	97.01 339	93.99 269	97.10 242	95.65 375	92.65 258	98.95 363	87.60 398	96.74 406	97.09 401

RPMNet		94.68 302	94.60 298	94.90 326	95.44 423	88.15 336	96.18 199	98.86 152	97.43 87	94.10 373	98.49 130	79.40 396	99.76 76	95.69 166	95.81 426	96.81 415

Patchmatch-RL test		94.66 303	94.49 304	95.19 309	98.54 198	88.91 317	92.57 393	98.74 192	91.46 344	98.32 139	97.75 243	77.31 409	98.81 374	96.06 142	99.61 119	97.85 366

CANet_DTU		94.65 304	94.21 316	95.96 270	95.90 405	89.68 296	93.92 353	97.83 303	93.19 299	90.12 441	95.64 376	88.52 326	99.57 201	93.27 296	99.47 179	98.62 288

pmmvs5		94.63 305	94.34 312	95.50 298	97.63 330	88.34 330	94.02 346	97.13 333	87.15 401	95.22 347	97.15 291	87.50 340	99.27 310	93.99 273	99.26 243	98.88 251

PAPM_NR		94.61 306	94.17 318	95.96 270	98.36 225	91.23 261	95.93 228	97.95 292	92.98 309	93.42 400	94.43 402	90.53 298	98.38 416	87.60 398	96.29 420	98.27 327

PatchMatch-RL		94.61 306	93.81 328	97.02 189	98.19 245	95.72 93	93.66 362	97.23 328	88.17 392	94.94 355	95.62 377	91.43 286	98.57 400	87.36 404	97.68 375	96.76 417

BH-RMVSNet		94.56 308	94.44 309	94.91 324	97.57 334	87.44 354	93.78 359	96.26 356	93.69 277	96.41 296	96.50 341	92.10 276	99.00 354	85.96 412	97.71 372	98.31 321

USDC		94.56 308	94.57 303	94.55 347	97.78 307	86.43 372	92.75 387	98.65 216	85.96 413	96.91 261	97.93 221	90.82 295	98.74 381	90.71 349	99.59 129	98.47 304

test1111		94.53 310	94.81 287	93.72 373	99.06 108	81.94 426	98.31 42	83.87 462	96.37 140	98.49 113	99.17 49	81.49 386	99.73 99	96.64 115	99.86 35	99.49 93

test_fmvs1		94.51 311	94.60 298	94.26 363	95.91 404	87.92 342	95.35 276	99.02 105	86.56 409	96.79 267	98.52 127	82.64 383	97.00 445	97.87 64	98.71 314	97.88 364

ppachtmachnet_test		94.49 312	94.84 284	93.46 379	96.16 395	82.10 423	90.59 437	97.48 322	90.53 359	97.01 252	97.59 256	91.01 292	99.36 277	93.97 275	99.18 254	98.94 235

test_yl		94.40 313	94.00 323	95.59 290	96.95 371	89.52 300	94.75 318	95.55 375	96.18 153	96.79 267	96.14 359	81.09 390	99.18 325	90.75 345	97.77 366	98.07 344

DCV-MVSNet		94.40 313	94.00 323	95.59 290	96.95 371	89.52 300	94.75 318	95.55 375	96.18 153	96.79 267	96.14 359	81.09 390	99.18 325	90.75 345	97.77 366	98.07 344

jason		94.39 315	94.04 322	95.41 304	98.29 231	87.85 346	92.74 389	96.75 349	85.38 422	95.29 345	96.15 357	88.21 332	99.65 163	94.24 260	99.34 224	98.74 273
jason: jason.
ECVR-MVS		94.37 316	94.48 305	94.05 368	98.95 124	83.10 416	98.31 42	82.48 464	96.20 150	98.23 151	99.16 50	81.18 389	99.66 160	95.95 152	99.83 52	99.38 132

EU-MVSNet		94.25 317	94.47 306	93.60 376	98.14 257	82.60 421	97.24 124	92.72 415	85.08 423	98.48 115	98.94 76	82.59 384	98.76 380	97.47 85	99.53 156	99.44 119

xiu_mvs_v2_base		94.22 318	94.63 296	92.99 395	97.32 358	84.84 400	92.12 407	97.84 301	91.96 332	94.17 371	93.43 411	96.07 138	99.71 121	91.27 328	97.48 385	94.42 447

sss		94.22 318	93.72 329	95.74 282	97.71 317	89.95 289	93.84 355	96.98 340	88.38 389	93.75 385	95.74 372	87.94 333	98.89 366	91.02 334	98.10 353	98.37 312

MVSTER		94.21 320	93.93 327	95.05 317	95.83 410	86.46 370	95.18 291	97.65 314	92.41 325	97.94 188	98.00 213	72.39 433	99.58 195	96.36 130	99.56 140	99.12 198

MAR-MVS		94.21 320	93.03 341	97.76 118	96.94 373	97.44 37	96.97 140	97.15 332	87.89 396	92.00 424	92.73 426	92.14 274	99.12 336	83.92 429	97.51 384	96.73 418
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
our_test_3		94.20 322	94.58 301	93.07 390	96.16 395	81.20 432	90.42 439	96.84 344	90.72 355	97.14 238	97.13 296	90.47 299	99.11 339	94.04 270	98.25 347	98.91 243

1112_ss		94.12 323	93.42 335	96.23 253	98.59 189	90.85 270	94.24 334	98.85 156	85.49 418	92.97 408	94.94 390	86.01 354	99.64 169	91.78 321	97.92 360	98.20 334

PS-MVSNAJ		94.10 324	94.47 306	93.00 394	97.35 353	84.88 397	91.86 412	97.84 301	91.96 332	94.17 371	92.50 430	95.82 148	99.71 121	91.27 328	97.48 385	94.40 448

CHOSEN 1792x2688		94.10 324	93.41 336	96.18 259	99.16 88	90.04 287	92.15 406	98.68 205	79.90 449	96.22 309	97.83 231	87.92 337	99.42 247	89.18 377	99.65 106	99.08 208

MG-MVS		94.08 326	94.00 323	94.32 360	97.09 367	85.89 382	93.19 380	95.96 363	92.52 321	94.93 356	97.51 263	89.54 314	98.77 378	87.52 402	97.71 372	98.31 321

ttmdpeth		94.05 327	94.15 319	93.75 372	95.81 412	85.32 388	96.00 218	94.93 388	92.07 328	94.19 370	99.09 58	85.73 357	96.41 453	90.98 335	98.52 330	99.53 75

PLC	91.02 16	94.05 327	92.90 344	97.51 139	98.00 272	95.12 131	94.25 333	98.25 260	86.17 411	91.48 429	95.25 384	91.01 292	99.19 324	85.02 424	96.69 409	98.22 332
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_vis1_rt		94.03 329	93.65 330	95.17 311	95.76 416	93.42 196	93.97 351	98.33 253	84.68 429	93.17 404	95.89 370	92.53 266	94.79 458	93.50 289	94.97 437	97.31 398

114514_t		93.96 330	93.22 339	96.19 258	99.06 108	90.97 266	95.99 221	98.94 132	73.88 462	93.43 399	96.93 312	92.38 270	99.37 273	89.09 378	99.28 238	98.25 329

PVSNet_Blended		93.96 330	93.65 330	94.91 324	97.79 304	87.40 356	91.43 421	98.68 205	84.50 432	94.51 363	94.48 401	93.04 246	99.30 299	89.77 369	98.61 325	98.02 354

AUN-MVS		93.95 332	92.69 352	97.74 119	97.80 299	95.38 113	95.57 259	95.46 377	91.26 348	92.64 417	96.10 362	74.67 422	99.55 207	93.72 284	96.97 396	98.30 323

lupinMVS		93.77 333	93.28 337	95.24 307	97.68 319	87.81 347	92.12 407	96.05 359	84.52 431	94.48 365	95.06 388	86.90 347	99.63 174	93.62 287	99.13 260	98.27 327

PatchT		93.75 334	93.57 332	94.29 362	95.05 432	87.32 358	96.05 212	92.98 411	97.54 82	94.25 368	98.72 99	75.79 418	99.24 318	95.92 155	95.81 426	96.32 427

SD_0403		93.73 335	93.43 334	94.64 339	97.85 281	86.35 374	97.47 110	97.94 293	93.50 284	93.71 386	96.73 327	93.77 227	98.84 371	73.48 458	96.39 416	98.72 276

EPNet		93.72 336	92.62 355	97.03 188	87.61 470	92.25 228	96.27 190	91.28 432	96.74 120	87.65 455	97.39 274	85.00 365	99.64 169	92.14 312	99.48 177	99.20 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		93.72 336	92.65 353	96.91 197	98.93 132	91.81 247	91.23 428	98.52 228	82.69 437	96.46 294	96.52 340	80.38 394	99.90 18	90.36 360	98.79 301	99.03 216

DPM-MVS		93.68 338	92.77 351	96.42 237	97.91 278	92.54 218	91.17 429	97.47 323	84.99 427	93.08 406	94.74 394	89.90 310	99.00 354	87.54 400	98.09 354	97.72 378

PMMVS2		93.66 339	94.07 321	92.45 410	97.57 334	80.67 436	86.46 455	96.00 361	93.99 269	97.10 242	97.38 276	89.90 310	97.82 435	88.76 382	99.47 179	98.86 254

OpenMVS_ROB	91.80 14	93.64 340	93.05 340	95.42 302	97.31 359	91.21 262	95.08 298	96.68 353	81.56 441	96.88 263	96.41 345	90.44 302	99.25 314	85.39 420	97.67 376	95.80 435

Patchmatch-test		93.60 341	93.25 338	94.63 341	96.14 399	87.47 353	96.04 214	94.50 393	93.57 280	96.47 293	96.97 309	76.50 412	98.61 397	90.67 352	98.41 341	97.81 370

WTY-MVS		93.55 342	93.00 343	95.19 309	97.81 295	87.86 344	93.89 354	96.00 361	89.02 378	94.07 375	95.44 383	86.27 352	99.33 286	87.69 396	96.82 403	98.39 310

Test_1112_low_res		93.53 343	92.86 345	95.54 297	98.60 187	88.86 319	92.75 387	98.69 203	82.66 438	92.65 416	96.92 314	84.75 367	99.56 203	90.94 337	97.76 368	98.19 335

mvsany_test1		93.47 344	93.03 341	94.79 333	94.05 449	92.12 235	90.82 435	90.01 448	85.02 426	97.26 229	98.28 167	93.57 232	97.03 443	92.51 308	95.75 431	95.23 443

MIMVSNet		93.42 345	92.86 345	95.10 315	98.17 251	88.19 334	98.13 58	93.69 400	92.07 328	95.04 353	98.21 180	80.95 392	99.03 353	81.42 440	98.06 355	98.07 344

FMVSNet5		93.39 346	92.35 357	96.50 230	95.83 410	90.81 273	97.31 119	98.27 258	92.74 318	96.27 305	98.28 167	62.23 449	99.67 152	90.86 339	99.36 216	99.03 216

SCA		93.38 347	93.52 333	92.96 396	96.24 389	81.40 430	93.24 377	94.00 398	91.58 341	94.57 361	96.97 309	87.94 333	99.42 247	89.47 373	97.66 378	98.06 348

tttt0517		93.31 348	92.56 356	95.57 292	98.71 169	87.86 344	97.44 112	87.17 456	95.79 182	97.47 219	96.84 318	64.12 447	99.81 44	96.20 139	99.32 231	99.02 219

MonoMVSNet		93.30 349	93.96 326	91.33 424	94.14 447	81.33 431	97.68 93	96.69 352	95.38 206	96.32 300	98.42 140	84.12 373	96.76 450	90.78 343	92.12 451	95.89 432

CR-MVSNet		93.29 350	92.79 348	94.78 334	95.44 423	88.15 336	96.18 199	97.20 329	84.94 428	94.10 373	98.57 121	77.67 404	99.39 265	95.17 207	95.81 426	96.81 415

cl22		93.25 351	92.84 347	94.46 353	94.30 442	86.00 381	91.09 432	96.64 354	90.74 354	95.79 328	96.31 351	78.24 401	98.77 378	94.15 264	98.34 343	98.62 288

wuyk23d		93.25 351	95.20 263	87.40 445	96.07 401	95.38 113	97.04 136	94.97 387	95.33 207	99.70 10	98.11 193	98.14 21	91.94 463	77.76 452	99.68 99	74.89 463

miper_enhance_ethall		93.14 353	92.78 350	94.20 364	93.65 452	85.29 390	89.97 443	97.85 299	85.05 424	96.15 315	94.56 397	85.74 356	99.14 332	93.74 282	98.34 343	98.17 338

baseline1		93.14 353	92.64 354	94.62 342	97.34 355	87.20 360	96.67 168	93.02 410	94.71 235	96.51 292	95.83 371	81.64 385	98.60 399	90.00 365	88.06 459	98.07 344

FE-MVS		92.95 355	92.22 360	95.11 313	97.21 362	88.33 331	98.54 26	93.66 403	89.91 368	96.21 310	98.14 186	70.33 440	99.50 221	87.79 394	98.24 348	97.51 389

X-MVStestdata		92.86 356	90.83 385	98.94 19	99.15 91	97.66 23	97.77 83	98.83 167	97.42 88	96.32 300	36.50 467	96.49 115	99.72 105	95.66 169	99.37 212	99.45 109

GA-MVS		92.83 357	92.15 362	94.87 328	96.97 370	87.27 359	90.03 442	96.12 358	91.83 335	94.05 376	94.57 396	76.01 416	98.97 362	92.46 309	97.34 391	98.36 317

CMPMVS	73.10 23	92.74 358	91.39 372	96.77 209	93.57 454	94.67 142	94.21 337	97.67 310	80.36 448	93.61 391	96.60 334	82.85 382	97.35 440	84.86 425	98.78 302	98.29 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thisisatest0530		92.71 359	91.76 368	95.56 295	98.42 220	88.23 333	96.03 215	87.35 455	94.04 268	96.56 288	95.47 381	64.03 448	99.77 70	94.78 239	99.11 264	98.68 284

HY-MVS	91.43 15	92.58 360	91.81 366	94.90 326	96.49 383	88.87 318	97.31 119	94.62 391	85.92 414	90.50 435	96.84 318	85.05 364	99.40 259	83.77 432	95.78 429	96.43 426

TR-MVS		92.54 361	92.20 361	93.57 377	96.49 383	86.66 368	93.51 369	94.73 390	89.96 367	94.95 354	93.87 408	90.24 308	98.61 397	81.18 442	94.88 438	95.45 441

PMMVS		92.39 362	91.08 379	96.30 249	93.12 456	92.81 212	90.58 438	95.96 363	79.17 452	91.85 426	92.27 431	90.29 307	98.66 393	89.85 368	96.68 410	97.43 392

1314		92.38 363	92.30 358	92.64 405	95.42 425	85.15 393	95.86 233	96.97 341	85.40 421	90.62 432	93.06 418	91.12 290	97.80 436	86.74 409	95.49 434	94.97 445

new_pmnet		92.34 364	91.69 369	94.32 360	96.23 391	89.16 309	92.27 404	92.88 412	84.39 434	95.29 345	96.35 350	85.66 358	96.74 451	84.53 427	97.56 381	97.05 402

CVMVSNet		92.33 365	92.79 348	90.95 426	97.26 360	75.84 457	95.29 284	92.33 421	81.86 439	96.27 305	98.19 181	81.44 387	98.46 411	94.23 261	98.29 346	98.55 295

PAPR		92.22 366	91.27 376	95.07 316	95.73 418	88.81 320	91.97 410	97.87 298	85.80 416	90.91 431	92.73 426	91.16 289	98.33 420	79.48 446	95.76 430	98.08 342

DSMNet-mixed		92.19 367	91.83 365	93.25 384	96.18 394	83.68 414	96.27 190	93.68 402	76.97 459	92.54 420	99.18 46	89.20 323	98.55 403	83.88 430	98.60 327	97.51 389

BH-w/o		92.14 368	91.94 363	92.73 403	97.13 366	85.30 389	92.46 397	95.64 370	89.33 374	94.21 369	92.74 425	89.60 312	98.24 424	81.68 439	94.66 440	94.66 446

PCF-MVS	89.43 18	92.12 369	90.64 389	96.57 223	97.80 299	93.48 193	89.88 447	98.45 234	74.46 461	96.04 318	95.68 374	90.71 297	99.31 295	73.73 457	99.01 278	96.91 408
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Syy-MVS		92.09 370	91.80 367	92.93 398	95.19 429	82.65 419	92.46 397	91.35 430	90.67 357	91.76 427	87.61 459	85.64 359	98.50 407	94.73 242	96.84 401	97.65 381

dmvs_re		92.08 371	91.27 376	94.51 350	97.16 364	92.79 215	95.65 250	92.64 417	94.11 265	92.74 413	90.98 446	83.41 378	94.44 461	80.72 443	94.07 444	96.29 428

reproduce_monomvs		92.05 372	92.26 359	91.43 422	95.42 425	75.72 458	95.68 246	97.05 338	94.47 250	97.95 187	98.35 150	55.58 463	99.05 348	96.36 130	99.44 188	99.51 82

thres600view7		92.03 373	91.43 371	93.82 370	98.19 245	84.61 402	96.27 190	90.39 441	96.81 117	96.37 298	93.11 413	73.44 431	99.49 227	80.32 444	97.95 359	97.36 394

PatchmatchNet		91.98 374	91.87 364	92.30 412	94.60 439	79.71 439	95.12 292	93.59 405	89.52 372	93.61 391	97.02 305	77.94 402	99.18 325	90.84 340	94.57 443	98.01 355
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVStest1		91.89 375	91.45 370	93.21 387	89.01 467	84.87 398	95.82 237	95.05 386	91.50 342	98.75 89	99.19 42	57.56 454	95.11 456	97.78 70	98.37 342	99.64 42

cascas		91.89 375	91.35 373	93.51 378	94.27 443	85.60 384	88.86 452	98.61 218	79.32 451	92.16 423	91.44 441	89.22 322	98.12 429	90.80 342	97.47 387	96.82 414

JIA-IIPM		91.79 377	90.69 388	95.11 313	93.80 451	90.98 265	94.16 339	91.78 426	96.38 139	90.30 438	99.30 32	72.02 434	98.90 365	88.28 390	90.17 455	95.45 441

thres100view900		91.76 378	91.26 378	93.26 383	98.21 242	84.50 403	96.39 179	90.39 441	96.87 114	96.33 299	93.08 417	73.44 431	99.42 247	78.85 449	97.74 369	95.85 433

thres400		91.68 379	91.00 380	93.71 374	98.02 266	84.35 407	95.70 243	90.79 437	96.26 145	95.90 325	92.13 434	73.62 428	99.42 247	78.85 449	97.74 369	97.36 394

tfpn200view9		91.55 380	91.00 380	93.21 387	98.02 266	84.35 407	95.70 243	90.79 437	96.26 145	95.90 325	92.13 434	73.62 428	99.42 247	78.85 449	97.74 369	95.85 433

WB-MVSnew		91.50 381	91.29 374	92.14 415	94.85 434	80.32 437	93.29 376	88.77 451	88.57 386	94.03 377	92.21 432	92.56 261	98.28 423	80.21 445	97.08 395	97.81 370

ADS-MVSNet2		91.47 382	90.51 391	94.36 356	95.51 421	85.63 383	95.05 301	95.70 368	83.46 435	92.69 414	96.84 318	79.15 398	99.41 257	85.66 416	90.52 453	98.04 352

EPNet_dtu		91.39 383	90.75 386	93.31 382	90.48 466	82.61 420	94.80 314	92.88 412	93.39 288	81.74 464	94.90 393	81.36 388	99.11 339	88.28 390	98.87 292	98.21 333
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		91.12 384	89.67 398	95.47 300	96.41 386	89.15 310	91.54 419	90.23 445	89.07 377	86.78 459	92.84 423	69.39 442	99.44 243	94.16 263	96.61 411	97.82 368

WBMVS		91.11 385	90.72 387	92.26 413	95.99 402	77.98 448	91.47 420	95.90 365	91.63 337	95.90 325	96.45 343	59.60 451	99.46 235	89.97 366	99.59 129	99.33 144

PVSNet	86.72 19	91.10 386	90.97 382	91.49 421	97.56 336	78.04 446	87.17 454	94.60 392	84.65 430	92.34 421	92.20 433	87.37 344	98.47 410	85.17 423	97.69 374	97.96 358

tpm		91.08 387	90.85 384	91.75 419	95.33 427	78.09 445	95.03 303	91.27 433	88.75 382	93.53 395	97.40 270	71.24 435	99.30 299	91.25 330	93.87 445	97.87 365

thres200		91.00 388	90.42 392	92.77 402	97.47 346	83.98 412	94.01 347	91.18 434	95.12 217	95.44 342	91.21 443	73.93 424	99.31 295	77.76 452	97.63 380	95.01 444

ADS-MVSNet		90.95 389	90.26 394	93.04 391	95.51 421	82.37 422	95.05 301	93.41 406	83.46 435	92.69 414	96.84 318	79.15 398	98.70 386	85.66 416	90.52 453	98.04 352

tpmvs		90.79 390	90.87 383	90.57 429	92.75 460	76.30 455	95.79 238	93.64 404	91.04 352	91.91 425	96.26 352	77.19 410	98.86 370	89.38 375	89.85 456	96.56 422

thisisatest0515		90.43 391	89.18 404	94.17 366	97.07 368	85.44 386	89.75 448	87.58 454	88.28 390	93.69 389	91.72 438	65.27 446	99.58 195	90.59 353	98.67 318	97.50 391

tpmrst		90.31 392	90.61 390	89.41 435	94.06 448	72.37 466	95.06 300	93.69 400	88.01 393	92.32 422	96.86 316	77.45 406	98.82 372	91.04 333	87.01 460	97.04 403

test0.0.03 1		90.11 393	89.21 401	92.83 400	93.89 450	86.87 366	91.74 415	88.74 452	92.02 330	94.71 359	91.14 444	73.92 425	94.48 460	83.75 433	92.94 447	97.16 400

testing3-2		90.09 394	90.38 393	89.24 436	98.07 262	69.88 469	95.12 292	90.71 440	96.65 122	93.60 393	94.03 406	55.81 462	99.33 286	90.69 351	98.71 314	98.51 299

MVS		90.02 395	89.20 402	92.47 409	94.71 437	86.90 365	95.86 233	96.74 350	64.72 464	90.62 432	92.77 424	92.54 264	98.39 415	79.30 447	95.56 433	92.12 456

pmmvs3		90.00 396	88.90 406	93.32 381	94.20 446	85.34 387	91.25 427	92.56 419	78.59 453	93.82 381	95.17 385	67.36 445	98.69 388	89.08 379	98.03 356	95.92 431

CHOSEN 280x420		89.98 397	89.19 403	92.37 411	95.60 420	81.13 433	86.22 456	97.09 335	81.44 443	87.44 456	93.15 412	73.99 423	99.47 232	88.69 384	99.07 271	96.52 423

test-LLR		89.97 398	89.90 396	90.16 430	94.24 444	74.98 459	89.89 444	89.06 449	92.02 330	89.97 442	90.77 447	73.92 425	98.57 400	91.88 317	97.36 389	96.92 406

FPMVS		89.92 399	88.63 407	93.82 370	98.37 224	96.94 49	91.58 418	93.34 407	88.00 394	90.32 437	97.10 300	70.87 438	91.13 464	71.91 461	96.16 424	93.39 454

test2506		89.86 400	89.16 405	91.97 417	98.95 124	76.83 454	98.54 26	61.07 472	96.20 150	97.07 248	99.16 50	55.19 466	99.69 137	96.43 127	99.83 52	99.38 132

CostFormer		89.75 401	89.25 399	91.26 425	94.69 438	78.00 447	95.32 280	91.98 424	81.50 442	90.55 434	96.96 311	71.06 437	98.89 366	88.59 386	92.63 449	96.87 409

testing3		89.72 402	88.26 411	94.10 367	97.66 324	84.30 409	94.80 314	88.25 453	94.66 236	95.07 349	92.51 429	41.15 472	99.43 245	91.81 320	98.44 339	98.55 295

testing91		89.67 403	88.55 408	93.04 391	95.90 405	81.80 427	92.71 391	93.71 399	93.71 275	90.18 439	90.15 451	57.11 455	99.22 322	87.17 407	96.32 419	98.12 340

baseline2		89.65 404	88.44 410	93.25 384	95.62 419	82.71 418	93.82 356	85.94 459	88.89 381	87.35 457	92.54 428	71.23 436	99.33 286	86.01 411	94.60 442	97.72 378

E-PMN		89.52 405	89.78 397	88.73 438	93.14 455	77.61 449	83.26 461	92.02 423	94.82 230	93.71 386	93.11 413	75.31 419	96.81 447	85.81 413	96.81 404	91.77 458

EPMVS		89.26 406	88.55 408	91.39 423	92.36 461	79.11 442	95.65 250	79.86 465	88.60 385	93.12 405	96.53 338	70.73 439	98.10 430	90.75 345	89.32 457	96.98 404

testing99		89.21 407	88.04 413	92.70 404	95.78 414	81.00 434	92.65 392	92.03 422	93.20 298	89.90 444	90.08 453	55.25 464	99.14 332	87.54 400	95.95 425	97.97 357

EMVS		89.06 408	89.22 400	88.61 439	93.00 457	77.34 451	82.91 462	90.92 435	94.64 238	92.63 418	91.81 437	76.30 414	97.02 444	83.83 431	96.90 399	91.48 459

testing11		88.93 409	87.63 418	92.80 401	95.87 407	81.49 429	92.48 396	91.54 428	91.62 338	88.27 453	90.24 449	55.12 467	99.11 339	87.30 405	96.28 421	97.81 370

KD-MVS_2432*1600		88.93 409	87.74 414	92.49 407	88.04 468	81.99 424	89.63 449	95.62 371	91.35 346	95.06 350	93.11 413	56.58 457	98.63 395	85.19 421	95.07 435	96.85 411

miper_refine_blended		88.93 409	87.74 414	92.49 407	88.04 468	81.99 424	89.63 449	95.62 371	91.35 346	95.06 350	93.11 413	56.58 457	98.63 395	85.19 421	95.07 435	96.85 411

IB-MVS	85.98 20	88.63 412	86.95 424	93.68 375	95.12 431	84.82 401	90.85 434	90.17 446	87.55 398	88.48 452	91.34 442	58.01 453	99.59 192	87.24 406	93.80 446	96.63 421
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
tpm2		88.47 413	87.69 417	90.79 427	94.98 433	77.34 451	95.09 296	91.83 425	77.51 458	89.40 447	96.41 345	67.83 444	98.73 382	83.58 434	92.60 450	96.29 428

MVS-HIRNet		88.40 414	90.20 395	82.99 446	97.01 369	60.04 471	93.11 381	85.61 460	84.45 433	88.72 451	99.09 58	84.72 368	98.23 425	82.52 436	96.59 412	90.69 461

myMVS_eth3d28		88.32 415	87.73 416	90.11 433	96.42 385	74.96 462	92.21 405	92.37 420	93.56 281	90.14 440	89.61 454	56.13 460	98.05 432	81.84 437	97.26 394	97.33 397

UBG		88.29 416	87.17 420	91.63 420	96.08 400	78.21 444	91.61 416	91.50 429	89.67 371	89.71 445	88.97 456	59.01 452	98.91 364	81.28 441	96.72 408	97.77 373

gg-mvs-nofinetune		88.28 417	86.96 423	92.23 414	92.84 459	84.44 405	98.19 55	74.60 468	99.08 17	87.01 458	99.47 16	56.93 456	98.23 425	78.91 448	95.61 432	94.01 450

dp		88.08 418	88.05 412	88.16 443	92.85 458	68.81 470	94.17 338	92.88 412	85.47 419	91.38 430	96.14 359	68.87 443	98.81 374	86.88 408	83.80 463	96.87 409

tpm cat1		88.01 419	87.33 419	90.05 434	94.48 440	76.28 456	94.47 326	94.35 395	73.84 463	89.26 448	95.61 378	73.64 427	98.30 422	84.13 428	86.20 461	95.57 440

test-mter		87.92 420	87.17 420	90.16 430	94.24 444	74.98 459	89.89 444	89.06 449	86.44 410	89.97 442	90.77 447	54.96 468	98.57 400	91.88 317	97.36 389	96.92 406

PAPM		87.64 421	85.84 428	93.04 391	96.54 381	84.99 396	88.42 453	95.57 374	79.52 450	83.82 461	93.05 419	80.57 393	98.41 413	62.29 464	92.79 448	95.71 436

ETVMVS		87.62 422	85.75 429	93.22 386	96.15 398	83.26 415	92.94 383	90.37 443	91.39 345	90.37 436	88.45 457	51.93 469	98.64 394	73.76 456	96.38 417	97.75 374

UWE-MVS		87.57 423	86.72 425	90.13 432	95.21 428	73.56 463	91.94 411	83.78 463	88.73 384	93.00 407	92.87 422	55.22 465	99.25 314	81.74 438	97.96 358	97.59 386

testing222		87.35 424	85.50 431	92.93 398	95.79 413	82.83 417	92.40 402	90.10 447	92.80 317	88.87 450	89.02 455	48.34 470	98.70 386	75.40 455	96.74 406	97.27 399

dmvs_testset		87.30 425	86.99 422	88.24 441	96.71 377	77.48 450	94.68 320	86.81 458	92.64 320	89.61 446	87.01 461	85.91 355	93.12 462	61.04 465	88.49 458	94.13 449

TESTMET0.1,1		87.20 426	86.57 426	89.07 437	93.62 453	72.84 465	89.89 444	87.01 457	85.46 420	89.12 449	90.20 450	56.00 461	97.72 437	90.91 338	96.92 397	96.64 419

myMVS_eth3d		87.16 427	85.61 430	91.82 418	95.19 429	79.32 440	92.46 397	91.35 430	90.67 357	91.76 427	87.61 459	41.96 471	98.50 407	82.66 435	96.84 401	97.65 381

MVE	73.61 22	86.48 428	85.92 427	88.18 442	96.23 391	85.28 391	81.78 463	75.79 467	86.01 412	82.53 463	91.88 436	92.74 254	87.47 466	71.42 462	94.86 439	91.78 457
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PVSNet_0	81.89 21	84.49 429	83.21 432	88.34 440	95.76 416	74.97 461	83.49 460	92.70 416	78.47 454	87.94 454	86.90 462	83.38 379	96.63 452	73.44 459	66.86 466	93.40 453

UWE-MVS-28		83.78 430	82.36 433	88.03 444	90.72 465	71.58 467	93.64 363	77.87 466	87.62 397	85.91 460	92.89 421	59.94 450	95.99 455	56.06 467	96.56 413	96.52 423

EGC-MVSNET		83.08 431	77.93 434	98.53 55	99.57 20	97.55 30	98.33 41	98.57 225	4.71 469	10.38 470	98.90 83	95.60 162	99.50 221	95.69 166	99.61 119	98.55 295

test_method		66.88 432	66.13 435	69.11 448	62.68 473	25.73 476	49.76 464	96.04 360	14.32 468	64.27 468	91.69 439	73.45 430	88.05 465	76.06 454	66.94 465	93.54 451

dongtai		63.43 433	63.37 436	63.60 449	83.91 471	53.17 473	85.14 457	43.40 475	77.91 457	80.96 465	79.17 465	36.36 473	77.10 467	37.88 468	45.63 467	60.54 464

tmp_tt		57.23 434	62.50 437	41.44 451	34.77 474	49.21 475	83.93 459	60.22 473	15.31 467	71.11 467	79.37 464	70.09 441	44.86 470	64.76 463	82.93 464	30.25 466

kuosan		54.81 435	54.94 438	54.42 450	74.43 472	50.03 474	84.98 458	44.27 474	61.80 465	62.49 469	70.43 466	35.16 474	58.04 469	19.30 469	41.61 468	55.19 465

cdsmvs_eth3d_5k		24.22 436	32.30 439	0.00 454	0.00 477	0.00 479	0.00 465	98.10 283	0.00 472	0.00 473	95.06 388	97.54 44	0.00 473	0.00 472	0.00 471	0.00 469

test123		12.59 437	15.49 440	3.87 452	6.07 475	2.55 477	90.75 436	2.59 477	2.52 470	5.20 472	13.02 469	4.96 475	1.85 472	5.20 470	9.09 469	7.23 467

testmvs		12.33 438	15.23 441	3.64 453	5.77 476	2.23 478	88.99 451	3.62 476	2.30 471	5.29 471	13.09 468	4.52 476	1.95 471	5.16 471	8.32 470	6.75 468

pcd_1.5k_mvsjas		7.98 439	10.65 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	95.82 148	0.00 473	0.00 472	0.00 471	0.00 469

ab-mvs-re		7.91 440	10.55 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	94.94 390	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						79.32 440								85.41 419

FOURS1					99.59 18	98.20 8	99.03 8	99.25 46	98.96 25	98.87 75

MSC_two_6792asdad				98.22 82	97.75 311	95.34 118		98.16 277					99.75 84	95.87 159	99.51 166	99.57 56

PC_three_1452									87.24 400	98.37 128	97.44 267	97.00 76	96.78 449	92.01 313	99.25 244	99.21 173

No_MVS				98.22 82	97.75 311	95.34 118		98.16 277					99.75 84	95.87 159	99.51 166	99.57 56

test_one_0601					99.05 114	95.50 108		98.87 149	97.21 103	98.03 176	98.30 162	96.93 82

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					98.43 218	95.94 86		98.56 226	90.72 355	96.66 280	97.07 301	95.02 186	99.74 93	91.08 332	98.93 286

RE-MVS-def			97.88 84		98.81 148	98.05 10	97.55 103	98.86 152	97.77 67	98.20 153	98.07 199	96.94 80		95.49 180	99.20 249	99.26 163

IU-MVS					99.22 74	95.40 111		98.14 280	85.77 417	98.36 131				95.23 201	99.51 166	99.49 93

OPU-MVS				97.64 130	98.01 268	95.27 121	96.79 155				97.35 279	96.97 78	98.51 406	91.21 331	99.25 244	99.14 190

test_241102_TWO								98.83 167	96.11 155	98.62 100	98.24 174	96.92 85	99.72 105	95.44 188	99.49 173	99.49 93

test_241102_ONE					99.22 74	95.35 116		98.83 167	96.04 163	99.08 53	98.13 188	97.87 28	99.33 286

9.14			96.69 190		98.53 199		96.02 216	98.98 123	93.23 294	97.18 236	97.46 265	96.47 117	99.62 179	92.99 301	99.32 231

save fliter					98.48 211	94.71 139	94.53 325	98.41 241	95.02 223

test_0728_THIRD									96.62 123	98.40 125	98.28 167	97.10 64	99.71 121	95.70 164	99.62 113	99.58 48

test_0728_SECOND				98.25 80	99.23 71	95.49 109	96.74 159	98.89 140					99.75 84	95.48 184	99.52 161	99.53 75

test0726					99.24 68	95.51 105	96.89 145	98.89 140	95.92 174	98.64 98	98.31 158	97.06 69

GSMVS																98.06 348

test_part2					99.03 116	96.07 81				98.08 169

sam_mvs1												77.80 403				98.06 348

sam_mvs												77.38 407

ambc				96.56 225	98.23 241	91.68 250	97.88 76	98.13 281		98.42 122	98.56 123	94.22 215	99.04 350	94.05 269	99.35 221	98.95 232

MTGPA								98.73 193

test_post1							94.98 305				10.37 471	76.21 415	99.04 350	89.47 373

test_post											10.87 470	76.83 411	99.07 346

patchmatchnet-post											96.84 318	77.36 408	99.42 247

GG-mvs-BLEND				90.60 428	91.00 463	84.21 410	98.23 49	72.63 471		82.76 462	84.11 463	56.14 459	96.79 448	72.20 460	92.09 452	90.78 460

MTMP							96.55 171	74.60 468

gm-plane-assit					91.79 462	71.40 468			81.67 440		90.11 452		98.99 356	84.86 425

test9_res														91.29 327	98.89 291	99.00 220

TEST9					97.84 287	95.23 123	93.62 364	98.39 244	86.81 406	93.78 382	95.99 364	94.68 197	99.52 216

test_8					97.81 295	95.07 132	93.54 368	98.38 246	87.04 402	93.71 386	95.96 367	94.58 202	99.52 216

agg_prior2														90.34 361	98.90 288	99.10 207

agg_prior					97.80 299	94.96 134		98.36 249		93.49 396			99.53 213

TestCases				98.06 96	99.08 104	96.16 76		99.16 57	94.35 255	97.78 201	98.07 199	95.84 145	99.12 336	91.41 325	99.42 201	98.91 243

test_prior4						95.38 113	93.61 366

test_prior2							93.33 375		94.21 259	94.02 378	96.25 353	93.64 231		91.90 316	98.96 280

test_prior				97.46 149	97.79 304	94.26 163		98.42 240					99.34 284			98.79 262

旧先验2							93.35 374		77.95 456	95.77 332			98.67 392	90.74 348

新几何2							93.43 370

新几何1				97.25 168	98.29 231	94.70 141		97.73 307	77.98 455	94.83 357	96.67 331	92.08 277	99.45 240	88.17 392	98.65 322	97.61 384

旧先验1					97.80 299	93.87 175		97.75 306			97.04 304	93.57 232			98.68 317	98.72 276

无先验							93.20 379	97.91 295	80.78 445				99.40 259	87.71 395		97.94 360

原ACMM2							92.82 385

原ACMM1				96.58 221	98.16 253	92.12 235		98.15 279	85.90 415	93.49 396	96.43 344	92.47 268	99.38 268	87.66 397	98.62 324	98.23 330

test222					98.17 251	93.24 203	92.74 389	97.61 319	75.17 460	94.65 360	96.69 330	90.96 294			98.66 320	97.66 380

testdata2													99.46 235	87.84 393

segment_acmp												95.34 172

testdata				95.70 285	98.16 253	90.58 276		97.72 308	80.38 447	95.62 335	97.02 305	92.06 278	98.98 358	89.06 380	98.52 330	97.54 388

testdata1							92.77 386		93.78 273

test12				97.46 149	97.61 331	94.07 167		97.78 305		93.57 394		93.31 238	99.42 247		98.78 302	98.89 247

plane_prior7					98.70 171	94.67 142

plane_prior6					98.38 223	94.37 156						91.91 283

plane_prior5								98.75 190					99.46 235	92.59 306	99.20 249	99.28 158

plane_prior4											96.77 324

plane_prior3						94.51 149			95.29 210	96.16 313

plane_prior2							96.50 173		96.36 141

plane_prior1					98.49 209

plane_prior						94.29 159	95.42 266		94.31 257						98.93 286

n2								0.00 478

nn								0.00 478

door-mid								98.17 273

lessismore_v0				97.05 184	99.36 53	92.12 235		84.07 461		98.77 87	98.98 70	85.36 361	99.74 93	97.34 91	99.37 212	99.30 150

LGP-MVS_train				98.74 38	99.15 91	97.02 46		99.02 105	95.15 215	98.34 135	98.23 176	97.91 25	99.70 130	94.41 252	99.73 81	99.50 85

test11								98.08 285

door								97.81 304

HQP5-MVS						92.47 222

HQP-NCC					97.85 281		94.26 330		93.18 300	92.86 410

ACMP_Plane					97.85 281		94.26 330		93.18 300	92.86 410

BP-MVS														90.51 356

HQP4-MVS										92.87 409			99.23 320			99.06 213

HQP3-MVS								98.43 237							98.74 310

HQP2-MVS												90.33 303

NP-MVS					98.14 257	93.72 181					95.08 386

MDTV_nov1_ep13_2view						57.28 472	94.89 309		80.59 446	94.02 378		78.66 400		85.50 418		97.82 368

MDTV_nov1_ep13			91.28 375		94.31 441	73.51 464	94.80 314	93.16 409	86.75 408	93.45 398	97.40 270	76.37 413	98.55 403	88.85 381	96.43 414

ACMMP++_ref															99.52 161

ACMMP++															99.55 146

Test By Simon												94.51 206

ITE_SJBPF				97.85 112	98.64 177	96.66 58		98.51 230	95.63 189	97.22 231	97.30 283	95.52 164	98.55 403	90.97 336	98.90 288	98.34 318

DeepMVS_CX				77.17 447	90.94 464	85.28 391		74.08 470	52.51 466	80.87 466	88.03 458	75.25 420	70.63 468	59.23 466	84.94 462	75.62 462

High-resolution multi-view benchmark