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.

LTVRE_ROB	99.19 1	99.88 5	99.87 5	99.88 12	99.91 15	99.90 5	99.96 1	99.92 5	99.90 8	99.97 7	99.87 31	99.81 5	99.95 43	99.54 25	99.99 13	99.80 24
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
LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 8	99.78 6	100.00 1	99.92 1	100.00 1	99.87 10

test_part1		99.89 3	99.88 4	99.94 2	99.91 15	99.92 2	99.92 3	99.90 11	99.98 2	99.99 3	99.97 9	99.50 21	99.98 6	99.73 16	100.00 1	99.92 3

UA-Net		99.78 14	99.76 15	99.86 17	99.72 107	99.71 63	99.91 4	99.95 4	99.96 3	99.71 97	99.91 20	99.15 53	99.97 17	99.50 31	100.00 1	99.90 5

UniMVSNet_ETH3D		99.85 8	99.83 8	99.90 5	99.89 22	99.91 3	99.89 5	99.71 91	99.93 5	99.95 11	99.89 26	99.71 9	99.96 34	99.51 29	99.97 30	99.84 15

TDRefinement		99.72 18	99.70 18	99.77 40	99.90 20	99.85 12	99.86 6	99.92 5	99.69 47	99.78 67	99.92 17	99.37 30	99.88 151	98.93 105	99.95 48	99.60 116

pmmvs6		99.86 7	99.86 7	99.83 22	99.94 10	99.90 5	99.83 7	99.91 8	99.85 20	99.94 12	99.95 12	99.73 8	99.90 123	99.65 17	99.97 30	99.69 51

OurMVSNet-221017-0		99.75 16	99.71 17	99.84 20	99.96 4	99.83 21	99.83 7	99.85 24	99.80 30	99.93 15	99.93 14	98.54 134	99.93 67	99.59 20	99.98 22	99.76 36

v7n		99.82 11	99.80 11	99.88 12	99.96 4	99.84 17	99.82 9	99.82 37	99.84 22	99.94 12	99.91 20	99.13 58	99.96 34	99.83 9	99.99 13	99.83 19

Anonymous20231211		99.62 33	99.57 38	99.76 46	99.61 144	99.60 102	99.81 10	99.73 79	99.82 26	99.90 23	99.90 22	97.97 194	99.86 184	99.42 40	99.96 41	99.80 24

ab-mvs		99.33 96	99.28 93	99.47 163	99.57 161	99.39 148	99.78 11	99.43 231	98.87 176	99.57 145	99.82 47	98.06 186	99.87 164	98.69 124	99.73 188	99.15 261

MVSFormer		99.41 71	99.44 58	99.31 210	99.57 161	98.40 255	99.77 12	99.80 47	99.73 37	99.63 122	99.30 261	98.02 189	99.98 6	99.43 36	99.69 202	99.55 142

test_djsdf		99.84 9	99.81 10	99.91 3	99.94 10	99.84 17	99.77 12	99.80 47	99.73 37	99.97 7	99.92 17	99.77 7	99.98 6	99.43 36	100.00 1	99.90 5

pm-mvs1		99.79 13	99.79 12	99.78 38	99.91 15	99.83 21	99.76 14	99.87 18	99.73 37	99.89 27	99.87 31	99.63 14	99.87 164	99.54 25	99.92 73	99.63 93

TransMVSNet (Re)		99.78 14	99.77 13	99.81 27	99.91 15	99.85 12	99.75 15	99.86 20	99.70 44	99.91 21	99.89 26	99.60 18	99.87 164	99.59 20	99.74 181	99.71 45

K. test v3		98.87 199	98.60 209	99.69 83	99.93 13	99.46 126	99.74 16	94.97 349	99.78 33	99.88 33	99.88 29	93.66 294	99.97 17	99.61 19	99.95 48	99.64 88

anonymousdsp		99.80 12	99.77 13	99.90 5	99.96 4	99.88 7	99.73 17	99.85 24	99.70 44	99.92 19	99.93 14	99.45 22	99.97 17	99.36 45	100.00 1	99.85 14

NR-MVSNet		99.40 74	99.31 81	99.68 84	99.43 223	99.55 114	99.73 17	99.50 208	99.46 93	99.88 33	99.36 247	97.54 224	99.87 164	98.97 97	99.87 107	99.63 93

IS-MVSNet		99.03 170	98.85 187	99.55 142	99.80 56	99.25 180	99.73 17	99.15 291	99.37 105	99.61 135	99.71 99	94.73 284	99.81 254	97.70 198	99.88 99	99.58 130

FC-MVSNet-test		99.70 20	99.65 23	99.86 17	99.88 24	99.86 11	99.72 20	99.78 57	99.90 8	99.82 49	99.83 41	98.45 149	99.87 164	99.51 29	99.97 30	99.86 12

mvs_tets		99.90 2	99.90 2	99.90 5	99.96 4	99.79 35	99.72 20	99.88 16	99.92 7	99.98 4	99.93 14	99.94 1	99.98 6	99.77 12	100.00 1	99.92 3

Gipuma		99.57 38	99.59 33	99.49 158	99.98 3	99.71 63	99.72 20	99.84 30	99.81 27	99.94 12	99.78 65	98.91 83	99.71 290	98.41 135	99.95 48	99.05 284
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND				97.36 320	97.59 353	96.87 311	99.70 23	88.49 359		94.64 353	97.26 356	80.66 355	99.12 348	91.50 340	96.50 348	96.08 350

jajsoiax		99.89 3	99.89 3	99.89 8	99.96 4	99.78 38	99.70 23	99.86 20	99.89 12	99.98 4	99.90 22	99.94 1	99.98 6	99.75 13	100.00 1	99.90 5

SixPastTwentyTwo		99.42 67	99.30 86	99.76 46	99.92 14	99.67 79	99.70 23	99.14 292	99.65 57	99.89 27	99.90 22	96.20 268	99.94 54	99.42 40	99.92 73	99.67 64

UGNet		99.38 80	99.34 75	99.49 158	98.90 313	98.90 229	99.70 23	99.35 256	99.86 17	98.57 297	99.81 50	98.50 144	99.93 67	99.38 42	99.98 22	99.66 74
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
EPP-MVSNet		99.17 142	99.00 160	99.66 93	99.80 56	99.43 138	99.70 23	99.24 282	99.48 84	99.56 152	99.77 72	94.89 281	99.93 67	98.72 121	99.89 91	99.63 93

3Dnovator	99.15 2	99.43 64	99.36 73	99.65 98	99.39 233	99.42 141	99.70 23	99.56 175	99.23 126	99.35 205	99.80 52	99.17 51	99.95 43	98.21 153	99.84 121	99.59 125

gg-mvs-nofinetune		95.87 318	95.17 322	97.97 304	98.19 347	96.95 308	99.69 29	89.23 358	99.89 12	96.24 348	99.94 13	81.19 353	99.51 341	93.99 333	98.20 333	97.44 342

MIMVSNet1		99.66 25	99.62 26	99.80 30	99.94 10	99.87 8	99.69 29	99.77 60	99.78 33	99.93 15	99.89 26	97.94 195	99.92 85	99.65 17	99.98 22	99.62 105

Vis-MVSNet		99.75 16	99.74 16	99.79 35	99.88 24	99.66 81	99.69 29	99.92 5	99.67 51	99.77 72	99.75 79	99.61 16	99.98 6	99.35 46	99.98 22	99.72 42
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJss		99.84 9	99.82 9	99.89 8	99.96 4	99.77 40	99.68 32	99.85 24	99.95 4	99.98 4	99.92 17	99.28 41	99.98 6	99.75 13	100.00 1	99.94 2

GBi-Net		99.42 67	99.31 81	99.73 68	99.49 199	99.77 40	99.68 32	99.70 95	99.44 95	99.62 129	99.83 41	97.21 239	99.90 123	98.96 99	99.90 83	99.53 154

test1		99.42 67	99.31 81	99.73 68	99.49 199	99.77 40	99.68 32	99.70 95	99.44 95	99.62 129	99.83 41	97.21 239	99.90 123	98.96 99	99.90 83	99.53 154

FMVSNet1		99.66 25	99.63 25	99.73 68	99.78 72	99.77 40	99.68 32	99.70 95	99.67 51	99.82 49	99.83 41	98.98 74	99.90 123	99.24 62	99.97 30	99.53 154

DTE-MVSNet		99.68 23	99.61 30	99.88 12	99.80 56	99.87 8	99.67 36	99.71 91	99.72 40	99.84 42	99.78 65	98.67 117	99.97 17	99.30 55	99.95 48	99.80 24

WR-MVS_H		99.61 35	99.53 48	99.87 15	99.80 56	99.83 21	99.67 36	99.75 71	99.58 76	99.85 39	99.69 112	98.18 179	99.94 54	99.28 60	99.95 48	99.83 19

QAPM		98.40 248	97.99 259	99.65 98	99.39 233	99.47 122	99.67 36	99.52 202	91.70 345	98.78 281	99.80 52	98.55 132	99.95 43	94.71 323	99.75 173	99.53 154

FIs		99.65 30	99.58 35	99.84 20	99.84 34	99.85 12	99.66 39	99.75 71	99.86 17	99.74 87	99.79 58	98.27 168	99.85 202	99.37 44	99.93 69	99.83 19

v8		99.68 23	99.69 19	99.65 98	99.80 56	99.40 146	99.66 39	99.76 65	99.64 59	99.93 15	99.85 36	98.66 119	99.84 217	99.88 6	99.99 13	99.71 45

v10		99.69 22	99.69 19	99.66 93	99.81 50	99.39 148	99.66 39	99.75 71	99.60 73	99.92 19	99.87 31	98.75 108	99.86 184	99.90 2	99.99 13	99.73 41

PS-CasMVS		99.66 25	99.58 35	99.89 8	99.80 56	99.85 12	99.66 39	99.73 79	99.62 63	99.84 42	99.71 99	98.62 123	99.96 34	99.30 55	99.96 41	99.86 12

PEN-MVS		99.66 25	99.59 33	99.89 8	99.83 37	99.87 8	99.66 39	99.73 79	99.70 44	99.84 42	99.73 86	98.56 131	99.96 34	99.29 58	99.94 61	99.83 19

ANet_high		99.88 5	99.87 5	99.91 3	99.99 1	99.91 3	99.65 44	100.00 1	99.90 8	100.00 1	99.97 9	99.61 16	99.97 17	99.75 13	100.00 1	99.84 15

OpenMVS	98.12 10	98.23 261	97.89 273	99.26 219	99.19 282	99.26 176	99.65 44	99.69 101	91.33 346	98.14 319	99.77 72	98.28 167	99.96 34	95.41 312	99.55 243	98.58 313

Anonymous20240529		99.42 67	99.34 75	99.65 98	99.53 178	99.60 102	99.63 46	99.39 245	99.47 89	99.76 74	99.78 65	98.13 181	99.86 184	98.70 122	99.68 205	99.49 177

LFMVS		98.46 242	98.19 249	99.26 219	99.24 274	98.52 248	99.62 47	96.94 342	99.87 15	99.31 214	99.58 183	91.04 317	99.81 254	98.68 125	99.42 269	99.45 192

VDDNet		98.97 182	98.82 192	99.42 177	99.71 110	98.81 233	99.62 47	98.68 310	99.81 27	99.38 201	99.80 52	94.25 288	99.85 202	98.79 114	99.32 284	99.59 125

VPA-MVSNet		99.66 25	99.62 26	99.79 35	99.68 128	99.75 48	99.62 47	99.69 101	99.85 20	99.80 59	99.81 50	98.81 93	99.91 103	99.47 33	99.88 99	99.70 48

3Dnovator+	98.92 3	99.35 87	99.24 101	99.67 86	99.35 243	99.47 122	99.62 47	99.50 208	99.44 95	99.12 246	99.78 65	98.77 105	99.94 54	97.87 183	99.72 193	99.62 105

canonicalmvs		99.02 172	99.00 160	99.09 239	99.10 298	98.70 239	99.61 51	99.66 112	99.63 62	98.64 291	97.65 350	99.04 70	99.54 338	98.79 114	98.92 308	99.04 285

nrg030		99.70 20	99.66 22	99.82 24	99.76 84	99.84 17	99.61 51	99.70 95	99.93 5	99.78 67	99.68 123	99.10 59	99.78 266	99.45 34	99.96 41	99.83 19

HPM-MVS		99.25 111	99.07 138	99.78 38	99.81 50	99.75 48	99.61 51	99.67 108	97.72 271	99.35 205	99.25 272	99.23 46	99.92 85	97.21 237	99.82 140	99.67 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	98.23 9	98.21 263	97.95 263	98.99 248	99.03 306	98.24 262	99.61 51	98.72 309	96.81 307	98.73 285	99.51 209	94.06 289	99.86 184	96.91 251	98.20 333	98.86 299

Vis-MVSNet (Re-imp)		98.77 208	98.58 214	99.34 201	99.78 72	98.88 230	99.61 51	99.56 175	99.11 147	99.24 225	99.56 194	93.00 300	99.78 266	97.43 219	99.89 91	99.35 223

tfpnnormal		99.43 64	99.38 67	99.60 124	99.87 28	99.75 48	99.59 56	99.78 57	99.71 41	99.90 23	99.69 112	98.85 91	99.90 123	97.25 234	99.78 164	99.15 261

XXY-MVS		99.71 19	99.67 21	99.81 27	99.89 22	99.72 61	99.59 56	99.82 37	99.39 103	99.82 49	99.84 40	99.38 28	99.91 103	99.38 42	99.93 69	99.80 24

MIMVSNet		98.43 244	98.20 246	99.11 237	99.53 178	98.38 258	99.58 58	98.61 314	98.96 163	99.33 210	99.76 75	90.92 319	99.81 254	97.38 222	99.76 170	99.15 261

CP-MVSNet		99.54 46	99.43 61	99.87 15	99.76 84	99.82 25	99.57 59	99.61 141	99.54 77	99.80 59	99.64 140	97.79 208	99.95 43	99.21 63	99.94 61	99.84 15

LS3D		99.24 114	99.11 123	99.61 122	98.38 342	99.79 35	99.57 59	99.68 104	99.61 67	99.15 241	99.71 99	98.70 112	99.91 103	97.54 212	99.68 205	99.13 268

EU-MVSNet		99.39 78	99.62 26	98.72 278	99.88 24	96.44 317	99.56 61	99.85 24	99.90 8	99.90 23	99.85 36	98.09 183	99.83 228	99.58 22	99.95 48	99.90 5

ACMH	98.42 6	99.59 36	99.54 44	99.72 74	99.86 30	99.62 94	99.56 61	99.79 53	98.77 189	99.80 59	99.85 36	99.64 13	99.85 202	98.70 122	99.89 91	99.70 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		99.43 64	99.30 86	99.80 30	99.83 37	99.81 28	99.52 63	99.70 95	98.35 233	99.51 170	99.50 212	99.31 36	99.88 151	98.18 158	99.84 121	99.69 51

wuyk23d		97.58 283	99.13 116	92.93 337	99.69 120	99.49 119	99.52 63	99.77 60	97.97 257	99.96 9	99.79 58	99.84 3	99.94 54	95.85 300	99.82 140	79.36 351

VDD-MVS		99.20 131	99.11 123	99.44 172	99.43 223	98.98 216	99.50 65	98.32 326	99.80 30	99.56 152	99.69 112	96.99 249	99.85 202	98.99 93	99.73 188	99.50 172

APDe-MVS		99.48 53	99.36 73	99.85 19	99.55 172	99.81 28	99.50 65	99.69 101	98.99 158	99.75 79	99.71 99	98.79 100	99.93 67	98.46 134	99.85 117	99.80 24

DSMNet-mixed		99.48 53	99.65 23	98.95 251	99.71 110	97.27 301	99.50 65	99.82 37	99.59 75	99.41 194	99.85 36	99.62 15	100.00 1	99.53 27	99.89 91	99.59 125

ACMMP		99.25 111	99.08 134	99.74 60	99.79 66	99.68 77	99.50 65	99.65 123	98.07 251	99.52 165	99.69 112	98.57 129	99.92 85	97.18 239	99.79 158	99.63 93
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
tttt0517		97.62 281	97.20 290	98.90 264	99.76 84	97.40 298	99.48 69	94.36 351	99.06 155	99.70 99	99.49 217	84.55 350	99.94 54	98.73 120	99.65 219	99.36 220

VPNet		99.46 60	99.37 70	99.71 78	99.82 43	99.59 105	99.48 69	99.70 95	99.81 27	99.69 102	99.58 183	97.66 220	99.86 184	99.17 73	99.44 264	99.67 64

Anonymous202405211		98.75 211	98.46 224	99.63 109	99.34 253	99.66 81	99.47 71	97.65 335	99.28 117	99.56 152	99.50 212	93.15 297	99.84 217	98.62 127	99.58 237	99.40 209

MVS_0304		98.88 197	98.71 200	99.39 189	98.85 320	98.91 228	99.45 72	99.30 268	98.56 206	97.26 342	99.68 123	96.18 269	99.96 34	99.17 73	99.94 61	99.29 235

FMVSNet2		99.35 87	99.28 93	99.55 142	99.49 199	99.35 161	99.45 72	99.57 170	99.44 95	99.70 99	99.74 82	97.21 239	99.87 164	99.03 90	99.94 61	99.44 197

TAMVS		99.49 51	99.45 56	99.63 109	99.48 205	99.42 141	99.45 72	99.57 170	99.66 55	99.78 67	99.83 41	97.85 204	99.86 184	99.44 35	99.96 41	99.61 112

baseline		99.63 31	99.62 26	99.66 93	99.80 56	99.62 94	99.44 75	99.80 47	99.71 41	99.72 92	99.69 112	99.15 53	99.83 228	99.32 51	99.94 61	99.53 154

RPSCF		99.18 138	99.02 154	99.64 105	99.83 37	99.85 12	99.44 75	99.82 37	98.33 238	99.50 171	99.78 65	97.90 198	99.65 325	96.78 260	99.83 131	99.44 197

CSCG		99.37 82	99.29 91	99.60 124	99.71 110	99.46 126	99.43 77	99.85 24	98.79 186	99.41 194	99.60 175	98.92 81	99.92 85	98.02 168	99.92 73	99.43 203

CS-MVS		99.09 160	99.03 152	99.25 222	99.45 218	99.49 119	99.41 78	99.82 37	99.10 148	98.03 324	98.48 339	99.30 38	99.89 136	98.30 145	99.41 270	98.35 323

CostFormer		96.71 304	96.79 303	96.46 332	98.90 313	90.71 356	99.41 78	98.68 310	94.69 338	98.14 319	99.34 255	86.32 347	99.80 259	97.60 209	98.07 339	98.88 297

Patchmatch-test		98.10 266	97.98 261	98.48 287	99.27 270	96.48 316	99.40 80	99.07 295	98.81 183	99.23 226	99.57 191	90.11 330	99.87 164	96.69 264	99.64 221	99.09 274

baseline1		97.73 277	97.33 285	98.96 250	99.30 264	97.73 289	99.40 80	98.42 322	99.33 111	99.46 178	99.21 281	91.18 315	99.82 238	98.35 140	91.26 352	99.32 229

V42		99.56 41	99.54 44	99.63 109	99.79 66	99.46 126	99.39 82	99.59 159	99.24 124	99.86 38	99.70 106	98.55 132	99.82 238	99.79 11	99.95 48	99.60 116

EPMVS		96.53 307	96.32 305	97.17 324	98.18 348	92.97 344	99.39 82	89.95 357	98.21 245	98.61 293	99.59 181	86.69 346	99.72 286	96.99 247	99.23 296	98.81 303

mPP-MVS		99.19 134	99.00 160	99.76 46	99.76 84	99.68 77	99.38 84	99.54 185	98.34 237	99.01 256	99.50 212	98.53 138	99.93 67	97.18 239	99.78 164	99.66 74

CP-MVS		99.23 115	99.05 144	99.75 55	99.66 134	99.66 81	99.38 84	99.62 134	98.38 226	99.06 254	99.27 268	98.79 100	99.94 54	97.51 215	99.82 140	99.66 74

FMVSNet5		97.80 274	97.25 288	99.42 177	98.83 322	98.97 218	99.38 84	99.80 47	98.87 176	99.25 222	99.69 112	80.60 356	99.91 103	98.96 99	99.90 83	99.38 214

COLMAP_ROB	98.06 12	99.45 62	99.37 70	99.70 82	99.83 37	99.70 70	99.38 84	99.78 57	99.53 79	99.67 108	99.78 65	99.19 49	99.86 184	97.32 224	99.87 107	99.55 142
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVS		99.27 108	99.11 123	99.75 55	99.71 110	99.71 63	99.37 88	99.61 141	99.29 114	98.76 283	99.47 224	98.47 145	99.88 151	97.62 206	99.73 188	99.67 64

X-MVStestdata		96.09 315	94.87 323	99.75 55	99.71 110	99.71 63	99.37 88	99.61 141	99.29 114	98.76 283	61.30 360	98.47 145	99.88 151	97.62 206	99.73 188	99.67 64

MVS_Test		99.28 104	99.31 81	99.19 231	99.35 243	98.79 235	99.36 90	99.49 213	99.17 135	99.21 232	99.67 129	98.78 102	99.66 318	99.09 86	99.66 216	99.10 271

MSP-MVS		99.04 169	98.79 196	99.81 27	99.78 72	99.73 57	99.35 91	99.57 170	98.54 211	99.54 159	98.99 305	96.81 252	99.93 67	96.97 248	99.53 251	99.77 32

EIA-MVS		99.12 151	99.01 157	99.45 170	99.36 241	99.62 94	99.34 92	99.79 53	98.41 222	98.84 273	98.89 320	98.75 108	99.84 217	98.15 162	99.51 254	98.89 296

LCM-MVSNet-Re		99.28 104	99.15 112	99.67 86	99.33 258	99.76 46	99.34 92	99.97 2	98.93 168	99.91 21	99.79 58	98.68 114	99.93 67	96.80 259	99.56 239	99.30 232

MTAPA		99.35 87	99.20 105	99.80 30	99.81 50	99.81 28	99.33 94	99.53 194	99.27 118	99.42 186	99.63 150	98.21 174	99.95 43	97.83 189	99.79 158	99.65 82

VNet		99.18 138	99.06 140	99.56 139	99.24 274	99.36 157	99.33 94	99.31 265	99.67 51	99.47 175	99.57 191	96.48 258	99.84 217	99.15 77	99.30 286	99.47 186

abl_6		99.36 85	99.23 103	99.75 55	99.71 110	99.74 54	99.33 94	99.76 65	99.07 151	99.65 116	99.63 150	99.09 61	99.92 85	97.13 242	99.76 170	99.58 130

MP-MVS		99.06 163	98.83 191	99.76 46	99.76 84	99.71 63	99.32 97	99.50 208	98.35 233	98.97 258	99.48 219	98.37 158	99.92 85	95.95 298	99.75 173	99.63 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Patchmtry		98.78 207	98.54 218	99.49 158	98.89 316	99.19 196	99.32 97	99.67 108	99.65 57	99.72 92	99.79 58	91.87 309	99.95 43	98.00 172	99.97 30	99.33 226

tpm		97.15 293	96.95 297	97.75 311	98.91 312	94.24 338	99.32 97	97.96 330	97.71 272	98.29 308	99.32 257	86.72 345	99.92 85	98.10 166	96.24 349	99.09 274

ACMH+	98.40 8	99.50 49	99.43 61	99.71 78	99.86 30	99.76 46	99.32 97	99.77 60	99.53 79	99.77 72	99.76 75	99.26 45	99.78 266	97.77 191	99.88 99	99.60 116

HFP-MVS		99.25 111	99.08 134	99.76 46	99.73 103	99.70 70	99.31 101	99.59 159	98.36 228	99.36 203	99.37 242	98.80 97	99.91 103	97.43 219	99.75 173	99.68 57

region2R		99.23 115	99.05 144	99.77 40	99.76 84	99.70 70	99.31 101	99.59 159	98.41 222	99.32 212	99.36 247	98.73 111	99.93 67	97.29 226	99.74 181	99.67 64

ACMMPR		99.23 115	99.06 140	99.76 46	99.74 100	99.69 74	99.31 101	99.59 159	98.36 228	99.35 205	99.38 241	98.61 125	99.93 67	97.43 219	99.75 173	99.67 64

1314		98.00 271	97.90 272	98.27 298	98.90 313	97.45 297	99.30 104	99.06 297	94.98 332	97.21 343	99.12 292	98.43 150	99.67 314	95.58 308	98.56 326	97.71 340

1121		98.56 229	98.24 242	99.52 149	99.49 199	99.24 185	99.30 104	99.22 284	95.77 322	98.52 300	99.29 264	97.39 231	99.85 202	95.79 303	99.34 281	99.46 190

MVS		95.72 321	94.63 325	98.99 248	98.56 338	97.98 283	99.30 104	98.86 302	72.71 354	97.30 340	99.08 296	98.34 162	99.74 282	89.21 344	98.33 331	99.26 238

tpmvs		97.39 288	97.69 279	96.52 331	98.41 341	91.76 349	99.30 104	98.94 301	97.74 270	97.85 332	99.55 200	92.40 305	99.73 284	96.25 286	98.73 321	98.06 335

TranMVSNet+NR-MVSNet		99.54 46	99.47 52	99.76 46	99.58 151	99.64 88	99.30 104	99.63 131	99.61 67	99.71 97	99.56 194	98.76 106	99.96 34	99.14 83	99.92 73	99.68 57

CR-MVSNet		98.35 253	98.20 246	98.83 270	99.05 303	98.12 270	99.30 104	99.67 108	97.39 288	99.16 239	99.79 58	91.87 309	99.91 103	98.78 117	98.77 315	98.44 320

RPMNet		98.60 223	98.53 220	98.83 270	99.05 303	98.12 270	99.30 104	99.62 134	99.86 17	99.16 239	99.74 82	92.53 304	99.92 85	98.75 118	98.77 315	98.44 320

DP-MVS		99.48 53	99.39 65	99.74 60	99.57 161	99.62 94	99.29 111	99.61 141	99.87 15	99.74 87	99.76 75	98.69 113	99.87 164	98.20 154	99.80 153	99.75 39

ZNCC-MVS		99.22 124	99.04 150	99.77 40	99.76 84	99.73 57	99.28 112	99.56 175	98.19 247	99.14 243	99.29 264	98.84 92	99.92 85	97.53 214	99.80 153	99.64 88

Anonymous20231206		99.35 87	99.31 81	99.47 163	99.74 100	99.06 213	99.28 112	99.74 76	99.23 126	99.72 92	99.53 204	97.63 222	99.88 151	99.11 85	99.84 121	99.48 181

test_0402		99.22 124	99.14 113	99.45 170	99.79 66	99.43 138	99.28 112	99.68 104	99.54 77	99.40 199	99.56 194	99.07 66	99.82 238	96.01 292	99.96 41	99.11 269

APD-MVS_3200maxsize		99.31 100	99.16 109	99.74 60	99.53 178	99.75 48	99.27 115	99.61 141	99.19 131	99.57 145	99.64 140	98.76 106	99.90 123	97.29 226	99.62 224	99.56 139

SR-MVS-dyc-post		99.27 108	99.11 123	99.73 68	99.54 173	99.74 54	99.26 116	99.62 134	99.16 137	99.52 165	99.64 140	98.41 152	99.91 103	97.27 229	99.61 231	99.54 149

RE-MVS-def			99.13 116		99.54 173	99.74 54	99.26 116	99.62 134	99.16 137	99.52 165	99.64 140	98.57 129		97.27 229	99.61 231	99.54 149

TSAR-MVS + MP.		99.34 92	99.24 101	99.63 109	99.82 43	99.37 154	99.26 116	99.35 256	98.77 189	99.57 145	99.70 106	99.27 44	99.88 151	97.71 196	99.75 173	99.65 82
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet		99.38 80	99.44 58	99.21 228	99.58 151	98.09 274	99.26 116	99.46 223	99.62 63	99.75 79	99.67 129	98.54 134	99.85 202	99.15 77	99.92 73	99.68 57

CVMVSNet		98.61 222	98.88 184	97.80 309	99.58 151	93.60 340	99.26 116	99.64 129	99.66 55	99.72 92	99.67 129	93.26 296	99.93 67	99.30 55	99.81 148	99.87 10

RRT_test8_iter05		97.35 291	97.25 288	97.63 314	98.81 326	93.13 342	99.26 116	99.89 13	99.51 81	99.83 47	99.68 123	79.03 359	99.88 151	99.53 27	99.72 193	99.89 9

EG-PatchMatch MVS		99.57 38	99.56 42	99.62 118	99.77 80	99.33 164	99.26 116	99.76 65	99.32 112	99.80 59	99.78 65	99.29 39	99.87 164	99.15 77	99.91 82	99.66 74

DWT-MVSNet_test		96.03 317	95.80 316	96.71 330	98.50 340	91.93 348	99.25 123	97.87 333	95.99 319	96.81 345	97.61 351	81.02 354	99.66 318	97.20 238	97.98 340	98.54 315

test0726					99.69 120	99.80 33	99.24 124	99.57 170	99.16 137	99.73 91	99.65 138	98.35 160

EI-MVSNet-UG-set		99.48 53	99.50 50	99.42 177	99.57 161	98.65 244	99.24 124	99.46 223	99.68 49	99.80 59	99.66 133	98.99 73	99.89 136	99.19 68	99.90 83	99.72 42

EI-MVSNet-Vis-set		99.47 59	99.49 51	99.42 177	99.57 161	98.66 242	99.24 124	99.46 223	99.67 51	99.79 64	99.65 138	98.97 76	99.89 136	99.15 77	99.89 91	99.71 45

EPNet		98.13 264	97.77 277	99.18 233	94.57 357	97.99 278	99.24 124	97.96 330	99.74 36	97.29 341	99.62 159	93.13 298	99.97 17	98.59 128	99.83 131	99.58 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.49 239	98.11 254	99.64 105	99.73 103	99.58 108	99.24 124	99.76 65	89.94 348	99.42 186	99.56 194	97.76 210	99.86 184	97.74 194	99.82 140	99.47 186

PatchT		98.45 243	98.32 239	98.83 270	98.94 311	98.29 261	99.24 124	98.82 305	99.84 22	99.08 250	99.76 75	91.37 312	99.94 54	98.82 112	99.00 305	98.26 327

DeepC-MVS	98.90 4	99.62 33	99.61 30	99.67 86	99.72 107	99.44 133	99.24 124	99.71 91	99.27 118	99.93 15	99.90 22	99.70 11	99.93 67	98.99 93	99.99 13	99.64 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ADS-MVSNet2		97.78 275	97.66 282	98.12 302	99.14 288	95.36 330	99.22 131	98.75 308	96.97 301	98.25 311	99.64 140	90.90 320	99.94 54	96.51 274	99.56 239	99.08 277

ADS-MVSNet		97.72 279	97.67 281	97.86 307	99.14 288	94.65 336	99.22 131	98.86 302	96.97 301	98.25 311	99.64 140	90.90 320	99.84 217	96.51 274	99.56 239	99.08 277

tpm2		96.35 310	96.22 307	96.73 328	98.88 319	91.75 350	99.21 133	98.51 318	93.27 341	97.89 329	99.21 281	84.83 349	99.70 292	96.04 291	98.18 336	98.75 306

test1172		99.23 115	99.05 144	99.74 60	99.52 183	99.75 48	99.20 134	99.61 141	98.97 160	99.48 173	99.58 183	98.41 152	99.91 103	97.15 241	99.55 243	99.57 136

SED-MVS		99.40 74	99.28 93	99.77 40	99.69 120	99.82 25	99.20 134	99.54 185	99.13 143	99.82 49	99.63 150	98.91 83	99.92 85	97.85 186	99.70 199	99.58 130

OPU-MVS				99.29 213	99.12 292	99.44 133	99.20 134				99.40 236	99.00 72	98.84 351	96.54 272	99.60 234	99.58 130

GST-MVS		99.16 143	98.96 171	99.75 55	99.73 103	99.73 57	99.20 134	99.55 180	98.22 244	99.32 212	99.35 252	98.65 121	99.91 103	96.86 254	99.74 181	99.62 105

PMVS	92.94 21	98.82 204	98.81 193	98.85 266	99.84 34	97.99 278	99.20 134	99.47 219	99.71 41	99.42 186	99.82 47	98.09 183	99.47 343	93.88 334	99.85 117	99.07 282
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dp		96.86 299	97.07 293	96.24 334	98.68 336	90.30 358	99.19 139	98.38 325	97.35 290	98.23 313	99.59 181	87.23 338	99.82 238	96.27 285	98.73 321	98.59 311

SR-MVS		99.19 134	99.00 160	99.74 60	99.51 188	99.72 61	99.18 140	99.60 152	98.85 178	99.47 175	99.58 183	98.38 157	99.92 85	96.92 250	99.54 249	99.57 136

thres100view900		96.39 309	96.03 311	97.47 317	99.63 140	95.93 324	99.18 140	97.57 336	98.75 193	98.70 288	97.31 355	87.04 340	99.67 314	87.62 348	98.51 328	96.81 346

thres600view7		96.60 306	96.16 308	97.93 305	99.63 140	96.09 323	99.18 140	97.57 336	98.77 189	98.72 286	97.32 354	87.04 340	99.72 286	88.57 345	98.62 324	97.98 337

SteuartSystems-ACMMP		99.30 101	99.14 113	99.76 46	99.87 28	99.66 81	99.18 140	99.60 152	98.55 208	99.57 145	99.67 129	99.03 71	99.94 54	97.01 246	99.80 153	99.69 51
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		98.74 213	98.44 226	99.64 105	99.61 144	99.38 151	99.18 140	99.55 180	96.49 311	99.27 220	99.37 242	97.11 245	99.92 85	95.74 305	99.67 212	99.62 105

ambc				99.20 230	99.35 243	98.53 247	99.17 145	99.46 223		99.67 108	99.80 52	98.46 148	99.70 292	97.92 177	99.70 199	99.38 214

Regformer-3		99.41 71	99.41 63	99.40 186	99.52 183	98.70 239	99.17 145	99.44 228	99.62 63	99.75 79	99.60 175	98.90 86	99.85 202	98.89 107	99.84 121	99.65 82

Regformer-4		99.45 62	99.44 58	99.50 155	99.52 183	98.94 221	99.17 145	99.53 194	99.64 59	99.76 74	99.60 175	98.96 79	99.90 123	98.91 106	99.84 121	99.67 64

PatchmatchNet		97.65 280	97.80 274	97.18 323	98.82 325	92.49 345	99.17 145	98.39 324	98.12 249	98.79 279	99.58 183	90.71 324	99.89 136	97.23 235	99.41 270	99.16 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS		99.11 155	98.95 173	99.59 126	99.13 290	99.59 105	99.17 145	99.65 123	97.88 263	99.25 222	99.46 227	98.97 76	99.80 259	97.26 231	99.82 140	99.37 217

MAR-MVS		98.24 260	97.92 269	99.19 231	98.78 330	99.65 86	99.17 145	99.14 292	95.36 327	98.04 323	98.81 325	97.47 226	99.72 286	95.47 311	99.06 300	98.21 330
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
PGM-MVS		99.20 131	99.01 157	99.77 40	99.75 94	99.71 63	99.16 151	99.72 88	97.99 255	99.42 186	99.60 175	98.81 93	99.93 67	96.91 251	99.74 181	99.66 74

LPG-MVS_test		99.22 124	99.05 144	99.74 60	99.82 43	99.63 92	99.16 151	99.73 79	97.56 277	99.64 118	99.69 112	99.37 30	99.89 136	96.66 267	99.87 107	99.69 51

Effi-MVS+-dtu		99.07 162	98.92 178	99.52 149	98.89 316	99.78 38	99.15 153	99.66 112	99.34 108	98.92 265	99.24 277	97.69 213	99.98 6	98.11 164	99.28 288	98.81 303

MDTV_nov1_ep13			97.73 278		98.70 335	90.83 355	99.15 153	98.02 329	98.51 213	98.82 275	99.61 168	90.98 318	99.66 318	96.89 253	98.92 308

DVP-MVS		99.32 98	99.17 108	99.77 40	99.69 120	99.80 33	99.14 155	99.31 265	99.16 137	99.62 129	99.61 168	98.35 160	99.91 103	97.88 180	99.72 193	99.61 112

test_0728_SECOND				99.83 22	99.70 117	99.79 35	99.14 155	99.61 141					99.92 85	97.88 180	99.72 193	99.77 32

test_post1							99.14 155				51.63 362	89.54 334	99.82 238	96.86 254

testing_2		99.58 37	99.56 42	99.62 118	99.81 50	99.44 133	99.14 155	99.43 231	99.69 47	99.82 49	99.79 58	99.14 55	99.79 262	99.31 54	99.95 48	99.63 93

v2v482		99.50 49	99.47 52	99.58 130	99.78 72	99.25 180	99.14 155	99.58 168	99.25 122	99.81 56	99.62 159	98.24 170	99.84 217	99.83 9	99.97 30	99.64 88

MDTV_nov1_ep13_2view						91.44 353	99.14 155		97.37 289	99.21 232		91.78 311		96.75 261		99.03 286

API-MVS		98.38 249	98.39 231	98.35 292	98.83 322	99.26 176	99.14 155	99.18 288	98.59 204	98.66 290	98.78 326	98.61 125	99.57 337	94.14 329	99.56 239	96.21 348

SF-MVS		99.10 159	98.93 174	99.62 118	99.58 151	99.51 117	99.13 162	99.65 123	97.97 257	99.42 186	99.61 168	98.86 89	99.87 164	96.45 278	99.68 205	99.49 177

SMA-MVS		99.19 134	99.00 160	99.73 68	99.46 215	99.73 57	99.13 162	99.52 202	97.40 287	99.57 145	99.64 140	98.93 80	99.83 228	97.61 208	99.79 158	99.63 93
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
casdiffmvs		99.63 31	99.61 30	99.67 86	99.79 66	99.59 105	99.13 162	99.85 24	99.79 32	99.76 74	99.72 92	99.33 35	99.82 238	99.21 63	99.94 61	99.59 125

ACMM	98.09 11	99.46 60	99.38 67	99.72 74	99.80 56	99.69 74	99.13 162	99.65 123	98.99 158	99.64 118	99.72 92	99.39 24	99.86 184	98.23 151	99.81 148	99.60 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		99.18 138	99.18 107	99.16 234	99.34 253	99.28 172	99.12 166	99.79 53	99.48 84	98.93 262	98.55 335	99.40 23	99.93 67	98.51 132	99.52 253	98.28 326

AllTest		99.21 129	99.07 138	99.63 109	99.78 72	99.64 88	99.12 166	99.83 32	98.63 200	99.63 122	99.72 92	98.68 114	99.75 280	96.38 281	99.83 131	99.51 166

v144192		99.55 44	99.54 44	99.58 130	99.78 72	99.20 195	99.11 168	99.62 134	99.18 132	99.89 27	99.72 92	98.66 119	99.87 164	99.88 6	99.97 30	99.66 74

v1144		99.54 46	99.53 48	99.59 126	99.79 66	99.28 172	99.10 169	99.61 141	99.20 130	99.84 42	99.73 86	98.67 117	99.84 217	99.86 8	99.98 22	99.64 88

#test#		99.12 151	98.90 182	99.76 46	99.73 103	99.70 70	99.10 169	99.59 159	97.60 276	99.36 203	99.37 242	98.80 97	99.91 103	96.84 257	99.75 173	99.68 57

tpmrst		97.73 277	98.07 256	96.73 328	98.71 334	92.00 347	99.10 169	98.86 302	98.52 212	98.92 265	99.54 202	91.90 307	99.82 238	98.02 168	99.03 303	98.37 322

FMVSNet3		98.80 206	98.63 208	99.32 207	99.13 290	98.72 238	99.10 169	99.48 215	99.23 126	99.62 129	99.64 140	92.57 302	99.86 184	98.96 99	99.90 83	99.39 212

thisisatest0530		97.45 286	96.95 297	98.94 252	99.68 128	97.73 289	99.09 173	94.19 353	98.61 203	99.56 152	99.30 261	84.30 351	99.93 67	98.27 148	99.54 249	99.16 259

MTMP							99.09 173	98.59 316

v148		99.40 74	99.41 63	99.39 189	99.76 84	98.94 221	99.09 173	99.59 159	99.17 135	99.81 56	99.61 168	98.41 152	99.69 298	99.32 51	99.94 61	99.53 154

MVP-Stereo		99.16 143	99.08 134	99.43 175	99.48 205	99.07 211	99.08 176	99.55 180	98.63 200	99.31 214	99.68 123	98.19 177	99.78 266	98.18 158	99.58 237	99.45 192
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		96.78 301	96.98 296	96.16 335	98.85 320	90.59 357	99.08 176	99.32 261	92.37 343	97.73 338	99.46 227	91.15 316	99.69 298	96.07 290	98.80 312	98.21 330

MVSTER		98.47 241	98.22 244	99.24 225	99.06 302	98.35 260	99.08 176	99.46 223	99.27 118	99.75 79	99.66 133	88.61 335	99.85 202	99.14 83	99.92 73	99.52 164

Fast-Effi-MVS+-dtu		99.20 131	99.12 120	99.43 175	99.25 272	99.69 74	99.05 179	99.82 37	99.50 82	98.97 258	99.05 299	98.98 74	99.98 6	98.20 154	99.24 294	98.62 309

v1921920		99.56 41	99.57 38	99.55 142	99.75 94	99.11 203	99.05 179	99.61 141	99.15 141	99.88 33	99.71 99	99.08 64	99.87 164	99.90 2	99.97 30	99.66 74

Fast-Effi-MVS+		99.02 172	98.87 185	99.46 166	99.38 236	99.50 118	99.04 181	99.79 53	97.17 296	98.62 292	98.74 328	99.34 34	99.95 43	98.32 143	99.41 270	98.92 294

v1192		99.57 38	99.57 38	99.57 135	99.77 80	99.22 189	99.04 181	99.60 152	99.18 132	99.87 37	99.72 92	99.08 64	99.85 202	99.89 5	99.98 22	99.66 74

alignmvs		98.28 256	97.96 262	99.25 222	99.12 292	98.93 225	99.03 183	98.42 322	99.64 59	98.72 286	97.85 348	90.86 322	99.62 329	98.88 108	99.13 297	99.19 253

test20.03		99.55 44	99.54 44	99.58 130	99.79 66	99.37 154	99.02 184	99.89 13	99.60 73	99.82 49	99.62 159	98.81 93	99.89 136	99.43 36	99.86 114	99.47 186

mvs_anonymous		99.28 104	99.39 65	98.94 252	99.19 282	97.81 286	99.02 184	99.55 180	99.78 33	99.85 39	99.80 52	98.24 170	99.86 184	99.57 23	99.50 256	99.15 261

APD-MVS		98.87 199	98.59 211	99.71 78	99.50 194	99.62 94	99.01 186	99.57 170	96.80 308	99.54 159	99.63 150	98.29 166	99.91 103	95.24 315	99.71 197	99.61 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CMPMVS	77.52 23	98.50 236	98.19 249	99.41 184	98.33 344	99.56 111	99.01 186	99.59 159	95.44 326	99.57 145	99.80 52	95.64 276	99.46 345	96.47 277	99.92 73	99.21 248
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_yl		98.25 258	97.95 263	99.13 235	99.17 285	98.47 249	99.00 188	98.67 312	98.97 160	99.22 230	99.02 303	91.31 313	99.69 298	97.26 231	98.93 306	99.24 241

DCV-MVSNet		98.25 258	97.95 263	99.13 235	99.17 285	98.47 249	99.00 188	98.67 312	98.97 160	99.22 230	99.02 303	91.31 313	99.69 298	97.26 231	98.93 306	99.24 241

tfpn200view9		96.30 312	95.89 312	97.53 315	99.58 151	96.11 321	99.00 188	97.54 339	98.43 219	98.52 300	96.98 357	86.85 342	99.67 314	87.62 348	98.51 328	96.81 346

v1240		99.56 41	99.58 35	99.51 152	99.80 56	99.00 214	99.00 188	99.65 123	99.15 141	99.90 23	99.75 79	99.09 61	99.88 151	99.90 2	99.96 41	99.67 64

thres400		96.40 308	95.89 312	97.92 306	99.58 151	96.11 321	99.00 188	97.54 339	98.43 219	98.52 300	96.98 357	86.85 342	99.67 314	87.62 348	98.51 328	97.98 337

Regformer-1		99.32 98	99.27 96	99.47 163	99.41 229	98.95 220	98.99 193	99.48 215	99.48 84	99.66 112	99.52 206	98.78 102	99.87 164	98.36 138	99.74 181	99.60 116

Regformer-2		99.34 92	99.27 96	99.53 148	99.41 229	99.10 207	98.99 193	99.53 194	99.47 89	99.66 112	99.52 206	98.80 97	99.89 136	98.31 144	99.74 181	99.60 116

UnsupCasMVSNet_eth		98.83 202	98.57 215	99.59 126	99.68 128	99.45 131	98.99 193	99.67 108	99.48 84	99.55 157	99.36 247	94.92 280	99.86 184	98.95 103	96.57 347	99.45 192

DeepC-MVS_fast	98.47 5	99.23 115	99.12 120	99.56 139	99.28 268	99.22 189	98.99 193	99.40 242	99.08 149	99.58 142	99.64 140	98.90 86	99.83 228	97.44 218	99.75 173	99.63 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RRT_MVS		98.75 211	98.54 218	99.41 184	98.14 351	98.61 245	98.98 197	99.66 112	99.31 113	99.84 42	99.75 79	91.98 306	99.98 6	99.20 66	99.95 48	99.62 105

UniMVSNet (Re)		99.37 82	99.26 98	99.68 84	99.51 188	99.58 108	98.98 197	99.60 152	99.43 100	99.70 99	99.36 247	97.70 211	99.88 151	99.20 66	99.87 107	99.59 125

UniMVSNet_NR-MVSNet		99.37 82	99.25 100	99.72 74	99.47 210	99.56 111	98.97 199	99.61 141	99.43 100	99.67 108	99.28 266	97.85 204	99.95 43	99.17 73	99.81 148	99.65 82

CDS-MVSNet		99.22 124	99.13 116	99.50 155	99.35 243	99.11 203	98.96 200	99.54 185	99.46 93	99.61 135	99.70 106	96.31 265	99.83 228	99.34 47	99.88 99	99.55 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP_NAP		99.28 104	99.11 123	99.79 35	99.75 94	99.81 28	98.95 201	99.53 194	98.27 242	99.53 163	99.73 86	98.75 108	99.87 164	97.70 198	99.83 131	99.68 57

PM-MVS		99.36 85	99.29 91	99.58 130	99.83 37	99.66 81	98.95 201	99.86 20	98.85 178	99.81 56	99.73 86	98.40 156	99.92 85	98.36 138	99.83 131	99.17 257

SD-MVS		99.01 176	99.30 86	98.15 300	99.50 194	99.40 146	98.94 203	99.61 141	99.22 129	99.75 79	99.82 47	99.54 20	95.51 355	97.48 216	99.87 107	99.54 149
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
PVSNet_Blended_VisFu		99.40 74	99.38 67	99.44 172	99.90 20	98.66 242	98.94 203	99.91 8	97.97 257	99.79 64	99.73 86	99.05 69	99.97 17	99.15 77	99.99 13	99.68 57

MDA-MVSNet-bldmvs		99.06 163	99.05 144	99.07 243	99.80 56	97.83 285	98.89 205	99.72 88	99.29 114	99.63 122	99.70 106	96.47 259	99.89 136	98.17 160	99.82 140	99.50 172

testtj		98.56 229	98.17 251	99.72 74	99.45 218	99.60 102	98.88 206	99.50 208	96.88 303	99.18 238	99.48 219	97.08 246	99.92 85	93.69 335	99.38 274	99.63 93

mvs-test1		98.83 202	98.70 203	99.22 227	98.89 316	99.65 86	98.88 206	99.66 112	99.34 108	98.29 308	98.94 315	97.69 213	99.96 34	98.11 164	98.54 327	98.04 336

ACMP	97.51 14	99.05 166	98.84 189	99.67 86	99.78 72	99.55 114	98.88 206	99.66 112	97.11 300	99.47 175	99.60 175	99.07 66	99.89 136	96.18 287	99.85 117	99.58 130
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OpenMVS_ROB	97.31 17	97.36 290	96.84 301	98.89 265	99.29 266	99.45 131	98.87 209	99.48 215	86.54 351	99.44 180	99.74 82	97.34 234	99.86 184	91.61 339	99.28 288	97.37 344

tmp_tt		95.75 320	95.42 320	96.76 326	89.90 358	94.42 337	98.86 210	97.87 333	78.01 352	99.30 218	99.69 112	97.70 211	95.89 354	99.29 58	98.14 337	99.95 1

HPM-MVS++		98.96 185	98.70 203	99.74 60	99.52 183	99.71 63	98.86 210	99.19 287	98.47 218	98.59 295	99.06 298	98.08 185	99.91 103	96.94 249	99.60 234	99.60 116

IterMVS-LS		99.41 71	99.47 52	99.25 222	99.81 50	98.09 274	98.85 212	99.76 65	99.62 63	99.83 47	99.64 140	98.54 134	99.97 17	99.15 77	99.99 13	99.68 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testgi		99.29 103	99.26 98	99.37 196	99.75 94	98.81 233	98.84 213	99.89 13	98.38 226	99.75 79	99.04 302	99.36 33	99.86 184	99.08 87	99.25 292	99.45 192

F-COLMAP		98.74 213	98.45 225	99.62 118	99.57 161	99.47 122	98.84 213	99.65 123	96.31 315	98.93 262	99.19 285	97.68 215	99.87 164	96.52 273	99.37 278	99.53 154

baseline2		96.83 300	96.28 306	98.46 288	99.09 300	96.91 310	98.83 215	93.87 354	97.23 295	96.23 349	98.36 341	88.12 336	99.90 123	96.68 265	98.14 337	98.57 314

DU-MVS		99.33 96	99.21 104	99.71 78	99.43 223	99.56 111	98.83 215	99.53 194	99.38 104	99.67 108	99.36 247	97.67 216	99.95 43	99.17 73	99.81 148	99.63 93

Baseline_NR-MVSNet		99.49 51	99.37 70	99.82 24	99.91 15	99.84 17	98.83 215	99.86 20	99.68 49	99.65 116	99.88 29	97.67 216	99.87 164	99.03 90	99.86 114	99.76 36

XVG-ACMP-BASELINE		99.23 115	99.10 131	99.63 109	99.82 43	99.58 108	98.83 215	99.72 88	98.36 228	99.60 137	99.71 99	98.92 81	99.91 103	97.08 244	99.84 121	99.40 209

MSLP-MVS++		99.05 166	99.09 132	98.91 258	99.21 277	98.36 259	98.82 219	99.47 219	98.85 178	98.90 268	99.56 194	98.78 102	99.09 349	98.57 129	99.68 205	99.26 238

9.14			98.64 206		99.45 218		98.81 220	99.60 152	97.52 281	99.28 219	99.56 194	98.53 138	99.83 228	95.36 314	99.64 221

D2MVS		99.22 124	99.19 106	99.29 213	99.69 120	98.74 237	98.81 220	99.41 235	98.55 208	99.68 104	99.69 112	98.13 181	99.87 164	98.82 112	99.98 22	99.24 241

pmmvs-eth3d		99.48 53	99.47 52	99.51 152	99.77 80	99.41 145	98.81 220	99.66 112	99.42 102	99.75 79	99.66 133	99.20 48	99.76 276	98.98 95	99.99 13	99.36 220

HQP_MVS		98.90 193	98.68 205	99.55 142	99.58 151	99.24 185	98.80 223	99.54 185	98.94 165	99.14 243	99.25 272	97.24 237	99.82 238	95.84 301	99.78 164	99.60 116

plane_prior2							98.80 223		98.94 165

JIA-IIPM		98.06 268	97.92 269	98.50 286	98.59 337	97.02 307	98.80 223	98.51 318	99.88 14	97.89 329	99.87 31	91.89 308	99.90 123	98.16 161	97.68 343	98.59 311

PAPM_NR		98.36 250	98.04 257	99.33 203	99.48 205	98.93 225	98.79 226	99.28 273	97.54 279	98.56 298	98.57 333	97.12 244	99.69 298	94.09 330	98.90 310	99.38 214

CHOSEN 1792x2688		99.39 78	99.30 86	99.65 98	99.88 24	99.25 180	98.78 227	99.88 16	98.66 197	99.96 9	99.79 58	97.45 227	99.93 67	99.34 47	99.99 13	99.78 31

ETH3D-3000-0.1		98.77 208	98.50 222	99.59 126	99.47 210	99.53 116	98.77 228	99.60 152	97.33 291	99.23 226	99.50 212	97.91 197	99.83 228	95.02 319	99.67 212	99.41 207

MS-PatchMatch		99.00 178	98.97 169	99.09 239	99.11 297	98.19 266	98.76 229	99.33 259	98.49 216	99.44 180	99.58 183	98.21 174	99.69 298	98.20 154	99.62 224	99.39 212

DPE-MVS		99.14 147	98.92 178	99.82 24	99.57 161	99.77 40	98.74 230	99.60 152	98.55 208	99.76 74	99.69 112	98.23 173	99.92 85	96.39 280	99.75 173	99.76 36

WTY-MVS		98.59 226	98.37 233	99.26 219	99.43 223	98.40 255	98.74 230	99.13 294	98.10 250	99.21 232	99.24 277	94.82 282	99.90 123	97.86 184	98.77 315	99.49 177

zzz-MVS		99.30 101	99.14 113	99.80 30	99.81 50	99.81 28	98.73 232	99.53 194	99.27 118	99.42 186	99.63 150	98.21 174	99.95 43	97.83 189	99.79 158	99.65 82

sss		98.90 193	98.77 197	99.27 217	99.48 205	98.44 252	98.72 233	99.32 261	97.94 261	99.37 202	99.35 252	96.31 265	99.91 103	98.85 109	99.63 223	99.47 186

CANet		99.11 155	99.05 144	99.28 215	98.83 322	98.56 246	98.71 234	99.41 235	99.25 122	99.23 226	99.22 279	97.66 220	99.94 54	99.19 68	99.97 30	99.33 226

AdaColmap		98.60 223	98.35 236	99.38 193	99.12 292	99.22 189	98.67 235	99.42 234	97.84 268	98.81 276	99.27 268	97.32 235	99.81 254	95.14 316	99.53 251	99.10 271

MP-MVS-pluss		99.14 147	98.92 178	99.80 30	99.83 37	99.83 21	98.61 236	99.63 131	96.84 306	99.44 180	99.58 183	98.81 93	99.91 103	97.70 198	99.82 140	99.67 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		98.82 204	98.57 215	99.58 130	99.21 277	99.31 167	98.61 236	99.25 279	98.65 198	98.43 305	99.26 270	97.86 202	99.81 254	96.55 271	99.27 291	99.61 112

BH-RMVSNet		98.41 246	98.14 253	99.21 228	99.21 277	98.47 249	98.60 238	98.26 327	98.35 233	98.93 262	99.31 259	97.20 242	99.66 318	94.32 326	99.10 299	99.51 166

LF4IMVS		99.01 176	98.92 178	99.27 217	99.71 110	99.28 172	98.59 239	99.77 60	98.32 239	99.39 200	99.41 234	98.62 123	99.84 217	96.62 270	99.84 121	98.69 307

OPM-MVS		99.26 110	99.13 116	99.63 109	99.70 117	99.61 100	98.58 240	99.48 215	98.50 214	99.52 165	99.63 150	99.14 55	99.76 276	97.89 179	99.77 168	99.51 166

MCST-MVS		99.02 172	98.81 193	99.65 98	99.58 151	99.49 119	98.58 240	99.07 295	98.40 224	99.04 255	99.25 272	98.51 143	99.80 259	97.31 225	99.51 254	99.65 82

PVSNet_BlendedMVS		99.03 170	99.01 157	99.09 239	99.54 173	97.99 278	98.58 240	99.82 37	97.62 275	99.34 208	99.71 99	98.52 141	99.77 274	97.98 173	99.97 30	99.52 164

OMC-MVS		98.90 193	98.72 199	99.44 172	99.39 233	99.42 141	98.58 240	99.64 129	97.31 292	99.44 180	99.62 159	98.59 127	99.69 298	96.17 288	99.79 158	99.22 246

diffmvs		99.34 92	99.32 80	99.39 189	99.67 133	98.77 236	98.57 244	99.81 46	99.61 67	99.48 173	99.41 234	98.47 145	99.86 184	98.97 97	99.90 83	99.53 154

DP-MVS Recon		98.50 236	98.23 243	99.31 210	99.49 199	99.46 126	98.56 245	99.63 131	94.86 335	98.85 272	99.37 242	97.81 206	99.59 335	96.08 289	99.44 264	98.88 297

new-patchmatchnet		99.35 87	99.57 38	98.71 280	99.82 43	96.62 315	98.55 246	99.75 71	99.50 82	99.88 33	99.87 31	99.31 36	99.88 151	99.43 36	100.00 1	99.62 105

pmmvs5		99.19 134	99.11 123	99.42 177	99.76 84	98.88 230	98.55 246	99.73 79	98.82 182	99.72 92	99.62 159	96.56 255	99.82 238	99.32 51	99.95 48	99.56 139

BH-untuned		98.22 262	98.09 255	98.58 284	99.38 236	97.24 302	98.55 246	98.98 300	97.81 269	99.20 237	98.76 327	97.01 248	99.65 325	94.83 320	98.33 331	98.86 299

CNVR-MVS		98.99 181	98.80 195	99.56 139	99.25 272	99.43 138	98.54 249	99.27 274	98.58 205	98.80 278	99.43 232	98.53 138	99.70 292	97.22 236	99.59 236	99.54 149

thres200		96.09 315	95.68 318	97.33 322	99.48 205	96.22 320	98.53 250	97.57 336	98.06 252	98.37 307	96.73 359	86.84 344	99.61 333	86.99 351	98.57 325	96.16 349

1112_ss		99.05 166	98.84 189	99.67 86	99.66 134	99.29 170	98.52 251	99.82 37	97.65 274	99.43 184	99.16 286	96.42 261	99.91 103	99.07 88	99.84 121	99.80 24

EPNet_dtu		97.62 281	97.79 276	97.11 325	96.67 356	92.31 346	98.51 252	98.04 328	99.24 124	95.77 350	99.47 224	93.78 293	99.66 318	98.98 95	99.62 224	99.37 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	97.35 16	98.36 250	97.99 259	99.48 161	99.32 259	99.24 185	98.50 253	99.51 205	95.19 331	98.58 296	98.96 313	96.95 250	99.83 228	95.63 306	99.25 292	99.37 217
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	97.92 13	98.03 269	97.55 283	99.46 166	99.47 210	99.44 133	98.50 253	99.62 134	86.79 349	99.07 253	99.26 270	98.26 169	99.62 329	97.28 228	99.73 188	99.31 231
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETH3D cwj APD-0.16		98.50 236	98.16 252	99.51 152	99.04 305	99.39 148	98.47 255	99.47 219	96.70 310	98.78 281	99.33 256	97.62 223	99.86 184	94.69 324	99.38 274	99.28 237

xiu_mvs_v1_base_debu		99.23 115	99.34 75	98.91 258	99.59 148	98.23 263	98.47 255	99.66 112	99.61 67	99.68 104	98.94 315	99.39 24	99.97 17	99.18 70	99.55 243	98.51 317

xiu_mvs_v1_base		99.23 115	99.34 75	98.91 258	99.59 148	98.23 263	98.47 255	99.66 112	99.61 67	99.68 104	98.94 315	99.39 24	99.97 17	99.18 70	99.55 243	98.51 317

xiu_mvs_v1_base_debi		99.23 115	99.34 75	98.91 258	99.59 148	98.23 263	98.47 255	99.66 112	99.61 67	99.68 104	98.94 315	99.39 24	99.97 17	99.18 70	99.55 243	98.51 317

TR-MVS		97.44 287	97.15 292	98.32 294	98.53 339	97.46 296	98.47 255	97.91 332	96.85 305	98.21 314	98.51 337	96.42 261	99.51 341	92.16 338	97.29 345	97.98 337

FPMVS		96.32 311	95.50 319	98.79 274	99.60 146	98.17 268	98.46 260	98.80 306	97.16 297	96.28 346	99.63 150	82.19 352	99.09 349	88.45 346	98.89 311	99.10 271

plane_prior						99.24 185	98.42 261		97.87 264						99.71 197

WR-MVS		99.11 155	98.93 174	99.66 93	99.30 264	99.42 141	98.42 261	99.37 252	99.04 156	99.57 145	99.20 283	96.89 251	99.86 184	98.66 126	99.87 107	99.70 48

MVS-HIRNet		97.86 273	98.22 244	96.76 326	99.28 268	91.53 352	98.38 263	92.60 355	99.13 143	99.31 214	99.96 11	97.18 243	99.68 309	98.34 141	99.83 131	99.07 282

ETH3 D test6400		97.76 276	97.19 291	99.50 155	99.38 236	99.26 176	98.34 264	99.49 213	92.99 342	98.54 299	99.20 283	95.92 274	99.82 238	91.14 342	99.66 216	99.40 209

N_pmnet		98.73 215	98.53 220	99.35 200	99.72 107	98.67 241	98.34 264	94.65 350	98.35 233	99.79 64	99.68 123	98.03 187	99.93 67	98.28 147	99.92 73	99.44 197

CNLPA		98.57 228	98.34 237	99.28 215	99.18 284	99.10 207	98.34 264	99.41 235	98.48 217	98.52 300	98.98 308	97.05 247	99.78 266	95.59 307	99.50 256	98.96 290

CDPH-MVS		98.56 229	98.20 246	99.61 122	99.50 194	99.46 126	98.32 267	99.41 235	95.22 329	99.21 232	99.10 295	98.34 162	99.82 238	95.09 318	99.66 216	99.56 139

Effi-MVS+		99.06 163	98.97 169	99.34 201	99.31 260	98.98 216	98.31 268	99.91 8	98.81 183	98.79 279	98.94 315	99.14 55	99.84 217	98.79 114	98.74 319	99.20 251

xxxxxxxxxxxxxcwj		99.11 155	98.96 171	99.54 146	99.53 178	99.25 180	98.29 269	99.76 65	99.07 151	99.42 186	99.61 168	98.86 89	99.87 164	96.45 278	99.68 205	99.49 177

save fliter					99.53 178	99.25 180	98.29 269	99.38 251	99.07 151

Patchmatch-RL test		98.60 223	98.36 234	99.33 203	99.77 80	99.07 211	98.27 271	99.87 18	98.91 171	99.74 87	99.72 92	90.57 326	99.79 262	98.55 130	99.85 117	99.11 269

jason		99.16 143	99.11 123	99.32 207	99.75 94	98.44 252	98.26 272	99.39 245	98.70 195	99.74 87	99.30 261	98.54 134	99.97 17	98.48 133	99.82 140	99.55 142
jason: jason.
XVG-OURS-SEG-HR		99.16 143	98.99 165	99.66 93	99.84 34	99.64 88	98.25 273	99.73 79	98.39 225	99.63 122	99.43 232	99.70 11	99.90 123	97.34 223	98.64 323	99.44 197

MDA-MVSNet_test_wron		98.95 188	98.99 165	98.85 266	99.64 138	97.16 304	98.23 274	99.33 259	98.93 168	99.56 152	99.66 133	97.39 231	99.83 228	98.29 146	99.88 99	99.55 142

YYNet1		98.95 188	98.99 165	98.84 268	99.64 138	97.14 305	98.22 275	99.32 261	98.92 170	99.59 140	99.66 133	97.40 229	99.83 228	98.27 148	99.90 83	99.55 142

CANet_DTU		98.91 191	98.85 187	99.09 239	98.79 328	98.13 269	98.18 276	99.31 265	99.48 84	98.86 271	99.51 209	96.56 255	99.95 43	99.05 89	99.95 48	99.19 253

MG-MVS		98.52 235	98.39 231	98.94 252	99.15 287	97.39 299	98.18 276	99.21 286	98.89 175	99.23 226	99.63 150	97.37 233	99.74 282	94.22 328	99.61 231	99.69 51

SCA		98.11 265	98.36 234	97.36 320	99.20 280	92.99 343	98.17 278	98.49 320	98.24 243	99.10 249	99.57 191	96.01 272	99.94 54	96.86 254	99.62 224	99.14 265

TSAR-MVS + GP.		99.12 151	99.04 150	99.38 193	99.34 253	99.16 198	98.15 279	99.29 270	98.18 248	99.63 122	99.62 159	99.18 50	99.68 309	98.20 154	99.74 181	99.30 232

new_pmnet		98.88 197	98.89 183	98.84 268	99.70 117	97.62 292	98.15 279	99.50 208	97.98 256	99.62 129	99.54 202	98.15 180	99.94 54	97.55 211	99.84 121	98.95 291

PatchMatch-RL		98.68 218	98.47 223	99.30 212	99.44 221	99.28 172	98.14 281	99.54 185	97.12 299	99.11 247	99.25 272	97.80 207	99.70 292	96.51 274	99.30 286	98.93 293

xiu_mvs_v2_base		99.02 172	99.11 123	98.77 275	99.37 239	98.09 274	98.13 282	99.51 205	99.47 89	99.42 186	98.54 336	99.38 28	99.97 17	98.83 110	99.33 283	98.24 328

lupinMVS		98.96 185	98.87 185	99.24 225	99.57 161	98.40 255	98.12 283	99.18 288	98.28 241	99.63 122	99.13 288	98.02 189	99.97 17	98.22 152	99.69 202	99.35 223

DELS-MVS		99.34 92	99.30 86	99.48 161	99.51 188	99.36 157	98.12 283	99.53 194	99.36 107	99.41 194	99.61 168	99.22 47	99.87 164	99.21 63	99.68 205	99.20 251
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
TEST9					99.35 243	99.35 161	98.11 285	99.41 235	94.83 337	97.92 327	98.99 305	98.02 189	99.85 202

train_agg		98.35 253	97.95 263	99.57 135	99.35 243	99.35 161	98.11 285	99.41 235	94.90 333	97.92 327	98.99 305	98.02 189	99.85 202	95.38 313	99.44 264	99.50 172

PMMVS2		99.48 53	99.45 56	99.57 135	99.76 84	98.99 215	98.09 287	99.90 11	98.95 164	99.78 67	99.58 183	99.57 19	99.93 67	99.48 32	99.95 48	99.79 30

Test_1112_low_res		98.95 188	98.73 198	99.63 109	99.68 128	99.15 200	98.09 287	99.80 47	97.14 298	99.46 178	99.40 236	96.11 270	99.89 136	99.01 92	99.84 121	99.84 15

test_8					99.34 253	99.31 167	98.08 289	99.40 242	94.90 333	97.87 331	98.97 311	98.02 189	99.84 217

IterMVS-SCA-FT		99.00 178	99.16 109	98.51 285	99.75 94	95.90 325	98.07 290	99.84 30	99.84 22	99.89 27	99.73 86	96.01 272	99.99 4	99.33 49	100.00 1	99.63 93

HyFIR lowres test		98.91 191	98.64 206	99.73 68	99.85 33	99.47 122	98.07 290	99.83 32	98.64 199	99.89 27	99.60 175	92.57 302	100.00 1	99.33 49	99.97 30	99.72 42

IterMVS		98.97 182	99.16 109	98.42 289	99.74 100	95.64 328	98.06 292	99.83 32	99.83 25	99.85 39	99.74 82	96.10 271	99.99 4	99.27 61	100.00 1	99.63 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							98.04 293

BH-w/o		97.20 292	97.01 295	97.76 310	99.08 301	95.69 327	98.03 294	98.52 317	95.76 323	97.96 326	98.02 346	95.62 277	99.47 343	92.82 337	97.25 346	98.12 334

无先验							98.01 295	99.23 283	95.83 321				99.85 202	95.79 303		99.44 197

pmmvs4		99.13 149	99.06 140	99.36 199	99.57 161	99.10 207	98.01 295	99.25 279	98.78 188	99.58 142	99.44 231	98.24 170	99.76 276	98.74 119	99.93 69	99.22 246

PS-MVSNAJ		99.00 178	99.08 134	98.76 276	99.37 239	98.10 273	98.00 297	99.51 205	99.47 89	99.41 194	98.50 338	99.28 41	99.97 17	98.83 110	99.34 281	98.20 332

test_prior4						99.19 196	98.00 297

agg_prior1		98.33 255	97.92 269	99.57 135	99.35 243	99.36 157	97.99 299	99.39 245	94.85 336	97.76 336	98.98 308	98.03 187	99.85 202	95.49 309	99.44 264	99.51 166

HQP-NCC					99.31 260		97.98 300		97.45 284	98.15 315

ACMP_Plane					99.31 260		97.98 300		97.45 284	98.15 315

HQP-MVS		98.36 250	98.02 258	99.39 189	99.31 260	98.94 221	97.98 300	99.37 252	97.45 284	98.15 315	98.83 323	96.67 253	99.70 292	94.73 321	99.67 212	99.53 154

UnsupCasMVSNet_bld		98.55 232	98.27 241	99.40 186	99.56 171	99.37 154	97.97 303	99.68 104	97.49 283	99.08 250	99.35 252	95.41 279	99.82 238	97.70 198	98.19 335	99.01 288

test_prior3		98.62 221	98.34 237	99.46 166	99.35 243	99.22 189	97.95 304	99.39 245	97.87 264	98.05 321	99.05 299	97.90 198	99.69 298	95.99 294	99.49 258	99.48 181

test_prior2							97.95 304		97.87 264	98.05 321	99.05 299	97.90 198		95.99 294	99.49 258

旧先验2							97.94 306		95.33 328	98.94 261			99.88 151	96.75 261

MVE	92.54 22	96.66 305	96.11 309	98.31 296	99.68 128	97.55 294	97.94 306	95.60 348	99.37 105	90.68 355	98.70 329	96.56 255	98.61 353	86.94 352	99.55 243	98.77 305
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
原ACMM2							97.92 308

MVS_111021_HR		99.12 151	99.02 154	99.40 186	99.50 194	99.11 203	97.92 308	99.71 91	98.76 192	99.08 250	99.47 224	99.17 51	99.54 338	97.85 186	99.76 170	99.54 149

MVS_111021_LR		99.13 149	99.03 152	99.42 177	99.58 151	99.32 166	97.91 310	99.73 79	98.68 196	99.31 214	99.48 219	99.09 61	99.66 318	97.70 198	99.77 168	99.29 235

pmmvs3		98.08 267	97.80 274	98.91 258	99.41 229	97.69 291	97.87 311	99.66 112	95.87 320	99.50 171	99.51 209	90.35 328	99.97 17	98.55 130	99.47 261	99.08 277

XVG-OURS		99.21 129	99.06 140	99.65 98	99.82 43	99.62 94	97.87 311	99.74 76	98.36 228	99.66 112	99.68 123	99.71 9	99.90 123	96.84 257	99.88 99	99.43 203

test222					99.51 188	99.08 210	97.83 313	99.29 270	95.21 330	98.68 289	99.31 259	97.28 236			99.38 274	99.43 203

miper_lstm_enhance		98.65 220	98.60 209	98.82 273	99.20 280	97.33 300	97.78 314	99.66 112	99.01 157	99.59 140	99.50 212	94.62 285	99.85 202	98.12 163	99.90 83	99.26 238

TinyColmap		98.97 182	98.93 174	99.07 243	99.46 215	98.19 266	97.75 315	99.75 71	98.79 186	99.54 159	99.70 106	98.97 76	99.62 329	96.63 269	99.83 131	99.41 207

our_test_3		98.85 201	99.09 132	98.13 301	99.66 134	94.90 335	97.72 316	99.58 168	99.07 151	99.64 118	99.62 159	98.19 177	99.93 67	98.41 135	99.95 48	99.55 142

testdata1							97.72 316		97.86 267

ET-MVSNet_ETH3D		96.78 301	96.07 310	98.91 258	99.26 271	97.92 284	97.70 318	96.05 346	97.96 260	92.37 354	98.43 340	87.06 339	99.90 123	98.27 148	97.56 344	98.91 295

cl_fuxian		98.72 216	98.71 200	98.72 278	99.12 292	97.22 303	97.68 319	99.56 175	98.90 172	99.54 159	99.48 219	96.37 264	99.73 284	97.88 180	99.88 99	99.21 248

ppachtmachnet_test		98.89 196	99.12 120	98.20 299	99.66 134	95.24 332	97.63 320	99.68 104	99.08 149	99.78 67	99.62 159	98.65 121	99.88 151	98.02 168	99.96 41	99.48 181

PAPR		97.56 284	97.07 293	99.04 246	98.80 327	98.11 272	97.63 320	99.25 279	94.56 339	98.02 325	98.25 344	97.43 228	99.68 309	90.90 343	98.74 319	99.33 226

test0.0.03 1		97.37 289	96.91 300	98.74 277	97.72 352	97.57 293	97.60 322	97.36 341	98.00 253	99.21 232	98.02 346	90.04 331	99.79 262	98.37 137	95.89 350	98.86 299

PVSNet_Blended		98.70 217	98.59 211	99.02 247	99.54 173	97.99 278	97.58 323	99.82 37	95.70 324	99.34 208	98.98 308	98.52 141	99.77 274	97.98 173	99.83 131	99.30 232

PMMVS		98.49 239	98.29 240	99.11 237	98.96 310	98.42 254	97.54 324	99.32 261	97.53 280	98.47 304	98.15 345	97.88 201	99.82 238	97.46 217	99.24 294	99.09 274

MSDG		99.08 161	98.98 168	99.37 196	99.60 146	99.13 201	97.54 324	99.74 76	98.84 181	99.53 163	99.55 200	99.10 59	99.79 262	97.07 245	99.86 114	99.18 255

test123		29.31 324	33.05 329	18.08 338	25.93 360	12.24 360	97.53 326	10.93 361	11.78 355	24.21 356	50.08 364	21.04 361	8.60 356	23.51 354	32.43 355	33.39 352

CLD-MVS		98.76 210	98.57 215	99.33 203	99.57 161	98.97 218	97.53 326	99.55 180	96.41 312	99.27 220	99.13 288	99.07 66	99.78 266	96.73 263	99.89 91	99.23 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
eth_miper_zixun_eth		98.68 218	98.71 200	98.60 282	99.10 298	96.84 312	97.52 328	99.54 185	98.94 165	99.58 142	99.48 219	96.25 267	99.76 276	98.01 171	99.93 69	99.21 248

miper_ehance_all_eth		98.59 226	98.59 211	98.59 283	98.98 309	97.07 306	97.49 329	99.52 202	98.50 214	99.52 165	99.37 242	96.41 263	99.71 290	97.86 184	99.62 224	99.00 289

cl-mvsnet_		98.54 233	98.41 229	98.92 256	99.03 306	97.80 287	97.46 330	99.59 159	98.90 172	99.60 137	99.46 227	93.85 291	99.78 266	97.97 175	99.89 91	99.17 257

cl-mvsnet1		98.54 233	98.42 228	98.92 256	99.03 306	97.80 287	97.46 330	99.59 159	98.90 172	99.60 137	99.46 227	93.87 290	99.78 266	97.97 175	99.89 91	99.18 255

test-LLR		97.15 293	96.95 297	97.74 312	98.18 348	95.02 333	97.38 332	96.10 343	98.00 253	97.81 333	98.58 331	90.04 331	99.91 103	97.69 204	98.78 313	98.31 324

TESTMET0.1,1		96.24 313	95.84 315	97.41 319	98.24 346	93.84 339	97.38 332	95.84 347	98.43 219	97.81 333	98.56 334	79.77 357	99.89 136	97.77 191	98.77 315	98.52 316

test-mter		96.23 314	95.73 317	97.74 312	98.18 348	95.02 333	97.38 332	96.10 343	97.90 262	97.81 333	98.58 331	79.12 358	99.91 103	97.69 204	98.78 313	98.31 324

IB-MVS	95.41 20	95.30 323	94.46 326	97.84 308	98.76 332	95.33 331	97.33 335	96.07 345	96.02 318	95.37 352	97.41 353	76.17 360	99.96 34	97.54 212	95.44 351	98.22 329
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
DPM-MVS		98.28 256	97.94 267	99.32 207	99.36 241	99.11 203	97.31 336	98.78 307	96.88 303	98.84 273	99.11 294	97.77 209	99.61 333	94.03 332	99.36 279	99.23 244

thisisatest0515		96.98 297	96.42 304	98.66 281	99.42 228	97.47 295	97.27 337	94.30 352	97.24 294	99.15 241	98.86 322	85.01 348	99.87 164	97.10 243	99.39 273	98.63 308

DeepPCF-MVS	98.42 6	99.18 138	99.02 154	99.67 86	99.22 276	99.75 48	97.25 338	99.47 219	98.72 194	99.66 112	99.70 106	99.29 39	99.63 328	98.07 167	99.81 148	99.62 105

cl-mvsnet2		97.56 284	97.28 286	98.40 290	98.37 343	96.75 313	97.24 339	99.37 252	97.31 292	99.41 194	99.22 279	87.30 337	99.37 347	97.70 198	99.62 224	99.08 277

GA-MVS		97.99 272	97.68 280	98.93 255	99.52 183	98.04 277	97.19 340	99.05 298	98.32 239	98.81 276	98.97 311	89.89 333	99.41 346	98.33 142	99.05 301	99.34 225

USDC		98.96 185	98.93 174	99.05 245	99.54 173	97.99 278	97.07 341	99.80 47	98.21 245	99.75 79	99.77 72	98.43 150	99.64 327	97.90 178	99.88 99	99.51 166

miper_enhance_ethall		98.03 269	97.94 267	98.32 294	98.27 345	96.43 318	96.95 342	99.41 235	96.37 314	99.43 184	98.96 313	94.74 283	99.69 298	97.71 196	99.62 224	98.83 302

CHOSEN 280x420		98.41 246	98.41 229	98.40 290	99.34 253	95.89 326	96.94 343	99.44 228	98.80 185	99.25 222	99.52 206	93.51 295	99.98 6	98.94 104	99.98 22	99.32 229

PCF-MVS	96.03 18	96.73 303	95.86 314	99.33 203	99.44 221	99.16 198	96.87 344	99.44 228	86.58 350	98.95 260	99.40 236	94.38 287	99.88 151	87.93 347	99.80 153	98.95 291
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testmvs		28.94 325	33.33 327	15.79 339	26.03 359	9.81 361	96.77 345	15.67 360	11.55 356	23.87 357	50.74 363	19.03 362	8.53 357	23.21 355	33.07 354	29.03 353

PVSNet	97.47 15	98.42 245	98.44 226	98.35 292	99.46 215	96.26 319	96.70 346	99.34 258	97.68 273	99.00 257	99.13 288	97.40 229	99.72 286	97.59 210	99.68 205	99.08 277

PAPM		95.61 322	94.71 324	98.31 296	99.12 292	96.63 314	96.66 347	98.46 321	90.77 347	96.25 347	98.68 330	93.01 299	99.69 298	81.60 353	97.86 342	98.62 309

cascas		96.99 296	96.82 302	97.48 316	97.57 355	95.64 328	96.43 348	99.56 175	91.75 344	97.13 344	97.61 351	95.58 278	98.63 352	96.68 265	99.11 298	98.18 333

PVSNet_0	95.53 19	95.85 319	95.31 321	97.47 317	98.78 330	93.48 341	95.72 349	99.40 242	96.18 317	97.37 339	97.73 349	95.73 275	99.58 336	95.49 309	81.40 353	99.36 220

E-PMN		97.14 295	97.43 284	96.27 333	98.79 328	91.62 351	95.54 350	99.01 299	99.44 95	98.88 269	99.12 292	92.78 301	99.68 309	94.30 327	99.03 303	97.50 341

EMVS		96.96 298	97.28 286	95.99 336	98.76 332	91.03 354	95.26 351	98.61 314	99.34 108	98.92 265	98.88 321	93.79 292	99.66 318	92.87 336	99.05 301	97.30 345

uanet_test		8.33 328	11.11 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		24.88 326	33.17 328	0.00 340	0.00 361	0.00 362	0.00 352	99.62 134	0.00 357	0.00 358	99.13 288	99.82 4	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		16.61 327	22.14 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	99.28 41	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		8.33 328	11.11 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		8.33 328	11.11 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		8.33 328	11.11 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		8.33 328	11.11 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		8.26 334	11.02 337	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	99.16 286	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		8.33 328	11.11 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	100.00 1	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ZD-MVS					99.43 223	99.61 100		99.43 231	96.38 313	99.11 247	99.07 297	97.86 202	99.92 85	94.04 331	99.49 258

IU-MVS					99.69 120	99.77 40		99.22 284	97.50 282	99.69 102				97.75 193	99.70 199	99.77 32

test_241102_TWO								99.54 185	99.13 143	99.76 74	99.63 150	98.32 165	99.92 85	97.85 186	99.69 202	99.75 39

test_241102_ONE					99.69 120	99.82 25		99.54 185	99.12 146	99.82 49	99.49 217	98.91 83	99.52 340

test_0728_THIRD									99.18 132	99.62 129	99.61 168	98.58 128	99.91 103	97.72 195	99.80 153	99.77 32

GSMVS																99.14 265

test_part2					99.62 143	99.67 79				99.55 157

sam_mvs1												90.81 323				99.14 265

sam_mvs												90.52 327

MTGPA								99.53 194

test_post											52.41 361	90.25 329	99.86 184

patchmatchnet-post											99.62 159	90.58 325	99.94 54

gm-plane-assit					97.59 353	89.02 359			93.47 340		98.30 342		99.84 217	96.38 281

test9_res														95.10 317	99.44 264	99.50 172

agg_prior2														94.58 325	99.46 263	99.50 172

agg_prior					99.35 243	99.36 157		99.39 245		97.76 336			99.85 202

TestCases				99.63 109	99.78 72	99.64 88		99.83 32	98.63 200	99.63 122	99.72 92	98.68 114	99.75 280	96.38 281	99.83 131	99.51 166

test_prior				99.46 166	99.35 243	99.22 189		99.39 245					99.69 298			99.48 181

新几何1				99.52 149	99.50 194	99.22 189		99.26 276	95.66 325	98.60 294	99.28 266	97.67 216	99.89 136	95.95 298	99.32 284	99.45 192

旧先验1					99.49 199	99.29 170		99.26 276			99.39 240	97.67 216			99.36 279	99.46 190

原ACMM1				99.37 196	99.47 210	98.87 232		99.27 274	96.74 309	98.26 310	99.32 257	97.93 196	99.82 238	95.96 297	99.38 274	99.43 203

testdata2													99.89 136	95.99 294

segment_acmp												98.37 158

testdata				99.42 177	99.51 188	98.93 225		99.30 268	96.20 316	98.87 270	99.40 236	98.33 164	99.89 136	96.29 284	99.28 288	99.44 197

test12				99.54 146	99.29 266	99.33 164		99.16 290		98.43 305		97.54 224	99.82 238		99.47 261	99.48 181

plane_prior7					99.58 151	99.38 151

plane_prior6					99.47 210	99.26 176						97.24 237

plane_prior5								99.54 185					99.82 238	95.84 301	99.78 164	99.60 116

plane_prior4											99.25 272

plane_prior3						99.31 167			98.36 228	99.14 243

plane_prior1					99.51 188

n2								0.00 362

nn								0.00 362

door-mid								99.83 32

lessismore_v0				99.64 105	99.86 30	99.38 151		90.66 356		99.89 27	99.83 41	94.56 286	99.97 17	99.56 24	99.92 73	99.57 136

LGP-MVS_train				99.74 60	99.82 43	99.63 92		99.73 79	97.56 277	99.64 118	99.69 112	99.37 30	99.89 136	96.66 267	99.87 107	99.69 51

test11								99.29 270

door								99.77 60

HQP5-MVS						98.94 221

BP-MVS														94.73 321

HQP4-MVS										98.15 315			99.70 292			99.53 154

HQP3-MVS								99.37 252							99.67 212

HQP2-MVS												96.67 253

NP-MVS					99.40 232	99.13 201					98.83 323

ACMMP++_ref															99.94 61

ACMMP++															99.79 158

Test By Simon												98.41 152

ITE_SJBPF				99.38 193	99.63 140	99.44 133		99.73 79	98.56 206	99.33 210	99.53 204	98.88 88	99.68 309	96.01 292	99.65 219	99.02 287

DeepMVS_CX				97.98 303	99.69 120	96.95 308		99.26 276	75.51 353	95.74 351	98.28 343	96.47 259	99.62 329	91.23 341	97.89 341	97.38 343

High-resolution multi-view benchmark