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 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

CS-MVS		98.09 42	98.01 44	98.32 69	98.45 167	96.69 54	98.52 26	99.69 2	98.07 43	96.07 244	97.19 223	96.88 66	99.86 25	97.50 43	99.73 56	98.41 246

CS-MVS-test		97.91 64	97.84 56	98.14 88	98.52 154	96.03 79	98.38 34	99.67 3	98.11 41	95.50 266	96.92 243	96.81 72	99.87 22	96.87 65	99.76 49	98.51 239

DROMVSNet		97.90 66	97.94 49	97.79 114	98.66 136	95.14 126	98.31 39	99.66 4	97.57 66	95.95 249	97.01 237	96.99 55	99.82 37	97.66 37	99.64 75	98.39 249

dcpmvs_2		97.12 120	97.99 45	94.51 292	99.11 89	84.00 343	97.75 76	99.65 5	97.38 79	99.14 32	98.42 87	95.16 138	99.96 2	95.52 124	99.78 46	99.58 33

LCM-MVSNet-Re		97.33 111	97.33 104	97.32 158	98.13 203	93.79 177	96.99 122	99.65 5	96.74 97	99.47 13	98.93 52	96.91 63	99.84 31	90.11 288	99.06 223	98.32 258

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 38	99.71 7	96.99 46	99.69 2	99.57 7	99.02 15	99.62 10	99.36 14	98.53 7	99.52 188	98.58 15	99.95 5	99.66 23
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
ANet_high		98.31 29	98.94 6	96.41 211	99.33 50	89.64 255	97.92 66	99.56 8	99.27 6	99.66 8	99.50 6	97.67 25	99.83 34	97.55 41	99.98 2	99.77 10

Vis-MVSNet		98.27 30	98.34 29	98.07 93	99.33 50	95.21 125	98.04 59	99.46 9	97.32 81	97.82 151	99.11 37	96.75 74	99.86 25	97.84 29	99.36 167	99.15 148
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TDRefinement		98.90 5	98.86 8	99.02 9	99.54 24	98.06 8	99.34 4	99.44 10	98.85 20	99.00 39	99.20 26	97.42 32	99.59 166	97.21 52	99.76 49	99.40 90

UA-Net		98.88 7	98.76 13	99.22 2	99.11 89	97.89 14	99.47 3	99.32 11	99.08 10	97.87 146	99.67 2	96.47 90	99.92 5	97.88 27	99.98 2	99.85 3

patch_mono-2		96.59 156	96.93 130	95.55 248	98.88 112	87.12 305	94.47 256	99.30 12	94.12 210	96.65 216	98.41 88	94.98 146	99.87 22	95.81 109	99.78 46	99.66 23

pmmvs6		99.07 4	99.24 4	98.56 50	99.81 2	96.38 64	98.87 9	99.30 12	99.01 16	99.63 9	99.66 3	99.27 2	99.68 132	97.75 34	99.89 26	99.62 29

bld_raw_conf005		98.51 20	98.52 24	98.47 56	99.57 18	95.91 83	98.75 13	99.27 14	98.28 35	99.17 29	99.27 21	93.85 178	99.83 34	98.63 12	99.91 17	99.66 23

FOURS1					99.59 16	98.20 4	99.03 7	99.25 15	98.96 18	98.87 43

mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 62	98.54 24	99.22 16	96.23 120	99.71 4	99.48 7	98.77 6	99.93 3	98.89 3	99.95 5	99.84 5

FC-MVSNet-test		98.16 35	98.37 28	97.56 131	99.49 33	93.10 196	98.35 35	99.21 17	98.43 29	98.89 42	98.83 59	94.30 167	99.81 40	97.87 28	99.91 17	99.77 10

PS-MVSNAJss		98.53 19	98.63 19	98.21 83	99.68 9	94.82 135	98.10 55	99.21 17	96.91 92	99.75 2	99.45 9	95.82 110	99.92 5	98.80 4	99.96 4	99.89 1

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 66	99.18 5	99.20 19	99.67 2	99.73 3	99.65 4	99.15 3	99.86 25	97.22 51	99.92 14	99.77 10

ACMH+	93.58 10	98.23 33	98.31 30	97.98 101	99.39 44	95.22 123	97.55 89	99.20 19	98.21 38	99.25 25	98.51 81	98.21 11	99.40 225	94.79 169	99.72 60	99.32 107

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 14	97.29 39	98.65 20	99.19 21	95.62 155	99.35 19	99.37 12	97.38 33	99.90 14	98.59 14	99.91 17	99.77 10

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 29	96.61 58	98.55 23	99.17 22	99.05 13	99.17 29	98.79 60	95.47 128	99.89 18	97.95 26	99.91 17	99.75 15

EIA-MVS		96.04 179	95.77 187	96.85 183	97.80 237	92.98 198	96.12 164	99.16 23	94.65 191	93.77 309	91.69 362	95.68 119	99.67 137	94.18 195	98.85 245	97.91 294

AllTest		97.20 119	96.92 132	98.06 95	99.08 92	96.16 71	97.14 113	99.16 23	94.35 201	97.78 152	98.07 132	95.84 107	99.12 280	91.41 253	99.42 153	98.91 194

TestCases				98.06 95	99.08 92	96.16 71		99.16 23	94.35 201	97.78 152	98.07 132	95.84 107	99.12 280	91.41 253	99.42 153	98.91 194

COLMAP_ROB	94.48 6	98.25 32	98.11 35	98.64 44	99.21 71	97.35 37	97.96 62	99.16 23	98.34 32	98.78 50	98.52 80	97.32 35	99.45 208	94.08 199	99.67 70	99.13 153
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20231211		98.55 17	98.76 13	97.94 103	98.79 119	94.37 153	98.84 11	99.15 27	99.37 3	99.67 6	99.43 11	95.61 122	99.72 92	98.12 21	99.86 30	99.73 17

PEN-MVS		98.75 10	98.85 10	98.44 58	99.58 17	95.67 94	98.45 31	99.15 27	99.33 5	99.30 21	99.00 45	97.27 38	99.92 5	97.64 38	99.92 14	99.75 15

v7n		98.73 11	98.99 5	97.95 102	99.64 12	94.20 162	98.67 16	99.14 29	99.08 10	99.42 15	99.23 24	96.53 85	99.91 13	99.27 2	99.93 10	99.73 17

PS-CasMVS		98.73 11	98.85 10	98.39 64	99.55 22	95.47 107	98.49 28	99.13 30	99.22 8	99.22 27	98.96 49	97.35 34	99.92 5	97.79 32	99.93 10	99.79 9

jajsoiax		98.77 9	98.79 12	98.74 35	99.66 11	96.48 62	98.45 31	99.12 31	95.83 147	99.67 6	99.37 12	98.25 10	99.92 5	98.77 5	99.94 8	99.82 6

FIs		97.93 60	98.07 37	97.48 143	99.38 45	92.95 199	98.03 61	99.11 32	98.04 45	98.62 58	98.66 70	93.75 182	99.78 51	97.23 50	99.84 34	99.73 17

abl_6		98.42 24	98.19 33	99.09 3	99.16 76	98.10 6	97.73 80	99.11 32	97.76 54	98.62 58	98.27 110	97.88 19	99.80 46	95.67 114	99.50 124	99.38 94

SF-MVS		97.60 91	97.39 100	98.22 80	98.93 108	95.69 91	97.05 118	99.10 34	95.32 167	97.83 149	97.88 161	96.44 92	99.72 92	94.59 179	99.39 161	99.25 130

Effi-MVS+		96.19 173	96.01 176	96.71 191	97.43 278	92.19 216	96.12 164	99.10 34	95.45 162	93.33 327	94.71 324	97.23 43	99.56 175	93.21 226	97.54 311	98.37 251

APDe-MVS		98.14 37	98.03 43	98.47 56	98.72 127	96.04 77	98.07 57	99.10 34	95.96 136	98.59 63	98.69 68	96.94 58	99.81 40	96.64 67	99.58 92	99.57 38

DTE-MVSNet		98.79 8	98.86 8	98.59 48	99.55 22	96.12 74	98.48 30	99.10 34	99.36 4	99.29 23	99.06 43	97.27 38	99.93 3	97.71 36	99.91 17	99.70 20

Gipuma		98.07 44	98.31 30	97.36 156	99.76 5	96.28 69	98.51 27	99.10 34	98.76 23	96.79 206	99.34 18	96.61 80	98.82 311	96.38 79	99.50 124	96.98 322
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
casdiffmvs		97.50 98	97.81 60	96.56 201	98.51 156	91.04 235	95.83 184	99.09 39	97.23 85	98.33 92	98.30 101	97.03 52	99.37 236	96.58 71	99.38 163	99.28 121

nrg030		98.54 18	98.62 21	98.32 69	99.22 64	95.66 95	97.90 67	99.08 40	98.31 33	99.02 37	98.74 64	97.68 24	99.61 164	97.77 33	99.85 33	99.70 20

diffmvs		96.04 179	96.23 166	95.46 253	97.35 282	88.03 286	93.42 297	99.08 40	94.09 212	96.66 214	96.93 241	93.85 178	99.29 257	96.01 96	98.67 260	99.06 170

PVSNet_Blended_VisFu		95.95 183	95.80 185	96.42 209	99.28 54	90.62 243	95.31 214	99.08 40	88.40 304	96.97 198	98.17 122	92.11 221	99.78 51	93.64 217	99.21 199	98.86 205

xxxxxxxxxxxxxcwj		97.24 117	97.03 125	97.89 107	98.48 162	94.71 139	94.53 254	99.07 43	95.02 181	97.83 149	97.88 161	96.44 92	99.72 92	94.59 179	99.39 161	99.25 130

PGM-MVS		97.88 68	97.52 92	98.96 16	99.20 72	97.62 22	97.09 116	99.06 44	95.45 162	97.55 156	97.94 152	97.11 44	99.78 51	94.77 172	99.46 137	99.48 65

RPSCF		97.87 69	97.51 93	98.95 17	99.15 79	98.43 3	97.56 88	99.06 44	96.19 123	98.48 72	98.70 67	94.72 151	99.24 265	94.37 187	99.33 182	99.17 144

canonicalmvs		97.23 118	97.21 114	97.30 159	97.65 262	94.39 151	97.84 70	99.05 46	97.42 74	96.68 213	93.85 337	97.63 26	99.33 246	96.29 82	98.47 274	98.18 274

TranMVSNet+NR-MVSNet		98.33 27	98.30 32	98.43 60	99.07 94	95.87 84	96.73 137	99.05 46	98.67 24	98.84 46	98.45 85	97.58 28	99.88 20	96.45 77	99.86 30	99.54 44

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 65	99.17 6	99.05 46	98.05 44	99.61 11	99.52 5	93.72 183	99.88 20	98.72 9	99.88 28	99.65 26

HPM-MVS		98.11 41	97.83 59	98.92 22	99.42 41	97.46 33	98.57 21	99.05 46	95.43 164	97.41 169	97.50 195	97.98 15	99.79 47	95.58 123	99.57 95	99.50 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		97.12 120	96.74 141	98.26 75	98.99 103	97.45 34	93.82 285	99.05 46	95.19 172	98.32 93	97.70 179	95.22 137	98.41 345	94.27 192	98.13 285	98.93 189

ACMH	93.61 9	98.44 23	98.76 13	97.51 136	99.43 39	93.54 187	98.23 45	99.05 46	97.40 78	99.37 18	99.08 41	98.79 5	99.47 201	97.74 35	99.71 63	99.50 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		97.83 73	97.65 76	98.35 68	98.80 118	95.86 86	95.92 180	99.04 52	97.51 71	98.22 103	97.81 169	94.68 154	99.78 51	97.14 57	99.75 54	99.41 89

HPM-MVS_fast		98.32 28	98.13 34	98.88 24	99.54 24	97.48 32	98.35 35	99.03 53	95.88 142	97.88 143	98.22 117	98.15 12	99.74 82	96.50 75	99.62 78	99.42 87

baseline		97.44 103	97.78 65	96.43 207	98.52 154	90.75 242	96.84 126	99.03 53	96.51 107	97.86 147	98.02 141	96.67 76	99.36 238	97.09 58	99.47 134	99.19 140

v10		97.55 94	97.97 46	96.31 215	98.60 145	89.64 255	97.44 97	99.02 55	96.60 101	98.72 56	99.16 33	93.48 187	99.72 92	98.76 6	99.92 14	99.58 33

UniMVSNet_NR-MVSNet		97.83 73	97.65 76	98.37 65	98.72 127	95.78 87	95.66 191	99.02 55	98.11 41	98.31 95	97.69 181	94.65 156	99.85 28	97.02 61	99.71 63	99.48 65

XVG-OURS-SEG-HR		97.38 107	97.07 122	98.30 73	99.01 102	97.41 36	94.66 249	99.02 55	95.20 171	98.15 111	97.52 193	98.83 4	98.43 344	94.87 165	96.41 337	99.07 168

MVSFormer		96.14 175	96.36 162	95.49 251	97.68 258	87.81 291	98.67 16	99.02 55	96.50 108	94.48 291	96.15 286	86.90 288	99.92 5	98.73 7	99.13 210	98.74 218

test_djsdf		98.73 11	98.74 16	98.69 40	99.63 13	96.30 68	98.67 16	99.02 55	96.50 108	99.32 20	99.44 10	97.43 31	99.92 5	98.73 7	99.95 5	99.86 2

LPG-MVS_test		97.94 57	97.67 73	98.74 35	99.15 79	97.02 44	97.09 116	99.02 55	95.15 174	98.34 88	98.23 114	97.91 17	99.70 117	94.41 184	99.73 56	99.50 51

LGP-MVS_train				98.74 35	99.15 79	97.02 44		99.02 55	95.15 174	98.34 88	98.23 114	97.91 17	99.70 117	94.41 184	99.73 56	99.50 51

DeepC-MVS	95.41 4	97.82 75	97.70 69	98.16 84	98.78 121	95.72 89	96.23 159	99.02 55	93.92 216	98.62 58	98.99 46	97.69 23	99.62 158	96.18 86	99.87 29	99.15 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pm-mvs1		98.47 22	98.67 17	97.86 110	99.52 28	94.58 145	98.28 42	99.00 63	97.57 66	99.27 24	99.22 25	98.32 9	99.50 193	97.09 58	99.75 54	99.50 51

VPA-MVSNet		98.27 30	98.46 25	97.70 122	99.06 95	93.80 176	97.76 75	99.00 63	98.40 30	99.07 36	98.98 47	96.89 64	99.75 72	97.19 55	99.79 43	99.55 43

XXY-MVS		97.54 95	97.70 69	97.07 171	99.46 35	92.21 213	97.22 109	99.00 63	94.93 185	98.58 64	98.92 53	97.31 36	99.41 223	94.44 182	99.43 150	99.59 32

DPE-MVS		97.64 87	97.35 103	98.50 53	98.85 114	96.18 70	95.21 222	98.99 66	95.84 146	98.78 50	98.08 130	96.84 70	99.81 40	93.98 206	99.57 95	99.52 48
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		97.69 84	97.36 102	98.70 39	99.50 32	96.84 49	95.38 208	98.99 66	92.45 258	98.11 115	98.31 97	97.25 41	99.77 60	96.60 69	99.62 78	99.48 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CSCG		97.40 106	97.30 105	97.69 124	98.95 105	94.83 134	97.28 105	98.99 66	96.35 116	98.13 114	95.95 298	95.99 103	99.66 143	94.36 190	99.73 56	98.59 233

GeoE		97.75 80	97.70 69	97.89 107	98.88 112	94.53 146	97.10 115	98.98 69	95.75 151	97.62 154	97.59 187	97.61 27	99.77 60	96.34 81	99.44 142	99.36 102

9.14			96.69 143		98.53 153		96.02 170	98.98 69	93.23 234	97.18 177	97.46 198	96.47 90	99.62 158	92.99 229	99.32 184

ETH3D-3000-0.1		96.89 135	96.46 159	98.16 84	98.62 142	95.69 91	95.96 175	98.98 69	93.36 229	97.04 190	97.31 216	94.93 148	99.63 150	92.60 232	99.34 175	99.17 144

XVG-ACMP-BASELINE		97.58 93	97.28 108	98.49 54	99.16 76	96.90 48	96.39 147	98.98 69	95.05 179	98.06 123	98.02 141	95.86 106	99.56 175	94.37 187	99.64 75	99.00 177

EG-PatchMatch MVS		97.69 84	97.79 61	97.40 154	99.06 95	93.52 188	95.96 175	98.97 73	94.55 197	98.82 47	98.76 63	97.31 36	99.29 257	97.20 54	99.44 142	99.38 94

CP-MVS		97.92 61	97.56 90	98.99 13	98.99 103	97.82 16	97.93 64	98.96 74	96.11 126	96.89 203	97.45 199	96.85 69	99.78 51	95.19 146	99.63 77	99.38 94

ACMMP		98.05 45	97.75 68	98.93 21	99.23 61	97.60 23	98.09 56	98.96 74	95.75 151	97.91 139	98.06 137	96.89 64	99.76 65	95.32 139	99.57 95	99.43 86
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
ETV-MVS		96.13 176	95.90 183	96.82 185	97.76 249	93.89 171	95.40 206	98.95 76	95.87 143	95.58 265	91.00 368	96.36 97	99.72 92	93.36 220	98.83 247	96.85 329

KD-MVS_self_test		97.86 71	98.07 37	97.25 163	99.22 64	92.81 202	97.55 89	98.94 77	97.10 88	98.85 44	98.88 56	95.03 143	99.67 137	97.39 48	99.65 73	99.26 126

mvsmamba		98.16 35	98.06 39	98.44 58	99.53 27	95.87 84	98.70 14	98.94 77	97.71 60	98.85 44	99.10 38	91.35 236	99.83 34	98.47 16	99.90 24	99.64 28

114514_t		93.96 264	93.22 271	96.19 220	99.06 95	90.97 237	95.99 172	98.94 77	73.88 373	93.43 324	96.93 241	92.38 217	99.37 236	89.09 303	99.28 191	98.25 268

SD-MVS		97.37 108	97.70 69	96.35 212	98.14 200	95.13 127	96.54 142	98.92 80	95.94 138	99.19 28	98.08 130	97.74 22	95.06 373	95.24 144	99.54 107	98.87 204
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
APD-MVS_3200maxsize		98.13 40	97.90 50	98.79 31	98.79 119	97.31 38	97.55 89	98.92 80	97.72 58	98.25 100	98.13 124	97.10 45	99.75 72	95.44 132	99.24 198	99.32 107

SteuartSystems-ACMMP		98.02 47	97.76 66	98.79 31	99.43 39	97.21 43	97.15 111	98.90 82	96.58 104	98.08 121	97.87 163	97.02 53	99.76 65	95.25 143	99.59 90	99.40 90
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		97.96 50	97.90 50	98.12 90	97.75 251	95.40 108	99.03 7	98.89 83	96.62 99	98.62 58	98.30 101	96.97 56	99.75 72	95.70 110	99.25 195	99.21 135

test_0728_SECOND				98.25 78	99.23 61	95.49 106	96.74 133	98.89 83					99.75 72	95.48 128	99.52 115	99.53 47

test0726					99.24 59	95.51 102	96.89 125	98.89 83	95.92 139	98.64 57	98.31 97	97.06 50

MSP-MVS		97.45 102	96.92 132	99.03 8	99.26 55	97.70 19	97.66 81	98.89 83	95.65 153	98.51 68	96.46 271	92.15 219	99.81 40	95.14 153	98.58 270	99.58 33
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
MIMVSNet1		98.51 20	98.45 27	98.67 41	99.72 6	96.71 52	98.76 12	98.89 83	98.49 28	99.38 17	99.14 36	95.44 130	99.84 31	96.47 76	99.80 42	99.47 68

ACMP	92.54 13	97.47 101	97.10 119	98.55 51	99.04 100	96.70 53	96.24 158	98.89 83	93.71 221	97.97 134	97.75 174	97.44 30	99.63 150	93.22 225	99.70 66	99.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1240		96.74 145	97.02 126	95.91 233	98.18 193	88.52 274	95.39 207	98.88 89	93.15 241	98.46 75	98.40 91	92.80 201	99.71 108	98.45 17	99.49 128	99.49 59

3Dnovator	96.53 2	97.61 90	97.64 79	97.50 139	97.74 254	93.65 185	98.49 28	98.88 89	96.86 94	97.11 182	98.55 78	95.82 110	99.73 87	95.94 100	99.42 153	99.13 153

test_one_0601					99.05 99	95.50 105		98.87 91	97.21 86	98.03 127	98.30 101	96.93 60

TransMVSNet (Re)		98.38 26	98.67 17	97.51 136	99.51 29	93.39 191	98.20 50	98.87 91	98.23 37	99.48 12	99.27 21	98.47 8	99.55 179	96.52 73	99.53 110	99.60 31

DU-MVS		97.79 77	97.60 86	98.36 66	98.73 125	95.78 87	95.65 194	98.87 91	97.57 66	98.31 95	97.83 165	94.69 152	99.85 28	97.02 61	99.71 63	99.46 70

SR-MVS-dyc-post		98.14 37	97.84 56	99.02 9	98.81 116	98.05 9	97.55 89	98.86 94	97.77 51	98.20 104	98.07 132	96.60 82	99.76 65	95.49 125	99.20 200	99.26 126

RE-MVS-def			97.88 54		98.81 116	98.05 9	97.55 89	98.86 94	97.77 51	98.20 104	98.07 132	96.94 58		95.49 125	99.20 200	99.26 126

Baseline_NR-MVSNet		97.72 82	97.79 61	97.50 139	99.56 20	93.29 192	95.44 201	98.86 94	98.20 39	98.37 82	99.24 23	94.69 152	99.55 179	95.98 98	99.79 43	99.65 26

RPMNet		94.68 237	94.60 232	94.90 273	95.44 345	88.15 282	96.18 161	98.86 94	97.43 73	94.10 298	98.49 82	79.40 323	99.76 65	95.69 112	95.81 343	96.81 333

test1172		98.08 43	97.76 66	99.05 6	98.78 121	98.07 7	97.41 101	98.85 98	97.57 66	98.15 111	97.96 147	96.60 82	99.76 65	95.30 140	99.18 204	99.33 106

test_part1		96.77 144	96.53 154	97.47 144	98.04 207	92.92 200	97.93 64	98.85 98	98.83 21	99.30 21	99.07 42	79.25 324	99.79 47	97.59 39	99.93 10	99.69 22

1112_ss		94.12 259	93.42 266	96.23 217	98.59 147	90.85 238	94.24 264	98.85 98	85.49 330	92.97 331	94.94 319	86.01 293	99.64 148	91.78 247	97.92 292	98.20 272

PHI-MVS		96.96 129	96.53 154	98.25 78	97.48 272	96.50 61	96.76 132	98.85 98	93.52 224	96.19 240	96.85 246	95.94 104	99.42 214	93.79 212	99.43 150	98.83 207

LS3D		97.77 79	97.50 95	98.57 49	96.24 320	97.58 25	98.45 31	98.85 98	98.58 27	97.51 159	97.94 152	95.74 118	99.63 150	95.19 146	98.97 228	98.51 239

ZNCC-MVS		97.92 61	97.62 83	98.83 26	99.32 52	97.24 41	97.45 96	98.84 103	95.76 149	96.93 200	97.43 201	97.26 40	99.79 47	96.06 89	99.53 110	99.45 75

HFP-MVS		97.94 57	97.64 79	98.83 26	99.15 79	97.50 30	97.59 86	98.84 103	96.05 129	97.49 161	97.54 190	97.07 48	99.70 117	95.61 120	99.46 137	99.30 113

region2R		97.92 61	97.59 87	98.92 22	99.22 64	97.55 27	97.60 85	98.84 103	96.00 134	97.22 173	97.62 185	96.87 68	99.76 65	95.48 128	99.43 150	99.46 70

#test#		97.62 89	97.22 113	98.83 26	99.15 79	97.50 30	96.81 128	98.84 103	94.25 205	97.49 161	97.54 190	97.07 48	99.70 117	94.37 187	99.46 137	99.30 113

MSLP-MVS++		96.42 166	96.71 142	95.57 245	97.82 232	90.56 246	95.71 186	98.84 103	94.72 189	96.71 212	97.39 207	94.91 149	98.10 359	95.28 141	99.02 225	98.05 286

CP-MVSNet		98.42 24	98.46 25	98.30 73	99.46 35	95.22 123	98.27 44	98.84 103	99.05 13	99.01 38	98.65 72	95.37 131	99.90 14	97.57 40	99.91 17	99.77 10

OpenMVS	94.22 8	95.48 200	95.20 200	96.32 214	97.16 297	91.96 222	97.74 78	98.84 103	87.26 313	94.36 293	98.01 143	93.95 176	99.67 137	90.70 276	98.75 254	97.35 316

SED-MVS		97.94 57	97.90 50	98.07 93	99.22 64	95.35 113	96.79 130	98.83 110	96.11 126	99.08 34	98.24 112	97.87 20	99.72 92	95.44 132	99.51 120	99.14 151

test_241102_TWO								98.83 110	96.11 126	98.62 58	98.24 112	96.92 62	99.72 92	95.44 132	99.49 128	99.49 59

test_241102_ONE					99.22 64	95.35 113		98.83 110	96.04 131	99.08 34	98.13 124	97.87 20	99.33 246

SR-MVS		98.00 49	97.66 74	99.01 11	98.77 123	97.93 11	97.38 102	98.83 110	97.32 81	98.06 123	97.85 164	96.65 77	99.77 60	95.00 162	99.11 214	99.32 107

XVS		97.96 50	97.63 81	98.94 18	99.15 79	97.66 20	97.77 73	98.83 110	97.42 74	96.32 231	97.64 183	96.49 88	99.72 92	95.66 116	99.37 164	99.45 75

X-MVStestdata		92.86 287	90.83 311	98.94 18	99.15 79	97.66 20	97.77 73	98.83 110	97.42 74	96.32 231	36.50 377	96.49 88	99.72 92	95.66 116	99.37 164	99.45 75

ACMMPR		97.95 54	97.62 83	98.94 18	99.20 72	97.56 26	97.59 86	98.83 110	96.05 129	97.46 167	97.63 184	96.77 73	99.76 65	95.61 120	99.46 137	99.49 59

ACMM	93.33 11	98.05 45	97.79 61	98.85 25	99.15 79	97.55 27	96.68 139	98.83 110	95.21 170	98.36 85	98.13 124	98.13 14	99.62 158	96.04 92	99.54 107	99.39 92
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v8		97.60 91	98.06 39	96.23 217	98.71 130	89.44 259	97.43 99	98.82 118	97.29 83	98.74 54	99.10 38	93.86 177	99.68 132	98.61 13	99.94 8	99.56 41

LF4IMVS		96.07 177	95.63 191	97.36 156	98.19 190	95.55 99	95.44 201	98.82 118	92.29 260	95.70 262	96.55 265	92.63 207	98.69 324	91.75 249	99.33 182	97.85 296

GST-MVS		97.82 75	97.49 96	98.81 29	99.23 61	97.25 40	97.16 110	98.79 120	95.96 136	97.53 157	97.40 203	96.93 60	99.77 60	95.04 159	99.35 172	99.42 87

ACMMP_NAP		97.89 67	97.63 81	98.67 41	99.35 48	96.84 49	96.36 150	98.79 120	95.07 178	97.88 143	98.35 93	97.24 42	99.72 92	96.05 91	99.58 92	99.45 75

v1921920		96.72 148	96.96 129	95.99 226	98.21 188	88.79 271	95.42 203	98.79 120	93.22 235	98.19 107	98.26 111	92.68 204	99.70 117	98.34 20	99.55 104	99.49 59

DP-MVS		97.87 69	97.89 53	97.81 113	98.62 142	94.82 135	97.13 114	98.79 120	98.98 17	98.74 54	98.49 82	95.80 116	99.49 195	95.04 159	99.44 142	99.11 161

mPP-MVS		97.91 64	97.53 91	99.04 7	99.22 64	97.87 15	97.74 78	98.78 124	96.04 131	97.10 183	97.73 177	96.53 85	99.78 51	95.16 150	99.50 124	99.46 70

v144192		96.69 151	96.90 134	96.03 225	98.25 184	88.92 266	95.49 199	98.77 125	93.05 243	98.09 119	98.29 105	92.51 213	99.70 117	98.11 22	99.56 98	99.47 68

v1192		96.83 139	97.06 123	96.15 222	98.28 179	89.29 261	95.36 209	98.77 125	93.73 220	98.11 115	98.34 94	93.02 198	99.67 137	98.35 19	99.58 92	99.50 51

APD-MVS		97.00 124	96.53 154	98.41 62	98.55 151	96.31 67	96.32 153	98.77 125	92.96 250	97.44 168	97.58 189	95.84 107	99.74 82	91.96 240	99.35 172	99.19 140
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CPTT-MVS		96.69 151	96.08 174	98.49 54	98.89 111	96.64 57	97.25 106	98.77 125	92.89 251	96.01 248	97.13 225	92.23 218	99.67 137	92.24 237	99.34 175	99.17 144

HQP_MVS		96.66 154	96.33 164	97.68 125	98.70 132	94.29 155	96.50 143	98.75 129	96.36 114	96.16 241	96.77 253	91.91 230	99.46 204	92.59 234	99.20 200	99.28 121

plane_prior5								98.75 129					99.46 204	92.59 234	99.20 200	99.28 121

ETH3D cwj APD-0.16		96.23 171	95.61 193	98.09 92	97.91 221	95.65 96	94.94 237	98.74 131	91.31 275	96.02 247	97.08 230	94.05 174	99.69 125	91.51 252	98.94 233	98.93 189

Patchmatch-RL test		94.66 238	94.49 238	95.19 262	98.54 152	88.91 267	92.57 316	98.74 131	91.46 272	98.32 93	97.75 174	77.31 337	98.81 313	96.06 89	99.61 84	97.85 296

SMA-MVS		97.48 100	97.11 118	98.60 46	98.83 115	96.67 55	96.74 133	98.73 133	91.61 269	98.48 72	98.36 92	96.53 85	99.68 132	95.17 148	99.54 107	99.45 75
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
Fast-Effi-MVS+-dtu		96.44 164	96.12 171	97.39 155	97.18 296	94.39 151	95.46 200	98.73 133	96.03 133	94.72 282	94.92 321	96.28 100	99.69 125	93.81 211	97.98 290	98.09 276

zzz-MVS		98.01 48	97.66 74	99.06 4	99.44 37	97.90 12	95.66 191	98.73 133	97.69 62	97.90 140	97.96 147	95.81 114	99.82 37	96.13 87	99.61 84	99.45 75

MTGPA								98.73 133

MTAPA		98.14 37	97.84 56	99.06 4	99.44 37	97.90 12	97.25 106	98.73 133	97.69 62	97.90 140	97.96 147	95.81 114	99.82 37	96.13 87	99.61 84	99.45 75

MP-MVS		97.64 87	97.18 115	99.00 12	99.32 52	97.77 18	97.49 95	98.73 133	96.27 117	95.59 264	97.75 174	96.30 98	99.78 51	93.70 216	99.48 132	99.45 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NR-MVSNet		97.96 50	97.86 55	98.26 75	98.73 125	95.54 100	98.14 53	98.73 133	97.79 50	99.42 15	97.83 165	94.40 165	99.78 51	95.91 102	99.76 49	99.46 70

QAPM		95.88 186	95.57 194	96.80 186	97.90 223	91.84 225	98.18 52	98.73 133	88.41 303	96.42 226	98.13 124	94.73 150	99.75 72	88.72 308	98.94 233	98.81 209

test_0402		97.84 72	97.97 46	97.47 144	99.19 74	94.07 165	96.71 138	98.73 133	98.66 25	98.56 65	98.41 88	96.84 70	99.69 125	94.82 167	99.81 39	98.64 227

TAPA-MVS	93.32 12	94.93 223	94.23 247	97.04 173	98.18 193	94.51 147	95.22 221	98.73 133	81.22 355	96.25 237	95.95 298	93.80 181	98.98 299	89.89 292	98.87 241	97.62 306
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETH3 D test6400		94.77 229	93.87 260	97.47 144	98.12 204	93.73 179	94.56 253	98.70 143	85.45 333	94.70 284	95.93 300	91.77 232	99.63 150	86.45 333	99.14 207	99.05 172

testtj		96.69 151	96.13 170	98.36 66	98.46 166	96.02 80	96.44 145	98.70 143	94.26 204	96.79 206	97.13 225	94.07 173	99.75 72	90.53 280	98.80 249	99.31 112

3Dnovator+	96.13 3	97.73 81	97.59 87	98.15 87	98.11 205	95.60 97	98.04 59	98.70 143	98.13 40	96.93 200	98.45 85	95.30 135	99.62 158	95.64 118	98.96 229	99.24 132

Test_1112_low_res		93.53 277	92.86 277	95.54 249	98.60 145	88.86 269	92.75 312	98.69 146	82.66 349	92.65 338	96.92 243	84.75 301	99.56 175	90.94 264	97.76 298	98.19 273

DP-MVS Recon		95.55 196	95.13 203	96.80 186	98.51 156	93.99 169	94.60 251	98.69 146	90.20 286	95.78 258	96.21 284	92.73 203	98.98 299	90.58 279	98.86 243	97.42 313

CHOSEN 1792x2688		94.10 260	93.41 267	96.18 221	99.16 76	90.04 250	92.15 324	98.68 148	79.90 360	96.22 238	97.83 165	87.92 282	99.42 214	89.18 302	99.65 73	99.08 166

PVSNet_BlendedMVS		95.02 222	94.93 213	95.27 259	97.79 243	87.40 300	94.14 272	98.68 148	88.94 298	94.51 289	98.01 143	93.04 195	99.30 253	89.77 294	99.49 128	99.11 161

PVSNet_Blended		93.96 264	93.65 263	94.91 271	97.79 243	87.40 300	91.43 334	98.68 148	84.50 343	94.51 289	94.48 330	93.04 195	99.30 253	89.77 294	98.61 267	98.02 289

v1144		96.84 136	97.08 121	96.13 223	98.42 169	89.28 262	95.41 205	98.67 151	94.21 206	97.97 134	98.31 97	93.06 194	99.65 145	98.06 24	99.62 78	99.45 75

CLD-MVS		95.47 201	95.07 206	96.69 193	98.27 181	92.53 206	91.36 335	98.67 151	91.22 277	95.78 258	94.12 335	95.65 121	98.98 299	90.81 268	99.72 60	98.57 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GBi-Net		96.99 125	96.80 138	97.56 131	97.96 217	93.67 181	98.23 45	98.66 153	95.59 157	97.99 130	99.19 27	89.51 265	99.73 87	94.60 176	99.44 142	99.30 113

test1		96.99 125	96.80 138	97.56 131	97.96 217	93.67 181	98.23 45	98.66 153	95.59 157	97.99 130	99.19 27	89.51 265	99.73 87	94.60 176	99.44 142	99.30 113

FMVSNet1		97.95 54	98.08 36	97.56 131	99.14 87	93.67 181	98.23 45	98.66 153	97.41 77	99.00 39	99.19 27	95.47 128	99.73 87	95.83 107	99.76 49	99.30 113

IterMVS-LS		96.92 131	97.29 106	95.79 237	98.51 156	88.13 284	95.10 225	98.66 153	96.99 89	98.46 75	98.68 69	92.55 209	99.74 82	96.91 63	99.79 43	99.50 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		95.30 209	94.38 244	98.05 98	98.64 137	96.04 77	95.61 197	98.66 153	89.00 297	93.22 328	96.40 275	92.90 199	99.35 241	87.45 327	97.53 312	98.77 216

USDC		94.56 243	94.57 237	94.55 290	97.78 247	86.43 315	92.75 312	98.65 158	85.96 325	96.91 202	97.93 154	90.82 242	98.74 319	90.71 275	99.59 90	98.47 243

PM-MVS		97.36 110	97.10 119	98.14 88	98.91 110	96.77 51	96.20 160	98.63 159	93.82 218	98.54 66	98.33 95	93.98 175	99.05 290	95.99 97	99.45 141	98.61 232

cascas		91.89 303	91.35 301	93.51 311	94.27 359	85.60 322	88.86 364	98.61 160	79.32 362	92.16 345	91.44 364	89.22 269	98.12 358	90.80 269	97.47 316	96.82 332

bld_raw_dy_0_64		97.69 84	97.61 85	97.91 105	99.54 24	94.27 159	98.06 58	98.60 161	96.60 101	98.79 49	98.95 50	89.62 259	99.84 31	98.43 18	99.91 17	99.62 29

Fast-Effi-MVS+		95.49 198	95.07 206	96.75 189	97.67 261	92.82 201	94.22 266	98.60 161	91.61 269	93.42 325	92.90 347	96.73 75	99.70 117	92.60 232	97.89 295	97.74 301

DeepPCF-MVS	94.58 5	96.90 133	96.43 160	98.31 72	97.48 272	97.23 42	92.56 317	98.60 161	92.84 252	98.54 66	97.40 203	96.64 79	98.78 315	94.40 186	99.41 159	98.93 189

OMC-MVS		96.48 162	96.00 177	97.91 105	98.30 176	96.01 81	94.86 241	98.60 161	91.88 266	97.18 177	97.21 222	96.11 101	99.04 291	90.49 284	99.34 175	98.69 224

testgi		96.07 177	96.50 158	94.80 279	99.26 55	87.69 294	95.96 175	98.58 165	95.08 177	98.02 129	96.25 281	97.92 16	97.60 364	88.68 310	98.74 255	99.11 161

EGC-MVSNET		83.08 343	77.93 346	98.53 52	99.57 18	97.55 27	98.33 38	98.57 166	4.71 379	10.38 380	98.90 55	95.60 123	99.50 193	95.69 112	99.61 84	98.55 237

ZD-MVS					98.43 168	95.94 82		98.56 167	90.72 281	96.66 214	97.07 231	95.02 144	99.74 82	91.08 260	98.93 235

VPNet		97.26 115	97.49 96	96.59 197	99.47 34	90.58 244	96.27 154	98.53 168	97.77 51	98.46 75	98.41 88	94.59 158	99.68 132	94.61 175	99.29 190	99.52 48

DELS-MVS		96.17 174	96.23 166	95.99 226	97.55 269	90.04 250	92.38 322	98.52 169	94.13 209	96.55 222	97.06 232	94.99 145	99.58 168	95.62 119	99.28 191	98.37 251
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
HyFIR lowres test		93.72 269	92.65 285	96.91 180	98.93 108	91.81 226	91.23 341	98.52 169	82.69 348	96.46 225	96.52 269	80.38 321	99.90 14	90.36 286	98.79 250	99.03 174

ITE_SJBPF				97.85 111	98.64 137	96.66 56		98.51 171	95.63 154	97.22 173	97.30 217	95.52 125	98.55 338	90.97 263	98.90 237	98.34 257

eth_miper_zixun_eth		94.89 224	94.93 213	94.75 281	95.99 332	86.12 318	91.35 336	98.49 172	93.40 227	97.12 181	97.25 220	86.87 290	99.35 241	95.08 158	98.82 248	98.78 213

TinyColmap		96.00 182	96.34 163	94.96 270	97.90 223	87.91 287	94.13 273	98.49 172	94.41 199	98.16 109	97.76 171	96.29 99	98.68 327	90.52 281	99.42 153	98.30 262

OPM-MVS		97.54 95	97.25 109	98.41 62	99.11 89	96.61 58	95.24 220	98.46 174	94.58 196	98.10 118	98.07 132	97.09 47	99.39 230	95.16 150	99.44 142	99.21 135
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		97.72 82	97.97 46	96.94 177	99.26 55	92.23 212	97.83 71	98.45 175	98.25 36	99.13 33	98.66 70	96.65 77	99.69 125	93.92 208	99.62 78	98.91 194

UnsupCasMVSNet_eth		95.91 184	95.73 188	96.44 206	98.48 162	91.52 230	95.31 214	98.45 175	95.76 149	97.48 164	97.54 190	89.53 264	98.69 324	94.43 183	94.61 356	99.13 153

PCF-MVS	89.43 18	92.12 300	90.64 314	96.57 200	97.80 237	93.48 189	89.88 359	98.45 175	74.46 372	96.04 246	95.68 304	90.71 244	99.31 250	73.73 371	99.01 227	96.91 326
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP3-MVS								98.43 178							98.74 255

HQP-MVS		95.17 215	94.58 235	96.92 178	97.85 225	92.47 207	94.26 260	98.43 178	93.18 237	92.86 333	95.08 315	90.33 248	99.23 267	90.51 282	98.74 255	99.05 172

DeepC-MVS_fast	94.34 7	96.74 145	96.51 157	97.44 150	97.69 257	94.15 163	96.02 170	98.43 178	93.17 240	97.30 171	97.38 209	95.48 127	99.28 259	93.74 213	99.34 175	98.88 202
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		95.91 184	95.39 197	97.46 147	97.79 243	94.26 160	93.33 302	98.42 181	94.21 206	94.02 302	96.25 281	93.64 184	99.34 243	91.90 242	98.96 229	98.79 211

test_prior				97.46 147	97.79 243	94.26 160		98.42 181					99.34 243			98.79 211

save fliter					98.48 162	94.71 139	94.53 254	98.41 183	95.02 181

CANet		95.86 187	95.65 190	96.49 204	96.41 316	90.82 239	94.36 258	98.41 183	94.94 183	92.62 341	96.73 256	92.68 204	99.71 108	95.12 156	99.60 88	98.94 185

Anonymous20240521		97.07 122	97.51 93	95.76 238	99.35 48	88.18 281	97.78 72	98.40 185	97.11 87	98.34 88	99.04 44	89.58 261	99.79 47	98.09 23	99.93 10	99.30 113

TEST9					97.84 229	95.23 120	93.62 291	98.39 186	86.81 319	93.78 307	95.99 293	94.68 154	99.52 188

train_agg		95.46 202	94.66 226	97.88 109	97.84 229	95.23 120	93.62 291	98.39 186	87.04 317	93.78 307	95.99 293	94.58 159	99.52 188	91.76 248	98.90 237	98.89 198

test_8					97.81 233	95.07 129	93.54 294	98.38 188	87.04 317	93.71 311	95.96 297	94.58 159	99.52 188

MSDG		95.33 207	95.13 203	95.94 232	97.40 280	91.85 224	91.02 346	98.37 189	95.30 168	96.31 233	95.99 293	94.51 162	98.38 348	89.59 296	97.65 308	97.60 308

agg_prior1		95.39 205	94.60 232	97.75 117	97.80 237	94.96 131	93.39 299	98.36 190	87.20 315	93.49 320	95.97 296	94.65 156	99.53 184	91.69 250	98.86 243	98.77 216

agg_prior					97.80 237	94.96 131		98.36 190		93.49 320			99.53 184

V42		97.04 123	97.16 116	96.68 194	98.59 147	91.05 234	96.33 152	98.36 190	94.60 193	97.99 130	98.30 101	93.32 189	99.62 158	97.40 47	99.53 110	99.38 94

MVS_111021_HR		96.73 147	96.54 153	97.27 160	98.35 174	93.66 184	93.42 297	98.36 190	94.74 188	96.58 218	96.76 255	96.54 84	98.99 297	94.87 165	99.27 193	99.15 148

c3_l		95.20 212	95.32 198	94.83 278	96.19 324	86.43 315	91.83 330	98.35 194	93.47 226	97.36 170	97.26 219	88.69 271	99.28 259	95.41 138	99.36 167	98.78 213

MVS_Test		96.27 169	96.79 140	94.73 282	96.94 305	86.63 312	96.18 161	98.33 195	94.94 183	96.07 244	98.28 106	95.25 136	99.26 262	97.21 52	97.90 294	98.30 262

CDPH-MVS		95.45 203	94.65 227	97.84 112	98.28 179	94.96 131	93.73 289	98.33 195	85.03 338	95.44 267	96.60 263	95.31 134	99.44 211	90.01 290	99.13 210	99.11 161

MVS_111021_LR		96.82 140	96.55 151	97.62 128	98.27 181	95.34 115	93.81 287	98.33 195	94.59 195	96.56 220	96.63 262	96.61 80	98.73 320	94.80 168	99.34 175	98.78 213

Anonymous20240529		97.96 50	98.04 41	97.71 120	98.69 134	94.28 158	97.86 69	98.31 198	98.79 22	99.23 26	98.86 58	95.76 117	99.61 164	95.49 125	99.36 167	99.23 133

Regformer-2		97.41 105	97.24 111	97.93 104	97.21 294	94.72 138	94.85 242	98.27 199	97.74 55	98.11 115	97.50 195	95.58 124	99.69 125	96.57 72	99.31 186	99.37 101

FMVSNet5		93.39 279	92.35 289	96.50 203	95.83 336	90.81 241	97.31 103	98.27 199	92.74 253	96.27 235	98.28 106	62.23 376	99.67 137	90.86 266	99.36 167	99.03 174

v2v482		96.78 143	97.06 123	95.95 230	98.57 149	88.77 272	95.36 209	98.26 201	95.18 173	97.85 148	98.23 114	92.58 208	99.63 150	97.80 31	99.69 67	99.45 75

PLC	91.02 16	94.05 263	92.90 276	97.51 136	98.00 215	95.12 128	94.25 263	98.25 202	86.17 323	91.48 349	95.25 313	91.01 239	99.19 270	85.02 346	96.69 332	98.22 270
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
miper_ehance_all_eth		94.69 235	94.70 225	94.64 283	95.77 338	86.22 317	91.32 339	98.24 203	91.67 268	97.05 189	96.65 261	88.39 275	99.22 269	94.88 164	98.34 277	98.49 242

DVP-MVS		97.78 78	97.65 76	98.16 84	99.24 59	95.51 102	96.74 133	98.23 204	95.92 139	98.40 79	98.28 106	97.06 50	99.71 108	95.48 128	99.52 115	99.26 126
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
iter_conf05		93.65 273	93.05 272	95.46 253	96.13 330	87.45 298	95.95 178	98.22 205	92.66 254	97.04 190	97.89 157	63.52 375	99.72 92	96.19 85	99.82 38	99.21 135

xiu_mvs_v1_base_debu		95.62 193	95.96 180	94.60 286	98.01 211	88.42 275	93.99 278	98.21 206	92.98 246	95.91 251	94.53 327	96.39 94	99.72 92	95.43 135	98.19 282	95.64 351

xiu_mvs_v1_base		95.62 193	95.96 180	94.60 286	98.01 211	88.42 275	93.99 278	98.21 206	92.98 246	95.91 251	94.53 327	96.39 94	99.72 92	95.43 135	98.19 282	95.64 351

xiu_mvs_v1_base_debi		95.62 193	95.96 180	94.60 286	98.01 211	88.42 275	93.99 278	98.21 206	92.98 246	95.91 251	94.53 327	96.39 94	99.72 92	95.43 135	98.19 282	95.64 351

miper_lstm_enhance		94.81 228	94.80 222	94.85 276	96.16 326	86.45 314	91.14 343	98.20 209	93.49 225	97.03 192	97.37 211	84.97 300	99.26 262	95.28 141	99.56 98	98.83 207

TSAR-MVS + MP.		97.42 104	97.23 112	98.00 100	99.38 45	95.00 130	97.63 84	98.20 209	93.00 245	98.16 109	98.06 137	95.89 105	99.72 92	95.67 114	99.10 216	99.28 121
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MVP-Stereo		95.69 190	95.28 199	96.92 178	98.15 199	93.03 197	95.64 196	98.20 209	90.39 284	96.63 217	97.73 177	91.63 233	99.10 285	91.84 246	97.31 320	98.63 229
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS++		96.99 125	96.38 161	98.81 29	98.64 137	97.59 24	95.97 174	98.20 209	95.51 160	95.06 274	96.53 267	94.10 172	99.70 117	94.29 191	99.15 206	99.13 153

NCCC		96.52 160	95.99 178	98.10 91	97.81 233	95.68 93	95.00 235	98.20 209	95.39 165	95.40 269	96.36 277	93.81 180	99.45 208	93.55 219	98.42 275	99.17 144

new-patchmatchnet		95.67 192	96.58 148	92.94 326	97.48 272	80.21 361	92.96 308	98.19 214	94.83 186	98.82 47	98.79 60	93.31 190	99.51 192	95.83 107	99.04 224	99.12 158

MCST-MVS		96.24 170	95.80 185	97.56 131	98.75 124	94.13 164	94.66 249	98.17 215	90.17 287	96.21 239	96.10 291	95.14 139	99.43 213	94.13 198	98.85 245	99.13 153

door-mid								98.17 215

CNVR-MVS		96.92 131	96.55 151	98.03 99	98.00 215	95.54 100	94.87 240	98.17 215	94.60 193	96.38 228	97.05 233	95.67 120	99.36 238	95.12 156	99.08 218	99.19 140

MSC_two_6792asdad				98.22 80	97.75 251	95.34 115		98.16 218					99.75 72	95.87 105	99.51 120	99.57 38

No_MVS				98.22 80	97.75 251	95.34 115		98.16 218					99.75 72	95.87 105	99.51 120	99.57 38

原ACMM1				96.58 198	98.16 197	92.12 217		98.15 220	85.90 327	93.49 320	96.43 272	92.47 214	99.38 233	87.66 322	98.62 266	98.23 269

IU-MVS					99.22 64	95.40 108		98.14 221	85.77 329	98.36 85				95.23 145	99.51 120	99.49 59

Regformer-4		97.53 97	97.47 98	97.71 120	97.35 282	93.91 170	95.26 218	98.14 221	97.97 47	98.34 88	97.89 157	95.49 126	99.71 108	97.41 46	99.42 153	99.51 50

ambc				96.56 201	98.23 187	91.68 228	97.88 68	98.13 223		98.42 78	98.56 77	94.22 170	99.04 291	94.05 203	99.35 172	98.95 183

WR-MVS		96.90 133	96.81 137	97.16 165	98.56 150	92.20 215	94.33 259	98.12 224	97.34 80	98.20 104	97.33 214	92.81 200	99.75 72	94.79 169	99.81 39	99.54 44

iter_conf_final		94.54 245	93.91 259	96.43 207	97.23 293	90.41 248	96.81 128	98.10 225	93.87 217	96.80 205	97.89 157	68.02 369	99.72 92	96.73 66	99.77 48	99.18 143

cdsmvs_eth3d_5k		24.22 346	32.30 349	0.00 364	0.00 387	0.00 388	0.00 375	98.10 225	0.00 382	0.00 383	95.06 317	97.54 29	0.00 383	0.00 381	0.00 381	0.00 379

Effi-MVS+-dtu		96.81 141	96.09 173	98.99 13	96.90 307	98.69 2	96.42 146	98.09 227	95.86 144	95.15 273	95.54 309	94.26 168	99.81 40	94.06 200	98.51 273	98.47 243

mvs-test1		96.20 172	95.50 196	98.32 69	96.90 307	98.16 5	95.07 229	98.09 227	95.86 144	93.63 314	94.32 333	94.26 168	99.71 108	94.06 200	97.27 322	97.07 319

cl____		94.73 230	94.64 228	95.01 268	95.85 335	87.00 307	91.33 337	98.08 229	93.34 230	97.10 183	97.33 214	84.01 307	99.30 253	95.14 153	99.56 98	98.71 223

DIV-MVS_self_test		94.73 230	94.64 228	95.01 268	95.86 334	87.00 307	91.33 337	98.08 229	93.34 230	97.10 183	97.34 213	84.02 306	99.31 250	95.15 152	99.55 104	98.72 221

test11								98.08 229

AdaColmap		95.11 216	94.62 231	96.58 198	97.33 288	94.45 150	94.92 238	98.08 229	93.15 241	93.98 305	95.53 310	94.34 166	99.10 285	85.69 338	98.61 267	96.20 345

pmmvs-eth3d		96.49 161	96.18 169	97.42 152	98.25 184	94.29 155	94.77 246	98.07 233	89.81 290	97.97 134	98.33 95	93.11 193	99.08 287	95.46 131	99.84 34	98.89 198

FMVSNet2		96.72 148	96.67 145	96.87 182	97.96 217	91.88 223	97.15 111	98.06 234	95.59 157	98.50 70	98.62 73	89.51 265	99.65 145	94.99 163	99.60 88	99.07 168

UnsupCasMVSNet_bld		94.72 234	94.26 246	96.08 224	98.62 142	90.54 247	93.38 300	98.05 235	90.30 285	97.02 193	96.80 252	89.54 262	99.16 276	88.44 312	96.18 340	98.56 235

Regformer-1		97.27 114	97.16 116	97.61 129	97.21 294	93.86 173	94.85 242	98.04 236	97.62 65	98.03 127	97.50 195	95.34 132	99.63 150	96.52 73	99.31 186	99.35 104

PAPM_NR		94.61 241	94.17 251	95.96 228	98.36 173	91.23 232	95.93 179	97.95 237	92.98 246	93.42 325	94.43 331	90.53 245	98.38 348	87.60 323	96.29 339	98.27 266

D2MVS		95.18 213	95.17 202	95.21 261	97.76 249	87.76 293	94.15 270	97.94 238	89.77 291	96.99 195	97.68 182	87.45 285	99.14 278	95.03 161	99.81 39	98.74 218

无先验							93.20 305	97.91 239	80.78 356				99.40 225	87.71 319		97.94 292

v148		96.58 158	96.97 127	95.42 255	98.63 141	87.57 295	95.09 226	97.90 240	95.91 141	98.24 101	97.96 147	93.42 188	99.39 230	96.04 92	99.52 115	99.29 120

CNLPA		95.04 219	94.47 240	96.75 189	97.81 233	95.25 119	94.12 274	97.89 241	94.41 199	94.57 286	95.69 303	90.30 251	98.35 351	86.72 332	98.76 253	96.64 337

PAPR		92.22 297	91.27 303	95.07 266	95.73 340	88.81 270	91.97 328	97.87 242	85.80 328	90.91 351	92.73 351	91.16 237	98.33 352	79.48 362	95.76 347	98.08 277

miper_enhance_ethall		93.14 285	92.78 282	94.20 300	93.65 366	85.29 327	89.97 355	97.85 243	85.05 337	96.15 243	94.56 326	85.74 294	99.14 278	93.74 213	98.34 277	98.17 275

Anonymous20231206		95.27 210	95.06 208	95.88 234	98.72 127	89.37 260	95.70 187	97.85 243	88.00 309	96.98 197	97.62 185	91.95 226	99.34 243	89.21 301	99.53 110	98.94 185

xiu_mvs_v2_base		94.22 254	94.63 230	92.99 324	97.32 289	84.84 335	92.12 325	97.84 245	91.96 264	94.17 296	93.43 338	96.07 102	99.71 108	91.27 256	97.48 314	94.42 360

PS-MVSNAJ		94.10 260	94.47 240	93.00 323	97.35 282	84.88 334	91.86 329	97.84 245	91.96 264	94.17 296	92.50 354	95.82 110	99.71 108	91.27 256	97.48 314	94.40 361

CANet_DTU		94.65 239	94.21 249	95.96 228	95.90 333	89.68 254	93.92 282	97.83 247	93.19 236	90.12 358	95.64 306	88.52 272	99.57 174	93.27 224	99.47 134	98.62 230

door								97.81 248

test12				97.46 147	97.61 265	94.07 165		97.78 249		93.57 318		93.31 190	99.42 214		98.78 251	98.89 198

旧先验1					97.80 237	93.87 172		97.75 250			97.04 234	93.57 186			98.68 259	98.72 221

新几何1				97.25 163	98.29 177	94.70 142		97.73 251	77.98 366	94.83 281	96.67 260	92.08 223	99.45 208	88.17 317	98.65 264	97.61 307

testdata				95.70 242	98.16 197	90.58 244		97.72 252	80.38 358	95.62 263	97.02 235	92.06 224	98.98 299	89.06 305	98.52 271	97.54 309

1121		94.26 252	93.26 269	97.27 160	98.26 183	94.73 137	95.86 181	97.71 253	77.96 367	94.53 288	96.71 257	91.93 228	99.40 225	87.71 319	98.64 265	97.69 304

test20.03		96.58 158	96.61 146	96.48 205	98.49 160	91.72 227	95.68 190	97.69 254	96.81 95	98.27 99	97.92 155	94.18 171	98.71 322	90.78 270	99.66 72	99.00 177

ab-mvs		96.59 156	96.59 147	96.60 196	98.64 137	92.21 213	98.35 35	97.67 255	94.45 198	96.99 195	98.79 60	94.96 147	99.49 195	90.39 285	99.07 220	98.08 277

CMPMVS	73.10 23	92.74 289	91.39 300	96.77 188	93.57 368	94.67 143	94.21 267	97.67 255	80.36 359	93.61 316	96.60 263	82.85 310	97.35 365	84.86 347	98.78 251	98.29 265
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_low_dy_conf_001		98.18 34	98.04 41	98.60 46	99.62 14	96.14 73	98.66 19	97.66 257	97.24 84	98.78 50	99.33 19	92.47 214	99.87 22	98.71 10	99.89 26	99.80 8

mvs_anonymous		95.36 206	96.07 175	93.21 318	96.29 318	81.56 356	94.60 251	97.66 257	93.30 232	96.95 199	98.91 54	93.03 197	99.38 233	96.60 69	97.30 321	98.69 224

FMVSNet3		95.26 211	94.94 211	96.22 219	96.53 313	90.06 249	95.99 172	97.66 257	94.11 211	97.99 130	97.91 156	80.22 322	99.63 150	94.60 176	99.44 142	98.96 182

EI-MVSNet-UG-set		97.32 112	97.40 99	97.09 170	97.34 286	92.01 221	95.33 212	97.65 260	97.74 55	98.30 97	98.14 123	95.04 142	99.69 125	97.55 41	99.52 115	99.58 33

EI-MVSNet-Vis-set		97.32 112	97.39 100	97.11 168	97.36 281	92.08 219	95.34 211	97.65 260	97.74 55	98.29 98	98.11 128	95.05 140	99.68 132	97.50 43	99.50 124	99.56 41

EI-MVSNet		96.63 155	96.93 130	95.74 239	97.26 291	88.13 284	95.29 216	97.65 260	96.99 89	97.94 137	98.19 119	92.55 209	99.58 168	96.91 63	99.56 98	99.50 51

MVSTER		94.21 256	93.93 258	95.05 267	95.83 336	86.46 313	95.18 223	97.65 260	92.41 259	97.94 137	98.00 145	72.39 359	99.58 168	96.36 80	99.56 98	99.12 158

IterMVS-SCA-FT		95.86 187	96.19 168	94.85 276	97.68 258	85.53 323	92.42 320	97.63 264	96.99 89	98.36 85	98.54 79	87.94 278	99.75 72	97.07 60	99.08 218	99.27 125

Regformer-3		97.25 116	97.29 106	97.11 168	97.35 282	92.32 210	95.26 218	97.62 265	97.67 64	98.17 108	97.89 157	95.05 140	99.56 175	97.16 56	99.42 153	99.46 70

test222					98.17 195	93.24 194	92.74 314	97.61 266	75.17 371	94.65 285	96.69 259	90.96 241			98.66 262	97.66 305

VNet		96.84 136	96.83 136	96.88 181	98.06 206	92.02 220	96.35 151	97.57 267	97.70 61	97.88 143	97.80 170	92.40 216	99.54 182	94.73 174	98.96 229	99.08 166

PMVS	89.60 17	96.71 150	96.97 127	95.95 230	99.51 29	97.81 17	97.42 100	97.49 268	97.93 48	95.95 249	98.58 74	96.88 66	96.91 367	89.59 296	99.36 167	93.12 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ppachtmachnet_test		94.49 247	94.84 218	93.46 312	96.16 326	82.10 352	90.59 349	97.48 269	90.53 283	97.01 194	97.59 187	91.01 239	99.36 238	93.97 207	99.18 204	98.94 185

DPM-MVS		93.68 271	92.77 283	96.42 209	97.91 221	92.54 205	91.17 342	97.47 270	84.99 339	93.08 330	94.74 323	89.90 256	99.00 295	87.54 325	98.09 287	97.72 302

IterMVS		95.42 204	95.83 184	94.20 300	97.52 270	83.78 345	92.41 321	97.47 270	95.49 161	98.06 123	98.49 82	87.94 278	99.58 168	96.02 94	99.02 225	99.23 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		94.83 226	94.91 215	94.57 289	96.81 309	87.10 306	94.23 265	97.34 272	88.74 301	97.14 179	97.11 228	91.94 227	98.23 355	92.99 229	97.92 292	98.37 251

MDA-MVSNet-bldmvs		95.69 190	95.67 189	95.74 239	98.48 162	88.76 273	92.84 309	97.25 273	96.00 134	97.59 155	97.95 151	91.38 235	99.46 204	93.16 227	96.35 338	98.99 180

PatchMatch-RL		94.61 241	93.81 261	97.02 175	98.19 190	95.72 89	93.66 290	97.23 274	88.17 307	94.94 279	95.62 307	91.43 234	98.57 335	87.36 328	97.68 305	96.76 335

CR-MVSNet		93.29 282	92.79 280	94.78 280	95.44 345	88.15 282	96.18 161	97.20 275	84.94 340	94.10 298	98.57 75	77.67 332	99.39 230	95.17 148	95.81 343	96.81 333

Patchmtry		95.03 221	94.59 234	96.33 213	94.83 352	90.82 239	96.38 149	97.20 275	96.59 103	97.49 161	98.57 75	77.67 332	99.38 233	92.95 231	99.62 78	98.80 210

API-MVS		95.09 218	95.01 210	95.31 258	96.61 311	94.02 167	96.83 127	97.18 277	95.60 156	95.79 256	94.33 332	94.54 161	98.37 350	85.70 337	98.52 271	93.52 364

MAR-MVS		94.21 256	93.03 274	97.76 116	96.94 305	97.44 35	96.97 123	97.15 278	87.89 311	92.00 346	92.73 351	92.14 220	99.12 280	83.92 351	97.51 313	96.73 336
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
pmmvs5		94.63 240	94.34 245	95.50 250	97.63 264	88.34 278	94.02 276	97.13 279	87.15 316	95.22 272	97.15 224	87.50 284	99.27 261	93.99 205	99.26 194	98.88 202

UGNet		96.81 141	96.56 150	97.58 130	96.64 310	93.84 175	97.75 76	97.12 280	96.47 111	93.62 315	98.88 56	93.22 192	99.53 184	95.61 120	99.69 67	99.36 102
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
h-mvs33		96.29 168	95.63 191	98.26 75	98.50 159	96.11 75	96.90 124	97.09 281	96.58 104	97.21 175	98.19 119	84.14 304	99.78 51	95.89 103	96.17 341	98.89 198

CHOSEN 280x420		89.98 321	89.19 327	92.37 335	95.60 342	81.13 359	86.22 368	97.09 281	81.44 354	87.44 369	93.15 339	73.99 349	99.47 201	88.69 309	99.07 220	96.52 341

CDS-MVSNet		94.88 225	94.12 252	97.14 167	97.64 263	93.57 186	93.96 281	97.06 283	90.05 288	96.30 234	96.55 265	86.10 292	99.47 201	90.10 289	99.31 186	98.40 247
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
BH-untuned		94.69 235	94.75 224	94.52 291	97.95 220	87.53 296	94.07 275	97.01 284	93.99 214	97.10 183	95.65 305	92.65 206	98.95 304	87.60 323	96.74 331	97.09 318

sss		94.22 254	93.72 262	95.74 239	97.71 256	89.95 252	93.84 284	96.98 285	88.38 305	93.75 310	95.74 302	87.94 278	98.89 306	91.02 262	98.10 286	98.37 251

1314		92.38 294	92.30 290	92.64 330	95.42 347	85.15 330	95.86 181	96.97 286	85.40 334	90.62 352	93.06 345	91.12 238	97.80 362	86.74 331	95.49 350	94.97 358

SixPastTwentyTwo		97.49 99	97.57 89	97.26 162	99.56 20	92.33 209	98.28 42	96.97 286	98.30 34	99.45 14	99.35 16	88.43 274	99.89 18	98.01 25	99.76 49	99.54 44

TSAR-MVS + GP.		96.47 163	96.12 171	97.49 142	97.74 254	95.23 120	94.15 270	96.90 288	93.26 233	98.04 126	96.70 258	94.41 164	98.89 306	94.77 172	99.14 207	98.37 251

our_test_3		94.20 258	94.58 235	93.07 320	96.16 326	81.20 358	90.42 351	96.84 289	90.72 281	97.14 179	97.13 225	90.47 246	99.11 283	94.04 204	98.25 281	98.91 194

alignmvs		96.01 181	95.52 195	97.50 139	97.77 248	94.71 139	96.07 166	96.84 289	97.48 72	96.78 210	94.28 334	85.50 296	99.40 225	96.22 83	98.73 258	98.40 247

CL-MVSNet_self_test		95.04 219	94.79 223	95.82 236	97.51 271	89.79 253	91.14 343	96.82 291	93.05 243	96.72 211	96.40 275	90.82 242	99.16 276	91.95 241	98.66 262	98.50 241

TAMVS		95.49 198	94.94 211	97.16 165	98.31 175	93.41 190	95.07 229	96.82 291	91.09 278	97.51 159	97.82 168	89.96 255	99.42 214	88.42 313	99.44 142	98.64 227

pmmvs4		94.82 227	94.19 250	96.70 192	97.42 279	92.75 204	92.09 327	96.76 293	86.80 320	95.73 261	97.22 221	89.28 268	98.89 306	93.28 223	99.14 207	98.46 245

jason		94.39 250	94.04 254	95.41 257	98.29 177	87.85 290	92.74 314	96.75 294	85.38 335	95.29 270	96.15 286	88.21 277	99.65 145	94.24 193	99.34 175	98.74 218
jason: jason.
MVS		90.02 319	89.20 326	92.47 333	94.71 353	86.90 309	95.86 181	96.74 295	64.72 375	90.62 352	92.77 349	92.54 211	98.39 347	79.30 363	95.56 349	92.12 368

IS-MVSNet		96.93 130	96.68 144	97.70 122	99.25 58	94.00 168	98.57 21	96.74 295	98.36 31	98.14 113	97.98 146	88.23 276	99.71 108	93.10 228	99.72 60	99.38 94

RRT_MVS		97.95 54	97.79 61	98.43 60	99.67 10	95.56 98	98.86 10	96.73 297	97.99 46	99.15 31	99.35 16	89.84 258	99.90 14	98.64 11	99.90 24	99.82 6

OpenMVS_ROB	91.80 14	93.64 274	93.05 272	95.42 255	97.31 290	91.21 233	95.08 228	96.68 298	81.56 352	96.88 204	96.41 273	90.44 247	99.25 264	85.39 342	97.67 306	95.80 349

cl22		93.25 283	92.84 279	94.46 293	94.30 358	86.00 319	91.09 345	96.64 299	90.74 280	95.79 256	96.31 279	78.24 329	98.77 316	94.15 197	98.34 277	98.62 230

EPP-MVSNet		96.84 136	96.58 148	97.65 126	99.18 75	93.78 178	98.68 15	96.34 300	97.91 49	97.30 171	98.06 137	88.46 273	99.85 28	93.85 210	99.40 160	99.32 107

BH-RMVSNet		94.56 243	94.44 243	94.91 271	97.57 266	87.44 299	93.78 288	96.26 301	93.69 222	96.41 227	96.50 270	92.10 222	99.00 295	85.96 335	97.71 302	98.31 260

MVS_0304		95.50 197	95.05 209	96.84 184	96.28 319	93.12 195	97.00 121	96.16 302	95.03 180	89.22 363	97.70 179	90.16 254	99.48 198	94.51 181	99.34 175	97.93 293

GA-MVS		92.83 288	92.15 292	94.87 275	96.97 302	87.27 303	90.03 354	96.12 303	91.83 267	94.05 301	94.57 325	76.01 344	98.97 303	92.46 236	97.34 319	98.36 256

lupinMVS		93.77 267	93.28 268	95.24 260	97.68 258	87.81 291	92.12 325	96.05 304	84.52 342	94.48 291	95.06 317	86.90 288	99.63 150	93.62 218	99.13 210	98.27 266

test_method		66.88 344	66.13 347	69.11 360	62.68 383	25.73 385	49.76 374	96.04 305	14.32 378	64.27 379	91.69 362	73.45 356	88.05 377	76.06 370	66.94 377	93.54 363

PMMVS2		93.66 272	94.07 253	92.45 334	97.57 266	80.67 360	86.46 367	96.00 306	93.99 214	97.10 183	97.38 209	89.90 256	97.82 361	88.76 307	99.47 134	98.86 205

WTY-MVS		93.55 276	93.00 275	95.19 262	97.81 233	87.86 288	93.89 283	96.00 306	89.02 296	94.07 300	95.44 312	86.27 291	99.33 246	87.69 321	96.82 328	98.39 249

PMMVS		92.39 293	91.08 305	96.30 216	93.12 370	92.81 202	90.58 350	95.96 308	79.17 363	91.85 348	92.27 355	90.29 252	98.66 329	89.85 293	96.68 333	97.43 312

MG-MVS		94.08 262	94.00 255	94.32 297	97.09 299	85.89 320	93.19 306	95.96 308	92.52 255	94.93 280	97.51 194	89.54 262	98.77 316	87.52 326	97.71 302	98.31 260

MDA-MVSNet_test_wron		94.73 230	94.83 220	94.42 294	97.48 272	85.15 330	90.28 353	95.87 310	92.52 255	97.48 164	97.76 171	91.92 229	99.17 275	93.32 221	96.80 330	98.94 185

YYNet1		94.73 230	94.84 218	94.41 295	97.47 276	85.09 332	90.29 352	95.85 311	92.52 255	97.53 157	97.76 171	91.97 225	99.18 271	93.31 222	96.86 327	98.95 183

ADS-MVSNet2		91.47 308	90.51 316	94.36 296	95.51 343	85.63 321	95.05 232	95.70 312	83.46 346	92.69 336	96.84 247	79.15 326	99.41 223	85.66 339	90.52 366	98.04 287

BH-w/o		92.14 299	91.94 293	92.73 329	97.13 298	85.30 326	92.46 319	95.64 313	89.33 294	94.21 295	92.74 350	89.60 260	98.24 354	81.68 358	94.66 355	94.66 359

KD-MVS_2432*1600		88.93 330	87.74 335	92.49 331	88.04 380	81.99 353	89.63 361	95.62 314	91.35 273	95.06 274	93.11 340	56.58 379	98.63 330	85.19 343	95.07 351	96.85 329

miper_refine_blended		88.93 330	87.74 335	92.49 331	88.04 380	81.99 353	89.63 361	95.62 314	91.35 273	95.06 274	93.11 340	56.58 379	98.63 330	85.19 343	95.07 351	96.85 329

VDD-MVS		97.37 108	97.25 109	97.74 118	98.69 134	94.50 149	97.04 119	95.61 316	98.59 26	98.51 68	98.72 65	92.54 211	99.58 168	96.02 94	99.49 128	99.12 158

PAPM		87.64 339	85.84 344	93.04 321	96.54 312	84.99 333	88.42 365	95.57 317	79.52 361	83.82 373	93.05 346	80.57 320	98.41 345	62.29 377	92.79 362	95.71 350

test_yl		94.40 248	94.00 255	95.59 243	96.95 303	89.52 257	94.75 247	95.55 318	96.18 124	96.79 206	96.14 288	81.09 317	99.18 271	90.75 271	97.77 296	98.07 279

DCV-MVSNet		94.40 248	94.00 255	95.59 243	96.95 303	89.52 257	94.75 247	95.55 318	96.18 124	96.79 206	96.14 288	81.09 317	99.18 271	90.75 271	97.77 296	98.07 279

AUN-MVS		93.95 266	92.69 284	97.74 118	97.80 237	95.38 110	95.57 198	95.46 320	91.26 276	92.64 339	96.10 291	74.67 348	99.55 179	93.72 215	96.97 323	98.30 262

hse-mvs2		95.77 189	95.09 205	97.79 114	97.84 229	95.51 102	95.66 191	95.43 321	96.58 104	97.21 175	96.16 285	84.14 304	99.54 182	95.89 103	96.92 324	98.32 258

VDDNet		96.98 128	96.84 135	97.41 153	99.40 43	93.26 193	97.94 63	95.31 322	99.26 7	98.39 81	99.18 30	87.85 283	99.62 158	95.13 155	99.09 217	99.35 104

wuyk23d		93.25 283	95.20 200	87.40 357	96.07 331	95.38 110	97.04 119	94.97 323	95.33 166	99.70 5	98.11 128	98.14 13	91.94 375	77.76 368	99.68 69	74.89 375

Vis-MVSNet (Re-imp)		95.11 216	94.85 217	95.87 235	99.12 88	89.17 263	97.54 94	94.92 324	96.50 108	96.58 218	97.27 218	83.64 308	99.48 198	88.42 313	99.67 70	98.97 181

TR-MVS		92.54 292	92.20 291	93.57 310	96.49 314	86.66 311	93.51 295	94.73 325	89.96 289	94.95 278	93.87 336	90.24 253	98.61 332	81.18 360	94.88 353	95.45 355

HY-MVS	91.43 15	92.58 291	91.81 296	94.90 273	96.49 314	88.87 268	97.31 103	94.62 326	85.92 326	90.50 355	96.84 247	85.05 298	99.40 225	83.77 354	95.78 346	96.43 342

PVSNet	86.72 19	91.10 311	90.97 308	91.49 340	97.56 268	78.04 365	87.17 366	94.60 327	84.65 341	92.34 343	92.20 356	87.37 286	98.47 342	85.17 345	97.69 304	97.96 291

Patchmatch-test		93.60 275	93.25 270	94.63 284	96.14 329	87.47 297	96.04 168	94.50 328	93.57 223	96.47 224	96.97 238	76.50 340	98.61 332	90.67 277	98.41 276	97.81 300

Anonymous202405211		96.34 167	95.98 179	97.43 151	98.25 184	93.85 174	96.74 133	94.41 329	97.72 58	98.37 82	98.03 140	87.15 287	99.53 184	94.06 200	99.07 220	98.92 193

tpm cat1		88.01 337	87.33 338	90.05 349	94.48 356	76.28 372	94.47 256	94.35 330	73.84 374	89.26 362	95.61 308	73.64 353	98.30 353	84.13 350	86.20 373	95.57 354

SCA		93.38 280	93.52 265	92.96 325	96.24 320	81.40 357	93.24 304	94.00 331	91.58 271	94.57 286	96.97 238	87.94 278	99.42 214	89.47 298	97.66 307	98.06 283

tpmrst		90.31 317	90.61 315	89.41 350	94.06 363	72.37 379	95.06 231	93.69 332	88.01 308	92.32 344	96.86 245	77.45 334	98.82 311	91.04 261	87.01 372	97.04 321

MIMVSNet		93.42 278	92.86 277	95.10 265	98.17 195	88.19 280	98.13 54	93.69 332	92.07 261	95.04 277	98.21 118	80.95 319	99.03 294	81.42 359	98.06 288	98.07 279

DSMNet-mixed		92.19 298	91.83 295	93.25 316	96.18 325	83.68 346	96.27 154	93.68 334	76.97 370	92.54 342	99.18 30	89.20 270	98.55 338	83.88 352	98.60 269	97.51 310

tpmvs		90.79 315	90.87 309	90.57 346	92.75 374	76.30 371	95.79 185	93.64 335	91.04 279	91.91 347	96.26 280	77.19 338	98.86 310	89.38 300	89.85 369	96.56 340

PatchmatchNet		91.98 302	91.87 294	92.30 336	94.60 355	79.71 362	95.12 224	93.59 336	89.52 292	93.61 316	97.02 235	77.94 330	99.18 271	90.84 267	94.57 358	98.01 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		90.95 314	90.26 318	93.04 321	95.51 343	82.37 351	95.05 232	93.41 337	83.46 346	92.69 336	96.84 247	79.15 326	98.70 323	85.66 339	90.52 366	98.04 287

FPMVS		89.92 323	88.63 331	93.82 304	98.37 172	96.94 47	91.58 332	93.34 338	88.00 309	90.32 356	97.10 229	70.87 364	91.13 376	71.91 374	96.16 342	93.39 366

MDTV_nov1_ep13			91.28 302		94.31 357	73.51 377	94.80 244	93.16 339	86.75 321	93.45 323	97.40 203	76.37 341	98.55 338	88.85 306	96.43 336

baseline1		93.14 285	92.64 286	94.62 285	97.34 286	87.20 304	96.67 140	93.02 340	94.71 190	96.51 223	95.83 301	81.64 312	98.60 334	90.00 291	88.06 371	98.07 279

PatchT		93.75 268	93.57 264	94.29 299	95.05 350	87.32 302	96.05 167	92.98 341	97.54 70	94.25 294	98.72 65	75.79 345	99.24 265	95.92 101	95.81 343	96.32 343

EPNet_dtu		91.39 309	90.75 312	93.31 314	90.48 379	82.61 349	94.80 244	92.88 342	93.39 228	81.74 376	94.90 322	81.36 315	99.11 283	88.28 315	98.87 241	98.21 271
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
new_pmnet		92.34 295	91.69 298	94.32 297	96.23 322	89.16 264	92.27 323	92.88 342	84.39 345	95.29 270	96.35 278	85.66 295	96.74 371	84.53 349	97.56 310	97.05 320

dp		88.08 336	88.05 334	88.16 356	92.85 372	68.81 381	94.17 268	92.88 342	85.47 331	91.38 350	96.14 288	68.87 368	98.81 313	86.88 330	83.80 375	96.87 327

EU-MVSNet		94.25 253	94.47 240	93.60 309	98.14 200	82.60 350	97.24 108	92.72 345	85.08 336	98.48 72	98.94 51	82.59 311	98.76 318	97.47 45	99.53 110	99.44 85

PVSNet_0	81.89 21	84.49 342	83.21 345	88.34 354	95.76 339	74.97 376	83.49 370	92.70 346	78.47 365	87.94 367	86.90 374	83.38 309	96.63 372	73.44 372	66.86 378	93.40 365

pmmvs3		90.00 320	88.90 330	93.32 313	94.20 362	85.34 325	91.25 340	92.56 347	78.59 364	93.82 306	95.17 314	67.36 371	98.69 324	89.08 304	98.03 289	95.92 346

CVMVSNet		92.33 296	92.79 280	90.95 343	97.26 291	75.84 373	95.29 216	92.33 348	81.86 350	96.27 235	98.19 119	81.44 314	98.46 343	94.23 194	98.29 280	98.55 237

E-PMN		89.52 327	89.78 321	88.73 352	93.14 369	77.61 367	83.26 371	92.02 349	94.82 187	93.71 311	93.11 340	75.31 346	96.81 368	85.81 336	96.81 329	91.77 370

CostFormer		89.75 325	89.25 323	91.26 342	94.69 354	78.00 366	95.32 213	91.98 350	81.50 353	90.55 354	96.96 240	71.06 363	98.89 306	88.59 311	92.63 363	96.87 327

tpm2		88.47 333	87.69 337	90.79 344	94.98 351	77.34 368	95.09 226	91.83 351	77.51 369	89.40 361	96.41 273	67.83 370	98.73 320	83.58 356	92.60 364	96.29 344

JIA-IIPM		91.79 304	90.69 313	95.11 264	93.80 365	90.98 236	94.16 269	91.78 352	96.38 112	90.30 357	99.30 20	72.02 360	98.90 305	88.28 315	90.17 368	95.45 355

N_pmnet		95.18 213	94.23 247	98.06 95	97.85 225	96.55 60	92.49 318	91.63 353	89.34 293	98.09 119	97.41 202	90.33 248	99.06 289	91.58 251	99.31 186	98.56 235

EPNet		93.72 269	92.62 287	97.03 174	87.61 382	92.25 211	96.27 154	91.28 354	96.74 97	87.65 368	97.39 207	85.00 299	99.64 148	92.14 238	99.48 132	99.20 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		91.08 312	90.85 310	91.75 339	95.33 348	78.09 364	95.03 234	91.27 355	88.75 300	93.53 319	97.40 203	71.24 361	99.30 253	91.25 258	93.87 359	97.87 295

thres200		91.00 313	90.42 317	92.77 328	97.47 276	83.98 344	94.01 277	91.18 356	95.12 176	95.44 267	91.21 366	73.93 350	99.31 250	77.76 368	97.63 309	95.01 357

EMVS		89.06 329	89.22 324	88.61 353	93.00 371	77.34 368	82.91 372	90.92 357	94.64 192	92.63 340	91.81 360	76.30 342	97.02 366	83.83 353	96.90 326	91.48 371

tfpn200view9		91.55 307	91.00 306	93.21 318	98.02 209	84.35 340	95.70 187	90.79 358	96.26 118	95.90 254	92.13 357	73.62 354	99.42 214	78.85 365	97.74 299	95.85 347

thres400		91.68 306	91.00 306	93.71 307	98.02 209	84.35 340	95.70 187	90.79 358	96.26 118	95.90 254	92.13 357	73.62 354	99.42 214	78.85 365	97.74 299	97.36 314

LFMVS		95.32 208	94.88 216	96.62 195	98.03 208	91.47 231	97.65 82	90.72 360	99.11 9	97.89 142	98.31 97	79.20 325	99.48 198	93.91 209	99.12 213	98.93 189

thres100view900		91.76 305	91.26 304	93.26 315	98.21 188	84.50 338	96.39 147	90.39 361	96.87 93	96.33 230	93.08 344	73.44 357	99.42 214	78.85 365	97.74 299	95.85 347

thres600view7		92.03 301	91.43 299	93.82 304	98.19 190	84.61 337	96.27 154	90.39 361	96.81 95	96.37 229	93.11 340	73.44 357	99.49 195	80.32 361	97.95 291	97.36 314

K. test v3		96.44 164	96.28 165	96.95 176	99.41 42	91.53 229	97.65 82	90.31 363	98.89 19	98.93 41	99.36 14	84.57 303	99.92 5	97.81 30	99.56 98	99.39 92

ET-MVSNet_ETH3D		91.12 310	89.67 322	95.47 252	96.41 316	89.15 265	91.54 333	90.23 364	89.07 295	86.78 372	92.84 348	69.39 367	99.44 211	94.16 196	96.61 334	97.82 298

IB-MVS	85.98 20	88.63 332	86.95 341	93.68 308	95.12 349	84.82 336	90.85 347	90.17 365	87.55 312	88.48 366	91.34 365	58.01 377	99.59 166	87.24 329	93.80 360	96.63 339
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
test-LLR		89.97 322	89.90 320	90.16 347	94.24 360	74.98 374	89.89 356	89.06 366	92.02 262	89.97 359	90.77 369	73.92 351	98.57 335	91.88 244	97.36 317	96.92 324

test-mter		87.92 338	87.17 339	90.16 347	94.24 360	74.98 374	89.89 356	89.06 366	86.44 322	89.97 359	90.77 369	54.96 384	98.57 335	91.88 244	97.36 317	96.92 324

test0.0.03 1		90.11 318	89.21 325	92.83 327	93.89 364	86.87 310	91.74 331	88.74 368	92.02 262	94.71 283	91.14 367	73.92 351	94.48 374	83.75 355	92.94 361	97.16 317

thisisatest0515		90.43 316	89.18 328	94.17 302	97.07 300	85.44 324	89.75 360	87.58 369	88.28 306	93.69 313	91.72 361	65.27 372	99.58 168	90.59 278	98.67 260	97.50 311

thisisatest0530		92.71 290	91.76 297	95.56 247	98.42 169	88.23 279	96.03 169	87.35 370	94.04 213	96.56 220	95.47 311	64.03 374	99.77 60	94.78 171	99.11 214	98.68 226

tttt0517		93.31 281	92.56 288	95.57 245	98.71 130	87.86 288	97.44 97	87.17 371	95.79 148	97.47 166	96.84 247	64.12 373	99.81 40	96.20 84	99.32 184	99.02 176

TESTMET0.1,1		87.20 340	86.57 342	89.07 351	93.62 367	72.84 378	89.89 356	87.01 372	85.46 332	89.12 364	90.20 371	56.00 382	97.72 363	90.91 265	96.92 324	96.64 337

baseline2		89.65 326	88.44 333	93.25 316	95.62 341	82.71 348	93.82 285	85.94 373	88.89 299	87.35 370	92.54 353	71.23 362	99.33 246	86.01 334	94.60 357	97.72 302

MVS-HIRNet		88.40 334	90.20 319	82.99 358	97.01 301	60.04 382	93.11 307	85.61 374	84.45 344	88.72 365	99.09 40	84.72 302	98.23 355	82.52 357	96.59 335	90.69 373

lessismore_v0				97.05 172	99.36 47	92.12 217		84.07 375		98.77 53	98.98 47	85.36 297	99.74 82	97.34 49	99.37 164	99.30 113

test1111		94.53 246	94.81 221	93.72 306	99.06 95	81.94 355	98.31 39	83.87 376	96.37 113	98.49 71	99.17 32	81.49 313	99.73 87	96.64 67	99.86 30	99.49 59

ECVR-MVS		94.37 251	94.48 239	94.05 303	98.95 105	83.10 347	98.31 39	82.48 377	96.20 121	98.23 102	99.16 33	81.18 316	99.66 143	95.95 99	99.83 36	99.38 94

EPMVS		89.26 328	88.55 332	91.39 341	92.36 375	79.11 363	95.65 194	79.86 378	88.60 302	93.12 329	96.53 267	70.73 365	98.10 359	90.75 271	89.32 370	96.98 322

MVE	73.61 22	86.48 341	85.92 343	88.18 355	96.23 322	85.28 328	81.78 373	75.79 379	86.01 324	82.53 375	91.88 359	92.74 202	87.47 378	71.42 375	94.86 354	91.78 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							96.55 141	74.60 380

gg-mvs-nofinetune		88.28 335	86.96 340	92.23 337	92.84 373	84.44 339	98.19 51	74.60 380	99.08 10	87.01 371	99.47 8	56.93 378	98.23 355	78.91 364	95.61 348	94.01 362

DeepMVS_CX				77.17 359	90.94 378	85.28 328		74.08 382	52.51 376	80.87 377	88.03 373	75.25 347	70.63 379	59.23 378	84.94 374	75.62 374

GG-mvs-BLEND				90.60 345	91.00 377	84.21 342	98.23 45	72.63 383		82.76 374	84.11 375	56.14 381	96.79 369	72.20 373	92.09 365	90.78 372

test2506		89.86 324	89.16 329	91.97 338	98.95 105	76.83 370	98.54 24	61.07 384	96.20 121	97.07 188	99.16 33	55.19 383	99.69 125	96.43 78	99.83 36	99.38 94

tmp_tt		57.23 345	62.50 348	41.44 361	34.77 384	49.21 384	83.93 369	60.22 385	15.31 377	71.11 378	79.37 376	70.09 366	44.86 380	64.76 376	82.93 376	30.25 376

testmvs		12.33 348	15.23 351	3.64 363	5.77 386	2.23 387	88.99 363	3.62 386	2.30 381	5.29 381	13.09 378	4.52 386	1.95 381	5.16 380	8.32 380	6.75 378

test123		12.59 347	15.49 350	3.87 362	6.07 385	2.55 386	90.75 348	2.59 387	2.52 380	5.20 382	13.02 379	4.96 385	1.85 382	5.20 379	9.09 379	7.23 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		7.98 349	10.65 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	95.82 110	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

n2								0.00 388

nn								0.00 388

ab-mvs-re		7.91 350	10.55 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	94.94 319	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

PC_three_1452									87.24 314	98.37 82	97.44 200	97.00 54	96.78 370	92.01 239	99.25 195	99.21 135

eth-test2					0.00 387

eth-test					0.00 387

OPU-MVS				97.64 127	98.01 211	95.27 118	96.79 130				97.35 212	96.97 56	98.51 341	91.21 259	99.25 195	99.14 151

test_0728_THIRD									96.62 99	98.40 79	98.28 106	97.10 45	99.71 108	95.70 110	99.62 78	99.58 33

GSMVS																98.06 283

test_part2					99.03 101	96.07 76				98.08 121

sam_mvs1												77.80 331				98.06 283

sam_mvs												77.38 335

test_post1							94.98 236				10.37 381	76.21 343	99.04 291	89.47 298

test_post											10.87 380	76.83 339	99.07 288

patchmatchnet-post											96.84 247	77.36 336	99.42 214

gm-plane-assit					91.79 376	71.40 380			81.67 351		90.11 372		98.99 297	84.86 347

test9_res														91.29 255	98.89 240	99.00 177

agg_prior2														90.34 287	98.90 237	99.10 165

test_prior4						95.38 110	93.61 293

test_prior2							93.33 302		94.21 206	94.02 302	96.25 281	93.64 184		91.90 242	98.96 229

旧先验2							93.35 301		77.95 368	95.77 260			98.67 328	90.74 274

新几何2							93.43 296

原ACMM2							92.82 310

testdata2													99.46 204	87.84 318

segment_acmp												95.34 132

testdata1							92.77 311		93.78 219

plane_prior7					98.70 132	94.67 143

plane_prior6					98.38 171	94.37 153						91.91 230

plane_prior4											96.77 253

plane_prior3						94.51 147			95.29 169	96.16 241

plane_prior2							96.50 143		96.36 114

plane_prior1					98.49 160

plane_prior						94.29 155	95.42 203		94.31 203						98.93 235

HQP5-MVS						92.47 207

HQP-NCC					97.85 225		94.26 260		93.18 237	92.86 333

ACMP_Plane					97.85 225		94.26 260		93.18 237	92.86 333

BP-MVS														90.51 282

HQP4-MVS										92.87 332			99.23 267			99.06 170

HQP2-MVS												90.33 248

NP-MVS					98.14 200	93.72 180					95.08 315

MDTV_nov1_ep13_2view						57.28 383	94.89 239		80.59 357	94.02 302		78.66 328		85.50 341		97.82 298

ACMMP++_ref															99.52 115

ACMMP++															99.55 104

Test By Simon												94.51 162

High-resolution multi-view benchmark