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 4	99.87 4	99.88 11	99.91 15	99.90 4	99.96 1	99.92 5	99.90 7	99.97 6	99.87 31	99.81 5	99.95 45	99.54 26	99.99 12	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 9

UA-Net		99.78 13	99.76 14	99.86 16	99.72 107	99.71 63	99.91 3	99.95 4	99.96 2	99.71 99	99.91 19	99.15 52	99.97 17	99.50 32	100.00 1	99.90 4

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 92	99.93 4	99.95 10	99.89 25	99.71 9	99.96 35	99.51 30	99.97 30	99.84 14

TDRefinement		99.72 17	99.70 17	99.77 39	99.90 19	99.85 12	99.86 5	99.92 5	99.69 50	99.78 68	99.92 16	99.37 29	99.88 155	98.93 108	99.95 49	99.60 117

pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 4	99.83 6	99.91 8	99.85 20	99.94 11	99.95 11	99.73 8	99.90 126	99.65 16	99.97 30	99.69 52

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 21	99.83 6	99.85 23	99.80 32	99.93 14	99.93 13	98.54 132	99.93 69	99.59 20	99.98 21	99.76 37

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 17	99.82 8	99.82 36	99.84 23	99.94 11	99.91 19	99.13 56	99.96 35	99.83 9	99.99 12	99.83 18

Anonymous20231211		99.62 33	99.57 38	99.76 45	99.61 145	99.60 102	99.81 9	99.73 80	99.82 28	99.90 22	99.90 21	97.97 193	99.86 188	99.42 41	99.96 42	99.80 24

ab-mvs		99.33 96	99.28 93	99.47 164	99.57 163	99.39 148	99.78 10	99.43 235	98.87 179	99.57 147	99.82 49	98.06 185	99.87 168	98.69 127	99.73 189	99.15 264

MVSFormer		99.41 71	99.44 57	99.31 213	99.57 163	98.40 260	99.77 11	99.80 46	99.73 39	99.63 124	99.30 264	98.02 188	99.98 7	99.43 37	99.69 204	99.55 143

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 17	99.77 11	99.80 46	99.73 39	99.97 6	99.92 16	99.77 7	99.98 7	99.43 37	100.00 1	99.90 4

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 21	99.76 13	99.87 17	99.73 39	99.89 26	99.87 31	99.63 14	99.87 168	99.54 26	99.92 74	99.63 95

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 12	99.75 14	99.86 19	99.70 47	99.91 20	99.89 25	99.60 18	99.87 168	99.59 20	99.74 182	99.71 46

K. test v3		98.87 199	98.60 209	99.69 84	99.93 13	99.46 127	99.74 15	94.97 356	99.78 35	99.88 32	99.88 28	93.66 296	99.97 17	99.61 18	99.95 49	99.64 90

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 7	99.73 16	99.85 23	99.70 47	99.92 18	99.93 13	99.45 21	99.97 17	99.36 47	100.00 1	99.85 13

NR-MVSNet		99.40 74	99.31 81	99.68 85	99.43 225	99.55 114	99.73 16	99.50 211	99.46 96	99.88 32	99.36 250	97.54 223	99.87 168	98.97 100	99.87 108	99.63 95

IS-MVSNet		99.03 170	98.85 187	99.55 142	99.80 56	99.25 180	99.73 16	99.15 296	99.37 108	99.61 137	99.71 100	94.73 286	99.81 259	97.70 203	99.88 100	99.58 131

FC-MVSNet-test		99.70 19	99.65 22	99.86 16	99.88 24	99.86 11	99.72 19	99.78 57	99.90 7	99.82 50	99.83 43	98.45 147	99.87 168	99.51 30	99.97 30	99.86 11

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 35	99.72 19	99.88 15	99.92 6	99.98 3	99.93 13	99.94 1	99.98 7	99.77 12	100.00 1	99.92 3

Gipuma		99.57 37	99.59 32	99.49 158	99.98 3	99.71 63	99.72 19	99.84 29	99.81 29	99.94 11	99.78 66	98.91 81	99.71 295	98.41 139	99.95 49	99.05 287
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND				97.36 325	97.59 358	96.87 316	99.70 22	88.49 366		94.64 360	97.26 363	80.66 360	99.12 355	91.50 347	96.50 355	96.08 357

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 38	99.70 22	99.86 19	99.89 11	99.98 3	99.90 21	99.94 1	99.98 7	99.75 13	100.00 1	99.90 4

SixPastTwentyTwo		99.42 67	99.30 86	99.76 45	99.92 14	99.67 79	99.70 22	99.14 297	99.65 60	99.89 26	99.90 21	96.20 269	99.94 56	99.42 41	99.92 74	99.67 65

UGNet		99.38 80	99.34 75	99.49 158	98.90 318	98.90 230	99.70 22	99.35 259	99.86 16	98.57 303	99.81 52	98.50 142	99.93 69	99.38 44	99.98 21	99.66 75
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 94	99.80 56	99.43 138	99.70 22	99.24 285	99.48 87	99.56 154	99.77 73	94.89 283	99.93 69	98.72 124	99.89 92	99.63 95

3Dnovator	99.15 2	99.43 64	99.36 73	99.65 99	99.39 236	99.42 141	99.70 22	99.56 177	99.23 129	99.35 208	99.80 54	99.17 50	99.95 45	98.21 157	99.84 122	99.59 126

gg-mvs-nofinetune		95.87 325	95.17 329	97.97 309	98.19 352	96.95 313	99.69 28	89.23 365	99.89 11	96.24 355	99.94 12	81.19 358	99.51 348	93.99 340	98.20 336	97.44 349

MIMVSNet1		99.66 24	99.62 25	99.80 29	99.94 10	99.87 8	99.69 28	99.77 60	99.78 35	99.93 14	99.89 25	97.94 194	99.92 88	99.65 16	99.98 21	99.62 106

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 24	99.66 81	99.69 28	99.92 5	99.67 54	99.77 73	99.75 80	99.61 16	99.98 7	99.35 48	99.98 21	99.72 43
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 40	99.68 31	99.85 23	99.95 3	99.98 3	99.92 16	99.28 40	99.98 7	99.75 13	100.00 1	99.94 2

GBi-Net		99.42 67	99.31 81	99.73 69	99.49 201	99.77 40	99.68 31	99.70 96	99.44 98	99.62 131	99.83 43	97.21 238	99.90 126	98.96 102	99.90 84	99.53 156

test1		99.42 67	99.31 81	99.73 69	99.49 201	99.77 40	99.68 31	99.70 96	99.44 98	99.62 131	99.83 43	97.21 238	99.90 126	98.96 102	99.90 84	99.53 156

FMVSNet1		99.66 24	99.63 24	99.73 69	99.78 72	99.77 40	99.68 31	99.70 96	99.67 54	99.82 50	99.83 43	98.98 72	99.90 126	99.24 64	99.97 30	99.53 156

test_part1		98.63 223	98.26 245	99.75 55	99.40 234	99.49 119	99.67 35	99.68 105	99.86 16	99.88 32	99.86 36	86.73 349	99.93 69	99.34 49	99.97 30	99.81 23

DTE-MVSNet		99.68 22	99.61 29	99.88 11	99.80 56	99.87 8	99.67 35	99.71 92	99.72 42	99.84 43	99.78 66	98.67 115	99.97 17	99.30 57	99.95 49	99.80 24

WR-MVS_H		99.61 35	99.53 47	99.87 14	99.80 56	99.83 21	99.67 35	99.75 72	99.58 79	99.85 40	99.69 113	98.18 178	99.94 56	99.28 62	99.95 49	99.83 18

QAPM		98.40 252	97.99 263	99.65 99	99.39 236	99.47 123	99.67 35	99.52 204	91.70 352	98.78 287	99.80 54	98.55 130	99.95 45	94.71 330	99.75 174	99.53 156

FIs		99.65 29	99.58 35	99.84 19	99.84 34	99.85 12	99.66 39	99.75 72	99.86 16	99.74 88	99.79 60	98.27 167	99.85 206	99.37 46	99.93 70	99.83 18

v8		99.68 22	99.69 18	99.65 99	99.80 56	99.40 146	99.66 39	99.76 65	99.64 62	99.93 14	99.85 37	98.66 117	99.84 222	99.88 6	99.99 12	99.71 46

v10		99.69 21	99.69 18	99.66 94	99.81 51	99.39 148	99.66 39	99.75 72	99.60 76	99.92 18	99.87 31	98.75 106	99.86 188	99.90 2	99.99 12	99.73 42

PS-CasMVS		99.66 24	99.58 35	99.89 7	99.80 56	99.85 12	99.66 39	99.73 80	99.62 66	99.84 43	99.71 100	98.62 121	99.96 35	99.30 57	99.96 42	99.86 11

PEN-MVS		99.66 24	99.59 32	99.89 7	99.83 38	99.87 8	99.66 39	99.73 80	99.70 47	99.84 43	99.73 87	98.56 129	99.96 35	99.29 60	99.94 62	99.83 18

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 44	100.00 1	99.90 7	100.00 1	99.97 9	99.61 16	99.97 17	99.75 13	100.00 1	99.84 14

OpenMVS	98.12 10	98.23 265	97.89 277	99.26 222	99.19 287	99.26 176	99.65 44	99.69 102	91.33 353	98.14 325	99.77 73	98.28 166	99.96 35	95.41 319	99.55 245	98.58 318

Anonymous20240529		99.42 67	99.34 75	99.65 99	99.53 180	99.60 102	99.63 46	99.39 248	99.47 92	99.76 75	99.78 66	98.13 180	99.86 188	98.70 125	99.68 207	99.49 179

Anonymous20240521		99.44 63	99.42 62	99.49 158	99.89 21	98.96 220	99.62 47	99.76 65	99.85 20	99.82 50	99.88 28	96.39 264	99.97 17	99.59 20	99.98 21	99.55 143

LFMVS		98.46 246	98.19 253	99.26 222	99.24 278	98.52 252	99.62 47	96.94 348	99.87 14	99.31 218	99.58 184	91.04 320	99.81 259	98.68 128	99.42 271	99.45 195

VDDNet		98.97 182	98.82 192	99.42 179	99.71 110	98.81 234	99.62 47	98.68 316	99.81 29	99.38 204	99.80 54	94.25 290	99.85 206	98.79 117	99.32 286	99.59 126

VPA-MVSNet		99.66 24	99.62 25	99.79 34	99.68 129	99.75 48	99.62 47	99.69 102	99.85 20	99.80 60	99.81 52	98.81 91	99.91 106	99.47 34	99.88 100	99.70 49

3Dnovator+	98.92 3	99.35 87	99.24 101	99.67 87	99.35 246	99.47 123	99.62 47	99.50 211	99.44 98	99.12 250	99.78 66	98.77 103	99.94 56	97.87 188	99.72 195	99.62 106

canonicalmvs		99.02 172	99.00 160	99.09 244	99.10 303	98.70 240	99.61 52	99.66 114	99.63 65	98.64 297	97.65 357	99.04 68	99.54 343	98.79 117	98.92 311	99.04 288

nrg030		99.70 19	99.66 21	99.82 23	99.76 84	99.84 17	99.61 52	99.70 96	99.93 4	99.78 68	99.68 124	99.10 57	99.78 270	99.45 35	99.96 42	99.83 18

HPM-MVS		99.25 111	99.07 138	99.78 37	99.81 51	99.75 48	99.61 52	99.67 110	97.72 276	99.35 208	99.25 276	99.23 45	99.92 88	97.21 242	99.82 141	99.67 65
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	98.23 9	98.21 267	97.95 267	98.99 253	99.03 311	98.24 267	99.61 52	98.72 315	96.81 314	98.73 291	99.51 211	94.06 291	99.86 188	96.91 256	98.20 336	98.86 304

Vis-MVSNet (Re-imp)		98.77 208	98.58 214	99.34 204	99.78 72	98.88 231	99.61 52	99.56 177	99.11 150	99.24 229	99.56 195	93.00 302	99.78 270	97.43 224	99.89 92	99.35 226

tfpnnormal		99.43 64	99.38 67	99.60 124	99.87 28	99.75 48	99.59 57	99.78 57	99.71 43	99.90 22	99.69 113	98.85 89	99.90 126	97.25 239	99.78 165	99.15 264

XXY-MVS		99.71 18	99.67 20	99.81 26	99.89 21	99.72 61	99.59 57	99.82 36	99.39 106	99.82 50	99.84 42	99.38 27	99.91 106	99.38 44	99.93 70	99.80 24

MIMVSNet		98.43 248	98.20 250	99.11 242	99.53 180	98.38 263	99.58 59	98.61 320	98.96 166	99.33 213	99.76 76	90.92 322	99.81 259	97.38 227	99.76 171	99.15 264

CP-MVSNet		99.54 45	99.43 60	99.87 14	99.76 84	99.82 25	99.57 60	99.61 143	99.54 80	99.80 60	99.64 141	97.79 207	99.95 45	99.21 66	99.94 62	99.84 14

LS3D		99.24 114	99.11 123	99.61 122	98.38 347	99.79 35	99.57 60	99.68 105	99.61 70	99.15 245	99.71 100	98.70 110	99.91 106	97.54 217	99.68 207	99.13 271

EU-MVSNet		99.39 78	99.62 25	98.72 283	99.88 24	96.44 322	99.56 62	99.85 23	99.90 7	99.90 22	99.85 37	98.09 182	99.83 233	99.58 23	99.95 49	99.90 4

ACMH	98.42 6	99.59 36	99.54 43	99.72 75	99.86 30	99.62 94	99.56 62	99.79 52	98.77 192	99.80 60	99.85 37	99.64 13	99.85 206	98.70 125	99.89 92	99.70 49
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 29	99.83 38	99.81 28	99.52 64	99.70 96	98.35 236	99.51 173	99.50 214	99.31 35	99.88 155	98.18 162	99.84 122	99.69 52

wuyk23d		97.58 288	99.13 116	92.93 344	99.69 120	99.49 119	99.52 64	99.77 60	97.97 262	99.96 8	99.79 60	99.84 3	99.94 56	95.85 307	99.82 141	79.36 358

VDD-MVS		99.20 131	99.11 123	99.44 173	99.43 225	98.98 216	99.50 66	98.32 332	99.80 32	99.56 154	99.69 113	96.99 248	99.85 206	98.99 96	99.73 189	99.50 174

APDe-MVS		99.48 52	99.36 73	99.85 18	99.55 174	99.81 28	99.50 66	99.69 102	98.99 161	99.75 80	99.71 100	98.79 98	99.93 69	98.46 137	99.85 118	99.80 24

DSMNet-mixed		99.48 52	99.65 22	98.95 256	99.71 110	97.27 306	99.50 66	99.82 36	99.59 78	99.41 197	99.85 37	99.62 15	100.00 1	99.53 28	99.89 92	99.59 126

ACMMP		99.25 111	99.08 134	99.74 61	99.79 66	99.68 77	99.50 66	99.65 125	98.07 256	99.52 168	99.69 113	98.57 127	99.92 88	97.18 244	99.79 159	99.63 95
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		97.62 286	97.20 295	98.90 269	99.76 84	97.40 303	99.48 70	94.36 358	99.06 158	99.70 101	99.49 219	84.55 355	99.94 56	98.73 123	99.65 221	99.36 223

VPNet		99.46 59	99.37 70	99.71 79	99.82 44	99.59 105	99.48 70	99.70 96	99.81 29	99.69 104	99.58 184	97.66 219	99.86 188	99.17 76	99.44 266	99.67 65

Anonymous202405211		98.75 211	98.46 225	99.63 110	99.34 256	99.66 81	99.47 72	97.65 341	99.28 120	99.56 154	99.50 214	93.15 299	99.84 222	98.62 130	99.58 239	99.40 212

MVS_0304		98.88 197	98.71 200	99.39 192	98.85 325	98.91 229	99.45 73	99.30 271	98.56 209	97.26 348	99.68 124	96.18 270	99.96 35	99.17 76	99.94 62	99.29 238

FMVSNet2		99.35 87	99.28 93	99.55 142	99.49 201	99.35 161	99.45 73	99.57 172	99.44 98	99.70 101	99.74 83	97.21 238	99.87 168	99.03 93	99.94 62	99.44 200

TAMVS		99.49 50	99.45 55	99.63 110	99.48 207	99.42 141	99.45 73	99.57 172	99.66 58	99.78 68	99.83 43	97.85 203	99.86 188	99.44 36	99.96 42	99.61 113

baseline		99.63 30	99.62 25	99.66 94	99.80 56	99.62 94	99.44 76	99.80 46	99.71 43	99.72 94	99.69 113	99.15 52	99.83 233	99.32 54	99.94 62	99.53 156

RPSCF		99.18 138	99.02 154	99.64 106	99.83 38	99.85 12	99.44 76	99.82 36	98.33 241	99.50 174	99.78 66	97.90 197	99.65 330	96.78 265	99.83 132	99.44 200

CSCG		99.37 82	99.29 91	99.60 124	99.71 110	99.46 127	99.43 78	99.85 23	98.79 189	99.41 197	99.60 176	98.92 79	99.92 88	98.02 172	99.92 74	99.43 206

CS-MVS		99.09 160	99.03 152	99.25 225	99.45 220	99.49 119	99.41 79	99.82 36	99.10 151	98.03 330	98.48 346	99.30 37	99.89 140	98.30 149	99.41 272	98.35 330

CostFormer		96.71 309	96.79 308	96.46 339	98.90 318	90.71 363	99.41 79	98.68 316	94.69 345	98.14 325	99.34 258	86.32 352	99.80 264	97.60 214	98.07 342	98.88 302

Patchmatch-test		98.10 270	97.98 265	98.48 292	99.27 273	96.48 321	99.40 81	99.07 300	98.81 186	99.23 230	99.57 192	90.11 333	99.87 168	96.69 269	99.64 223	99.09 277

baseline1		97.73 282	97.33 290	98.96 255	99.30 267	97.73 294	99.40 81	98.42 328	99.33 114	99.46 181	99.21 285	91.18 318	99.82 243	98.35 144	91.26 359	99.32 232

V42		99.56 40	99.54 43	99.63 110	99.79 66	99.46 127	99.39 83	99.59 161	99.24 127	99.86 39	99.70 107	98.55 130	99.82 243	99.79 11	99.95 49	99.60 117

EPMVS		96.53 312	96.32 310	97.17 331	98.18 353	92.97 351	99.39 83	89.95 364	98.21 248	98.61 299	99.59 182	86.69 351	99.72 291	96.99 252	99.23 298	98.81 308

mPP-MVS		99.19 134	99.00 160	99.76 45	99.76 84	99.68 77	99.38 85	99.54 187	98.34 240	99.01 260	99.50 214	98.53 136	99.93 69	97.18 244	99.78 165	99.66 75

CP-MVS		99.23 115	99.05 144	99.75 55	99.66 135	99.66 81	99.38 85	99.62 136	98.38 229	99.06 258	99.27 271	98.79 98	99.94 56	97.51 220	99.82 141	99.66 75

FMVSNet5		97.80 279	97.25 293	99.42 179	98.83 327	98.97 218	99.38 85	99.80 46	98.87 179	99.25 226	99.69 113	80.60 361	99.91 106	98.96 102	99.90 84	99.38 217

COLMAP_ROB	98.06 12	99.45 61	99.37 70	99.70 83	99.83 38	99.70 70	99.38 85	99.78 57	99.53 82	99.67 110	99.78 66	99.19 48	99.86 188	97.32 229	99.87 108	99.55 143
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DIV-MVS_2432*1600		99.63 30	99.59 32	99.76 45	99.84 34	99.90 4	99.37 89	99.79 52	99.83 26	99.88 32	99.85 37	98.42 150	99.90 126	99.60 19	99.73 189	99.49 179

XVS		99.27 108	99.11 123	99.75 55	99.71 110	99.71 63	99.37 89	99.61 143	99.29 117	98.76 289	99.47 227	98.47 143	99.88 155	97.62 211	99.73 189	99.67 65

X-MVStestdata		96.09 320	94.87 330	99.75 55	99.71 110	99.71 63	99.37 89	99.61 143	99.29 117	98.76 289	61.30 367	98.47 143	99.88 155	97.62 211	99.73 189	99.67 65

MVS_Test		99.28 104	99.31 81	99.19 234	99.35 246	98.79 236	99.36 92	99.49 216	99.17 138	99.21 236	99.67 130	98.78 100	99.66 323	99.09 89	99.66 218	99.10 274

MSP-MVS		99.04 169	98.79 196	99.81 26	99.78 72	99.73 57	99.35 93	99.57 172	98.54 214	99.54 161	98.99 312	96.81 252	99.93 69	96.97 253	99.53 253	99.77 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
EIA-MVS		99.12 151	99.01 157	99.45 171	99.36 244	99.62 94	99.34 94	99.79 52	98.41 225	98.84 279	98.89 327	98.75 106	99.84 222	98.15 166	99.51 256	98.89 301

LCM-MVSNet-Re		99.28 104	99.15 112	99.67 87	99.33 261	99.76 46	99.34 94	99.97 2	98.93 171	99.91 20	99.79 60	98.68 112	99.93 69	96.80 264	99.56 241	99.30 235

MTAPA		99.35 87	99.20 105	99.80 29	99.81 51	99.81 28	99.33 96	99.53 196	99.27 121	99.42 189	99.63 151	98.21 173	99.95 45	97.83 194	99.79 159	99.65 83

VNet		99.18 138	99.06 140	99.56 139	99.24 278	99.36 157	99.33 96	99.31 268	99.67 54	99.47 178	99.57 192	96.48 258	99.84 222	99.15 80	99.30 288	99.47 189

abl_6		99.36 85	99.23 103	99.75 55	99.71 110	99.74 54	99.33 96	99.76 65	99.07 154	99.65 118	99.63 151	99.09 59	99.92 88	97.13 247	99.76 171	99.58 131

MP-MVS		99.06 163	98.83 191	99.76 45	99.76 84	99.71 63	99.32 99	99.50 211	98.35 236	98.97 262	99.48 222	98.37 157	99.92 88	95.95 305	99.75 174	99.63 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Patchmtry		98.78 207	98.54 219	99.49 158	98.89 321	99.19 196	99.32 99	99.67 110	99.65 60	99.72 94	99.79 60	91.87 312	99.95 45	98.00 176	99.97 30	99.33 229

tpm		97.15 298	96.95 302	97.75 316	98.91 317	94.24 343	99.32 99	97.96 336	97.71 277	98.29 314	99.32 260	86.72 350	99.92 88	98.10 170	96.24 356	99.09 277

ACMH+	98.40 8	99.50 48	99.43 60	99.71 79	99.86 30	99.76 46	99.32 99	99.77 60	99.53 82	99.77 73	99.76 76	99.26 44	99.78 270	97.77 196	99.88 100	99.60 117

HFP-MVS		99.25 111	99.08 134	99.76 45	99.73 103	99.70 70	99.31 103	99.59 161	98.36 231	99.36 206	99.37 245	98.80 95	99.91 106	97.43 224	99.75 174	99.68 58

region2R		99.23 115	99.05 144	99.77 39	99.76 84	99.70 70	99.31 103	99.59 161	98.41 225	99.32 215	99.36 250	98.73 109	99.93 69	97.29 231	99.74 182	99.67 65

ACMMPR		99.23 115	99.06 140	99.76 45	99.74 100	99.69 74	99.31 103	99.59 161	98.36 231	99.35 208	99.38 244	98.61 123	99.93 69	97.43 224	99.75 174	99.67 65

1314		98.00 275	97.90 276	98.27 303	98.90 318	97.45 302	99.30 106	99.06 302	94.98 339	97.21 349	99.12 296	98.43 148	99.67 319	95.58 315	98.56 329	97.71 347

1121		98.56 233	98.24 246	99.52 149	99.49 201	99.24 185	99.30 106	99.22 287	95.77 329	98.52 306	99.29 267	97.39 230	99.85 206	95.79 310	99.34 283	99.46 193

MVS		95.72 328	94.63 332	98.99 253	98.56 343	97.98 288	99.30 106	98.86 308	72.71 361	97.30 346	99.08 300	98.34 161	99.74 286	89.21 351	98.33 334	99.26 241

tpmvs		97.39 293	97.69 283	96.52 338	98.41 346	91.76 356	99.30 106	98.94 307	97.74 275	97.85 338	99.55 201	92.40 308	99.73 289	96.25 291	98.73 324	98.06 342

TranMVSNet+NR-MVSNet		99.54 45	99.47 51	99.76 45	99.58 153	99.64 88	99.30 106	99.63 133	99.61 70	99.71 99	99.56 195	98.76 104	99.96 35	99.14 86	99.92 74	99.68 58

CR-MVSNet		98.35 257	98.20 250	98.83 275	99.05 308	98.12 275	99.30 106	99.67 110	97.39 293	99.16 243	99.79 60	91.87 312	99.91 106	98.78 120	98.77 318	98.44 327

RPMNet		98.60 227	98.53 221	98.83 275	99.05 308	98.12 275	99.30 106	99.62 136	99.86 16	99.16 243	99.74 83	92.53 306	99.92 88	98.75 121	98.77 318	98.44 327

DP-MVS		99.48 52	99.39 65	99.74 61	99.57 163	99.62 94	99.29 113	99.61 143	99.87 14	99.74 88	99.76 76	98.69 111	99.87 168	98.20 158	99.80 154	99.75 40

ZNCC-MVS		99.22 124	99.04 150	99.77 39	99.76 84	99.73 57	99.28 114	99.56 177	98.19 250	99.14 247	99.29 267	98.84 90	99.92 88	97.53 219	99.80 154	99.64 90

Anonymous20231206		99.35 87	99.31 81	99.47 164	99.74 100	99.06 213	99.28 114	99.74 77	99.23 129	99.72 94	99.53 206	97.63 221	99.88 155	99.11 88	99.84 122	99.48 184

test_0402		99.22 124	99.14 113	99.45 171	99.79 66	99.43 138	99.28 114	99.68 105	99.54 80	99.40 202	99.56 195	99.07 64	99.82 243	96.01 299	99.96 42	99.11 272

hse-mvs3		98.61 225	98.34 239	99.44 173	99.60 147	98.67 242	99.27 117	99.44 231	99.68 51	99.32 215	99.49 219	92.50 307	100.00 1	99.24 64	96.51 354	99.65 83

APD-MVS_3200maxsize		99.31 100	99.16 109	99.74 61	99.53 180	99.75 48	99.27 117	99.61 143	99.19 134	99.57 147	99.64 141	98.76 104	99.90 126	97.29 231	99.62 226	99.56 140

SR-MVS-dyc-post		99.27 108	99.11 123	99.73 69	99.54 175	99.74 54	99.26 119	99.62 136	99.16 140	99.52 168	99.64 141	98.41 151	99.91 106	97.27 234	99.61 233	99.54 151

RE-MVS-def			99.13 116		99.54 175	99.74 54	99.26 119	99.62 136	99.16 140	99.52 168	99.64 141	98.57 127		97.27 234	99.61 233	99.54 151

TSAR-MVS + MP.		99.34 92	99.24 101	99.63 110	99.82 44	99.37 154	99.26 119	99.35 259	98.77 192	99.57 147	99.70 107	99.27 43	99.88 155	97.71 201	99.75 174	99.65 83
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 57	99.21 231	99.58 153	98.09 279	99.26 119	99.46 226	99.62 66	99.75 80	99.67 130	98.54 132	99.85 206	99.15 80	99.92 74	99.68 58

CVMVSNet		98.61 225	98.88 184	97.80 314	99.58 153	93.60 347	99.26 119	99.64 131	99.66 58	99.72 94	99.67 130	93.26 298	99.93 69	99.30 57	99.81 149	99.87 9

RRT_test8_iter05		97.35 296	97.25 293	97.63 319	98.81 331	93.13 349	99.26 119	99.89 12	99.51 84	99.83 48	99.68 124	79.03 366	99.88 155	99.53 28	99.72 195	99.89 8

EG-PatchMatch MVS		99.57 37	99.56 42	99.62 119	99.77 80	99.33 164	99.26 119	99.76 65	99.32 115	99.80 60	99.78 66	99.29 38	99.87 168	99.15 80	99.91 83	99.66 75

DWT-MVSNet_test		96.03 322	95.80 321	96.71 337	98.50 345	91.93 355	99.25 126	97.87 339	95.99 326	96.81 352	97.61 358	81.02 359	99.66 323	97.20 243	97.98 343	98.54 320

test0726					99.69 120	99.80 33	99.24 127	99.57 172	99.16 140	99.73 92	99.65 139	98.35 159

EI-MVSNet-UG-set		99.48 52	99.50 49	99.42 179	99.57 163	98.65 247	99.24 127	99.46 226	99.68 51	99.80 60	99.66 134	98.99 71	99.89 140	99.19 71	99.90 84	99.72 43

EI-MVSNet-Vis-set		99.47 58	99.49 50	99.42 179	99.57 163	98.66 244	99.24 127	99.46 226	99.67 54	99.79 65	99.65 139	98.97 74	99.89 140	99.15 80	99.89 92	99.71 46

EPNet		98.13 268	97.77 281	99.18 236	94.57 364	97.99 283	99.24 127	97.96 336	99.74 38	97.29 347	99.62 160	93.13 300	99.97 17	98.59 131	99.83 132	99.58 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.49 243	98.11 258	99.64 106	99.73 103	99.58 108	99.24 127	99.76 65	89.94 355	99.42 189	99.56 195	97.76 209	99.86 188	97.74 199	99.82 141	99.47 189

PatchT		98.45 247	98.32 242	98.83 275	98.94 316	98.29 266	99.24 127	98.82 311	99.84 23	99.08 254	99.76 76	91.37 315	99.94 56	98.82 115	99.00 307	98.26 334

DeepC-MVS	98.90 4	99.62 33	99.61 29	99.67 87	99.72 107	99.44 134	99.24 127	99.71 92	99.27 121	99.93 14	99.90 21	99.70 11	99.93 69	98.99 96	99.99 12	99.64 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ADS-MVSNet2		97.78 280	97.66 286	98.12 307	99.14 293	95.36 335	99.22 134	98.75 314	96.97 308	98.25 317	99.64 141	90.90 323	99.94 56	96.51 279	99.56 241	99.08 280

ADS-MVSNet		97.72 284	97.67 285	97.86 312	99.14 293	94.65 341	99.22 134	98.86 308	96.97 308	98.25 317	99.64 141	90.90 323	99.84 222	96.51 279	99.56 241	99.08 280

tpm2		96.35 315	96.22 312	96.73 335	98.88 324	91.75 357	99.21 136	98.51 324	93.27 348	97.89 335	99.21 285	84.83 354	99.70 297	96.04 298	98.18 339	98.75 311

test1172		99.23 115	99.05 144	99.74 61	99.52 185	99.75 48	99.20 137	99.61 143	98.97 163	99.48 176	99.58 184	98.41 151	99.91 106	97.15 246	99.55 245	99.57 137

SED-MVS		99.40 74	99.28 93	99.77 39	99.69 120	99.82 25	99.20 137	99.54 187	99.13 146	99.82 50	99.63 151	98.91 81	99.92 88	97.85 191	99.70 201	99.58 131

OPU-MVS				99.29 216	99.12 297	99.44 134	99.20 137				99.40 239	99.00 70	98.84 358	96.54 277	99.60 236	99.58 131

GST-MVS		99.16 143	98.96 171	99.75 55	99.73 103	99.73 57	99.20 137	99.55 182	98.22 247	99.32 215	99.35 255	98.65 119	99.91 106	96.86 259	99.74 182	99.62 106

PMVS	92.94 21	98.82 204	98.81 193	98.85 271	99.84 34	97.99 283	99.20 137	99.47 222	99.71 43	99.42 189	99.82 49	98.09 182	99.47 350	93.88 341	99.85 118	99.07 285
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dp		96.86 304	97.07 298	96.24 341	98.68 341	90.30 365	99.19 142	98.38 331	97.35 295	98.23 319	99.59 182	87.23 342	99.82 243	96.27 290	98.73 324	98.59 316

SR-MVS		99.19 134	99.00 160	99.74 61	99.51 190	99.72 61	99.18 143	99.60 154	98.85 181	99.47 178	99.58 184	98.38 156	99.92 88	96.92 255	99.54 251	99.57 137

thres100view900		96.39 314	96.03 316	97.47 322	99.63 141	95.93 329	99.18 143	97.57 342	98.75 196	98.70 294	97.31 362	87.04 344	99.67 319	87.62 355	98.51 331	96.81 353

thres600view7		96.60 311	96.16 313	97.93 310	99.63 141	96.09 328	99.18 143	97.57 342	98.77 192	98.72 292	97.32 361	87.04 344	99.72 291	88.57 352	98.62 327	97.98 344

SteuartSystems-ACMMP		99.30 101	99.14 113	99.76 45	99.87 28	99.66 81	99.18 143	99.60 154	98.55 211	99.57 147	99.67 130	99.03 69	99.94 56	97.01 251	99.80 154	99.69 52
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		98.74 213	98.44 228	99.64 106	99.61 145	99.38 151	99.18 143	99.55 182	96.49 318	99.27 224	99.37 245	97.11 244	99.92 88	95.74 312	99.67 214	99.62 106

ambc				99.20 233	99.35 246	98.53 250	99.17 148	99.46 226		99.67 110	99.80 54	98.46 146	99.70 297	97.92 182	99.70 201	99.38 217

Regformer-3		99.41 71	99.41 63	99.40 189	99.52 185	98.70 240	99.17 148	99.44 231	99.62 66	99.75 80	99.60 176	98.90 84	99.85 206	98.89 110	99.84 122	99.65 83

Regformer-4		99.45 61	99.44 57	99.50 155	99.52 185	98.94 222	99.17 148	99.53 196	99.64 62	99.76 75	99.60 176	98.96 77	99.90 126	98.91 109	99.84 122	99.67 65

PatchmatchNet		97.65 285	97.80 278	97.18 330	98.82 330	92.49 352	99.17 148	98.39 330	98.12 252	98.79 285	99.58 184	90.71 327	99.89 140	97.23 240	99.41 272	99.16 262
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	99.13 295	99.59 105	99.17 148	99.65 125	97.88 268	99.25 226	99.46 230	98.97 74	99.80 264	97.26 236	99.82 141	99.37 220

MAR-MVS		98.24 264	97.92 273	99.19 234	98.78 335	99.65 86	99.17 148	99.14 297	95.36 334	98.04 329	98.81 332	97.47 225	99.72 291	95.47 318	99.06 302	98.21 337
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 39	99.75 94	99.71 63	99.16 154	99.72 89	97.99 260	99.42 189	99.60 176	98.81 91	99.93 69	96.91 256	99.74 182	99.66 75

LPG-MVS_test		99.22 124	99.05 144	99.74 61	99.82 44	99.63 92	99.16 154	99.73 80	97.56 282	99.64 120	99.69 113	99.37 29	99.89 140	96.66 272	99.87 108	99.69 52

Effi-MVS+-dtu		99.07 162	98.92 178	99.52 149	98.89 321	99.78 38	99.15 156	99.66 114	99.34 111	98.92 269	99.24 281	97.69 212	99.98 7	98.11 168	99.28 290	98.81 308

MDTV_nov1_ep13			97.73 282		98.70 340	90.83 362	99.15 156	98.02 335	98.51 216	98.82 281	99.61 169	90.98 321	99.66 323	96.89 258	98.92 311

DVP-MVS		99.32 98	99.17 108	99.77 39	99.69 120	99.80 33	99.14 158	99.31 268	99.16 140	99.62 131	99.61 169	98.35 159	99.91 106	97.88 185	99.72 195	99.61 113
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.83 21	99.70 117	99.79 35	99.14 158	99.61 143					99.92 88	97.88 185	99.72 195	99.77 33

test_post1							99.14 158				51.63 369	89.54 337	99.82 243	96.86 259

v2v482		99.50 48	99.47 51	99.58 130	99.78 72	99.25 180	99.14 158	99.58 170	99.25 125	99.81 57	99.62 160	98.24 169	99.84 222	99.83 9	99.97 30	99.64 90

MDTV_nov1_ep13_2view						91.44 360	99.14 158		97.37 294	99.21 236		91.78 314		96.75 266		99.03 289

API-MVS		98.38 253	98.39 233	98.35 297	98.83 327	99.26 176	99.14 158	99.18 293	98.59 207	98.66 296	98.78 333	98.61 123	99.57 342	94.14 336	99.56 241	96.21 355

SF-MVS		99.10 159	98.93 174	99.62 119	99.58 153	99.51 117	99.13 164	99.65 125	97.97 262	99.42 189	99.61 169	98.86 87	99.87 168	96.45 283	99.68 207	99.49 179

SMA-MVS		99.19 134	99.00 160	99.73 69	99.46 217	99.73 57	99.13 164	99.52 204	97.40 292	99.57 147	99.64 141	98.93 78	99.83 233	97.61 213	99.79 159	99.63 95
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 30	99.61 29	99.67 87	99.79 66	99.59 105	99.13 164	99.85 23	99.79 34	99.76 75	99.72 93	99.33 34	99.82 243	99.21 66	99.94 62	99.59 126

ACMM	98.09 11	99.46 59	99.38 67	99.72 75	99.80 56	99.69 74	99.13 164	99.65 125	98.99 161	99.64 120	99.72 93	99.39 23	99.86 188	98.23 155	99.81 149	99.60 117
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		99.18 138	99.18 107	99.16 237	99.34 256	99.28 172	99.12 168	99.79 52	99.48 87	98.93 266	98.55 342	99.40 22	99.93 69	98.51 135	99.52 255	98.28 333

AllTest		99.21 129	99.07 138	99.63 110	99.78 72	99.64 88	99.12 168	99.83 31	98.63 203	99.63 124	99.72 93	98.68 112	99.75 284	96.38 286	99.83 132	99.51 168

v144192		99.55 43	99.54 43	99.58 130	99.78 72	99.20 195	99.11 170	99.62 136	99.18 135	99.89 26	99.72 93	98.66 117	99.87 168	99.88 6	99.97 30	99.66 75

v1144		99.54 45	99.53 47	99.59 126	99.79 66	99.28 172	99.10 171	99.61 143	99.20 133	99.84 43	99.73 87	98.67 115	99.84 222	99.86 8	99.98 21	99.64 90

#test#		99.12 151	98.90 182	99.76 45	99.73 103	99.70 70	99.10 171	99.59 161	97.60 281	99.36 206	99.37 245	98.80 95	99.91 106	96.84 262	99.75 174	99.68 58

tpmrst		97.73 282	98.07 260	96.73 335	98.71 339	92.00 354	99.10 171	98.86 308	98.52 215	98.92 269	99.54 203	91.90 310	99.82 243	98.02 172	99.03 305	98.37 329

FMVSNet3		98.80 206	98.63 208	99.32 210	99.13 295	98.72 239	99.10 171	99.48 218	99.23 129	99.62 131	99.64 141	92.57 304	99.86 188	98.96 102	99.90 84	99.39 215

thisisatest0530		97.45 291	96.95 302	98.94 257	99.68 129	97.73 294	99.09 175	94.19 360	98.61 206	99.56 154	99.30 264	84.30 356	99.93 69	98.27 152	99.54 251	99.16 262

MTMP							99.09 175	98.59 322

v148		99.40 74	99.41 63	99.39 192	99.76 84	98.94 222	99.09 175	99.59 161	99.17 138	99.81 57	99.61 169	98.41 151	99.69 303	99.32 54	99.94 62	99.53 156

MVP-Stereo		99.16 143	99.08 134	99.43 177	99.48 207	99.07 211	99.08 178	99.55 182	98.63 203	99.31 218	99.68 124	98.19 176	99.78 270	98.18 162	99.58 239	99.45 195
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat1		96.78 306	96.98 301	96.16 342	98.85 325	90.59 364	99.08 178	99.32 264	92.37 350	97.73 344	99.46 230	91.15 319	99.69 303	96.07 297	98.80 315	98.21 337

MVSTER		98.47 245	98.22 248	99.24 228	99.06 307	98.35 265	99.08 178	99.46 226	99.27 121	99.75 80	99.66 134	88.61 339	99.85 206	99.14 86	99.92 74	99.52 166

Fast-Effi-MVS+-dtu		99.20 131	99.12 120	99.43 177	99.25 276	99.69 74	99.05 181	99.82 36	99.50 85	98.97 262	99.05 303	98.98 72	99.98 7	98.20 158	99.24 296	98.62 314

v1921920		99.56 40	99.57 38	99.55 142	99.75 94	99.11 203	99.05 181	99.61 143	99.15 144	99.88 32	99.71 100	99.08 62	99.87 168	99.90 2	99.97 30	99.66 75

Fast-Effi-MVS+		99.02 172	98.87 185	99.46 167	99.38 239	99.50 118	99.04 183	99.79 52	97.17 303	98.62 298	98.74 335	99.34 33	99.95 45	98.32 147	99.41 272	98.92 299

v1192		99.57 37	99.57 38	99.57 135	99.77 80	99.22 189	99.04 183	99.60 154	99.18 135	99.87 38	99.72 93	99.08 62	99.85 206	99.89 5	99.98 21	99.66 75

alignmvs		98.28 260	97.96 266	99.25 225	99.12 297	98.93 226	99.03 185	98.42 328	99.64 62	98.72 292	97.85 355	90.86 325	99.62 334	98.88 111	99.13 299	99.19 256

test20.03		99.55 43	99.54 43	99.58 130	99.79 66	99.37 154	99.02 186	99.89 12	99.60 76	99.82 50	99.62 160	98.81 91	99.89 140	99.43 37	99.86 115	99.47 189

mvs_anonymous		99.28 104	99.39 65	98.94 257	99.19 287	97.81 291	99.02 186	99.55 182	99.78 35	99.85 40	99.80 54	98.24 169	99.86 188	99.57 24	99.50 258	99.15 264

APD-MVS		98.87 199	98.59 211	99.71 79	99.50 196	99.62 94	99.01 188	99.57 172	96.80 315	99.54 161	99.63 151	98.29 165	99.91 106	95.24 322	99.71 199	99.61 113
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CMPMVS	77.52 23	98.50 240	98.19 253	99.41 187	98.33 349	99.56 111	99.01 188	99.59 161	95.44 333	99.57 147	99.80 54	95.64 277	99.46 352	96.47 282	99.92 74	99.21 251
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_yl		98.25 262	97.95 267	99.13 240	99.17 290	98.47 254	99.00 190	98.67 318	98.97 163	99.22 234	99.02 310	91.31 316	99.69 303	97.26 236	98.93 309	99.24 244

DCV-MVSNet		98.25 262	97.95 267	99.13 240	99.17 290	98.47 254	99.00 190	98.67 318	98.97 163	99.22 234	99.02 310	91.31 316	99.69 303	97.26 236	98.93 309	99.24 244

tfpn200view9		96.30 317	95.89 317	97.53 320	99.58 153	96.11 326	99.00 190	97.54 345	98.43 222	98.52 306	96.98 364	86.85 346	99.67 319	87.62 355	98.51 331	96.81 353

v1240		99.56 40	99.58 35	99.51 152	99.80 56	99.00 214	99.00 190	99.65 125	99.15 144	99.90 22	99.75 80	99.09 59	99.88 155	99.90 2	99.96 42	99.67 65

thres400		96.40 313	95.89 317	97.92 311	99.58 153	96.11 326	99.00 190	97.54 345	98.43 222	98.52 306	96.98 364	86.85 346	99.67 319	87.62 355	98.51 331	97.98 344

Regformer-1		99.32 98	99.27 96	99.47 164	99.41 231	98.95 221	98.99 195	99.48 218	99.48 87	99.66 114	99.52 208	98.78 100	99.87 168	98.36 142	99.74 182	99.60 117

Regformer-2		99.34 92	99.27 96	99.53 148	99.41 231	99.10 207	98.99 195	99.53 196	99.47 92	99.66 114	99.52 208	98.80 95	99.89 140	98.31 148	99.74 182	99.60 117

UnsupCasMVSNet_eth		98.83 202	98.57 215	99.59 126	99.68 129	99.45 132	98.99 195	99.67 110	99.48 87	99.55 159	99.36 250	94.92 282	99.86 188	98.95 106	96.57 353	99.45 195

DeepC-MVS_fast	98.47 5	99.23 115	99.12 120	99.56 139	99.28 271	99.22 189	98.99 195	99.40 245	99.08 152	99.58 144	99.64 141	98.90 84	99.83 233	97.44 223	99.75 174	99.63 95
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	98.54 219	99.41 187	98.14 356	98.61 248	98.98 199	99.66 114	99.31 116	99.84 43	99.75 80	91.98 309	99.98 7	99.20 69	99.95 49	99.62 106

UniMVSNet (Re)		99.37 82	99.26 98	99.68 85	99.51 190	99.58 108	98.98 199	99.60 154	99.43 103	99.70 101	99.36 250	97.70 210	99.88 155	99.20 69	99.87 108	99.59 126

UniMVSNet_NR-MVSNet		99.37 82	99.25 100	99.72 75	99.47 212	99.56 111	98.97 201	99.61 143	99.43 103	99.67 110	99.28 269	97.85 203	99.95 45	99.17 76	99.81 149	99.65 83

CDS-MVSNet		99.22 124	99.13 116	99.50 155	99.35 246	99.11 203	98.96 202	99.54 187	99.46 96	99.61 137	99.70 107	96.31 266	99.83 233	99.34 49	99.88 100	99.55 143
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 34	99.75 94	99.81 28	98.95 203	99.53 196	98.27 245	99.53 166	99.73 87	98.75 106	99.87 168	97.70 203	99.83 132	99.68 58

PM-MVS		99.36 85	99.29 91	99.58 130	99.83 38	99.66 81	98.95 203	99.86 19	98.85 181	99.81 57	99.73 87	98.40 155	99.92 88	98.36 142	99.83 132	99.17 260

SD-MVS		99.01 176	99.30 86	98.15 305	99.50 196	99.40 146	98.94 205	99.61 143	99.22 132	99.75 80	99.82 49	99.54 20	95.51 362	97.48 221	99.87 108	99.54 151
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	99.44 173	99.90 19	98.66 244	98.94 205	99.91 8	97.97 262	99.79 65	99.73 87	99.05 67	99.97 17	99.15 80	99.99 12	99.68 58

MDA-MVSNet-bldmvs		99.06 163	99.05 144	99.07 248	99.80 56	97.83 290	98.89 207	99.72 89	99.29 117	99.63 124	99.70 107	96.47 259	99.89 140	98.17 164	99.82 141	99.50 174

testtj		98.56 233	98.17 255	99.72 75	99.45 220	99.60 102	98.88 208	99.50 211	96.88 310	99.18 242	99.48 222	97.08 245	99.92 88	93.69 342	99.38 276	99.63 95

mvs-test1		98.83 202	98.70 203	99.22 230	98.89 321	99.65 86	98.88 208	99.66 114	99.34 111	98.29 314	98.94 322	97.69 212	99.96 35	98.11 168	98.54 330	98.04 343

ACMP	97.51 14	99.05 166	98.84 189	99.67 87	99.78 72	99.55 114	98.88 208	99.66 114	97.11 307	99.47 178	99.60 176	99.07 64	99.89 140	96.18 294	99.85 118	99.58 131
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OpenMVS_ROB	97.31 17	97.36 295	96.84 306	98.89 270	99.29 269	99.45 132	98.87 211	99.48 218	86.54 358	99.44 183	99.74 83	97.34 233	99.86 188	91.61 346	99.28 290	97.37 351

tmp_tt		95.75 327	95.42 325	96.76 333	89.90 365	94.42 342	98.86 212	97.87 339	78.01 359	99.30 222	99.69 113	97.70 210	95.89 361	99.29 60	98.14 340	99.95 1

HPM-MVS++		98.96 185	98.70 203	99.74 61	99.52 185	99.71 63	98.86 212	99.19 292	98.47 221	98.59 301	99.06 302	98.08 184	99.91 106	96.94 254	99.60 236	99.60 117

IterMVS-LS		99.41 71	99.47 51	99.25 225	99.81 51	98.09 279	98.85 214	99.76 65	99.62 66	99.83 48	99.64 141	98.54 132	99.97 17	99.15 80	99.99 12	99.68 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testgi		99.29 103	99.26 98	99.37 199	99.75 94	98.81 234	98.84 215	99.89 12	98.38 229	99.75 80	99.04 306	99.36 32	99.86 188	99.08 90	99.25 294	99.45 195

F-COLMAP		98.74 213	98.45 226	99.62 119	99.57 163	99.47 123	98.84 215	99.65 125	96.31 322	98.93 266	99.19 289	97.68 214	99.87 168	96.52 278	99.37 280	99.53 156

baseline2		96.83 305	96.28 311	98.46 293	99.09 305	96.91 315	98.83 217	93.87 361	97.23 300	96.23 356	98.36 348	88.12 340	99.90 126	96.68 270	98.14 340	98.57 319

DU-MVS		99.33 96	99.21 104	99.71 79	99.43 225	99.56 111	98.83 217	99.53 196	99.38 107	99.67 110	99.36 250	97.67 215	99.95 45	99.17 76	99.81 149	99.63 95

Baseline_NR-MVSNet		99.49 50	99.37 70	99.82 23	99.91 15	99.84 17	98.83 217	99.86 19	99.68 51	99.65 118	99.88 28	97.67 215	99.87 168	99.03 93	99.86 115	99.76 37

XVG-ACMP-BASELINE		99.23 115	99.10 131	99.63 110	99.82 44	99.58 108	98.83 217	99.72 89	98.36 231	99.60 139	99.71 100	98.92 79	99.91 106	97.08 249	99.84 122	99.40 212

MSLP-MVS++		99.05 166	99.09 132	98.91 263	99.21 282	98.36 264	98.82 221	99.47 222	98.85 181	98.90 272	99.56 195	98.78 100	99.09 356	98.57 132	99.68 207	99.26 241

9.14			98.64 206		99.45 220		98.81 222	99.60 154	97.52 286	99.28 223	99.56 195	98.53 136	99.83 233	95.36 321	99.64 223

D2MVS		99.22 124	99.19 106	99.29 216	99.69 120	98.74 238	98.81 222	99.41 238	98.55 211	99.68 106	99.69 113	98.13 180	99.87 168	98.82 115	99.98 21	99.24 244

pmmvs-eth3d		99.48 52	99.47 51	99.51 152	99.77 80	99.41 145	98.81 222	99.66 114	99.42 105	99.75 80	99.66 134	99.20 47	99.76 280	98.98 98	99.99 12	99.36 223

HQP_MVS		98.90 193	98.68 205	99.55 142	99.58 153	99.24 185	98.80 225	99.54 187	98.94 168	99.14 247	99.25 276	97.24 236	99.82 243	95.84 308	99.78 165	99.60 117

plane_prior2							98.80 225		98.94 168

JIA-IIPM		98.06 272	97.92 273	98.50 291	98.59 342	97.02 312	98.80 225	98.51 324	99.88 13	97.89 335	99.87 31	91.89 311	99.90 126	98.16 165	97.68 347	98.59 316

PAPM_NR		98.36 254	98.04 261	99.33 206	99.48 207	98.93 226	98.79 228	99.28 276	97.54 284	98.56 304	98.57 340	97.12 243	99.69 303	94.09 337	98.90 313	99.38 217

CHOSEN 1792x2688		99.39 78	99.30 86	99.65 99	99.88 24	99.25 180	98.78 229	99.88 15	98.66 200	99.96 8	99.79 60	97.45 226	99.93 69	99.34 49	99.99 12	99.78 32

ETH3D-3000-0.1		98.77 208	98.50 223	99.59 126	99.47 212	99.53 116	98.77 230	99.60 154	97.33 296	99.23 230	99.50 214	97.91 196	99.83 233	95.02 326	99.67 214	99.41 210

MS-PatchMatch		99.00 178	98.97 169	99.09 244	99.11 302	98.19 271	98.76 231	99.33 262	98.49 219	99.44 183	99.58 184	98.21 173	99.69 303	98.20 158	99.62 226	99.39 215

DPE-MVS		99.14 147	98.92 178	99.82 23	99.57 163	99.77 40	98.74 232	99.60 154	98.55 211	99.76 75	99.69 113	98.23 172	99.92 88	96.39 285	99.75 174	99.76 37
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
WTY-MVS		98.59 230	98.37 235	99.26 222	99.43 225	98.40 260	98.74 232	99.13 299	98.10 253	99.21 236	99.24 281	94.82 284	99.90 126	97.86 189	98.77 318	99.49 179

zzz-MVS		99.30 101	99.14 113	99.80 29	99.81 51	99.81 28	98.73 234	99.53 196	99.27 121	99.42 189	99.63 151	98.21 173	99.95 45	97.83 194	99.79 159	99.65 83

AUN-MVS		97.82 278	97.38 289	99.14 239	99.27 273	98.53 250	98.72 235	99.02 304	98.10 253	97.18 350	99.03 309	89.26 338	99.85 206	97.94 181	97.91 344	99.03 289

sss		98.90 193	98.77 197	99.27 220	99.48 207	98.44 257	98.72 235	99.32 264	97.94 266	99.37 205	99.35 255	96.31 266	99.91 106	98.85 112	99.63 225	99.47 189

CANet		99.11 155	99.05 144	99.28 218	98.83 327	98.56 249	98.71 237	99.41 238	99.25 125	99.23 230	99.22 283	97.66 219	99.94 56	99.19 71	99.97 30	99.33 229

AdaColmap		98.60 227	98.35 238	99.38 196	99.12 297	99.22 189	98.67 238	99.42 237	97.84 273	98.81 282	99.27 271	97.32 234	99.81 259	95.14 323	99.53 253	99.10 274

MP-MVS-pluss		99.14 147	98.92 178	99.80 29	99.83 38	99.83 21	98.61 239	99.63 133	96.84 313	99.44 183	99.58 184	98.81 91	99.91 106	97.70 203	99.82 141	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		98.82 204	98.57 215	99.58 130	99.21 282	99.31 167	98.61 239	99.25 282	98.65 201	98.43 311	99.26 274	97.86 201	99.81 259	96.55 276	99.27 293	99.61 113

BH-RMVSNet		98.41 250	98.14 257	99.21 231	99.21 282	98.47 254	98.60 241	98.26 333	98.35 236	98.93 266	99.31 262	97.20 241	99.66 323	94.32 333	99.10 301	99.51 168

LF4IMVS		99.01 176	98.92 178	99.27 220	99.71 110	99.28 172	98.59 242	99.77 60	98.32 242	99.39 203	99.41 237	98.62 121	99.84 222	96.62 275	99.84 122	98.69 312

OPM-MVS		99.26 110	99.13 116	99.63 110	99.70 117	99.61 100	98.58 243	99.48 218	98.50 217	99.52 168	99.63 151	99.14 54	99.76 280	97.89 184	99.77 169	99.51 168

MCST-MVS		99.02 172	98.81 193	99.65 99	99.58 153	99.49 119	98.58 243	99.07 300	98.40 227	99.04 259	99.25 276	98.51 141	99.80 264	97.31 230	99.51 256	99.65 83

PVSNet_BlendedMVS		99.03 170	99.01 157	99.09 244	99.54 175	97.99 283	98.58 243	99.82 36	97.62 280	99.34 211	99.71 100	98.52 139	99.77 278	97.98 177	99.97 30	99.52 166

OMC-MVS		98.90 193	98.72 199	99.44 173	99.39 236	99.42 141	98.58 243	99.64 131	97.31 297	99.44 183	99.62 160	98.59 125	99.69 303	96.17 295	99.79 159	99.22 249

diffmvs		99.34 92	99.32 80	99.39 192	99.67 134	98.77 237	98.57 247	99.81 45	99.61 70	99.48 176	99.41 237	98.47 143	99.86 188	98.97 100	99.90 84	99.53 156

DP-MVS Recon		98.50 240	98.23 247	99.31 213	99.49 201	99.46 127	98.56 248	99.63 133	94.86 342	98.85 278	99.37 245	97.81 205	99.59 340	96.08 296	99.44 266	98.88 302

new-patchmatchnet		99.35 87	99.57 38	98.71 285	99.82 44	96.62 320	98.55 249	99.75 72	99.50 85	99.88 32	99.87 31	99.31 35	99.88 155	99.43 37	100.00 1	99.62 106

pmmvs5		99.19 134	99.11 123	99.42 179	99.76 84	98.88 231	98.55 249	99.73 80	98.82 185	99.72 94	99.62 160	96.56 255	99.82 243	99.32 54	99.95 49	99.56 140

BH-untuned		98.22 266	98.09 259	98.58 289	99.38 239	97.24 307	98.55 249	98.98 306	97.81 274	99.20 241	98.76 334	97.01 247	99.65 330	94.83 327	98.33 334	98.86 304

CNVR-MVS		98.99 181	98.80 195	99.56 139	99.25 276	99.43 138	98.54 252	99.27 277	98.58 208	98.80 284	99.43 235	98.53 136	99.70 297	97.22 241	99.59 238	99.54 151

thres200		96.09 320	95.68 323	97.33 327	99.48 207	96.22 325	98.53 253	97.57 342	98.06 257	98.37 313	96.73 366	86.84 348	99.61 338	86.99 358	98.57 328	96.16 356

1112_ss		99.05 166	98.84 189	99.67 87	99.66 135	99.29 170	98.52 254	99.82 36	97.65 279	99.43 187	99.16 290	96.42 261	99.91 106	99.07 91	99.84 122	99.80 24

EPNet_dtu		97.62 286	97.79 280	97.11 332	96.67 361	92.31 353	98.51 255	98.04 334	99.24 127	95.77 357	99.47 227	93.78 295	99.66 323	98.98 98	99.62 226	99.37 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	97.35 16	98.36 254	97.99 263	99.48 162	99.32 262	99.24 185	98.50 256	99.51 207	95.19 338	98.58 302	98.96 320	96.95 249	99.83 233	95.63 313	99.25 294	99.37 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	97.92 13	98.03 273	97.55 287	99.46 167	99.47 212	99.44 134	98.50 256	99.62 136	86.79 356	99.07 257	99.26 274	98.26 168	99.62 334	97.28 233	99.73 189	99.31 234
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETH3D cwj APD-0.16		98.50 240	98.16 256	99.51 152	99.04 310	99.39 148	98.47 258	99.47 222	96.70 317	98.78 287	99.33 259	97.62 222	99.86 188	94.69 331	99.38 276	99.28 240

xiu_mvs_v1_base_debu		99.23 115	99.34 75	98.91 263	99.59 150	98.23 268	98.47 258	99.66 114	99.61 70	99.68 106	98.94 322	99.39 23	99.97 17	99.18 73	99.55 245	98.51 322

xiu_mvs_v1_base		99.23 115	99.34 75	98.91 263	99.59 150	98.23 268	98.47 258	99.66 114	99.61 70	99.68 106	98.94 322	99.39 23	99.97 17	99.18 73	99.55 245	98.51 322

xiu_mvs_v1_base_debi		99.23 115	99.34 75	98.91 263	99.59 150	98.23 268	98.47 258	99.66 114	99.61 70	99.68 106	98.94 322	99.39 23	99.97 17	99.18 73	99.55 245	98.51 322

TR-MVS		97.44 292	97.15 297	98.32 299	98.53 344	97.46 301	98.47 258	97.91 338	96.85 312	98.21 320	98.51 344	96.42 261	99.51 348	92.16 345	97.29 349	97.98 344

FPMVS		96.32 316	95.50 324	98.79 279	99.60 147	98.17 273	98.46 263	98.80 312	97.16 304	96.28 353	99.63 151	82.19 357	99.09 356	88.45 353	98.89 314	99.10 274

plane_prior						99.24 185	98.42 264		97.87 269						99.71 199

WR-MVS		99.11 155	98.93 174	99.66 94	99.30 267	99.42 141	98.42 264	99.37 255	99.04 159	99.57 147	99.20 287	96.89 250	99.86 188	98.66 129	99.87 108	99.70 49

MVS-HIRNet		97.86 277	98.22 248	96.76 333	99.28 271	91.53 359	98.38 266	92.60 362	99.13 146	99.31 218	99.96 10	97.18 242	99.68 314	98.34 145	99.83 132	99.07 285

ETH3 D test6400		97.76 281	97.19 296	99.50 155	99.38 239	99.26 176	98.34 267	99.49 216	92.99 349	98.54 305	99.20 287	95.92 275	99.82 243	91.14 349	99.66 218	99.40 212

N_pmnet		98.73 215	98.53 221	99.35 203	99.72 107	98.67 242	98.34 267	94.65 357	98.35 236	99.79 65	99.68 124	98.03 186	99.93 69	98.28 151	99.92 74	99.44 200

CNLPA		98.57 232	98.34 239	99.28 218	99.18 289	99.10 207	98.34 267	99.41 238	98.48 220	98.52 306	98.98 315	97.05 246	99.78 270	95.59 314	99.50 258	98.96 295

CDPH-MVS		98.56 233	98.20 250	99.61 122	99.50 196	99.46 127	98.32 270	99.41 238	95.22 336	99.21 236	99.10 299	98.34 161	99.82 243	95.09 325	99.66 218	99.56 140

Effi-MVS+		99.06 163	98.97 169	99.34 204	99.31 263	98.98 216	98.31 271	99.91 8	98.81 186	98.79 285	98.94 322	99.14 54	99.84 222	98.79 117	98.74 322	99.20 254

xxxxxxxxxxxxxcwj		99.11 155	98.96 171	99.54 146	99.53 180	99.25 180	98.29 272	99.76 65	99.07 154	99.42 189	99.61 169	98.86 87	99.87 168	96.45 283	99.68 207	99.49 179

save fliter					99.53 180	99.25 180	98.29 272	99.38 254	99.07 154

Patchmatch-RL test		98.60 227	98.36 236	99.33 206	99.77 80	99.07 211	98.27 274	99.87 17	98.91 174	99.74 88	99.72 93	90.57 329	99.79 267	98.55 133	99.85 118	99.11 272

jason		99.16 143	99.11 123	99.32 210	99.75 94	98.44 257	98.26 275	99.39 248	98.70 198	99.74 88	99.30 264	98.54 132	99.97 17	98.48 136	99.82 141	99.55 143
jason: jason.
XVG-OURS-SEG-HR		99.16 143	98.99 165	99.66 94	99.84 34	99.64 88	98.25 276	99.73 80	98.39 228	99.63 124	99.43 235	99.70 11	99.90 126	97.34 228	98.64 326	99.44 200

MDA-MVSNet_test_wron		98.95 188	98.99 165	98.85 271	99.64 139	97.16 309	98.23 277	99.33 262	98.93 171	99.56 154	99.66 134	97.39 230	99.83 233	98.29 150	99.88 100	99.55 143

YYNet1		98.95 188	98.99 165	98.84 273	99.64 139	97.14 310	98.22 278	99.32 264	98.92 173	99.59 142	99.66 134	97.40 228	99.83 233	98.27 152	99.90 84	99.55 143

CANet_DTU		98.91 191	98.85 187	99.09 244	98.79 333	98.13 274	98.18 279	99.31 268	99.48 87	98.86 277	99.51 211	96.56 255	99.95 45	99.05 92	99.95 49	99.19 256

MG-MVS		98.52 239	98.39 233	98.94 257	99.15 292	97.39 304	98.18 279	99.21 291	98.89 178	99.23 230	99.63 151	97.37 232	99.74 286	94.22 335	99.61 233	99.69 52

SCA		98.11 269	98.36 236	97.36 325	99.20 285	92.99 350	98.17 281	98.49 326	98.24 246	99.10 253	99.57 192	96.01 273	99.94 56	96.86 259	99.62 226	99.14 268

TSAR-MVS + GP.		99.12 151	99.04 150	99.38 196	99.34 256	99.16 198	98.15 282	99.29 273	98.18 251	99.63 124	99.62 160	99.18 49	99.68 314	98.20 158	99.74 182	99.30 235

new_pmnet		98.88 197	98.89 183	98.84 273	99.70 117	97.62 297	98.15 282	99.50 211	97.98 261	99.62 131	99.54 203	98.15 179	99.94 56	97.55 216	99.84 122	98.95 296

PatchMatch-RL		98.68 220	98.47 224	99.30 215	99.44 223	99.28 172	98.14 284	99.54 187	97.12 306	99.11 251	99.25 276	97.80 206	99.70 297	96.51 279	99.30 288	98.93 298

xiu_mvs_v2_base		99.02 172	99.11 123	98.77 280	99.37 242	98.09 279	98.13 285	99.51 207	99.47 92	99.42 189	98.54 343	99.38 27	99.97 17	98.83 113	99.33 285	98.24 335

lupinMVS		98.96 185	98.87 185	99.24 228	99.57 163	98.40 260	98.12 286	99.18 293	98.28 244	99.63 124	99.13 292	98.02 188	99.97 17	98.22 156	99.69 204	99.35 226

DELS-MVS		99.34 92	99.30 86	99.48 162	99.51 190	99.36 157	98.12 286	99.53 196	99.36 110	99.41 197	99.61 169	99.22 46	99.87 168	99.21 66	99.68 207	99.20 254
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					99.35 246	99.35 161	98.11 288	99.41 238	94.83 344	97.92 333	98.99 312	98.02 188	99.85 206

train_agg		98.35 257	97.95 267	99.57 135	99.35 246	99.35 161	98.11 288	99.41 238	94.90 340	97.92 333	98.99 312	98.02 188	99.85 206	95.38 320	99.44 266	99.50 174

PMMVS2		99.48 52	99.45 55	99.57 135	99.76 84	98.99 215	98.09 290	99.90 11	98.95 167	99.78 68	99.58 184	99.57 19	99.93 69	99.48 33	99.95 49	99.79 30

Test_1112_low_res		98.95 188	98.73 198	99.63 110	99.68 129	99.15 200	98.09 290	99.80 46	97.14 305	99.46 181	99.40 239	96.11 271	99.89 140	99.01 95	99.84 122	99.84 14

test_8					99.34 256	99.31 167	98.08 292	99.40 245	94.90 340	97.87 337	98.97 318	98.02 188	99.84 222

IterMVS-SCA-FT		99.00 178	99.16 109	98.51 290	99.75 94	95.90 330	98.07 293	99.84 29	99.84 23	99.89 26	99.73 87	96.01 273	99.99 5	99.33 52	100.00 1	99.63 95

HyFIR lowres test		98.91 191	98.64 206	99.73 69	99.85 33	99.47 123	98.07 293	99.83 31	98.64 202	99.89 26	99.60 176	92.57 304	100.00 1	99.33 52	99.97 30	99.72 43

IterMVS		98.97 182	99.16 109	98.42 294	99.74 100	95.64 333	98.06 295	99.83 31	99.83 26	99.85 40	99.74 83	96.10 272	99.99 5	99.27 63	100.00 1	99.63 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							98.04 296

BH-w/o		97.20 297	97.01 300	97.76 315	99.08 306	95.69 332	98.03 297	98.52 323	95.76 330	97.96 332	98.02 353	95.62 278	99.47 350	92.82 344	97.25 350	98.12 341

无先验							98.01 298	99.23 286	95.83 328				99.85 206	95.79 310		99.44 200

pmmvs4		99.13 149	99.06 140	99.36 202	99.57 163	99.10 207	98.01 298	99.25 282	98.78 191	99.58 144	99.44 234	98.24 169	99.76 280	98.74 122	99.93 70	99.22 249

PS-MVSNAJ		99.00 178	99.08 134	98.76 281	99.37 242	98.10 278	98.00 300	99.51 207	99.47 92	99.41 197	98.50 345	99.28 40	99.97 17	98.83 113	99.34 283	98.20 339

test_prior4						99.19 196	98.00 300

agg_prior1		98.33 259	97.92 273	99.57 135	99.35 246	99.36 157	97.99 302	99.39 248	94.85 343	97.76 342	98.98 315	98.03 186	99.85 206	95.49 316	99.44 266	99.51 168

HQP-NCC					99.31 263		97.98 303		97.45 289	98.15 321

ACMP_Plane					99.31 263		97.98 303		97.45 289	98.15 321

HQP-MVS		98.36 254	98.02 262	99.39 192	99.31 263	98.94 222	97.98 303	99.37 255	97.45 289	98.15 321	98.83 330	96.67 253	99.70 297	94.73 328	99.67 214	99.53 156

UnsupCasMVSNet_bld		98.55 236	98.27 244	99.40 189	99.56 173	99.37 154	97.97 306	99.68 105	97.49 288	99.08 254	99.35 255	95.41 280	99.82 243	97.70 203	98.19 338	99.01 293

test_prior3		98.62 224	98.34 239	99.46 167	99.35 246	99.22 189	97.95 307	99.39 248	97.87 269	98.05 327	99.05 303	97.90 197	99.69 303	95.99 301	99.49 260	99.48 184

test_prior2							97.95 307		97.87 269	98.05 327	99.05 303	97.90 197		95.99 301	99.49 260

旧先验2							97.94 309		95.33 335	98.94 265			99.88 155	96.75 266

MVE	92.54 22	96.66 310	96.11 314	98.31 301	99.68 129	97.55 299	97.94 309	95.60 355	99.37 108	90.68 362	98.70 336	96.56 255	98.61 360	86.94 359	99.55 245	98.77 310
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
原ACMM2							97.92 311

MVS_111021_HR		99.12 151	99.02 154	99.40 189	99.50 196	99.11 203	97.92 311	99.71 92	98.76 195	99.08 254	99.47 227	99.17 50	99.54 343	97.85 191	99.76 171	99.54 151

MVS_111021_LR		99.13 149	99.03 152	99.42 179	99.58 153	99.32 166	97.91 313	99.73 80	98.68 199	99.31 218	99.48 222	99.09 59	99.66 323	97.70 203	99.77 169	99.29 238

pmmvs3		98.08 271	97.80 278	98.91 263	99.41 231	97.69 296	97.87 314	99.66 114	95.87 327	99.50 174	99.51 211	90.35 331	99.97 17	98.55 133	99.47 263	99.08 280

XVG-OURS		99.21 129	99.06 140	99.65 99	99.82 44	99.62 94	97.87 314	99.74 77	98.36 231	99.66 114	99.68 124	99.71 9	99.90 126	96.84 262	99.88 100	99.43 206

test222					99.51 190	99.08 210	97.83 316	99.29 273	95.21 337	98.68 295	99.31 262	97.28 235			99.38 276	99.43 206

miper_lstm_enhance		98.65 222	98.60 209	98.82 278	99.20 285	97.33 305	97.78 317	99.66 114	99.01 160	99.59 142	99.50 214	94.62 287	99.85 206	98.12 167	99.90 84	99.26 241

TinyColmap		98.97 182	98.93 174	99.07 248	99.46 217	98.19 271	97.75 318	99.75 72	98.79 189	99.54 161	99.70 107	98.97 74	99.62 334	96.63 274	99.83 132	99.41 210

our_test_3		98.85 201	99.09 132	98.13 306	99.66 135	94.90 340	97.72 319	99.58 170	99.07 154	99.64 120	99.62 160	98.19 176	99.93 69	98.41 139	99.95 49	99.55 143

testdata1							97.72 319		97.86 272

ET-MVSNet_ETH3D		96.78 306	96.07 315	98.91 263	99.26 275	97.92 289	97.70 321	96.05 353	97.96 265	92.37 361	98.43 347	87.06 343	99.90 126	98.27 152	97.56 348	98.91 300

cl_fuxian		98.72 216	98.71 200	98.72 283	99.12 297	97.22 308	97.68 322	99.56 177	98.90 175	99.54 161	99.48 222	96.37 265	99.73 289	97.88 185	99.88 100	99.21 251

ppachtmachnet_test		98.89 196	99.12 120	98.20 304	99.66 135	95.24 337	97.63 323	99.68 105	99.08 152	99.78 68	99.62 160	98.65 119	99.88 155	98.02 172	99.96 42	99.48 184

PAPR		97.56 289	97.07 298	99.04 251	98.80 332	98.11 277	97.63 323	99.25 282	94.56 346	98.02 331	98.25 351	97.43 227	99.68 314	90.90 350	98.74 322	99.33 229

test0.0.03 1		97.37 294	96.91 305	98.74 282	97.72 357	97.57 298	97.60 325	97.36 347	98.00 258	99.21 236	98.02 353	90.04 334	99.79 267	98.37 141	95.89 357	98.86 304

PVSNet_Blended		98.70 218	98.59 211	99.02 252	99.54 175	97.99 283	97.58 326	99.82 36	95.70 331	99.34 211	98.98 315	98.52 139	99.77 278	97.98 177	99.83 132	99.30 235

PMMVS		98.49 243	98.29 243	99.11 242	98.96 315	98.42 259	97.54 327	99.32 264	97.53 285	98.47 310	98.15 352	97.88 200	99.82 243	97.46 222	99.24 296	99.09 277

MSDG		99.08 161	98.98 168	99.37 199	99.60 147	99.13 201	97.54 327	99.74 77	98.84 184	99.53 166	99.55 201	99.10 57	99.79 267	97.07 250	99.86 115	99.18 258

test123		29.31 331	33.05 336	18.08 345	25.93 367	12.24 367	97.53 329	10.93 368	11.78 362	24.21 363	50.08 371	21.04 368	8.60 363	23.51 361	32.43 362	33.39 359

CLD-MVS		98.76 210	98.57 215	99.33 206	99.57 163	98.97 218	97.53 329	99.55 182	96.41 319	99.27 224	99.13 292	99.07 64	99.78 270	96.73 268	99.89 92	99.23 247
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		98.68 220	98.71 200	98.60 287	99.10 303	96.84 317	97.52 331	99.54 187	98.94 168	99.58 144	99.48 222	96.25 268	99.76 280	98.01 175	99.93 70	99.21 251

miper_ehance_all_eth		98.59 230	98.59 211	98.59 288	98.98 314	97.07 311	97.49 332	99.52 204	98.50 217	99.52 168	99.37 245	96.41 263	99.71 295	97.86 189	99.62 226	99.00 294

cl-mvsnet_		98.54 237	98.41 231	98.92 261	99.03 311	97.80 292	97.46 333	99.59 161	98.90 175	99.60 139	99.46 230	93.85 293	99.78 270	97.97 179	99.89 92	99.17 260

cl-mvsnet1		98.54 237	98.42 230	98.92 261	99.03 311	97.80 292	97.46 333	99.59 161	98.90 175	99.60 139	99.46 230	93.87 292	99.78 270	97.97 179	99.89 92	99.18 258

test-LLR		97.15 298	96.95 302	97.74 317	98.18 353	95.02 338	97.38 335	96.10 350	98.00 258	97.81 339	98.58 338	90.04 334	99.91 106	97.69 209	98.78 316	98.31 331

TESTMET0.1,1		96.24 318	95.84 320	97.41 324	98.24 351	93.84 346	97.38 335	95.84 354	98.43 222	97.81 339	98.56 341	79.77 362	99.89 140	97.77 196	98.77 318	98.52 321

test-mter		96.23 319	95.73 322	97.74 317	98.18 353	95.02 338	97.38 335	96.10 350	97.90 267	97.81 339	98.58 338	79.12 365	99.91 106	97.69 209	98.78 316	98.31 331

IB-MVS	95.41 20	95.30 330	94.46 333	97.84 313	98.76 337	95.33 336	97.33 338	96.07 352	96.02 325	95.37 359	97.41 360	76.17 367	99.96 35	97.54 217	95.44 358	98.22 336
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		98.28 260	97.94 271	99.32 210	99.36 244	99.11 203	97.31 339	98.78 313	96.88 310	98.84 279	99.11 298	97.77 208	99.61 338	94.03 339	99.36 281	99.23 247

thisisatest0515		96.98 302	96.42 309	98.66 286	99.42 230	97.47 300	97.27 340	94.30 359	97.24 299	99.15 245	98.86 329	85.01 353	99.87 168	97.10 248	99.39 275	98.63 313

DeepPCF-MVS	98.42 6	99.18 138	99.02 154	99.67 87	99.22 280	99.75 48	97.25 341	99.47 222	98.72 197	99.66 114	99.70 107	99.29 38	99.63 333	98.07 171	99.81 149	99.62 106

cl-mvsnet2		97.56 289	97.28 291	98.40 295	98.37 348	96.75 318	97.24 342	99.37 255	97.31 297	99.41 197	99.22 283	87.30 341	99.37 354	97.70 203	99.62 226	99.08 280

GA-MVS		97.99 276	97.68 284	98.93 260	99.52 185	98.04 282	97.19 343	99.05 303	98.32 242	98.81 282	98.97 318	89.89 336	99.41 353	98.33 146	99.05 303	99.34 228

CL-MVSNet_2432*1600		98.71 217	98.56 218	99.15 238	99.22 280	98.66 244	97.14 344	99.51 207	98.09 255	99.54 161	99.27 271	96.87 251	99.74 286	98.43 138	98.96 308	99.03 289

KD-MVS_2432*1600		95.89 323	95.41 326	97.31 328	94.96 362	93.89 344	97.09 345	99.22 287	97.23 300	98.88 273	99.04 306	79.23 363	99.54 343	96.24 292	96.81 351	98.50 325

miper_refine_blended		95.89 323	95.41 326	97.31 328	94.96 362	93.89 344	97.09 345	99.22 287	97.23 300	98.88 273	99.04 306	79.23 363	99.54 343	96.24 292	96.81 351	98.50 325

USDC		98.96 185	98.93 174	99.05 250	99.54 175	97.99 283	97.07 347	99.80 46	98.21 248	99.75 80	99.77 73	98.43 148	99.64 332	97.90 183	99.88 100	99.51 168

miper_enhance_ethall		98.03 273	97.94 271	98.32 299	98.27 350	96.43 323	96.95 348	99.41 238	96.37 321	99.43 187	98.96 320	94.74 285	99.69 303	97.71 201	99.62 226	98.83 307

CHOSEN 280x420		98.41 250	98.41 231	98.40 295	99.34 256	95.89 331	96.94 349	99.44 231	98.80 188	99.25 226	99.52 208	93.51 297	99.98 7	98.94 107	99.98 21	99.32 232

PCF-MVS	96.03 18	96.73 308	95.86 319	99.33 206	99.44 223	99.16 198	96.87 350	99.44 231	86.58 357	98.95 264	99.40 239	94.38 289	99.88 155	87.93 354	99.80 154	98.95 296
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testmvs		28.94 332	33.33 334	15.79 346	26.03 366	9.81 368	96.77 351	15.67 367	11.55 363	23.87 364	50.74 370	19.03 369	8.53 364	23.21 362	33.07 361	29.03 360

PVSNet	97.47 15	98.42 249	98.44 228	98.35 297	99.46 217	96.26 324	96.70 352	99.34 261	97.68 278	99.00 261	99.13 292	97.40 228	99.72 291	97.59 215	99.68 207	99.08 280

PAPM		95.61 329	94.71 331	98.31 301	99.12 297	96.63 319	96.66 353	98.46 327	90.77 354	96.25 354	98.68 337	93.01 301	99.69 303	81.60 360	97.86 346	98.62 314

cascas		96.99 301	96.82 307	97.48 321	97.57 360	95.64 333	96.43 354	99.56 177	91.75 351	97.13 351	97.61 358	95.58 279	98.63 359	96.68 270	99.11 300	98.18 340

bset_n11_16_dypcd		98.69 219	98.45 226	99.42 179	99.69 120	98.52 252	96.06 355	96.80 349	99.71 43	99.73 92	99.54 203	95.14 281	99.96 35	99.39 43	99.95 49	99.79 30

PVSNet_0	95.53 19	95.85 326	95.31 328	97.47 322	98.78 335	93.48 348	95.72 356	99.40 245	96.18 324	97.37 345	97.73 356	95.73 276	99.58 341	95.49 316	81.40 360	99.36 223

E-PMN		97.14 300	97.43 288	96.27 340	98.79 333	91.62 358	95.54 357	99.01 305	99.44 98	98.88 273	99.12 296	92.78 303	99.68 314	94.30 334	99.03 305	97.50 348

EMVS		96.96 303	97.28 291	95.99 343	98.76 337	91.03 361	95.26 358	98.61 320	99.34 111	98.92 269	98.88 328	93.79 294	99.66 323	92.87 343	99.05 303	97.30 352

uanet_test		8.33 335	11.11 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		24.88 333	33.17 335	0.00 347	0.00 368	0.00 369	0.00 359	99.62 136	0.00 364	0.00 365	99.13 292	99.82 4	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		16.61 334	22.14 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	99.28 40	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		8.33 335	11.11 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		8.33 335	11.11 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		8.33 335	11.11 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		8.33 335	11.11 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		8.26 341	11.02 344	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	99.16 290	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		8.33 335	11.11 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	100.00 1	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ZD-MVS					99.43 225	99.61 100		99.43 235	96.38 320	99.11 251	99.07 301	97.86 201	99.92 88	94.04 338	99.49 260

IU-MVS					99.69 120	99.77 40		99.22 287	97.50 287	99.69 104				97.75 198	99.70 201	99.77 33

test_241102_TWO								99.54 187	99.13 146	99.76 75	99.63 151	98.32 164	99.92 88	97.85 191	99.69 204	99.75 40

test_241102_ONE					99.69 120	99.82 25		99.54 187	99.12 149	99.82 50	99.49 219	98.91 81	99.52 347

test_0728_THIRD									99.18 135	99.62 131	99.61 169	98.58 126	99.91 106	97.72 200	99.80 154	99.77 33

GSMVS																99.14 268

test_part2					99.62 144	99.67 79				99.55 159

sam_mvs1												90.81 326				99.14 268

sam_mvs												90.52 330

MTGPA								99.53 196

test_post											52.41 368	90.25 332	99.86 188

patchmatchnet-post											99.62 160	90.58 328	99.94 56

gm-plane-assit					97.59 358	89.02 366			93.47 347		98.30 349		99.84 222	96.38 286

test9_res														95.10 324	99.44 266	99.50 174

agg_prior2														94.58 332	99.46 265	99.50 174

agg_prior					99.35 246	99.36 157		99.39 248		97.76 342			99.85 206

TestCases				99.63 110	99.78 72	99.64 88		99.83 31	98.63 203	99.63 124	99.72 93	98.68 112	99.75 284	96.38 286	99.83 132	99.51 168

test_prior				99.46 167	99.35 246	99.22 189		99.39 248					99.69 303			99.48 184

新几何1				99.52 149	99.50 196	99.22 189		99.26 279	95.66 332	98.60 300	99.28 269	97.67 215	99.89 140	95.95 305	99.32 286	99.45 195

旧先验1					99.49 201	99.29 170		99.26 279			99.39 243	97.67 215			99.36 281	99.46 193

原ACMM1				99.37 199	99.47 212	98.87 233		99.27 277	96.74 316	98.26 316	99.32 260	97.93 195	99.82 243	95.96 304	99.38 276	99.43 206

testdata2													99.89 140	95.99 301

segment_acmp												98.37 157

testdata				99.42 179	99.51 190	98.93 226		99.30 271	96.20 323	98.87 276	99.40 239	98.33 163	99.89 140	96.29 289	99.28 290	99.44 200

test12				99.54 146	99.29 269	99.33 164		99.16 295		98.43 311		97.54 223	99.82 243		99.47 263	99.48 184

plane_prior7					99.58 153	99.38 151

plane_prior6					99.47 212	99.26 176						97.24 236

plane_prior5								99.54 187					99.82 243	95.84 308	99.78 165	99.60 117

plane_prior4											99.25 276

plane_prior3						99.31 167			98.36 231	99.14 247

plane_prior1					99.51 190

n2								0.00 369

nn								0.00 369

door-mid								99.83 31

lessismore_v0				99.64 106	99.86 30	99.38 151		90.66 363		99.89 26	99.83 43	94.56 288	99.97 17	99.56 25	99.92 74	99.57 137

LGP-MVS_train				99.74 61	99.82 44	99.63 92		99.73 80	97.56 282	99.64 120	99.69 113	99.37 29	99.89 140	96.66 272	99.87 108	99.69 52

test11								99.29 273

door								99.77 60

HQP5-MVS						98.94 222

BP-MVS														94.73 328

HQP4-MVS										98.15 321			99.70 297			99.53 156

HQP3-MVS								99.37 255							99.67 214

HQP2-MVS												96.67 253

NP-MVS					99.40 234	99.13 201					98.83 330

ACMMP++_ref															99.94 62

ACMMP++															99.79 159

Test By Simon												98.41 151

ITE_SJBPF				99.38 196	99.63 141	99.44 134		99.73 80	98.56 209	99.33 213	99.53 206	98.88 86	99.68 314	96.01 299	99.65 221	99.02 292

DeepMVS_CX				97.98 308	99.69 120	96.95 313		99.26 279	75.51 360	95.74 358	98.28 350	96.47 259	99.62 334	91.23 348	97.89 345	97.38 350

High-resolution multi-view benchmark