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.

SMA-MVS		99.71 32	99.62 44	99.98 16	99.99 45	99.95 19	100.00 1	99.42 121	97.61 123	100.00 1	100.00 1	99.55 24	100.00 1	100.00 1	100.00 1	100.00 1

tfpn1000		99.47 69	99.34 72	99.87 81	99.89 96	99.80 80	100.00 1	99.72 61	97.83 98	99.96 88	99.98 141	99.94 4	99.82 152	98.87 161	97.87 176	100.00 1

tfpn_ndepth		99.52 62	99.39 64	99.89 75	99.90 94	99.83 72	100.00 1	99.72 61	97.95 89	99.96 88	99.98 141	99.94 4	99.85 147	99.17 149	97.91 173	100.00 1

test_part1								99.42 121				99.53 26			100.00 1	100.00 1

ESAPD		99.76 15	99.69 18	100.00 1	100.00 1	99.99 1	100.00 1	99.42 121	98.23 64	100.00 1	100.00 1	99.53 26	100.00 1	100.00 1	100.00 1	100.00 1

CHOSEN 280x420		99.85 3	99.87 1	99.80 95	99.99 45	99.97 8	99.97 198	99.98 13	98.96 21	100.00 1	100.00 1	99.96 3	99.42 207	100.00 1	100.00 1	100.00 1

HSP-MVS		99.81 9	99.77 8	99.94 59	100.00 1	99.86 67	100.00 1	99.42 121	98.87 29	100.00 1	100.00 1	99.65 18	99.96 114	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 7	99.77 8	99.99 5	100.00 1	99.96 10	100.00 1	99.43 115	99.05 12	100.00 1	100.00 1	99.45 43	99.99 82	100.00 1	100.00 1	100.00 1

ACMMP_Plus		99.67 46	99.57 51	99.97 24	99.98 72	99.92 36	100.00 1	99.42 121	97.83 98	100.00 1	100.00 1	98.89 99	100.00 1	99.98 59	100.00 1	100.00 1

zzz-MVS		99.68 43	99.59 47	99.97 24	99.99 45	99.91 39	100.00 1	99.42 121	98.32 61	99.94 109	100.00 1	98.65 109	100.00 1	99.96 66	100.00 1	100.00 1

MTAPA		99.68 43	99.59 47	99.97 24	99.99 45	99.91 39	100.00 1	99.42 121	98.32 61	99.94 109	100.00 1	98.65 109	100.00 1	99.96 66	100.00 1	100.00 1

test9_res														100.00 1	100.00 1	100.00 1

train_agg		99.71 32	99.63 39	99.97 24	100.00 1	99.95 19	100.00 1	99.42 121	97.70 109	100.00 1	100.00 1	99.51 31	99.97 99	100.00 1	100.00 1	100.00 1

agg_prior3		99.71 32	99.63 39	99.97 24	100.00 1	99.95 19	100.00 1	99.42 121	97.71 108	100.00 1	100.00 1	99.51 31	99.97 99	100.00 1	100.00 1	100.00 1

agg_prior1		99.71 32	99.63 39	99.95 47	100.00 1	99.88 59	100.00 1	99.42 121	97.72 106	100.00 1	100.00 1	99.51 31	99.97 99	100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 23	99.67 27	99.96 36	100.00 1	99.89 49	100.00 1	99.76 46	97.95 89	100.00 1	100.00 1	99.31 55	100.00 1	99.99 45	100.00 1	100.00 1

region2R		99.72 29	99.64 35	99.97 24	100.00 1	99.90 45	100.00 1	99.74 53	97.86 97	100.00 1	100.00 1	99.19 73	100.00 1	99.99 45	100.00 1	100.00 1

#test#		99.75 18	99.68 22	99.96 36	100.00 1	99.89 49	100.00 1	99.76 46	98.07 77	100.00 1	100.00 1	99.31 55	100.00 1	99.99 45	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 40	99.63 39	99.87 81	99.99 45	99.64 105	99.95 207	99.44 106	98.35 59	100.00 1	100.00 1	98.98 88	99.97 99	99.98 59	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 37	99.64 35	99.87 81	100.00 1	99.64 105	99.98 192	99.44 106	98.35 59	99.99 76	100.00 1	99.04 83	99.96 114	99.98 59	100.00 1	100.00 1

Regformer-3		99.73 26	99.65 31	99.96 36	100.00 1	99.89 49	100.00 1	99.44 106	98.40 52	100.00 1	100.00 1	99.03 86	99.97 99	99.99 45	100.00 1	100.00 1

Regformer-4		99.73 26	99.65 31	99.95 47	100.00 1	99.89 49	100.00 1	99.44 106	98.40 52	100.00 1	100.00 1	99.03 86	99.97 99	99.99 45	100.00 1	100.00 1

Regformer-1		99.75 18	99.68 22	99.98 16	100.00 1	99.94 28	100.00 1	99.44 106	98.43 49	100.00 1	100.00 1	99.23 69	99.99 82	99.99 45	100.00 1	100.00 1

Regformer-2		99.75 18	99.68 22	99.97 24	100.00 1	99.94 28	100.00 1	99.44 106	98.42 51	100.00 1	100.00 1	99.22 70	99.99 82	99.99 45	100.00 1	100.00 1

XVS		99.79 12	99.73 14	99.98 16	100.00 1	99.94 28	100.00 1	99.75 50	98.67 40	100.00 1	100.00 1	99.16 76	100.00 1	100.00 1	100.00 1	100.00 1

test_prior3		99.81 9	99.78 6	99.90 72	100.00 1	99.75 85	100.00 1	99.73 55	98.82 33	100.00 1	100.00 1	99.47 40	99.97 99	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 204	96.06 231	99.98 16	100.00 1	99.94 28	100.00 1	99.75 50	98.67 40	100.00 1	66.97 356	99.16 76	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 72	100.00 1	99.75 85		99.73 55					99.97 99			100.00 1

新几何1				99.99 5	100.00 1	99.96 10		99.81 42	97.89 93	100.00 1	100.00 1	99.20 72	100.00 1	97.91 207	100.00 1	100.00 1

旧先验1					99.99 45	99.88 59		99.82 41			100.00 1	99.27 66			100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 84				100.00 1	99.33 140		100.00 1

原ACMM1				99.93 62	100.00 1	99.80 80		99.66 71	98.18 66	100.00 1	100.00 1	99.43 45	100.00 1	99.50 127	100.00 1	100.00 1

test222					99.99 45	99.90 45	100.00 1	99.69 70	97.66 112	100.00 1	100.00 1	99.30 61			100.00 1	100.00 1

testdata				99.66 116	99.99 45	98.97 157		99.73 55	97.96 88	100.00 1	100.00 1	99.42 46	100.00 1	99.28 144	100.00 1	100.00 1

1314		99.38 74	99.19 84	99.96 36	98.88 231	99.89 49	99.24 303	99.93 32	98.88 27	98.79 203	100.00 1	97.02 142	100.00 1	100.00 1	100.00 1	100.00 1

1121		99.63 52	99.51 56	99.99 5	99.99 45	99.96 10	99.24 303	99.74 53	97.89 93	100.00 1	100.00 1	99.39 49	100.00 1	99.33 140	100.00 1	100.00 1

MVS		99.22 100	98.96 106	99.98 16	99.00 220	99.95 19	99.24 303	99.94 18	98.14 70	98.88 196	100.00 1	95.63 179	100.00 1	99.85 83	100.00 1	100.00 1

SD-MVS		99.81 9	99.75 12	99.99 5	99.99 45	99.96 10	100.00 1	99.42 121	99.01 18	100.00 1	100.00 1	99.33 51	100.00 1	100.00 1	100.00 1	100.00 1

MSLP-MVS++		99.89 1	99.85 2	99.99 5	100.00 1	99.96 10	100.00 1	99.95 15	99.11 6	100.00 1	100.00 1	99.60 19	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 6	99.99 5	100.00 1	99.98 6	100.00 1	99.44 106	99.06 10	100.00 1	100.00 1	99.56 23	99.99 82	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 48	99.55 55	99.97 24	99.99 45	99.91 39	100.00 1	99.48 82	97.54 130	100.00 1	100.00 1	98.97 89	99.99 82	99.98 59	100.00 1	100.00 1

ACMMPR		99.74 23	99.67 27	99.96 36	100.00 1	99.89 49	100.00 1	99.76 46	97.95 89	100.00 1	100.00 1	99.29 62	100.00 1	99.99 45	100.00 1	100.00 1

MP-MVS		99.61 55	99.49 59	99.98 16	99.99 45	99.94 28	100.00 1	99.42 121	97.82 100	99.99 76	100.00 1	98.20 119	100.00 1	99.99 45	100.00 1	100.00 1

PGM-MVS		99.69 40	99.61 45	99.95 47	99.99 45	99.85 70	100.00 1	99.58 73	97.69 111	100.00 1	100.00 1	99.44 44	100.00 1	99.79 91	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 1	100.00 1	99.73 55	99.19 5	100.00 1	100.00 1	99.31 55	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 26	99.64 35	99.99 5	100.00 1	99.97 8	100.00 1	99.42 121	98.02 80	100.00 1	100.00 1	99.32 54	99.99 82	100.00 1	100.00 1	100.00 1

test12				99.95 47	99.99 45	99.89 49		99.42 121		100.00 1		99.24 68	99.97 99		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 55	99.69 18	99.35 146	99.99 45	98.06 211	100.00 1	99.36 182	99.83 2	100.00 1	100.00 1	98.95 92	99.99 82	100.00 1	99.11 124	100.00 1

abl_6		99.53 61	99.40 62	99.92 68	100.00 1	99.76 83	100.00 1	99.42 121	97.21 149	100.00 1	100.00 1	98.12 121	100.00 1	99.82 90	100.00 1	100.00 1

mPP-MVS		99.69 40	99.60 46	99.97 24	100.00 1	99.91 39	100.00 1	99.42 121	97.91 92	100.00 1	100.00 1	99.04 83	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS_fast		99.60 58	99.49 59	99.91 69	99.99 45	99.78 82	100.00 1	99.42 121	97.09 157	100.00 1	100.00 1	98.95 92	99.96 114	99.98 59	100.00 1	100.00 1

HPM-MVS		99.59 59	99.50 57	99.89 75	100.00 1	99.70 100	100.00 1	99.42 121	97.46 133	100.00 1	100.00 1	98.60 111	99.96 114	99.99 45	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPNet_dtu		98.53 154	98.23 162	99.43 138	99.92 91	99.01 153	99.96 201	99.47 83	98.80 35	99.96 88	99.96 160	98.56 113	99.30 214	87.78 320	99.68 116	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		99.00 121	98.91 117	99.25 155	99.90 94	97.79 227	100.00 1	99.99 10	98.79 36	98.28 236	100.00 1	93.63 201	99.95 120	99.66 109	99.95 102	100.00 1

APD-MVS		99.68 43	99.58 49	99.97 24	99.99 45	99.96 10	100.00 1	99.42 121	97.53 131	100.00 1	100.00 1	99.27 66	99.97 99	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 1	100.00 1	99.77 45	99.07 8	100.00 1	100.00 1	99.39 49	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 1	100.00 1	99.71 63	99.07 8	100.00 1	100.00 1	99.59 21	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 73	99.25 75	99.81 92	99.97 76	99.48 118	100.00 1	99.42 121	95.53 222	100.00 1	100.00 1	98.37 118	99.95 120	99.97 64	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 47	100.00 1	99.84 71	100.00 1	99.42 121	97.77 103	100.00 1	100.00 1	99.07 80	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 13	99.71 16	99.98 16	99.76 126	99.95 19	100.00 1	99.42 121	98.69 38	100.00 1	100.00 1	99.52 29	99.99 82	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.62 53	99.56 54	99.82 89	99.92 91	99.45 120	100.00 1	99.78 44	98.92 25	99.73 143	100.00 1	97.70 129	100.00 1	99.93 73	100.00 1	100.00 1
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
CPTT-MVS		99.49 66	99.38 66	99.85 85	100.00 1	99.54 109	100.00 1	99.42 121	97.58 128	99.98 81	100.00 1	97.43 136	100.00 1	99.99 45	100.00 1	100.00 1

DP-MVS Recon		99.76 15	99.69 18	99.98 16	100.00 1	99.95 19	100.00 1	99.52 76	97.99 82	99.99 76	100.00 1	99.72 15	100.00 1	99.96 66	100.00 1	100.00 1

MVS_111021_LR		99.70 37	99.65 31	99.88 80	99.96 81	99.70 100	100.00 1	99.97 14	98.96 21	100.00 1	100.00 1	97.93 125	99.95 120	99.99 45	100.00 1	100.00 1

DP-MVS		98.86 134	98.54 148	99.81 92	99.97 76	99.45 120	99.52 283	99.40 155	94.35 250	98.36 230	100.00 1	96.13 169	99.97 99	99.12 152	100.00 1	100.00 1

QAPM		98.99 123	98.66 136	99.96 36	99.01 216	99.87 62	99.88 224	99.93 32	97.99 82	98.68 210	100.00 1	93.17 207	100.00 1	99.32 142	100.00 1	100.00 1

HyFIR lowres test		99.32 80	99.24 77	99.58 128	99.95 82	99.26 133	100.00 1	99.99 10	96.72 177	99.29 174	99.91 180	99.49 37	99.47 202	99.74 99	98.08 167	100.00 1

PAPM_NR		99.74 23	99.66 30	99.99 5	100.00 1	99.96 10	100.00 1	99.47 83	97.87 96	100.00 1	100.00 1	99.60 19	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 15	99.68 22	99.99 5	100.00 1	99.96 10	100.00 1	99.47 83	98.16 67	100.00 1	100.00 1	99.51 31	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 187	96.95 194	99.33 148	99.67 136	98.10 208	100.00 1	99.47 83	97.42 137	99.26 175	99.69 212	98.83 101	99.89 139	99.43 129	78.77 331	100.00 1
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
MVS_111021_HR		99.71 32	99.63 39	99.93 62	99.95 82	99.83 72	100.00 1	100.00 1	98.89 26	100.00 1	100.00 1	97.85 126	99.95 120	100.00 1	100.00 1	100.00 1

CSCG		99.28 90	99.35 70	99.05 164	99.99 45	97.15 248	100.00 1	99.47 83	97.44 135	99.42 167	100.00 1	97.83 127	100.00 1	99.99 45	100.00 1	100.00 1

API-MVS		99.72 29	99.70 17	99.79 99	99.97 76	99.37 126	99.96 201	99.94 18	98.48 47	100.00 1	100.00 1	98.92 96	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 13	99.76 10	99.85 85	99.01 216	99.95 19	100.00 1	99.75 50	99.37 3	99.99 76	100.00 1	99.76 14	99.60 174	100.00 1	100.00 1	100.00 1

ACMMP		99.65 48	99.57 51	99.89 75	99.99 45	99.66 103	99.75 251	99.73 55	98.16 67	99.75 142	100.00 1	98.90 98	100.00 1	99.96 66	99.88 108	100.00 1
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
CNLPA		99.72 29	99.65 31	99.91 69	99.97 76	99.72 95	100.00 1	99.47 83	98.43 49	99.88 121	100.00 1	99.14 78	100.00 1	99.97 64	100.00 1	100.00 1

PHI-MVS		99.50 64	99.39 64	99.82 89	100.00 1	99.45 120	100.00 1	99.94 18	96.38 199	100.00 1	100.00 1	98.18 120	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 88	99.25 75	99.44 136	100.00 1	98.32 186	100.00 1	99.86 39	98.04 79	100.00 1	100.00 1	96.10 170	100.00 1	99.55 119	99.73 115	100.00 1

PVSNet_0	93.57 19	96.41 233	95.74 245	98.41 194	99.84 98	95.22 272	100.00 1	100.00 1	98.08 76	97.55 264	99.78 201	84.40 305	100.00 1	100.00 1	81.99 323	100.00 1

DeepPCF-MVS	98.03 4	98.54 153	99.72 15	94.98 303	99.99 45	84.94 329	100.00 1	99.42 121	99.98 1	100.00 1	100.00 1	98.11 122	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 18	99.67 27	99.99 5	99.99 45	99.96 10	99.73 256	99.52 76	99.06 10	100.00 1	100.00 1	98.80 104	100.00 1	99.95 71	100.00 1	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
MG-MVS		99.75 18	99.68 22	99.97 24	100.00 1	99.91 39	99.98 192	99.47 83	99.09 7	100.00 1	100.00 1	98.59 112	100.00 1	99.95 71	100.00 1	100.00 1

AdaColmap		99.44 71	99.26 74	99.95 47	100.00 1	99.86 67	99.70 262	99.99 10	98.53 46	99.90 117	100.00 1	95.34 181	100.00 1	99.92 74	100.00 1	100.00 1

MAR-MVS		99.49 66	99.36 68	99.89 75	99.97 76	99.66 103	99.74 252	99.95 15	97.89 93	100.00 1	100.00 1	96.71 160	100.00 1	100.00 1	100.00 1	100.00 1
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
3Dnovator+	95.58 15	99.03 115	98.71 134	99.96 36	98.99 223	99.89 49	100.00 1	99.51 80	98.96 21	98.32 233	100.00 1	92.78 211	100.00 1	99.87 80	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 112	98.76 130	99.96 36	98.86 234	99.90 45	99.98 192	99.93 32	98.95 24	98.49 226	100.00 1	92.91 210	100.00 1	99.71 101	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 182	97.37 187	98.45 192	99.94 86	95.70 270	100.00 1	99.40 155	97.65 119	99.53 161	100.00 1	99.31 55	99.66 173	80.48 332	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MP-MVS-pluss		99.61 55	99.50 57	99.97 24	99.98 72	99.92 36	100.00 1	99.42 121	97.53 131	99.77 139	100.00 1	98.77 105	100.00 1	99.99 45	100.00 1	99.99 89
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS++		99.82 7	99.76 10	99.99 5	99.99 45	99.98 6	100.00 1	99.83 40	98.88 27	99.96 88	100.00 1	99.21 71	100.00 1	100.00 1	100.00 1	99.99 89

thres100view900		99.25 95	99.01 99	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.59 113	97.85 178	99.98 91

tfpn200view9		99.26 93	99.03 97	99.96 36	99.81 100	99.89 49	100.00 1	99.94 18	97.23 146	99.83 125	99.96 160	97.04 139	100.00 1	99.59 113	97.85 178	99.98 91

thres200		99.27 91	99.04 96	99.96 36	99.81 100	99.90 45	100.00 1	99.94 18	97.31 145	99.83 125	99.96 160	97.04 139	100.00 1	99.62 112	97.88 175	99.98 91

LCM-MVSNet-Re		96.52 228	97.21 191	94.44 306	99.27 199	85.80 328	99.85 228	96.61 346	95.98 208	92.75 309	98.48 301	93.97 198	97.55 308	99.58 117	98.43 143	99.98 91

JIA-IIPM		97.09 200	96.34 215	99.36 145	98.88 231	98.59 170	99.81 237	99.43 115	84.81 331	99.96 88	90.34 343	98.55 114	99.52 196	97.00 230	98.28 157	99.98 91

tfpn111		99.24 98	99.00 101	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.54 122	97.77 186	99.97 96

conf0.01		99.32 80	99.16 86	99.80 95	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.97 96

conf0.002		99.32 80	99.16 86	99.80 95	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.97 96

conf200view11		99.25 95	99.01 99	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.59 113	97.85 178	99.97 96

view600		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

view800		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

conf0.05thres1000		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

tfpn		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

thres600view7		99.24 98	99.00 101	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.54 122	97.77 186	99.97 96

thres400		99.26 93	99.03 97	99.95 47	99.81 100	99.89 49	100.00 1	99.94 18	97.23 146	99.83 125	99.96 160	97.04 139	100.00 1	99.59 113	97.85 178	99.97 96

OMC-MVS		99.27 91	99.38 66	98.96 171	99.95 82	97.06 252	100.00 1	99.40 155	98.83 31	99.88 121	100.00 1	97.01 143	99.86 143	99.47 128	99.84 111	99.97 96

CANet		99.40 72	99.24 77	99.89 75	99.99 45	99.76 83	100.00 1	99.73 55	98.40 52	99.78 138	100.00 1	95.28 182	99.96 114	100.00 1	99.99 88	99.96 107

GG-mvs-BLEND				99.59 124	99.54 163	99.49 114	99.17 317	99.52 76		99.96 88	99.68 217	100.00 1	99.33 213	99.71 101	99.99 88	99.96 107

gg-mvs-nofinetune		96.95 208	96.10 229	99.50 134	99.41 185	99.36 127	99.07 326	99.52 76	83.69 334	99.96 88	83.60 351	100.00 1	99.20 216	99.68 105	99.99 88	99.96 107

VNet		99.04 114	98.75 131	99.90 72	99.81 100	99.75 85	99.50 285	99.47 83	98.36 58	100.00 1	99.99 140	94.66 192	100.00 1	99.90 76	97.09 207	99.96 107

BH-w/o		98.82 137	98.81 126	98.88 176	99.62 147	96.71 259	100.00 1	99.28 219	97.09 157	98.81 202	100.00 1	94.91 190	99.96 114	99.54 122	100.00 1	99.96 107

MVS_0304		98.98 125	98.66 136	99.94 59	99.95 82	99.86 67	100.00 1	99.29 211	98.38 55	99.81 136	100.00 1	90.60 249	100.00 1	100.00 1	99.91 105	99.95 112

Patchmatch-test		97.83 178	97.42 184	99.06 162	99.08 207	97.66 231	98.66 333	99.21 242	93.65 271	98.25 239	99.58 234	99.47 40	99.57 179	90.25 309	98.59 134	99.95 112

HY-MVS	96.53 9	99.50 64	99.35 70	99.96 36	99.81 100	99.93 33	99.64 273	100.00 1	97.97 86	99.84 123	99.85 188	98.94 94	99.99 82	99.86 81	98.23 159	99.95 112

PatchMatch-RL		99.02 119	98.78 128	99.74 110	99.99 45	99.29 130	100.00 1	100.00 1	98.38 55	99.89 120	99.81 197	93.14 208	99.99 82	97.85 208	99.98 95	99.95 112

thresconf0.02		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpn_n400		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpnconf		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpnview11		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

LFMVS		97.42 188	96.62 206	99.81 92	99.80 114	99.50 111	99.16 318	99.56 74	94.48 247	100.00 1	100.00 1	79.35 329	100.00 1	99.89 78	97.37 204	99.94 116

WTY-MVS		99.54 60	99.40 62	99.95 47	99.81 100	99.93 33	100.00 1	100.00 1	97.98 84	99.84 123	100.00 1	98.94 94	99.98 95	99.86 81	98.21 160	99.94 116

PMMVS		99.12 107	98.97 105	99.58 128	99.57 160	98.98 155	100.00 1	99.30 207	97.14 152	99.96 88	100.00 1	96.53 165	99.82 152	99.70 103	98.49 138	99.94 116

F-COLMAP		99.64 50	99.64 35	99.67 115	99.99 45	99.07 146	100.00 1	99.44 106	98.30 63	99.90 117	100.00 1	99.18 74	99.99 82	99.91 75	100.00 1	99.94 116

PLC	98.56 2	99.70 37	99.74 13	99.58 128	100.00 1	98.79 162	100.00 1	99.54 75	98.58 45	99.96 88	100.00 1	99.59 21	100.00 1	100.00 1	100.00 1	99.94 116
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		98.14 170	97.74 178	99.33 148	99.59 154	98.28 188	99.27 300	99.21 242	96.42 196	99.15 182	99.94 176	88.87 274	99.79 157	98.88 160	98.29 155	99.93 125

PatchmatchNet		99.03 115	98.96 106	99.26 154	99.49 179	98.33 183	99.38 293	99.45 97	96.64 183	99.96 88	99.58 234	99.49 37	99.50 199	97.63 213	99.00 128	99.93 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LS3D		99.31 87	99.13 93	99.87 81	99.99 45	99.71 96	99.55 280	99.46 95	97.32 143	99.82 134	100.00 1	96.85 157	99.97 99	99.14 150	100.00 1	99.92 127

GSMVS																99.91 128

sam_mvs1												99.29 62				99.91 128

Patchmatch-RL test		93.49 282	93.63 278	93.05 314	91.78 331	83.41 331	98.21 338	96.95 342	91.58 297	91.05 312	97.64 312	99.40 48	95.83 329	94.11 277	81.95 324	99.91 128

canonicalmvs		99.03 115	98.73 132	99.94 59	99.75 128	99.95 19	100.00 1	99.30 207	97.64 121	100.00 1	100.00 1	95.22 184	99.97 99	99.76 97	96.90 210	99.91 128

test-LLR		99.03 115	98.91 117	99.40 141	99.40 189	99.28 131	100.00 1	99.45 97	96.70 178	99.42 167	99.12 257	99.31 55	99.01 224	96.82 236	99.99 88	99.91 128

TESTMET0.1,1		99.08 110	98.96 106	99.44 136	99.63 145	99.38 124	100.00 1	99.45 97	95.53 222	99.48 164	100.00 1	99.71 16	99.02 223	96.84 235	99.99 88	99.91 128

test-mter		98.96 127	98.82 124	99.40 141	99.40 189	99.28 131	100.00 1	99.45 97	95.44 231	99.42 167	99.12 257	99.70 17	99.01 224	96.82 236	99.99 88	99.91 128

MSDG		98.90 132	98.63 141	99.70 113	99.92 91	99.25 135	100.00 1	99.37 176	95.71 216	99.40 171	100.00 1	96.58 161	99.95 120	96.80 238	99.94 103	99.91 128

mvs-test1		98.89 133	98.99 104	98.60 188	99.77 124	95.96 265	100.00 1	98.94 301	97.61 123	99.93 114	99.92 179	95.89 171	99.93 133	99.36 137	99.50 120	99.90 136

alignmvs		99.38 74	99.21 80	99.91 69	99.73 129	99.92 36	100.00 1	99.51 80	97.61 123	100.00 1	100.00 1	99.06 81	99.93 133	99.83 86	97.12 206	99.90 136

PVSNet_Blended		99.48 68	99.36 68	99.83 88	99.98 72	99.60 107	100.00 1	100.00 1	97.79 102	100.00 1	100.00 1	96.57 162	99.99 82	100.00 1	99.88 108	99.90 136

EPMVS		99.25 95	99.13 93	99.60 121	99.60 151	99.20 140	99.60 276	100.00 1	96.93 168	99.92 115	99.36 248	99.05 82	99.71 165	98.77 166	98.94 129	99.90 136

PCF-MVS	98.23 3	98.69 145	98.37 156	99.62 117	99.78 122	99.02 150	99.23 309	99.06 292	96.43 194	98.08 242	100.00 1	94.72 191	99.95 120	98.16 192	99.91 105	99.90 136
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Patchmatch-test1		98.23 169	97.91 174	99.17 159	99.41 185	98.25 194	99.99 176	99.45 97	96.91 169	99.76 141	99.69 212	89.65 264	99.37 210	97.55 216	98.79 131	99.89 141

GA-MVS		97.72 180	97.27 189	99.06 162	99.24 201	97.93 219	100.00 1	99.24 233	95.80 215	98.99 191	99.64 225	89.77 262	99.36 211	95.12 261	97.62 202	99.89 141

diffmvs		98.52 155	98.11 165	99.73 112	99.59 154	99.47 119	100.00 1	99.19 245	93.68 269	99.70 152	99.75 203	95.07 186	99.84 148	97.57 215	98.48 139	99.89 141

tpmvs		98.59 148	98.38 155	99.23 156	99.69 132	97.90 220	99.31 298	99.47 83	94.52 245	99.68 153	99.28 253	97.64 130	99.89 139	97.71 211	98.17 163	99.89 141

EPNet		99.62 53	99.69 18	99.42 139	99.99 45	98.37 180	100.00 1	99.89 38	98.83 31	100.00 1	100.00 1	98.97 89	100.00 1	99.90 76	99.61 119	99.89 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		99.33 79	99.18 85	99.78 107	99.82 99	99.49 114	100.00 1	99.95 15	97.36 139	99.63 155	100.00 1	96.45 166	99.95 120	99.79 91	99.65 118	99.89 141

dp		98.72 141	98.61 143	99.03 166	99.53 166	97.39 237	99.45 287	99.39 169	95.62 219	99.94 109	99.52 240	98.83 101	99.82 152	96.77 240	98.42 144	99.89 141

sss		99.45 70	99.34 72	99.80 95	99.76 126	99.50 111	100.00 1	99.91 37	97.72 106	99.98 81	99.94 176	98.45 116	100.00 1	99.53 125	98.75 132	99.89 141

Test_1112_low_res		98.83 136	98.60 144	99.51 132	99.69 132	98.75 163	99.99 176	99.14 258	96.81 171	98.84 200	99.06 261	97.45 134	99.89 139	98.66 170	97.75 188	99.89 141

1112_ss		98.91 131	98.71 134	99.51 132	99.69 132	98.75 163	99.99 176	99.15 254	96.82 170	98.84 200	100.00 1	97.45 134	99.89 139	98.66 170	97.75 188	99.89 141

MDTV_nov1_ep13_2view						99.24 137	99.56 279		96.31 202	99.96 88		98.86 100		98.92 158		99.89 141

Vis-MVSNet (Re-imp)		98.99 123	98.89 121	99.29 151	99.64 144	98.89 159	99.98 192	99.31 205	96.74 174	99.48 164	100.00 1	98.11 122	99.10 219	98.39 183	98.34 152	99.89 141

UA-Net		99.06 112	98.83 123	99.74 110	99.52 171	99.40 123	99.08 324	99.45 97	97.64 121	99.83 125	100.00 1	95.80 173	99.94 131	98.35 185	99.80 114	99.88 153

ADS-MVSNet2		98.28 166	98.51 150	97.62 245	99.51 174	95.03 275	99.24 303	99.41 153	95.52 224	99.96 88	99.70 210	97.57 132	97.94 303	97.11 228	98.54 135	99.88 153

ADS-MVSNet		98.70 144	98.51 150	99.28 152	99.51 174	98.39 177	99.24 303	99.44 106	95.52 224	99.96 88	99.70 210	97.57 132	99.58 178	97.11 228	98.54 135	99.88 153

mvs_anonymous		98.80 138	98.60 144	99.38 144	99.57 160	99.24 137	100.00 1	99.21 242	95.87 210	98.92 193	99.82 194	96.39 167	99.03 222	99.13 151	98.50 137	99.88 153

PatchFormer-LS_test		99.14 106	99.12 95	99.19 158	99.54 163	97.86 225	99.66 271	99.40 155	96.94 167	99.96 88	100.00 1	99.29 62	99.84 148	98.96 156	98.29 155	99.88 153

tpm		98.24 168	98.22 163	98.32 202	99.13 203	95.79 269	99.53 282	99.12 269	95.20 232	99.96 88	99.36 248	97.58 131	99.28 215	97.41 222	96.67 211	99.88 153

IS-MVSNet		99.08 110	98.91 117	99.59 124	99.65 140	99.38 124	99.78 242	99.24 233	96.70 178	99.51 162	100.00 1	98.44 117	99.52 196	98.47 181	98.39 147	99.88 153

Fast-Effi-MVS+		98.40 162	98.02 171	99.55 131	99.63 145	99.06 147	100.00 1	99.15 254	95.07 233	99.42 167	99.95 172	93.26 206	99.73 162	97.44 220	98.24 158	99.87 160

DWT-MVSNet_test		99.22 100	99.22 79	99.22 157	99.56 162	97.98 215	99.89 221	99.39 169	97.20 150	99.96 88	100.00 1	99.52 29	99.82 152	99.11 153	98.34 152	99.87 160

MVS-HIRNet		94.12 277	92.73 286	98.29 204	99.33 195	95.95 266	99.38 293	99.19 245	74.54 341	98.26 238	86.34 347	86.07 296	99.06 221	91.60 296	99.87 110	99.85 162

CR-MVSNet		98.02 174	97.71 181	98.93 172	99.31 196	98.86 160	99.13 320	99.00 299	96.53 190	99.96 88	98.98 277	96.94 155	98.10 295	91.18 298	98.40 145	99.84 163

RPMNet		95.10 272	93.82 275	98.93 172	99.31 196	98.86 160	99.13 320	99.38 173	79.82 339	99.96 88	95.13 334	95.69 178	98.10 295	77.54 339	98.40 145	99.84 163

ab-mvs		98.42 160	98.02 171	99.61 118	99.71 130	99.00 154	99.10 322	99.64 72	96.70 178	99.04 189	99.81 197	90.64 248	99.98 95	99.64 110	97.93 172	99.84 163

CVMVSNet		98.56 151	98.47 152	98.82 179	99.11 204	97.67 230	99.74 252	99.47 83	97.57 129	99.06 188	100.00 1	95.72 177	98.97 228	98.21 191	97.33 205	99.83 166

tpm2		98.64 146	98.58 146	98.81 181	99.42 184	97.12 249	99.69 266	99.37 176	93.63 272	99.94 109	99.67 218	98.96 91	99.47 202	98.62 176	97.95 171	99.83 166

DeepC-MVS	97.84 5	99.00 121	98.80 127	99.60 121	99.93 88	99.03 149	100.00 1	99.40 155	98.61 44	99.33 172	100.00 1	92.23 216	99.95 120	99.74 99	99.96 100	99.83 166
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_Test		98.93 130	98.65 139	99.77 109	99.62 147	99.50 111	99.99 176	99.19 245	95.52 224	99.96 88	99.86 186	96.54 164	99.98 95	98.65 172	98.48 139	99.82 169

tpmrst		98.98 125	98.93 112	99.14 161	99.61 149	97.74 228	99.52 283	99.36 182	96.05 207	99.98 81	99.64 225	99.04 83	99.86 143	98.94 157	98.19 162	99.82 169

Effi-MVS+		98.58 149	98.24 160	99.61 118	99.60 151	99.26 133	97.85 340	99.10 271	96.22 204	99.97 85	99.89 182	93.75 199	99.77 158	99.43 129	98.34 152	99.81 171

lupinMVS		99.29 89	99.16 86	99.69 114	99.45 181	99.49 114	100.00 1	99.15 254	97.45 134	99.97 85	100.00 1	96.76 158	99.76 159	99.67 107	100.00 1	99.81 171

tpm cat1		98.05 172	97.76 177	98.92 174	99.50 177	97.10 251	99.77 246	99.30 207	90.20 306	99.72 144	98.71 293	97.71 128	99.86 143	96.75 241	98.20 161	99.81 171

tpmp4_e23		98.71 142	98.66 136	98.84 178	99.60 151	96.98 254	99.83 230	99.35 190	94.29 251	99.90 117	99.50 242	99.17 75	99.73 162	98.22 190	98.13 164	99.81 171

CostFormer		98.84 135	98.77 129	99.04 165	99.41 185	97.58 233	99.67 269	99.35 190	94.66 241	99.96 88	99.36 248	99.28 65	99.74 161	99.41 135	97.81 183	99.81 171

PatchT		95.90 258	94.95 271	98.75 183	99.03 214	98.39 177	99.08 324	99.32 200	85.52 327	99.96 88	94.99 336	97.94 124	98.05 301	80.20 333	98.47 141	99.81 171

BH-untuned		98.64 146	98.65 139	98.60 188	99.59 154	96.17 264	100.00 1	99.28 219	96.67 182	98.41 229	100.00 1	94.52 193	99.83 150	99.41 135	100.00 1	99.81 171

MIMVSNet		97.06 203	96.73 202	98.05 230	99.38 193	96.64 261	98.47 336	99.35 190	93.41 277	99.48 164	98.53 300	89.66 263	97.70 307	94.16 276	98.11 166	99.80 178

BH-RMVSNet		98.46 158	98.08 167	99.59 124	99.61 149	99.19 141	100.00 1	99.28 219	97.06 161	98.95 192	100.00 1	88.99 272	99.82 152	98.83 164	100.00 1	99.77 179

CDS-MVSNet		98.96 127	98.95 110	99.01 167	99.48 180	98.36 181	99.93 213	99.37 176	96.79 172	99.31 173	99.83 191	99.77 13	98.91 231	98.07 201	97.98 170	99.77 179
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		98.52 155	98.25 159	99.34 147	99.68 135	98.55 172	99.68 268	99.41 153	97.34 142	99.94 109	100.00 1	90.38 254	99.70 166	99.03 155	98.84 130	99.76 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_normal		96.24 243	95.12 267	99.60 121	96.00 320	99.16 143	100.00 1	99.29 211	96.43 194	88.96 319	98.40 306	80.06 324	99.90 137	98.53 178	98.39 147	99.75 182

DI_MVS_plusplus_test		96.28 242	95.16 265	99.61 118	96.01 319	99.18 142	100.00 1	99.29 211	96.49 193	89.08 316	98.42 305	80.06 324	99.90 137	98.74 168	98.39 147	99.75 182

Test4		92.63 293	90.45 304	99.17 159	92.81 330	99.02 150	99.98 192	99.13 263	96.60 186	82.04 339	96.27 318	51.35 348	98.75 252	97.55 216	98.36 151	99.75 182

EPP-MVSNet		99.10 109	99.00 101	99.40 141	99.51 174	98.68 167	99.92 215	99.43 115	95.47 228	99.65 154	100.00 1	99.51 31	99.76 159	99.53 125	98.00 169	99.75 182

xiu_mvs_v2_base		99.51 63	99.41 61	99.82 89	99.70 131	99.73 88	99.92 215	99.40 155	98.15 69	100.00 1	100.00 1	98.50 115	100.00 1	99.85 83	99.13 123	99.74 186

PS-MVSNAJ		99.64 50	99.57 51	99.85 85	99.78 122	99.81 78	99.95 207	99.42 121	98.38 55	100.00 1	100.00 1	98.75 106	100.00 1	99.88 79	99.99 88	99.74 186

MVSFormer		98.94 129	98.82 124	99.28 152	99.45 181	99.49 114	100.00 1	99.13 263	95.46 229	99.97 85	100.00 1	96.76 158	98.59 268	98.63 174	100.00 1	99.74 186

jason		99.11 108	98.96 106	99.59 124	99.17 202	99.31 129	100.00 1	99.13 263	97.38 138	99.83 125	100.00 1	95.54 180	99.72 164	99.57 118	99.97 98	99.74 186
jason: jason.
TAMVS		98.76 139	98.73 132	98.86 177	99.44 183	97.69 229	99.57 278	99.34 195	96.57 187	99.12 183	99.81 197	98.83 101	99.16 217	97.97 206	97.91 173	99.73 190

VDD-MVS		96.58 227	95.99 234	98.34 200	99.52 171	95.33 271	99.18 312	99.38 173	96.64 183	99.77 139	100.00 1	72.51 340	100.00 1	100.00 1	96.94 209	99.70 191

RPSCF		97.37 189	98.24 160	94.76 305	99.80 114	84.57 330	99.99 176	99.05 294	94.95 236	99.82 134	100.00 1	94.03 196	100.00 1	98.15 193	98.38 150	99.70 191

AllTest		98.55 152	98.40 154	98.99 168	99.93 88	97.35 240	100.00 1	99.40 155	97.08 159	99.09 184	99.98 141	93.37 203	99.95 120	96.94 231	99.84 111	99.68 193

TestCases				98.99 168	99.93 88	97.35 240		99.40 155	97.08 159	99.09 184	99.98 141	93.37 203	99.95 120	96.94 231	99.84 111	99.68 193

xiu_mvs_v1_base_debu		99.35 76	99.21 80	99.79 99	99.67 136	99.71 96	99.78 242	99.36 182	98.13 71	100.00 1	100.00 1	97.00 148	100.00 1	99.83 86	99.07 125	99.66 195

xiu_mvs_v1_base		99.35 76	99.21 80	99.79 99	99.67 136	99.71 96	99.78 242	99.36 182	98.13 71	100.00 1	100.00 1	97.00 148	100.00 1	99.83 86	99.07 125	99.66 195

xiu_mvs_v1_base_debi		99.35 76	99.21 80	99.79 99	99.67 136	99.71 96	99.78 242	99.36 182	98.13 71	100.00 1	100.00 1	97.00 148	100.00 1	99.83 86	99.07 125	99.66 195

LP		95.89 260	95.35 261	97.51 249	98.92 228	95.15 274	98.66 333	99.39 169	89.43 317	97.22 272	99.03 271	94.37 195	97.74 306	83.71 327	97.44 203	99.63 198

OpenMVS	95.20 17	98.76 139	98.41 153	99.78 107	98.89 230	99.81 78	99.99 176	99.76 46	98.02 80	98.02 247	100.00 1	91.44 234	100.00 1	99.63 111	99.97 98	99.55 199

cascas		98.43 159	98.07 168	99.50 134	99.65 140	99.02 150	100.00 1	99.22 238	94.21 254	99.72 144	99.98 141	92.03 219	99.93 133	99.68 105	98.12 165	99.54 200

CANet_DTU		99.02 119	98.90 120	99.41 140	99.88 97	98.71 165	100.00 1	99.29 211	98.84 30	100.00 1	100.00 1	94.02 197	100.00 1	98.08 200	99.96 100	99.52 201

DSMNet-mixed		95.18 271	95.21 264	95.08 300	96.03 318	90.21 321	99.65 272	93.64 351	92.91 286	98.34 232	97.40 313	90.05 259	95.51 332	91.02 300	97.86 177	99.51 202

Fast-Effi-MVS+-dtu		98.38 163	98.56 147	97.82 241	99.58 158	94.44 295	100.00 1	99.16 253	96.75 173	99.51 162	99.63 229	95.03 188	99.60 174	97.71 211	99.67 117	99.42 203

UGNet		98.41 161	98.11 165	99.31 150	99.54 163	98.55 172	99.18 312	100.00 1	98.64 43	99.79 137	99.04 266	87.61 284	100.00 1	99.30 143	99.89 107	99.40 204
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
VDDNet		96.39 236	95.55 252	98.90 175	99.27 199	97.45 234	99.15 319	99.92 36	91.28 299	99.98 81	100.00 1	73.55 338	100.00 1	99.85 83	96.98 208	99.24 205

test0.0.03 1		98.12 171	98.03 170	98.39 195	99.11 204	98.07 210	100.00 1	99.93 32	96.70 178	96.91 279	99.95 172	99.31 55	98.19 286	91.93 295	98.44 142	98.91 206

testgi		96.18 246	95.93 237	96.93 271	98.98 224	94.20 299	100.00 1	99.07 284	97.16 151	96.06 289	99.86 186	84.08 310	97.79 305	90.38 306	97.80 184	98.81 207

Effi-MVS+-dtu		98.51 157	98.86 122	97.47 252	99.77 124	94.21 298	100.00 1	98.94 301	97.61 123	99.91 116	98.75 291	95.89 171	99.51 198	99.36 137	99.48 121	98.68 208

testpf		97.02 206	96.87 197	97.44 253	99.50 177	95.18 273	95.70 345	99.33 197	91.35 298	98.00 249	99.22 255	96.29 168	98.49 277	94.42 270	98.07 168	98.67 209

DeepMVS_CX				89.98 318	98.90 229	71.46 344		99.18 249	97.61 123	96.92 277	99.83 191	86.07 296	99.83 150	96.02 244	97.65 201	98.65 210

COLMAP_ROB	97.10 7	98.29 165	98.17 164	98.65 186	99.94 86	97.39 237	99.30 299	99.40 155	95.64 217	97.75 258	100.00 1	92.69 213	99.95 120	98.89 159	99.92 104	98.62 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-OURS-SEG-HR		98.27 167	98.31 158	98.14 219	99.59 154	95.92 267	100.00 1	99.36 182	98.48 47	99.21 177	100.00 1	89.27 269	99.94 131	99.76 97	99.17 122	98.56 212

XVG-OURS		98.30 164	98.36 157	98.13 222	99.58 158	95.91 268	100.00 1	99.36 182	98.69 38	99.23 176	100.00 1	91.20 238	99.92 136	99.34 139	97.82 182	98.56 212

test2356		92.55 295	93.94 273	88.39 321	93.49 329	78.42 335	100.00 1	99.40 155	91.64 295	93.93 303	98.74 292	91.11 242	88.67 344	79.37 335	97.79 185	98.08 214

test1235678		88.02 311	88.73 309	85.90 327	87.57 340	71.70 343	100.00 1	97.42 339	87.52 325	86.19 332	96.39 317	81.60 318	87.93 345	70.72 343	91.18 271	98.08 214

HQP4-MVS										99.17 178			99.57 179			97.77 216

HQP-MVS		97.73 179	97.85 175	97.39 254	99.07 208	94.82 279	100.00 1	99.40 155	99.04 13	99.17 178	99.97 151	88.61 276	99.57 179	99.79 91	95.58 213	97.77 216

VPNet		96.41 233	95.76 244	98.33 201	98.61 240	98.30 187	99.48 286	99.45 97	96.98 162	98.87 197	99.88 183	81.57 319	98.93 229	99.22 148	87.82 308	97.76 218

VPA-MVSNet		97.03 205	96.43 213	98.82 179	98.64 239	99.32 128	99.38 293	99.47 83	96.73 176	98.91 195	98.94 282	87.00 290	99.40 208	99.23 146	89.59 292	97.76 218

HQP_MVS		97.71 181	97.82 176	97.37 255	99.00 220	94.80 282	100.00 1	99.40 155	99.00 19	99.08 186	99.97 151	88.58 278	99.55 188	99.79 91	95.57 217	97.76 218

plane_prior5								99.40 155					99.55 188	99.79 91	95.57 217	97.76 218

nrg030		97.64 182	97.27 189	98.75 183	98.34 249	99.53 110	100.00 1	99.22 238	96.21 205	98.27 237	99.95 172	94.40 194	98.98 227	99.23 146	89.78 291	97.75 222

v144192		96.40 235	95.81 240	98.17 217	97.89 287	98.11 207	99.99 176	99.06 292	93.39 278	98.75 205	99.09 259	90.43 253	98.66 262	93.10 287	90.55 279	97.75 222

v1921920		96.16 248	95.50 253	98.14 219	97.88 288	97.96 217	99.99 176	99.07 284	93.33 280	98.60 216	99.24 254	89.37 268	98.71 257	91.28 297	90.74 277	97.75 222

v1192		96.18 246	95.49 255	98.26 208	98.01 280	98.15 205	99.99 176	99.08 282	93.36 279	98.54 221	98.97 280	89.47 267	98.89 234	91.15 299	90.82 274	97.75 222

v148		96.29 241	95.84 239	97.63 243	97.74 290	96.53 262	100.00 1	99.07 284	93.52 275	98.01 248	99.42 247	91.22 237	98.60 266	96.37 243	87.22 311	97.75 222

v1141		96.66 220	96.12 227	98.26 208	98.05 276	98.28 188	100.00 1	99.09 274	93.71 263	98.59 217	99.04 266	91.57 228	98.73 253	95.05 262	90.00 285	97.75 222

v1240		95.96 256	95.25 263	98.07 226	97.91 286	97.87 224	99.96 201	99.07 284	93.24 282	98.64 214	98.96 281	88.98 273	98.61 264	89.58 312	90.92 273	97.75 222

divwei89l23v2f112		96.68 219	96.15 225	98.25 212	98.03 279	98.27 190	100.00 1	99.09 274	93.79 262	98.59 217	99.04 266	91.54 230	98.73 253	95.29 260	89.99 286	97.75 222

v2v482		96.70 218	96.18 222	98.27 206	98.04 278	98.39 177	100.00 1	99.13 263	94.19 255	98.58 219	99.08 260	90.48 252	98.67 261	95.69 250	90.44 280	97.75 222

v1		96.66 220	96.12 227	98.30 203	98.09 270	98.33 183	100.00 1	99.09 274	93.69 268	98.68 210	99.04 266	91.42 235	98.83 241	95.05 262	89.99 286	97.75 222

EI-MVSNet		97.98 175	97.93 173	98.16 218	99.11 204	97.84 226	99.74 252	99.29 211	94.39 249	98.65 212	100.00 1	97.21 137	98.88 237	97.62 214	95.31 222	97.75 222

MDA-MVSNet-bldmvs		91.65 302	89.94 305	96.79 285	96.72 313	96.70 260	99.42 290	98.94 301	88.89 319	66.97 349	98.37 307	81.43 320	95.91 328	89.24 318	89.46 294	97.75 222

UniMVSNet_NR-MVSNet		97.16 197	96.80 199	98.22 213	98.38 248	98.41 175	100.00 1	99.45 97	96.14 206	97.76 255	99.64 225	95.05 187	98.50 275	97.98 203	86.84 312	97.75 222

DU-MVS		96.93 209	96.49 210	98.22 213	98.31 251	98.41 175	100.00 1	99.37 176	96.41 197	97.76 255	99.65 221	92.14 217	98.50 275	97.98 203	86.84 312	97.75 222

UniMVSNet (Re)		97.29 193	96.85 198	98.59 190	98.49 245	99.13 144	100.00 1	99.42 121	96.52 191	98.24 240	98.90 285	94.93 189	98.89 234	97.54 218	87.61 309	97.75 222

NR-MVSNet		96.63 224	96.04 232	98.38 196	98.31 251	98.98 155	99.22 311	99.35 190	95.87 210	94.43 301	99.65 221	92.73 212	98.40 281	96.78 239	88.05 306	97.75 222

TranMVSNet+NR-MVSNet		96.45 232	96.01 233	97.79 242	98.00 282	97.62 232	100.00 1	99.35 190	95.98 208	97.31 269	99.64 225	90.09 257	98.00 302	96.89 234	86.80 315	97.75 222

Patchmtry		96.81 210	96.37 214	98.14 219	99.31 196	98.55 172	98.91 329	99.00 299	90.45 303	97.92 251	98.98 277	96.94 155	98.12 290	94.27 273	91.53 267	97.75 222

N_pmnet		91.88 300	93.37 281	87.40 323	97.24 311	66.33 348	99.90 218	91.05 353	89.77 312	95.65 293	98.58 299	90.05 259	98.11 292	85.39 324	92.72 257	97.75 222

XXY-MVS		97.14 199	96.63 205	98.67 185	98.65 238	98.92 158	99.54 281	99.29 211	95.57 221	97.63 260	99.83 191	87.79 283	99.35 212	98.39 183	92.95 255	97.75 222

IterMVS-LS		97.56 185	97.44 183	97.92 239	99.38 193	97.90 220	99.89 221	99.10 271	94.41 248	98.32 233	99.54 239	97.21 137	98.11 292	97.50 219	91.62 266	97.75 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		97.64 182	97.74 178	97.36 256	99.01 216	94.76 287	100.00 1	99.34 195	99.30 4	99.00 190	99.97 151	87.49 285	99.57 179	99.96 66	95.58 213	97.75 222
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FIs		97.95 176	97.73 180	98.62 187	98.53 244	99.24 137	100.00 1	99.43 115	96.74 174	97.87 253	99.82 194	95.27 183	98.89 234	98.78 165	93.07 253	97.74 244

v1144		96.51 229	95.97 236	98.13 222	97.98 283	98.04 213	99.99 176	99.08 282	93.51 276	98.62 215	98.98 277	90.98 247	98.62 263	93.79 281	90.79 276	97.74 244

testing_2		90.79 304	88.26 310	98.35 199	89.11 337	98.56 171	99.70 262	99.14 258	93.70 267	73.80 342	94.06 338	55.19 345	98.76 251	97.17 226	92.50 259	97.74 244

v1neww		96.77 211	96.27 218	98.26 208	98.16 263	98.23 195	100.00 1	99.09 274	93.94 259	98.73 207	99.05 263	91.65 224	98.82 242	95.75 247	90.04 282	97.74 244

v7new		96.77 211	96.27 218	98.26 208	98.16 263	98.23 195	100.00 1	99.09 274	93.94 259	98.73 207	99.05 263	91.65 224	98.82 242	95.75 247	90.04 282	97.74 244

v7		96.64 223	96.14 226	98.13 222	98.18 260	97.97 216	99.99 176	99.09 274	93.62 273	98.76 204	99.30 251	91.13 241	98.70 258	94.37 271	90.80 275	97.74 244

v6		96.77 211	96.26 220	98.29 204	98.10 269	98.27 190	100.00 1	99.09 274	93.95 258	98.73 207	99.06 261	91.51 231	98.86 239	95.83 245	90.04 282	97.74 244

YYNet1		92.44 296	90.92 301	97.03 266	96.20 316	97.06 252	99.99 176	99.14 258	88.21 322	67.93 346	98.43 304	88.63 275	96.28 324	90.64 301	89.08 298	97.74 244

MDA-MVSNet_test_wron		92.61 294	91.09 300	97.19 263	96.71 314	97.26 245	100.00 1	99.14 258	88.61 320	67.90 347	98.32 309	89.03 271	96.57 311	90.47 305	89.59 292	97.74 244

WR-MVS		97.09 200	96.64 204	98.46 191	98.43 246	99.09 145	99.97 198	99.33 197	95.62 219	97.76 255	99.67 218	91.17 239	98.56 272	98.49 180	89.28 295	97.74 244

semantic-postprocess				97.62 245	99.40 189	96.83 257		99.14 258	94.65 242	97.55 264	99.72 205	89.64 265	98.31 284	95.62 253	92.05 262	97.73 254

FC-MVSNet-test		97.84 177	97.63 182	98.45 192	98.30 253	99.05 148	100.00 1	99.43 115	96.63 185	97.61 262	99.82 194	95.19 185	98.57 271	98.64 173	93.05 254	97.73 254

MVSTER		98.58 149	98.52 149	98.77 182	99.65 140	99.68 102	100.00 1	99.29 211	95.63 218	98.65 212	99.80 200	99.78 6	98.88 237	98.59 177	95.31 222	97.73 254

FMVSNet3		97.30 192	96.95 194	98.37 197	99.65 140	99.25 135	99.71 260	99.28 219	94.23 252	98.53 222	98.91 284	93.30 205	98.11 292	95.31 257	93.60 247	97.73 254

FMVSNet2		96.22 244	95.60 251	98.06 229	99.53 166	98.33 183	99.45 287	99.27 226	93.71 263	98.03 245	98.84 287	84.23 307	98.10 295	93.97 279	93.40 250	97.73 254

OPM-MVS		97.21 194	97.18 192	97.32 257	98.08 273	94.66 289	100.00 1	99.28 219	98.65 42	98.92 193	99.98 141	86.03 298	99.56 183	98.28 189	95.41 219	97.72 259

pmmvs5		95.94 257	95.61 250	96.95 269	97.42 307	94.66 289	100.00 1	98.08 327	93.60 274	97.05 275	99.43 246	87.02 289	98.46 279	95.76 246	92.12 261	97.72 259

pm-mvs1		95.76 263	95.01 269	98.00 232	98.23 257	97.45 234	99.24 303	99.04 297	93.13 285	95.93 291	99.72 205	86.28 295	98.84 240	95.62 253	87.92 307	97.72 259

GBi-Net		96.07 252	95.80 241	96.89 273	99.53 166	94.87 276	99.18 312	99.27 226	93.71 263	98.53 222	98.81 288	84.23 307	98.07 298	95.31 257	93.60 247	97.72 259

test1		96.07 252	95.80 241	96.89 273	99.53 166	94.87 276	99.18 312	99.27 226	93.71 263	98.53 222	98.81 288	84.23 307	98.07 298	95.31 257	93.60 247	97.72 259

FMVSNet1		94.45 274	93.63 278	96.89 273	98.87 233	94.87 276	99.18 312	99.27 226	90.95 302	97.31 269	98.81 288	72.89 339	98.07 298	92.61 289	92.81 256	97.72 259

IterMVS		96.76 215	96.46 212	97.63 243	99.41 185	96.89 255	99.99 176	99.13 263	94.74 240	97.59 263	99.66 220	89.63 266	98.28 285	95.71 249	92.31 260	97.72 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		97.17 196	96.80 199	98.27 206	97.68 292	98.64 169	100.00 1	99.18 249	94.22 253	98.55 220	99.71 207	93.67 200	98.47 278	95.66 251	92.57 258	97.71 266

pcd1.5k->3k		40.07 331	42.58 332	32.57 344	97.97 284	0.00 361	0.00 352	99.25 230	0.00 356	0.00 357	0.14 358	91.10 243	0.00 359	0.00 356	94.75 238	97.70 267

v7n		96.06 254	95.42 259	97.99 234	97.58 298	97.35 240	99.86 227	99.11 270	92.81 287	97.91 252	99.49 243	90.99 246	98.92 230	92.51 291	88.49 304	97.70 267

PS-MVSNAJss		98.03 173	98.06 169	97.94 236	97.63 295	97.33 243	99.89 221	99.23 236	96.27 203	98.03 245	99.59 233	98.75 106	98.78 246	98.52 179	94.61 242	97.70 267

LPG-MVS_test		97.31 191	97.32 188	97.28 259	98.85 235	94.60 292	100.00 1	99.37 176	97.35 140	98.85 198	99.98 141	86.66 292	99.56 183	99.55 119	95.26 224	97.70 267

LGP-MVS_train				97.28 259	98.85 235	94.60 292		99.37 176	97.35 140	98.85 198	99.98 141	86.66 292	99.56 183	99.55 119	95.26 224	97.70 267

SixPastTwentyTwo		95.71 264	95.49 255	96.38 292	97.42 307	93.01 307	99.84 229	98.23 320	94.75 238	95.98 290	99.97 151	85.35 302	98.43 280	94.71 266	93.17 252	97.69 272

ACMM	97.17 6	97.37 189	97.40 185	97.29 258	99.01 216	94.64 291	100.00 1	99.25 230	98.07 77	98.44 228	99.98 141	87.38 286	99.55 188	99.25 145	95.19 229	97.69 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EU-MVSNet		96.63 224	96.53 208	96.94 270	97.59 297	96.87 256	99.76 249	99.47 83	96.35 200	96.85 281	99.78 201	92.57 214	96.27 325	95.33 256	91.08 272	97.68 274

K. test v3		95.46 269	95.14 266	96.40 291	97.53 300	93.40 305	99.99 176	99.23 236	95.49 227	92.70 310	99.73 204	84.26 306	98.12 290	93.94 280	93.38 251	97.68 274

lessismore_v0				96.05 296	97.55 299	91.80 317		99.22 238		91.87 311	99.91 180	83.50 312	98.68 259	92.48 292	90.42 281	97.68 274

XVG-ACMP-BASELINE		96.60 226	96.52 209	96.84 281	98.41 247	93.29 306	99.99 176	99.32 200	97.76 105	98.51 225	99.29 252	81.95 317	99.54 191	98.40 182	95.03 235	97.68 274

v748		95.47 268	94.95 271	97.04 265	97.46 306	94.76 287	99.93 213	99.10 271	91.95 293	97.11 274	99.18 256	89.94 261	98.68 259	94.01 278	85.88 318	97.67 278

ACMP	97.00 8	97.19 195	97.16 193	97.27 261	98.97 225	94.58 294	100.00 1	99.32 200	97.97 86	97.45 266	99.98 141	85.79 300	99.56 183	99.70 103	95.24 227	97.67 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jajsoiax		97.07 202	96.79 201	97.89 240	97.28 310	97.12 249	99.95 207	99.19 245	96.55 188	97.31 269	99.69 212	87.35 288	98.91 231	98.70 169	95.12 233	97.66 280

PS-CasMVS		96.34 239	95.78 243	98.03 231	98.18 260	98.27 190	99.71 260	99.32 200	94.75 238	96.82 282	99.65 221	86.98 291	98.15 288	97.74 210	88.85 300	97.66 280

CP-MVSNet		96.73 216	96.25 221	98.18 216	98.21 258	98.67 168	99.77 246	99.32 200	95.06 234	97.20 273	99.65 221	90.10 256	98.19 286	98.06 202	88.90 299	97.66 280

ACMH	96.25 11	96.77 211	96.62 206	97.21 262	98.96 226	94.43 296	99.64 273	99.33 197	97.43 136	96.55 285	99.97 151	83.52 311	99.54 191	99.07 154	95.13 232	97.66 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		97.00 207	96.69 203	97.94 236	97.41 309	97.27 244	99.60 276	99.18 249	96.51 192	97.35 268	99.69 212	86.53 294	98.91 231	98.84 162	95.09 234	97.65 284

PEN-MVS		96.01 255	95.48 257	97.58 247	97.74 290	97.26 245	99.90 218	99.29 211	94.55 244	96.79 283	99.55 237	87.38 286	97.84 304	96.92 233	87.24 310	97.65 284

ACMH+	96.20 13	96.49 231	96.33 216	97.00 267	99.06 212	93.80 301	99.81 237	99.31 205	97.32 143	95.89 292	99.97 151	82.62 315	99.54 191	98.34 186	94.63 241	97.65 284

OurMVSNet-221017-0		96.14 250	95.98 235	96.62 288	97.49 303	93.44 304	99.92 215	98.16 323	95.86 212	97.65 259	99.95 172	85.71 301	98.78 246	94.93 265	94.18 245	97.64 287

pmmvs6		93.64 279	92.87 284	95.94 298	97.47 305	91.41 320	98.92 328	99.02 298	87.84 324	95.01 296	99.61 232	77.24 334	98.77 249	94.33 272	86.41 316	97.63 288

v10		96.14 250	95.50 253	98.07 226	98.19 259	97.96 217	99.83 230	99.07 284	92.10 292	98.07 244	98.94 282	91.07 245	98.61 264	92.41 294	89.82 290	97.63 288

v8		96.35 238	95.73 246	98.21 215	98.11 268	98.23 195	99.94 211	99.07 284	92.66 288	98.29 235	99.00 276	91.46 233	98.77 249	94.17 274	88.83 301	97.62 290

DTE-MVSNet		95.52 266	94.99 270	97.08 264	97.49 303	96.45 263	100.00 1	99.25 230	93.82 261	96.17 287	99.57 236	87.81 282	97.18 309	94.57 267	86.26 317	97.62 290

test_djsdf		97.55 186	97.38 186	98.07 226	97.50 301	97.99 214	100.00 1	99.13 263	95.46 229	98.47 227	99.85 188	92.01 221	98.59 268	98.63 174	95.36 220	97.62 290

MIMVSNet1		91.96 297	91.20 298	94.23 310	94.94 327	91.69 318	99.34 296	99.22 238	88.23 321	94.18 302	98.45 302	75.52 337	93.41 338	79.37 335	91.49 268	97.60 293

FMVSNet5		95.32 270	95.43 258	94.99 302	99.39 192	92.99 309	99.25 302	99.24 233	90.45 303	97.44 267	98.45 302	95.78 174	94.39 334	87.02 321	91.88 264	97.59 294

LTVRE_ROB	95.29 16	96.32 240	96.10 229	96.99 268	98.55 242	93.88 300	99.45 287	99.28 219	94.50 246	96.46 286	99.52 240	84.86 303	99.48 201	97.26 225	95.03 235	97.59 294
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
TransMVSNet (Re)		94.78 273	93.72 276	97.93 238	98.34 249	97.88 222	99.23 309	97.98 331	91.60 296	94.55 299	99.71 207	87.89 281	98.36 282	89.30 317	84.92 319	97.56 296

Baseline_NR-MVSNet		96.16 248	95.70 247	97.56 248	98.28 254	96.79 258	100.00 1	97.86 334	91.93 294	97.63 260	99.47 245	92.14 217	98.35 283	97.13 227	86.83 314	97.54 297

USDC		95.90 258	95.70 247	96.50 290	98.60 241	92.56 313	100.00 1	98.30 319	97.77 103	96.92 277	99.94 176	81.25 323	99.45 204	93.54 285	94.96 237	97.49 298

ITE_SJBPF				96.84 281	98.96 226	93.49 303		98.12 325	98.12 74	98.35 231	99.97 151	84.45 304	99.56 183	95.63 252	95.25 226	97.49 298

Anonymous20231206		93.45 284	93.17 282	94.30 308	95.00 326	89.69 322	99.98 192	98.43 318	93.30 281	94.50 300	98.59 298	90.52 250	95.73 330	77.46 340	90.73 278	97.48 300

WR-MVS_H		96.73 216	96.32 217	97.95 235	98.26 255	97.88 222	99.72 258	99.43 115	95.06 234	96.99 276	98.68 294	93.02 209	98.53 273	97.43 221	88.33 305	97.43 301

tfpnnormal		96.36 237	95.69 249	98.37 197	98.55 242	98.71 165	99.69 266	99.45 97	93.16 284	96.69 284	99.71 207	88.44 280	98.99 226	94.17 274	91.38 269	97.41 302

TinyColmap		95.50 267	95.12 267	96.64 287	98.69 237	93.00 308	99.40 291	97.75 335	96.40 198	96.14 288	99.87 184	79.47 328	99.50 199	93.62 282	94.72 240	97.40 303

UnsupCasMVSNet_eth		94.25 276	93.89 274	95.34 299	97.63 295	92.13 314	99.73 256	99.36 182	94.88 237	92.78 307	98.63 297	82.72 313	96.53 315	94.57 267	84.73 320	97.36 304

PVSNet_BlendedMVS		98.71 142	98.62 142	98.98 170	99.98 72	99.60 107	100.00 1	100.00 1	97.23 146	100.00 1	99.03 271	96.57 162	99.99 82	100.00 1	94.75 238	97.35 305

testus		91.61 303	92.92 283	87.70 322	93.91 328	74.98 339	100.00 1	98.19 322	92.59 289	93.90 304	98.68 294	81.43 320	90.77 342	79.82 334	97.71 194	97.34 306

anonymousdsp		97.16 197	96.88 196	98.00 232	97.08 312	98.06 211	99.81 237	99.15 254	94.58 243	97.84 254	99.62 231	90.49 251	98.60 266	97.98 203	95.32 221	97.33 307

V42		96.65 222	96.16 224	98.11 225	98.17 262	98.23 195	99.99 176	99.09 274	93.97 256	98.74 206	99.05 263	91.09 244	98.82 242	95.46 255	89.90 289	97.27 308

LF4IMVS		96.19 245	96.18 222	96.23 295	98.26 255	92.09 315	100.00 1	97.89 333	97.82 100	97.94 250	99.87 184	82.71 314	99.38 209	97.41 222	93.71 246	97.20 309

new_pmnet		94.11 278	93.47 280	96.04 297	96.60 315	92.82 310	99.97 198	98.91 304	90.21 305	95.26 294	98.05 310	85.89 299	98.14 289	84.28 326	92.01 263	97.16 310

test20.03		93.11 291	92.85 285	93.88 313	95.19 325	91.83 316	100.00 1	98.87 305	93.68 269	92.76 308	98.88 286	89.20 270	92.71 339	77.88 338	89.19 296	97.09 311

pmmvs3		90.62 306	89.36 307	94.40 307	90.53 335	91.49 319	100.00 1	96.73 344	84.21 333	93.65 306	96.65 315	82.56 316	94.83 333	82.28 329	77.62 335	96.89 312

test_0402		94.35 275	93.70 277	96.32 293	97.92 285	93.60 302	99.61 275	98.85 306	88.19 323	94.68 298	99.48 244	80.01 327	98.58 270	89.39 315	95.15 231	96.77 313

EG-PatchMatch MVS		92.94 292	92.49 287	94.29 309	95.87 321	87.07 327	99.07 326	98.11 326	93.19 283	88.98 318	98.66 296	70.89 341	99.08 220	92.43 293	95.21 228	96.72 314

TDRefinement		91.93 298	90.48 303	96.27 294	81.60 346	92.65 312	99.10 322	97.61 338	93.96 257	93.77 305	99.85 188	80.03 326	99.53 195	97.82 209	70.59 343	96.63 315

MS-PatchMatch		95.66 265	95.87 238	95.05 301	97.80 289	89.25 323	98.88 330	99.30 207	96.35 200	96.86 280	99.01 274	81.35 322	99.43 205	93.30 286	99.98 95	96.46 316

MVP-Stereo		96.51 229	96.48 211	96.60 289	95.65 322	94.25 297	98.84 331	98.16 323	95.85 214	95.23 295	99.04 266	92.54 215	99.13 218	92.98 288	99.98 95	96.43 317
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v52		95.88 262	95.35 261	97.48 251	97.66 293	97.38 239	99.88 224	99.07 284	92.19 290	98.09 241	99.02 273	90.06 258	98.72 255	93.58 283	88.61 303	96.34 318

V4		95.89 260	95.36 260	97.49 250	97.65 294	97.40 236	99.88 224	99.07 284	92.19 290	98.08 242	99.01 274	90.20 255	98.72 255	93.57 284	88.63 302	96.34 318

UnsupCasMVSNet_bld		89.50 308	88.00 311	93.99 312	95.30 324	88.86 324	98.52 335	99.28 219	85.50 330	87.80 330	94.11 337	61.63 344	96.96 310	90.63 302	79.26 328	96.15 320

OpenMVS_ROB	88.34 20	91.89 299	91.12 299	94.19 311	95.55 323	87.63 326	99.26 301	98.03 328	86.61 326	90.65 315	96.82 314	70.14 342	98.78 246	86.54 323	96.50 212	96.15 320

ambc				88.45 319	86.84 341	70.76 345	97.79 342	98.02 330		90.91 313	95.14 333	38.69 353	98.51 274	94.97 264	84.23 321	96.09 322

PM-MVS		88.39 310	87.41 312	91.31 316	91.73 332	82.02 333	99.79 241	96.62 345	91.06 301	90.71 314	95.73 321	48.60 350	95.96 327	90.56 303	81.91 325	95.97 323

v17		93.53 281	92.42 288	96.88 277	98.09 270	98.16 203	99.83 230	98.74 308	89.95 309	89.05 317	95.65 322	91.99 222	96.54 312	90.31 307	78.15 334	95.95 324

v16		93.48 283	92.34 290	96.89 273	98.13 265	98.19 201	99.82 235	98.74 308	89.94 310	88.90 321	95.62 324	91.65 224	96.54 312	90.03 310	78.22 333	95.95 324

v18		93.55 280	92.42 288	96.91 272	98.12 266	98.26 193	99.83 230	98.74 308	89.99 308	88.94 320	95.64 323	92.02 220	96.54 312	90.27 308	78.26 332	95.94 326

v15		93.28 285	92.08 291	96.87 278	98.07 274	98.22 199	99.82 235	98.74 308	89.83 311	88.74 323	95.44 328	91.55 229	96.47 316	89.70 311	76.87 336	95.93 327

v13		93.18 288	91.97 295	96.81 283	98.12 266	98.12 206	99.74 252	98.73 314	89.47 316	88.52 326	95.40 332	91.72 223	96.41 319	89.23 319	76.77 339	95.92 328

V14		93.24 286	92.04 292	96.87 278	98.07 274	98.20 200	99.80 240	98.74 308	89.76 313	88.64 324	95.53 325	91.47 232	96.47 316	89.49 313	76.84 337	95.92 328

V9		93.20 287	91.99 294	96.85 280	98.05 276	98.16 203	99.77 246	98.73 314	89.67 314	88.64 324	95.44 328	91.34 236	96.47 316	89.46 314	76.79 338	95.92 328

pmmvs-eth3d		91.73 301	90.67 302	94.92 304	91.63 333	92.71 311	99.90 218	98.54 317	91.19 300	88.08 328	95.50 326	79.31 330	96.13 326	90.55 304	81.32 326	95.91 331

v12		93.18 288	91.97 295	96.80 284	98.09 270	98.10 208	99.76 249	98.73 314	89.61 315	88.45 327	95.41 331	91.63 227	96.39 321	89.32 316	76.76 340	95.91 331

v11		93.14 290	91.93 297	96.77 286	98.01 280	98.17 202	99.72 258	98.74 308	89.39 318	88.81 322	95.49 327	91.15 240	96.29 323	86.78 322	79.25 329	95.86 333

Anonymous20231211		84.35 315	82.41 316	90.15 317	89.14 336	78.88 334	98.71 332	98.22 321	70.20 343	86.88 331	95.44 328	47.11 352	95.72 331	81.14 330	74.88 341	95.84 334

new-patchmatchnet		90.30 307	89.46 306	92.84 315	90.77 334	88.55 325	99.83 230	98.80 307	90.07 307	87.86 329	95.00 335	78.77 331	94.30 335	84.86 325	79.15 330	95.68 335

test123		79.44 318	79.23 320	80.05 333	80.03 348	71.72 342	100.00 1	77.93 360	62.52 345	94.81 297	99.69 212	78.21 332	74.53 355	92.57 290	27.33 355	93.90 336

LCM-MVSNet		79.01 320	76.93 322	85.27 329	78.28 349	68.01 346	96.57 343	98.03 328	55.10 349	82.03 340	93.27 339	31.99 356	93.95 336	82.72 328	74.37 342	93.84 337

CMPMVS	66.12 22	90.65 305	92.04 292	86.46 325	96.18 317	66.87 347	98.03 339	99.38 173	83.38 335	85.49 334	99.55 237	77.59 333	98.80 245	94.44 269	94.31 244	93.72 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		88.62 309	89.13 308	87.08 324	87.71 338	77.22 336	99.96 201	97.71 336	85.51 328	83.47 337	96.26 319	95.76 175	90.96 340	66.63 346	89.17 297	93.07 339

test12356		86.30 312	87.15 313	83.77 331	84.24 343	72.00 341	99.94 211	96.80 343	84.63 332	85.16 335	96.51 316	77.02 335	86.31 348	70.06 344	89.93 288	93.00 340

PMMVS2		79.15 319	77.28 321	84.76 330	82.34 345	72.66 340	99.70 262	95.11 350	71.68 342	84.78 336	90.87 341	32.05 355	89.99 343	75.53 341	63.45 344	91.64 341

tmp_tt		75.80 322	74.26 323	80.43 332	52.91 359	53.67 357	87.42 350	97.98 331	61.80 346	67.04 348	100.00 1	76.43 336	96.40 320	96.47 242	28.26 354	91.23 342

.test1245		85.18 313	84.81 314	86.28 326	87.71 338	77.22 336	99.96 201	97.71 336	85.51 328	83.47 337	96.26 319	95.76 175	90.96 340	66.63 346	50.30 347	90.64 343

testmvs		80.17 316	81.95 317	74.80 337	58.54 357	59.58 355	100.00 1	87.14 357	76.09 340	99.61 156	100.00 1	67.06 343	74.19 356	98.84 162	50.30 347	90.64 343

testmv		79.83 317	79.77 318	79.99 334	78.22 350	63.55 351	99.67 269	95.51 349	80.89 337	76.24 341	92.90 340	52.16 347	86.10 349	62.45 349	79.90 327	90.33 345

no-one		74.65 323	71.03 324	85.51 328	80.99 347	75.42 338	99.70 262	97.15 340	81.02 336	66.48 350	80.55 354	29.10 357	93.65 337	73.74 342	30.53 353	87.47 346

Gipuma		84.73 314	83.50 315	88.40 320	97.50 301	82.21 332	88.87 348	99.05 294	65.81 344	85.71 333	90.49 342	53.70 346	96.31 322	78.64 337	91.74 265	86.67 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		77.92 321	79.45 319	73.34 339	76.87 351	46.81 358	98.24 337	99.05 294	59.89 348	73.55 343	98.34 308	36.81 354	86.55 346	80.96 331	91.35 270	86.65 348

ANet_high		66.05 328	63.44 330	73.88 338	61.14 356	63.45 352	95.68 346	87.18 356	79.93 338	47.35 353	80.68 353	22.35 358	72.33 357	61.24 350	35.42 352	85.88 349

PNet_i23d		69.93 325	67.61 327	76.86 335	75.89 352	62.08 354	97.85 340	88.23 355	61.04 347	55.65 352	84.10 350	19.35 359	83.21 350	66.45 348	59.98 345	85.29 350

MVE	68.59 21	67.22 327	64.68 329	74.84 336	74.67 354	62.32 353	95.84 344	90.87 354	50.98 351	58.72 351	81.05 352	12.20 363	78.95 352	61.06 351	56.75 346	83.24 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	60.66 23	65.98 329	65.05 328	68.75 343	55.06 358	38.40 359	88.19 349	96.98 341	48.30 354	44.82 355	88.52 345	12.22 362	86.49 347	67.58 345	83.79 322	81.35 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		62.75 330	59.23 331	73.34 339	61.53 355	59.39 356	93.28 347	86.17 359	53.70 350	46.89 354	77.89 355	12.35 360	79.96 351	60.86 352	40.35 351	80.68 353

EMVS		69.88 326	69.09 326	72.24 342	84.70 342	65.82 349	99.96 201	87.08 358	49.82 353	71.51 345	84.74 349	49.30 349	75.32 354	50.97 354	43.71 350	75.59 354

E-PMN		70.72 324	70.06 325	72.69 341	83.92 344	65.48 350	99.95 207	92.72 352	49.88 352	72.30 344	86.26 348	47.17 351	77.43 353	53.83 353	44.49 349	75.17 355

wuyk23d		28.28 332	29.73 334	23.92 345	75.89 352	32.61 360	66.50 351	12.88 361	16.09 355	14.59 356	16.59 357	12.35 360	32.36 358	39.36 355	13.36 356	6.79 356

cdsmvs_eth3d_5k		24.41 333	32.55 333	0.00 346	0.00 360	0.00 361	0.00 352	99.39 169	0.00 356	0.00 357	100.00 1	93.55 202	0.00 359	0.00 356	0.00 357	0.00 357

pcd_1.5k_mvsjas		8.24 335	10.99 336	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.14 358	98.75 106	0.00 359	0.00 356	0.00 357	0.00 357

sosnet-low-res		0.01 336	0.02 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.14 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.01 336	0.02 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.14 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.01 336	0.02 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.14 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.01 336	0.02 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.14 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

ab-mvs-re		8.33 334	11.11 335	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	100.00 1	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.01 336	0.02 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.14 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

test_part3							100.00 1		98.23 64		100.00 1		100.00 1	100.00 1

test_part2					100.00 1	99.99 1				100.00 1

sam_mvs												99.33 51

MTGPA								99.42 121

test_post1							99.32 297				88.24 346	99.33 51	99.59 176	98.31 187

test_post											89.05 344	99.49 37	99.59 176

patchmatchnet-post											97.79 311	99.41 47	99.54 191

MTMP								99.18 249

gm-plane-assit					99.52 171	97.26 245			95.86 212		100.00 1		99.43 205	98.76 167

TEST9					100.00 1	99.95 19	100.00 1	99.42 121	97.65 119	100.00 1	100.00 1	99.53 26	99.97 99

test_8					100.00 1	99.91 39	100.00 1	99.42 121	97.70 109	100.00 1	100.00 1	99.51 31	99.98 95

agg_prior					100.00 1	99.88 59		99.42 121		100.00 1			99.97 99

test_prior4						99.93 33	100.00 1

test_prior2							100.00 1		98.82 33	100.00 1	100.00 1	99.47 40		100.00 1	100.00 1

旧先验2							100.00 1		98.11 75	100.00 1			100.00 1	99.67 107

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 224

segment_acmp												99.55 24

testdata1							100.00 1		98.77 37

plane_prior7					99.00 220	94.78 286

plane_prior6					99.06 212	94.80 282						88.58 278

plane_prior4											99.97 151

plane_prior3						94.79 285			99.03 16	99.08 186

plane_prior2							100.00 1		99.00 19

plane_prior1					99.02 215

plane_prior						94.80 282	100.00 1		99.03 16						95.58 213

n2								0.00 362

nn								0.00 362

door-mid								96.32 347

test11								99.42 121

door								96.13 348

HQP5-MVS						94.82 279

HQP-NCC					99.07 208		100.00 1		99.04 13	99.17 178

ACMP_Plane					99.07 208		100.00 1		99.04 13	99.17 178

BP-MVS														99.79 91

HQP3-MVS								99.40 155							95.58 213

HQP2-MVS												88.61 276

NP-MVS					99.07 208	94.81 281					99.97 151

MDTV_nov1_ep13			98.94 111		99.53 166	98.36 181	99.39 292	99.46 95	96.54 189	99.99 76	99.63 229	98.92 96	99.86 143	98.30 188	98.71 133

ACMMP++_ref															94.58 243

ACMMP++															95.17 230

Test By Simon												99.10 79

High-resolution multi-view benchmark