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.

MVS_111021_HR		98.72 22	98.62 18	99.01 71	99.36 84	97.18 98	99.93 50	99.90 1	96.81 24	98.67 73	99.77 52	93.92 78	99.89 69	99.27 31	99.94 44	99.96 58

MVS_111021_LR		98.42 40	98.38 31	98.53 101	99.39 82	95.79 139	99.87 71	99.86 2	96.70 27	98.78 68	99.79 45	92.03 115	99.90 66	99.17 32	99.86 60	99.88 74

CHOSEN 1792x2688		96.81 94	96.53 91	97.64 141	98.91 103	93.07 207	99.65 147	99.80 3	95.64 57	95.39 149	98.86 132	84.35 194	99.90 66	96.98 106	99.16 99	99.95 63

HyFIR lowres test		96.66 105	96.43 92	97.36 152	99.05 89	93.91 181	99.70 133	99.80 3	90.54 205	96.26 132	98.08 174	92.15 113	98.23 197	96.84 110	95.46 167	99.93 66

tfpn111		96.69 102	95.87 120	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.20 160

conf200view11		96.73 101	95.92 110	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.20 160

thres100view900		96.74 99	95.92 110	99.18 43	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.27 154

tfpn200view9		96.79 95	95.99 102	99.19 42	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.27 154

view600		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

view800		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

conf0.05thres1000		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

tfpn		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

thres600view7		96.69 102	95.87 120	99.14 52	98.90 104	98.78 25	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.16 166

thres400		96.78 96	95.99 102	99.16 46	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.16 166

thres200		96.96 88	96.21 97	99.22 41	98.97 96	98.84 22	99.85 88	99.71 5	93.17 123	96.26 132	98.88 128	89.87 139	99.51 119	94.26 147	94.91 172	99.31 149

PVSNet	91.05 13	97.13 84	96.69 86	98.45 110	99.52 76	95.81 138	99.95 31	99.65 16	94.73 75	99.04 58	99.21 108	84.48 192	99.95 51	94.92 130	98.74 105	99.58 114

PVSNet_0	88.03 19	91.80 211	90.27 223	96.38 176	98.27 136	90.46 262	99.94 45	99.61 17	93.99 100	86.26 273	97.39 187	71.13 302	99.89 69	98.77 53	67.05 327	98.79 185

WTY-MVS		98.10 54	97.60 60	99.60 13	98.92 101	99.28 5	99.89 64	99.52 18	95.58 58	98.24 93	99.39 97	93.33 91	99.74 101	97.98 83	95.58 166	99.78 84

HY-MVS	92.50 7	97.79 64	97.17 72	99.63 9	98.98 95	99.32 3	97.49 292	99.52 18	95.69 56	98.32 88	97.41 186	93.32 92	99.77 92	98.08 78	95.75 163	99.81 80

EPNet		98.49 36	98.40 29	98.77 83	99.62 69	96.80 110	99.90 59	99.51 20	97.60 8	99.20 51	99.36 100	93.71 86	99.91 65	97.99 81	98.71 106	99.61 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PGM-MVS		98.34 44	98.13 44	98.99 72	99.92 27	97.00 103	99.75 117	99.50 21	93.90 105	99.37 44	99.76 56	93.24 95	100.00 1	97.75 91	99.96 37	99.98 44

ACMMP		97.74 66	97.44 64	98.66 89	99.92 27	96.13 131	99.18 199	99.45 22	94.84 72	96.41 130	99.71 67	91.40 122	99.99 28	97.99 81	98.03 121	99.87 75
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
MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 151	99.44 23	97.33 12	99.00 62	99.72 65	94.03 76	99.98 32	98.73 54	100.00 1	100.00 1

EPMVS		96.53 108	96.01 101	98.09 129	98.43 131	96.12 134	96.36 309	99.43 24	93.53 116	97.64 103	95.04 261	94.41 59	98.38 186	91.13 192	98.11 117	99.75 87

CHOSEN 280x420		99.01 10	99.03 5	98.95 75	99.38 83	98.87 20	98.46 261	99.42 25	97.03 17	99.02 59	99.09 112	99.35 1	98.21 198	99.73 16	99.78 69	99.77 85

sss		97.57 70	97.03 77	99.18 43	98.37 132	98.04 64	99.73 126	99.38 26	93.46 117	98.76 69	99.06 114	91.21 124	99.89 69	96.33 113	97.01 143	99.62 105

PAPM		98.60 26	98.42 26	99.14 52	96.05 211	98.96 13	99.90 59	99.35 27	96.68 28	98.35 87	99.66 78	96.45 21	98.51 169	99.45 25	99.89 55	99.96 58

UGNet		95.33 145	94.57 151	97.62 142	98.55 126	94.85 163	98.67 247	99.32 28	95.75 55	96.80 120	96.27 222	72.18 296	99.96 43	94.58 140	99.05 100	98.04 194
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
VNet		97.21 82	96.57 90	99.13 57	98.97 96	97.82 70	99.03 218	99.21 29	94.31 87	99.18 54	98.88 128	86.26 178	99.89 69	98.93 44	94.32 184	99.69 95

PVSNet_BlendedMVS		96.05 130	95.82 122	96.72 167	99.59 70	96.99 104	99.95 31	99.10 30	94.06 98	98.27 90	95.80 230	89.00 153	99.95 51	99.12 33	87.53 238	93.24 290

PVSNet_Blended		97.94 58	97.64 58	98.83 81	99.59 70	96.99 104	100.00 1	99.10 30	95.38 61	98.27 90	99.08 113	89.00 153	99.95 51	99.12 33	99.25 97	99.57 115

UniMVSNet_NR-MVSNet		92.95 191	92.11 194	95.49 191	94.61 243	95.28 156	99.83 95	99.08 32	91.49 182	89.21 235	96.86 205	87.14 169	96.73 272	93.20 168	77.52 302	94.46 217

CSCG		97.10 85	97.04 76	97.27 154	99.89 36	91.92 235	99.90 59	99.07 33	88.67 234	95.26 152	99.82 40	93.17 97	99.98 32	98.15 73	99.47 89	99.90 71

PatchMatch-RL		96.04 131	95.40 133	97.95 132	99.59 70	95.22 159	99.52 165	99.07 33	93.96 102	96.49 125	98.35 169	82.28 204	99.82 87	90.15 210	99.22 98	98.81 184

VPA-MVSNet		92.70 195	91.55 201	96.16 180	95.09 234	96.20 128	98.88 230	99.00 35	91.02 199	91.82 191	95.29 251	76.05 278	97.96 209	95.62 125	81.19 268	94.30 232

CVMVSNet		94.68 160	94.94 145	93.89 249	96.80 197	86.92 295	99.06 213	98.98 36	94.45 81	94.23 174	99.02 115	85.60 183	95.31 301	90.91 198	95.39 169	99.43 132

UniMVSNet (Re)		93.07 189	92.13 193	95.88 186	94.84 239	96.24 127	99.88 66	98.98 36	92.49 156	89.25 233	95.40 240	87.09 170	97.14 247	93.13 172	78.16 296	94.26 234

tfpnnormal		89.29 262	87.61 268	94.34 233	94.35 246	94.13 177	98.95 225	98.94 38	83.94 292	84.47 283	95.51 237	74.84 285	97.39 223	77.05 315	80.41 276	91.48 312

MVS		96.60 106	95.56 131	99.72 4	96.85 194	99.22 8	98.31 271	98.94 38	91.57 180	90.90 197	99.61 82	86.66 174	99.96 43	97.36 96	99.88 57	99.99 12

WR-MVS_H		91.30 223	90.35 218	94.15 237	94.17 249	92.62 221	99.17 200	98.94 38	88.87 231	86.48 269	94.46 284	84.36 193	96.61 275	88.19 230	78.51 292	93.21 291

FIs		94.10 171	93.43 173	96.11 181	94.70 242	96.82 109	99.58 156	98.93 41	92.54 153	89.34 231	97.31 188	87.62 164	97.10 252	94.22 149	86.58 242	94.40 223

conf0.01		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

conf0.002		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

thresconf0.02		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpn_n400		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnconf		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnview11		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpn1000		96.90 92	96.29 95	98.74 85	99.00 93	98.09 62	99.92 52	98.91 42	92.08 167	95.85 138	98.65 154	97.39 8	98.83 147	90.56 201	94.23 187	99.31 149

tfpn_ndepth		97.21 82	96.63 87	98.92 77	99.06 88	98.28 56	99.95 31	98.91 42	92.96 127	96.49 125	98.67 152	97.40 7	99.07 139	91.87 186	94.38 179	99.41 134

EPNet_dtu		95.71 138	95.39 134	96.66 169	98.92 101	93.41 197	99.57 157	98.90 50	96.19 41	97.52 106	98.56 161	92.65 105	97.36 225	77.89 309	98.33 112	99.20 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FC-MVSNet-test		93.81 176	93.15 180	95.80 189	94.30 247	96.20 128	99.42 176	98.89 51	92.33 159	89.03 238	97.27 190	87.39 167	96.83 269	93.20 168	86.48 243	94.36 226

API-MVS		97.86 60	97.66 57	98.47 108	99.52 76	95.41 152	99.47 171	98.87 52	91.68 178	98.84 65	99.85 21	92.34 109	99.99 28	98.44 67	99.96 37	100.00 1

1314		96.84 93	95.96 107	99.48 25	96.74 201	98.52 48	98.31 271	98.86 53	95.82 48	89.91 211	98.98 120	87.49 165	99.96 43	97.80 87	99.73 71	99.96 58

MSLP-MVS++		99.13 5	99.01 6	99.49 23	99.94 14	98.46 52	99.98 6	98.86 53	97.10 15	99.80 9	99.94 4	95.92 30	100.00 1	99.51 22	100.00 1	100.00 1

AdaColmap		97.23 81	96.80 82	98.51 102	99.99 1	95.60 148	99.09 205	98.84 55	93.32 120	96.74 121	99.72 65	86.04 179	100.00 1	98.01 79	99.43 92	99.94 65

IB-MVS	92.85 6	94.99 153	93.94 161	98.16 123	97.72 171	95.69 147	99.99 3	98.81 56	94.28 88	92.70 187	96.90 202	95.08 44	99.17 138	96.07 116	73.88 317	99.60 109
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
3Dnovator	91.47 12	96.28 127	95.34 136	99.08 60	96.82 196	97.47 83	99.45 174	98.81 56	95.52 59	89.39 229	99.00 119	81.97 212	99.95 51	97.27 98	99.83 62	99.84 77

PHI-MVS		98.41 41	98.21 39	99.03 68	99.86 40	97.10 102	99.98 6	98.80 58	90.78 204	99.62 23	99.78 50	95.30 40	100.00 1	99.80 7	99.93 49	99.99 12

MAR-MVS		97.43 72	97.19 70	98.15 126	99.47 79	94.79 167	99.05 216	98.76 59	92.65 144	98.66 74	99.82 40	88.52 159	99.98 32	98.12 74	99.63 77	99.67 97
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
DU-MVS		92.46 201	91.45 204	95.49 191	94.05 250	95.28 156	99.81 98	98.74 60	92.25 160	89.21 235	96.64 213	81.66 219	96.73 272	93.20 168	77.52 302	94.46 217

无先验							99.49 168	98.71 61	93.46 117				100.00 1	94.36 143		99.99 12

NR-MVSNet		91.56 216	90.22 225	95.60 190	94.05 250	95.76 141	98.25 275	98.70 62	91.16 196	80.78 297	96.64 213	83.23 201	96.57 276	91.41 189	77.73 300	94.46 217

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 63	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 12

WR-MVS		92.31 203	91.25 205	95.48 193	94.45 244	95.29 155	99.60 154	98.68 64	90.10 211	88.07 250	96.89 203	80.68 236	96.80 271	93.14 171	79.67 287	94.36 226

ab-mvs		94.69 159	93.42 174	98.51 102	98.07 147	96.26 123	96.49 307	98.68 64	90.31 209	94.54 167	97.00 200	76.30 274	99.71 105	95.98 118	93.38 202	99.56 116

QAPM		95.40 144	94.17 158	99.10 58	96.92 190	97.71 72	99.40 177	98.68 64	89.31 220	88.94 239	98.89 126	82.48 203	99.96 43	93.12 173	99.83 62	99.62 105

test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 31	98.65 67	95.78 50	99.73 14	99.76 56	96.00 26	99.80 88	99.78 9	100.00 1	99.99 12

test_prior				99.43 27	99.94 14	98.49 50		98.65 67					99.80 88			99.99 12

TranMVSNet+NR-MVSNet		91.68 215	90.61 212	94.87 213	93.69 257	93.98 179	99.69 134	98.65 67	91.03 198	88.44 244	96.83 209	80.05 245	96.18 287	90.26 209	76.89 309	94.45 222

旧先验1					99.76 54	97.52 78		98.64 70			99.85 21	95.63 34			99.94 44	99.99 12

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 70	98.47 2	99.13 55	99.92 6	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

PVSNet_Blended_VisFu		97.27 79	96.81 81	98.66 89	98.81 113	96.67 111	99.92 52	98.64 70	94.51 80	96.38 131	98.49 163	89.05 152	99.88 75	97.10 103	98.34 111	99.43 132

新几何1				99.42 30	99.75 56	98.27 57		98.63 73	92.69 140	99.55 28	99.82 40	94.40 60	100.00 1	91.21 190	99.94 44	99.99 12

1121		98.03 56	97.57 62	99.40 33	99.74 57	98.21 58	98.31 271	98.62 74	92.78 135	99.53 29	99.83 37	95.08 44	100.00 1	94.36 143	99.92 51	99.99 12

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 74	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 21	100.00 1	100.00 1

HFP-MVS		98.56 30	98.37 32	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	94.77 73	99.31 46	99.85 21	94.22 69	100.00 1	98.70 55	99.98 26	99.98 44

#test#		98.59 28	98.41 27	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	95.00 68	99.31 46	99.85 21	94.22 69	100.00 1	98.78 52	99.98 26	99.98 44

ACMMPR		98.50 35	98.32 36	99.05 66	99.96 8	97.18 98	99.95 31	98.60 78	94.77 73	99.31 46	99.84 35	93.73 85	100.00 1	98.70 55	99.98 26	99.98 44

VPNet		91.81 209	90.46 215	95.85 188	94.74 241	95.54 149	98.98 221	98.59 79	92.14 165	90.77 199	97.44 185	68.73 309	97.54 219	94.89 133	77.89 298	94.46 217

test0.0.03 1		93.86 173	93.61 165	94.64 221	95.02 238	92.18 229	99.93 50	98.58 80	94.07 95	87.96 251	98.50 162	93.90 81	94.96 306	81.33 291	93.17 204	96.78 202

DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 87	98.65 39	100.00 1	98.58 80	97.70 7	98.21 94	99.24 106	92.58 107	99.94 59	98.63 62	99.94 44	99.92 69
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
CP-MVSNet		91.23 226	90.22 225	94.26 234	93.96 252	92.39 225	99.09 205	98.57 82	88.95 229	86.42 270	96.57 215	79.19 252	96.37 280	90.29 208	78.95 289	94.02 248

OpenMVS	90.15 15	94.77 157	93.59 168	98.33 119	96.07 210	97.48 82	99.56 159	98.57 82	90.46 206	86.51 267	98.95 124	78.57 259	99.94 59	93.86 153	99.74 70	97.57 200

HPM-MVS++		99.07 6	98.88 11	99.63 9	99.90 33	99.02 12	99.95 31	98.56 84	97.56 9	99.44 37	99.85 21	95.38 39	100.00 1	99.31 30	99.99 13	99.87 75

testdata				98.42 113	99.47 79	95.33 154		98.56 84	93.78 109	99.79 11	99.85 21	93.64 88	99.94 59	94.97 129	99.94 44	100.00 1

EPP-MVSNet		96.69 102	96.60 88	96.96 159	97.74 167	93.05 209	99.37 182	98.56 84	88.75 233	95.83 143	99.01 117	96.01 25	98.56 166	96.92 109	97.20 139	99.25 156

DeepPCF-MVS	95.94 2	97.71 67	98.98 8	93.92 247	99.63 68	81.76 320	99.96 19	98.56 84	99.47 1	99.19 53	99.99 1	94.16 73	100.00 1	99.92 3	99.93 49	100.00 1

region2R		98.54 32	98.37 32	99.05 66	99.96 8	97.18 98	99.96 19	98.55 88	94.87 71	99.45 36	99.85 21	94.07 75	100.00 1	98.67 57	100.00 1	99.98 44

test222					99.55 74	97.41 87	99.34 185	98.55 88	91.86 174	99.27 49	99.83 37	93.84 83			99.95 40	99.99 12

tpmvs		94.28 170	93.57 169	96.40 175	98.55 126	91.50 250	95.70 320	98.55 88	87.47 253	92.15 189	94.26 286	91.42 121	98.95 144	88.15 231	95.85 160	98.76 186

GG-mvs-BLEND				98.54 100	98.21 140	98.01 65	93.87 326	98.52 91		97.92 99	97.92 179	99.02 2	97.94 211	98.17 72	99.58 83	99.67 97

Regformer-3		98.58 29	98.41 27	99.10 58	99.84 46	97.57 76	99.66 144	98.52 91	95.79 49	99.01 60	99.77 52	94.40 60	99.75 97	98.82 50	99.83 62	99.98 44

Regformer-4		98.56 30	98.39 30	99.08 60	99.84 46	97.52 78	99.66 144	98.52 91	95.76 52	99.01 60	99.77 52	94.33 66	99.75 97	98.80 51	99.83 62	99.98 44

Regformer-1		98.79 20	98.60 20	99.36 36	99.85 41	98.34 54	99.87 71	98.52 91	96.05 45	99.41 40	99.79 45	94.93 52	99.76 94	99.07 35	99.90 53	99.99 12

Regformer-2		98.78 21	98.59 21	99.36 36	99.85 41	98.32 55	99.87 71	98.52 91	96.04 46	99.41 40	99.79 45	94.92 53	99.76 94	99.05 36	99.90 53	99.98 44

PS-CasMVS		90.63 239	89.51 242	93.99 245	93.83 254	91.70 245	98.98 221	98.52 91	88.48 237	86.15 274	96.53 217	75.46 280	96.31 283	88.83 226	78.86 291	93.95 262

CANet		98.27 47	97.82 55	99.63 9	99.72 63	99.10 10	99.98 6	98.51 97	97.00 18	98.52 79	99.71 67	87.80 162	99.95 51	99.75 11	99.38 93	99.83 78

gg-mvs-nofinetune		93.51 183	91.86 198	98.47 108	97.72 171	97.96 66	92.62 331	98.51 97	74.70 329	97.33 109	69.59 345	98.91 3	97.79 214	97.77 90	99.56 84	99.67 97

EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 84	99.83 49	96.59 115	99.40 177	98.51 97	95.29 64	98.51 80	99.76 56	93.60 89	99.71 105	98.53 65	99.52 86	99.95 63

原ACMM1				98.96 74	99.73 61	96.99 104		98.51 97	94.06 98	99.62 23	99.85 21	94.97 51	99.96 43	95.11 128	99.95 40	99.92 69

EI-MVSNet-UG-set		98.14 52	97.99 49	98.60 94	99.80 52	96.27 122	99.36 184	98.50 101	95.21 66	98.30 89	99.75 61	93.29 94	99.73 104	98.37 69	99.30 96	99.81 80

LS3D		95.84 135	95.11 143	98.02 131	99.85 41	95.10 160	98.74 240	98.50 101	87.22 258	93.66 176	99.86 17	87.45 166	99.95 51	90.94 197	99.81 68	99.02 179

PEN-MVS		90.19 250	89.06 249	93.57 255	93.06 285	90.90 256	99.06 213	98.47 103	88.11 242	85.91 276	96.30 221	76.67 270	95.94 295	87.07 247	76.91 308	93.89 268

DeepC-MVS_fast	96.59 1	98.81 19	98.54 24	99.62 12	99.90 33	98.85 21	99.24 195	98.47 103	98.14 4	99.08 56	99.91 7	93.09 98	100.00 1	99.04 40	99.99 13	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PLC	95.54 3	97.93 59	97.89 54	98.05 130	99.82 50	94.77 168	99.92 52	98.46 105	93.93 104	97.20 111	99.27 102	95.44 38	99.97 41	97.41 95	99.51 88	99.41 134
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UA-Net		96.54 107	95.96 107	98.27 121	98.23 139	95.71 145	98.00 286	98.45 106	93.72 111	98.41 83	99.27 102	88.71 157	99.66 113	91.19 191	97.69 124	99.44 131

alignmvs		97.81 62	97.33 67	99.25 40	98.77 116	98.66 37	99.99 3	98.44 107	94.40 84	98.41 83	99.47 91	93.65 87	99.42 133	98.57 63	94.26 186	99.67 97

test11								98.44 107

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 43	98.72 117	97.71 72	99.98 6	98.44 107	96.85 20	99.80 9	99.91 7	97.57 4	99.85 79	99.44 26	99.99 13	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
MDTV_nov1_ep13			95.69 124		97.90 154	94.15 176	95.98 316	98.44 107	93.12 124	97.98 98	95.74 231	95.10 43	98.58 165	90.02 211	96.92 145

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 52	98.44 107	92.06 170	98.40 85	99.84 35	95.68 33	100.00 1	98.19 71	99.71 73	99.97 54

TEST9					99.92 27	98.92 16	99.96 19	98.43 112	93.90 105	99.71 16	99.86 17	95.88 31	99.85 79

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 16	99.96 19	98.43 112	94.35 85	99.71 16	99.86 17	95.94 28	99.85 79	99.69 19	99.98 26	99.99 12

test_8					99.92 27	98.88 19	99.96 19	98.43 112	94.35 85	99.69 18	99.85 21	95.94 28	99.85 79

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 19	98.43 112	94.07 95	99.67 19	99.85 21	96.05 24	99.85 79	99.69 19	99.98 26	99.99 12

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 19	98.43 112	94.63 78	99.63 21	99.85 21	95.79 32	99.85 79	99.72 17	99.99 13	99.99 12

agg_prior					99.93 24	98.77 26		98.43 112		99.63 21			99.85 79

PAPM_NR		98.12 53	97.93 53	98.70 86	99.94 14	96.13 131	99.82 96	98.43 112	94.56 79	97.52 106	99.70 69	94.40 60	99.98 32	97.00 105	99.98 26	99.99 12

PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 31	98.43 112	95.35 62	98.03 97	99.75 61	94.03 76	99.98 32	98.11 75	99.83 62	99.99 12

XVS		98.70 23	98.55 22	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	99.78 50	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

X-MVStestdata		93.83 174	92.06 196	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	41.37 356	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

test_part1								98.41 122				97.20 11			99.99 13	99.99 12

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 122	96.14 43	99.49 33	99.91 7	97.20 11	100.00 1	99.99 1	99.99 13	99.99 12

test12				99.43 27	99.74 57	98.56 46		98.40 124		99.65 20		94.76 55	99.75 97		99.98 26	99.99 12

PatchmatchNet		95.94 133	95.45 132	97.39 151	97.83 160	94.41 172	96.05 315	98.40 124	92.86 128	97.09 115	95.28 252	94.21 72	98.07 204	89.26 223	98.11 117	99.70 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
APDe-MVS		99.06 8	98.91 10	99.51 21	99.94 14	98.76 32	99.91 56	98.39 126	97.20 14	99.46 35	99.85 21	95.53 37	99.79 90	99.86 5	100.00 1	99.99 12

MP-MVS		98.23 50	97.97 50	99.03 68	99.94 14	97.17 101	99.95 31	98.39 126	94.70 76	98.26 92	99.81 43	91.84 119	100.00 1	98.85 49	99.97 35	99.93 66

CP-MVS		98.45 38	98.32 36	98.87 79	99.96 8	96.62 113	99.97 12	98.39 126	94.43 83	98.90 64	99.87 15	94.30 67	100.00 1	99.04 40	99.99 13	99.99 12

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 57	98.67 36	99.77 110	98.38 129	96.73 26	99.88 3	99.74 63	94.89 54	99.59 116	99.80 7	99.98 26	99.97 54

mPP-MVS		98.39 43	98.20 40	98.97 73	99.97 3	96.92 107	99.95 31	98.38 129	95.04 67	98.61 77	99.80 44	93.39 90	100.00 1	98.64 61	100.00 1	99.98 44

ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 67	98.62 41	99.85 88	98.37 131	94.68 77	99.53 29	99.83 37	92.87 99	100.00 1	98.66 60	99.84 61	99.99 12

APD-MVS		98.62 25	98.35 35	99.41 31	99.90 33	98.51 49	99.87 71	98.36 132	94.08 94	99.74 13	99.73 64	94.08 74	99.74 101	99.42 27	99.99 13	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		98.82 18	98.55 22	99.65 8	99.87 39	98.95 14	99.86 86	98.35 133	93.19 122	99.83 7	99.94 4	96.17 23	100.00 1	99.74 13	99.99 13	100.00 1

CPTT-MVS		97.64 69	97.32 68	98.58 96	99.97 3	95.77 140	99.96 19	98.35 133	89.90 215	98.36 86	99.79 45	91.18 128	99.99 28	98.37 69	99.99 13	99.99 12

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 65	98.73 33	99.94 45	98.34 135	96.38 34	99.81 8	99.76 56	94.59 57	99.98 32	99.84 6	99.96 37	99.97 54

CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 71	98.33 136	93.97 101	99.76 12	99.87 15	94.99 50	99.75 97	98.55 64	100.00 1	99.98 44

HSP-MVS		99.07 6	99.11 4	98.95 75	99.93 24	97.24 95	99.95 31	98.32 137	97.50 10	99.52 32	99.88 12	97.43 6	99.71 105	99.50 23	99.98 26	99.89 72

Patchmatch-test1		94.39 168	93.46 172	97.17 155	97.10 183	94.44 171	98.86 235	98.32 137	93.30 121	96.17 134	95.38 243	76.48 273	97.34 227	88.12 233	97.43 130	99.74 88

APD-MVS_3200maxsize		98.25 49	98.08 46	98.78 82	99.81 51	96.60 114	99.82 96	98.30 139	93.95 103	99.37 44	99.77 52	92.84 100	99.76 94	98.95 42	99.92 51	99.97 54

TESTMET0.1,1		96.74 99	96.26 96	98.16 123	97.36 180	96.48 117	99.96 19	98.29 140	91.93 172	95.77 144	98.07 175	95.54 35	98.29 192	90.55 202	98.89 101	99.70 93

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 41	97.93 67	99.80 101	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

MTGPA								98.28 141

MTAPA		98.29 46	97.96 52	99.30 38	99.85 41	97.93 67	99.39 180	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

114514_t		97.41 76	96.83 80	99.14 52	99.51 78	97.83 69	99.89 64	98.27 144	88.48 237	99.06 57	99.66 78	90.30 136	99.64 115	96.32 114	99.97 35	99.96 58

Vis-MVSNet		95.72 136	95.15 142	97.45 147	97.62 173	94.28 174	99.28 192	98.24 145	94.27 89	96.84 118	98.94 125	79.39 248	98.76 152	93.25 167	98.49 108	99.30 151
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	91.53 11	96.31 124	95.24 138	99.52 20	96.88 193	98.64 40	99.72 131	98.24 145	95.27 65	88.42 247	98.98 120	82.76 202	99.94 59	97.10 103	99.83 62	99.96 58

DTE-MVSNet		89.40 259	88.24 262	92.88 266	92.66 293	89.95 271	99.10 204	98.22 147	87.29 256	85.12 280	96.22 223	76.27 275	95.30 302	83.56 279	75.74 312	93.41 284

VDDNet		93.12 188	91.91 197	96.76 165	96.67 204	92.65 220	98.69 244	98.21 148	82.81 297	97.75 102	99.28 101	61.57 328	99.48 127	98.09 77	94.09 189	98.15 192

test-LLR		96.47 115	96.04 100	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	94.07 95	95.55 146	96.38 218	93.90 81	98.27 195	90.42 204	98.83 103	99.64 103

test-mter		96.39 121	95.93 109	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	91.81 176	95.55 146	96.38 218	95.17 41	98.27 195	90.42 204	98.83 103	99.64 103

DWT-MVSNet_test		97.31 77	97.19 70	97.66 140	98.24 138	94.67 169	98.86 235	98.20 151	93.60 115	98.09 95	98.89 126	97.51 5	98.78 150	94.04 151	97.28 134	99.55 117

MP-MVS-pluss		98.07 55	97.64 58	99.38 35	99.74 57	98.41 53	99.74 120	98.18 152	93.35 119	96.45 127	99.85 21	92.64 106	99.97 41	98.91 47	99.89 55	99.77 85
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PS-MVSNAJ		98.44 39	98.20 40	99.16 46	98.80 114	98.92 16	99.54 163	98.17 153	97.34 11	99.85 5	99.85 21	91.20 125	99.89 69	99.41 28	99.67 75	98.69 187

HPM-MVS		97.96 57	97.72 56	98.68 87	99.84 46	96.39 121	99.90 59	98.17 153	92.61 146	98.62 76	99.57 84	91.87 118	99.67 112	98.87 48	99.99 13	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
tpmrst		96.27 128	95.98 104	97.13 156	97.96 151	93.15 206	96.34 310	98.17 153	92.07 168	98.71 72	95.12 255	93.91 80	98.73 153	94.91 132	96.62 147	99.50 126

ADS-MVSNet		94.79 155	94.02 160	97.11 158	97.87 156	93.79 183	94.24 322	98.16 156	90.07 212	96.43 128	94.48 282	90.29 137	98.19 199	87.44 239	97.23 137	99.36 144

HPM-MVS_fast		97.80 63	97.50 63	98.68 87	99.79 53	96.42 118	99.88 66	98.16 156	91.75 177	98.94 63	99.54 87	91.82 120	99.65 114	97.62 93	99.99 13	99.99 12

Vis-MVSNet (Re-imp)		96.32 123	95.98 104	97.35 153	97.93 153	94.82 164	99.47 171	98.15 158	91.83 175	95.09 163	99.11 111	91.37 123	97.47 221	93.47 163	97.43 130	99.74 88

abl_6		97.67 68	97.34 66	98.66 89	99.68 66	96.11 135	99.68 139	98.14 159	93.80 108	99.27 49	99.70 69	88.65 158	99.98 32	97.46 94	99.72 72	99.89 72

CNLPA		97.76 65	97.38 65	98.92 77	99.53 75	96.84 108	99.87 71	98.14 159	93.78 109	96.55 124	99.69 72	92.28 110	99.98 32	97.13 101	99.44 91	99.93 66

PatchFormer-LS_test		97.01 87	96.79 83	97.69 139	98.26 137	94.80 165	98.66 250	98.13 161	93.70 112	97.86 101	98.80 144	95.54 35	98.67 157	94.12 150	96.00 155	99.60 109

JIA-IIPM		91.76 214	90.70 211	94.94 208	96.11 209	87.51 291	93.16 329	98.13 161	75.79 326	97.58 105	77.68 341	92.84 100	97.97 207	88.47 229	96.54 148	99.33 148

cdsmvs_eth3d_5k		23.43 332	31.24 333	0.00 346	0.00 360	0.00 361	0.00 352	98.09 163	0.00 356	0.00 357	99.67 76	83.37 199	0.00 359	0.00 356	0.00 357	0.00 357

xiu_mvs_v2_base		98.23 50	97.97 50	99.02 70	98.69 118	98.66 37	99.52 165	98.08 164	97.05 16	99.86 4	99.86 17	90.65 133	99.71 105	99.39 29	98.63 107	98.69 187

tpm cat1		93.51 183	92.52 189	96.47 172	97.77 164	91.47 251	96.13 313	98.06 165	80.98 313	92.91 184	93.78 294	89.66 140	98.87 145	87.03 249	96.39 151	99.09 177

DeepC-MVS	94.51 4	96.92 91	96.40 93	98.45 110	99.16 86	95.90 137	99.66 144	98.06 165	96.37 37	94.37 171	99.49 90	83.29 200	99.90 66	97.63 92	99.61 81	99.55 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EU-MVSNet		90.14 252	90.34 219	89.54 306	92.55 294	81.06 323	98.69 244	98.04 167	91.41 186	86.59 266	96.84 208	80.83 233	93.31 328	86.20 258	81.91 264	94.26 234

MVS_0304		97.52 71	96.79 83	99.69 6	99.59 70	99.30 4	99.97 12	98.01 168	96.99 19	98.84 65	99.79 45	78.90 256	99.96 43	99.74 13	99.32 95	99.81 80

TAPA-MVS	92.12 8	94.42 167	93.60 167	96.90 161	99.33 85	91.78 239	99.78 105	98.00 169	89.89 216	94.52 168	99.47 91	91.97 116	99.18 137	69.90 323	99.52 86	99.73 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UnsupCasMVSNet_eth		85.52 291	83.99 289	90.10 302	89.36 322	83.51 310	96.65 305	97.99 170	89.14 221	75.89 313	93.83 292	63.25 325	93.92 320	81.92 289	67.90 326	92.88 297

LFMVS		94.75 158	93.56 170	98.30 120	99.03 90	95.70 146	98.74 240	97.98 171	87.81 245	98.47 81	99.39 97	67.43 314	99.53 117	98.01 79	95.20 170	99.67 97

dp		95.05 151	94.43 153	96.91 160	97.99 150	92.73 216	96.29 311	97.98 171	89.70 218	95.93 137	94.67 278	93.83 84	98.45 175	86.91 253	96.53 149	99.54 121

PMMVS		96.76 97	96.76 85	96.76 165	98.28 135	92.10 230	99.91 56	97.98 171	94.12 92	99.53 29	99.39 97	86.93 172	98.73 153	96.95 108	97.73 123	99.45 129

F-COLMAP		96.93 90	96.95 78	96.87 162	99.71 64	91.74 241	99.85 88	97.95 174	93.11 125	95.72 145	99.16 110	92.35 108	99.94 59	95.32 126	99.35 94	98.92 181

OMC-MVS		97.28 78	97.23 69	97.41 149	99.76 54	93.36 201	99.65 147	97.95 174	96.03 47	97.41 108	99.70 69	89.61 141	99.51 119	96.73 111	98.25 116	99.38 141

tpm2		95.47 143	95.18 141	96.35 177	96.91 191	91.70 245	96.96 303	97.93 176	88.04 244	98.44 82	95.40 240	93.32 92	97.97 207	94.00 152	95.61 165	99.38 141

TSAR-MVS + GP.		98.60 26	98.51 25	98.86 80	99.73 61	96.63 112	99.97 12	97.92 177	98.07 5	98.76 69	99.55 85	95.00 49	99.94 59	99.91 4	97.68 125	99.99 12

tpmp4_e23		95.15 150	94.69 150	96.55 171	97.84 159	91.77 240	97.10 299	97.91 178	88.33 240	97.19 112	95.06 259	93.92 78	98.51 169	89.64 214	95.19 171	99.37 143

CDS-MVSNet		96.34 122	96.07 99	97.13 156	97.37 179	94.96 161	99.53 164	97.91 178	91.55 181	95.37 150	98.32 170	95.05 46	97.13 249	93.80 158	95.75 163	99.30 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP3-MVS								97.89 180							89.60 208

HQP-MVS		94.61 162	94.50 152	94.92 210	95.78 217	91.85 236	99.87 71	97.89 180	96.82 21	93.37 177	98.65 154	80.65 237	98.39 182	97.92 85	89.60 208	94.53 212

HQP_MVS		94.49 166	94.36 154	94.87 213	95.71 226	91.74 241	99.84 91	97.87 182	96.38 34	93.01 181	98.59 158	80.47 241	98.37 187	97.79 88	89.55 211	94.52 214

plane_prior5								97.87 182					98.37 187	97.79 88	89.55 211	94.52 214

xiu_mvs_v1_base_debu		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 187	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 187	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base_debi		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 187	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

CostFormer		96.10 129	95.88 113	96.78 164	97.03 186	92.55 222	97.08 300	97.83 187	90.04 214	98.72 71	94.89 270	95.01 48	98.29 192	96.54 112	95.77 162	99.50 126

TAMVS		95.85 134	95.58 130	96.65 170	97.07 184	93.50 190	99.17 200	97.82 188	91.39 187	95.02 164	98.01 176	92.20 111	97.30 233	93.75 160	95.83 161	99.14 172

VDD-MVS		93.77 178	92.94 181	96.27 178	98.55 126	90.22 265	98.77 239	97.79 189	90.85 202	96.82 119	99.42 93	61.18 330	99.77 92	98.95 42	94.13 188	98.82 183

cascas		94.64 161	93.61 165	97.74 138	97.82 161	96.26 123	99.96 19	97.78 190	85.76 276	94.00 175	97.54 183	76.95 268	99.21 136	97.23 99	95.43 168	97.76 199

test_normal		92.44 202	90.54 214	98.12 127	91.85 304	96.18 130	99.68 139	97.73 191	92.66 142	75.76 315	93.74 295	70.49 303	99.04 141	95.71 124	97.27 135	99.13 174

DI_MVS_plusplus_test		92.48 199	90.60 213	98.11 128	91.88 303	96.13 131	99.64 151	97.73 191	92.69 140	76.02 311	93.79 293	70.49 303	99.07 139	95.88 120	97.26 136	99.14 172

CLD-MVS		94.06 172	93.90 162	94.55 226	96.02 212	90.69 258	99.98 6	97.72 193	96.62 30	91.05 196	98.85 137	77.21 265	98.47 171	98.11 75	89.51 213	94.48 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MS-PatchMatch		90.65 237	90.30 220	91.71 289	94.22 248	85.50 302	98.24 276	97.70 194	88.67 234	86.42 270	96.37 220	67.82 313	98.03 205	83.62 278	99.62 78	91.60 310

XXY-MVS		91.82 208	90.46 215	95.88 186	93.91 253	95.40 153	98.87 233	97.69 195	88.63 236	87.87 252	97.08 195	74.38 289	97.89 212	91.66 188	84.07 256	94.35 229

EI-MVSNet		93.73 179	93.40 177	94.74 217	96.80 197	92.69 217	99.06 213	97.67 196	88.96 228	91.39 193	99.02 115	88.75 156	97.30 233	91.07 193	87.85 233	94.22 237

MVSTER		95.53 141	95.22 139	96.45 173	98.56 125	97.72 71	99.91 56	97.67 196	92.38 158	91.39 193	97.14 192	97.24 10	97.30 233	94.80 134	87.85 233	94.34 230

CANet_DTU		96.76 97	96.15 98	98.60 94	98.78 115	97.53 77	99.84 91	97.63 198	97.25 13	99.20 51	99.64 80	81.36 226	99.98 32	92.77 175	98.89 101	98.28 190

RPMNet		89.39 260	87.20 272	95.94 184	96.29 206	92.66 218	92.01 334	97.63 198	70.19 337	96.94 116	85.87 337	87.25 168	96.03 292	62.69 334	95.96 157	99.13 174

LPG-MVS_test		92.96 190	92.71 185	93.71 252	95.43 231	88.67 280	99.75 117	97.62 200	92.81 132	90.05 205	98.49 163	75.24 282	98.40 180	95.84 122	89.12 215	94.07 245

LGP-MVS_train				93.71 252	95.43 231	88.67 280		97.62 200	92.81 132	90.05 205	98.49 163	75.24 282	98.40 180	95.84 122	89.12 215	94.07 245

FMVSNet3		92.69 196	91.58 200	95.99 183	98.29 134	97.42 86	99.26 194	97.62 200	89.80 217	89.68 220	95.32 247	81.62 221	96.27 284	87.01 250	85.65 246	94.29 233

OPM-MVS		93.21 187	92.80 183	94.44 229	93.12 283	90.85 257	99.77 110	97.61 203	96.19 41	91.56 192	98.65 154	75.16 284	98.47 171	93.78 159	89.39 214	93.99 256

IS-MVSNet		96.29 126	95.90 112	97.45 147	98.13 145	94.80 165	99.08 207	97.61 203	92.02 171	95.54 148	98.96 122	90.64 134	98.08 202	93.73 161	97.41 132	99.47 128

CMPMVS	61.59 21	84.75 297	85.14 279	83.57 317	90.32 318	62.54 341	96.98 302	97.59 205	74.33 330	69.95 330	96.66 211	64.17 322	98.32 190	87.88 235	88.41 228	89.84 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
lupinMVS		97.85 61	97.60 60	98.62 92	97.28 181	97.70 74	99.99 3	97.55 206	95.50 60	99.43 38	99.67 76	90.92 131	98.71 155	98.40 68	99.62 78	99.45 129

XVG-OURS		94.82 154	94.74 148	95.06 201	98.00 149	89.19 275	99.08 207	97.55 206	94.10 93	94.71 166	99.62 81	80.51 239	99.74 101	96.04 117	93.06 206	96.25 206

XVG-OURS-SEG-HR		94.79 155	94.70 149	95.08 200	98.05 148	89.19 275	99.08 207	97.54 208	93.66 113	94.87 165	99.58 83	78.78 257	99.79 90	97.31 97	93.40 201	96.25 206

PatchT		90.38 243	88.75 255	95.25 196	95.99 213	90.16 266	91.22 338	97.54 208	76.80 323	97.26 110	86.01 336	91.88 117	96.07 291	66.16 331	95.91 159	99.51 124

BH-RMVSNet		95.18 147	94.31 155	97.80 135	98.17 143	95.23 158	99.76 116	97.53 210	92.52 154	94.27 173	99.25 105	76.84 269	98.80 148	90.89 199	99.54 85	99.35 146

ACMP	92.05 9	92.74 194	92.42 191	93.73 250	95.91 216	88.72 279	99.81 98	97.53 210	94.13 91	87.00 260	98.23 171	74.07 290	98.47 171	96.22 115	88.86 220	93.99 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pcd1.5k->3k		37.58 331	39.62 331	31.46 343	92.73 292	0.00 361	0.00 352	97.52 212	0.00 356	0.00 357	0.00 358	78.40 263	0.00 359	0.00 356	87.90 232	94.37 225

ACMM	91.95 10	92.88 192	92.52 189	93.98 246	95.75 222	89.08 277	99.77 110	97.52 212	93.00 126	89.95 210	97.99 177	76.17 276	98.46 174	93.63 162	88.87 219	94.39 224
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TR-MVS		94.54 163	93.56 170	97.49 145	97.96 151	94.34 173	98.71 242	97.51 214	90.30 210	94.51 169	98.69 151	75.56 279	98.77 151	92.82 174	95.99 156	99.35 146

BH-w/o		95.71 138	95.38 135	96.68 168	98.49 130	92.28 226	99.84 91	97.50 215	92.12 166	92.06 190	98.79 149	84.69 190	98.67 157	95.29 127	99.66 76	99.09 177

mvs_anonymous		95.65 140	95.03 144	97.53 143	98.19 141	95.74 142	99.33 186	97.49 216	90.87 201	90.47 201	97.10 194	88.23 160	97.16 243	95.92 119	97.66 126	99.68 96

DP-MVS		94.54 163	93.42 174	97.91 134	99.46 81	94.04 178	98.93 227	97.48 217	81.15 312	90.04 208	99.55 85	87.02 171	99.95 51	88.97 225	98.11 117	99.73 90

ACMH	89.72 17	90.64 238	89.63 237	93.66 254	95.64 229	88.64 282	98.55 254	97.45 218	89.03 224	81.62 294	97.61 182	69.75 306	98.41 178	89.37 221	87.62 237	93.92 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-ACMP-BASELINE		91.22 227	90.75 210	92.63 270	93.73 256	85.61 300	98.52 258	97.44 219	92.77 136	89.90 212	96.85 206	66.64 316	98.39 182	92.29 177	88.61 224	93.89 268

mvs_tets		91.81 209	91.08 207	94.00 244	91.63 308	90.58 259	98.67 247	97.43 220	92.43 157	87.37 257	97.05 198	71.76 297	97.32 230	94.75 137	88.68 223	94.11 243

LTVRE_ROB	88.28 18	90.29 247	89.05 250	94.02 242	95.08 235	90.15 267	97.19 298	97.43 220	84.91 285	83.99 285	97.06 197	74.00 291	98.28 194	84.08 273	87.71 235	93.62 281
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
jajsoiax		91.92 207	91.18 206	94.15 237	91.35 310	90.95 255	99.00 220	97.42 222	92.61 146	87.38 256	97.08 195	72.46 295	97.36 225	94.53 141	88.77 221	94.13 242

K. test v3		88.05 272	87.24 271	90.47 298	91.82 306	82.23 317	98.96 224	97.42 222	89.05 223	76.93 308	95.60 235	68.49 310	95.42 299	85.87 262	81.01 273	93.75 276

FMVSNet2		91.02 230	89.56 239	95.41 194	97.53 175	95.74 142	98.98 221	97.41 224	87.05 259	88.43 245	95.00 264	71.34 299	96.24 286	85.12 267	85.21 251	94.25 236

jason		97.24 80	96.86 79	98.38 118	95.73 223	97.32 94	99.97 12	97.40 225	95.34 63	98.60 78	99.54 87	87.70 163	98.56 166	97.94 84	99.47 89	99.25 156
jason: jason.
testpf		89.10 264	88.73 256	90.24 300	97.59 174	83.48 311	74.22 349	97.39 226	79.66 317	89.64 224	93.92 290	86.38 176	95.76 296	85.42 264	94.31 185	91.49 311

PS-MVSNAJss		93.64 182	93.31 179	94.61 222	92.11 298	92.19 228	99.12 202	97.38 227	92.51 155	88.45 243	96.99 201	91.20 125	97.29 236	94.36 143	87.71 235	94.36 226

MSDG		94.37 169	93.36 178	97.40 150	98.88 108	93.95 180	99.37 182	97.38 227	85.75 279	90.80 198	99.17 109	84.11 195	99.88 75	86.35 257	98.43 110	98.36 189

canonicalmvs		97.09 86	96.32 94	99.39 34	98.93 100	98.95 14	99.72 131	97.35 229	94.45 81	97.88 100	99.42 93	86.71 173	99.52 118	98.48 66	93.97 197	99.72 92

UnsupCasMVSNet_bld		79.97 309	77.03 312	88.78 310	85.62 330	81.98 318	93.66 327	97.35 229	75.51 327	70.79 328	83.05 338	48.70 342	94.91 307	78.31 308	60.29 342	89.46 335

MVS-HIRNet		86.22 283	83.19 298	95.31 195	96.71 203	90.29 264	92.12 333	97.33 231	62.85 340	86.82 263	70.37 344	69.37 307	97.49 220	75.12 318	97.99 122	98.15 192

BH-untuned		95.18 147	94.83 146	96.22 179	98.36 133	91.22 252	99.80 101	97.32 232	90.91 200	91.08 195	98.67 152	83.51 197	98.54 168	94.23 148	99.61 81	98.92 181

PCF-MVS	94.20 5	95.18 147	94.10 159	98.43 112	98.55 126	95.99 136	97.91 288	97.31 233	90.35 208	89.48 228	99.22 107	85.19 189	99.89 69	90.40 206	98.47 109	99.41 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVSFormer		96.94 89	96.60 88	97.95 132	97.28 181	97.70 74	99.55 161	97.27 234	91.17 194	99.43 38	99.54 87	90.92 131	96.89 265	94.67 138	99.62 78	99.25 156

test_djsdf		92.83 193	92.29 192	94.47 228	91.90 302	92.46 223	99.55 161	97.27 234	91.17 194	89.96 209	96.07 228	81.10 229	96.89 265	94.67 138	88.91 217	94.05 247

GA-MVS		93.83 174	92.84 182	96.80 163	95.73 223	93.57 189	99.88 66	97.24 236	92.57 152	92.92 183	96.66 211	78.73 258	97.67 217	87.75 236	94.06 196	99.17 165

Effi-MVS+		96.30 125	95.69 124	98.16 123	97.85 158	96.26 123	97.41 293	97.21 237	90.37 207	98.65 75	98.58 160	86.61 175	98.70 156	97.11 102	97.37 133	99.52 123

Patchmatch-test		92.65 198	91.50 202	96.10 182	96.85 194	90.49 261	91.50 336	97.19 238	82.76 298	90.23 202	95.59 236	95.02 47	98.00 206	77.41 312	96.98 144	99.82 79

diffmvs		95.25 146	94.26 156	98.23 122	98.13 145	96.59 115	99.12 202	97.18 239	85.78 275	97.64 103	96.70 210	85.92 180	98.87 145	90.40 206	97.45 129	99.24 159

ACMH+	89.98 16	90.35 244	89.54 240	92.78 268	95.99 213	86.12 297	98.81 237	97.18 239	89.38 219	83.14 289	97.76 181	68.42 311	98.43 176	89.11 224	86.05 245	93.78 275

anonymousdsp		91.79 213	90.92 209	94.41 232	90.76 315	92.93 212	98.93 227	97.17 241	89.08 222	87.46 255	95.30 248	78.43 262	96.92 264	92.38 176	88.73 222	93.39 286

nrg030		93.51 183	92.53 188	96.45 173	94.36 245	97.20 97	99.81 98	97.16 242	91.60 179	89.86 214	97.46 184	86.37 177	97.68 216	95.88 120	80.31 278	94.46 217

MVS_Test		96.46 116	95.74 123	98.61 93	98.18 142	97.23 96	99.31 187	97.15 243	91.07 197	98.84 65	97.05 198	88.17 161	98.97 143	94.39 142	97.50 128	99.61 107

MIMVSNet		90.30 246	88.67 257	95.17 199	96.45 205	91.64 247	92.39 332	97.15 243	85.99 272	90.50 200	93.19 302	66.95 315	94.86 308	82.01 288	93.43 200	99.01 180

v7n		89.65 256	88.29 261	93.72 251	92.22 296	90.56 260	99.07 211	97.10 245	85.42 283	86.73 264	94.72 274	80.06 244	97.13 249	81.14 292	78.12 297	93.49 283

Fast-Effi-MVS+		95.02 152	94.19 157	97.52 144	97.88 155	94.55 170	99.97 12	97.08 246	88.85 232	94.47 170	97.96 178	84.59 191	98.41 178	89.84 212	97.10 141	99.59 111

Effi-MVS+-dtu		94.53 165	95.30 137	92.22 283	97.77 164	82.54 314	99.59 155	97.06 247	94.92 69	95.29 151	95.37 245	85.81 181	97.89 212	94.80 134	97.07 142	96.23 208

mvs-test1		95.53 141	95.97 106	94.20 236	97.77 164	85.44 303	99.95 31	97.06 247	94.92 69	96.58 123	98.72 150	85.81 181	98.98 142	94.80 134	98.11 117	98.18 191

v1neww		91.44 217	90.28 221	94.91 211	93.50 261	93.43 193	99.73 126	97.06 247	87.55 247	90.08 203	95.11 256	81.98 210	97.32 230	87.41 241	80.15 280	93.99 256

v7new		91.44 217	90.28 221	94.91 211	93.50 261	93.43 193	99.73 126	97.06 247	87.55 247	90.08 203	95.11 256	81.98 210	97.32 230	87.41 241	80.15 280	93.99 256

v1141		91.36 221	90.14 229	95.00 204	93.33 273	93.79 183	99.78 105	97.05 251	87.52 251	89.75 218	94.89 270	82.13 206	97.21 239	86.84 256	80.00 284	94.00 253

divwei89l23v2f112		91.37 220	90.15 228	95.00 204	93.35 271	93.78 186	99.78 105	97.05 251	87.54 249	89.73 219	94.89 270	82.24 205	97.21 239	86.91 253	79.90 286	94.00 253

v6		91.44 217	90.27 223	94.93 209	93.44 265	93.44 192	99.73 126	97.05 251	87.57 246	90.05 205	95.10 258	81.87 215	97.39 223	87.45 238	80.17 279	93.98 260

v1		91.36 221	90.14 229	95.04 202	93.35 271	93.80 182	99.77 110	97.05 251	87.53 250	89.77 217	94.91 268	81.99 209	97.33 229	86.90 255	79.98 285	94.00 253

IterMVS		90.91 232	90.17 227	93.12 261	96.78 200	90.42 263	98.89 229	97.05 251	89.03 224	86.49 268	95.42 239	76.59 271	95.02 304	87.22 246	84.09 255	93.93 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test4		88.80 267	85.91 276	97.48 146	87.33 326	95.72 144	99.29 191	97.04 256	92.82 131	70.35 329	91.46 309	44.37 344	97.43 222	93.37 166	97.17 140	99.29 153

v1192		90.62 240	89.25 245	94.72 219	93.13 281	93.07 207	99.50 167	97.02 257	86.33 269	89.56 227	95.01 262	79.22 251	97.09 254	82.34 286	81.16 269	94.01 250

v2v482		91.30 223	90.07 232	95.01 203	93.13 281	93.79 183	99.77 110	97.02 257	88.05 243	89.25 233	95.37 245	80.73 235	97.15 245	87.28 245	80.04 283	94.09 244

V42		91.28 225	90.12 231	94.74 217	93.42 267	93.46 191	99.68 139	97.02 257	87.36 255	89.85 215	95.05 260	81.31 227	97.34 227	87.34 244	80.07 282	93.40 285

testing_2		85.10 295	81.72 302	95.22 197	82.25 335	94.16 175	97.54 291	97.01 260	88.15 241	62.23 337	86.43 334	44.43 343	97.18 242	92.28 182	85.20 252	94.31 231

semantic-postprocess				92.93 265	96.72 202	89.96 270		96.99 261	88.95 229	86.63 265	95.67 233	76.50 272	95.00 305	87.04 248	84.04 258	93.84 272

v144192		90.79 235	89.52 241	94.59 223	93.11 284	92.77 214	99.56 159	96.99 261	86.38 268	89.82 216	94.95 267	80.50 240	97.10 252	83.98 275	80.41 276	93.90 266

v1921920		90.46 242	89.12 247	94.50 227	92.96 288	92.46 223	99.49 168	96.98 263	86.10 271	89.61 226	95.30 248	78.55 260	97.03 259	82.17 287	80.89 275	94.01 250

v1144		91.09 229	89.83 234	94.87 213	93.25 278	93.69 188	99.62 153	96.98 263	86.83 263	89.64 224	94.99 265	80.94 231	97.05 255	85.08 268	81.16 269	93.87 270

v7		91.20 228	89.99 233	94.82 216	93.57 258	93.41 197	99.57 157	96.98 263	86.83 263	89.88 213	95.22 253	81.01 230	97.14 247	85.53 263	81.31 267	93.90 266

v748		88.94 266	87.72 267	92.61 271	91.91 301	87.50 292	99.07 211	96.97 266	84.76 286	85.79 277	93.63 297	79.19 252	97.04 256	83.16 281	75.03 316	93.28 288

LP		86.76 276	84.85 280	92.50 274	95.08 235	85.89 299	89.97 339	96.97 266	75.28 328	84.97 281	90.68 312	80.78 234	95.13 303	61.64 336	88.31 229	96.46 205

GBi-Net		90.88 233	89.82 235	94.08 239	97.53 175	91.97 231	98.43 263	96.95 268	87.05 259	89.68 220	94.72 274	71.34 299	96.11 288	87.01 250	85.65 246	94.17 239

test1		90.88 233	89.82 235	94.08 239	97.53 175	91.97 231	98.43 263	96.95 268	87.05 259	89.68 220	94.72 274	71.34 299	96.11 288	87.01 250	85.65 246	94.17 239

FMVSNet1		88.50 269	86.64 273	94.08 239	95.62 230	91.97 231	98.43 263	96.95 268	83.00 296	86.08 275	94.72 274	59.09 333	96.11 288	81.82 290	84.07 256	94.17 239

v8		90.54 241	89.17 246	94.66 220	93.43 266	93.40 200	99.20 197	96.94 271	85.76 276	87.56 254	94.51 280	81.96 213	97.19 241	84.94 269	78.25 295	93.38 287

v1240		90.20 249	88.79 254	94.44 229	93.05 286	92.27 227	99.38 181	96.92 272	85.89 273	89.36 230	94.87 273	77.89 264	97.03 259	80.66 294	81.08 271	94.01 250

V4		89.55 257	88.41 259	92.98 263	92.21 297	90.03 268	98.87 233	96.91 273	84.51 289	86.84 262	94.21 288	79.37 249	97.15 245	84.45 272	78.28 293	91.76 308

tpm		93.70 181	93.41 176	94.58 224	95.36 233	87.41 293	97.01 301	96.90 274	90.85 202	96.72 122	94.14 289	90.40 135	96.84 268	90.75 200	88.54 226	99.51 124

v148		90.70 236	89.63 237	93.92 247	92.97 287	90.97 254	99.75 117	96.89 275	87.51 252	88.27 248	95.01 262	81.67 218	97.04 256	87.40 243	77.17 306	93.75 276

v52		89.55 257	88.41 259	92.98 263	92.32 295	90.01 269	98.88 230	96.89 275	84.51 289	86.89 261	94.22 287	79.23 250	97.16 243	84.46 271	78.27 294	91.76 308

IterMVS-LS		92.69 196	92.11 194	94.43 231	96.80 197	92.74 215	99.45 174	96.89 275	88.98 226	89.65 223	95.38 243	88.77 155	96.34 282	90.98 196	82.04 263	94.22 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		90.25 248	88.82 253	94.57 225	93.53 260	93.43 193	99.08 207	96.87 278	85.00 284	87.34 258	94.51 280	80.93 232	97.02 261	82.85 283	79.23 288	93.26 289

ADS-MVSNet2		93.80 177	93.88 163	93.55 256	97.87 156	85.94 298	94.24 322	96.84 279	90.07 212	96.43 128	94.48 282	90.29 137	95.37 300	87.44 239	97.23 137	99.36 144

Fast-Effi-MVS+-dtu		93.72 180	93.86 164	93.29 259	97.06 185	86.16 296	99.80 101	96.83 280	92.66 142	92.58 188	97.83 180	81.39 225	97.67 217	89.75 213	96.87 146	96.05 210

pmmvs4		92.10 206	91.07 208	95.18 198	92.82 290	94.96 161	99.48 170	96.83 280	87.45 254	88.66 242	96.56 216	83.78 196	96.83 269	89.29 222	84.77 254	93.75 276

AllTest		92.48 199	91.64 199	95.00 204	99.01 91	88.43 284	98.94 226	96.82 282	86.50 266	88.71 240	98.47 167	74.73 286	99.88 75	85.39 265	96.18 152	96.71 203

TestCases				95.00 204	99.01 91	88.43 284		96.82 282	86.50 266	88.71 240	98.47 167	74.73 286	99.88 75	85.39 265	96.18 152	96.71 203

COLMAP_ROB	90.47 14	92.18 205	91.49 203	94.25 235	99.00 93	88.04 289	98.42 266	96.70 284	82.30 302	88.43 245	99.01 117	76.97 267	99.85 79	86.11 260	96.50 150	94.86 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
1112_ss		96.01 132	95.20 140	98.42 113	97.80 162	96.41 119	99.65 147	96.66 285	92.71 138	92.88 185	99.40 95	92.16 112	99.30 134	91.92 184	93.66 198	99.55 117

Test_1112_low_res		95.72 136	94.83 146	98.42 113	97.79 163	96.41 119	99.65 147	96.65 286	92.70 139	92.86 186	96.13 226	92.15 113	99.30 134	91.88 185	93.64 199	99.55 117

test2356		86.43 280	87.59 269	82.95 320	85.90 328	69.43 333	99.79 104	96.63 287	85.76 276	83.44 288	94.99 265	80.45 243	86.52 342	68.12 328	93.21 203	92.90 295

RPSCF		91.80 211	92.79 184	88.83 309	98.15 144	69.87 332	98.11 282	96.60 288	83.93 293	94.33 172	99.27 102	79.60 247	99.46 128	91.99 183	93.16 205	97.18 201

YYNet1		85.50 293	83.33 296	92.00 285	90.89 314	88.38 287	99.22 196	96.55 289	79.60 318	57.26 341	92.72 303	79.09 255	93.78 324	77.25 313	77.37 305	93.84 272

MDA-MVSNet_test_wron		85.51 292	83.32 297	92.10 284	90.96 313	88.58 283	99.20 197	96.52 290	79.70 316	57.12 342	92.69 304	79.11 254	93.86 322	77.10 314	77.46 304	93.86 271

MTMP								96.49 291

pm-mvs1		89.36 261	87.81 266	94.01 243	93.40 269	91.93 234	98.62 251	96.48 292	86.25 270	83.86 286	96.14 225	73.68 292	97.04 256	86.16 259	75.73 313	93.04 294

CR-MVSNet		93.45 186	92.62 186	95.94 184	96.29 206	92.66 218	92.01 334	96.23 293	92.62 145	96.94 116	93.31 300	91.04 129	96.03 292	79.23 302	95.96 157	99.13 174

Patchmtry		89.70 255	88.49 258	93.33 258	96.24 208	89.94 273	91.37 337	96.23 293	78.22 320	87.69 253	93.31 300	91.04 129	96.03 292	80.18 296	82.10 262	94.02 248

MVP-Stereo		90.93 231	90.45 217	92.37 280	91.25 312	88.76 278	98.05 285	96.17 295	87.27 257	84.04 284	95.30 248	78.46 261	97.27 238	83.78 277	99.70 74	91.09 313
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs6		85.69 289	83.84 294	91.26 292	90.00 320	84.41 308	97.82 289	96.15 296	75.86 325	81.29 295	95.39 242	61.21 329	96.87 267	83.52 280	73.29 319	92.50 300

EG-PatchMatch MVS		85.35 294	83.81 295	89.99 304	90.39 317	81.89 319	98.21 279	96.09 297	81.78 309	74.73 316	93.72 296	51.56 341	97.12 251	79.16 303	88.61 224	90.96 315

DeepMVS_CX				82.92 321	95.98 215	58.66 345		96.01 298	92.72 137	78.34 305	95.51 237	58.29 334	98.08 202	82.57 284	85.29 249	92.03 305

test20.03		84.72 298	83.99 289	86.91 314	88.19 325	80.62 325	98.88 230	95.94 299	88.36 239	78.87 302	94.62 279	68.75 308	89.11 335	66.52 330	75.82 311	91.00 314

MDA-MVSNet-bldmvs		84.09 300	81.52 304	91.81 288	91.32 311	88.00 290	98.67 247	95.92 300	80.22 315	55.60 343	93.32 299	68.29 312	93.60 326	73.76 319	76.61 310	93.82 274

lessismore_v0				90.53 296	90.58 316	80.90 324		95.80 301		77.01 307	95.84 229	66.15 317	96.95 262	83.03 282	75.05 315	93.74 279

ITE_SJBPF				92.38 279	95.69 228	85.14 304		95.71 302	92.81 132	89.33 232	98.11 173	70.23 305	98.42 177	85.91 261	88.16 231	93.59 282

FMVSNet5		88.32 270	87.47 270	90.88 293	96.90 192	88.39 286	97.28 297	95.68 303	82.60 299	84.67 282	92.40 306	79.83 246	91.16 331	76.39 317	81.51 266	93.09 292

testgi		89.01 265	88.04 264	91.90 287	93.49 263	84.89 306	99.73 126	95.66 304	93.89 107	85.14 279	98.17 172	59.68 332	94.66 310	77.73 310	88.88 218	96.16 209

new_pmnet		84.49 299	82.92 299	89.21 307	90.03 319	82.60 313	96.89 304	95.62 305	80.59 314	75.77 314	89.17 314	65.04 321	94.79 309	72.12 320	81.02 272	90.23 322

pmmvs5		90.17 251	89.09 248	93.40 257	92.10 299	89.77 274	99.74 120	95.58 306	85.88 274	87.24 259	95.74 231	73.41 293	96.48 278	88.54 227	83.56 259	93.95 262

v18		86.59 277	84.57 281	92.65 269	93.41 268	93.43 193	98.69 244	95.55 307	82.44 300	74.71 317	87.68 323	82.11 207	94.21 311	80.14 297	66.37 330	90.32 319

v17		86.51 279	84.49 282	92.57 273	93.38 270	93.29 203	98.61 252	95.54 308	82.32 301	74.69 318	87.63 324	82.03 208	94.17 313	80.02 298	66.17 331	90.26 321

USDC		90.00 253	88.96 251	93.10 262	94.81 240	88.16 288	98.71 242	95.54 308	93.66 113	83.75 287	97.20 191	65.58 318	98.31 191	83.96 276	87.49 239	92.85 298

v16		86.52 278	84.49 282	92.60 272	93.45 264	93.31 202	98.60 253	95.52 310	82.30 302	74.59 319	87.70 322	81.95 214	94.18 312	79.93 299	66.38 329	90.30 320

v15		86.26 282	84.19 285	92.47 275	93.29 275	93.28 204	98.53 257	95.47 311	82.24 304	74.34 320	87.34 326	81.71 217	94.07 314	79.39 300	65.42 332	90.06 327

V14		86.22 283	84.15 286	92.41 278	93.30 274	93.16 205	98.47 260	95.47 311	82.10 305	74.27 321	87.41 325	81.73 216	94.02 316	79.26 301	65.37 334	90.04 328

v12		86.10 286	84.01 288	92.37 280	93.23 280	92.96 211	98.33 270	95.45 313	81.87 308	74.05 325	87.15 329	81.60 222	93.98 319	79.09 305	65.28 336	90.18 325

V9		86.16 285	84.07 287	92.43 276	93.27 277	93.04 210	98.40 267	95.45 313	81.98 307	74.18 323	87.31 327	81.58 223	94.06 315	79.12 304	65.33 335	90.20 324

v13		86.06 288	83.97 292	92.34 282	93.25 278	92.85 213	98.26 274	95.44 315	81.70 311	74.02 326	87.11 331	81.58 223	94.00 318	78.94 306	65.41 333	90.18 325

MIMVSNet1		82.58 304	80.51 306	88.78 310	86.68 327	84.20 309	96.65 305	95.41 316	78.75 319	78.59 304	92.44 305	51.88 340	89.76 334	65.26 333	78.95 289	92.38 301

OurMVSNet-221017-0		89.81 254	89.48 244	90.83 295	91.64 307	81.21 321	98.17 280	95.38 317	91.48 183	85.65 278	97.31 188	72.66 294	97.29 236	88.15 231	84.83 253	93.97 261

v11		86.09 287	83.98 291	92.42 277	93.29 275	93.41 197	98.52 258	95.30 318	81.73 310	74.27 321	87.20 328	81.24 228	93.85 323	77.68 311	66.61 328	90.00 329

Anonymous20231206		86.32 281	85.42 277	89.02 308	89.11 323	80.53 326	99.05 216	95.28 319	85.43 282	82.82 290	93.92 290	74.40 288	93.44 327	66.99 329	81.83 265	93.08 293

new-patchmatchnet		81.19 305	79.34 307	86.76 315	82.86 334	80.36 327	97.92 287	95.27 320	82.09 306	72.02 327	86.87 332	62.81 326	90.74 333	71.10 321	63.08 338	89.19 336

OpenMVS_ROB	79.82 20	83.77 303	81.68 303	90.03 303	88.30 324	82.82 312	98.46 261	95.22 321	73.92 332	76.00 312	91.29 310	55.00 337	96.94 263	68.40 326	88.51 227	90.34 318

test_0402		85.58 290	83.94 293	90.50 297	93.81 255	85.04 305	98.55 254	95.20 322	76.01 324	79.72 301	95.13 254	64.15 323	96.26 285	66.04 332	86.88 241	90.21 323

SixPastTwentyTwo		88.73 268	88.01 265	90.88 293	91.85 304	82.24 316	98.22 278	95.18 323	88.97 227	82.26 292	96.89 203	71.75 298	96.67 274	84.00 274	82.98 260	93.72 280

Gipuma		66.95 319	65.00 318	72.79 330	91.52 309	67.96 334	66.16 350	95.15 324	47.89 344	58.54 340	67.99 347	29.74 349	87.54 340	50.20 345	77.83 299	62.87 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		89.25 263	88.85 252	90.45 299	92.81 291	81.19 322	98.12 281	94.79 325	91.44 185	86.29 272	97.11 193	65.30 320	98.11 201	88.53 228	85.25 250	92.07 303

FPMVS		68.72 315	68.72 316	68.71 334	65.95 348	44.27 356	95.97 317	94.74 326	51.13 343	53.26 345	90.50 313	25.11 352	83.00 346	60.80 337	80.97 274	78.87 343

pmmvs-eth3d		84.03 301	81.97 301	90.20 301	84.15 332	87.09 294	98.10 283	94.73 327	83.05 295	74.10 324	87.77 321	65.56 319	94.01 317	81.08 293	69.24 323	89.49 334

TDRefinement		84.76 296	82.56 300	91.38 291	74.58 342	84.80 307	97.36 294	94.56 328	84.73 287	80.21 299	96.12 227	63.56 324	98.39 182	87.92 234	63.97 337	90.95 316

ambc				83.23 318	77.17 341	62.61 340	87.38 343	94.55 329		76.72 309	86.65 333	30.16 348	96.36 281	84.85 270	69.86 320	90.73 317

Anonymous20231211		74.17 313	71.17 315	83.17 319	80.58 336	67.02 337	96.27 312	94.45 330	57.31 342	69.60 331	86.25 335	33.67 346	92.96 329	61.86 335	60.50 341	89.54 333

TinyColmap		87.87 273	86.51 274	91.94 286	95.05 237	85.57 301	97.65 290	94.08 331	84.40 291	81.82 293	96.85 206	62.14 327	98.33 189	80.25 295	86.37 244	91.91 307

TransMVSNet (Re)		87.25 274	85.28 278	93.16 260	93.56 259	91.03 253	98.54 256	94.05 332	83.69 294	81.09 296	96.16 224	75.32 281	96.40 279	76.69 316	68.41 324	92.06 304

Baseline_NR-MVSNet		90.33 245	89.51 242	92.81 267	92.84 289	89.95 271	99.77 110	93.94 333	84.69 288	89.04 237	95.66 234	81.66 219	96.52 277	90.99 195	76.98 307	91.97 306

LCM-MVSNet		67.77 316	64.73 319	76.87 326	62.95 352	56.25 347	89.37 341	93.74 334	44.53 346	61.99 338	80.74 339	20.42 355	86.53 341	69.37 324	59.50 343	87.84 337

1111		79.11 310	78.74 309	80.23 323	78.33 338	67.13 335	97.31 295	93.65 335	71.34 334	68.35 333	87.87 319	85.42 187	88.46 336	52.93 343	73.46 318	85.11 339

.test1245		71.48 314	71.80 314	70.51 333	78.33 338	67.13 335	97.31 295	93.65 335	71.34 334	68.35 333	87.87 319	85.42 187	88.46 336	52.93 343	11.01 353	55.94 352

testus		83.91 302	84.49 282	82.17 322	85.68 329	66.11 338	99.68 139	93.53 337	86.55 265	82.60 291	94.91 268	56.70 336	88.19 338	68.46 325	92.31 207	92.21 302

test1235678		78.45 311	77.88 310	80.16 324	77.83 340	62.18 342	98.36 268	93.45 338	77.46 322	69.08 332	88.23 316	60.33 331	85.41 343	58.46 339	77.68 301	92.90 295

Patchmatch-RL test		86.90 275	85.98 275	89.67 305	84.45 331	75.59 329	89.71 340	92.43 339	86.89 262	77.83 306	90.94 311	94.22 69	93.63 325	87.75 236	69.61 321	99.79 83

test12356		75.26 312	75.12 313	75.67 329	74.02 343	60.60 344	96.43 308	92.15 340	74.17 331	66.35 335	88.11 317	52.29 339	84.36 345	57.41 340	75.12 314	82.05 340

pmmvs3		80.27 307	77.77 311	87.76 313	80.32 337	82.43 315	98.23 277	91.97 341	72.74 333	78.75 303	87.97 318	57.30 335	90.99 332	70.31 322	62.37 339	89.87 330

LCM-MVSNet-Re		92.31 203	92.60 187	91.43 290	97.53 175	79.27 328	99.02 219	91.83 342	92.07 168	80.31 298	94.38 285	83.50 198	95.48 298	97.22 100	97.58 127	99.54 121

PM-MVS		80.47 306	78.88 308	85.26 316	83.79 333	72.22 331	95.89 318	91.08 343	85.71 280	76.56 310	88.30 315	36.64 345	93.90 321	82.39 285	69.57 322	89.66 332

door								90.31 344

testmv		67.54 317	65.93 317	72.37 331	64.46 351	54.05 348	95.09 321	90.07 345	68.90 339	55.16 344	77.63 342	30.39 347	82.61 347	49.42 346	62.26 340	80.45 342

no-one		63.48 321	59.26 322	76.14 327	66.71 347	65.06 339	92.75 330	89.92 346	68.96 338	46.96 348	66.55 348	21.74 354	87.68 339	57.07 341	22.69 351	75.68 345

DSMNet-mixed		88.28 271	88.24 262	88.42 312	89.64 321	75.38 330	98.06 284	89.86 347	85.59 281	88.20 249	92.14 307	76.15 277	91.95 330	78.46 307	96.05 154	97.92 195

door-mid								89.69 348

PMVS	49.05 23	53.75 324	51.34 326	60.97 339	40.80 357	34.68 357	74.82 348	89.62 349	37.55 349	28.67 354	72.12 343	7.09 360	81.63 348	43.17 351	68.21 325	66.59 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		65.23 320	62.94 321	72.13 332	44.90 356	50.03 352	81.05 345	89.42 350	38.45 348	48.51 347	99.90 11	54.09 338	78.70 349	91.84 187	18.26 352	87.64 338

PMMVS2		67.15 318	64.15 320	76.14 327	70.56 346	62.07 343	93.89 325	87.52 351	58.09 341	60.02 339	78.32 340	22.38 353	84.54 344	59.56 338	47.03 344	81.80 341

ANet_high		56.10 323	52.24 324	67.66 335	49.27 355	56.82 346	83.94 344	82.02 352	70.47 336	33.28 353	64.54 349	17.23 357	69.16 353	45.59 350	23.85 350	77.02 344

PNet_i23d		56.44 322	53.54 323	65.14 337	65.34 349	50.33 351	89.06 342	79.57 353	45.77 345	35.75 352	68.95 346	10.75 359	74.40 350	48.48 347	38.20 345	70.70 346

wuykxyi23d		50.36 328	45.43 329	65.16 336	51.13 354	51.75 349	77.46 347	78.42 354	41.45 347	26.98 355	54.30 355	6.13 361	74.03 351	46.82 349	26.19 347	69.71 347

MVE	53.74 22	51.54 326	47.86 328	62.60 338	59.56 353	50.93 350	79.41 346	77.69 355	35.69 351	36.27 351	61.76 352	5.79 363	69.63 352	37.97 352	36.61 346	67.24 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		52.30 325	52.18 325	52.67 340	71.51 344	45.40 353	93.62 328	76.60 356	36.01 350	43.50 349	64.13 350	27.11 351	67.31 354	31.06 353	26.06 348	45.30 355

EMVS		51.44 327	51.22 327	52.11 341	70.71 345	44.97 355	94.04 324	75.66 357	35.34 352	42.40 350	61.56 353	28.93 350	65.87 355	27.64 354	24.73 349	45.49 354

N_pmnet		80.06 308	80.78 305	77.89 325	91.94 300	45.28 354	98.80 238	56.82 358	78.10 321	80.08 300	93.33 298	77.03 266	95.76 296	68.14 327	82.81 261	92.64 299

testmvs		40.60 329	44.45 330	29.05 344	19.49 359	14.11 360	99.68 139	18.47 359	20.74 353	64.59 336	98.48 166	10.95 358	17.09 358	56.66 342	11.01 353	55.94 352

test123		37.68 330	39.14 332	33.31 342	19.94 358	24.83 359	98.36 268	9.75 360	15.53 354	51.31 346	87.14 330	19.62 356	17.74 357	47.10 348	3.47 356	57.36 351

wuyk23d		20.37 333	20.84 334	18.99 345	65.34 349	27.73 358	50.43 351	7.67 361	9.50 355	8.01 356	6.34 357	6.13 361	26.24 356	23.40 355	10.69 355	2.99 356

pcd_1.5k_mvsjas		7.60 335	10.13 336	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	91.20 125	0.00 359	0.00 356	0.00 357	0.00 357

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

n2								0.00 362

nn								0.00 362

ab-mvs-re		8.28 334	11.04 335	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	99.40 95	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																99.59 111

test_part3							99.88 66		96.14 43		99.91 7		100.00 1	99.99 1

test_part2					99.89 36	99.25 6				99.49 33

sam_mvs1												94.72 56				99.59 111

sam_mvs												94.25 68

test_post1							95.78 319				59.23 354	93.20 96	97.74 215	91.06 194

test_post											63.35 351	94.43 58	98.13 200

patchmatchnet-post											91.70 308	95.12 42	97.95 210

gm-plane-assit					96.97 189	93.76 187			91.47 184		98.96 122		98.79 149	94.92 130

test9_res														99.71 18	99.99 13	100.00 1

agg_prior2														99.48 24	100.00 1	100.00 1

test_prior4						98.05 63	99.94 45

test_prior2							99.95 31		95.78 50	99.73 14	99.76 56	96.00 26		99.78 9	100.00 1

旧先验2							99.46 173		94.21 90	99.85 5			99.95 51	96.96 107

新几何2							99.40 177

原ACMM2							99.90 59

testdata2													99.99 28	90.54 203

segment_acmp												96.68 14

testdata1							99.28 192		96.35 38

plane_prior7					95.71 226	91.59 249

plane_prior6					95.76 221	91.72 244						80.47 241

plane_prior4											98.59 158

plane_prior3						91.64 247			96.63 29	93.01 181

plane_prior2							99.84 91		96.38 34

plane_prior1					95.73 223

plane_prior						91.74 241	99.86 86		96.76 25						89.59 210

HQP5-MVS						91.85 236

HQP-NCC					95.78 217		99.87 71		96.82 21	93.37 177

ACMP_Plane					95.78 217		99.87 71		96.82 21	93.37 177

BP-MVS														97.92 85

HQP4-MVS										93.37 177			98.39 182			94.53 212

HQP2-MVS												80.65 237

NP-MVS					95.77 220	91.79 238					98.65 154

MDTV_nov1_ep13_2view						96.26 123	96.11 314		91.89 173	98.06 96		94.40 60		94.30 146		99.67 97

ACMMP++_ref															87.04 240

ACMMP++															88.23 230

Test By Simon												92.82 102

High-resolution multi-view benchmark