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.

ANet_high		99.88 5	99.87 5	99.91 2	99.99 1	99.91 3	99.65 44	100.00 1	99.90 8	100.00 1	99.97 9	99.61 17	99.97 15	99.75 14	100.00 1	99.84 13

LCM-MVSNet-Re		99.28 103	99.15 111	99.67 82	99.33 247	99.76 43	99.34 92	99.97 2	98.93 159	99.91 21	99.79 57	98.68 108	99.93 65	96.80 246	99.56 220	99.30 219

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 7	99.78 6	100.00 1	99.92 1	100.00 1	99.87 8

UA-Net		99.78 14	99.76 15	99.86 17	99.72 106	99.71 56	99.91 4	99.95 4	99.96 2	99.71 92	99.91 19	99.15 53	99.97 15	99.50 30	100.00 1	99.90 4

Vis-MVSNet		99.75 16	99.74 16	99.79 35	99.88 24	99.66 74	99.69 29	99.92 5	99.67 51	99.77 68	99.75 78	99.61 17	99.98 6	99.35 45	99.98 22	99.72 39
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TDRefinement		99.72 18	99.70 18	99.77 40	99.90 20	99.85 12	99.86 6	99.92 5	99.69 47	99.78 63	99.92 16	99.37 30	99.88 144	98.93 103	99.95 48	99.60 111

LTVRE_ROB	99.19 1	99.88 5	99.87 5	99.88 11	99.91 16	99.90 5	99.96 1	99.92 5	99.90 8	99.97 7	99.87 30	99.81 5	99.95 41	99.54 25	99.99 13	99.80 23
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Effi-MVS+		99.06 157	98.97 164	99.34 191	99.31 249	98.98 202	98.31 258	99.91 8	98.81 171	98.79 260	98.94 293	99.14 55	99.84 207	98.79 112	98.74 298	99.20 235

pmmvs6		99.86 7	99.86 7	99.83 22	99.94 11	99.90 5	99.83 7	99.91 8	99.85 20	99.94 12	99.95 11	99.73 9	99.90 115	99.65 17	99.97 30	99.69 48

PVSNet_Blended_VisFu		99.40 74	99.38 67	99.44 163	99.90 20	98.66 229	98.94 195	99.91 8	97.97 243	99.79 60	99.73 84	99.05 69	99.97 15	99.15 75	99.99 13	99.68 54

PMMVS2		99.48 53	99.45 56	99.57 129	99.76 84	98.99 201	98.09 276	99.90 11	98.95 156	99.78 63	99.58 174	99.57 20	99.93 65	99.48 31	99.95 48	99.79 29

testgi		99.29 102	99.26 97	99.37 186	99.75 93	98.81 220	98.84 205	99.89 12	98.38 213	99.75 74	99.04 281	99.36 33	99.86 174	99.08 85	99.25 271	99.45 181

test20.03		99.55 44	99.54 44	99.58 124	99.79 66	99.37 142	99.02 177	99.89 12	99.60 73	99.82 47	99.62 152	98.81 87	99.89 129	99.43 35	99.86 109	99.47 175

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 5	99.79 33	99.72 20	99.88 14	99.92 7	99.98 4	99.93 13	99.94 1	99.98 6	99.77 12	100.00 1	99.92 3

CHOSEN 1792x2688		99.39 77	99.30 86	99.65 94	99.88 24	99.25 167	98.78 219	99.88 14	98.66 185	99.96 9	99.79 57	97.45 215	99.93 65	99.34 46	99.99 13	99.78 30

Patchmatch-RL test		98.60 214	98.36 221	99.33 193	99.77 80	99.07 197	98.27 260	99.87 16	98.91 162	99.74 82	99.72 90	90.57 307	99.79 250	98.55 128	99.85 112	99.11 252

pm-mvs1		99.79 13	99.79 12	99.78 38	99.91 16	99.83 21	99.76 14	99.87 16	99.73 37	99.89 27	99.87 30	99.63 15	99.87 156	99.54 25	99.92 71	99.63 89

jajsoiax		99.89 3	99.89 4	99.89 7	99.96 5	99.78 36	99.70 23	99.86 18	99.89 12	99.98 4	99.90 21	99.94 1	99.98 6	99.75 14	100.00 1	99.90 4

PM-MVS		99.36 84	99.29 91	99.58 124	99.83 37	99.66 74	98.95 193	99.86 18	98.85 166	99.81 52	99.73 84	98.40 148	99.92 83	98.36 137	99.83 126	99.17 240

TransMVSNet (Re)		99.78 14	99.77 13	99.81 27	99.91 16	99.85 12	99.75 15	99.86 18	99.70 44	99.91 21	99.89 25	99.60 19	99.87 156	99.59 20	99.74 175	99.71 42

Baseline_NR-MVSNet		99.49 51	99.37 70	99.82 24	99.91 16	99.84 17	98.83 207	99.86 18	99.68 49	99.65 110	99.88 28	97.67 205	99.87 156	99.03 88	99.86 109	99.76 34

anonymousdsp		99.80 12	99.77 13	99.90 4	99.96 5	99.88 7	99.73 17	99.85 22	99.70 44	99.92 19	99.93 13	99.45 22	99.97 15	99.36 44	100.00 1	99.85 12

PS-MVSNAJss		99.84 9	99.82 9	99.89 7	99.96 5	99.77 38	99.68 32	99.85 22	99.95 3	99.98 4	99.92 16	99.28 41	99.98 6	99.75 14	100.00 1	99.94 2

EU-MVSNet		99.39 77	99.62 26	98.72 266	99.88 24	96.44 297	99.56 61	99.85 22	99.90 8	99.90 23	99.85 35	98.09 174	99.83 218	99.58 22	99.95 48	99.90 4

casdiffmvs		99.63 31	99.61 30	99.67 82	99.79 66	99.59 97	99.13 156	99.85 22	99.79 32	99.76 70	99.72 90	99.33 35	99.82 227	99.21 62	99.94 60	99.59 120

OurMVSNet-221017-0		99.75 16	99.71 17	99.84 20	99.96 5	99.83 21	99.83 7	99.85 22	99.80 30	99.93 15	99.93 13	98.54 127	99.93 65	99.59 20	99.98 22	99.76 34

CSCG		99.37 81	99.29 91	99.60 119	99.71 109	99.46 116	99.43 77	99.85 22	98.79 174	99.41 178	99.60 166	98.92 80	99.92 83	98.02 168	99.92 71	99.43 192

IterMVS-SCA-FT		99.00 172	99.16 108	98.51 270	99.75 93	95.90 304	98.07 279	99.84 28	99.84 22	99.89 27	99.73 84	96.01 258	99.99 4	99.33 48	100.00 1	99.63 89

Gipuma		99.57 38	99.59 33	99.49 149	99.98 3	99.71 56	99.72 20	99.84 28	99.81 27	99.94 12	99.78 64	98.91 82	99.71 276	98.41 134	99.95 48	99.05 266
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
AllTest		99.21 125	99.07 135	99.63 105	99.78 72	99.64 81	99.12 159	99.83 30	98.63 188	99.63 116	99.72 90	98.68 108	99.75 267	96.38 265	99.83 126	99.51 156

TestCases				99.63 105	99.78 72	99.64 81		99.83 30	98.63 188	99.63 116	99.72 90	98.68 108	99.75 267	96.38 265	99.83 126	99.51 156

door-mid								99.83 30

IterMVS		98.97 176	99.16 108	98.42 274	99.74 99	95.64 307	98.06 281	99.83 30	99.83 25	99.85 39	99.74 80	96.10 257	99.99 4	99.27 60	100.00 1	99.63 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		98.91 185	98.64 198	99.73 65	99.85 33	99.47 112	98.07 279	99.83 30	98.64 187	99.89 27	99.60 166	92.57 284	100.00 1	99.33 48	99.97 30	99.72 39

CS-MVS		99.09 154	99.03 147	99.25 212	99.45 209	99.49 109	99.41 78	99.82 35	99.10 141	98.03 303	98.48 317	99.30 38	99.89 129	98.30 144	99.41 250	98.35 303

Fast-Effi-MVS+-dtu		99.20 127	99.12 118	99.43 166	99.25 261	99.69 67	99.05 172	99.82 35	99.50 81	98.97 238	99.05 278	98.98 73	99.98 6	98.20 153	99.24 273	98.62 289

v7n		99.82 11	99.80 11	99.88 11	99.96 5	99.84 17	99.82 9	99.82 35	99.84 22	99.94 12	99.91 19	99.13 58	99.96 32	99.83 9	99.99 13	99.83 18

DSMNet-mixed		99.48 53	99.65 23	98.95 241	99.71 109	97.27 285	99.50 65	99.82 35	99.59 75	99.41 178	99.85 35	99.62 16	100.00 1	99.53 27	99.89 89	99.59 120

PVSNet_BlendedMVS		99.03 164	99.01 152	99.09 229	99.54 169	97.99 264	98.58 231	99.82 35	97.62 260	99.34 191	99.71 97	98.52 134	99.77 260	97.98 172	99.97 30	99.52 154

PVSNet_Blended		98.70 209	98.59 203	99.02 237	99.54 169	97.99 264	97.58 311	99.82 35	95.70 304	99.34 191	98.98 287	98.52 134	99.77 260	97.98 172	99.83 126	99.30 219

XXY-MVS		99.71 19	99.67 21	99.81 27	99.89 22	99.72 54	99.59 56	99.82 35	99.39 102	99.82 47	99.84 39	99.38 28	99.91 96	99.38 41	99.93 68	99.80 23

1112_ss		99.05 160	98.84 182	99.67 82	99.66 130	99.29 158	98.52 242	99.82 35	97.65 259	99.43 171	99.16 266	96.42 249	99.91 96	99.07 86	99.84 116	99.80 23

RPSCF		99.18 134	99.02 149	99.64 101	99.83 37	99.85 12	99.44 75	99.82 35	98.33 225	99.50 159	99.78 64	97.90 188	99.65 309	96.78 247	99.83 126	99.44 186

test_normal		99.89 3	99.90 2	99.87 14	99.97 4	99.94 2	99.92 3	99.81 44	99.95 3	99.99 3	99.98 7	99.75 8	99.85 191	99.76 13	100.00 1	99.84 13

diffmvs		99.34 91	99.32 80	99.39 179	99.67 129	98.77 223	98.57 235	99.81 44	99.61 67	99.48 161	99.41 218	98.47 138	99.86 174	98.97 95	99.90 81	99.53 144

MVSFormer		99.41 71	99.44 58	99.31 200	99.57 156	98.40 241	99.77 12	99.80 46	99.73 37	99.63 116	99.30 243	98.02 180	99.98 6	99.43 35	99.69 194	99.55 134

test_djsdf		99.84 9	99.81 10	99.91 2	99.94 11	99.84 17	99.77 12	99.80 46	99.73 37	99.97 7	99.92 16	99.77 7	99.98 6	99.43 35	100.00 1	99.90 4

baseline		99.63 31	99.62 26	99.66 89	99.80 56	99.62 87	99.44 75	99.80 46	99.71 41	99.72 87	99.69 110	99.15 53	99.83 218	99.32 50	99.94 60	99.53 144

FMVSNet5		97.80 260	97.25 272	99.42 168	98.83 304	98.97 204	99.38 84	99.80 46	98.87 164	99.25 205	99.69 110	80.60 337	99.91 96	98.96 97	99.90 81	99.38 201

Test_1112_low_res		98.95 182	98.73 191	99.63 105	99.68 124	99.15 186	98.09 276	99.80 46	97.14 281	99.46 165	99.40 220	96.11 256	99.89 129	99.01 90	99.84 116	99.84 13

USDC		98.96 179	98.93 168	99.05 235	99.54 169	97.99 264	97.07 324	99.80 46	98.21 232	99.75 74	99.77 71	98.43 144	99.64 311	97.90 175	99.88 95	99.51 156

ETV-MVS		99.12 147	99.01 152	99.45 161	99.36 230	99.62 87	99.34 92	99.79 52	98.41 209	98.84 254	98.89 298	98.75 102	99.84 207	98.15 161	99.51 234	98.89 277

EIA-MVS		99.18 134	99.18 106	99.16 224	99.34 242	99.28 160	99.12 159	99.79 52	99.48 83	98.93 243	98.55 313	99.40 23	99.93 65	98.51 131	99.52 233	98.28 306

Fast-Effi-MVS+		99.02 166	98.87 178	99.46 157	99.38 226	99.50 108	99.04 174	99.79 52	97.17 279	98.62 272	98.74 306	99.34 34	99.95 41	98.32 142	99.41 250	98.92 275

ACMH	98.42 6	99.59 36	99.54 44	99.72 70	99.86 30	99.62 87	99.56 61	99.79 52	98.77 177	99.80 55	99.85 35	99.64 14	99.85 191	98.70 120	99.89 89	99.70 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		99.43 64	99.38 67	99.60 119	99.87 28	99.75 45	99.59 56	99.78 56	99.71 41	99.90 23	99.69 110	98.85 86	99.90 115	97.25 221	99.78 158	99.15 243

FC-MVSNet-test		99.70 20	99.65 23	99.86 17	99.88 24	99.86 11	99.72 20	99.78 56	99.90 8	99.82 47	99.83 40	98.45 142	99.87 156	99.51 28	99.97 30	99.86 10

COLMAP_ROB	98.06 12	99.45 62	99.37 70	99.70 78	99.83 37	99.70 63	99.38 84	99.78 56	99.53 79	99.67 102	99.78 64	99.19 49	99.86 174	97.32 213	99.87 102	99.55 134
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
door								99.77 59

MIMVSNet1		99.66 25	99.62 26	99.80 30	99.94 11	99.87 8	99.69 29	99.77 59	99.78 33	99.93 15	99.89 25	97.94 186	99.92 83	99.65 17	99.98 22	99.62 101

wuyk23d		97.58 268	99.13 115	92.93 320	99.69 119	99.49 109	99.52 63	99.77 59	97.97 243	99.96 9	99.79 57	99.84 3	99.94 52	95.85 284	99.82 135	79.36 332

ACMH+	98.40 8	99.50 49	99.43 61	99.71 74	99.86 30	99.76 43	99.32 97	99.77 59	99.53 79	99.77 68	99.76 74	99.26 45	99.78 254	97.77 184	99.88 95	99.60 111

LF4IMVS		99.01 170	98.92 171	99.27 206	99.71 109	99.28 160	98.59 230	99.77 59	98.32 226	99.39 183	99.41 218	98.62 117	99.84 207	96.62 257	99.84 116	98.69 287

v8		99.68 23	99.69 19	99.65 94	99.80 56	99.40 135	99.66 39	99.76 64	99.64 59	99.93 15	99.85 35	98.66 113	99.84 207	99.88 6	99.99 13	99.71 42

abl_6		99.36 84	99.23 102	99.75 53	99.71 109	99.74 50	99.33 94	99.76 64	99.07 144	99.65 110	99.63 145	99.09 61	99.92 83	97.13 229	99.76 164	99.58 125

114514_t		98.49 226	98.11 240	99.64 101	99.73 102	99.58 100	99.24 120	99.76 64	89.94 327	99.42 172	99.56 184	97.76 199	99.86 174	97.74 186	99.82 135	99.47 175

EG-PatchMatch MVS		99.57 38	99.56 42	99.62 114	99.77 80	99.33 152	99.26 115	99.76 64	99.32 111	99.80 55	99.78 64	99.29 39	99.87 156	99.15 75	99.91 80	99.66 71

IterMVS-LS		99.41 71	99.47 52	99.25 212	99.81 50	98.09 260	98.85 204	99.76 64	99.62 63	99.83 46	99.64 137	98.54 127	99.97 15	99.15 75	99.99 13	99.68 54
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		99.35 86	99.57 38	98.71 267	99.82 43	96.62 295	98.55 237	99.75 69	99.50 81	99.88 33	99.87 30	99.31 36	99.88 144	99.43 35	100.00 1	99.62 101

FIs		99.65 30	99.58 35	99.84 20	99.84 34	99.85 12	99.66 39	99.75 69	99.86 17	99.74 82	99.79 57	98.27 159	99.85 191	99.37 43	99.93 68	99.83 18

v10		99.69 22	99.69 19	99.66 89	99.81 50	99.39 137	99.66 39	99.75 69	99.60 73	99.92 19	99.87 30	98.75 102	99.86 174	99.90 2	99.99 13	99.73 38

WR-MVS_H		99.61 35	99.53 48	99.87 14	99.80 56	99.83 21	99.67 36	99.75 69	99.58 76	99.85 39	99.69 110	98.18 170	99.94 52	99.28 59	99.95 48	99.83 18

TinyColmap		98.97 176	98.93 168	99.07 233	99.46 206	98.19 252	97.75 304	99.75 69	98.79 174	99.54 150	99.70 104	98.97 75	99.62 313	96.63 256	99.83 126	99.41 196

Anonymous20231206		99.35 86	99.31 81	99.47 154	99.74 99	99.06 199	99.28 112	99.74 74	99.23 124	99.72 87	99.53 194	97.63 211	99.88 144	99.11 83	99.84 116	99.48 169

XVG-OURS		99.21 125	99.06 137	99.65 94	99.82 43	99.62 87	97.87 300	99.74 74	98.36 215	99.66 106	99.68 121	99.71 10	99.90 115	96.84 244	99.88 95	99.43 192

MSDG		99.08 155	98.98 163	99.37 186	99.60 142	99.13 187	97.54 312	99.74 74	98.84 169	99.53 153	99.55 190	99.10 59	99.79 250	97.07 232	99.86 109	99.18 239

pmmvs5		99.19 130	99.11 121	99.42 168	99.76 84	98.88 217	98.55 237	99.73 77	98.82 170	99.72 87	99.62 152	96.56 243	99.82 227	99.32 50	99.95 48	99.56 131

Anonymous20231211		99.62 33	99.57 38	99.76 44	99.61 140	99.60 94	99.81 10	99.73 77	99.82 26	99.90 23	99.90 21	97.97 185	99.86 174	99.42 39	99.96 41	99.80 23

PS-CasMVS		99.66 25	99.58 35	99.89 7	99.80 56	99.85 12	99.66 39	99.73 77	99.62 63	99.84 42	99.71 97	98.62 117	99.96 32	99.30 54	99.96 41	99.86 10

PEN-MVS		99.66 25	99.59 33	99.89 7	99.83 37	99.87 8	99.66 39	99.73 77	99.70 44	99.84 42	99.73 84	98.56 124	99.96 32	99.29 57	99.94 60	99.83 18

XVG-OURS-SEG-HR		99.16 139	98.99 160	99.66 89	99.84 34	99.64 81	98.25 262	99.73 77	98.39 212	99.63 116	99.43 216	99.70 12	99.90 115	97.34 212	98.64 302	99.44 186

LPG-MVS_test		99.22 121	99.05 141	99.74 58	99.82 43	99.63 85	99.16 145	99.73 77	97.56 262	99.64 112	99.69 110	99.37 30	99.89 129	96.66 254	99.87 102	99.69 48

LGP-MVS_train				99.74 58	99.82 43	99.63 85		99.73 77	97.56 262	99.64 112	99.69 110	99.37 30	99.89 129	96.66 254	99.87 102	99.69 48

MVS_111021_LR		99.13 145	99.03 147	99.42 168	99.58 147	99.32 154	97.91 299	99.73 77	98.68 184	99.31 197	99.48 207	99.09 61	99.66 302	97.70 189	99.77 162	99.29 222

ITE_SJBPF				99.38 183	99.63 136	99.44 123		99.73 77	98.56 194	99.33 193	99.53 194	98.88 85	99.68 293	96.01 276	99.65 206	99.02 269

PGM-MVS		99.20 127	99.01 152	99.77 40	99.75 93	99.71 56	99.16 145	99.72 86	97.99 241	99.42 172	99.60 166	98.81 87	99.93 65	96.91 238	99.74 175	99.66 71

MDA-MVSNet-bldmvs		99.06 157	99.05 141	99.07 233	99.80 56	97.83 271	98.89 197	99.72 86	99.29 112	99.63 116	99.70 104	96.47 247	99.89 129	98.17 159	99.82 135	99.50 162

XVG-ACMP-BASELINE		99.23 113	99.10 128	99.63 105	99.82 43	99.58 100	98.83 207	99.72 86	98.36 215	99.60 131	99.71 97	98.92 80	99.91 96	97.08 231	99.84 116	99.40 197

UniMVSNet_ETH3D		99.85 8	99.83 8	99.90 4	99.89 22	99.91 3	99.89 5	99.71 89	99.93 5	99.95 11	99.89 25	99.71 10	99.96 32	99.51 28	99.97 30	99.84 13

DTE-MVSNet		99.68 23	99.61 30	99.88 11	99.80 56	99.87 8	99.67 36	99.71 89	99.72 40	99.84 42	99.78 64	98.67 111	99.97 15	99.30 54	99.95 48	99.80 23

MVS_111021_HR		99.12 147	99.02 149	99.40 176	99.50 186	99.11 189	97.92 297	99.71 89	98.76 180	99.08 230	99.47 210	99.17 51	99.54 322	97.85 181	99.76 164	99.54 141

DeepC-MVS	98.90 4	99.62 33	99.61 30	99.67 82	99.72 106	99.44 123	99.24 120	99.71 89	99.27 116	99.93 15	99.90 21	99.70 12	99.93 65	98.99 91	99.99 13	99.64 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		99.70 20	99.66 22	99.82 24	99.76 84	99.84 17	99.61 51	99.70 93	99.93 5	99.78 63	99.68 121	99.10 59	99.78 254	99.45 33	99.96 41	99.83 18

VPNet		99.46 60	99.37 70	99.71 74	99.82 43	99.59 97	99.48 69	99.70 93	99.81 27	99.69 97	99.58 174	97.66 209	99.86 174	99.17 71	99.44 243	99.67 61

HPM-MVS_fast		99.43 64	99.30 86	99.80 30	99.83 37	99.81 26	99.52 63	99.70 93	98.35 220	99.51 157	99.50 202	99.31 36	99.88 144	98.18 157	99.84 116	99.69 48

GBi-Net		99.42 67	99.31 81	99.73 65	99.49 191	99.77 38	99.68 32	99.70 93	99.44 94	99.62 123	99.83 40	97.21 227	99.90 115	98.96 97	99.90 81	99.53 144

test1		99.42 67	99.31 81	99.73 65	99.49 191	99.77 38	99.68 32	99.70 93	99.44 94	99.62 123	99.83 40	97.21 227	99.90 115	98.96 97	99.90 81	99.53 144

FMVSNet1		99.66 25	99.63 25	99.73 65	99.78 72	99.77 38	99.68 32	99.70 93	99.67 51	99.82 47	99.83 40	98.98 73	99.90 115	99.24 61	99.97 30	99.53 144

APDe-MVS		99.48 53	99.36 73	99.85 19	99.55 168	99.81 26	99.50 65	99.69 99	98.99 151	99.75 74	99.71 97	98.79 94	99.93 65	98.46 133	99.85 112	99.80 23

VPA-MVSNet		99.66 25	99.62 26	99.79 35	99.68 124	99.75 45	99.62 47	99.69 99	99.85 20	99.80 55	99.81 49	98.81 87	99.91 96	99.47 32	99.88 95	99.70 45

OpenMVS	98.12 10	98.23 248	97.89 258	99.26 209	99.19 271	99.26 164	99.65 44	99.69 99	91.33 325	98.14 298	99.77 71	98.28 158	99.96 32	95.41 296	99.55 224	98.58 293

ppachtmachnet_test		98.89 190	99.12 118	98.20 282	99.66 130	95.24 311	97.63 308	99.68 102	99.08 142	99.78 63	99.62 152	98.65 115	99.88 144	98.02 168	99.96 41	99.48 169

UnsupCasMVSNet_bld		98.55 221	98.27 228	99.40 176	99.56 167	99.37 142	97.97 292	99.68 102	97.49 267	99.08 230	99.35 235	95.41 264	99.82 227	97.70 189	98.19 314	99.01 270

test_0402		99.22 121	99.14 112	99.45 161	99.79 66	99.43 127	99.28 112	99.68 102	99.54 77	99.40 182	99.56 184	99.07 66	99.82 227	96.01 276	99.96 41	99.11 252

LS3D		99.24 112	99.11 121	99.61 117	98.38 324	99.79 33	99.57 59	99.68 102	99.61 67	99.15 223	99.71 97	98.70 106	99.91 96	97.54 202	99.68 196	99.13 251

HPM-MVS		99.25 109	99.07 135	99.78 38	99.81 50	99.75 45	99.61 51	99.67 106	97.72 256	99.35 188	99.25 253	99.23 46	99.92 83	97.21 225	99.82 135	99.67 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CR-MVSNet		98.35 240	98.20 233	98.83 258	99.05 289	98.12 256	99.30 104	99.67 106	97.39 272	99.16 221	99.79 57	91.87 290	99.91 96	98.78 115	98.77 294	98.44 300

Patchmtry		98.78 202	98.54 209	99.49 149	98.89 298	99.19 182	99.32 97	99.67 106	99.65 57	99.72 87	99.79 57	91.87 290	99.95 41	98.00 171	99.97 30	99.33 213

UnsupCasMVSNet_eth		98.83 196	98.57 206	99.59 121	99.68 124	99.45 121	98.99 186	99.67 106	99.48 83	99.55 148	99.36 230	94.92 265	99.86 174	98.95 101	96.57 327	99.45 181

miper_lstm_enhance		98.65 211	98.60 201	98.82 261	99.20 269	97.33 284	97.78 303	99.66 110	99.01 150	99.59 132	99.50 202	94.62 269	99.85 191	98.12 163	99.90 81	99.26 224

Effi-MVS+-dtu		99.07 156	98.92 171	99.52 142	98.89 298	99.78 36	99.15 147	99.66 110	99.34 107	98.92 246	99.24 258	97.69 202	99.98 6	98.11 164	99.28 267	98.81 283

xiu_mvs_v1_base_debu		99.23 113	99.34 75	98.91 246	99.59 144	98.23 249	98.47 246	99.66 110	99.61 67	99.68 98	98.94 293	99.39 24	99.97 15	99.18 68	99.55 224	98.51 297

mvs-test1		98.83 196	98.70 194	99.22 217	98.89 298	99.65 79	98.88 198	99.66 110	99.34 107	98.29 287	98.94 293	97.69 202	99.96 32	98.11 164	98.54 306	98.04 317

xiu_mvs_v1_base		99.23 113	99.34 75	98.91 246	99.59 144	98.23 249	98.47 246	99.66 110	99.61 67	99.68 98	98.94 293	99.39 24	99.97 15	99.18 68	99.55 224	98.51 297

pmmvs-eth3d		99.48 53	99.47 52	99.51 145	99.77 80	99.41 134	98.81 212	99.66 110	99.42 101	99.75 74	99.66 130	99.20 48	99.76 262	98.98 93	99.99 13	99.36 207

xiu_mvs_v1_base_debi		99.23 113	99.34 75	98.91 246	99.59 144	98.23 249	98.47 246	99.66 110	99.61 67	99.68 98	98.94 293	99.39 24	99.97 15	99.18 68	99.55 224	98.51 297

canonicalmvs		99.02 166	99.00 155	99.09 229	99.10 285	98.70 226	99.61 51	99.66 110	99.63 62	98.64 271	97.65 328	99.04 70	99.54 322	98.79 112	98.92 287	99.04 267

pmmvs3		98.08 254	97.80 259	98.91 246	99.41 219	97.69 275	97.87 300	99.66 110	95.87 300	99.50 159	99.51 199	90.35 309	99.97 15	98.55 128	99.47 240	99.08 260

ACMP	97.51 14	99.05 160	98.84 182	99.67 82	99.78 72	99.55 106	98.88 198	99.66 110	97.11 283	99.47 162	99.60 166	99.07 66	99.89 129	96.18 271	99.85 112	99.58 125
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1240		99.56 41	99.58 35	99.51 145	99.80 56	99.00 200	99.00 181	99.65 120	99.15 137	99.90 23	99.75 78	99.09 61	99.88 144	99.90 2	99.96 41	99.67 61

ACMMP		99.25 109	99.08 131	99.74 58	99.79 66	99.68 70	99.50 65	99.65 120	98.07 237	99.52 155	99.69 110	98.57 123	99.92 83	97.18 227	99.79 152	99.63 89
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
PHI-MVS		99.11 151	98.95 167	99.59 121	99.13 279	99.59 97	99.17 139	99.65 120	97.88 248	99.25 205	99.46 213	98.97 75	99.80 247	97.26 218	99.82 135	99.37 204

F-COLMAP		98.74 206	98.45 213	99.62 114	99.57 156	99.47 112	98.84 205	99.65 120	96.31 295	98.93 243	99.19 265	97.68 204	99.87 156	96.52 259	99.37 257	99.53 144

ACMM	98.09 11	99.46 60	99.38 67	99.72 70	99.80 56	99.69 67	99.13 156	99.65 120	98.99 151	99.64 112	99.72 90	99.39 24	99.86 174	98.23 150	99.81 143	99.60 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CVMVSNet		98.61 213	98.88 177	97.80 292	99.58 147	93.60 319	99.26 115	99.64 125	99.66 55	99.72 87	99.67 126	93.26 278	99.93 65	99.30 54	99.81 143	99.87 8

OMC-MVS		98.90 187	98.72 192	99.44 163	99.39 223	99.42 130	98.58 231	99.64 125	97.31 275	99.44 167	99.62 152	98.59 121	99.69 283	96.17 272	99.79 152	99.22 232

MP-MVS-pluss		99.14 143	98.92 171	99.80 30	99.83 37	99.83 21	98.61 227	99.63 127	96.84 289	99.44 167	99.58 174	98.81 87	99.91 96	97.70 189	99.82 135	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		99.54 46	99.47 52	99.76 44	99.58 147	99.64 81	99.30 104	99.63 127	99.61 67	99.71 92	99.56 184	98.76 100	99.96 32	99.14 81	99.92 71	99.68 54

DP-MVS Recon		98.50 224	98.23 230	99.31 200	99.49 191	99.46 116	98.56 236	99.63 127	94.86 315	98.85 253	99.37 226	97.81 195	99.59 319	96.08 273	99.44 243	98.88 278

cdsmvs_eth3d_5k		24.88 310	33.17 311	0.00 323	0.00 340	0.00 341	0.00 334	99.62 130	0.00 336	0.00 338	99.13 268	99.82 4	0.00 339	0.00 336	0.00 336	0.00 335

v144192		99.55 44	99.54 44	99.58 124	99.78 72	99.20 181	99.11 161	99.62 130	99.18 130	99.89 27	99.72 90	98.66 113	99.87 156	99.88 6	99.97 30	99.66 71

CP-MVS		99.23 113	99.05 141	99.75 53	99.66 130	99.66 74	99.38 84	99.62 130	98.38 213	99.06 234	99.27 249	98.79 94	99.94 52	97.51 204	99.82 135	99.66 71

TAPA-MVS	97.92 13	98.03 256	97.55 268	99.46 157	99.47 202	99.44 123	98.50 244	99.62 130	86.79 328	99.07 233	99.26 251	98.26 160	99.62 313	97.28 217	99.73 182	99.31 218
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_0728_SECOND				99.83 22	99.70 116	99.79 33	99.14 149	99.61 134					99.92 83	97.88 177	99.72 187	99.77 31

v1921920		99.56 41	99.57 38	99.55 136	99.75 93	99.11 189	99.05 172	99.61 134	99.15 137	99.88 33	99.71 97	99.08 64	99.87 156	99.90 2	99.97 30	99.66 71

v1144		99.54 46	99.53 48	99.59 121	99.79 66	99.28 160	99.10 162	99.61 134	99.20 128	99.84 42	99.73 84	98.67 111	99.84 207	99.86 8	99.98 22	99.64 85

XVS		99.27 107	99.11 121	99.75 53	99.71 109	99.71 56	99.37 88	99.61 134	99.29 112	98.76 263	99.47 210	98.47 138	99.88 144	97.62 196	99.73 182	99.67 61

X-MVStestdata		96.09 299	94.87 306	99.75 53	99.71 109	99.71 56	99.37 88	99.61 134	99.29 112	98.76 263	61.30 339	98.47 138	99.88 144	97.62 196	99.73 182	99.67 61

SD-MVS		99.01 170	99.30 86	98.15 283	99.50 186	99.40 135	98.94 195	99.61 134	99.22 127	99.75 74	99.82 46	99.54 21	95.51 336	97.48 205	99.87 102	99.54 141
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APD-MVS_3200maxsize		99.31 99	99.16 108	99.74 58	99.53 172	99.75 45	99.27 114	99.61 134	99.19 129	99.57 136	99.64 137	98.76 100	99.90 115	97.29 215	99.62 211	99.56 131

UniMVSNet_NR-MVSNet		99.37 81	99.25 99	99.72 70	99.47 202	99.56 103	98.97 191	99.61 134	99.43 99	99.67 102	99.28 247	97.85 193	99.95 41	99.17 71	99.81 143	99.65 79

CP-MVSNet		99.54 46	99.43 61	99.87 14	99.76 84	99.82 25	99.57 59	99.61 134	99.54 77	99.80 55	99.64 137	97.79 197	99.95 41	99.21 62	99.94 60	99.84 13

DP-MVS		99.48 53	99.39 65	99.74 58	99.57 156	99.62 87	99.29 111	99.61 134	99.87 15	99.74 82	99.76 74	98.69 107	99.87 156	98.20 153	99.80 148	99.75 37

9.14			98.64 198		99.45 209		98.81 212	99.60 144	97.52 266	99.28 202	99.56 184	98.53 131	99.83 218	95.36 298	99.64 208

SR-MVS		99.19 130	99.00 155	99.74 58	99.51 180	99.72 54	99.18 133	99.60 144	98.85 166	99.47 162	99.58 174	98.38 149	99.92 83	96.92 237	99.54 229	99.57 129

DPE-MVS		99.14 143	98.92 171	99.82 24	99.57 156	99.77 38	98.74 221	99.60 144	98.55 196	99.76 70	99.69 110	98.23 164	99.92 83	96.39 264	99.75 167	99.76 34

v1192		99.57 38	99.57 38	99.57 129	99.77 80	99.22 175	99.04 174	99.60 144	99.18 130	99.87 37	99.72 90	99.08 64	99.85 191	99.89 5	99.98 22	99.66 71

UniMVSNet (Re)		99.37 81	99.26 97	99.68 80	99.51 180	99.58 100	98.98 190	99.60 144	99.43 99	99.70 94	99.36 230	97.70 200	99.88 144	99.20 65	99.87 102	99.59 120

SteuartSystems-ACMMP		99.30 100	99.14 112	99.76 44	99.87 28	99.66 74	99.18 133	99.60 144	98.55 196	99.57 136	99.67 126	99.03 71	99.94 52	97.01 233	99.80 148	99.69 48
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		99.25 109	99.08 131	99.76 44	99.73 102	99.70 63	99.31 101	99.59 150	98.36 215	99.36 186	99.37 226	98.80 91	99.91 96	97.43 208	99.75 167	99.68 54

v148		99.40 74	99.41 63	99.39 179	99.76 84	98.94 207	99.09 166	99.59 150	99.17 133	99.81 52	99.61 161	98.41 146	99.69 283	99.32 50	99.94 60	99.53 144

region2R		99.23 113	99.05 141	99.77 40	99.76 84	99.70 63	99.31 101	99.59 150	98.41 209	99.32 195	99.36 230	98.73 105	99.93 65	97.29 215	99.74 175	99.67 61

#test#		99.12 147	98.90 175	99.76 44	99.73 102	99.70 63	99.10 162	99.59 150	97.60 261	99.36 186	99.37 226	98.80 91	99.91 96	96.84 244	99.75 167	99.68 54

V42		99.56 41	99.54 44	99.63 105	99.79 66	99.46 116	99.39 82	99.59 150	99.24 122	99.86 38	99.70 104	98.55 125	99.82 227	99.79 11	99.95 48	99.60 111

ACMMPR		99.23 113	99.06 137	99.76 44	99.74 99	99.69 67	99.31 101	99.59 150	98.36 215	99.35 188	99.38 225	98.61 119	99.93 65	97.43 208	99.75 167	99.67 61

CMPMVS	77.52 23	98.50 224	98.19 236	99.41 175	98.33 325	99.56 103	99.01 179	99.59 150	95.44 306	99.57 136	99.80 51	95.64 261	99.46 328	96.47 263	99.92 71	99.21 234
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
our_test_3		98.85 195	99.09 129	98.13 284	99.66 130	94.90 314	97.72 305	99.58 157	99.07 144	99.64 112	99.62 152	98.19 168	99.93 65	98.41 134	99.95 48	99.55 134

v2v482		99.50 49	99.47 52	99.58 124	99.78 72	99.25 167	99.14 149	99.58 157	99.25 120	99.81 52	99.62 152	98.24 161	99.84 207	99.83 9	99.97 30	99.64 85

test0726					99.69 119	99.80 31	99.24 120	99.57 159	99.16 135	99.73 86	99.65 135	98.35 152

MSP-MVS		99.04 163	98.79 189	99.81 27	99.78 72	99.73 51	99.35 91	99.57 159	98.54 199	99.54 150	98.99 284	96.81 240	99.93 65	96.97 235	99.53 231	99.77 31

APD-MVS		98.87 193	98.59 203	99.71 74	99.50 186	99.62 87	99.01 179	99.57 159	96.80 291	99.54 150	99.63 145	98.29 157	99.91 96	95.24 299	99.71 190	99.61 107
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
FMVSNet2		99.35 86	99.28 93	99.55 136	99.49 191	99.35 149	99.45 72	99.57 159	99.44 94	99.70 94	99.74 80	97.21 227	99.87 156	99.03 88	99.94 60	99.44 186

TAMVS		99.49 51	99.45 56	99.63 105	99.48 197	99.42 130	99.45 72	99.57 159	99.66 55	99.78 63	99.83 40	97.85 193	99.86 174	99.44 34	99.96 41	99.61 107

cascas		96.99 279	96.82 285	97.48 298	97.57 334	95.64 307	96.43 330	99.56 164	91.75 323	97.13 324	97.61 329	95.58 263	98.63 333	96.68 252	99.11 277	98.18 314

Vis-MVSNet (Re-imp)		98.77 203	98.58 205	99.34 191	99.78 72	98.88 217	99.61 51	99.56 164	99.11 140	99.24 208	99.56 184	93.00 282	99.78 254	97.43 208	99.89 89	99.35 210

3Dnovator	99.15 2	99.43 64	99.36 73	99.65 94	99.39 223	99.42 130	99.70 23	99.56 164	99.23 124	99.35 188	99.80 51	99.17 51	99.95 41	98.21 152	99.84 116	99.59 120

GST-MVS		99.16 139	98.96 166	99.75 53	99.73 102	99.73 51	99.20 130	99.55 167	98.22 231	99.32 195	99.35 235	98.65 115	99.91 96	96.86 241	99.74 175	99.62 101

MVP-Stereo		99.16 139	99.08 131	99.43 166	99.48 197	99.07 197	99.08 169	99.55 167	98.63 188	99.31 197	99.68 121	98.19 168	99.78 254	98.18 157	99.58 218	99.45 181
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mvs_anonymous		99.28 103	99.39 65	98.94 242	99.19 271	97.81 272	99.02 177	99.55 167	99.78 33	99.85 39	99.80 51	98.24 161	99.86 174	99.57 23	99.50 236	99.15 243

CPTT-MVS		98.74 206	98.44 214	99.64 101	99.61 140	99.38 139	99.18 133	99.55 167	96.49 293	99.27 203	99.37 226	97.11 233	99.92 83	95.74 289	99.67 201	99.62 101

CLD-MVS		98.76 204	98.57 206	99.33 193	99.57 156	98.97 204	97.53 314	99.55 167	96.41 294	99.27 203	99.13 268	99.07 66	99.78 254	96.73 250	99.89 89	99.23 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		98.90 187	98.68 196	99.55 136	99.58 147	99.24 171	98.80 215	99.54 172	98.94 157	99.14 225	99.25 253	97.24 225	99.82 227	95.84 285	99.78 158	99.60 111

plane_prior5								99.54 172					99.82 227	95.84 285	99.78 158	99.60 111

mPP-MVS		99.19 130	99.00 155	99.76 44	99.76 84	99.68 70	99.38 84	99.54 172	98.34 224	99.01 236	99.50 202	98.53 131	99.93 65	97.18 227	99.78 158	99.66 71

CDS-MVSNet		99.22 121	99.13 115	99.50 147	99.35 232	99.11 189	98.96 192	99.54 172	99.46 92	99.61 129	99.70 104	96.31 251	99.83 218	99.34 46	99.88 95	99.55 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PatchMatch-RL		98.68 210	98.47 211	99.30 202	99.44 212	99.28 160	98.14 270	99.54 172	97.12 282	99.11 228	99.25 253	97.80 196	99.70 277	96.51 260	99.30 265	98.93 274

test_part1				0.00 323		0.00 341	0.00 334	99.53 177				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ACMMP_NAP		99.28 103	99.11 121	99.79 35	99.75 93	99.81 26	98.95 193	99.53 177	98.27 229	99.53 153	99.73 84	98.75 102	99.87 156	97.70 189	99.83 126	99.68 54

zzz-MVS		99.30 100	99.14 112	99.80 30	99.81 50	99.81 26	98.73 223	99.53 177	99.27 116	99.42 172	99.63 145	98.21 165	99.95 41	97.83 182	99.79 152	99.65 79

MTGPA								99.53 177

MTAPA		99.35 86	99.20 104	99.80 30	99.81 50	99.81 26	99.33 94	99.53 177	99.27 116	99.42 172	99.63 145	98.21 165	99.95 41	97.83 182	99.79 152	99.65 79

Regformer-4		99.45 62	99.44 58	99.50 147	99.52 176	98.94 207	99.17 139	99.53 177	99.64 59	99.76 70	99.60 166	98.96 78	99.90 115	98.91 104	99.84 116	99.67 61

Regformer-2		99.34 91	99.27 95	99.53 141	99.41 219	99.10 193	98.99 186	99.53 177	99.47 88	99.66 106	99.52 196	98.80 91	99.89 129	98.31 143	99.74 175	99.60 111

DU-MVS		99.33 95	99.21 103	99.71 74	99.43 214	99.56 103	98.83 207	99.53 177	99.38 103	99.67 102	99.36 230	97.67 205	99.95 41	99.17 71	99.81 143	99.63 89

DELS-MVS		99.34 91	99.30 86	99.48 152	99.51 180	99.36 145	98.12 272	99.53 177	99.36 106	99.41 178	99.61 161	99.22 47	99.87 156	99.21 62	99.68 196	99.20 235
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
SMA-MVS		99.19 130	99.00 155	99.73 65	99.46 206	99.73 51	99.13 156	99.52 186	97.40 271	99.57 136	99.64 137	98.93 79	99.83 218	97.61 198	99.79 152	99.63 89

QAPM		98.40 235	97.99 245	99.65 94	99.39 223	99.47 112	99.67 36	99.52 186	91.70 324	98.78 262	99.80 51	98.55 125	99.95 41	94.71 306	99.75 167	99.53 144

xiu_mvs_v2_base		99.02 166	99.11 121	98.77 263	99.37 228	98.09 260	98.13 271	99.51 188	99.47 88	99.42 172	98.54 314	99.38 28	99.97 15	98.83 108	99.33 262	98.24 309

PS-MVSNAJ		99.00 172	99.08 131	98.76 264	99.37 228	98.10 259	98.00 286	99.51 188	99.47 88	99.41 178	98.50 316	99.28 41	99.97 15	98.83 108	99.34 260	98.20 313

PLC	97.35 16	98.36 237	97.99 245	99.48 152	99.32 248	99.24 171	98.50 244	99.51 188	95.19 311	98.58 276	98.96 292	96.95 238	99.83 218	95.63 290	99.25 271	99.37 204
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testtj		98.56 218	98.17 238	99.72 70	99.45 209	99.60 94	98.88 198	99.50 191	96.88 286	99.18 220	99.48 207	97.08 234	99.92 83	93.69 316	99.38 254	99.63 89

MP-MVS		99.06 157	98.83 184	99.76 44	99.76 84	99.71 56	99.32 97	99.50 191	98.35 220	98.97 238	99.48 207	98.37 150	99.92 83	95.95 282	99.75 167	99.63 89
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NR-MVSNet		99.40 74	99.31 81	99.68 80	99.43 214	99.55 106	99.73 17	99.50 191	99.46 92	99.88 33	99.36 230	97.54 212	99.87 156	98.97 95	99.87 102	99.63 89

new_pmnet		98.88 191	98.89 176	98.84 256	99.70 116	97.62 276	98.15 268	99.50 191	97.98 242	99.62 123	99.54 192	98.15 171	99.94 52	97.55 201	99.84 116	98.95 272

3Dnovator+	98.92 3	99.35 86	99.24 100	99.67 82	99.35 232	99.47 112	99.62 47	99.50 191	99.44 94	99.12 227	99.78 64	98.77 99	99.94 52	97.87 179	99.72 187	99.62 101

MVS_Test		99.28 103	99.31 81	99.19 221	99.35 232	98.79 222	99.36 90	99.49 196	99.17 133	99.21 214	99.67 126	98.78 96	99.66 302	99.09 84	99.66 204	99.10 254

OPM-MVS		99.26 108	99.13 115	99.63 105	99.70 116	99.61 93	98.58 231	99.48 197	98.50 202	99.52 155	99.63 145	99.14 55	99.76 262	97.89 176	99.77 162	99.51 156

Regformer-1		99.32 97	99.27 95	99.47 154	99.41 219	98.95 206	98.99 186	99.48 197	99.48 83	99.66 106	99.52 196	98.78 96	99.87 156	98.36 137	99.74 175	99.60 111

FMVSNet3		98.80 200	98.63 200	99.32 197	99.13 279	98.72 225	99.10 162	99.48 197	99.23 124	99.62 123	99.64 137	92.57 284	99.86 174	98.96 97	99.90 81	99.39 199

OpenMVS_ROB	97.31 17	97.36 274	96.84 284	98.89 253	99.29 255	99.45 121	98.87 201	99.48 197	86.54 330	99.44 167	99.74 80	97.34 222	99.86 174	91.61 320	99.28 267	97.37 325

MSLP-MVS++		99.05 160	99.09 129	98.91 246	99.21 266	98.36 245	98.82 211	99.47 201	98.85 166	98.90 249	99.56 184	98.78 96	99.09 331	98.57 127	99.68 196	99.26 224

DeepPCF-MVS	98.42 6	99.18 134	99.02 149	99.67 82	99.22 265	99.75 45	97.25 322	99.47 201	98.72 182	99.66 106	99.70 104	99.29 39	99.63 312	98.07 167	99.81 143	99.62 101

PMVS	92.94 21	98.82 198	98.81 186	98.85 254	99.84 34	97.99 264	99.20 130	99.47 201	99.71 41	99.42 172	99.82 46	98.09 174	99.47 326	93.88 315	99.85 112	99.07 264
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc				99.20 220	99.35 232	98.53 233	99.17 139	99.46 204		99.67 102	99.80 51	98.46 141	99.70 277	97.92 174	99.70 192	99.38 201

EI-MVSNet-UG-set		99.48 53	99.50 50	99.42 168	99.57 156	98.65 231	99.24 120	99.46 204	99.68 49	99.80 55	99.66 130	98.99 72	99.89 129	99.19 66	99.90 81	99.72 39

EI-MVSNet-Vis-set		99.47 59	99.49 51	99.42 168	99.57 156	98.66 229	99.24 120	99.46 204	99.67 51	99.79 60	99.65 135	98.97 75	99.89 129	99.15 75	99.89 89	99.71 42

EI-MVSNet		99.38 79	99.44 58	99.21 218	99.58 147	98.09 260	99.26 115	99.46 204	99.62 63	99.75 74	99.67 126	98.54 127	99.85 191	99.15 75	99.92 71	99.68 54

MVSTER		98.47 228	98.22 231	99.24 215	99.06 288	98.35 246	99.08 169	99.46 204	99.27 116	99.75 74	99.66 130	88.61 317	99.85 191	99.14 81	99.92 71	99.52 154

CHOSEN 280x420		98.41 233	98.41 217	98.40 275	99.34 242	95.89 305	96.94 325	99.44 209	98.80 173	99.25 205	99.52 196	93.51 277	99.98 6	98.94 102	99.98 22	99.32 216

Regformer-3		99.41 71	99.41 63	99.40 176	99.52 176	98.70 226	99.17 139	99.44 209	99.62 63	99.75 74	99.60 166	98.90 83	99.85 191	98.89 105	99.84 116	99.65 79

PCF-MVS	96.03 18	96.73 287	95.86 297	99.33 193	99.44 212	99.16 184	96.87 326	99.44 209	86.58 329	98.95 241	99.40 220	94.38 271	99.88 144	87.93 327	99.80 148	98.95 272
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testing_2		99.58 37	99.56 42	99.62 114	99.81 50	99.44 123	99.14 149	99.43 212	99.69 47	99.82 47	99.79 57	99.14 55	99.79 250	99.31 53	99.95 48	99.63 89

ab-mvs		99.33 95	99.28 93	99.47 154	99.57 156	99.39 137	99.78 11	99.43 212	98.87 164	99.57 136	99.82 46	98.06 177	99.87 156	98.69 122	99.73 182	99.15 243

AdaColmap		98.60 214	98.35 223	99.38 183	99.12 281	99.22 175	98.67 226	99.42 214	97.84 253	98.81 257	99.27 249	97.32 223	99.81 242	95.14 300	99.53 231	99.10 254

D2MVS		99.22 121	99.19 105	99.29 203	99.69 119	98.74 224	98.81 212	99.41 215	98.55 196	99.68 98	99.69 110	98.13 172	99.87 156	98.82 110	99.98 22	99.24 227

CANet		99.11 151	99.05 141	99.28 204	98.83 304	98.56 232	98.71 225	99.41 215	99.25 120	99.23 209	99.22 261	97.66 209	99.94 52	99.19 66	99.97 30	99.33 213

TEST9					99.35 232	99.35 149	98.11 274	99.41 215	94.83 317	97.92 306	98.99 284	98.02 180	99.85 191

train_agg		98.35 240	97.95 249	99.57 129	99.35 232	99.35 149	98.11 274	99.41 215	94.90 313	97.92 306	98.99 284	98.02 180	99.85 191	95.38 297	99.44 243	99.50 162

CDPH-MVS		98.56 218	98.20 233	99.61 117	99.50 186	99.46 116	98.32 257	99.41 215	95.22 309	99.21 214	99.10 275	98.34 154	99.82 227	95.09 302	99.66 204	99.56 131

CNLPA		98.57 217	98.34 224	99.28 204	99.18 273	99.10 193	98.34 255	99.41 215	98.48 204	98.52 279	98.98 287	97.05 235	99.78 254	95.59 291	99.50 236	98.96 271

test_8					99.34 242	99.31 155	98.08 278	99.40 221	94.90 313	97.87 310	98.97 290	98.02 180	99.84 207

PVSNet_0	95.53 19	95.85 303	95.31 304	97.47 299	98.78 311	93.48 320	95.72 331	99.40 221	96.18 297	97.37 319	97.73 327	95.73 260	99.58 320	95.49 293	81.40 333	99.36 207

DeepC-MVS_fast	98.47 5	99.23 113	99.12 118	99.56 133	99.28 257	99.22 175	98.99 186	99.40 221	99.08 142	99.58 134	99.64 137	98.90 83	99.83 218	97.44 207	99.75 167	99.63 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous20240529		99.42 67	99.34 75	99.65 94	99.53 172	99.60 94	99.63 46	99.39 224	99.47 88	99.76 70	99.78 64	98.13 172	99.86 174	98.70 120	99.68 196	99.49 167

agg_prior1		98.33 242	97.92 254	99.57 129	99.35 232	99.36 145	97.99 288	99.39 224	94.85 316	97.76 315	98.98 287	98.03 178	99.85 191	95.49 293	99.44 243	99.51 156

agg_prior					99.35 232	99.36 145		99.39 224		97.76 315			99.85 191

test_prior3		98.62 212	98.34 224	99.46 157	99.35 232	99.22 175	97.95 293	99.39 224	97.87 249	98.05 300	99.05 278	97.90 188	99.69 283	95.99 278	99.49 238	99.48 169

test_prior				99.46 157	99.35 232	99.22 175		99.39 224					99.69 283			99.48 169

jason		99.16 139	99.11 121	99.32 197	99.75 93	98.44 238	98.26 261	99.39 224	98.70 183	99.74 82	99.30 243	98.54 127	99.97 15	98.48 132	99.82 135	99.55 134
jason: jason.
save fliter					99.53 172	99.25 167	98.29 259	99.38 230	99.07 144

WR-MVS		99.11 151	98.93 168	99.66 89	99.30 253	99.42 130	98.42 252	99.37 231	99.04 148	99.57 136	99.20 264	96.89 239	99.86 174	98.66 124	99.87 102	99.70 45

HQP3-MVS								99.37 231							99.67 201

HQP-MVS		98.36 237	98.02 244	99.39 179	99.31 249	98.94 207	97.98 289	99.37 231	97.45 268	98.15 294	98.83 301	96.67 241	99.70 277	94.73 304	99.67 201	99.53 144

TSAR-MVS + MP.		99.34 91	99.24 100	99.63 105	99.82 43	99.37 142	99.26 115	99.35 234	98.77 177	99.57 136	99.70 104	99.27 44	99.88 144	97.71 188	99.75 167	99.65 79
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
UGNet		99.38 79	99.34 75	99.49 149	98.90 295	98.90 215	99.70 23	99.35 234	99.86 17	98.57 277	99.81 49	98.50 137	99.93 65	99.38 41	99.98 22	99.66 71
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
PVSNet	97.47 15	98.42 232	98.44 214	98.35 276	99.46 206	96.26 298	96.70 328	99.34 236	97.68 258	99.00 237	99.13 268	97.40 217	99.72 272	97.59 200	99.68 196	99.08 260

MS-PatchMatch		99.00 172	98.97 164	99.09 229	99.11 284	98.19 252	98.76 220	99.33 237	98.49 203	99.44 167	99.58 174	98.21 165	99.69 283	98.20 153	99.62 211	99.39 199

MDA-MVSNet_test_wron		98.95 182	98.99 160	98.85 254	99.64 134	97.16 287	98.23 263	99.33 237	98.93 159	99.56 143	99.66 130	97.39 219	99.83 218	98.29 145	99.88 95	99.55 134

YYNet1		98.95 182	98.99 160	98.84 256	99.64 134	97.14 288	98.22 264	99.32 239	98.92 161	99.59 132	99.66 130	97.40 217	99.83 218	98.27 147	99.90 81	99.55 134

tpm cat1		96.78 285	96.98 279	96.16 318	98.85 302	90.59 336	99.08 169	99.32 239	92.37 322	97.73 317	99.46 213	91.15 297	99.69 283	96.07 274	98.80 291	98.21 311

sss		98.90 187	98.77 190	99.27 206	99.48 197	98.44 238	98.72 224	99.32 239	97.94 246	99.37 185	99.35 235	96.31 251	99.91 96	98.85 107	99.63 210	99.47 175

PMMVS		98.49 226	98.29 227	99.11 227	98.96 292	98.42 240	97.54 312	99.32 239	97.53 265	98.47 283	98.15 323	97.88 191	99.82 227	97.46 206	99.24 273	99.09 257

DVP-MVS		99.32 97	99.17 107	99.77 40	99.69 119	99.80 31	99.14 149	99.31 243	99.16 135	99.62 123	99.61 161	98.35 152	99.91 96	97.88 177	99.72 187	99.61 107

CANet_DTU		98.91 185	98.85 180	99.09 229	98.79 309	98.13 255	98.18 265	99.31 243	99.48 83	98.86 252	99.51 199	96.56 243	99.95 41	99.05 87	99.95 48	99.19 237

VNet		99.18 134	99.06 137	99.56 133	99.24 263	99.36 145	99.33 94	99.31 243	99.67 51	99.47 162	99.57 181	96.48 246	99.84 207	99.15 75	99.30 265	99.47 175

MVS_0304		98.88 191	98.71 193	99.39 179	98.85 302	98.91 214	99.45 72	99.30 246	98.56 194	97.26 322	99.68 121	96.18 254	99.96 32	99.17 71	99.94 60	99.29 222

testdata				99.42 168	99.51 180	98.93 211		99.30 246	96.20 296	98.87 251	99.40 220	98.33 156	99.89 129	96.29 268	99.28 267	99.44 186

test222					99.51 180	99.08 196	97.83 302	99.29 248	95.21 310	98.68 269	99.31 241	97.28 224			99.38 254	99.43 192

TSAR-MVS + GP.		99.12 147	99.04 146	99.38 183	99.34 242	99.16 184	98.15 268	99.29 248	98.18 234	99.63 116	99.62 152	99.18 50	99.68 293	98.20 153	99.74 175	99.30 219

test11								99.29 248

PAPM_NR		98.36 237	98.04 243	99.33 193	99.48 197	98.93 211	98.79 218	99.28 251	97.54 264	98.56 278	98.57 311	97.12 232	99.69 283	94.09 312	98.90 289	99.38 201

原ACMM1				99.37 186	99.47 202	98.87 219		99.27 252	96.74 292	98.26 289	99.32 239	97.93 187	99.82 227	95.96 281	99.38 254	99.43 192

CNVR-MVS		98.99 175	98.80 188	99.56 133	99.25 261	99.43 127	98.54 240	99.27 252	98.58 193	98.80 259	99.43 216	98.53 131	99.70 277	97.22 223	99.59 217	99.54 141

新几何1				99.52 142	99.50 186	99.22 175		99.26 254	95.66 305	98.60 274	99.28 247	97.67 205	99.89 129	95.95 282	99.32 263	99.45 181

旧先验1					99.49 191	99.29 158		99.26 254			99.39 224	97.67 205			99.36 258	99.46 179

DeepMVS_CX				97.98 286	99.69 119	96.95 290		99.26 254	75.51 332	95.74 331	98.28 321	96.47 247	99.62 313	91.23 322	97.89 321	97.38 324

pmmvs4		99.13 145	99.06 137	99.36 189	99.57 156	99.10 193	98.01 284	99.25 257	98.78 176	99.58 134	99.44 215	98.24 161	99.76 262	98.74 117	99.93 68	99.22 232

NCCC		98.82 198	98.57 206	99.58 124	99.21 266	99.31 155	98.61 227	99.25 257	98.65 186	98.43 284	99.26 251	97.86 192	99.81 242	96.55 258	99.27 270	99.61 107

PAPR		97.56 269	97.07 275	99.04 236	98.80 308	98.11 258	97.63 308	99.25 257	94.56 319	98.02 304	98.25 322	97.43 216	99.68 293	90.90 323	98.74 298	99.33 213

EPP-MVSNet		99.17 138	99.00 155	99.66 89	99.80 56	99.43 127	99.70 23	99.24 260	99.48 83	99.56 143	99.77 71	94.89 266	99.93 65	98.72 119	99.89 89	99.63 89

无先验							98.01 284	99.23 261	95.83 301				99.85 191	95.79 287		99.44 186

1121		98.56 218	98.24 229	99.52 142	99.49 191	99.24 171	99.30 104	99.22 262	95.77 302	98.52 279	99.29 246	97.39 219	99.85 191	95.79 287	99.34 260	99.46 179

MG-MVS		98.52 223	98.39 218	98.94 242	99.15 276	97.39 283	98.18 265	99.21 263	98.89 163	99.23 209	99.63 145	97.37 221	99.74 269	94.22 310	99.61 215	99.69 48

HPM-MVS++		98.96 179	98.70 194	99.74 58	99.52 176	99.71 56	98.86 202	99.19 264	98.47 205	98.59 275	99.06 277	98.08 176	99.91 96	96.94 236	99.60 216	99.60 111

lupinMVS		98.96 179	98.87 178	99.24 215	99.57 156	98.40 241	98.12 272	99.18 265	98.28 228	99.63 116	99.13 268	98.02 180	99.97 15	98.22 151	99.69 194	99.35 210

API-MVS		98.38 236	98.39 218	98.35 276	98.83 304	99.26 164	99.14 149	99.18 265	98.59 192	98.66 270	98.78 304	98.61 119	99.57 321	94.14 311	99.56 220	96.21 329

test12				99.54 140	99.29 255	99.33 152		99.16 267		98.43 284		97.54 212	99.82 227		99.47 240	99.48 169

IS-MVSNet		99.03 164	98.85 180	99.55 136	99.80 56	99.25 167	99.73 17	99.15 268	99.37 104	99.61 129	99.71 97	94.73 268	99.81 242	97.70 189	99.88 95	99.58 125

SixPastTwentyTwo		99.42 67	99.30 86	99.76 44	99.92 15	99.67 72	99.70 23	99.14 269	99.65 57	99.89 27	99.90 21	96.20 253	99.94 52	99.42 39	99.92 71	99.67 61

MAR-MVS		98.24 247	97.92 254	99.19 221	98.78 311	99.65 79	99.17 139	99.14 269	95.36 307	98.04 302	98.81 303	97.47 214	99.72 272	95.47 295	99.06 279	98.21 311
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
WTY-MVS		98.59 216	98.37 220	99.26 209	99.43 214	98.40 241	98.74 221	99.13 271	98.10 236	99.21 214	99.24 258	94.82 267	99.90 115	97.86 180	98.77 294	99.49 167

Patchmatch-test		98.10 253	97.98 247	98.48 272	99.27 259	96.48 296	99.40 80	99.07 272	98.81 171	99.23 209	99.57 181	90.11 311	99.87 156	96.69 251	99.64 208	99.09 257

MCST-MVS		99.02 166	98.81 186	99.65 94	99.58 147	99.49 109	98.58 231	99.07 272	98.40 211	99.04 235	99.25 253	98.51 136	99.80 247	97.31 214	99.51 234	99.65 79

1314		98.00 257	97.90 257	98.27 281	98.90 295	97.45 281	99.30 104	99.06 274	94.98 312	97.21 323	99.12 272	98.43 144	99.67 298	95.58 292	98.56 305	97.71 321

GA-MVS		97.99 258	97.68 265	98.93 245	99.52 176	98.04 263	97.19 323	99.05 275	98.32 226	98.81 257	98.97 290	89.89 314	99.41 329	98.33 141	99.05 280	99.34 212

E-PMN		97.14 278	97.43 269	96.27 316	98.79 309	91.62 330	95.54 332	99.01 276	99.44 94	98.88 250	99.12 272	92.78 283	99.68 293	94.30 309	99.03 282	97.50 322

BH-untuned		98.22 249	98.09 241	98.58 269	99.38 226	97.24 286	98.55 237	98.98 277	97.81 254	99.20 219	98.76 305	97.01 236	99.65 309	94.83 303	98.33 310	98.86 280

tpmvs		97.39 272	97.69 264	96.52 314	98.41 323	91.76 328	99.30 104	98.94 278	97.74 255	97.85 311	99.55 190	92.40 287	99.73 271	96.25 270	98.73 300	98.06 316

MVS		95.72 305	94.63 308	98.99 238	98.56 320	97.98 269	99.30 104	98.86 279	72.71 333	97.30 320	99.08 276	98.34 154	99.74 269	89.21 324	98.33 310	99.26 224

ADS-MVSNet		97.72 264	97.67 266	97.86 290	99.14 277	94.65 315	99.22 127	98.86 279	96.97 284	98.25 290	99.64 137	90.90 301	99.84 207	96.51 260	99.56 220	99.08 260

tpmrst		97.73 262	98.07 242	96.73 310	98.71 316	92.00 325	99.10 162	98.86 279	98.52 200	98.92 246	99.54 192	91.90 288	99.82 227	98.02 168	99.03 282	98.37 302

PatchT		98.45 230	98.32 226	98.83 258	98.94 293	98.29 247	99.24 120	98.82 282	99.84 22	99.08 230	99.76 74	91.37 293	99.94 52	98.82 110	99.00 284	98.26 307

FPMVS		96.32 295	95.50 302	98.79 262	99.60 142	98.17 254	98.46 250	98.80 283	97.16 280	96.28 326	99.63 145	82.19 333	99.09 331	88.45 326	98.89 290	99.10 254

DPM-MVS		98.28 243	97.94 253	99.32 197	99.36 230	99.11 189	97.31 320	98.78 284	96.88 286	98.84 254	99.11 274	97.77 198	99.61 317	94.03 313	99.36 258	99.23 230

RPMNet		98.53 222	98.44 214	98.83 258	99.05 289	98.12 256	99.30 104	98.78 284	99.86 17	99.16 221	99.74 80	92.53 286	99.91 96	98.75 116	98.77 294	98.44 300

ADS-MVSNet2		97.78 261	97.66 267	98.12 285	99.14 277	95.36 309	99.22 127	98.75 286	96.97 284	98.25 290	99.64 137	90.90 301	99.94 52	96.51 260	99.56 220	99.08 260

HY-MVS	98.23 9	98.21 250	97.95 249	98.99 238	99.03 291	98.24 248	99.61 51	98.72 287	96.81 290	98.73 265	99.51 199	94.06 273	99.86 174	96.91 238	98.20 312	98.86 280

VDDNet		98.97 176	98.82 185	99.42 168	99.71 109	98.81 220	99.62 47	98.68 288	99.81 27	99.38 184	99.80 51	94.25 272	99.85 191	98.79 112	99.32 263	99.59 120

CostFormer		96.71 288	96.79 286	96.46 315	98.90 295	90.71 335	99.41 78	98.68 288	94.69 318	98.14 298	99.34 238	86.32 328	99.80 247	97.60 199	98.07 318	98.88 278

test_yl		98.25 245	97.95 249	99.13 225	99.17 274	98.47 235	99.00 181	98.67 290	98.97 153	99.22 212	99.02 282	91.31 294	99.69 283	97.26 218	98.93 285	99.24 227

DCV-MVSNet		98.25 245	97.95 249	99.13 225	99.17 274	98.47 235	99.00 181	98.67 290	98.97 153	99.22 212	99.02 282	91.31 294	99.69 283	97.26 218	98.93 285	99.24 227

EMVS		96.96 281	97.28 271	95.99 319	98.76 313	91.03 333	95.26 333	98.61 292	99.34 107	98.92 246	98.88 299	93.79 274	99.66 302	92.87 317	99.05 280	97.30 326

MIMVSNet		98.43 231	98.20 233	99.11 227	99.53 172	98.38 244	99.58 58	98.61 292	98.96 155	99.33 193	99.76 74	90.92 300	99.81 242	97.38 211	99.76 164	99.15 243

MTMP							99.09 166	98.59 294

BH-w/o		97.20 275	97.01 278	97.76 293	99.08 287	95.69 306	98.03 283	98.52 295	95.76 303	97.96 305	98.02 324	95.62 262	99.47 326	92.82 318	97.25 326	98.12 315

tpm2		96.35 294	96.22 290	96.73 310	98.88 301	91.75 329	99.21 129	98.51 296	93.27 321	97.89 308	99.21 262	84.83 330	99.70 277	96.04 275	98.18 315	98.75 286

JIA-IIPM		98.06 255	97.92 254	98.50 271	98.59 319	97.02 289	98.80 215	98.51 296	99.88 14	97.89 308	99.87 30	91.89 289	99.90 115	98.16 160	97.68 323	98.59 291

SCA		98.11 252	98.36 221	97.36 302	99.20 269	92.99 321	98.17 267	98.49 298	98.24 230	99.10 229	99.57 181	96.01 258	99.94 52	96.86 241	99.62 211	99.14 247

PAPM		95.61 306	94.71 307	98.31 279	99.12 281	96.63 294	96.66 329	98.46 299	90.77 326	96.25 327	98.68 308	93.01 281	99.69 283	81.60 333	97.86 322	98.62 289

PatchFormer-LS_test		96.95 282	97.07 275	96.62 313	98.76 313	91.85 327	99.18 133	98.45 300	97.29 276	97.73 317	97.22 335	88.77 316	99.76 262	98.13 162	98.04 319	98.25 308

alignmvs		98.28 243	97.96 248	99.25 212	99.12 281	98.93 211	99.03 176	98.42 301	99.64 59	98.72 266	97.85 326	90.86 303	99.62 313	98.88 106	99.13 276	99.19 237

baseline1		97.73 262	97.33 270	98.96 240	99.30 253	97.73 273	99.40 80	98.42 301	99.33 110	99.46 165	99.21 262	91.18 296	99.82 227	98.35 139	91.26 332	99.32 216

PatchmatchNet		97.65 265	97.80 259	97.18 305	98.82 307	92.49 323	99.17 139	98.39 303	98.12 235	98.79 260	99.58 174	90.71 305	99.89 129	97.23 222	99.41 250	99.16 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dp		96.86 283	97.07 275	96.24 317	98.68 318	90.30 337	99.19 132	98.38 304	97.35 274	98.23 292	99.59 172	87.23 319	99.82 227	96.27 269	98.73 300	98.59 291

VDD-MVS		99.20 127	99.11 121	99.44 163	99.43 214	98.98 202	99.50 65	98.32 305	99.80 30	99.56 143	99.69 110	96.99 237	99.85 191	98.99 91	99.73 182	99.50 162

BH-RMVSNet		98.41 233	98.14 239	99.21 218	99.21 266	98.47 235	98.60 229	98.26 306	98.35 220	98.93 243	99.31 241	97.20 230	99.66 302	94.32 308	99.10 278	99.51 156

EPNet_dtu		97.62 266	97.79 261	97.11 307	96.67 335	92.31 324	98.51 243	98.04 307	99.24 122	95.77 330	99.47 210	93.78 275	99.66 302	98.98 93	99.62 211	99.37 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			97.73 263		98.70 317	90.83 334	99.15 147	98.02 308	98.51 201	98.82 256	99.61 161	90.98 299	99.66 302	96.89 240	98.92 287

EPNet		98.13 251	97.77 262	99.18 223	94.57 336	97.99 264	99.24 120	97.96 309	99.74 36	97.29 321	99.62 152	93.13 280	99.97 15	98.59 126	99.83 126	99.58 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		97.15 276	96.95 280	97.75 294	98.91 294	94.24 317	99.32 97	97.96 309	97.71 257	98.29 287	99.32 239	86.72 326	99.92 83	98.10 166	96.24 329	99.09 257

DI_MVS_plusplus_test		98.80 200	98.65 197	99.27 206	99.57 156	98.90 215	98.44 251	97.95 311	99.02 149	99.51 157	99.23 260	96.18 254	99.76 262	98.52 130	99.42 248	99.14 247

TR-MVS		97.44 271	97.15 274	98.32 278	98.53 321	97.46 280	98.47 246	97.91 312	96.85 288	98.21 293	98.51 315	96.42 249	99.51 324	92.16 319	97.29 325	97.98 318

tmp_tt		95.75 304	95.42 303	96.76 308	89.90 337	94.42 316	98.86 202	97.87 313	78.01 331	99.30 201	99.69 110	97.70 200	95.89 335	99.29 57	98.14 316	99.95 1

DWT-MVSNet_test		96.03 301	95.80 299	96.71 312	98.50 322	91.93 326	99.25 119	97.87 313	95.99 299	96.81 325	97.61 329	81.02 335	99.66 302	97.20 226	97.98 320	98.54 295

Anonymous202405211		98.75 205	98.46 212	99.63 105	99.34 242	99.66 74	99.47 71	97.65 315	99.28 115	99.56 143	99.50 202	93.15 279	99.84 207	98.62 125	99.58 218	99.40 197

thres100view900		96.39 293	96.03 294	97.47 299	99.63 136	95.93 303	99.18 133	97.57 316	98.75 181	98.70 268	97.31 333	87.04 321	99.67 298	87.62 328	98.51 307	96.81 327

thres600view7		96.60 290	96.16 291	97.93 288	99.63 136	96.09 302	99.18 133	97.57 316	98.77 177	98.72 266	97.32 332	87.04 321	99.72 272	88.57 325	98.62 303	97.98 318

thres200		96.09 299	95.68 301	97.33 304	99.48 197	96.22 299	98.53 241	97.57 316	98.06 238	98.37 286	96.73 338	86.84 325	99.61 317	86.99 331	98.57 304	96.16 330

tfpn200view9		96.30 296	95.89 295	97.53 297	99.58 147	96.11 300	99.00 181	97.54 319	98.43 206	98.52 279	96.98 336	86.85 323	99.67 298	87.62 328	98.51 307	96.81 327

thres400		96.40 292	95.89 295	97.92 289	99.58 147	96.11 300	99.00 181	97.54 319	98.43 206	98.52 279	96.98 336	86.85 323	99.67 298	87.62 328	98.51 307	97.98 318

test0.0.03 1		97.37 273	96.91 283	98.74 265	97.72 331	97.57 277	97.60 310	97.36 321	98.00 239	99.21 214	98.02 324	90.04 312	99.79 250	98.37 136	95.89 330	98.86 280

LFMVS		98.46 229	98.19 236	99.26 209	99.24 263	98.52 234	99.62 47	96.94 322	99.87 15	99.31 197	99.58 174	91.04 298	99.81 242	98.68 123	99.42 248	99.45 181

test-LLR		97.15 276	96.95 280	97.74 295	98.18 328	95.02 312	97.38 316	96.10 323	98.00 239	97.81 312	98.58 309	90.04 312	99.91 96	97.69 194	98.78 292	98.31 304

test-mter		96.23 298	95.73 300	97.74 295	98.18 328	95.02 312	97.38 316	96.10 323	97.90 247	97.81 312	98.58 309	79.12 339	99.91 96	97.69 194	98.78 292	98.31 304

IB-MVS	95.41 20	95.30 307	94.46 309	97.84 291	98.76 313	95.33 310	97.33 319	96.07 325	96.02 298	95.37 332	97.41 331	76.17 340	99.96 32	97.54 202	95.44 331	98.22 310
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
ET-MVSNet_ETH3D		96.78 285	96.07 293	98.91 246	99.26 260	97.92 270	97.70 307	96.05 326	97.96 245	92.37 334	98.43 318	87.06 320	99.90 115	98.27 147	97.56 324	98.91 276

TESTMET0.1,1		96.24 297	95.84 298	97.41 301	98.24 326	93.84 318	97.38 316	95.84 327	98.43 206	97.81 312	98.56 312	79.77 338	99.89 129	97.77 184	98.77 294	98.52 296

MVE	92.54 22	96.66 289	96.11 292	98.31 279	99.68 124	97.55 278	97.94 295	95.60 328	99.37 104	90.68 335	98.70 307	96.56 243	98.61 334	86.94 332	99.55 224	98.77 285
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
K. test v3		98.87 193	98.60 201	99.69 79	99.93 14	99.46 116	99.74 16	94.97 329	99.78 33	99.88 33	99.88 28	93.66 276	99.97 15	99.61 19	99.95 48	99.64 85

N_pmnet		98.73 208	98.53 210	99.35 190	99.72 106	98.67 228	98.34 255	94.65 330	98.35 220	99.79 60	99.68 121	98.03 178	99.93 65	98.28 146	99.92 71	99.44 186

tttt0517		97.62 266	97.20 273	98.90 252	99.76 84	97.40 282	99.48 69	94.36 331	99.06 147	99.70 94	99.49 206	84.55 331	99.94 52	98.73 118	99.65 206	99.36 207

thisisatest0515		96.98 280	96.42 287	98.66 268	99.42 218	97.47 279	97.27 321	94.30 332	97.24 277	99.15 223	98.86 300	85.01 329	99.87 156	97.10 230	99.39 253	98.63 288

thisisatest0530		97.45 270	96.95 280	98.94 242	99.68 124	97.73 273	99.09 166	94.19 333	98.61 191	99.56 143	99.30 243	84.30 332	99.93 65	98.27 147	99.54 229	99.16 241

baseline2		96.83 284	96.28 289	98.46 273	99.09 286	96.91 292	98.83 207	93.87 334	97.23 278	96.23 329	98.36 319	88.12 318	99.90 115	96.68 252	98.14 316	98.57 294

MVS-HIRNet		97.86 259	98.22 231	96.76 308	99.28 257	91.53 331	98.38 254	92.60 335	99.13 139	99.31 197	99.96 10	97.18 231	99.68 293	98.34 140	99.83 126	99.07 264

lessismore_v0				99.64 101	99.86 30	99.38 139		90.66 336		99.89 27	99.83 40	94.56 270	99.97 15	99.56 24	99.92 71	99.57 129

EPMVS		96.53 291	96.32 288	97.17 306	98.18 328	92.97 322	99.39 82	89.95 337	98.21 232	98.61 273	99.59 172	86.69 327	99.72 272	96.99 234	99.23 275	98.81 283

gg-mvs-nofinetune		95.87 302	95.17 305	97.97 287	98.19 327	96.95 290	99.69 29	89.23 338	99.89 12	96.24 328	99.94 12	81.19 334	99.51 324	93.99 314	98.20 312	97.44 323

GG-mvs-BLEND				97.36 302	97.59 332	96.87 293	99.70 23	88.49 339		94.64 333	97.26 334	80.66 336	99.12 330	91.50 321	96.50 328	96.08 331

testmvs		28.94 309	33.33 310	15.79 322	26.03 338	9.81 340	96.77 327	15.67 340	11.55 335	23.87 337	50.74 342	19.03 342	8.53 338	23.21 335	33.07 334	29.03 334

test123		29.31 308	33.05 312	18.08 321	25.93 339	12.24 339	97.53 314	10.93 341	11.78 334	24.21 336	50.08 343	21.04 341	8.60 337	23.51 334	32.43 335	33.39 333

pcd_1.5k_mvsjas		16.61 311	22.14 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	100.00 1	99.28 41	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		8.33 312	11.11 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	100.00 1	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		8.33 312	11.11 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	100.00 1	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		8.33 312	11.11 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	100.00 1	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		8.33 312	11.11 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	100.00 1	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

n2								0.00 342

nn								0.00 342

ab-mvs-re		8.26 317	11.02 319	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	99.16 266	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		8.33 312	11.11 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	100.00 1	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

save filter2										98.97 238	99.40 220	98.45 142	99.90 115	97.22 223	99.70 192	99.48 169

test_0728_THIRD									99.18 130	99.62 123	99.61 161	98.58 122	99.91 96	97.72 187	99.80 148	99.77 31

GSMVS																99.14 247

test_part2					99.62 139	99.67 72				99.55 148

sam_mvs1												90.81 304				99.14 247

sam_mvs												90.52 308

test_post1							99.14 149				51.63 341	89.54 315	99.82 227	96.86 241

test_post											52.41 340	90.25 310	99.86 174

patchmatchnet-post											99.62 152	90.58 306	99.94 52

gm-plane-assit					97.59 332	89.02 338			93.47 320		98.30 320		99.84 207	96.38 265

test9_res														95.10 301	99.44 243	99.50 162

agg_prior2														94.58 307	99.46 242	99.50 162

test_prior4						99.19 182	98.00 286

test_prior2							97.95 293		97.87 249	98.05 300	99.05 278	97.90 188		95.99 278	99.49 238

旧先验2							97.94 295		95.33 308	98.94 242			99.88 144	96.75 248

新几何2							98.04 282

原ACMM2							97.92 297

testdata2													99.89 129	95.99 278

segment_acmp												98.37 150

testdata1							97.72 305		97.86 252

plane_prior7					99.58 147	99.38 139

plane_prior6					99.47 202	99.26 164						97.24 225

plane_prior4											99.25 253

plane_prior3						99.31 155			98.36 215	99.14 225

plane_prior2							98.80 215		98.94 157

plane_prior1					99.51 180

plane_prior						99.24 171	98.42 252		97.87 249						99.71 190

HQP5-MVS						98.94 207

HQP-NCC					99.31 249		97.98 289		97.45 268	98.15 294

ACMP_Plane					99.31 249		97.98 289		97.45 268	98.15 294

BP-MVS														94.73 304

HQP4-MVS										98.15 294			99.70 277			99.53 144

HQP2-MVS												96.67 241

NP-MVS					99.40 222	99.13 187					98.83 301

MDTV_nov1_ep13_2view						91.44 332	99.14 149		97.37 273	99.21 214		91.78 292		96.75 248		99.03 268

ACMMP++_ref															99.94 60

ACMMP++															99.79 152

Test By Simon												98.41 146

High-resolution multi-view benchmark