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.

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 41	99.89 1	99.75 35	99.56 49	99.02 10	99.88 3	99.85 27	99.18 5	99.96 19	99.22 33	99.92 12	99.90 1

zzz-MVS		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 202	99.47 131	98.79 40	99.68 38	99.81 55	98.43 64	99.97 11	98.88 60	99.90 24	99.83 23

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 67	99.47 131	98.79 40	99.68 38	99.81 55	98.43 64	99.97 11	98.88 60	99.90 24	99.83 23

MP-MVS-pluss		99.37 38	99.20 47	99.88 4	99.90 3	99.87 2	99.30 216	99.52 77	97.18 185	99.60 62	99.79 74	98.79 38	99.95 33	98.83 73	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 156	99.48 115	98.05 99	99.76 29	99.86 23	98.82 35	99.93 57	98.82 77	99.91 17	99.84 12

ESAPD		99.31 45	99.13 53	99.87 6	99.81 32	99.83 7	99.37 196	99.48 115	97.97 109	99.77 24	99.78 80	98.96 21	99.95 33	97.15 219	99.84 58	99.83 23

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 15	99.65 77	99.66 25	98.13 83	99.66 49	99.68 124	98.96 21	99.96 19	98.62 96	99.87 39	99.84 12

HPM-MVS++		99.39 37	99.23 46	99.87 6	99.75 56	99.84 6	99.43 169	99.51 86	98.68 47	99.27 137	99.53 182	98.64 55	99.96 19	98.44 120	99.80 71	99.79 46

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 47	99.68 19	98.98 19	99.37 111	99.74 100	98.81 36	99.94 42	98.79 78	99.86 49	99.84 12

X-MVStestdata		96.55 278	95.45 299	99.87 6	99.85 23	99.83 7	99.69 47	99.68 19	98.98 19	99.37 111	64.01 363	98.81 36	99.94 42	98.79 78	99.86 49	99.84 12

MP-MVS		99.33 42	99.15 51	99.87 6	99.88 11	99.82 13	99.66 67	99.46 141	98.09 90	99.48 89	99.74 100	98.29 73	99.96 19	97.93 155	99.87 39	99.82 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 14	99.59 94	99.51 86	98.62 49	99.79 18	99.83 38	99.28 3	99.97 11	98.48 115	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 67	99.67 22	98.15 81	99.68 38	99.69 119	99.06 9	99.96 19	98.69 88	99.87 39	99.84 12

#test#		99.43 28	99.29 37	99.86 13	99.87 15	99.80 15	99.55 120	99.67 22	97.83 124	99.68 38	99.69 119	99.06 9	99.96 19	98.39 121	99.87 39	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 67	99.67 22	98.15 81	99.67 44	99.69 119	98.95 26	99.96 19	98.69 88	99.87 39	99.84 12

PGM-MVS		99.45 22	99.31 31	99.86 13	99.87 15	99.78 23	99.58 101	99.65 30	97.84 123	99.71 32	99.80 66	99.12 8	99.97 11	98.33 128	99.87 39	99.83 23

mPP-MVS		99.44 25	99.30 33	99.86 13	99.88 11	99.79 19	99.69 47	99.48 115	98.12 85	99.50 85	99.75 95	98.78 39	99.97 11	98.57 104	99.89 32	99.83 23

SMA-MVS		99.44 25	99.30 33	99.85 18	99.70 87	99.83 7	99.56 114	99.47 131	97.45 161	99.78 22	99.82 45	99.18 5	99.91 76	98.83 73	99.89 32	99.80 41

HSP-MVS		99.41 33	99.26 44	99.85 18	99.89 8	99.80 15	99.67 58	99.37 198	98.70 45	99.77 24	99.49 196	98.21 76	99.95 33	98.46 118	99.77 77	99.81 36

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 18	99.76 44	99.83 7	99.63 81	99.54 63	98.36 65	99.79 18	99.82 45	98.86 32	99.95 33	98.62 96	99.81 69	99.78 50

HPM-MVS_fast		99.51 12	99.40 14	99.85 18	99.91 1	99.79 19	99.76 28	99.56 49	97.72 137	99.76 29	99.75 95	99.13 7	99.92 66	99.07 47	99.92 12	99.85 8

CP-MVS		99.45 22	99.32 26	99.85 18	99.83 28	99.75 24	99.69 47	99.52 77	98.07 94	99.53 80	99.63 146	98.93 28	99.97 11	98.74 81	99.91 17	99.83 23

APD-MVS		99.27 51	99.08 58	99.84 23	99.75 56	99.79 19	99.50 137	99.50 100	97.16 187	99.77 24	99.82 45	98.78 39	99.94 42	97.56 190	99.86 49	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
abl_6		99.44 25	99.31 31	99.83 24	99.85 23	99.75 24	99.66 67	99.59 38	98.13 83	99.82 14	99.81 55	98.60 57	99.96 19	98.46 118	99.88 35	99.79 46

HPM-MVS		99.42 30	99.28 39	99.83 24	99.90 3	99.72 28	99.81 15	99.54 63	97.59 146	99.68 38	99.63 146	98.91 29	99.94 42	98.58 102	99.91 17	99.84 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MCST-MVS		99.43 28	99.30 33	99.82 26	99.79 35	99.74 27	99.29 220	99.40 181	98.79 40	99.52 82	99.62 151	98.91 29	99.90 89	98.64 93	99.75 80	99.82 32

ACMMP		99.45 22	99.32 26	99.82 26	99.89 8	99.67 35	99.62 84	99.69 18	98.12 85	99.63 54	99.84 36	98.73 49	99.96 19	98.55 110	99.83 64	99.81 36
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
3Dnovator+	97.12 13	99.18 59	98.97 73	99.82 26	99.17 212	99.68 33	99.81 15	99.51 86	99.20 4	98.72 234	99.89 10	95.68 147	99.97 11	98.86 67	99.86 49	99.81 36

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 81	99.39 184	98.91 29	99.78 22	99.85 27	99.36 2	99.94 42	98.84 70	99.88 35	99.82 32

3Dnovator	97.25 9	99.24 55	99.05 60	99.81 29	99.12 220	99.66 37	99.84 9	99.74 10	99.09 8	98.92 211	99.90 7	95.94 138	99.98 5	98.95 56	99.92 12	99.79 46

UA-Net		99.42 30	99.29 37	99.80 31	99.62 115	99.55 54	99.50 137	99.70 15	98.79 40	99.77 24	99.96 1	97.45 94	99.96 19	98.92 58	99.90 24	99.89 2

CDPH-MVS		99.13 64	98.91 82	99.80 31	99.75 56	99.71 29	99.15 254	99.41 174	96.60 233	99.60 62	99.55 172	98.83 34	99.90 89	97.48 198	99.83 64	99.78 50

QAPM		98.67 126	98.30 139	99.80 31	99.20 202	99.67 35	99.77 25	99.72 11	94.74 295	98.73 233	99.90 7	95.78 144	99.98 5	96.96 232	99.88 35	99.76 55

NCCC		99.34 41	99.19 48	99.79 34	99.61 119	99.65 40	99.30 216	99.48 115	98.86 31	99.21 160	99.63 146	98.72 50	99.90 89	98.25 132	99.63 103	99.80 41

CNVR-MVS		99.42 30	99.30 33	99.78 35	99.62 115	99.71 29	99.26 233	99.52 77	98.82 35	99.39 107	99.71 110	98.96 21	99.85 116	98.59 101	99.80 71	99.77 52

DP-MVS		99.16 62	98.95 78	99.78 35	99.77 41	99.53 58	99.41 180	99.50 100	97.03 206	99.04 193	99.88 15	97.39 95	99.92 66	98.66 91	99.90 24	99.87 4

train_agg		99.02 88	98.77 101	99.77 37	99.67 94	99.65 40	99.05 275	99.41 174	96.28 257	98.95 207	99.49 196	98.76 44	99.91 76	97.63 183	99.72 86	99.75 56

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 111	99.59 49	99.36 202	99.46 141	99.07 9	99.79 18	99.82 45	98.85 33	99.92 66	98.68 90	99.87 39	99.82 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		99.01 91	98.76 103	99.76 39	99.67 94	99.62 43	98.99 290	99.40 181	96.26 260	98.87 217	99.49 196	98.77 42	99.91 76	97.69 180	99.72 86	99.75 56

agg_prior3		98.97 96	98.71 107	99.75 40	99.67 94	99.60 47	99.04 280	99.41 174	95.93 281	98.87 217	99.48 202	98.61 56	99.91 76	97.63 183	99.72 86	99.75 56

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 81	99.52 61	99.49 146	99.49 105	98.94 26	99.83 11	99.76 90	99.01 12	99.94 42	99.15 41	99.87 39	99.80 41

新几何1				99.75 40	99.75 56	99.59 49		99.54 63	96.76 221	99.29 129	99.64 142	98.43 64	99.94 42	96.92 236	99.66 98	99.72 72

1121		99.09 77	98.87 87	99.75 40	99.74 67	99.60 47	99.27 225	99.48 115	96.82 220	99.25 145	99.65 135	98.38 68	99.93 57	97.53 193	99.67 97	99.73 66

test12				99.75 40	99.64 108	99.61 45		99.29 233		99.21 160		98.38 68	99.89 97		99.74 82	99.74 61

CPTT-MVS		99.11 73	98.90 83	99.74 45	99.80 34	99.46 68	99.59 94	99.49 105	97.03 206	99.63 54	99.69 119	97.27 100	99.96 19	97.82 163	99.84 58	99.81 36

LS3D		99.27 51	99.12 55	99.74 45	99.18 207	99.75 24	99.56 114	99.57 44	98.45 59	99.49 88	99.85 27	97.77 88	99.94 42	98.33 128	99.84 58	99.52 124

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 44	99.41 74	99.58 101	99.49 105	99.02 10	99.88 3	99.80 66	99.00 18	99.94 42	99.45 15	99.92 12	99.84 12

VNet		99.11 73	98.90 83	99.73 47	99.52 133	99.56 52	99.41 180	99.39 184	99.01 13	99.74 31	99.78 80	95.56 148	99.92 66	99.52 7	98.18 188	99.72 72

Regformer-1		99.53 9	99.47 8	99.72 49	99.71 81	99.44 71	99.49 146	99.46 141	98.95 24	99.83 11	99.76 90	99.01 12	99.93 57	99.17 38	99.87 39	99.80 41

114514_t		98.93 98	98.67 111	99.72 49	99.85 23	99.53 58	99.62 84	99.59 38	92.65 328	99.71 32	99.78 80	98.06 81	99.90 89	98.84 70	99.91 17	99.74 61

PHI-MVS		99.30 46	99.17 50	99.70 51	99.56 130	99.52 61	99.58 101	99.80 8	97.12 191	99.62 57	99.73 104	98.58 58	99.90 89	98.61 98	99.91 17	99.68 85

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 44	99.29 85	99.58 101	99.44 162	99.01 13	99.87 6	99.80 66	98.97 20	99.91 76	99.44 16	99.92 12	99.83 23

test_prior3		99.21 56	99.05 60	99.68 52	99.67 94	99.48 65	98.96 299	99.56 49	98.34 66	99.01 196	99.52 187	98.68 52	99.83 130	97.96 152	99.74 82	99.74 61

test_prior				99.68 52	99.67 94	99.48 65		99.56 49					99.83 130			99.74 61

MVS_0304		99.06 82	98.86 91	99.66 55	99.51 135	99.36 78	99.22 242	99.51 86	98.95 24	99.58 66	99.65 135	93.74 233	99.98 5	99.66 1	99.95 6	99.64 99

PAPM_NR		99.04 85	98.84 94	99.66 55	99.74 67	99.44 71	99.39 189	99.38 190	97.70 140	99.28 133	99.28 259	98.34 71	99.85 116	96.96 232	99.45 107	99.69 81

MVS_111021_HR		99.41 33	99.32 26	99.66 55	99.72 75	99.47 67	98.95 303	99.85 6	98.82 35	99.54 79	99.73 104	98.51 59	99.74 166	98.91 59	99.88 35	99.77 52

AdaColmap		99.01 91	98.80 98	99.66 55	99.56 130	99.54 55	99.18 249	99.70 15	98.18 80	99.35 117	99.63 146	96.32 129	99.90 89	97.48 198	99.77 77	99.55 116

原ACMM1				99.65 59	99.73 72	99.33 80		99.47 131	97.46 158	99.12 176	99.66 134	98.67 54	99.91 76	97.70 179	99.69 93	99.71 79

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 75	99.40 76	99.05 275	99.66 25	99.14 6	99.57 69	99.80 66	98.46 62	99.94 42	99.57 4	99.84 58	99.60 107
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
DP-MVS Recon		99.12 69	98.95 78	99.65 59	99.74 67	99.70 31	99.27 225	99.57 44	96.40 251	99.42 100	99.68 124	98.75 47	99.80 147	97.98 151	99.72 86	99.44 146

MVS_111021_LR		99.41 33	99.33 25	99.65 59	99.77 41	99.51 63	98.94 305	99.85 6	98.82 35	99.65 52	99.74 100	98.51 59	99.80 147	98.83 73	99.89 32	99.64 99

HyFIR lowres test		99.11 73	98.92 80	99.65 59	99.90 3	99.37 77	99.02 284	99.91 3	97.67 143	99.59 65	99.75 95	95.90 140	99.73 173	99.53 6	99.02 135	99.86 5

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 101	99.60 92	99.45 153	99.01 13	99.90 1	99.83 38	98.98 19	99.93 57	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 64	99.78 36	99.15 100	99.61 90	99.45 153	99.01 13	99.89 2	99.82 45	99.01 12	99.92 66	99.56 5	99.95 6	99.85 8

F-COLMAP		99.19 57	99.04 63	99.64 64	99.78 36	99.27 88	99.42 176	99.54 63	97.29 176	99.41 102	99.59 160	98.42 67	99.93 57	98.19 134	99.69 93	99.73 66

DeepC-MVS	98.35 2	99.30 46	99.19 48	99.64 64	99.82 29	99.23 92	99.62 84	99.55 56	98.94 26	99.63 54	99.95 2	95.82 143	99.94 42	99.37 17	99.97 3	99.73 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_Blended_VisFu		99.36 39	99.28 39	99.61 68	99.86 20	99.07 108	99.47 156	99.93 2	97.66 144	99.71 32	99.86 23	97.73 89	99.96 19	99.47 13	99.82 68	99.79 46

WTY-MVS		99.06 82	98.88 86	99.61 68	99.62 115	99.16 97	99.37 196	99.56 49	98.04 100	99.53 80	99.62 151	96.84 112	99.94 42	98.85 69	98.49 169	99.72 72

CANet		99.25 54	99.14 52	99.59 70	99.41 158	99.16 97	99.35 206	99.57 44	98.82 35	99.51 84	99.61 155	96.46 124	99.95 33	99.59 2	99.98 2	99.65 93

1112_ss		98.98 94	98.77 101	99.59 70	99.68 93	99.02 119	99.25 235	99.48 115	97.23 182	99.13 173	99.58 163	96.93 111	99.90 89	98.87 64	98.78 156	99.84 12

CNLPA		99.14 63	98.99 70	99.59 70	99.58 124	99.41 74	99.16 251	99.44 162	98.45 59	99.19 166	99.49 196	98.08 80	99.89 97	97.73 174	99.75 80	99.48 135

alignmvs		98.81 114	98.56 127	99.58 73	99.43 154	99.42 73	99.51 132	98.96 280	98.61 50	99.35 117	98.92 291	94.78 187	99.77 160	99.35 18	98.11 204	99.54 118

Test_1112_low_res		98.89 100	98.66 114	99.57 74	99.69 90	98.95 133	99.03 281	99.47 131	96.98 208	99.15 172	99.23 266	96.77 116	99.89 97	98.83 73	98.78 156	99.86 5

IS-MVSNet		99.05 84	98.87 87	99.57 74	99.73 72	99.32 81	99.75 35	99.20 253	98.02 103	99.56 70	99.86 23	96.54 122	99.67 194	98.09 141	99.13 126	99.73 66

Vis-MVSNet		99.12 69	98.97 73	99.56 76	99.78 36	99.10 104	99.68 56	99.66 25	98.49 56	99.86 7	99.87 20	94.77 191	99.84 122	99.19 35	99.41 110	99.74 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
0601test		98.86 104	98.63 116	99.54 77	99.49 142	99.18 96	99.50 137	99.07 268	98.22 76	99.61 59	99.51 190	95.37 153	99.84 122	98.60 100	98.33 174	99.59 111

testdata				99.54 77	99.75 56	98.95 133		99.51 86	97.07 202	99.43 97	99.70 113	98.87 31	99.94 42	97.76 170	99.64 101	99.72 72

LFMVS		97.90 204	97.35 247	99.54 77	99.52 133	99.01 121	99.39 189	98.24 330	97.10 195	99.65 52	99.79 74	84.79 342	99.91 76	99.28 27	98.38 173	99.69 81

ab-mvs		98.86 104	98.63 116	99.54 77	99.64 108	99.19 94	99.44 164	99.54 63	97.77 131	99.30 125	99.81 55	94.20 214	99.93 57	99.17 38	98.82 152	99.49 133

MAR-MVS		98.86 104	98.63 116	99.54 77	99.37 168	99.66 37	99.45 160	99.54 63	96.61 231	99.01 196	99.40 224	97.09 104	99.86 110	97.68 182	99.53 106	99.10 168
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
sss		99.17 60	99.05 60	99.53 82	99.62 115	98.97 128	99.36 202	99.62 31	97.83 124	99.67 44	99.65 135	97.37 98	99.95 33	99.19 35	99.19 123	99.68 85

EPP-MVSNet		99.13 64	98.99 70	99.53 82	99.65 107	99.06 109	99.81 15	99.33 219	97.43 163	99.60 62	99.88 15	97.14 103	99.84 122	99.13 42	98.94 141	99.69 81

PLC	97.94 4	99.02 88	98.85 93	99.53 82	99.66 104	99.01 121	99.24 237	99.52 77	96.85 217	99.27 137	99.48 202	98.25 75	99.91 76	97.76 170	99.62 104	99.65 93
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		98.98 94	98.80 98	99.53 82	99.76 44	99.19 94	98.75 320	99.55 56	97.25 179	99.47 90	99.77 87	97.82 86	99.87 106	96.93 235	99.90 24	99.54 118

PatchMatch-RL		98.84 113	98.62 120	99.52 86	99.71 81	99.28 86	99.06 273	99.77 9	97.74 135	99.50 85	99.53 182	95.41 152	99.84 122	97.17 218	99.64 101	99.44 146

OpenMVS	96.50 16	98.47 133	98.12 147	99.52 86	99.04 235	99.53 58	99.82 13	99.72 11	94.56 301	98.08 281	99.88 15	94.73 194	99.98 5	97.47 200	99.76 79	99.06 178

Fast-Effi-MVS+		98.70 123	98.43 130	99.51 88	99.51 135	99.28 86	99.52 128	99.47 131	96.11 274	99.01 196	99.34 247	96.20 133	99.84 122	97.88 158	98.82 152	99.39 152

canonicalmvs		99.02 88	98.86 91	99.51 88	99.42 155	99.32 81	99.80 19	99.48 115	98.63 48	99.31 124	98.81 300	97.09 104	99.75 165	99.27 29	97.90 210	99.47 139

PAPR		98.63 130	98.34 135	99.51 88	99.40 163	99.03 118	98.80 315	99.36 199	96.33 253	99.00 203	99.12 276	98.46 62	99.84 122	95.23 284	99.37 115	99.66 89

Effi-MVS+		98.81 114	98.59 125	99.48 91	99.46 148	99.12 103	98.08 345	99.50 100	97.50 156	99.38 109	99.41 220	96.37 128	99.81 143	99.11 44	98.54 166	99.51 129

MVS		97.28 267	96.55 274	99.48 91	98.78 287	98.95 133	99.27 225	99.39 184	83.53 348	98.08 281	99.54 175	96.97 109	99.87 106	94.23 308	99.16 124	99.63 103

MVS_Test		99.10 76	98.97 73	99.48 91	99.49 142	99.14 101	99.67 58	99.34 211	97.31 174	99.58 66	99.76 90	97.65 91	99.82 139	98.87 64	99.07 132	99.46 142

casdiffmvs		99.09 77	98.97 73	99.47 94	99.47 146	99.10 104	99.74 40	99.38 190	97.86 118	99.32 122	99.79 74	97.08 106	99.77 160	99.24 31	98.82 152	99.54 118

HY-MVS	97.30 7	98.85 111	98.64 115	99.47 94	99.42 155	99.08 107	99.62 84	99.36 199	97.39 169	99.28 133	99.68 124	96.44 126	99.92 66	98.37 124	98.22 184	99.40 151

PCF-MVS	97.08 14	97.66 245	97.06 265	99.47 94	99.61 119	99.09 106	98.04 346	99.25 248	91.24 335	98.51 258	99.70 113	94.55 202	99.91 76	92.76 325	99.85 53	99.42 149
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
lupinMVS		99.13 64	99.01 69	99.46 97	99.51 135	98.94 136	99.05 275	99.16 257	97.86 118	99.80 16	99.56 169	97.39 95	99.86 110	98.94 57	99.85 53	99.58 114

jason		99.13 64	99.03 65	99.45 98	99.46 148	98.87 144	99.12 258	99.26 246	98.03 102	99.79 18	99.65 135	97.02 107	99.85 116	99.02 51	99.90 24	99.65 93
jason: jason.
CHOSEN 1792x2688		99.19 57	99.10 57	99.45 98	99.89 8	98.52 196	99.39 189	99.94 1	98.73 44	99.11 178	99.89 10	95.50 150	99.94 42	99.50 8	99.97 3	99.89 2

MG-MVS		99.13 64	99.02 68	99.45 98	99.57 126	98.63 183	99.07 269	99.34 211	98.99 18	99.61 59	99.82 45	97.98 83	99.87 106	97.00 228	99.80 71	99.85 8

test_normal		97.44 262	96.77 272	99.44 101	97.75 327	99.00 123	99.10 266	98.64 318	97.71 138	93.93 331	98.82 299	87.39 332	99.83 130	98.61 98	98.97 139	99.49 133

MSLP-MVS++		99.46 21	99.47 8	99.44 101	99.60 121	99.16 97	99.41 180	99.71 13	98.98 19	99.45 93	99.78 80	99.19 4	99.54 214	99.28 27	99.84 58	99.63 103

DI_MVS_plusplus_test		97.45 261	96.79 270	99.44 101	97.76 326	99.04 111	99.21 245	98.61 321	97.74 135	94.01 328	98.83 298	87.38 333	99.83 130	98.63 94	98.90 146	99.44 146

PVSNet_Blended		99.08 80	98.97 73	99.42 104	99.76 44	98.79 167	98.78 317	99.91 3	96.74 222	99.67 44	99.49 196	97.53 92	99.88 104	98.98 54	99.85 53	99.60 107

BH-RMVSNet		98.41 138	98.08 151	99.40 105	99.41 158	98.83 151	99.30 216	98.77 301	97.70 140	98.94 209	99.65 135	92.91 245	99.74 166	96.52 258	99.55 105	99.64 99

UGNet		98.87 101	98.69 109	99.40 105	99.22 199	98.72 175	99.44 164	99.68 19	99.24 3	99.18 169	99.42 217	92.74 249	99.96 19	99.34 22	99.94 10	99.53 123
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
TSAR-MVS + GP.		99.36 39	99.36 19	99.36 107	99.67 94	98.61 188	99.07 269	99.33 219	99.00 17	99.82 14	99.81 55	99.06 9	99.84 122	99.09 45	99.42 109	99.65 93

diffmvs		98.99 93	98.87 87	99.35 108	99.45 152	98.74 172	99.62 84	99.45 153	97.43 163	99.13 173	99.72 108	97.23 101	99.87 106	98.86 67	98.90 146	99.45 145

Anonymous20240529		98.09 171	97.68 201	99.34 109	99.66 104	98.44 203	99.40 187	99.43 170	93.67 317	99.22 157	99.89 10	90.23 305	99.93 57	99.26 30	98.33 174	99.66 89

xiu_mvs_v1_base_debu		99.29 48	99.27 41	99.34 109	99.63 111	98.97 128	99.12 258	99.51 86	98.86 31	99.84 8	99.47 206	98.18 77	99.99 1	99.50 8	99.31 116	99.08 173

xiu_mvs_v1_base		99.29 48	99.27 41	99.34 109	99.63 111	98.97 128	99.12 258	99.51 86	98.86 31	99.84 8	99.47 206	98.18 77	99.99 1	99.50 8	99.31 116	99.08 173

xiu_mvs_v1_base_debi		99.29 48	99.27 41	99.34 109	99.63 111	98.97 128	99.12 258	99.51 86	98.86 31	99.84 8	99.47 206	98.18 77	99.99 1	99.50 8	99.31 116	99.08 173

PMMVS		98.80 117	98.62 120	99.34 109	99.27 192	98.70 176	98.76 319	99.31 226	97.34 171	99.21 160	99.07 278	97.20 102	99.82 139	98.56 107	98.87 149	99.52 124

CSCG		99.32 43	99.32 26	99.32 114	99.85 23	98.29 208	99.71 43	99.66 25	98.11 87	99.41 102	99.80 66	98.37 70	99.96 19	98.99 53	99.96 5	99.72 72

AllTest		98.87 101	98.72 105	99.31 115	99.86 20	98.48 201	99.56 114	99.61 32	97.85 121	99.36 114	99.85 27	95.95 136	99.85 116	96.66 254	99.83 64	99.59 111

TestCases				99.31 115	99.86 20	98.48 201		99.61 32	97.85 121	99.36 114	99.85 27	95.95 136	99.85 116	96.66 254	99.83 64	99.59 111

Vis-MVSNet (Re-imp)		98.87 101	98.72 105	99.31 115	99.71 81	98.88 143	99.80 19	99.44 162	97.91 116	99.36 114	99.78 80	95.49 151	99.43 228	97.91 156	99.11 127	99.62 105

PS-MVSNAJ		99.32 43	99.32 26	99.30 118	99.57 126	98.94 136	98.97 297	99.46 141	98.92 28	99.71 32	99.24 265	99.01 12	99.98 5	99.35 18	99.66 98	98.97 187

VPA-MVSNet		98.29 147	97.95 164	99.30 118	99.16 214	99.54 55	99.50 137	99.58 43	98.27 71	99.35 117	99.37 233	92.53 264	99.65 198	99.35 18	94.46 299	98.72 220

EPNet		98.86 104	98.71 107	99.30 118	97.20 335	98.18 212	99.62 84	98.91 287	99.28 2	98.63 252	99.81 55	95.96 135	99.99 1	99.24 31	99.72 86	99.73 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v2_base		99.26 53	99.25 45	99.29 121	99.53 132	98.91 141	99.02 284	99.45 153	98.80 39	99.71 32	99.26 262	98.94 27	99.98 5	99.34 22	99.23 120	98.98 186

MVSFormer		99.17 60	99.12 55	99.29 121	99.51 135	98.94 136	99.88 1	99.46 141	97.55 151	99.80 16	99.65 135	97.39 95	99.28 258	99.03 49	99.85 53	99.65 93

nrg030		98.64 129	98.42 131	99.28 123	99.05 234	99.69 32	99.81 15	99.46 141	98.04 100	99.01 196	99.82 45	96.69 119	99.38 231	99.34 22	94.59 298	98.78 208

thresconf0.02		98.24 150	97.89 171	99.27 124	99.76 44	99.04 111	99.67 58	97.71 341	97.10 195	99.55 73	99.54 175	92.70 253	99.79 150	96.90 238	98.12 198	98.97 187

tfpn_n400		98.24 150	97.89 171	99.27 124	99.76 44	99.04 111	99.67 58	97.71 341	97.10 195	99.55 73	99.54 175	92.70 253	99.79 150	96.90 238	98.12 198	98.97 187

tfpnconf		98.24 150	97.89 171	99.27 124	99.76 44	99.04 111	99.67 58	97.71 341	97.10 195	99.55 73	99.54 175	92.70 253	99.79 150	96.90 238	98.12 198	98.97 187

tfpnview11		98.24 150	97.89 171	99.27 124	99.76 44	99.04 111	99.67 58	97.71 341	97.10 195	99.55 73	99.54 175	92.70 253	99.79 150	96.90 238	98.12 198	98.97 187

Anonymous202405211		98.30 145	97.98 161	99.26 128	99.57 126	98.16 213	99.41 180	98.55 324	96.03 279	99.19 166	99.74 100	91.87 280	99.92 66	99.16 40	98.29 178	99.70 80

tfpn1000		98.33 142	98.02 156	99.25 129	99.78 36	98.73 173	99.70 44	97.55 349	97.48 157	99.69 37	99.53 182	92.37 271	99.85 116	97.82 163	98.26 183	99.16 164

CANet_DTU		98.97 96	98.87 87	99.25 129	99.33 175	98.42 206	99.08 268	99.30 228	99.16 5	99.43 97	99.75 95	95.27 157	99.97 11	98.56 107	99.95 6	99.36 153

CDS-MVSNet		99.09 77	99.03 65	99.25 129	99.42 155	98.73 173	99.45 160	99.46 141	98.11 87	99.46 92	99.77 87	98.01 82	99.37 235	98.70 86	98.92 144	99.66 89
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		98.38 140	98.09 150	99.24 132	99.26 194	99.32 81	99.56 114	99.55 56	97.45 161	98.71 235	99.83 38	93.23 237	99.63 205	98.88 60	96.32 259	98.76 213

TAMVS		99.12 69	99.08 58	99.24 132	99.46 148	98.55 190	99.51 132	99.46 141	98.09 90	99.45 93	99.82 45	98.34 71	99.51 215	98.70 86	98.93 142	99.67 88

FIs		98.78 118	98.63 116	99.23 134	99.18 207	99.54 55	99.83 12	99.59 38	98.28 70	98.79 228	99.81 55	96.75 117	99.37 235	99.08 46	96.38 257	98.78 208

Test4		95.05 306	93.67 314	99.22 135	96.07 337	98.94 136	99.20 247	99.27 245	97.71 138	89.96 345	97.59 336	66.18 354	99.25 267	98.06 148	98.96 140	99.47 139

OMC-MVS		99.08 80	99.04 63	99.20 136	99.67 94	98.22 211	99.28 222	99.52 77	98.07 94	99.66 49	99.81 55	97.79 87	99.78 158	97.79 166	99.81 69	99.60 107

Anonymous20240521		98.30 145	98.00 158	99.18 137	98.98 245	99.46 68	99.78 22	99.49 105	96.91 214	98.00 286	99.25 263	96.51 123	99.38 231	98.15 138	94.95 287	98.71 222

COLMAP_ROB	97.56 6	98.86 104	98.75 104	99.17 138	99.88 11	98.53 192	99.34 209	99.59 38	97.55 151	98.70 241	99.89 10	95.83 142	99.90 89	98.10 140	99.90 24	99.08 173
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDDNet		97.55 250	97.02 266	99.16 139	99.49 142	98.12 217	99.38 194	99.30 228	95.35 288	99.68 38	99.90 7	82.62 348	99.93 57	99.31 25	98.13 196	99.42 149

mvs_anonymous		99.03 87	98.99 70	99.16 139	99.38 166	98.52 196	99.51 132	99.38 190	97.79 129	99.38 109	99.81 55	97.30 99	99.45 219	99.35 18	98.99 137	99.51 129

conf0.01		98.21 157	97.89 171	99.15 141	99.76 44	99.04 111	99.67 58	97.71 341	97.10 195	99.55 73	99.54 175	92.70 253	99.79 150	96.90 238	98.12 198	98.61 282

conf0.002		98.21 157	97.89 171	99.15 141	99.76 44	99.04 111	99.67 58	97.71 341	97.10 195	99.55 73	99.54 175	92.70 253	99.79 150	96.90 238	98.12 198	98.61 282

tfpn_ndepth		98.17 161	97.84 179	99.15 141	99.75 56	98.76 171	99.61 90	97.39 351	96.92 213	99.61 59	99.38 229	92.19 273	99.86 110	97.57 188	98.13 196	98.82 204

FC-MVSNet-test		98.75 121	98.62 120	99.15 141	99.08 228	99.45 70	99.86 8	99.60 35	98.23 75	98.70 241	99.82 45	96.80 113	99.22 273	99.07 47	96.38 257	98.79 207

UniMVSNet (Re)		98.29 147	98.00 158	99.13 145	99.00 240	99.36 78	99.49 146	99.51 86	97.95 111	98.97 206	99.13 273	96.30 130	99.38 231	98.36 126	93.34 316	98.66 259

1314		98.68 125	98.54 128	99.11 146	98.89 270	98.65 181	99.27 225	99.49 105	96.89 215	97.99 287	99.56 169	97.72 90	99.83 130	97.74 173	99.27 119	98.84 203

CHOSEN 280x420		99.12 69	99.13 53	99.08 147	99.66 104	97.89 225	98.43 335	99.71 13	98.88 30	99.62 57	99.76 90	96.63 120	99.70 189	99.46 14	99.99 1	99.66 89

PAPM		97.59 249	97.09 264	99.07 148	99.06 231	98.26 210	98.30 340	99.10 263	94.88 292	98.08 281	99.34 247	96.27 131	99.64 200	89.87 333	98.92 144	99.31 157

WR-MVS		98.06 174	97.73 197	99.06 149	98.86 277	99.25 90	99.19 248	99.35 203	97.30 175	98.66 244	99.43 215	93.94 224	99.21 277	98.58 102	94.28 303	98.71 222

API-MVS		99.04 85	99.03 65	99.06 149	99.40 163	99.31 84	99.55 120	99.56 49	98.54 53	99.33 121	99.39 228	98.76 44	99.78 158	96.98 230	99.78 75	98.07 315

SD-MVS		99.41 33	99.52 6	99.05 151	99.74 67	99.68 33	99.46 159	99.52 77	99.11 7	99.88 3	99.91 5	99.43 1	97.70 337	98.72 85	99.93 11	99.77 52

PVSNet_BlendedMVS		98.86 104	98.80 98	99.03 152	99.76 44	98.79 167	99.28 222	99.91 3	97.42 166	99.67 44	99.37 233	97.53 92	99.88 104	98.98 54	97.29 241	98.42 303

NR-MVSNet		97.97 193	97.61 211	99.02 153	98.87 274	99.26 89	99.47 156	99.42 172	97.63 145	97.08 303	99.50 193	95.07 167	99.13 283	97.86 160	93.59 314	98.68 237

VPNet		97.84 211	97.44 235	99.01 154	99.21 200	98.94 136	99.48 151	99.57 44	98.38 64	99.28 133	99.73 104	88.89 316	99.39 230	99.19 35	93.27 317	98.71 222

CP-MVSNet		98.09 171	97.78 186	99.01 154	98.97 249	99.24 91	99.67 58	99.46 141	97.25 179	98.48 261	99.64 142	93.79 229	99.06 290	98.63 94	94.10 307	98.74 218

GA-MVS		97.85 209	97.47 226	99.00 156	99.38 166	97.99 220	98.57 330	99.15 258	97.04 205	98.90 214	99.30 256	89.83 308	99.38 231	96.70 251	98.33 174	99.62 105

MVSTER		98.49 132	98.32 137	99.00 156	99.35 171	99.02 119	99.54 123	99.38 190	97.41 167	99.20 163	99.73 104	93.86 228	99.36 239	98.87 64	97.56 222	98.62 273

tfpnnormal		97.84 211	97.47 226	98.98 158	99.20 202	99.22 93	99.64 79	99.61 32	96.32 254	98.27 273	99.70 113	93.35 236	99.44 224	95.69 274	95.40 275	98.27 310

testing_2		94.44 311	92.93 317	98.98 158	94.16 344	99.00 123	99.42 176	99.28 240	96.60 233	84.86 347	96.84 342	70.91 351	99.27 261	98.23 133	96.08 263	98.68 237

test_djsdf		98.67 126	98.57 126	98.98 158	98.70 298	98.91 141	99.88 1	99.46 141	97.55 151	99.22 157	99.88 15	95.73 146	99.28 258	99.03 49	97.62 217	98.75 215

UniMVSNet_NR-MVSNet		98.22 154	97.97 162	98.96 161	98.92 264	98.98 125	99.48 151	99.53 73	97.76 132	98.71 235	99.46 210	96.43 127	99.22 273	98.57 104	92.87 322	98.69 232

DU-MVS		98.08 173	97.79 184	98.96 161	98.87 274	98.98 125	99.41 180	99.45 153	97.87 117	98.71 235	99.50 193	94.82 184	99.22 273	98.57 104	92.87 322	98.68 237

PS-CasMVS		97.93 199	97.59 213	98.95 163	98.99 241	99.06 109	99.68 56	99.52 77	97.13 189	98.31 270	99.68 124	92.44 270	99.05 291	98.51 113	94.08 308	98.75 215

anonymousdsp		98.44 135	98.28 140	98.94 164	98.50 314	98.96 132	99.77 25	99.50 100	97.07 202	98.87 217	99.77 87	94.76 192	99.28 258	98.66 91	97.60 218	98.57 294

v6		98.12 167	97.84 179	98.94 164	98.94 257	98.83 151	99.66 67	99.34 211	96.49 238	99.30 125	99.37 233	94.95 173	99.34 245	97.77 169	94.74 289	98.67 248

TAPA-MVS	97.07 15	97.74 232	97.34 250	98.94 164	99.70 87	97.53 240	99.25 235	99.51 86	91.90 332	99.30 125	99.63 146	98.78 39	99.64 200	88.09 338	99.87 39	99.65 93
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1neww		98.12 167	97.84 179	98.93 167	98.97 249	98.81 160	99.66 67	99.35 203	96.49 238	99.29 129	99.37 233	95.02 169	99.32 249	97.73 174	94.73 290	98.67 248

v7new		98.12 167	97.84 179	98.93 167	98.97 249	98.81 160	99.66 67	99.35 203	96.49 238	99.29 129	99.37 233	95.02 169	99.32 249	97.73 174	94.73 290	98.67 248

v8		97.95 198	97.63 210	98.93 167	98.95 254	98.81 160	99.80 19	99.41 174	96.03 279	99.10 181	99.42 217	94.92 177	99.30 255	96.94 234	94.08 308	98.66 259

v1		98.05 180	97.76 193	98.93 167	98.92 264	98.80 165	99.57 107	99.35 203	96.39 252	99.28 133	99.36 240	94.86 182	99.32 249	97.38 206	94.72 292	98.68 237

JIA-IIPM		97.50 257	97.02 266	98.93 167	98.73 293	97.80 235	99.30 216	98.97 278	91.73 333	98.91 212	94.86 348	95.10 166	99.71 183	97.58 186	97.98 208	99.28 159

v7n		97.87 207	97.52 217	98.92 172	98.76 291	98.58 189	99.84 9	99.46 141	96.20 265	98.91 212	99.70 113	94.89 180	99.44 224	96.03 267	93.89 312	98.75 215

v2v482		98.06 174	97.77 190	98.92 172	98.90 267	98.82 158	99.57 107	99.36 199	96.65 228	99.19 166	99.35 244	94.20 214	99.25 267	97.72 178	94.97 285	98.69 232

thres600view7		97.86 208	97.51 219	98.92 172	99.72 75	97.95 224	99.59 94	98.74 305	97.94 112	99.27 137	98.62 307	91.75 282	99.86 110	93.73 313	98.19 187	98.96 193

thres400		97.77 225	97.38 243	98.92 172	99.69 90	97.96 222	99.50 137	98.73 314	97.83 124	99.17 170	98.45 316	91.67 288	99.83 130	93.22 318	98.18 188	98.96 193

v1192		97.81 217	97.44 235	98.91 176	98.88 271	98.68 177	99.51 132	99.34 211	96.18 267	99.20 163	99.34 247	94.03 222	99.36 239	95.32 283	95.18 279	98.69 232

v1141		98.05 180	97.76 193	98.91 176	98.91 266	98.78 169	99.57 107	99.35 203	96.41 250	99.23 155	99.36 240	94.93 176	99.27 261	97.38 206	94.72 292	98.68 237

mvs_tets		98.40 139	98.23 142	98.91 176	98.67 302	98.51 198	99.66 67	99.53 73	98.19 77	98.65 250	99.81 55	92.75 247	99.44 224	99.31 25	97.48 231	98.77 211

divwei89l23v2f112		98.06 174	97.78 186	98.91 176	98.90 267	98.77 170	99.57 107	99.35 203	96.45 245	99.24 150	99.37 233	94.92 177	99.27 261	97.50 196	94.71 294	98.68 237

Anonymous20231211		97.88 205	97.54 216	98.90 180	99.71 81	98.53 192	99.48 151	99.57 44	94.16 311	98.81 225	99.68 124	93.23 237	99.42 229	98.84 70	94.42 301	98.76 213

PS-MVSNAJss		98.92 99	98.92 80	98.90 180	98.78 287	98.53 192	99.78 22	99.54 63	98.07 94	99.00 203	99.76 90	99.01 12	99.37 235	99.13 42	97.23 242	98.81 205

WR-MVS_H		98.13 165	97.87 178	98.90 180	99.02 238	98.84 148	99.70 44	99.59 38	97.27 177	98.40 264	99.19 269	95.53 149	99.23 270	98.34 127	93.78 313	98.61 282

view600		97.97 193	97.66 203	98.89 183	99.75 56	97.81 231	99.69 47	98.80 297	98.02 103	99.25 145	98.88 292	91.95 275	99.89 97	94.36 300	98.29 178	98.96 193

view800		97.97 193	97.66 203	98.89 183	99.75 56	97.81 231	99.69 47	98.80 297	98.02 103	99.25 145	98.88 292	91.95 275	99.89 97	94.36 300	98.29 178	98.96 193

conf0.05thres1000		97.97 193	97.66 203	98.89 183	99.75 56	97.81 231	99.69 47	98.80 297	98.02 103	99.25 145	98.88 292	91.95 275	99.89 97	94.36 300	98.29 178	98.96 193

tfpn		97.97 193	97.66 203	98.89 183	99.75 56	97.81 231	99.69 47	98.80 297	98.02 103	99.25 145	98.88 292	91.95 275	99.89 97	94.36 300	98.29 178	98.96 193

mvs-test1		98.86 104	98.84 94	98.89 183	99.33 175	97.77 236	99.44 164	99.30 228	98.47 57	99.10 181	99.43 215	96.78 114	99.95 33	98.73 83	99.02 135	98.96 193

XVG-OURS-SEG-HR		98.69 124	98.62 120	98.89 183	99.71 81	97.74 237	99.12 258	99.54 63	98.44 62	99.42 100	99.71 110	94.20 214	99.92 66	98.54 112	98.90 146	99.00 183

PVSNet	96.02 17	98.85 111	98.84 94	98.89 183	99.73 72	97.28 243	98.32 339	99.60 35	97.86 118	99.50 85	99.57 167	96.75 117	99.86 110	98.56 107	99.70 92	99.54 118

jajsoiax		98.43 136	98.28 140	98.88 190	98.60 309	98.43 204	99.82 13	99.53 73	98.19 77	98.63 252	99.80 66	93.22 239	99.44 224	99.22 33	97.50 227	98.77 211

pm-mvs1		97.68 241	97.28 257	98.88 190	99.06 231	98.62 185	99.50 137	99.45 153	96.32 254	97.87 290	99.79 74	92.47 266	99.35 242	97.54 192	93.54 315	98.67 248

VDD-MVS		97.73 233	97.35 247	98.88 190	99.47 146	97.12 250	99.34 209	98.85 293	98.19 77	99.67 44	99.85 27	82.98 346	99.92 66	99.49 12	98.32 177	99.60 107

XVG-OURS		98.73 122	98.68 110	98.88 190	99.70 87	97.73 238	98.92 306	99.55 56	98.52 55	99.45 93	99.84 36	95.27 157	99.91 76	98.08 145	98.84 151	99.00 183

v144192		97.92 202	97.60 212	98.87 194	98.83 280	98.65 181	99.55 120	99.34 211	96.20 265	99.32 122	99.40 224	94.36 209	99.26 266	96.37 263	95.03 284	98.70 227

v7		98.05 180	97.78 186	98.87 194	98.99 241	98.67 178	99.64 79	99.34 211	96.31 256	99.29 129	99.51 190	94.78 187	99.27 261	97.03 226	95.15 281	98.66 259

CR-MVSNet		98.17 161	97.93 166	98.87 194	99.18 207	98.49 199	99.22 242	99.33 219	96.96 209	99.56 70	99.38 229	94.33 210	99.00 298	94.83 291	98.58 162	99.14 165

RPMNet		96.61 277	95.85 285	98.87 194	99.18 207	98.49 199	99.22 242	99.08 265	88.72 344	99.56 70	97.38 339	94.08 221	99.00 298	86.87 343	98.58 162	99.14 165

v10		97.85 209	97.52 217	98.86 198	98.99 241	98.67 178	99.75 35	99.41 174	95.70 285	98.98 205	99.41 220	94.75 193	99.23 270	96.01 268	94.63 297	98.67 248

V42		98.06 174	97.79 184	98.86 198	98.98 245	98.84 148	99.69 47	99.34 211	96.53 237	99.30 125	99.37 233	94.67 197	99.32 249	97.57 188	94.66 295	98.42 303

TransMVSNet (Re)		97.15 270	96.58 273	98.86 198	99.12 220	98.85 147	99.49 146	98.91 287	95.48 287	97.16 302	99.80 66	93.38 235	99.11 286	94.16 310	91.73 327	98.62 273

v1144		97.98 190	97.69 200	98.85 201	98.87 274	98.66 180	99.54 123	99.35 203	96.27 259	99.23 155	99.35 244	94.67 197	99.23 270	96.73 249	95.16 280	98.68 237

v1921920		97.80 220	97.45 229	98.84 202	98.80 281	98.53 192	99.52 128	99.34 211	96.15 271	99.24 150	99.47 206	93.98 223	99.29 257	95.40 281	95.13 282	98.69 232

FMVSNet3		98.03 183	97.76 193	98.84 202	99.39 165	98.98 125	99.40 187	99.38 190	96.67 227	99.07 187	99.28 259	92.93 242	98.98 300	97.10 222	96.65 250	98.56 295

TR-MVS		97.76 226	97.41 240	98.82 204	99.06 231	97.87 226	98.87 311	98.56 323	96.63 230	98.68 243	99.22 267	92.49 265	99.65 198	95.40 281	97.79 212	98.95 200

pmmvs4		98.13 165	97.90 167	98.81 205	98.61 308	98.87 144	98.99 290	99.21 252	96.44 246	99.06 191	99.58 163	95.90 140	99.11 286	97.18 217	96.11 262	98.46 302

Patchmtry		97.75 230	97.40 241	98.81 205	99.10 225	98.87 144	99.11 264	99.33 219	94.83 293	98.81 225	99.38 229	94.33 210	99.02 295	96.10 265	95.57 273	98.53 296

FMVSNet2		97.72 235	97.36 245	98.80 207	99.51 135	98.84 148	99.45 160	99.42 172	96.49 238	98.86 222	99.29 258	90.26 302	98.98 300	96.44 260	96.56 253	98.58 293

v1240		97.69 239	97.32 253	98.79 208	98.85 278	98.43 204	99.48 151	99.36 199	96.11 274	99.27 137	99.36 240	93.76 231	99.24 269	94.46 297	95.23 278	98.70 227

PatchT		97.03 274	96.44 275	98.79 208	98.99 241	98.34 207	99.16 251	99.07 268	92.13 329	99.52 82	97.31 341	94.54 203	98.98 300	88.54 336	98.73 158	99.03 180

tfpn111		97.81 217	97.49 223	98.78 210	99.72 75	97.86 227	99.59 94	98.74 305	97.93 113	99.26 141	98.62 307	91.75 282	99.86 110	93.57 314	98.18 188	98.61 282

conf200view11		97.78 224	97.45 229	98.77 211	99.72 75	97.86 227	99.59 94	98.74 305	97.93 113	99.26 141	98.62 307	91.75 282	99.83 130	93.22 318	98.18 188	98.61 282

Patchmatch-test		97.93 199	97.65 208	98.77 211	99.18 207	97.07 255	99.03 281	99.14 260	96.16 269	98.74 232	99.57 167	94.56 201	99.72 177	93.36 317	99.11 127	99.52 124

TranMVSNet+NR-MVSNet		97.93 199	97.66 203	98.76 213	98.78 287	98.62 185	99.65 77	99.49 105	97.76 132	98.49 260	99.60 158	94.23 213	98.97 307	98.00 150	92.90 320	98.70 227

gg-mvs-nofinetune		96.17 294	95.32 301	98.73 214	98.79 283	98.14 215	99.38 194	94.09 358	91.07 337	98.07 284	91.04 354	89.62 311	99.35 242	96.75 248	99.09 130	98.68 237

tfpn200view9		97.72 235	97.38 243	98.72 215	99.69 90	97.96 222	99.50 137	98.73 314	97.83 124	99.17 170	98.45 316	91.67 288	99.83 130	93.22 318	98.18 188	98.37 307

PEN-MVS		97.76 226	97.44 235	98.72 215	98.77 290	98.54 191	99.78 22	99.51 86	97.06 204	98.29 272	99.64 142	92.63 261	98.89 310	98.09 141	93.16 318	98.72 220

thres100view900		97.76 226	97.45 229	98.69 217	99.72 75	97.86 227	99.59 94	98.74 305	97.93 113	99.26 141	98.62 307	91.75 282	99.83 130	93.22 318	98.18 188	98.37 307

EI-MVSNet		98.67 126	98.67 111	98.68 218	99.35 171	97.97 221	99.50 137	99.38 190	96.93 212	99.20 163	99.83 38	97.87 84	99.36 239	98.38 123	97.56 222	98.71 222

Baseline_NR-MVSNet		97.76 226	97.45 229	98.68 218	99.09 227	98.29 208	99.41 180	98.85 293	95.65 286	98.63 252	99.67 129	94.82 184	99.10 288	98.07 147	92.89 321	98.64 264

V4		97.80 220	97.51 219	98.67 220	98.79 283	98.63 183	99.87 4	99.44 162	95.87 282	99.01 196	99.46 210	94.52 204	99.33 246	96.64 257	93.97 310	98.05 316

v52		97.79 222	97.50 221	98.66 221	98.80 281	98.62 185	99.87 4	99.44 162	95.87 282	99.01 196	99.46 210	94.44 208	99.33 246	96.65 256	93.96 311	98.05 316

thres200		97.61 248	97.28 257	98.62 222	99.64 108	98.03 218	99.26 233	98.74 305	97.68 142	99.09 185	98.32 318	91.66 290	99.81 143	92.88 324	98.22 184	98.03 319

Fast-Effi-MVS+-dtu		98.77 120	98.83 97	98.60 223	99.41 158	96.99 262	99.52 128	99.49 105	98.11 87	99.24 150	99.34 247	96.96 110	99.79 150	97.95 154	99.45 107	99.02 182

BH-untuned		98.42 137	98.36 133	98.59 224	99.49 142	96.70 274	99.27 225	99.13 261	97.24 181	98.80 227	99.38 229	95.75 145	99.74 166	97.07 225	99.16 124	99.33 156

IterMVS-LS		98.46 134	98.42 131	98.58 225	99.59 123	98.00 219	99.37 196	99.43 170	96.94 211	99.07 187	99.59 160	97.87 84	99.03 294	98.32 130	95.62 272	98.71 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		97.73 233	97.45 229	98.57 226	99.45 152	97.50 241	99.02 284	98.98 277	96.11 274	99.41 102	99.14 272	90.28 301	98.74 313	95.74 272	98.93 142	99.47 139

IB-MVS	95.67 18	96.22 292	95.44 300	98.57 226	99.21 200	96.70 274	98.65 327	97.74 340	96.71 224	97.27 299	98.54 314	86.03 336	99.92 66	98.47 117	86.30 347	99.10 168
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
ADS-MVSNet		98.20 159	98.08 151	98.56 228	99.33 175	96.48 281	99.23 238	99.15 258	96.24 262	99.10 181	99.67 129	94.11 219	99.71 183	96.81 245	99.05 133	99.48 135

test0.0.03 1		97.71 238	97.42 239	98.56 228	98.41 317	97.82 230	98.78 317	98.63 319	97.34 171	98.05 285	98.98 288	94.45 206	98.98 300	95.04 287	97.15 246	98.89 201

test-LLR		98.06 174	97.90 167	98.55 230	98.79 283	97.10 251	98.67 324	97.75 338	97.34 171	98.61 255	98.85 296	94.45 206	99.45 219	97.25 211	99.38 111	99.10 168

test-mter		97.49 259	97.13 263	98.55 230	98.79 283	97.10 251	98.67 324	97.75 338	96.65 228	98.61 255	98.85 296	88.23 327	99.45 219	97.25 211	99.38 111	99.10 168

v148		97.79 222	97.55 214	98.50 232	98.74 292	97.72 239	99.54 123	99.33 219	96.26 260	98.90 214	99.51 190	94.68 196	99.14 280	97.83 162	93.15 319	98.63 271

LPG-MVS_test		98.22 154	98.13 146	98.49 233	99.33 175	97.05 257	99.58 101	99.55 56	97.46 158	99.24 150	99.83 38	92.58 262	99.72 177	98.09 141	97.51 225	98.68 237

LGP-MVS_train				98.49 233	99.33 175	97.05 257		99.55 56	97.46 158	99.24 150	99.83 38	92.58 262	99.72 177	98.09 141	97.51 225	98.68 237

cascas		97.69 239	97.43 238	98.48 235	98.60 309	97.30 242	98.18 344	99.39 184	92.96 325	98.41 263	98.78 303	93.77 230	99.27 261	98.16 137	98.61 159	98.86 202

ACMM	97.58 5	98.37 141	98.34 135	98.48 235	99.41 158	97.10 251	99.56 114	99.45 153	98.53 54	99.04 193	99.85 27	93.00 241	99.71 183	98.74 81	97.45 232	98.64 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		98.78 118	98.89 85	98.47 237	99.33 175	96.91 268	99.57 107	99.30 228	98.47 57	99.41 102	98.99 285	96.78 114	99.74 166	98.73 83	99.38 111	98.74 218

DTE-MVSNet		97.51 256	97.19 262	98.46 238	98.63 305	98.13 216	99.84 9	99.48 115	96.68 226	97.97 288	99.67 129	92.92 243	98.56 316	96.88 244	92.60 325	98.70 227

OPM-MVS		98.19 160	98.10 148	98.45 239	98.88 271	97.07 255	99.28 222	99.38 190	98.57 52	99.22 157	99.81 55	92.12 274	99.66 196	98.08 145	97.54 224	98.61 282

GG-mvs-BLEND				98.45 239	98.55 312	98.16 213	99.43 169	93.68 359		97.23 300	98.46 315	89.30 313	99.22 273	95.43 280	98.22 184	97.98 321

ACMP	97.20 11	98.06 174	97.94 165	98.45 239	99.37 168	97.01 260	99.44 164	99.49 105	97.54 154	98.45 262	99.79 74	91.95 275	99.72 177	97.91 156	97.49 230	98.62 273
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		98.27 149	98.22 143	98.44 242	99.29 187	96.97 264	99.39 189	99.47 131	98.97 22	99.11 178	99.61 155	92.71 251	99.69 192	97.78 167	97.63 215	98.67 248

ACMH	97.28 8	98.10 170	97.99 160	98.44 242	99.41 158	96.96 266	99.60 92	99.56 49	98.09 90	98.15 278	99.91 5	90.87 298	99.70 189	98.88 60	97.45 232	98.67 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v748		97.52 253	97.23 260	98.41 244	98.69 299	97.23 248	99.87 4	99.45 153	95.72 284	98.51 258	99.53 182	94.13 218	99.30 255	96.78 247	92.39 326	98.70 227

TESTMET0.1,1		97.55 250	97.27 259	98.40 245	98.93 262	96.53 279	98.67 324	97.61 348	96.96 209	98.64 251	99.28 259	88.63 322	99.45 219	97.30 210	99.38 111	99.21 162

LTVRE_ROB	97.16 12	98.02 185	97.90 167	98.40 245	99.23 197	96.80 272	99.70 44	99.60 35	97.12 191	98.18 277	99.70 113	91.73 286	99.72 177	98.39 121	97.45 232	98.68 237
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
HQP-MVS		98.02 185	97.90 167	98.37 247	99.19 204	96.83 269	98.98 294	99.39 184	98.24 72	98.66 244	99.40 224	92.47 266	99.64 200	97.19 215	97.58 220	98.64 264

EPMVS		97.82 216	97.65 208	98.35 248	98.88 271	95.98 292	99.49 146	94.71 357	97.57 149	99.26 141	99.48 202	92.46 269	99.71 183	97.87 159	99.08 131	99.35 154

CLD-MVS		98.16 163	98.10 148	98.33 249	99.29 187	96.82 271	98.75 320	99.44 162	97.83 124	99.13 173	99.55 172	92.92 243	99.67 194	98.32 130	97.69 214	98.48 299
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-w/o		98.00 189	97.89 171	98.32 250	99.35 171	96.20 290	99.01 288	98.90 289	96.42 248	98.38 265	99.00 284	95.26 159	99.72 177	96.06 266	98.61 159	99.03 180

ACMH+	97.24 10	97.92 202	97.78 186	98.32 250	99.46 148	96.68 276	99.56 114	99.54 63	98.41 63	97.79 294	99.87 20	90.18 306	99.66 196	98.05 149	97.18 245	98.62 273

CVMVSNet		98.57 131	98.67 111	98.30 252	99.35 171	95.59 297	99.50 137	99.55 56	98.60 51	99.39 107	99.83 38	94.48 205	99.45 219	98.75 80	98.56 165	99.85 8

GBi-Net		97.68 241	97.48 224	98.29 253	99.51 135	97.26 245	99.43 169	99.48 115	96.49 238	99.07 187	99.32 253	90.26 302	98.98 300	97.10 222	96.65 250	98.62 273

test1		97.68 241	97.48 224	98.29 253	99.51 135	97.26 245	99.43 169	99.48 115	96.49 238	99.07 187	99.32 253	90.26 302	98.98 300	97.10 222	96.65 250	98.62 273

FMVSNet1		96.84 275	96.36 276	98.29 253	99.32 182	97.26 245	99.43 169	99.48 115	95.11 290	98.55 257	99.32 253	83.95 345	98.98 300	95.81 271	96.26 260	98.62 273

v18		96.42 282	95.80 289	98.26 256	98.95 254	98.82 158	99.76 28	99.28 240	94.58 298	94.12 323	97.70 327	95.22 162	98.16 320	94.83 291	87.80 337	97.79 334

v16		96.39 284	95.76 290	98.26 256	98.96 252	98.81 160	99.76 28	99.28 240	94.57 299	94.10 324	97.70 327	95.04 168	98.16 320	94.70 293	87.77 338	97.80 329

V14		96.26 287	95.60 293	98.26 256	98.94 257	98.83 151	99.76 28	99.29 233	94.49 304	93.96 329	97.66 330	94.99 172	98.13 324	94.41 298	86.90 343	97.80 329

V9		96.25 288	95.58 294	98.26 256	98.94 257	98.83 151	99.75 35	99.29 233	94.45 306	93.96 329	97.62 332	94.94 174	98.14 323	94.40 299	86.87 344	97.81 327

v17		96.42 282	95.81 287	98.25 260	98.94 257	98.80 165	99.76 28	99.28 240	94.57 299	94.18 322	97.71 326	95.23 161	98.16 320	94.86 289	87.73 339	97.80 329

v15		96.28 286	95.62 292	98.25 260	98.94 257	98.83 151	99.76 28	99.29 233	94.52 303	94.02 327	97.61 333	95.02 169	98.13 324	94.53 295	86.92 342	97.80 329

v13		96.24 289	95.58 294	98.25 260	98.98 245	98.83 151	99.75 35	99.29 233	94.35 308	93.89 332	97.60 334	95.17 164	98.11 326	94.27 307	86.86 345	97.81 327

v12		96.24 289	95.58 294	98.23 263	98.96 252	98.81 160	99.76 28	99.29 233	94.42 307	93.85 333	97.60 334	95.12 165	98.09 327	94.32 304	86.85 346	97.80 329

EPNet_dtu		98.03 183	97.96 163	98.23 263	98.27 319	95.54 300	99.23 238	98.75 302	99.02 10	97.82 292	99.71 110	96.11 134	99.48 216	93.04 322	99.65 100	99.69 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		98.16 163	98.14 145	98.22 265	99.30 184	95.55 298	99.07 269	98.97 278	97.57 149	99.43 97	99.60 158	92.72 250	99.60 208	97.38 206	99.20 122	99.50 132

v11		96.23 291	95.57 297	98.21 266	98.93 262	98.83 151	99.72 41	99.29 233	94.29 309	94.05 326	97.64 331	94.88 181	98.04 328	92.89 323	88.43 335	97.77 335

XVG-ACMP-BASELINE		97.83 213	97.71 199	98.20 267	99.11 222	96.33 286	99.41 180	99.52 77	98.06 98	99.05 192	99.50 193	89.64 310	99.73 173	97.73 174	97.38 238	98.53 296

OurMVSNet-221017-0		97.88 205	97.77 190	98.19 268	98.71 297	96.53 279	99.88 1	99.00 275	97.79 129	98.78 229	99.94 3	91.68 287	99.35 242	97.21 213	96.99 248	98.69 232

PatchmatchNet		98.31 144	98.36 133	98.19 268	99.16 214	95.32 305	99.27 225	98.92 284	97.37 170	99.37 111	99.58 163	94.90 179	99.70 189	97.43 204	99.21 121	99.54 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs5		97.52 253	97.30 255	98.16 270	98.57 311	96.73 273	99.27 225	98.90 289	96.14 272	98.37 266	99.53 182	91.54 292	99.14 280	97.51 195	95.87 267	98.63 271

testgi		97.65 246	97.50 221	98.13 271	99.36 170	96.45 282	99.42 176	99.48 115	97.76 132	97.87 290	99.45 213	91.09 295	98.81 312	94.53 295	98.52 167	99.13 167

ITE_SJBPF				98.08 272	99.29 187	96.37 284		98.92 284	98.34 66	98.83 224	99.75 95	91.09 295	99.62 206	95.82 270	97.40 236	98.25 312

semantic-postprocess				98.06 273	99.57 126	96.36 285		99.49 105	97.18 185	98.71 235	99.72 108	92.70 253	99.14 280	97.44 203	95.86 268	98.67 248

SixPastTwentyTwo		97.50 257	97.33 252	98.03 274	98.65 303	96.23 289	99.77 25	98.68 317	97.14 188	97.90 289	99.93 4	90.45 300	99.18 279	97.00 228	96.43 256	98.67 248

tpm		97.67 244	97.55 214	98.03 274	99.02 238	95.01 312	99.43 169	98.54 325	96.44 246	99.12 176	99.34 247	91.83 281	99.60 208	97.75 172	96.46 255	99.48 135

IterMVS		97.83 213	97.77 190	98.02 276	99.58 124	96.27 288	99.02 284	99.48 115	97.22 183	98.71 235	99.70 113	92.75 247	99.13 283	97.46 201	96.00 265	98.67 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDA-MVSNet_test_wron		95.45 302	94.60 307	98.01 277	98.16 321	97.21 249	99.11 264	99.24 249	93.49 321	80.73 352	98.98 288	93.02 240	98.18 318	94.22 309	94.45 300	98.64 264

K. test v3		97.10 272	96.79 270	98.01 277	98.72 295	96.33 286	99.87 4	97.05 352	97.59 146	96.16 313	99.80 66	88.71 318	99.04 292	96.69 252	96.55 254	98.65 262

MVP-Stereo		97.81 217	97.75 196	97.99 279	97.53 328	96.60 278	98.96 299	98.85 293	97.22 183	97.23 300	99.36 240	95.28 156	99.46 218	95.51 278	99.78 75	97.92 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		95.42 303	94.57 308	97.97 280	89.83 353	96.11 291	99.48 151	98.75 302	96.74 222	96.68 308	99.88 15	88.65 321	99.71 183	98.37 124	82.74 349	98.09 314

PVSNet_0	94.43 19	96.09 296	95.47 298	97.94 281	99.31 183	94.34 321	97.81 347	99.70 15	97.12 191	97.46 296	98.75 304	89.71 309	99.79 150	97.69 180	81.69 350	99.68 85

DWT-MVSNet_test		97.53 252	97.40 241	97.93 282	99.03 237	94.86 314	99.57 107	98.63 319	96.59 235	98.36 267	98.79 301	89.32 312	99.74 166	98.14 139	98.16 195	99.20 163

MDA-MVSNet-bldmvs		94.96 307	93.98 312	97.92 283	98.24 320	97.27 244	99.15 254	99.33 219	93.80 316	80.09 353	99.03 283	88.31 326	97.86 334	93.49 316	94.36 302	98.62 273

YYNet1		95.36 304	94.51 309	97.92 283	97.89 323	97.10 251	99.10 266	99.23 250	93.26 324	80.77 351	99.04 282	92.81 246	98.02 329	94.30 305	94.18 306	98.64 264

tpmrst		98.33 142	98.48 129	97.90 285	99.16 214	94.78 315	99.31 214	99.11 262	97.27 177	99.45 93	99.59 160	95.33 154	99.84 122	98.48 115	98.61 159	99.09 172

ADS-MVSNet2		98.02 185	98.07 153	97.87 286	99.33 175	95.19 309	99.23 238	99.08 265	96.24 262	99.10 181	99.67 129	94.11 219	98.93 309	96.81 245	99.05 133	99.48 135

PatchFormer-LS_test		98.01 188	98.05 154	97.87 286	99.15 217	94.76 316	99.42 176	98.93 282	97.12 191	98.84 223	98.59 312	93.74 233	99.80 147	98.55 110	98.17 194	99.06 178

test_0402		96.64 276	96.24 277	97.85 288	98.85 278	96.43 283	99.44 164	99.26 246	93.52 320	96.98 306	99.52 187	88.52 323	99.20 278	92.58 327	97.50 227	97.93 324

tpmvs		97.98 190	98.02 156	97.84 289	99.04 235	94.73 317	99.31 214	99.20 253	96.10 278	98.76 231	99.42 217	94.94 174	99.81 143	96.97 231	98.45 170	98.97 187

TinyColmap		97.12 271	96.89 268	97.83 290	99.07 229	95.52 301	98.57 330	98.74 305	97.58 148	97.81 293	99.79 74	88.16 328	99.56 211	95.10 285	97.21 243	98.39 306

pmmvs6		96.53 279	96.09 280	97.82 291	98.69 299	95.47 302	99.37 196	99.47 131	93.46 322	97.41 297	99.78 80	87.06 334	99.33 246	96.92 236	92.70 324	98.65 262

EU-MVSNet		97.98 190	98.03 155	97.81 292	98.72 295	96.65 277	99.66 67	99.66 25	98.09 90	98.35 268	99.82 45	95.25 160	98.01 330	97.41 205	95.30 277	98.78 208

lessismore_v0				97.79 293	98.69 299	95.44 304		94.75 356		95.71 317	99.87 20	88.69 319	99.32 249	95.89 269	94.93 288	98.62 273

LP		97.04 273	96.80 269	97.77 294	98.90 267	95.23 307	98.97 297	99.06 270	94.02 312	98.09 280	99.41 220	93.88 226	98.82 311	90.46 331	98.42 172	99.26 160

USDC		97.34 265	97.20 261	97.75 295	99.07 229	95.20 308	98.51 333	99.04 272	97.99 108	98.31 270	99.86 23	89.02 314	99.55 213	95.67 276	97.36 239	98.49 298

tpm2		97.44 262	97.34 250	97.74 296	99.15 217	94.36 320	99.45 160	98.94 281	93.45 323	98.90 214	99.44 214	91.35 293	99.59 210	97.31 209	98.07 205	99.29 158

CostFormer		97.72 235	97.73 197	97.71 297	99.15 217	94.02 323	99.54 123	99.02 274	94.67 296	99.04 193	99.35 244	92.35 272	99.77 160	98.50 114	97.94 209	99.34 155

LF4IMVS		97.52 253	97.46 228	97.70 298	98.98 245	95.55 298	99.29 220	98.82 296	98.07 94	98.66 244	99.64 142	89.97 307	99.61 207	97.01 227	96.68 249	97.94 323

ppachtmachnet_test		97.49 259	97.45 229	97.61 299	98.62 306	95.24 306	98.80 315	99.46 141	96.11 274	98.22 274	99.62 151	96.45 125	98.97 307	93.77 312	95.97 266	98.61 282

dp		97.75 230	97.80 183	97.59 300	99.10 225	93.71 327	99.32 211	98.88 291	96.48 244	99.08 186	99.55 172	92.67 260	99.82 139	96.52 258	98.58 162	99.24 161

our_test_3		97.65 246	97.68 201	97.55 301	98.62 306	94.97 313	98.84 313	99.30 228	96.83 219	98.19 276	99.34 247	97.01 108	99.02 295	95.00 288	96.01 264	98.64 264

tpmp4_e23		97.34 265	97.29 256	97.52 302	99.25 196	93.73 325	99.58 101	99.19 256	94.00 313	98.20 275	99.41 220	90.74 299	99.74 166	97.13 221	98.07 205	99.07 177

MVS-HIRNet		95.75 299	95.16 303	97.51 303	99.30 184	93.69 328	98.88 310	95.78 354	85.09 347	98.78 229	92.65 350	91.29 294	99.37 235	94.85 290	99.85 53	99.46 142

tpm cat1		97.39 264	97.36 245	97.50 304	99.17 212	93.73 325	99.43 169	99.31 226	91.27 334	98.71 235	99.08 277	94.31 212	99.77 160	96.41 262	98.50 168	99.00 183

new_pmnet		96.38 285	96.03 281	97.41 305	98.13 322	95.16 311	99.05 275	99.20 253	93.94 314	97.39 298	98.79 301	91.61 291	99.04 292	90.43 332	95.77 269	98.05 316

UnsupCasMVSNet_eth		96.44 280	96.12 279	97.40 306	98.65 303	95.65 295	99.36 202	99.51 86	97.13 189	96.04 316	98.99 285	88.40 325	98.17 319	96.71 250	90.27 330	98.40 305

pmmvs-eth3d		95.34 305	94.73 306	97.15 307	95.53 340	95.94 293	99.35 206	99.10 263	95.13 289	93.55 334	97.54 337	88.15 329	97.91 332	94.58 294	89.69 333	97.61 337

FMVSNet5		96.43 281	96.19 278	97.15 307	99.11 222	95.89 294	99.32 211	99.52 77	94.47 305	98.34 269	99.07 278	87.54 331	97.07 340	92.61 326	95.72 270	98.47 300

DeepPCF-MVS	98.18 3	98.81 114	99.37 17	97.12 309	99.60 121	91.75 337	98.61 328	99.44 162	99.35 1	99.83 11	99.85 27	98.70 51	99.81 143	99.02 51	99.91 17	99.81 36

MS-PatchMatch		97.24 269	97.32 253	96.99 310	98.45 316	93.51 330	98.82 314	99.32 225	97.41 167	98.13 279	99.30 256	88.99 315	99.56 211	95.68 275	99.80 71	97.90 326

RPSCF		98.22 154	98.62 120	96.99 310	99.82 29	91.58 338	99.72 41	99.44 162	96.61 231	99.66 49	99.89 10	95.92 139	99.82 139	97.46 201	99.10 129	99.57 115

DSMNet-mixed		97.25 268	97.35 247	96.95 312	97.84 324	93.61 329	99.57 107	96.63 353	96.13 273	98.87 217	98.61 311	94.59 200	97.70 337	95.08 286	98.86 150	99.55 116

MIMVSNet1		95.51 301	95.04 304	96.92 313	97.38 330	95.60 296	99.52 128	99.50 100	93.65 318	96.97 307	99.17 270	85.28 340	96.56 344	88.36 337	95.55 274	98.60 289

LCM-MVSNet-Re		97.83 213	98.15 144	96.87 314	99.30 184	92.25 336	99.59 94	98.26 329	97.43 163	96.20 312	99.13 273	96.27 131	98.73 314	98.17 136	98.99 137	99.64 99

EG-PatchMatch MVS		95.97 297	95.69 291	96.81 315	97.78 325	92.79 333	99.16 251	98.93 282	96.16 269	94.08 325	99.22 267	82.72 347	99.47 217	95.67 276	97.50 227	98.17 313

Anonymous20231206		96.22 292	96.03 281	96.79 316	97.31 333	94.14 322	99.63 81	99.08 265	96.17 268	97.04 304	99.06 280	93.94 224	97.76 336	86.96 342	95.06 283	98.47 300

test20.03		96.12 295	95.96 284	96.63 317	97.44 329	95.45 303	99.51 132	99.38 190	96.55 236	96.16 313	99.25 263	93.76 231	96.17 345	87.35 341	94.22 305	98.27 310

pmmvs3		94.09 314	93.25 316	96.60 318	94.76 343	94.49 318	98.92 306	98.18 333	89.66 339	96.48 310	98.06 321	86.28 335	97.33 339	89.68 334	87.20 341	97.97 322

UnsupCasMVSNet_bld		93.53 316	92.51 318	96.58 319	97.38 330	93.82 324	98.24 341	99.48 115	91.10 336	93.10 336	96.66 343	74.89 350	98.37 317	94.03 311	87.71 340	97.56 339

OpenMVS_ROB	92.34 20	94.38 312	93.70 313	96.41 320	97.38 330	93.17 331	99.06 273	98.75 302	86.58 345	94.84 321	98.26 320	81.53 349	99.32 249	89.01 335	97.87 211	96.76 341

Patchmatch-RL test		95.84 298	95.81 287	95.95 321	95.61 338	90.57 339	98.24 341	98.39 326	95.10 291	95.20 318	98.67 306	94.78 187	97.77 335	96.28 264	90.02 331	99.51 129

new-patchmatchnet		94.48 310	94.08 311	95.67 322	95.08 342	92.41 334	99.18 249	99.28 240	94.55 302	93.49 335	97.37 340	87.86 330	97.01 341	91.57 328	88.36 336	97.61 337

PM-MVS		92.96 317	92.23 319	95.14 323	95.61 338	89.98 341	99.37 196	98.21 331	94.80 294	95.04 320	97.69 329	65.06 355	97.90 333	94.30 305	89.98 332	97.54 340

testpf		95.66 300	96.02 283	94.58 324	98.35 318	92.32 335	97.25 352	97.91 337	92.83 326	97.03 305	98.99 285	88.69 319	98.61 315	95.72 273	97.40 236	92.80 350

Gipuma		90.99 321	90.15 322	93.51 325	98.73 293	90.12 340	93.98 356	99.45 153	79.32 350	92.28 338	94.91 347	69.61 352	97.98 331	87.42 339	95.67 271	92.45 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				93.34 326	99.29 187	82.27 351		99.22 251	85.15 346	96.33 311	99.05 281	90.97 297	99.73 173	93.57 314	97.77 213	98.01 320

ambc				93.06 327	92.68 348	82.36 350	98.47 334	98.73 314		95.09 319	97.41 338	55.55 358	99.10 288	96.42 261	91.32 328	97.71 336

test2356		94.07 315	94.46 310	92.89 328	95.18 341	86.13 344	97.60 350	99.06 270	93.61 319	96.15 315	98.28 319	85.60 339	93.95 351	86.68 344	98.00 207	98.59 290

1111		92.30 319	92.21 320	92.55 329	93.30 345	86.27 342	99.15 254	98.74 305	91.94 330	90.85 342	97.82 324	84.18 343	95.21 347	79.65 350	94.27 304	96.19 344

testus		94.61 309	95.30 302	92.54 330	96.44 336	84.18 346	98.36 336	99.03 273	94.18 310	96.49 309	98.57 313	88.74 317	95.09 349	87.41 340	98.45 170	98.36 309

N_pmnet		94.95 308	95.83 286	92.31 331	98.47 315	79.33 354	99.12 258	92.81 363	93.87 315	97.68 295	99.13 273	93.87 227	99.01 297	91.38 329	96.19 261	98.59 290

CMPMVS	69.68 23	94.13 313	94.90 305	91.84 332	97.24 334	80.01 353	98.52 332	99.48 115	89.01 342	91.99 339	99.67 129	85.67 338	99.13 283	95.44 279	97.03 247	96.39 343
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
no-one		83.04 327	80.12 329	91.79 333	89.44 354	85.65 345	99.32 211	98.32 327	89.06 341	79.79 355	89.16 356	44.86 362	96.67 343	84.33 347	46.78 358	93.05 349

test1235678		92.91 318	93.30 315	91.71 334	93.14 347	83.01 348	98.75 320	98.58 322	92.80 327	92.45 337	97.91 323	88.51 324	93.54 352	82.26 348	95.35 276	98.59 290

LCM-MVSNet		86.80 324	85.22 327	91.53 335	87.81 355	80.96 352	98.23 343	98.99 276	71.05 353	90.13 344	96.51 344	48.45 361	96.88 342	90.51 330	85.30 348	96.76 341

PMMVS2		86.87 323	85.37 326	91.35 336	90.21 352	83.80 347	98.89 309	97.45 350	83.13 349	91.67 341	95.03 346	48.49 360	94.70 350	85.86 345	77.62 351	95.54 346

test12356		91.74 320	92.19 321	90.37 337	91.22 349	82.41 349	98.61 328	98.28 328	90.66 338	91.82 340	97.92 322	84.90 341	92.61 353	81.64 349	94.66 295	96.09 345

testmv		87.91 322	87.80 323	88.24 338	87.68 356	77.50 356	99.07 269	97.66 347	89.27 340	86.47 346	96.22 345	68.35 353	92.49 355	76.63 354	88.82 334	94.72 348

tmp_tt		82.80 328	81.52 328	86.66 339	66.61 364	68.44 362	92.79 358	97.92 335	68.96 355	80.04 354	99.85 27	85.77 337	96.15 346	97.86 160	43.89 359	95.39 347

MVE	76.82 21	76.91 333	74.31 335	84.70 340	85.38 360	76.05 359	96.88 353	93.17 361	67.39 356	71.28 357	89.01 357	21.66 370	87.69 359	71.74 357	72.29 353	90.35 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		79.43 331	77.68 332	84.67 341	86.18 358	71.69 361	96.50 354	93.68 359	75.17 351	71.33 356	91.18 353	32.18 365	90.62 357	78.57 353	74.34 352	91.71 354

ANet_high		77.30 332	74.86 334	84.62 342	75.88 362	77.61 355	97.63 349	93.15 362	88.81 343	64.27 358	89.29 355	36.51 363	83.93 362	75.89 355	52.31 357	92.33 353

wuykxyi23d		74.42 335	71.19 336	84.14 343	76.16 361	74.29 360	96.00 355	92.57 364	69.57 354	63.84 359	87.49 358	21.98 367	88.86 358	75.56 356	57.50 356	89.26 357

E-PMN		80.61 329	79.88 330	82.81 344	90.75 351	76.38 358	97.69 348	95.76 355	66.44 357	83.52 348	92.25 351	62.54 357	87.16 360	68.53 358	61.40 354	84.89 359

FPMVS		84.93 325	85.65 325	82.75 345	86.77 357	63.39 363	98.35 338	98.92 284	74.11 352	83.39 349	98.98 288	50.85 359	92.40 356	84.54 346	94.97 285	92.46 351

EMVS		80.02 330	79.22 331	82.43 346	91.19 350	76.40 357	97.55 351	92.49 365	66.36 358	83.01 350	91.27 352	64.63 356	85.79 361	65.82 359	60.65 355	85.08 358

PMVS	70.75 22	75.98 334	74.97 333	79.01 347	70.98 363	55.18 364	93.37 357	98.21 331	65.08 359	61.78 360	93.83 349	21.74 369	92.53 354	78.59 352	91.12 329	89.34 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		83.42 326	86.17 324	75.15 348	93.30 345	86.27 342	99.15 254	98.74 305	91.94 330	90.85 342	97.82 324	84.18 343	95.21 347	79.65 350	39.90 360	43.98 361

wuyk23d		40.18 337	41.29 340	36.84 349	86.18 358	49.12 365	79.73 359	22.81 367	27.64 360	25.46 363	28.45 364	21.98 367	48.89 363	55.80 360	23.56 363	12.51 363

pcd1.5k->3k		40.85 336	43.49 338	32.93 350	98.95 254	0.00 368	0.00 360	99.53 73	0.00 363	0.00 364	0.27 365	95.32 155	0.00 366	0.00 363	97.30 240	98.80 206

test123		39.01 339	42.50 339	28.53 351	39.17 365	20.91 366	98.75 320	19.17 368	19.83 362	38.57 361	66.67 360	33.16 364	15.42 364	37.50 362	29.66 362	49.26 360

testmvs		39.17 338	43.78 337	25.37 352	36.04 366	16.84 367	98.36 336	26.56 366	20.06 361	38.51 362	67.32 359	29.64 366	15.30 365	37.59 361	39.90 360	43.98 361

cdsmvs_eth3d_5k		24.64 340	32.85 341	0.00 353	0.00 367	0.00 368	0.00 360	99.51 86	0.00 363	0.00 364	99.56 169	96.58 121	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		8.27 342	11.03 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.27 365	99.01 12	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.02 343	0.03 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.27 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.02 343	0.03 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.27 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.02 343	0.03 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.27 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.02 343	0.03 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.27 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		8.30 341	11.06 342	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	99.58 163	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.02 343	0.03 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.27 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																99.52 124

test_part3							99.37 196		97.97 109		99.78 80		99.95 33	97.15 219

test_part2					99.81 32	99.83 7				99.77 24

test_part1								99.48 115				98.96 21			99.84 58	99.83 23

sam_mvs1												94.86 182				99.52 124

sam_mvs												94.72 195

MTGPA								99.47 131

test_post1							99.23 238				65.14 362	94.18 217	99.71 183	97.58 186

test_post											65.99 361	94.65 199	99.73 173

patchmatchnet-post											98.70 305	94.79 186	99.74 166

MTMP							99.54 123	98.88 291

gm-plane-assit					98.54 313	92.96 332			94.65 297		99.15 271		99.64 200	97.56 190

test9_res														97.49 197	99.72 86	99.75 56

TEST9					99.67 94	99.65 40	99.05 275	99.41 174	96.22 264	98.95 207	99.49 196	98.77 42	99.91 76

test_8					99.67 94	99.61 45	99.03 281	99.41 174	96.28 257	98.93 210	99.48 202	98.76 44	99.91 76

agg_prior2														97.21 213	99.73 85	99.75 56

agg_prior					99.67 94	99.62 43		99.40 181		98.87 217			99.91 76

test_prior4						99.56 52	98.99 290

test_prior2							98.96 299		98.34 66	99.01 196	99.52 187	98.68 52		97.96 152	99.74 82

旧先验2							98.96 299		96.70 225	99.47 90			99.94 42	98.19 134

新几何2							99.01 288

旧先验1					99.74 67	99.59 49		99.54 63			99.69 119	98.47 61			99.68 96	99.73 66

无先验							98.99 290	99.51 86	96.89 215				99.93 57	97.53 193		99.72 72

原ACMM2							98.95 303

test222					99.75 56	99.49 64	98.91 308	99.49 105	96.42 248	99.34 120	99.65 135	98.28 74			99.69 93	99.72 72

testdata2													99.95 33	96.67 253

segment_acmp												98.96 21

testdata1							98.85 312		98.32 69

plane_prior7					99.29 187	97.03 259

plane_prior6					99.27 192	96.98 263						92.71 251

plane_prior5								99.47 131					99.69 192	97.78 167	97.63 215	98.67 248

plane_prior4											99.61 155

plane_prior3						97.00 261			98.69 46	99.11 178

plane_prior2							99.39 189		98.97 22

plane_prior1					99.26 194

plane_prior						96.97 264	99.21 245		98.45 59						97.60 218

n2								0.00 369

nn								0.00 369

door-mid								98.05 334

test11								99.35 203

door								97.92 335

HQP5-MVS						96.83 269

HQP-NCC					99.19 204		98.98 294		98.24 72	98.66 244

ACMP_Plane					99.19 204		98.98 294		98.24 72	98.66 244

BP-MVS														97.19 215

HQP4-MVS										98.66 244			99.64 200			98.64 264

HQP3-MVS								99.39 184							97.58 220

HQP2-MVS												92.47 266

NP-MVS					99.23 197	96.92 267					99.40 224

MDTV_nov1_ep13_2view						95.18 310	99.35 206		96.84 218	99.58 66		95.19 163		97.82 163		99.46 142

MDTV_nov1_ep13			98.32 137		99.11 222	94.44 319	99.27 225	98.74 305	97.51 155	99.40 106	99.62 151	94.78 187	99.76 164	97.59 185	98.81 155

ACMMP++_ref															97.19 244

ACMMP++															97.43 235

Test By Simon												98.75 47

High-resolution multi-view benchmark