High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 3	100.00 1	99.85 9

pcd1.5k->3k		41.59 333	44.35 334	33.30 346	99.87 12	0.00 364	0.00 355	99.58 36	0.00 359	0.00 360	0.00 361	99.70 2	0.00 362	0.00 359	99.99 11	99.91 2

mvs_tets		99.63 5	99.67 5	99.49 45	99.88 8	98.61 73	99.34 16	99.71 12	99.27 46	99.90 5	99.74 8	99.68 3	99.97 3	99.55 20	99.99 11	99.88 5

jajsoiax		99.58 8	99.61 7	99.48 46	99.87 12	98.61 73	99.28 30	99.66 19	99.09 69	99.89 8	99.68 19	99.53 4	99.97 3	99.50 22	99.99 11	99.87 6

pmmvs6		99.67 3	99.70 3	99.60 12	99.90 5	99.27 16	99.53 10	99.76 7	99.64 10	99.84 9	99.83 3	99.50 5	99.87 73	99.36 28	99.92 49	99.64 41

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 18	99.85 18	99.11 42	99.90 1	99.78 5	99.63 12	99.78 10	99.67 21	99.48 6	99.81 144	99.30 32	99.97 23	99.77 16
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
XVG-OURS		98.53 119	98.34 127	99.11 92	99.50 87	98.82 60	95.97 287	99.50 65	97.30 188	99.05 116	98.98 138	99.35 7	99.32 326	95.72 219	99.68 144	99.18 211

XVG-OURS-SEG-HR		98.49 123	98.28 133	99.14 89	99.49 93	98.83 58	96.54 263	99.48 74	97.32 186	99.11 106	98.61 205	99.33 8	99.30 329	96.23 193	98.38 287	99.28 189

Anonymous20231211		99.71 2	99.70 3	99.74 2	99.97 2	99.52 2	99.74 4	99.82 4	99.73 6	99.91 4	99.89 2	99.27 9	99.94 20	99.02 50	99.94 33	99.75 21

ACMH	96.65 7	99.25 31	99.24 36	99.26 77	99.72 34	98.38 91	99.07 53	99.55 54	98.30 116	99.65 23	99.45 57	99.22 10	99.76 201	98.44 77	99.77 106	99.64 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cdsmvs_eth3d_5k		24.66 334	32.88 335	0.00 349	0.00 363	0.00 364	0.00 355	99.10 193	0.00 359	0.00 360	97.58 276	99.21 11	0.00 362	0.00 359	0.00 360	0.00 360

wuyk23d		96.06 261	97.62 186	91.38 340	98.65 268	98.57 77	98.85 73	96.95 304	96.86 211	99.90 5	99.16 99	99.18 12	98.40 351	89.23 327	99.77 106	77.18 356

wuykxyi23d		99.36 25	99.31 28	99.50 43	99.81 21	98.67 69	98.08 135	99.75 8	98.03 127	99.90 5	99.60 34	99.18 12	99.94 20	99.46 25	99.98 19	99.89 3

TransMVSNet (Re)		99.44 15	99.47 15	99.36 58	99.80 22	98.58 76	99.27 32	99.57 43	99.39 34	99.75 12	99.62 28	99.17 14	99.83 119	99.06 48	99.62 159	99.66 34

ANet_high		99.57 9	99.67 5	99.28 72	99.89 7	98.09 106	99.14 45	99.93 1	99.82 2	99.93 2	99.81 4	99.17 14	99.94 20	99.31 31	100.00 1	99.82 10

pm-mvs1		99.44 15	99.48 13	99.33 68	99.80 22	98.63 70	99.29 26	99.63 25	99.30 43	99.65 23	99.60 34	99.16 16	99.82 131	99.07 47	99.83 80	99.56 76

test_djsdf		99.52 11	99.51 11	99.53 32	99.86 16	98.74 62	99.39 14	99.56 49	99.11 62	99.70 15	99.73 10	99.00 17	99.97 3	99.26 33	99.98 19	99.89 3

DeepC-MVS	97.60 4	98.97 55	98.93 52	99.10 94	99.35 126	97.98 120	98.01 151	99.46 83	97.56 163	99.54 36	99.50 47	98.97 18	99.84 104	98.06 94	99.92 49	99.49 112
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testgi		98.32 139	98.39 120	98.13 216	99.57 63	95.54 234	97.78 171	99.49 71	97.37 181	99.19 98	97.65 273	98.96 19	99.49 304	96.50 182	98.99 258	99.34 172

anonymousdsp		99.51 12	99.47 15	99.62 6	99.88 8	99.08 47	99.34 16	99.69 15	98.93 84	99.65 23	99.72 11	98.93 20	99.95 13	99.11 45	100.00 1	99.82 10

ACMM	96.08 12	98.91 60	98.73 68	99.48 46	99.55 74	99.14 35	98.07 137	99.37 108	97.62 155	99.04 119	98.96 143	98.84 21	99.79 176	97.43 126	99.65 156	99.49 112
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		99.34 27	99.36 21	99.27 75	99.73 29	98.26 95	99.17 42	99.78 5	99.11 62	99.27 83	99.48 51	98.82 22	99.95 13	98.94 53	99.93 39	99.59 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	96.62 9	99.08 43	99.00 50	99.33 68	99.71 35	98.83 58	98.60 84	99.58 36	99.11 62	99.53 38	99.18 93	98.81 23	99.67 248	96.71 165	99.77 106	99.50 105

SD-MVS		98.40 133	98.68 80	97.54 249	98.96 207	97.99 116	97.88 163	99.36 112	98.20 122	99.63 26	99.04 126	98.76 24	95.33 357	96.56 177	99.74 117	99.31 182

HPM-MVS_fast		99.01 48	98.82 57	99.57 16	99.71 35	99.35 9	99.00 60	99.50 65	97.33 184	98.94 137	98.86 161	98.75 25	99.82 131	97.53 120	99.71 129	99.56 76

XXY-MVS		99.14 38	99.15 44	99.10 94	99.76 27	97.74 145	98.85 73	99.62 28	98.48 107	99.37 65	99.49 50	98.75 25	99.86 78	98.20 89	99.80 94	99.71 28

LPG-MVS_test		98.71 81	98.46 109	99.47 49	99.57 63	98.97 51	98.23 121	99.48 74	96.60 224	99.10 108	99.06 119	98.71 27	99.83 119	95.58 226	99.78 102	99.62 46

LGP-MVS_train				99.47 49	99.57 63	98.97 51		99.48 74	96.60 224	99.10 108	99.06 119	98.71 27	99.83 119	95.58 226	99.78 102	99.62 46

TDRefinement		99.42 19	99.38 19	99.55 20	99.76 27	99.33 11	99.68 5	99.71 12	99.38 36	99.53 38	99.61 30	98.64 29	99.80 156	98.24 86	99.84 74	99.52 98

nrg030		99.40 21	99.35 22	99.54 25	99.58 58	99.13 38	98.98 63	99.48 74	99.68 7	99.46 51	99.26 80	98.62 30	99.73 221	99.17 44	99.92 49	99.76 19

HPM-MVS		98.79 70	98.53 96	99.59 15	99.65 48	99.29 13	99.16 43	99.43 94	96.74 215	98.61 174	98.38 227	98.62 30	99.87 73	96.47 183	99.67 150	99.59 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
canonicalmvs		98.34 137	98.26 134	98.58 168	98.46 283	97.82 137	98.96 64	99.46 83	99.19 55	97.46 256	95.46 330	98.59 32	99.46 310	98.08 93	98.71 272	98.46 276

EG-PatchMatch MVS		98.99 50	99.01 49	98.94 119	99.50 87	97.47 158	98.04 142	99.59 34	98.15 126	99.40 61	99.36 68	98.58 33	99.76 201	98.78 60	99.68 144	99.59 59

v52		99.59 6	99.60 8	99.55 20	99.87 12	99.00 48	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 25

V4		99.59 6	99.60 8	99.55 20	99.87 12	99.00 48	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 25

Effi-MVS+		98.02 163	97.82 173	98.62 160	98.53 280	97.19 171	97.33 213	99.68 16	97.30 188	96.68 292	97.46 285	98.56 36	99.80 156	96.63 170	98.20 293	98.86 248

abl_6		98.99 50	98.78 61	99.61 9	99.45 107	99.46 4	98.60 84	99.50 65	98.59 100	99.24 91	99.04 126	98.54 37	99.89 56	96.45 185	99.62 159	99.50 105

Fast-Effi-MVS+		97.67 188	97.38 200	98.57 170	98.71 250	97.43 161	97.23 220	99.45 86	94.82 275	96.13 307	96.51 306	98.52 38	99.91 43	96.19 196	98.83 265	98.37 284

xiu_mvs_v1_base_debu		97.86 176	98.17 140	96.92 270	98.98 204	93.91 276	96.45 267	99.17 181	97.85 145	98.41 191	97.14 298	98.47 39	99.92 34	98.02 96	99.05 250	96.92 324

xiu_mvs_v1_base		97.86 176	98.17 140	96.92 270	98.98 204	93.91 276	96.45 267	99.17 181	97.85 145	98.41 191	97.14 298	98.47 39	99.92 34	98.02 96	99.05 250	96.92 324

xiu_mvs_v1_base_debi		97.86 176	98.17 140	96.92 270	98.98 204	93.91 276	96.45 267	99.17 181	97.85 145	98.41 191	97.14 298	98.47 39	99.92 34	98.02 96	99.05 250	96.92 324

v7n		99.53 10	99.57 10	99.41 54	99.88 8	98.54 81	99.45 11	99.61 30	99.66 9	99.68 20	99.66 22	98.44 42	99.95 13	99.73 8	99.96 28	99.75 21

COLMAP_ROB	96.50 10	98.99 50	98.85 55	99.41 54	99.58 58	99.10 43	98.74 76	99.56 49	99.09 69	99.33 73	99.19 91	98.40 43	99.72 230	95.98 206	99.76 115	99.42 144
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TranMVSNet+NR-MVSNet		99.17 36	99.07 48	99.46 51	99.37 121	98.87 57	98.39 115	99.42 97	99.42 32	99.36 67	99.06 119	98.38 44	99.95 13	98.34 82	99.90 58	99.57 71

ACMP	95.32 15	98.41 131	98.09 152	99.36 58	99.51 85	98.79 61	97.68 182	99.38 104	95.76 251	98.81 156	98.82 170	98.36 45	99.82 131	94.75 237	99.77 106	99.48 118
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_0402		98.76 75	98.71 72	98.93 120	99.56 70	98.14 104	98.45 111	99.34 122	99.28 45	98.95 133	98.91 149	98.34 46	99.79 176	95.63 223	99.91 54	98.86 248

xiu_mvs_v2_base		97.16 225	97.49 191	96.17 297	98.54 278	92.46 297	95.45 314	98.84 238	97.25 192	97.48 255	96.49 307	98.31 47	99.90 47	96.34 191	98.68 274	96.15 340

VPA-MVSNet		99.30 29	99.30 32	99.28 72	99.49 93	98.36 93	99.00 60	99.45 86	99.63 12	99.52 40	99.44 58	98.25 48	99.88 63	99.09 46	99.84 74	99.62 46

MVS_111021_LR		98.30 141	98.12 148	98.83 133	99.16 168	98.03 114	96.09 284	99.30 139	97.58 159	98.10 203	98.24 239	98.25 48	99.34 323	96.69 166	99.65 156	99.12 220

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 35	99.10 43	99.29 26	99.53 59	99.53 24	99.46 51	99.41 62	98.23 50	99.95 13	98.89 56	99.95 30	99.81 12

DTE-MVSNet		99.43 18	99.35 22	99.66 4	99.71 35	99.30 12	99.31 21	99.51 64	99.64 10	99.56 34	99.46 53	98.23 50	99.97 3	98.78 60	99.93 39	99.72 25

testing_2		98.93 58	98.99 51	98.76 144	99.57 63	97.03 178	97.85 167	99.13 188	98.46 108	99.44 55	99.44 58	98.22 52	99.74 216	98.85 57	99.94 33	99.51 100

Gipuma		99.03 46	99.16 42	98.64 156	99.94 3	98.51 83	99.32 18	99.75 8	99.58 21	98.60 176	99.62 28	98.22 52	99.51 302	97.70 113	99.73 120	97.89 294
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet-Re		98.64 97	98.48 104	99.11 92	98.85 230	98.51 83	98.49 98	99.83 3	98.37 109	99.69 17	99.46 53	98.21 54	99.92 34	94.13 257	99.30 218	98.91 243

tfpnnormal		98.90 61	98.90 53	98.91 123	99.67 45	97.82 137	99.00 60	99.44 89	99.45 29	99.51 44	99.24 83	98.20 55	99.86 78	95.92 208	99.69 139	99.04 226

OPM-MVS		98.56 110	98.32 131	99.25 78	99.41 116	98.73 65	97.13 232	99.18 175	97.10 204	98.75 161	98.92 148	98.18 56	99.65 261	96.68 167	99.56 183	99.37 160

PEN-MVS		99.41 20	99.34 24	99.62 6	99.73 29	99.14 35	99.29 26	99.54 58	99.62 16	99.56 34	99.42 60	98.16 57	99.96 8	98.78 60	99.93 39	99.77 16

DeepPCF-MVS	96.93 5	98.32 139	98.01 159	99.23 80	98.39 288	98.97 51	95.03 323	99.18 175	96.88 210	99.33 73	98.78 175	98.16 57	99.28 332	96.74 160	99.62 159	99.44 136

MVS_111021_HR		98.25 149	98.08 155	98.75 146	99.09 179	97.46 159	95.97 287	99.27 148	97.60 158	97.99 210	98.25 238	98.15 59	99.38 320	96.87 152	99.57 176	99.42 144

Fast-Effi-MVS+-dtu		98.27 145	98.09 152	98.81 135	98.43 286	98.11 105	97.61 193	99.50 65	98.64 96	97.39 264	97.52 280	98.12 60	99.95 13	96.90 150	98.71 272	98.38 282

pcd_1.5k_mvsjas		8.17 337	10.90 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	98.07 61	0.00 362	0.00 359	0.00 360	0.00 360

PS-MVSNAJss		99.46 14	99.49 12	99.35 63	99.90 5	98.15 102	99.20 36	99.65 20	99.48 25	99.92 3	99.71 14	98.07 61	99.96 8	99.53 21	100.00 1	99.93 1

PS-MVSNAJ		97.08 229	97.39 199	96.16 299	98.56 275	92.46 297	95.24 319	98.85 237	97.25 192	97.49 254	95.99 315	98.07 61	99.90 47	96.37 189	98.67 275	96.12 341

UA-Net		99.47 13	99.40 17	99.70 3	99.49 93	99.29 13	99.80 3	99.72 11	99.82 2	99.04 119	99.81 4	98.05 64	99.96 8	98.85 57	99.99 11	99.86 8

ACMMP_Plus		98.75 76	98.48 104	99.57 16	99.58 58	99.29 13	97.82 170	99.25 154	96.94 207	98.78 157	99.12 107	98.02 65	99.84 104	97.13 140	99.67 150	99.59 59

MP-MVS-pluss		98.57 109	98.23 135	99.60 12	99.69 43	99.35 9	97.16 230	99.38 104	94.87 273	98.97 130	98.99 135	98.01 66	99.88 63	97.29 131	99.70 132	99.58 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PGM-MVS		98.66 94	98.37 123	99.55 20	99.53 80	99.18 26	98.23 121	99.49 71	97.01 205	98.69 164	98.88 158	98.00 67	99.89 56	95.87 212	99.59 166	99.58 66

SteuartSystems-ACMMP		98.79 70	98.54 95	99.54 25	99.73 29	99.16 29	98.23 121	99.31 132	97.92 131	98.90 140	98.90 152	98.00 67	99.88 63	96.15 200	99.72 125	99.58 66
Skip Steuart: Steuart Systems R&D Blog.
v748		99.44 15	99.48 13	99.33 68	99.88 8	98.43 88	99.42 12	99.53 59	99.63 12	99.69 17	99.60 34	97.99 69	99.91 43	99.60 14	99.96 28	99.66 34

TinyColmap		97.89 172	97.98 160	97.60 245	98.86 227	94.35 265	96.21 279	99.44 89	97.45 177	99.06 111	98.88 158	97.99 69	99.28 332	94.38 252	99.58 172	99.18 211

HFP-MVS		98.71 81	98.44 113	99.51 41	99.49 93	99.16 29	98.52 92	99.31 132	97.47 170	98.58 179	98.50 220	97.97 71	99.85 89	96.57 174	99.59 166	99.53 92

#test#		98.50 122	98.16 143	99.51 41	99.49 93	99.16 29	98.03 143	99.31 132	96.30 235	98.58 179	98.50 220	97.97 71	99.85 89	95.68 222	99.59 166	99.53 92

3Dnovator	98.27 2	98.81 69	98.73 68	99.05 104	98.76 244	97.81 139	99.25 33	99.30 139	98.57 104	98.55 182	99.33 73	97.95 73	99.90 47	97.16 136	99.67 150	99.44 136

APD-MVS_3200maxsize		98.84 66	98.61 90	99.53 32	99.19 161	99.27 16	98.49 98	99.33 127	98.64 96	99.03 122	98.98 138	97.89 74	99.85 89	96.54 179	99.42 203	99.46 130

CP-MVS		98.70 83	98.42 116	99.52 39	99.36 122	99.12 40	98.72 78	99.36 112	97.54 165	98.30 196	98.40 226	97.86 75	99.89 56	96.53 180	99.72 125	99.56 76

TSAR-MVS + MP.		98.63 99	98.49 103	99.06 103	99.64 51	97.90 129	98.51 96	98.94 220	96.96 206	99.24 91	98.89 157	97.83 76	99.81 144	96.88 151	99.49 199	99.48 118

region2R		98.69 88	98.40 118	99.54 25	99.53 80	99.17 27	98.52 92	99.31 132	97.46 175	98.44 188	98.51 217	97.83 76	99.88 63	96.46 184	99.58 172	99.58 66

APDe-MVS		98.99 50	98.79 60	99.60 12	99.21 151	99.15 34	98.87 70	99.48 74	97.57 161	99.35 69	99.24 83	97.83 76	99.89 56	97.88 103	99.70 132	99.75 21

FMVSNet1		99.17 36	99.17 40	99.17 83	99.55 74	98.24 96	99.20 36	99.44 89	99.21 48	99.43 56	99.55 41	97.82 79	99.86 78	98.42 79	99.89 64	99.41 146

PHI-MVS		98.29 144	97.95 162	99.34 66	98.44 285	99.16 29	98.12 131	99.38 104	96.01 247	98.06 206	98.43 224	97.80 80	99.67 248	95.69 221	99.58 172	99.20 205

ACMMPR		98.70 83	98.42 116	99.54 25	99.52 82	99.14 35	98.52 92	99.31 132	97.47 170	98.56 181	98.54 215	97.75 81	99.88 63	96.57 174	99.59 166	99.58 66

SMA-MVS		98.47 125	98.11 149	99.53 32	99.16 168	99.27 16	98.05 141	99.30 139	94.34 286	99.22 95	99.10 110	97.72 82	99.79 176	96.45 185	99.68 144	99.53 92

ACMMP		98.75 76	98.50 100	99.52 39	99.56 70	99.16 29	98.87 70	99.37 108	97.16 201	98.82 154	99.01 132	97.71 83	99.87 73	96.29 192	99.69 139	99.54 87
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
CSCG		98.68 91	98.50 100	99.20 82	99.45 107	98.63 70	98.56 88	99.57 43	97.87 143	98.85 148	98.04 255	97.66 84	99.84 104	96.72 162	99.81 90	99.13 219

AllTest		98.44 129	98.20 137	99.16 86	99.50 87	98.55 78	98.25 120	99.58 36	96.80 213	98.88 145	99.06 119	97.65 85	99.57 284	94.45 246	99.61 163	99.37 160

TestCases				99.16 86	99.50 87	98.55 78		99.58 36	96.80 213	98.88 145	99.06 119	97.65 85	99.57 284	94.45 246	99.61 163	99.37 160

test20.03		98.78 73	98.77 63	98.78 140	99.46 104	97.20 170	97.78 171	99.24 158	99.04 72	99.41 59	98.90 152	97.65 85	99.76 201	97.70 113	99.79 98	99.39 153

ITE_SJBPF				98.87 128	99.22 145	98.48 85		99.35 118	97.50 167	98.28 197	98.60 206	97.64 88	99.35 322	93.86 265	99.27 223	98.79 257

mPP-MVS		98.64 97	98.34 127	99.54 25	99.54 78	99.17 27	98.63 81	99.24 158	97.47 170	98.09 204	98.68 188	97.62 89	99.89 56	96.22 194	99.62 159	99.57 71

CLD-MVS		97.49 200	97.16 210	98.48 187	99.07 183	97.03 178	94.71 329	99.21 161	94.46 280	98.06 206	97.16 296	97.57 90	99.48 307	94.46 245	99.78 102	98.95 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_part1								99.28 143				97.56 91			99.57 176	99.53 92

ESAPD		98.25 149	97.83 172	99.50 43	99.36 122	99.10 43	97.25 218	99.28 143	96.66 221	99.05 116	98.71 183	97.56 91	99.86 78	93.00 283	99.57 176	99.53 92

DeepC-MVS_fast	96.85 6	98.30 141	98.15 145	98.75 146	98.61 270	97.23 167	97.76 175	99.09 194	97.31 187	98.75 161	98.66 192	97.56 91	99.64 263	96.10 202	99.55 185	99.39 153
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PM-MVS		98.82 67	98.72 71	99.12 91	99.64 51	98.54 81	97.98 154	99.68 16	97.62 155	99.34 72	99.18 93	97.54 94	99.77 196	97.79 106	99.74 117	99.04 226

XVG-ACMP-BASELINE		98.56 110	98.34 127	99.22 81	99.54 78	98.59 75	97.71 179	99.46 83	97.25 192	98.98 128	98.99 135	97.54 94	99.84 104	95.88 209	99.74 117	99.23 199

XVS		98.72 80	98.45 111	99.53 32	99.46 104	99.21 22	98.65 79	99.34 122	98.62 98	97.54 250	98.63 201	97.50 96	99.83 119	96.79 156	99.53 190	99.56 76

X-MVStestdata		94.32 299	92.59 316	99.53 32	99.46 104	99.21 22	98.65 79	99.34 122	98.62 98	97.54 250	45.85 356	97.50 96	99.83 119	96.79 156	99.53 190	99.56 76

v13		99.24 32	99.39 18	98.77 142	99.63 53	96.79 186	99.24 34	99.65 20	99.39 34	99.62 27	99.70 16	97.50 96	99.84 104	99.78 5	100.00 1	99.67 32

DELS-MVS		98.27 145	98.20 137	98.48 187	98.86 227	96.70 192	95.60 309	99.20 165	97.73 149	98.45 187	98.71 183	97.50 96	99.82 131	98.21 88	99.59 166	98.93 240
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
TSAR-MVS + GP.		98.18 155	97.98 160	98.77 142	98.71 250	97.88 130	96.32 274	98.66 259	96.33 232	99.23 94	98.51 217	97.48 100	99.40 316	97.16 136	99.46 200	99.02 229

Regformer-4		98.73 79	98.68 80	98.89 126	99.02 198	97.22 169	97.17 228	99.06 197	99.21 48	99.17 102	98.85 163	97.45 101	99.86 78	98.48 76	99.70 132	99.60 53

new-patchmatchnet		98.35 136	98.74 67	97.18 261	99.24 139	92.23 301	96.42 270	99.48 74	98.30 116	99.69 17	99.53 45	97.44 102	99.82 131	98.84 59	99.77 106	99.49 112

v12		99.21 33	99.37 20	98.74 150	99.60 56	96.72 191	99.19 40	99.65 20	99.35 40	99.62 27	99.69 17	97.43 103	99.83 119	99.76 6	100.00 1	99.66 34

PMVS	91.26 20	97.86 176	97.94 164	97.65 241	99.71 35	97.94 126	98.52 92	98.68 258	98.99 76	97.52 252	99.35 69	97.41 104	98.18 352	91.59 306	99.67 150	96.82 331
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS		98.46 127	98.09 152	99.54 25	99.57 63	99.22 21	98.50 97	99.19 171	97.61 157	97.58 246	98.66 192	97.40 105	99.88 63	94.72 240	99.60 165	99.54 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSDG		97.71 185	97.52 190	98.28 209	98.91 219	96.82 185	94.42 334	99.37 108	97.65 153	98.37 195	98.29 236	97.40 105	99.33 325	94.09 258	99.22 228	98.68 270

Regformer-2		98.60 106	98.46 109	99.02 110	98.85 230	97.71 147	96.91 242	99.09 194	98.98 78	99.01 123	98.64 197	97.37 107	99.84 104	97.75 112	99.57 176	99.52 98

V9		99.18 35	99.34 24	98.70 151	99.58 58	96.63 194	99.14 45	99.64 24	99.30 43	99.61 29	99.68 19	97.33 108	99.83 119	99.75 7	100.00 1	99.65 38

LS3D		98.63 99	98.38 122	99.36 58	97.25 334	99.38 6	99.12 49	99.32 130	99.21 48	98.44 188	98.88 158	97.31 109	99.80 156	96.58 172	99.34 212	98.92 241

EI-MVSNet-UG-set		98.69 88	98.71 72	98.62 160	99.10 176	96.37 204	97.23 220	98.87 232	99.20 51	99.19 98	98.99 135	97.30 110	99.85 89	98.77 63	99.79 98	99.65 38

WR-MVS_H		99.33 28	99.22 37	99.65 5	99.71 35	99.24 20	99.32 18	99.55 54	99.46 28	99.50 45	99.34 71	97.30 110	99.93 26	98.90 54	99.93 39	99.77 16

EI-MVSNet-Vis-set		98.68 91	98.70 75	98.63 158	99.09 179	96.40 202	97.23 220	98.86 236	99.20 51	99.18 101	98.97 140	97.29 112	99.85 89	98.72 65	99.78 102	99.64 41

pmmvs-eth3d		98.47 125	98.34 127	98.86 130	99.30 133	97.76 142	97.16 230	99.28 143	95.54 260	99.42 58	99.19 91	97.27 113	99.63 264	97.89 101	99.97 23	99.20 205

CNVR-MVS		98.17 157	97.87 171	99.07 98	98.67 262	98.24 96	97.01 235	98.93 223	97.25 192	97.62 242	98.34 231	97.27 113	99.57 284	96.42 188	99.33 213	99.39 153

OMC-MVS		97.88 174	97.49 191	99.04 106	98.89 224	98.63 70	96.94 238	99.25 154	95.02 268	98.53 184	98.51 217	97.27 113	99.47 308	93.50 276	99.51 193	99.01 230

Regformer-1		98.55 114	98.44 113	98.87 128	98.85 230	97.29 164	96.91 242	98.99 218	98.97 79	98.99 126	98.64 197	97.26 116	99.81 144	97.79 106	99.57 176	99.51 100

v11		99.12 41	99.31 28	98.53 179	99.59 57	96.11 214	99.08 50	99.65 20	99.15 57	99.60 30	99.69 17	97.26 116	99.83 119	99.81 3	100.00 1	99.66 34

Regformer-3		98.61 105	98.61 90	98.63 158	99.02 198	96.53 195	97.17 228	98.84 238	99.13 61	99.10 108	98.85 163	97.24 118	99.79 176	98.41 80	99.70 132	99.57 71

testmv		98.51 121	98.47 106	98.61 163	99.24 139	96.53 195	96.66 257	99.73 10	98.56 106	99.50 45	99.23 87	97.24 118	99.87 73	96.16 199	99.93 39	99.44 136

V14		99.14 38	99.30 32	98.66 154	99.56 70	96.53 195	99.08 50	99.63 25	99.24 47	99.60 30	99.66 22	97.23 120	99.82 131	99.73 8	100.00 1	99.65 38

DP-MVS		98.93 58	98.81 59	99.28 72	99.21 151	98.45 87	98.46 110	99.33 127	99.63 12	99.48 47	99.15 103	97.23 120	99.75 207	97.17 135	99.66 155	99.63 45

MVS_Test		98.18 155	98.36 124	97.67 239	98.48 281	94.73 251	98.18 125	99.02 210	97.69 151	98.04 208	99.11 108	97.22 122	99.56 287	98.57 71	98.90 264	98.71 264

Anonymous20240521		99.36 25	99.31 28	99.53 32	99.80 22	98.97 51	99.54 9	99.48 74	99.44 30	99.58 33	99.55 41	97.17 123	99.88 63	99.34 29	99.91 54	99.74 24

MCST-MVS		98.00 166	97.63 185	99.10 94	99.24 139	98.17 101	96.89 244	98.73 255	95.66 252	97.92 211	97.70 270	97.17 123	99.66 256	96.18 198	99.23 227	99.47 126

v15		99.11 42	99.27 34	98.62 160	99.52 82	96.43 199	99.01 56	99.63 25	99.18 56	99.59 32	99.64 26	97.13 125	99.81 144	99.71 10	100.00 1	99.64 41

FC-MVSNet-test		99.27 30	99.25 35	99.34 66	99.77 26	98.37 92	99.30 25	99.57 43	99.61 18	99.40 61	99.50 47	97.12 126	99.85 89	99.02 50	99.94 33	99.80 13

MIMVSNet1		99.38 23	99.32 27	99.55 20	99.86 16	99.19 25	99.41 13	99.59 34	99.59 19	99.71 14	99.57 39	97.12 126	99.90 47	99.21 39	99.87 69	99.54 87

3Dnovator+	97.89 3	98.69 88	98.51 98	99.24 79	98.81 240	98.40 89	99.02 55	99.19 171	98.99 76	98.07 205	99.28 76	97.11 128	99.84 104	96.84 154	99.32 215	99.47 126

v17		99.07 44	99.22 37	98.61 163	99.50 87	96.42 200	99.01 56	99.60 32	99.15 57	99.48 47	99.61 30	97.05 129	99.81 144	99.64 12	99.98 19	99.61 50

v16		99.07 44	99.22 37	98.61 163	99.50 87	96.42 200	99.01 56	99.60 32	99.15 57	99.46 51	99.61 30	97.04 130	99.81 144	99.64 12	99.97 23	99.61 50

MSLP-MVS++		98.02 163	98.14 147	97.64 243	98.58 273	95.19 243	97.48 206	99.23 160	97.47 170	97.90 214	98.62 203	97.04 130	98.81 349	97.55 118	99.41 204	98.94 239

APD-MVS		98.10 159	97.67 179	99.42 52	99.11 175	98.93 56	97.76 175	99.28 143	94.97 270	98.72 163	98.77 177	97.04 130	99.85 89	93.79 267	99.54 186	99.49 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
segment_acmp												97.02 133

CP-MVSNet		99.21 33	99.09 46	99.56 18	99.65 48	98.96 55	99.13 47	99.34 122	99.42 32	99.33 73	99.26 80	97.01 134	99.94 20	98.74 64	99.93 39	99.79 14

ambc				98.24 211	98.82 238	95.97 220	98.62 82	99.00 217		99.27 83	99.21 88	96.99 135	99.50 303	96.55 178	99.50 198	99.26 193

zzz-MVS		98.79 70	98.52 97	99.61 9	99.67 45	99.36 7	97.33 213	99.20 165	98.83 88	98.89 142	98.90 152	96.98 136	99.92 34	97.16 136	99.70 132	99.56 76

MTAPA		98.88 62	98.64 85	99.61 9	99.67 45	99.36 7	98.43 112	99.20 165	98.83 88	98.89 142	98.90 152	96.98 136	99.92 34	97.16 136	99.70 132	99.56 76

v8		99.01 48	99.16 42	98.57 170	99.47 100	96.31 206	98.90 68	99.47 81	99.03 73	99.52 40	99.57 39	96.93 138	99.81 144	99.60 14	99.98 19	99.60 53

v1neww		98.70 83	98.76 64	98.52 180	99.47 100	96.30 208	98.03 143	99.18 175	97.92 131	99.26 88	99.08 113	96.91 139	99.78 186	99.19 41	99.82 83	99.47 126

v7new		98.70 83	98.76 64	98.52 180	99.47 100	96.30 208	98.03 143	99.18 175	97.92 131	99.26 88	99.08 113	96.91 139	99.78 186	99.19 41	99.82 83	99.47 126

v18		99.02 47	99.17 40	98.57 170	99.45 107	96.31 206	98.94 65	99.58 36	99.06 71	99.43 56	99.58 38	96.91 139	99.80 156	99.60 14	99.97 23	99.59 59

v6		98.70 83	98.76 64	98.52 180	99.47 100	96.30 208	98.03 143	99.18 175	97.92 131	99.27 83	99.08 113	96.91 139	99.78 186	99.19 41	99.82 83	99.48 118

QAPM		97.31 213	96.81 225	98.82 134	98.80 242	97.49 157	99.06 54	99.19 171	90.22 331	97.69 239	99.16 99	96.91 139	99.90 47	90.89 320	99.41 204	99.07 223

CDPH-MVS		97.26 217	96.66 236	99.07 98	99.00 201	98.15 102	96.03 285	99.01 213	91.21 325	97.79 233	97.85 263	96.89 144	99.69 237	92.75 291	99.38 207	99.39 153

PVSNet_Blended_VisFu		98.17 157	98.15 145	98.22 212	99.73 29	95.15 244	97.36 212	99.68 16	94.45 282	98.99 126	99.27 78	96.87 145	99.94 20	97.13 140	99.91 54	99.57 71

V42		98.78 73	98.78 61	98.76 144	99.44 110	97.04 177	98.27 119	99.19 171	97.87 143	99.25 90	99.16 99	96.84 146	99.78 186	99.21 39	99.84 74	99.46 130

PMMVS2		98.07 162	98.08 155	98.04 225	99.41 116	94.59 257	94.59 332	99.40 99	97.50 167	98.82 154	98.83 167	96.83 147	99.84 104	97.50 122	99.81 90	99.71 28

PVSNet_BlendedMVS		97.55 197	97.53 189	97.60 245	98.92 216	93.77 283	96.64 258	99.43 94	94.49 278	97.62 242	99.18 93	96.82 148	99.67 248	94.73 238	99.93 39	99.36 166

PVSNet_Blended		96.88 238	96.68 233	97.47 252	98.92 216	93.77 283	94.71 329	99.43 94	90.98 326	97.62 242	97.36 292	96.82 148	99.67 248	94.73 238	99.56 183	98.98 233

ab-mvs		98.41 131	98.36 124	98.59 167	99.19 161	97.23 167	99.32 18	98.81 244	97.66 152	98.62 172	99.40 65	96.82 148	99.80 156	95.88 209	99.51 193	98.75 262

FIs		99.14 38	99.09 46	99.29 71	99.70 41	98.28 94	99.13 47	99.52 63	99.48 25	99.24 91	99.41 62	96.79 151	99.82 131	98.69 66	99.88 65	99.76 19

UniMVSNet (Re)		98.87 63	98.71 72	99.35 63	99.24 139	98.73 65	97.73 178	99.38 104	98.93 84	99.12 105	98.73 181	96.77 152	99.86 78	98.63 68	99.80 94	99.46 130

API-MVS		97.04 233	96.91 220	97.42 255	97.88 313	98.23 100	98.18 125	98.50 266	97.57 161	97.39 264	96.75 303	96.77 152	99.15 338	90.16 324	99.02 254	94.88 350

DU-MVS		98.82 67	98.63 86	99.39 57	99.16 168	98.74 62	97.54 202	99.25 154	98.84 87	99.06 111	98.76 179	96.76 154	99.93 26	98.57 71	99.77 106	99.50 105

Baseline_NR-MVSNet		98.98 54	98.86 54	99.36 58	99.82 20	98.55 78	97.47 208	99.57 43	99.37 37	99.21 96	99.61 30	96.76 154	99.83 119	98.06 94	99.83 80	99.71 28

VPNet		98.87 63	98.83 56	99.01 111	99.70 41	97.62 153	98.43 112	99.35 118	99.47 27	99.28 81	99.05 124	96.72 156	99.82 131	98.09 92	99.36 208	99.59 59

UniMVSNet_NR-MVSNet		98.86 65	98.68 80	99.40 56	99.17 166	98.74 62	97.68 182	99.40 99	99.14 60	99.06 111	98.59 207	96.71 157	99.93 26	98.57 71	99.77 106	99.53 92

LF4IMVS		97.90 171	97.69 178	98.52 180	99.17 166	97.66 149	97.19 227	99.47 81	96.31 234	97.85 219	98.20 243	96.71 157	99.52 297	94.62 241	99.72 125	98.38 282

v148		98.45 128	98.60 92	98.00 227	99.44 110	94.98 247	97.44 209	99.06 197	98.30 116	99.32 78	98.97 140	96.65 159	99.62 266	98.37 81	99.85 72	99.39 153

v7		98.67 93	98.73 68	98.50 185	99.43 114	96.21 212	98.00 152	99.31 132	97.58 159	99.17 102	99.18 93	96.63 160	99.80 156	99.42 27	99.88 65	99.48 118

v10		98.97 55	99.11 45	98.55 175	99.44 110	96.21 212	98.90 68	99.55 54	98.73 94	99.48 47	99.60 34	96.63 160	99.83 119	99.70 11	99.99 11	99.61 50

v1		98.63 99	98.70 75	98.41 194	99.39 118	95.96 221	97.64 187	99.20 165	97.92 131	99.36 67	99.07 118	96.63 160	99.78 186	99.25 35	99.90 58	99.50 105

v1141		98.63 99	98.70 75	98.41 194	99.39 118	95.96 221	97.64 187	99.21 161	97.92 131	99.35 69	99.08 113	96.61 163	99.78 186	99.25 35	99.90 58	99.50 105

divwei89l23v2f112		98.63 99	98.70 75	98.41 194	99.39 118	95.96 221	97.64 187	99.21 161	97.92 131	99.35 69	99.08 113	96.61 163	99.78 186	99.25 35	99.90 58	99.50 105

OpenMVS	96.65 7	97.09 228	96.68 233	98.32 204	98.32 292	97.16 174	98.86 72	99.37 108	89.48 335	96.29 305	99.15 103	96.56 165	99.90 47	92.90 285	99.20 231	97.89 294

UGNet		98.53 119	98.45 111	98.79 137	97.94 309	96.96 181	99.08 50	98.54 264	99.10 66	96.82 289	99.47 52	96.55 166	99.84 104	98.56 74	99.94 33	99.55 84
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
TEST9					98.71 250	98.08 109	95.96 291	99.03 206	91.40 322	95.85 314	97.53 278	96.52 167	99.76 201

Test By Simon												96.52 167

agg_prior1		97.06 230	96.40 246	99.03 107	98.68 259	97.99 116	95.76 302	99.01 213	91.73 315	95.59 318	97.50 281	96.49 169	99.77 196	93.71 268	99.14 242	99.34 172

train_agg		97.10 227	96.45 245	99.07 98	98.71 250	98.08 109	95.96 291	99.03 206	91.64 316	95.85 314	97.53 278	96.47 170	99.76 201	93.67 269	99.16 238	99.36 166

test_8					98.67 262	98.01 115	95.91 297	99.02 210	91.64 316	95.79 316	97.50 281	96.47 170	99.76 201

Effi-MVS+-dtu		98.26 147	97.90 168	99.35 63	98.02 306	99.49 3	98.02 150	99.16 184	98.29 119	97.64 241	97.99 257	96.44 172	99.95 13	96.66 168	98.93 263	98.60 271

mvs-test1		97.83 182	97.48 194	98.89 126	98.02 306	99.20 24	97.20 224	99.16 184	98.29 119	96.46 303	97.17 295	96.44 172	99.92 34	96.66 168	97.90 313	97.54 317

ppachtmachnet_test		97.50 198	97.74 176	96.78 277	98.70 254	91.23 321	94.55 333	99.05 201	96.36 231	99.21 96	98.79 174	96.39 174	99.78 186	96.74 160	99.82 83	99.34 172

OurMVSNet-221017-0		99.37 24	99.31 28	99.53 32	99.91 4	98.98 50	99.63 6	99.58 36	99.44 30	99.78 10	99.76 6	96.39 174	99.92 34	99.44 26	99.92 49	99.68 31

NR-MVSNet		98.95 57	98.82 57	99.36 58	99.16 168	98.72 67	99.22 35	99.20 165	99.10 66	99.72 13	98.76 179	96.38 176	99.86 78	98.00 99	99.82 83	99.50 105

v1192		98.60 106	98.66 83	98.41 194	99.27 135	95.88 225	97.52 203	99.36 112	97.41 178	99.33 73	99.20 90	96.37 177	99.82 131	99.57 18	99.92 49	99.55 84

v1144		98.60 106	98.66 83	98.41 194	99.36 122	95.90 224	97.58 197	99.34 122	97.51 166	99.27 83	99.15 103	96.34 178	99.80 156	99.47 24	99.93 39	99.51 100

mvs_anonymous		97.83 182	98.16 143	96.87 273	98.18 301	91.89 303	97.31 215	98.90 229	97.37 181	98.83 151	99.46 53	96.28 179	99.79 176	98.90 54	98.16 296	98.95 237

DSMNet-mixed		97.42 207	97.60 187	96.87 273	99.15 172	91.46 308	98.54 91	99.12 190	92.87 303	97.58 246	99.63 27	96.21 180	99.90 47	95.74 218	99.54 186	99.27 190

agg_prior3		96.95 237	96.27 250	99.00 113	98.68 259	97.91 127	95.96 291	99.01 213	90.74 328	95.60 317	97.45 286	96.14 181	99.74 216	93.67 269	99.16 238	99.36 166

TAPA-MVS	96.21 11	96.63 248	95.95 255	98.65 155	98.93 212	98.09 106	96.93 239	99.28 143	83.58 350	98.13 202	97.78 266	96.13 182	99.40 316	93.52 274	99.29 221	98.45 277
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1240		98.55 114	98.62 87	98.32 204	99.22 145	95.58 233	97.51 205	99.45 86	97.16 201	99.45 54	99.24 83	96.12 183	99.85 89	99.60 14	99.88 65	99.55 84

RPSCF		98.62 104	98.36 124	99.42 52	99.65 48	99.42 5	98.55 90	99.57 43	97.72 150	98.90 140	99.26 80	96.12 183	99.52 297	95.72 219	99.71 129	99.32 178

MS-PatchMatch		97.68 187	97.75 175	97.45 253	98.23 298	93.78 282	97.29 216	98.84 238	96.10 243	98.64 168	98.65 194	96.04 185	99.36 321	96.84 154	99.14 242	99.20 205

v1921920		98.54 117	98.60 92	98.38 200	99.20 160	95.76 229	97.56 199	99.36 112	97.23 197	99.38 63	99.17 98	96.02 186	99.84 104	99.57 18	99.90 58	99.54 87

HPM-MVS++		98.10 159	97.64 184	99.48 46	99.09 179	99.13 38	97.52 203	98.75 252	97.46 175	96.90 284	97.83 264	96.01 187	99.84 104	95.82 216	99.35 210	99.46 130

Anonymous20231206		98.21 152	98.21 136	98.20 213	99.51 85	95.43 239	98.13 129	99.32 130	96.16 241	98.93 138	98.82 170	96.00 188	99.83 119	97.32 130	99.73 120	99.36 166

EI-MVSNet		98.40 133	98.51 98	98.04 225	99.10 176	94.73 251	97.20 224	98.87 232	98.97 79	99.06 111	99.02 130	96.00 188	99.80 156	98.58 69	99.82 83	99.60 53

IterMVS-LS		98.55 114	98.70 75	98.09 218	99.48 98	94.73 251	97.22 223	99.39 101	98.97 79	99.38 63	99.31 75	96.00 188	99.93 26	98.58 69	99.97 23	99.60 53
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NCCC		97.86 176	97.47 195	99.05 104	98.61 270	98.07 111	96.98 236	98.90 229	97.63 154	97.04 276	97.93 261	95.99 191	99.66 256	95.31 229	98.82 266	99.43 141

our_test_3		97.39 209	97.73 177	96.34 288	98.70 254	89.78 325	94.61 331	98.97 219	96.50 226	99.04 119	98.85 163	95.98 192	99.84 104	97.26 133	99.67 150	99.41 146

v2v482		98.56 110	98.62 87	98.37 201	99.42 115	95.81 228	97.58 197	99.16 184	97.90 139	99.28 81	99.01 132	95.98 192	99.79 176	99.33 30	99.90 58	99.51 100

MVS		93.19 317	92.09 321	96.50 286	96.91 339	94.03 272	98.07 137	98.06 281	68.01 354	94.56 336	96.48 308	95.96 194	99.30 329	83.84 345	96.89 331	96.17 337

MVP-Stereo		98.08 161	97.92 166	98.57 170	98.96 207	96.79 186	97.90 162	99.18 175	96.41 230	98.46 186	98.95 144	95.93 195	99.60 273	96.51 181	98.98 260	99.31 182
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior3		97.48 203	97.00 215	98.95 117	98.69 257	97.95 124	95.74 304	99.03 206	96.48 227	96.11 308	97.63 274	95.92 196	99.59 277	94.16 253	99.20 231	99.30 185

test_prior2							95.74 304		96.48 227	96.11 308	97.63 274	95.92 196		94.16 253	99.20 231

no-one		97.98 169	98.10 151	97.61 244	99.55 74	93.82 281	96.70 254	98.94 220	96.18 237	99.52 40	99.41 62	95.90 198	99.81 144	96.72 162	99.99 11	99.20 205

AdaColmap		97.14 226	96.71 231	98.46 189	98.34 291	97.80 140	96.95 237	98.93 223	95.58 259	96.92 280	97.66 272	95.87 199	99.53 293	90.97 317	99.14 242	98.04 291

v144192		98.54 117	98.57 94	98.45 191	99.21 151	95.98 219	97.63 190	99.36 112	97.15 203	99.32 78	99.18 93	95.84 200	99.84 104	99.50 22	99.91 54	99.54 87

PatchMatch-RL		97.24 220	96.78 226	98.61 163	99.03 196	97.83 134	96.36 272	99.06 197	93.49 298	97.36 267	97.78 266	95.75 201	99.49 304	93.44 277	98.77 267	98.52 274

F-COLMAP		97.30 214	96.68 233	99.14 89	99.19 161	98.39 90	97.27 217	99.30 139	92.93 301	96.62 294	98.00 256	95.73 202	99.68 242	92.62 293	98.46 286	99.35 171

PMMVS		96.51 252	95.98 254	98.09 218	97.53 324	95.84 226	94.92 325	98.84 238	91.58 319	96.05 312	95.58 322	95.68 203	99.66 256	95.59 225	98.09 306	98.76 261

N_pmnet		97.63 191	97.17 209	98.99 114	99.27 135	97.86 132	95.98 286	93.41 340	95.25 265	99.47 50	98.90 152	95.63 204	99.85 89	96.91 148	99.73 120	99.27 190

WR-MVS		98.40 133	98.19 139	99.03 107	99.00 201	97.65 150	96.85 246	98.94 220	98.57 104	98.89 142	98.50 220	95.60 205	99.85 89	97.54 119	99.85 72	99.59 59

CANet		97.87 175	97.76 174	98.19 214	97.75 314	95.51 236	96.76 250	99.05 201	97.74 148	96.93 279	98.21 242	95.59 206	99.89 56	97.86 105	99.93 39	99.19 210

1314		95.74 266	95.60 263	96.17 297	97.53 324	92.75 294	98.07 137	98.31 273	91.22 324	94.25 338	96.68 304	95.53 207	99.03 340	91.64 304	97.18 326	96.74 332

114514_t		96.50 254	95.77 257	98.69 152	99.48 98	97.43 161	97.84 168	99.55 54	81.42 352	96.51 299	98.58 208	95.53 207	99.67 248	93.41 278	99.58 172	98.98 233

test12				98.93 120	98.58 273	97.83 134		98.66 259		96.53 297		95.51 209	99.69 237		99.13 245	99.27 190

旧先验1					98.82 238	97.45 160		98.76 249			98.34 231	95.50 210			99.01 256	99.23 199

YYNet1		97.60 192	97.67 179	97.39 257	99.04 193	93.04 293	95.27 317	98.38 271	97.25 192	98.92 139	98.95 144	95.48 211	99.73 221	96.99 145	98.74 268	99.41 146

MDA-MVSNet_test_wron		97.60 192	97.66 182	97.41 256	99.04 193	93.09 290	95.27 317	98.42 269	97.26 191	98.88 145	98.95 144	95.43 212	99.73 221	97.02 144	98.72 269	99.41 146

原ACMM1				98.35 202	98.90 220	96.25 211		98.83 243	92.48 307	96.07 311	98.10 250	95.39 213	99.71 231	92.61 294	98.99 258	99.08 221

USDC		97.41 208	97.40 197	97.44 254	98.94 210	93.67 285	95.17 320	99.53 59	94.03 291	98.97 130	99.10 110	95.29 214	99.34 323	95.84 215	99.73 120	99.30 185

testdata				98.09 218	98.93 212	95.40 240		98.80 246	90.08 333	97.45 257	98.37 228	95.26 215	99.70 233	93.58 273	98.95 262	99.17 215

BH-untuned		96.83 240	96.75 228	97.08 263	98.74 246	93.33 289	96.71 253	98.26 274	96.72 216	98.44 188	97.37 291	95.20 216	99.47 308	91.89 300	97.43 320	98.44 278

MVE	83.40 22	92.50 321	91.92 323	94.25 327	98.83 235	91.64 306	92.71 346	83.52 359	95.92 248	86.46 357	95.46 330	95.20 216	95.40 356	80.51 352	98.64 276	95.73 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
BH-RMVSNet		96.83 240	96.58 240	97.58 247	98.47 282	94.05 271	96.67 256	97.36 294	96.70 220	97.87 216	97.98 258	95.14 218	99.44 313	90.47 323	98.58 280	99.25 195

pmmvs4		97.58 194	97.28 205	98.51 184	98.84 233	96.93 183	95.40 316	98.52 265	93.60 295	98.61 174	98.65 194	95.10 219	99.60 273	96.97 146	99.79 98	98.99 232

EU-MVSNet		97.66 189	98.50 100	95.13 317	99.63 53	85.84 339	98.35 116	98.21 275	98.23 121	99.54 36	99.46 53	95.02 220	99.68 242	98.24 86	99.87 69	99.87 6

DP-MVS Recon		97.33 212	96.92 218	98.57 170	99.09 179	97.99 116	96.79 247	99.35 118	93.18 299	97.71 237	98.07 254	95.00 221	99.31 327	93.97 260	99.13 245	98.42 280

HQP_MVS		97.99 168	97.67 179	98.93 120	99.19 161	97.65 150	97.77 173	99.27 148	98.20 122	97.79 233	97.98 258	94.90 222	99.70 233	94.42 248	99.51 193	99.45 134

plane_prior6					98.99 203	97.70 148						94.90 222

CPTT-MVS		97.84 181	97.36 201	99.27 75	99.31 131	98.46 86	98.29 117	99.27 148	94.90 272	97.83 224	98.37 228	94.90 222	99.84 104	93.85 266	99.54 186	99.51 100

new_pmnet		96.99 235	96.76 227	97.67 239	98.72 248	94.89 249	95.95 294	98.20 276	92.62 306	98.55 182	98.54 215	94.88 225	99.52 297	93.96 261	99.44 202	98.59 272

diffmvs		97.49 200	97.36 201	97.91 229	98.38 289	95.70 232	97.95 157	99.31 132	94.87 273	96.14 306	98.78 175	94.84 226	99.43 314	97.69 115	98.26 289	98.59 272

VDD-MVS		98.56 110	98.39 120	99.07 98	99.13 174	98.07 111	98.59 86	97.01 301	99.59 19	99.11 106	99.27 78	94.82 227	99.79 176	98.34 82	99.63 158	99.34 172

jason		97.45 205	97.35 203	97.76 235	99.24 139	93.93 275	95.86 298	98.42 269	94.24 288	98.50 185	98.13 244	94.82 227	99.91 43	97.22 134	99.73 120	99.43 141
jason: jason.
TAMVS		98.24 151	98.05 157	98.80 136	99.07 183	97.18 172	97.88 163	98.81 244	96.66 221	99.17 102	99.21 88	94.81 229	99.77 196	96.96 147	99.88 65	99.44 136

MVS_0304		98.02 163	97.88 170	98.46 189	98.22 299	96.39 203	96.50 264	99.49 71	98.03 127	97.24 270	98.33 233	94.80 230	99.90 47	98.31 85	99.95 30	99.08 221

新几何1				98.91 123	98.94 210	97.76 142		98.76 249	87.58 343	96.75 291	98.10 250	94.80 230	99.78 186	92.73 292	99.00 257	99.20 205

VNet		98.42 130	98.30 132	98.79 137	98.79 243	97.29 164	98.23 121	98.66 259	99.31 42	98.85 148	98.80 172	94.80 230	99.78 186	98.13 91	99.13 245	99.31 182

MAR-MVS		96.47 255	95.70 259	98.79 137	97.92 310	99.12 40	98.28 118	98.60 263	92.16 313	95.54 325	96.17 313	94.77 233	99.52 297	89.62 326	98.23 290	97.72 306
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
testus		95.52 270	95.32 269	96.13 301	97.91 311	89.49 327	93.62 342	99.61 30	92.41 308	97.38 266	95.42 332	94.72 234	99.63 264	88.06 331	98.72 269	99.26 193

HSP-MVS		98.34 137	97.94 164	99.54 25	99.57 63	99.25 19	98.57 87	98.84 238	97.55 164	99.31 80	97.71 269	94.61 235	99.88 63	96.14 201	99.19 235	99.48 118

PAPR		95.29 274	94.47 282	97.75 236	97.50 328	95.14 245	94.89 326	98.71 257	91.39 323	95.35 329	95.48 329	94.57 236	99.14 339	84.95 341	97.37 321	98.97 236

1121		96.73 245	96.00 253	98.91 123	98.95 209	97.76 142	98.07 137	98.73 255	87.65 342	96.54 296	98.13 244	94.52 237	99.73 221	92.38 297	99.02 254	99.24 198

test222					98.92 216	96.93 183	95.54 310	98.78 248	85.72 347	96.86 287	98.11 249	94.43 238			99.10 249	99.23 199

PLC	94.65 16	96.51 252	95.73 258	98.85 131	98.75 245	97.91 127	96.42 270	99.06 197	90.94 327	95.59 318	97.38 290	94.41 239	99.59 277	90.93 318	98.04 311	99.05 225
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		97.17 224	96.71 231	98.55 175	98.56 275	98.05 113	96.33 273	98.93 223	96.91 209	97.06 275	97.39 289	94.38 240	99.45 312	91.66 302	99.18 237	98.14 288

MDA-MVSNet-bldmvs		97.94 170	97.91 167	98.06 223	99.44 110	94.96 248	96.63 259	99.15 187	98.35 110	98.83 151	99.11 108	94.31 241	99.85 89	96.60 171	98.72 269	99.37 160

OpenMVS_ROB	95.38 14	95.84 265	95.18 274	97.81 232	98.41 287	97.15 175	97.37 211	98.62 262	83.86 349	98.65 166	98.37 228	94.29 242	99.68 242	88.41 329	98.62 278	96.60 334

TR-MVS		95.55 269	95.12 275	96.86 276	97.54 323	93.94 274	96.49 266	96.53 315	94.36 285	97.03 277	96.61 305	94.26 243	99.16 337	86.91 334	96.31 336	97.47 319

GBi-Net		98.65 95	98.47 106	99.17 83	98.90 220	98.24 96	99.20 36	99.44 89	98.59 100	98.95 133	99.55 41	94.14 244	99.86 78	97.77 108	99.69 139	99.41 146

test1		98.65 95	98.47 106	99.17 83	98.90 220	98.24 96	99.20 36	99.44 89	98.59 100	98.95 133	99.55 41	94.14 244	99.86 78	97.77 108	99.69 139	99.41 146

FMVSNet2		98.49 123	98.40 118	98.75 146	98.90 220	97.14 176	98.61 83	99.13 188	98.59 100	99.19 98	99.28 76	94.14 244	99.82 131	97.97 100	99.80 94	99.29 188

PAPM_NR		96.82 242	96.32 249	98.30 207	99.07 183	96.69 193	97.48 206	98.76 249	95.81 250	96.61 295	96.47 309	94.12 247	99.17 336	90.82 322	97.78 315	99.06 224

test_normal		97.58 194	97.41 196	98.10 217	99.03 196	95.72 230	96.21 279	97.05 300	96.71 218	98.65 166	98.12 248	93.87 248	99.69 237	97.68 117	99.35 210	98.88 246

HQP2-MVS												93.84 249

HQP-MVS		97.00 234	96.49 244	98.55 175	98.67 262	96.79 186	96.29 275	99.04 204	96.05 244	95.55 322	96.84 301	93.84 249	99.54 291	92.82 288	99.26 225	99.32 178

DI_MVS_plusplus_test		97.57 196	97.40 197	98.07 222	99.06 186	95.71 231	96.58 262	96.96 302	96.71 218	98.69 164	98.13 244	93.81 251	99.68 242	97.45 124	99.19 235	98.80 256

MVSFormer		98.26 147	98.43 115	97.77 234	98.88 225	93.89 279	99.39 14	99.56 49	99.11 62	98.16 199	98.13 244	93.81 251	99.97 3	99.26 33	99.57 176	99.43 141

lupinMVS		97.06 230	96.86 222	97.65 241	98.88 225	93.89 279	95.48 313	97.97 282	93.53 296	98.16 199	97.58 276	93.81 251	99.91 43	96.77 158	99.57 176	99.17 215

MG-MVS		96.77 244	96.61 238	97.26 260	98.31 293	93.06 291	95.93 295	98.12 279	96.45 229	97.92 211	98.73 181	93.77 254	99.39 318	91.19 316	99.04 253	99.33 177

PVSNet	93.40 17	95.67 267	95.70 259	95.57 313	98.83 235	88.57 328	92.50 347	97.72 288	92.69 305	96.49 302	96.44 310	93.72 255	99.43 314	93.61 271	99.28 222	98.71 264

pmmvs5		97.64 190	97.49 191	98.08 221	99.14 173	95.12 246	96.70 254	99.05 201	93.77 293	98.62 172	98.83 167	93.23 256	99.75 207	98.33 84	99.76 115	99.36 166

CANet_DTU		97.26 217	97.06 213	97.84 231	97.57 321	94.65 255	96.19 282	98.79 247	97.23 197	95.14 331	98.24 239	93.22 257	99.84 104	97.34 129	99.84 74	99.04 226

UnsupCasMVSNet_bld		97.30 214	96.92 218	98.45 191	99.28 134	96.78 190	96.20 281	99.27 148	95.42 263	98.28 197	98.30 235	93.16 258	99.71 231	94.99 233	97.37 321	98.87 247

IterMVS		97.73 184	98.11 149	96.57 284	99.24 139	90.28 323	95.52 312	99.21 161	98.86 86	99.33 73	99.33 73	93.11 259	99.94 20	98.49 75	99.94 33	99.48 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test1		96.44 256	96.72 229	95.60 312	98.24 296	88.35 330	95.85 300	96.88 308	96.11 242	97.67 240	98.57 209	93.10 260	99.69 237	94.79 236	99.22 228	98.77 259

BH-w/o		95.13 276	94.89 280	95.86 306	98.20 300	91.31 318	95.65 307	97.37 293	93.64 294	96.52 298	95.70 321	93.04 261	99.02 341	88.10 330	95.82 339	97.24 322

cascas		94.79 289	94.33 290	96.15 300	96.02 352	92.36 300	92.34 349	99.26 153	85.34 348	95.08 332	94.96 341	92.96 262	98.53 350	94.41 251	98.59 279	97.56 316

semantic-postprocess				96.87 273	99.27 135	91.16 322		99.25 154	99.10 66	99.41 59	99.35 69	92.91 263	99.96 8	98.65 67	99.94 33	99.49 112

MVS-HIRNet		94.32 299	95.62 262	90.42 341	98.46 283	75.36 358	96.29 275	89.13 356	95.25 265	95.38 328	99.75 7	92.88 264	99.19 335	94.07 259	99.39 206	96.72 333

sss		97.21 221	96.93 217	98.06 223	98.83 235	95.22 242	96.75 251	98.48 267	94.49 278	97.27 269	97.90 262	92.77 265	99.80 156	96.57 174	99.32 215	99.16 218

test1235678		97.06 230	96.84 224	97.73 237	98.55 277	94.46 264	94.80 327	99.36 112	96.85 212	98.83 151	98.26 237	92.72 266	99.82 131	92.49 296	99.70 132	98.91 243

SixPastTwentyTwo		98.75 76	98.62 87	99.16 86	99.83 19	97.96 123	99.28 30	98.20 276	99.37 37	99.70 15	99.65 25	92.65 267	99.93 26	99.04 49	99.84 74	99.60 53

UnsupCasMVSNet_eth		97.89 172	97.60 187	98.75 146	99.31 131	97.17 173	97.62 191	99.35 118	98.72 95	98.76 160	98.68 188	92.57 268	99.74 216	97.76 111	95.60 341	99.34 172

CHOSEN 1792x2688		97.49 200	97.14 212	98.54 178	99.68 44	96.09 217	96.50 264	99.62 28	91.58 319	98.84 150	98.97 140	92.36 269	99.88 63	96.76 159	99.95 30	99.67 32

PCF-MVS	92.86 18	94.36 297	93.00 315	98.42 193	98.70 254	97.56 154	93.16 345	99.11 192	79.59 353	97.55 249	97.43 287	92.19 270	99.73 221	79.85 353	99.45 201	97.97 293
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPP-MVSNet		98.30 141	98.04 158	99.07 98	99.56 70	97.83 134	99.29 26	98.07 280	99.03 73	98.59 177	99.13 106	92.16 271	99.90 47	96.87 152	99.68 144	99.49 112

1112_ss		97.29 216	96.86 222	98.58 168	99.34 128	96.32 205	96.75 251	99.58 36	93.14 300	96.89 285	97.48 283	92.11 272	99.86 78	96.91 148	99.54 186	99.57 71

CDS-MVSNet		97.69 186	97.35 203	98.69 152	98.73 247	97.02 180	96.92 241	98.75 252	95.89 249	98.59 177	98.67 190	92.08 273	99.74 216	96.72 162	99.81 90	99.32 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS-MVSNet		98.19 154	97.90 168	99.08 97	99.57 63	97.97 121	99.31 21	98.32 272	99.01 75	98.98 128	99.03 129	91.59 274	99.79 176	95.49 228	99.80 94	99.48 118

Test_1112_low_res		96.99 235	96.55 242	98.31 206	99.35 126	95.47 238	95.84 301	99.53 59	91.51 321	96.80 290	98.48 223	91.36 275	99.83 119	96.58 172	99.53 190	99.62 46

WTY-MVS		96.67 246	96.27 250	97.87 230	98.81 240	94.61 256	96.77 249	97.92 284	94.94 271	97.12 271	97.74 268	91.11 276	99.82 131	93.89 263	98.15 297	99.18 211

PVSNet_0	89.98 21	91.15 329	90.30 329	93.70 333	97.72 315	84.34 350	90.24 351	97.42 292	90.20 332	93.79 344	93.09 353	90.90 277	98.89 347	86.57 335	72.76 356	97.87 296

Test4		97.43 206	97.18 208	98.18 215	99.05 191	96.02 218	96.62 260	99.09 194	96.25 236	98.63 171	97.70 270	90.49 278	99.68 242	97.50 122	99.30 218	98.83 250

VDDNet		98.21 152	97.95 162	99.01 111	99.58 58	97.74 145	99.01 56	97.29 296	99.67 8	98.97 130	99.50 47	90.45 279	99.80 156	97.88 103	99.20 231	99.48 118

lessismore_v0				98.97 115	99.73 29	97.53 156		86.71 357		99.37 65	99.52 46	89.93 280	99.92 34	98.99 52	99.72 125	99.44 136

HY-MVS	95.94 13	95.90 263	95.35 268	97.55 248	97.95 308	94.79 250	98.81 75	96.94 305	92.28 311	95.17 330	98.57 209	89.90 281	99.75 207	91.20 315	97.33 325	98.10 289

K. test v3		98.00 166	97.66 182	99.03 107	99.79 25	97.56 154	99.19 40	92.47 348	99.62 16	99.52 40	99.66 22	89.61 282	99.96 8	99.25 35	99.81 90	99.56 76

CMPMVS	75.91 23	96.29 257	95.44 266	98.84 132	96.25 349	98.69 68	97.02 234	99.12 190	88.90 338	97.83 224	98.86 161	89.51 283	98.90 346	91.92 299	99.51 193	98.92 241
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		96.25 259	97.21 207	93.38 337	99.10 176	80.56 357	97.20 224	98.19 278	96.94 207	99.00 125	99.02 130	89.50 284	99.80 156	96.36 190	99.59 166	99.78 15

DeepMVS_CX				93.44 336	98.24 296	94.21 268		94.34 331	64.28 355	91.34 350	94.87 344	89.45 285	92.77 358	77.54 355	93.14 351	93.35 353

EPNet		96.14 260	95.44 266	98.25 210	90.76 359	95.50 237	97.92 159	94.65 325	98.97 79	92.98 346	98.85 163	89.12 286	99.87 73	95.99 205	99.68 144	99.39 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		97.46 204	97.16 210	98.34 203	99.55 74	96.10 215	98.94 65	98.44 268	98.32 115	98.16 199	98.62 203	88.76 287	99.73 221	93.88 264	99.79 98	99.18 211

LP		96.60 250	96.57 241	96.68 279	97.64 320	91.70 305	98.11 132	97.74 287	97.29 190	97.91 213	99.24 83	88.35 288	99.85 89	97.11 142	95.76 340	98.49 275

EPNet_dtu		94.93 280	94.78 281	95.38 315	93.58 358	87.68 333	96.78 248	95.69 322	97.35 183	89.14 354	98.09 252	88.15 289	99.49 304	94.95 235	99.30 218	98.98 233
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
alignmvs		97.35 210	96.88 221	98.78 140	98.54 278	98.09 106	97.71 179	97.69 290	99.20 51	97.59 245	95.90 320	88.12 290	99.55 290	98.18 90	98.96 261	98.70 266

FMVSNet3		97.50 198	97.24 206	98.29 208	98.08 304	95.83 227	97.86 166	98.91 228	97.89 140	98.95 133	98.95 144	87.06 291	99.81 144	97.77 108	99.69 139	99.23 199

RPMNet		96.82 242	96.66 236	97.28 258	97.71 316	94.22 266	98.11 132	96.90 307	99.37 37	96.91 282	99.34 71	86.72 292	99.81 144	97.53 120	97.36 323	97.81 300

HyFIR lowres test		97.19 223	96.60 239	98.96 116	99.62 55	97.28 166	95.17 320	99.50 65	94.21 289	99.01 123	98.32 234	86.61 293	99.99 2	97.10 143	99.84 74	99.60 53

PAPM		91.88 326	90.34 328	96.51 285	98.06 305	92.56 295	92.44 348	97.17 297	86.35 345	90.38 353	96.01 314	86.61 293	99.21 334	70.65 356	95.43 342	97.75 304

CHOSEN 280x420		95.51 272	95.47 264	95.65 311	98.25 294	88.27 331	93.25 344	98.88 231	93.53 296	94.65 334	97.15 297	86.17 295	99.93 26	97.41 127	99.93 39	98.73 263

EMVS		93.83 311	94.02 298	93.23 338	96.83 342	84.96 345	89.77 353	96.32 317	97.92 131	97.43 259	96.36 311	86.17 295	98.93 345	87.68 332	97.73 316	95.81 343

MIMVSNet		96.62 249	96.25 252	97.71 238	99.04 193	94.66 254	99.16 43	96.92 306	97.23 197	97.87 216	99.10 110	86.11 297	99.65 261	91.65 303	99.21 230	98.82 252

tpmvs		95.02 279	95.25 271	94.33 325	96.39 348	85.87 338	98.08 135	96.83 309	95.46 262	95.51 326	98.69 186	85.91 298	99.53 293	94.16 253	96.23 337	97.58 315

test12356		94.85 285	95.12 275	94.03 330	98.25 294	83.12 352	93.85 340	99.33 127	94.17 290	97.28 268	97.20 293	85.83 299	99.75 207	90.85 321	99.33 213	99.22 203

MDTV_nov1_ep13_2view						74.92 359	97.69 181		90.06 334	97.75 236		85.78 300		93.52 274		98.69 267

ADS-MVSNet2		95.43 273	94.98 278	96.76 278	98.14 302	91.74 304	97.92 159	97.76 286	90.23 329	96.51 299	98.91 149	85.61 301	99.85 89	92.88 286	96.90 329	98.69 267

ADS-MVSNet		95.24 275	94.93 279	96.18 296	98.14 302	90.10 324	97.92 159	97.32 295	90.23 329	96.51 299	98.91 149	85.61 301	99.74 216	92.88 286	96.90 329	98.69 267

tpmrst		95.07 277	95.46 265	93.91 331	97.11 336	84.36 349	97.62 191	96.96 302	94.98 269	96.35 304	98.80 172	85.46 303	99.59 277	95.60 224	96.23 337	97.79 303

CR-MVSNet		96.28 258	95.95 255	97.28 258	97.71 316	94.22 266	98.11 132	98.92 226	92.31 310	96.91 282	99.37 66	85.44 304	99.81 144	97.39 128	97.36 323	97.81 300

Patchmtry		97.35 210	96.97 216	98.50 185	97.31 333	96.47 198	98.18 125	98.92 226	98.95 83	98.78 157	99.37 66	85.44 304	99.85 89	95.96 207	99.83 80	99.17 215

PatchmatchNet		95.58 268	95.67 261	95.30 316	97.34 332	87.32 334	97.65 186	96.65 312	95.30 264	97.07 274	98.69 186	84.77 306	99.75 207	94.97 234	98.64 276	98.83 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												84.74 307				98.81 253

E-PMN		94.17 303	94.37 288	93.58 334	96.86 340	85.71 341	90.11 352	97.07 299	98.17 125	97.82 226	97.19 294	84.62 308	98.94 344	89.77 325	97.68 317	96.09 342

LFMVS		97.20 222	96.72 229	98.64 156	98.72 248	96.95 182	98.93 67	94.14 338	99.74 5	98.78 157	99.01 132	84.45 309	99.73 221	97.44 125	99.27 223	99.25 195

patchmatchnet-post											98.77 177	84.37 310	99.85 89

PatchT		96.65 247	96.35 247	97.54 249	97.40 330	95.32 241	97.98 154	96.64 313	99.33 41	96.89 285	99.42 60	84.32 311	99.81 144	97.69 115	97.49 318	97.48 318

Patchmatch-RL test		97.26 217	97.02 214	97.99 228	99.52 82	95.53 235	96.13 283	99.71 12	97.47 170	99.27 83	99.16 99	84.30 312	99.62 266	97.89 101	99.77 106	98.81 253

sam_mvs												84.29 313

MDTV_nov1_ep13			95.22 272		97.06 337	83.20 351	97.74 177	96.16 318	94.37 284	96.99 278	98.83 167	83.95 314	99.53 293	93.90 262	97.95 312

test_post											21.25 359	83.86 315	99.70 233

Patchmatch-test		96.55 251	96.34 248	97.17 262	98.35 290	93.06 291	98.40 114	97.79 285	97.33 184	98.41 191	98.67 190	83.68 316	99.69 237	95.16 230	99.31 217	98.77 259

GA-MVS		95.86 264	95.32 269	97.49 251	98.60 272	94.15 270	93.83 341	97.93 283	95.49 261	96.68 292	97.42 288	83.21 317	99.30 329	96.22 194	98.55 281	99.01 230

JIA-IIPM		95.52 270	95.03 277	97.00 267	96.85 341	94.03 272	96.93 239	95.82 320	99.20 51	94.63 335	99.71 14	83.09 318	99.60 273	94.42 248	94.64 345	97.36 320

test_post1							97.59 196				20.48 360	83.07 319	99.66 256	94.16 253

tpm cat1		93.29 316	93.13 314	93.75 332	97.39 331	84.74 346	97.39 210	97.65 291	83.39 351	94.16 339	98.41 225	82.86 320	99.39 318	91.56 307	95.35 343	97.14 323

test-LLR		93.90 310	93.85 300	94.04 328	96.53 344	84.62 347	94.05 337	92.39 349	96.17 238	94.12 340	95.07 334	82.30 321	99.67 248	95.87 212	98.18 294	97.82 298

test0.0.03 1		94.51 295	93.69 305	96.99 268	96.05 350	93.61 286	94.97 324	93.49 339	96.17 238	97.57 248	94.88 342	82.30 321	99.01 343	93.60 272	94.17 350	98.37 284

PatchFormer-LS_test		94.08 306	93.91 299	94.59 323	96.93 338	86.86 336	97.55 201	96.57 314	94.27 287	94.38 337	93.64 352	80.96 323	99.59 277	96.44 187	94.48 348	97.31 321

MVSTER		96.86 239	96.55 242	97.79 233	97.91 311	94.21 268	97.56 199	98.87 232	97.49 169	99.06 111	99.05 124	80.72 324	99.80 156	98.44 77	99.82 83	99.37 160

tmp_tt		78.77 332	78.73 333	78.90 344	58.45 360	74.76 360	94.20 336	78.26 362	39.16 356	86.71 356	92.82 354	80.50 325	75.19 359	86.16 336	92.29 352	86.74 354

view600		94.87 281	94.41 283	96.26 291	99.22 145	91.37 311	98.49 98	94.45 327	98.75 90	97.85 219	95.98 316	80.38 326	99.75 207	86.06 337	98.49 282	97.66 307

view800		94.87 281	94.41 283	96.26 291	99.22 145	91.37 311	98.49 98	94.45 327	98.75 90	97.85 219	95.98 316	80.38 326	99.75 207	86.06 337	98.49 282	97.66 307

conf0.05thres1000		94.87 281	94.41 283	96.26 291	99.22 145	91.37 311	98.49 98	94.45 327	98.75 90	97.85 219	95.98 316	80.38 326	99.75 207	86.06 337	98.49 282	97.66 307

tfpn		94.87 281	94.41 283	96.26 291	99.22 145	91.37 311	98.49 98	94.45 327	98.75 90	97.85 219	95.98 316	80.38 326	99.75 207	86.06 337	98.49 282	97.66 307

thres200		93.72 312	93.14 313	95.46 314	98.66 267	91.29 319	96.61 261	94.63 326	97.39 180	96.83 288	93.71 350	79.88 330	99.56 287	82.40 350	98.13 298	95.54 345

tfpn111		94.33 298	93.78 302	95.96 303	99.06 186	91.35 315	98.03 143	94.24 334	98.33 112	97.40 261	94.98 338	79.84 331	99.68 242	83.94 344	98.22 292	96.86 327

conf200view11		94.24 301	93.67 306	95.94 304	99.06 186	91.35 315	98.03 143	94.24 334	98.33 112	97.40 261	94.98 338	79.84 331	99.62 266	83.05 346	98.08 307	96.86 327

thres100view900		94.19 302	93.67 306	95.75 309	99.06 186	91.35 315	98.03 143	94.24 334	98.33 112	97.40 261	94.98 338	79.84 331	99.62 266	83.05 346	98.08 307	96.29 335

thres600view7		94.45 296	93.83 301	96.29 289	99.06 186	91.53 307	97.99 153	94.24 334	98.34 111	97.44 258	95.01 336	79.84 331	99.67 248	84.33 343	98.23 290	97.66 307

tfpn200view9		94.03 307	93.44 310	95.78 308	98.93 212	91.44 309	97.60 194	94.29 332	97.94 129	97.10 272	94.31 347	79.67 335	99.62 266	83.05 346	98.08 307	96.29 335

thres400		94.14 304	93.44 310	96.24 295	98.93 212	91.44 309	97.60 194	94.29 332	97.94 129	97.10 272	94.31 347	79.67 335	99.62 266	83.05 346	98.08 307	97.66 307

pmmvs3		95.03 278	94.40 287	96.93 269	97.70 318	92.53 296	95.08 322	97.71 289	88.57 339	97.71 237	98.08 253	79.39 337	99.82 131	96.19 196	99.11 248	98.43 279

tpm		94.67 294	94.34 289	95.66 310	97.68 319	88.42 329	97.88 163	94.90 324	94.46 280	96.03 313	98.56 212	78.66 338	99.79 176	95.88 209	95.01 344	98.78 258

CostFormer		93.97 309	93.78 302	94.51 324	97.53 324	85.83 340	97.98 154	95.96 319	89.29 337	94.99 333	98.63 201	78.63 339	99.62 266	94.54 243	96.50 334	98.09 290

dp		93.47 314	93.59 308	93.13 339	96.64 343	81.62 356	97.66 184	96.42 316	92.80 304	96.11 308	98.64 197	78.55 340	99.59 277	93.31 279	92.18 354	98.16 287

EPMVS		93.72 312	93.27 312	95.09 318	96.04 351	87.76 332	98.13 129	85.01 358	94.69 276	96.92 280	98.64 197	78.47 341	99.31 327	95.04 231	96.46 335	98.20 286

PNet_i23d		91.80 327	92.35 319	90.14 342	98.65 268	73.10 361	89.22 354	99.02 210	95.23 267	97.87 216	97.82 265	78.45 342	98.89 347	88.73 328	86.14 355	98.42 280

conf0.01		94.82 286	94.07 292	97.06 265	99.21 151	94.53 258	98.47 104	92.69 342	95.61 253	97.81 227	95.54 323	77.71 343	99.80 156	91.49 308	98.11 299	96.86 327

conf0.002		94.82 286	94.07 292	97.06 265	99.21 151	94.53 258	98.47 104	92.69 342	95.61 253	97.81 227	95.54 323	77.71 343	99.80 156	91.49 308	98.11 299	96.86 327

thresconf0.02		94.70 290	94.07 292	96.58 280	99.21 151	94.53 258	98.47 104	92.69 342	95.61 253	97.81 227	95.54 323	77.71 343	99.80 156	91.49 308	98.11 299	95.42 346

tfpn_n400		94.70 290	94.07 292	96.58 280	99.21 151	94.53 258	98.47 104	92.69 342	95.61 253	97.81 227	95.54 323	77.71 343	99.80 156	91.49 308	98.11 299	95.42 346

tfpnconf		94.70 290	94.07 292	96.58 280	99.21 151	94.53 258	98.47 104	92.69 342	95.61 253	97.81 227	95.54 323	77.71 343	99.80 156	91.49 308	98.11 299	95.42 346

tfpnview11		94.70 290	94.07 292	96.58 280	99.21 151	94.53 258	98.47 104	92.69 342	95.61 253	97.81 227	95.54 323	77.71 343	99.80 156	91.49 308	98.11 299	95.42 346

tfpn1000		94.81 288	94.25 291	96.47 287	99.01 200	93.47 288	98.56 88	92.30 351	96.17 238	97.90 214	96.29 312	76.70 349	99.77 196	93.02 282	98.29 288	96.16 338

tfpn_ndepth		94.12 305	93.51 309	95.94 304	98.86 227	93.60 287	98.16 128	91.90 353	94.66 277	97.41 260	95.24 333	76.24 350	99.73 221	91.21 314	97.88 314	94.50 351

tpm2		93.09 318	92.58 317	94.62 322	97.56 322	86.53 337	97.66 184	95.79 321	86.15 346	94.07 342	98.23 241	75.95 351	99.53 293	90.91 319	96.86 332	97.81 300

FPMVS		93.44 315	92.23 320	97.08 263	99.25 138	97.86 132	95.61 308	97.16 298	92.90 302	93.76 345	98.65 194	75.94 352	95.66 355	79.30 354	97.49 318	97.73 305

tpmp4_e23		92.91 319	92.45 318	94.29 326	97.41 329	85.62 342	97.95 157	96.77 310	87.55 344	91.33 351	98.57 209	74.21 353	99.59 277	91.62 305	96.64 333	97.65 314

DWT-MVSNet_test		92.75 320	92.05 322	94.85 319	96.48 346	87.21 335	97.83 169	94.99 323	92.22 312	92.72 347	94.11 349	70.75 354	99.46 310	95.01 232	94.33 349	97.87 296

FMVSNet5		96.01 262	95.20 273	98.41 194	97.53 324	96.10 215	98.74 76	99.50 65	97.22 200	98.03 209	99.04 126	69.80 355	99.88 63	97.27 132	99.71 129	99.25 195

gg-mvs-nofinetune		92.37 322	91.20 326	95.85 307	95.80 353	92.38 299	99.31 21	81.84 360	99.75 4	91.83 349	99.74 8	68.29 356	99.02 341	87.15 333	97.12 327	96.16 338

GG-mvs-BLEND				94.76 321	94.54 355	92.13 302	99.31 21	80.47 361		88.73 355	91.01 355	67.59 357	98.16 353	82.30 351	94.53 347	93.98 352

test2356		91.64 328	90.19 331	96.00 302	94.30 356	89.58 326	90.84 350	96.68 311	91.76 314	95.48 327	93.69 351	67.05 358	99.52 297	84.83 342	97.08 328	98.91 243

TESTMET0.1,1		92.19 325	91.77 324	93.46 335	96.48 346	82.80 354	94.05 337	91.52 354	94.45 282	94.00 343	94.88 342	66.65 359	99.56 287	95.78 217	98.11 299	98.02 292

IB-MVS	91.63 19	92.24 324	90.90 327	96.27 290	97.22 335	91.24 320	94.36 335	93.33 341	92.37 309	92.24 348	94.58 346	66.20 360	99.89 56	93.16 281	94.63 346	97.66 307
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
testpf		89.08 330	90.27 330	85.50 343	94.03 357	82.85 353	96.87 245	91.09 355	91.61 318	90.96 352	94.86 345	66.15 361	95.83 354	94.58 242	92.27 353	77.82 355

test-mter		92.33 323	91.76 325	94.04 328	96.53 344	84.62 347	94.05 337	92.39 349	94.00 292	94.12 340	95.07 334	65.63 362	99.67 248	95.87 212	98.18 294	97.82 298

1111		93.99 308	93.72 304	94.80 320	99.33 129	85.20 343	95.97 287	99.39 101	97.88 141	98.64 168	98.56 212	57.79 363	99.80 156	96.02 203	99.87 69	99.40 152

.test1245		79.71 331	84.30 332	65.96 345	99.33 129	85.20 343	95.97 287	99.39 101	97.88 141	98.64 168	98.56 212	57.79 363	99.80 156	96.02 203	15.07 357	12.86 358

test123		17.04 336	20.11 337	7.82 347	10.25 362	4.91 362	94.80 327	4.47 364	4.93 357	10.00 359	24.28 358	9.69 365	3.64 360	10.14 357	12.43 359	14.92 357

testmvs		17.12 335	20.53 336	6.87 348	12.05 361	4.20 363	93.62 342	6.73 363	4.62 358	10.41 358	24.33 357	8.28 366	3.56 361	9.69 358	15.07 357	12.86 358

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.12 338	10.83 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	97.48 283	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																98.81 253

test_part3							97.25 218		96.66 221		98.71 183		99.86 78	93.00 283

test_part2					99.36 122	99.10 43				99.05 116

MTGPA								99.20 165

MTMP								91.91 352

gm-plane-assit					94.83 354	81.97 355			88.07 341		94.99 337		99.60 273	91.76 301

test9_res														93.28 280	99.15 241	99.38 159

agg_prior2														92.50 295	99.16 238	99.37 160

agg_prior					98.68 259	97.99 116		99.01 213		95.59 318			99.77 196

test_prior4						97.97 121	95.86 298

test_prior				98.95 117	98.69 257	97.95 124		99.03 206					99.59 277			99.30 185

旧先验2							95.76 302		88.56 340	97.52 252			99.66 256	94.48 244

新几何2							95.93 295

无先验							95.74 304	98.74 254	89.38 336				99.73 221	92.38 297		99.22 203

原ACMM2							95.53 311

testdata2													99.79 176	92.80 290

testdata1							95.44 315		96.32 233

plane_prior7					99.19 161	97.87 131

plane_prior5								99.27 148					99.70 233	94.42 248	99.51 193	99.45 134

plane_prior4											97.98 258

plane_prior3						97.78 141			97.41 178	97.79 233

plane_prior2							97.77 173		98.20 122

plane_prior1					99.05 191

plane_prior						97.65 150	97.07 233		96.72 216						99.36 208

n2								0.00 365

nn								0.00 365

door-mid								99.57 43

test11								98.87 232

door								99.41 98

HQP5-MVS						96.79 186

HQP-NCC					98.67 262		96.29 275		96.05 244	95.55 322

ACMP_Plane					98.67 262		96.29 275		96.05 244	95.55 322

BP-MVS														92.82 288

HQP4-MVS										95.56 321			99.54 291			99.32 178

HQP3-MVS								99.04 204							99.26 225

NP-MVS					98.84 233	97.39 163					96.84 301

ACMMP++_ref															99.77 106

ACMMP++															99.68 144

High-resolution multi-view benchmark