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.

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 3	100.00 1	99.42 124	99.03 19	100.00 1	100.00 1	99.50 33	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 3		99.42 124	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 22	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 124	99.03 19	100.00 1	100.00 1	99.56 19	100.00 1	100.00 1	100.00 1	100.00 1

xxxxxxxxxxxxxcwj		99.77 18	99.70 20	99.97 31	99.99 52	99.93 41	100.00 1	99.31 216	98.93 32	100.00 1	100.00 1	99.05 89	99.99 89	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 54	99.57 57	99.95 56	99.99 52	99.85 83	100.00 1	99.42 124	97.67 121	100.00 1	100.00 1	99.05 89	99.99 89	100.00 1	100.00 1	100.00 1

ETH3D cwj APD-0.16		99.63 61	99.53 65	99.94 71	99.97 92	99.82 88	100.00 1	99.42 124	97.39 151	100.00 1	100.00 1	98.98 99	99.99 89	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 35	99.62 46	99.97 31	99.99 52	99.90 57	100.00 1	99.79 40	97.97 94	99.97 102	100.00 1	98.97 101	100.00 1	99.94 88	100.00 1	100.00 1

ETH3 D test6400		99.68 47	99.59 50	99.97 31	99.99 52	99.93 41	100.00 1	99.42 124	97.97 94	100.00 1	100.00 1	98.90 111	99.99 89	100.00 1	100.00 1	100.00 1

9.14			99.57 57		99.99 52		100.00 1	99.42 124	97.54 136	100.00 1	100.00 1	99.15 83	99.99 89	100.00 1	100.00 1

testtj		99.70 39	99.61 47	99.99 9	100.00 1	99.98 13	100.00 1	99.53 67	97.71 117	100.00 1	100.00 1	99.23 73	100.00 1	100.00 1	100.00 1	100.00 1

ETH3D-3000-0.1		99.66 54	99.57 57	99.95 56	99.99 52	99.85 83	100.00 1	99.42 124	97.58 132	100.00 1	100.00 1	99.12 85	99.99 89	100.00 1	100.00 1	100.00 1

MSP-MVS		99.83 7	99.78 6	100.00 1	100.00 1	99.99 3	100.00 1	99.42 124	99.04 14	100.00 1	100.00 1	99.53 23	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									98.79 46	100.00 1	100.00 1	99.61 13	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_SECOND				100.00 1	99.99 52	99.99 3	100.00 1	99.42 124					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS		99.68 47	99.58 54	99.98 22	100.00 1	99.95 28	100.00 1	99.64 62	97.59 131	100.00 1	100.00 1	98.99 98	99.99 89	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 61	99.51 67	100.00 1	99.90 110	100.00 1	100.00 1	99.43 116	99.00 25	100.00 1	100.00 1	99.58 18	100.00 1	97.64 233	100.00 1	100.00 1

SMA-MVS		99.69 43	99.59 50	99.98 22	99.99 52	99.93 41	100.00 1	99.43 116	97.50 142	100.00 1	100.00 1	99.43 47	100.00 1	100.00 1	100.00 1	100.00 1

DPE-MVS		99.79 14	99.73 16	99.99 9	99.99 52	99.98 13	100.00 1	99.42 124	98.91 35	100.00 1	100.00 1	99.22 75	100.00 1	100.00 1	100.00 1	100.00 1

CHOSEN 280x420		99.85 3	99.87 1	99.80 102	99.99 52	99.97 17	99.97 210	99.98 13	98.96 28	100.00 1	100.00 1	99.96 3	99.42 219	100.00 1	100.00 1	100.00 1

MP-MVS-pluss		99.61 66	99.50 69	99.97 31	99.98 88	99.92 48	100.00 1	99.42 124	97.53 138	99.77 158	100.00 1	98.77 119	100.00 1	99.99 57	100.00 1	99.99 103
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DVP-MVS		99.81 11	99.77 9	99.94 71	100.00 1	99.86 80	100.00 1	99.42 124	98.87 39	100.00 1	100.00 1	99.65 12	99.96 127	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 9	99.77 9	99.99 9	100.00 1	99.96 19	100.00 1	99.43 116	99.05 13	100.00 1	100.00 1	99.45 42	99.99 89	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		99.67 52	99.57 57	99.97 31	99.98 88	99.92 48	100.00 1	99.42 124	97.83 107	100.00 1	100.00 1	98.89 113	100.00 1	99.98 71	100.00 1	100.00 1

zzz-MVS		99.68 47	99.59 50	99.97 31	99.99 52	99.91 51	100.00 1	99.42 124	98.32 71	99.94 129	100.00 1	98.65 123	100.00 1	99.96 80	100.00 1	100.00 1

MTAPA		99.68 47	99.59 50	99.97 31	99.99 52	99.91 51	100.00 1	99.42 124	98.32 71	99.94 129	100.00 1	98.65 123	100.00 1	99.96 80	100.00 1	100.00 1

test9_res														100.00 1	100.00 1	100.00 1

train_agg		99.71 35	99.63 42	99.97 31	100.00 1	99.95 28	100.00 1	99.42 124	97.70 118	100.00 1	100.00 1	99.51 28	99.97 113	100.00 1	100.00 1	100.00 1

agg_prior1		99.71 35	99.63 42	99.95 56	100.00 1	99.88 74	100.00 1	99.42 124	97.72 114	100.00 1	100.00 1	99.51 28	99.97 113	100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 26	99.67 30	99.96 45	100.00 1	99.89 64	100.00 1	99.76 44	97.95 99	100.00 1	100.00 1	99.31 59	100.00 1	99.99 57	100.00 1	100.00 1

region2R		99.72 32	99.64 38	99.97 31	100.00 1	99.90 57	100.00 1	99.74 51	97.86 106	100.00 1	100.00 1	99.19 79	100.00 1	99.99 57	100.00 1	100.00 1

#test#		99.75 21	99.68 25	99.96 45	100.00 1	99.89 64	100.00 1	99.76 44	98.07 85	100.00 1	100.00 1	99.31 59	100.00 1	99.99 57	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 43	99.63 42	99.87 88	99.99 52	99.64 112	99.95 218	99.44 106	98.35 69	100.00 1	100.00 1	98.98 99	99.97 113	99.98 71	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 38	99.87 88	100.00 1	99.64 112	99.98 204	99.44 106	98.35 69	99.99 93	100.00 1	99.04 92	99.96 127	99.98 71	100.00 1	100.00 1

Regformer-3		99.73 29	99.65 34	99.96 45	100.00 1	99.89 64	100.00 1	99.44 106	98.40 63	100.00 1	100.00 1	99.03 96	99.97 113	99.99 57	100.00 1	100.00 1

Regformer-4		99.73 29	99.65 34	99.95 56	100.00 1	99.89 64	100.00 1	99.44 106	98.40 63	100.00 1	100.00 1	99.03 96	99.97 113	99.99 57	100.00 1	100.00 1

Regformer-1		99.75 21	99.68 25	99.98 22	100.00 1	99.94 36	100.00 1	99.44 106	98.43 60	100.00 1	100.00 1	99.23 73	99.99 89	99.99 57	100.00 1	100.00 1

Regformer-2		99.75 21	99.68 25	99.97 31	100.00 1	99.94 36	100.00 1	99.44 106	98.42 62	100.00 1	100.00 1	99.22 75	99.99 89	99.99 57	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 12	99.99 9	99.99 52	99.98 13	100.00 1	99.83 35	98.88 37	99.96 106	100.00 1	99.21 77	100.00 1	100.00 1	100.00 1	99.99 103

XVS		99.79 14	99.73 16	99.98 22	100.00 1	99.94 36	100.00 1	99.75 48	98.67 51	100.00 1	100.00 1	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

test_prior3		99.81 11	99.78 6	99.90 80	100.00 1	99.75 96	100.00 1	99.73 53	98.82 43	100.00 1	100.00 1	99.47 39	99.97 113	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 43	100.00 1	100.00 1	99.47 39		100.00 1	100.00 1

X-MVStestdata		97.04 222	96.06 245	99.98 22	100.00 1	99.94 36	100.00 1	99.75 48	98.67 51	100.00 1	66.97 351	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

新几何1				99.99 9	100.00 1	99.96 19		99.81 38	97.89 102	100.00 1	100.00 1	99.20 78	100.00 1	97.91 226	100.00 1	100.00 1

旧先验1					99.99 52	99.88 74		99.82 36			100.00 1	99.27 69			100.00 1	100.00 1

原ACMM1				99.93 74	100.00 1	99.80 91		99.66 61	98.18 74	100.00 1	100.00 1	99.43 47	100.00 1	99.50 150	100.00 1	100.00 1

test222					99.99 52	99.90 57	100.00 1	99.69 60	97.66 122	100.00 1	100.00 1	99.30 65			100.00 1	100.00 1

testdata				99.66 119	99.99 52	98.97 170		99.73 53	97.96 98	100.00 1	100.00 1	99.42 49	100.00 1	99.28 163	100.00 1	100.00 1

1314		99.38 86	99.19 101	99.96 45	98.88 258	99.89 64	99.24 300	99.93 26	98.88 37	98.79 216	100.00 1	97.02 168	100.00 1	100.00 1	100.00 1	100.00 1

1121		99.63 61	99.51 67	99.99 9	99.99 52	99.96 19	99.24 300	99.74 51	97.89 102	100.00 1	100.00 1	99.39 53	100.00 1	99.33 159	100.00 1	100.00 1

MVS		99.22 109	98.96 115	99.98 22	99.00 247	99.95 28	99.24 300	99.94 18	98.14 78	98.88 207	100.00 1	95.63 198	100.00 1	99.85 99	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 9	99.99 52	99.96 19	100.00 1	99.42 124	99.01 24	100.00 1	100.00 1	99.33 55	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MSLP-MVS++		99.89 1	99.85 2	99.99 9	100.00 1	99.96 19	100.00 1	99.95 15	99.11 7	100.00 1	100.00 1	99.60 14	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 6	99.99 9	100.00 1	99.98 13	100.00 1	99.44 106	99.06 11	100.00 1	100.00 1	99.56 19	99.99 89	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 56	99.55 64	99.97 31	99.99 52	99.91 51	100.00 1	99.48 74	97.54 136	100.00 1	100.00 1	98.97 101	99.99 89	99.98 71	100.00 1	100.00 1

ACMMPR		99.74 26	99.67 30	99.96 45	100.00 1	99.89 64	100.00 1	99.76 44	97.95 99	100.00 1	100.00 1	99.29 66	100.00 1	99.99 57	100.00 1	100.00 1

MP-MVS		99.61 66	99.49 71	99.98 22	99.99 52	99.94 36	100.00 1	99.42 124	97.82 108	99.99 93	100.00 1	98.20 135	100.00 1	99.99 57	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		99.69 43	99.61 47	99.95 56	99.99 52	99.85 83	100.00 1	99.58 64	97.69 120	100.00 1	100.00 1	99.44 43	100.00 1	99.79 108	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 3	100.00 1	99.73 53	99.19 5	100.00 1	100.00 1	99.31 59	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 29	99.64 38	99.99 9	100.00 1	99.97 17	100.00 1	99.42 124	98.02 88	100.00 1	100.00 1	99.32 58	99.99 89	100.00 1	100.00 1	100.00 1

test12				99.95 56	99.99 52	99.89 64		99.42 124		100.00 1		99.24 72	99.97 113		100.00 1	100.00 1

abl_6		99.53 72	99.40 74	99.92 76	100.00 1	99.76 94	100.00 1	99.42 124	97.21 165	100.00 1	100.00 1	98.12 138	100.00 1	99.82 107	100.00 1	100.00 1

mPP-MVS		99.69 43	99.60 49	99.97 31	100.00 1	99.91 51	100.00 1	99.42 124	97.91 101	100.00 1	100.00 1	99.04 92	100.00 1	100.00 1	100.00 1	100.00 1

MVSFormer		98.94 134	98.82 130	99.28 159	99.45 206	99.49 122	100.00 1	99.13 279	95.46 246	99.97 102	100.00 1	96.76 179	98.59 277	98.63 195	100.00 1	99.74 195

lupinMVS		99.29 99	99.16 104	99.69 115	99.45 206	99.49 122	100.00 1	99.15 269	97.45 146	99.97 102	100.00 1	96.76 179	99.76 174	99.67 130	100.00 1	99.81 178

HPM-MVS_fast		99.60 69	99.49 71	99.91 77	99.99 52	99.78 92	100.00 1	99.42 124	97.09 171	100.00 1	100.00 1	98.95 105	99.96 127	99.98 71	100.00 1	100.00 1

HPM-MVS		99.59 70	99.50 69	99.89 83	100.00 1	99.70 106	100.00 1	99.42 124	97.46 145	100.00 1	100.00 1	98.60 125	99.96 127	99.99 57	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
APD-MVS		99.68 47	99.58 54	99.97 31	99.99 52	99.96 19	100.00 1	99.42 124	97.53 138	100.00 1	100.00 1	99.27 69	99.97 113	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 3	100.00 1	99.77 43	99.07 9	100.00 1	100.00 1	99.39 53	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 3	100.00 1	99.71 59	99.07 9	100.00 1	100.00 1	99.59 16	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 85	99.25 89	99.81 98	99.97 92	99.48 126	100.00 1	99.42 124	95.53 239	100.00 1	100.00 1	98.37 133	99.95 133	99.97 78	100.00 1	100.00 1

CP-MVS		99.67 52	99.58 54	99.95 56	100.00 1	99.84 86	100.00 1	99.42 124	97.77 111	100.00 1	100.00 1	99.07 87	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 19	99.98 22	99.76 136	99.95 28	100.00 1	99.42 124	98.69 49	100.00 1	100.00 1	99.52 26	99.99 89	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
BH-w/o		98.82 143	98.81 132	98.88 185	99.62 163	96.71 263	100.00 1	99.28 228	97.09 171	98.81 214	100.00 1	94.91 208	99.96 127	99.54 146	100.00 1	99.96 113

DELS-MVS		99.62 64	99.56 63	99.82 95	99.92 107	99.45 127	100.00 1	99.78 42	98.92 34	99.73 162	100.00 1	97.70 149	100.00 1	99.93 89	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
BH-untuned		98.64 153	98.65 144	98.60 197	99.59 170	96.17 269	100.00 1	99.28 228	96.67 197	98.41 238	100.00 1	94.52 212	99.83 163	99.41 154	100.00 1	99.81 178

CPTT-MVS		99.49 78	99.38 77	99.85 91	100.00 1	99.54 117	100.00 1	99.42 124	97.58 132	99.98 98	100.00 1	97.43 160	100.00 1	99.99 57	100.00 1	100.00 1

BH-RMVSNet		98.46 165	98.08 178	99.59 128	99.61 165	99.19 152	100.00 1	99.28 228	97.06 175	98.95 203	100.00 1	88.99 273	99.82 166	98.83 184	100.00 1	99.77 191

DP-MVS Recon		99.76 19	99.69 22	99.98 22	100.00 1	99.95 28	100.00 1	99.52 68	97.99 90	99.99 93	100.00 1	99.72 9	100.00 1	99.96 80	100.00 1	100.00 1

MVS_111021_LR		99.70 39	99.65 34	99.88 87	99.96 98	99.70 106	100.00 1	99.97 14	98.96 28	100.00 1	100.00 1	97.93 143	99.95 133	99.99 57	100.00 1	100.00 1

DP-MVS		98.86 140	98.54 154	99.81 98	99.97 92	99.45 127	99.52 279	99.40 169	94.35 272	98.36 239	100.00 1	96.13 189	99.97 113	99.12 170	100.00 1	100.00 1

QAPM		98.99 127	98.66 143	99.96 45	99.01 243	99.87 77	99.88 233	99.93 26	97.99 90	98.68 221	100.00 1	93.17 227	100.00 1	99.32 161	100.00 1	100.00 1

PAPM_NR		99.74 26	99.66 33	99.99 9	100.00 1	99.96 19	100.00 1	99.47 75	97.87 105	100.00 1	100.00 1	99.60 14	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 19	99.68 25	99.99 9	100.00 1	99.96 19	100.00 1	99.47 75	98.16 75	100.00 1	100.00 1	99.51 28	100.00 1	100.00 1	100.00 1	100.00 1

MVS_111021_HR		99.71 35	99.63 42	99.93 74	99.95 99	99.83 87	100.00 1	100.00 1	98.89 36	100.00 1	100.00 1	97.85 145	99.95 133	100.00 1	100.00 1	100.00 1

CSCG		99.28 100	99.35 83	99.05 171	99.99 52	97.15 253	100.00 1	99.47 75	97.44 148	99.42 176	100.00 1	97.83 146	100.00 1	99.99 57	100.00 1	100.00 1

API-MVS		99.72 32	99.70 20	99.79 104	99.97 92	99.37 135	99.96 213	99.94 18	98.48 58	100.00 1	100.00 1	98.92 109	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 91	99.01 243	99.95 28	100.00 1	99.75 48	99.37 3	99.99 93	100.00 1	99.76 8	99.60 184	100.00 1	100.00 1	100.00 1

CNLPA		99.72 32	99.65 34	99.91 77	99.97 92	99.72 101	100.00 1	99.47 75	98.43 60	99.88 143	100.00 1	99.14 84	100.00 1	99.97 78	100.00 1	100.00 1

PHI-MVS		99.50 76	99.39 76	99.82 95	100.00 1	99.45 127	100.00 1	99.94 18	96.38 213	100.00 1	100.00 1	98.18 136	100.00 1	100.00 1	100.00 1	100.00 1

F-COLMAP		99.64 58	99.64 38	99.67 117	99.99 52	99.07 158	100.00 1	99.44 106	98.30 73	99.90 138	100.00 1	99.18 80	99.99 89	99.91 91	100.00 1	99.94 121

DeepPCF-MVS	98.03 4	98.54 160	99.72 18	94.98 306	99.99 52	84.94 336	100.00 1	99.42 124	99.98 1	100.00 1	100.00 1	98.11 139	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 21	99.67 30	99.99 9	99.99 52	99.96 19	99.73 255	99.52 68	99.06 11	100.00 1	100.00 1	98.80 118	100.00 1	99.95 86	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 21	99.68 25	99.97 31	100.00 1	99.91 51	99.98 204	99.47 75	99.09 8	100.00 1	100.00 1	98.59 126	100.00 1	99.95 86	100.00 1	100.00 1

AdaColmap		99.44 82	99.26 88	99.95 56	100.00 1	99.86 80	99.70 260	99.99 10	98.53 57	99.90 138	100.00 1	95.34 200	100.00 1	99.92 90	100.00 1	100.00 1

PLC	98.56 2	99.70 39	99.74 15	99.58 132	100.00 1	98.79 175	100.00 1	99.54 66	98.58 56	99.96 106	100.00 1	99.59 16	100.00 1	100.00 1	100.00 1	99.94 121
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		99.49 78	99.36 81	99.89 83	99.97 92	99.66 110	99.74 252	99.95 15	97.89 102	100.00 1	100.00 1	96.71 181	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	99.03 119	98.71 141	99.96 45	98.99 250	99.89 64	100.00 1	99.51 72	98.96 28	98.32 243	100.00 1	92.78 232	100.00 1	99.87 96	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 116	98.76 136	99.96 45	98.86 261	99.90 57	99.98 204	99.93 26	98.95 31	98.49 235	100.00 1	92.91 231	100.00 1	99.71 119	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 194	97.37 202	98.45 204	99.94 102	95.70 275	100.00 1	99.40 169	97.65 123	99.53 170	100.00 1	99.31 59	99.66 183	80.48 337	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		99.31 97	99.13 105	99.87 88	99.99 52	99.71 102	99.55 276	99.46 90	97.32 158	99.82 154	100.00 1	96.85 178	99.97 113	99.14 168	100.00 1	99.92 130

GST-MVS		99.64 58	99.53 65	99.95 56	100.00 1	99.86 80	100.00 1	99.79 40	97.72 114	99.95 127	100.00 1	98.39 132	100.00 1	99.96 80	99.99 103	100.00 1

CANet		99.40 84	99.24 91	99.89 83	99.99 52	99.76 94	100.00 1	99.73 53	98.40 63	99.78 157	100.00 1	95.28 201	99.96 127	100.00 1	99.99 103	99.96 113

GG-mvs-BLEND				99.59 128	99.54 182	99.49 122	99.17 314	99.52 68		99.96 106	99.68 233	100.00 1	99.33 225	99.71 119	99.99 103	99.96 113

gg-mvs-nofinetune		96.95 225	96.10 243	99.50 138	99.41 210	99.36 136	99.07 326	99.52 68	83.69 335	99.96 106	83.60 348	100.00 1	99.20 229	99.68 128	99.99 103	99.96 113

PS-MVSNAJ		99.64 58	99.57 57	99.85 91	99.78 132	99.81 89	99.95 218	99.42 124	98.38 66	100.00 1	100.00 1	98.75 120	100.00 1	99.88 95	99.99 103	99.74 195

test-LLR		99.03 119	98.91 123	99.40 148	99.40 215	99.28 141	100.00 1	99.45 97	96.70 193	99.42 176	99.12 281	99.31 59	99.01 237	96.82 256	99.99 103	99.91 132

TESTMET0.1,1		99.08 114	98.96 115	99.44 142	99.63 159	99.38 132	100.00 1	99.45 97	95.53 239	99.48 173	100.00 1	99.71 10	99.02 236	96.84 255	99.99 103	99.91 132

test-mter		98.96 131	98.82 130	99.40 148	99.40 215	99.28 141	100.00 1	99.45 97	95.44 248	99.42 176	99.12 281	99.70 11	99.01 237	96.82 256	99.99 103	99.91 132

MVP-Stereo		96.51 241	96.48 229	96.60 291	95.65 330	94.25 303	98.84 331	98.16 327	95.85 230	95.23 313	99.04 288	92.54 237	99.13 231	92.98 302	99.98 111	96.43 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		95.66 276	95.87 253	95.05 304	97.80 300	89.25 330	98.88 330	99.30 219	96.35 214	96.86 295	99.01 292	81.35 323	99.43 217	93.30 300	99.98 111	96.46 328

PatchMatch-RL		99.02 123	98.78 134	99.74 111	99.99 52	99.29 140	100.00 1	100.00 1	98.38 66	99.89 141	99.81 215	93.14 229	99.99 89	97.85 228	99.98 111	99.95 118

jason		99.11 112	98.96 115	99.59 128	99.17 229	99.31 139	100.00 1	99.13 279	97.38 152	99.83 147	100.00 1	95.54 199	99.72 180	99.57 142	99.97 114	99.74 195
jason: jason.
OpenMVS	95.20 17	98.76 145	98.41 160	99.78 108	98.89 257	99.81 89	99.99 186	99.76 44	98.02 88	98.02 260	100.00 1	91.44 244	100.00 1	99.63 136	99.97 114	99.55 207

CANet_DTU		99.02 123	98.90 126	99.41 146	99.88 112	98.71 180	100.00 1	99.29 223	98.84 40	100.00 1	100.00 1	94.02 217	100.00 1	98.08 218	99.96 116	99.52 209

DeepC-MVS	97.84 5	99.00 125	98.80 133	99.60 126	99.93 104	99.03 162	100.00 1	99.40 169	98.61 55	99.33 181	100.00 1	92.23 238	99.95 133	99.74 116	99.96 116	99.83 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 1792x2688		99.00 125	98.91 123	99.25 162	99.90 110	97.79 231	100.00 1	99.99 10	98.79 46	98.28 246	100.00 1	93.63 221	99.95 133	99.66 133	99.95 118	100.00 1

baseline2		98.99 127	98.93 121	99.18 167	99.26 227	99.15 155	100.00 1	99.46 90	96.71 192	96.79 298	100.00 1	99.42 49	99.25 228	98.75 188	99.94 119	99.15 215

MSDG		98.90 138	98.63 146	99.70 114	99.92 107	99.25 145	100.00 1	99.37 188	95.71 232	99.40 180	100.00 1	96.58 182	99.95 133	96.80 258	99.94 119	99.91 132

COLMAP_ROB	97.10 7	98.29 175	98.17 175	98.65 195	99.94 102	97.39 242	99.30 296	99.40 169	95.64 234	97.75 271	100.00 1	92.69 235	99.95 133	98.89 180	99.92 121	98.62 220
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PCF-MVS	98.23 3	98.69 150	98.37 165	99.62 123	99.78 132	99.02 163	99.23 306	99.06 305	96.43 209	98.08 255	100.00 1	94.72 209	99.95 133	98.16 216	99.91 122	99.90 141
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UGNet		98.41 168	98.11 176	99.31 157	99.54 182	98.55 188	99.18 309	100.00 1	98.64 54	99.79 156	99.04 288	87.61 286	100.00 1	99.30 162	99.89 123	99.40 212
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PVSNet_Blended		99.48 80	99.36 81	99.83 94	99.98 88	99.60 114	100.00 1	100.00 1	97.79 110	100.00 1	100.00 1	96.57 183	99.99 89	100.00 1	99.88 124	99.90 141

ACMMP		99.65 56	99.57 57	99.89 83	99.99 52	99.66 110	99.75 251	99.73 53	98.16 75	99.75 161	100.00 1	98.90 111	100.00 1	99.96 80	99.88 124	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
MVS-HIRNet		94.12 289	92.73 297	98.29 215	99.33 221	95.95 271	99.38 289	99.19 259	74.54 340	98.26 249	86.34 345	86.07 298	99.06 234	91.60 311	99.87 126	99.85 166

CS-MVS		99.33 94	99.23 94	99.65 120	99.63 159	99.18 153	100.00 1	99.27 236	97.45 146	99.96 106	100.00 1	97.22 162	99.87 155	99.67 130	99.86 127	99.90 141

AllTest		98.55 159	98.40 162	98.99 176	99.93 104	97.35 244	100.00 1	99.40 169	97.08 173	99.09 194	99.98 165	93.37 223	99.95 133	96.94 251	99.84 128	99.68 202

TestCases				98.99 176	99.93 104	97.35 244		99.40 169	97.08 173	99.09 194	99.98 165	93.37 223	99.95 133	96.94 251	99.84 128	99.68 202

OMC-MVS		99.27 101	99.38 77	98.96 179	99.95 99	97.06 257	100.00 1	99.40 169	98.83 41	99.88 143	100.00 1	97.01 169	99.86 156	99.47 151	99.84 128	99.97 110

UA-Net		99.06 116	98.83 129	99.74 111	99.52 190	99.40 131	99.08 324	99.45 97	97.64 125	99.83 147	100.00 1	95.80 194	99.94 144	98.35 208	99.80 131	99.88 158

PVSNet	94.91 18	99.30 98	99.25 89	99.44 142	100.00 1	98.32 202	100.00 1	99.86 34	98.04 87	100.00 1	100.00 1	96.10 190	100.00 1	99.55 143	99.73 132	100.00 1

EIA-MVS		99.26 103	99.19 101	99.45 141	99.63 159	98.75 176	100.00 1	99.27 236	96.93 178	99.95 127	100.00 1	97.47 156	99.79 170	99.74 116	99.72 133	99.82 174

EPNet_dtu		98.53 161	98.23 172	99.43 144	99.92 107	99.01 165	99.96 213	99.47 75	98.80 45	99.96 106	99.96 182	98.56 127	99.30 226	87.78 328	99.68 134	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+-dtu		98.38 170	98.56 153	97.82 253	99.58 175	94.44 301	100.00 1	99.16 268	96.75 187	99.51 171	99.63 246	95.03 206	99.60 184	97.71 231	99.67 135	99.42 211

ETV-MVS		99.34 92	99.24 91	99.64 121	99.58 175	99.33 137	100.00 1	99.25 242	97.57 134	99.96 106	100.00 1	97.44 159	99.79 170	99.70 121	99.65 136	99.81 178

PVSNet_Blended_VisFu		99.33 94	99.18 103	99.78 108	99.82 114	99.49 122	100.00 1	99.95 15	97.36 153	99.63 167	100.00 1	96.45 187	99.95 133	99.79 108	99.65 136	99.89 147

thisisatest0515		99.42 83	99.31 86	99.74 111	99.59 170	99.55 116	100.00 1	99.46 90	96.65 198	99.92 135	100.00 1	99.44 43	99.85 160	99.09 172	99.63 138	99.81 178

EPNet		99.62 64	99.69 22	99.42 145	99.99 52	98.37 196	100.00 1	99.89 32	98.83 41	100.00 1	100.00 1	98.97 101	100.00 1	99.90 92	99.61 139	99.89 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thisisatest0530		99.37 88	99.27 87	99.69 115	99.59 170	99.41 130	100.00 1	99.46 90	96.46 208	99.90 138	100.00 1	99.44 43	99.85 160	98.97 175	99.58 140	99.80 188

mvs-test1		98.89 139	98.99 113	98.60 197	99.77 134	95.96 270	100.00 1	98.94 316	97.61 127	99.93 134	99.92 197	95.89 191	99.93 146	99.36 156	99.50 141	99.90 141

Effi-MVS+-dtu		98.51 163	98.86 128	97.47 262	99.77 134	94.21 304	100.00 1	98.94 316	97.61 127	99.91 137	98.75 308	95.89 191	99.51 209	99.36 156	99.48 142	98.68 218

tttt0517		99.34 92	99.23 94	99.67 117	99.57 178	99.38 132	100.00 1	99.46 90	96.33 216	99.89 141	100.00 1	99.44 43	99.84 162	98.93 177	99.46 143	99.78 190

XVG-OURS-SEG-HR		98.27 177	98.31 167	98.14 227	99.59 170	95.92 272	100.00 1	99.36 194	98.48 58	99.21 186	100.00 1	89.27 270	99.94 144	99.76 114	99.17 144	98.56 221

xiu_mvs_v2_base		99.51 73	99.41 73	99.82 95	99.70 142	99.73 100	99.92 224	99.40 169	98.15 77	100.00 1	100.00 1	98.50 129	100.00 1	99.85 99	99.13 145	99.74 195

TSAR-MVS + GP.		99.61 66	99.69 22	99.35 153	99.99 52	98.06 216	100.00 1	99.36 194	99.83 2	100.00 1	100.00 1	98.95 105	99.99 89	100.00 1	99.11 146	100.00 1

xiu_mvs_v1_base_debu		99.35 89	99.21 97	99.79 104	99.67 147	99.71 102	99.78 243	99.36 194	98.13 79	100.00 1	100.00 1	97.00 172	100.00 1	99.83 102	99.07 147	99.66 204

xiu_mvs_v1_base		99.35 89	99.21 97	99.79 104	99.67 147	99.71 102	99.78 243	99.36 194	98.13 79	100.00 1	100.00 1	97.00 172	100.00 1	99.83 102	99.07 147	99.66 204

xiu_mvs_v1_base_debi		99.35 89	99.21 97	99.79 104	99.67 147	99.71 102	99.78 243	99.36 194	98.13 79	100.00 1	100.00 1	97.00 172	100.00 1	99.83 102	99.07 147	99.66 204

PatchmatchNet		99.03 119	98.96 115	99.26 161	99.49 199	98.33 200	99.38 289	99.45 97	96.64 199	99.96 106	99.58 255	99.49 35	99.50 210	97.63 234	99.00 150	99.93 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		99.25 106	99.13 105	99.60 126	99.60 167	99.20 151	99.60 272	100.00 1	96.93 178	99.92 135	99.36 272	99.05 89	99.71 181	98.77 186	98.94 151	99.90 141

Vis-MVSNet		98.52 162	98.25 169	99.34 154	99.68 146	98.55 188	99.68 265	99.41 167	97.34 156	99.94 129	100.00 1	90.38 258	99.70 182	99.03 174	98.84 152	99.76 193
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SCA		98.30 173	97.98 184	99.23 163	99.41 210	98.25 206	99.99 186	99.45 97	96.91 181	99.76 160	99.58 255	89.65 265	99.54 201	98.31 210	98.79 153	99.91 132

sss		99.45 81	99.34 85	99.80 102	99.76 136	99.50 119	100.00 1	99.91 31	97.72 114	99.98 98	99.94 193	98.45 130	100.00 1	99.53 148	98.75 154	99.89 147

MDTV_nov1_ep13			98.94 120		99.53 185	98.36 198	99.39 288	99.46 90	96.54 204	99.99 93	99.63 246	98.92 109	99.86 156	98.30 212	98.71 155

MVS_0304		95.11 283	94.84 284	95.91 301	99.60 167	91.24 327	98.64 333	99.88 33	94.51 266	99.62 168	98.31 323	69.19 342	98.82 255	95.22 281	98.60 156	97.66 291

Patchmatch-test		97.83 189	97.42 198	99.06 169	99.08 234	97.66 235	98.66 332	99.21 256	93.65 288	98.25 250	99.58 255	99.47 39	99.57 189	90.25 322	98.59 157	99.95 118

ADS-MVSNet2		98.28 176	98.51 156	97.62 258	99.51 195	95.03 281	99.24 300	99.41 167	95.52 241	99.96 106	99.70 227	97.57 153	97.94 318	97.11 248	98.54 158	99.88 158

ADS-MVSNet		98.70 149	98.51 156	99.28 159	99.51 195	98.39 193	99.24 300	99.44 106	95.52 241	99.96 106	99.70 227	97.57 153	99.58 188	97.11 248	98.54 158	99.88 158

mvs_anonymous		98.80 144	98.60 150	99.38 151	99.57 178	99.24 147	100.00 1	99.21 256	95.87 226	98.92 204	99.82 212	96.39 188	99.03 235	99.13 169	98.50 160	99.88 158

PMMVS		99.12 111	98.97 114	99.58 132	99.57 178	98.98 168	100.00 1	99.30 219	97.14 168	99.96 106	100.00 1	96.53 186	99.82 166	99.70 121	98.49 161	99.94 121

MVS_Test		98.93 135	98.65 144	99.77 110	99.62 163	99.50 119	99.99 186	99.19 259	95.52 241	99.96 106	99.86 204	96.54 185	99.98 109	98.65 193	98.48 162	99.82 174

PatchT		95.90 272	94.95 283	98.75 191	99.03 241	98.39 193	99.08 324	99.32 211	85.52 331	99.96 106	94.99 335	97.94 142	98.05 315	80.20 338	98.47 163	99.81 178

test0.0.03 1		98.12 181	98.03 181	98.39 207	99.11 231	98.07 215	100.00 1	99.93 26	96.70 193	96.91 294	99.95 189	99.31 59	98.19 298	91.93 309	98.44 164	98.91 216

LCM-MVSNet-Re		96.52 240	97.21 210	94.44 309	99.27 225	85.80 335	99.85 235	96.61 345	95.98 224	92.75 325	98.48 316	93.97 218	97.55 323	99.58 141	98.43 165	99.98 105

dp		98.72 147	98.61 148	99.03 174	99.53 185	97.39 242	99.45 283	99.39 182	95.62 236	99.94 129	99.52 263	98.83 115	99.82 166	96.77 261	98.42 166	99.89 147

CR-MVSNet		98.02 184	97.71 193	98.93 180	99.31 222	98.86 173	99.13 319	99.00 314	96.53 205	99.96 106	98.98 294	96.94 175	98.10 308	91.18 313	98.40 167	99.84 167

RPMNet		95.10 284	93.82 287	98.93 180	99.31 222	98.86 173	99.13 319	99.38 185	79.82 338	99.96 106	95.13 333	95.69 197	98.10 308	77.54 342	98.40 167	99.84 167

IS-MVSNet		99.08 114	98.91 123	99.59 128	99.65 152	99.38 132	99.78 243	99.24 245	96.70 193	99.51 171	100.00 1	98.44 131	99.52 207	98.47 203	98.39 169	99.88 158

casdiffmvs		98.65 152	98.38 163	99.46 140	99.52 190	98.74 179	100.00 1	99.15 269	96.91 181	99.05 199	100.00 1	92.75 233	99.83 163	99.70 121	98.38 170	99.81 178

RPSCF		97.37 207	98.24 170	94.76 308	99.80 128	84.57 337	99.99 186	99.05 307	94.95 253	99.82 154	100.00 1	94.03 216	100.00 1	98.15 217	98.38 170	99.70 200

Effi-MVS+		98.58 156	98.24 170	99.61 124	99.60 167	99.26 143	97.85 339	99.10 287	96.22 220	99.97 102	99.89 200	93.75 219	99.77 173	99.43 152	98.34 172	99.81 178

DWT-MVSNet_test		99.22 109	99.22 96	99.22 165	99.56 181	97.98 220	99.89 230	99.39 182	97.20 166	99.96 106	100.00 1	99.52 26	99.82 166	99.11 171	98.34 172	99.87 164

baseline		98.69 150	98.45 159	99.41 146	99.52 190	98.67 183	100.00 1	99.17 267	97.03 176	99.13 192	100.00 1	93.17 227	99.74 177	99.70 121	98.34 172	99.81 178

Vis-MVSNet (Re-imp)		98.99 127	98.89 127	99.29 158	99.64 157	98.89 172	99.98 204	99.31 216	96.74 188	99.48 173	100.00 1	98.11 139	99.10 232	98.39 206	98.34 172	99.89 147

diffmvs		98.96 131	98.73 138	99.63 122	99.54 182	99.16 154	100.00 1	99.18 262	97.33 157	99.96 106	100.00 1	94.60 211	99.91 150	99.66 133	98.33 176	99.82 174

TR-MVS		98.14 180	97.74 190	99.33 155	99.59 170	98.28 204	99.27 297	99.21 256	96.42 210	99.15 191	99.94 193	88.87 276	99.79 170	98.88 181	98.29 177	99.93 128

JIA-IIPM		97.09 218	96.34 235	99.36 152	98.88 258	98.59 186	99.81 239	99.43 116	84.81 333	99.96 106	90.34 341	98.55 128	99.52 207	97.00 250	98.28 178	99.98 105

Fast-Effi-MVS+		98.40 169	98.02 182	99.55 135	99.63 159	99.06 159	100.00 1	99.15 269	95.07 250	99.42 176	99.95 189	93.26 226	99.73 179	97.44 238	98.24 179	99.87 164

HY-MVS	96.53 9	99.50 76	99.35 83	99.96 45	99.81 118	99.93 41	99.64 268	100.00 1	97.97 94	99.84 145	99.85 206	98.94 107	99.99 89	99.86 97	98.23 180	99.95 118

WTY-MVS		99.54 71	99.40 74	99.95 56	99.81 118	99.93 41	100.00 1	100.00 1	97.98 92	99.84 145	100.00 1	98.94 107	99.98 109	99.86 97	98.21 181	99.94 121

tpm cat1		98.05 182	97.76 189	98.92 182	99.50 198	97.10 256	99.77 248	99.30 219	90.20 319	99.72 163	98.71 309	97.71 148	99.86 156	96.75 262	98.20 182	99.81 178

tpmrst		98.98 130	98.93 121	99.14 168	99.61 165	97.74 232	99.52 279	99.36 194	96.05 223	99.98 98	99.64 242	99.04 92	99.86 156	98.94 176	98.19 183	99.82 174

tpmvs		98.59 155	98.38 163	99.23 163	99.69 143	97.90 225	99.31 295	99.47 75	94.52 265	99.68 165	99.28 276	97.64 151	99.89 151	97.71 231	98.17 184	99.89 147

cascas		98.43 166	98.07 179	99.50 138	99.65 152	99.02 163	100.00 1	99.22 251	94.21 275	99.72 163	99.98 165	92.03 241	99.93 146	99.68 128	98.12 185	99.54 208

MIMVSNet		97.06 221	96.73 220	98.05 240	99.38 219	96.64 266	98.47 335	99.35 202	93.41 293	99.48 173	98.53 315	89.66 264	97.70 322	94.16 293	98.11 186	99.80 188

HyFIR lowres test		99.32 96	99.24 91	99.58 132	99.95 99	99.26 143	100.00 1	99.99 10	96.72 191	99.29 183	99.91 198	99.49 35	99.47 213	99.74 116	98.08 187	100.00 1

EPP-MVSNet		99.10 113	99.00 111	99.40 148	99.51 195	98.68 182	99.92 224	99.43 116	95.47 245	99.65 166	100.00 1	99.51 28	99.76 174	99.53 148	98.00 188	99.75 194

CDS-MVSNet		98.96 131	98.95 119	99.01 175	99.48 201	98.36 198	99.93 223	99.37 188	96.79 186	99.31 182	99.83 209	99.77 7	98.91 246	98.07 219	97.98 189	99.77 191
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		98.64 153	98.58 152	98.81 189	99.42 209	97.12 254	99.69 263	99.37 188	93.63 289	99.94 129	99.67 234	98.96 104	99.47 213	98.62 197	97.95 190	99.83 170

ab-mvs		98.42 167	98.02 182	99.61 124	99.71 140	99.00 167	99.10 322	99.64 62	96.70 193	99.04 200	99.81 215	90.64 252	99.98 109	99.64 135	97.93 191	99.84 167

TAMVS		98.76 145	98.73 138	98.86 186	99.44 208	97.69 233	99.57 274	99.34 207	96.57 202	99.12 193	99.81 215	98.83 115	99.16 230	97.97 225	97.91 192	99.73 199

thres200		99.27 101	99.04 107	99.96 45	99.81 118	99.90 57	100.00 1	99.94 18	97.31 160	99.83 147	99.96 182	97.04 165	100.00 1	99.62 137	97.88 193	99.98 105

DSMNet-mixed		95.18 282	95.21 278	95.08 303	96.03 328	90.21 328	99.65 267	93.64 349	92.91 304	98.34 241	97.40 327	90.05 261	95.51 337	91.02 315	97.86 194	99.51 210

thres100view900		99.25 106	99.01 110	99.95 56	99.81 118	99.87 77	100.00 1	99.94 18	97.13 169	99.83 147	99.96 182	97.01 169	100.00 1	99.59 138	97.85 195	99.98 105

tfpn200view9		99.26 103	99.03 108	99.96 45	99.81 118	99.89 64	100.00 1	99.94 18	97.23 162	99.83 147	99.96 182	97.04 165	100.00 1	99.59 138	97.85 195	99.98 105

thres400		99.26 103	99.03 108	99.95 56	99.81 118	99.89 64	100.00 1	99.94 18	97.23 162	99.83 147	99.96 182	97.04 165	100.00 1	99.59 138	97.85 195	99.97 110

XVG-OURS		98.30 173	98.36 166	98.13 230	99.58 175	95.91 273	100.00 1	99.36 194	98.69 49	99.23 185	100.00 1	91.20 246	99.92 149	99.34 158	97.82 198	98.56 221

CostFormer		98.84 141	98.77 135	99.04 173	99.41 210	97.58 237	99.67 266	99.35 202	94.66 260	99.96 106	99.36 272	99.28 68	99.74 177	99.41 154	97.81 199	99.81 178

testgi		96.18 259	95.93 251	96.93 282	98.98 251	94.20 305	100.00 1	99.07 298	97.16 167	96.06 307	99.86 204	84.08 312	97.79 321	90.38 321	97.80 200	98.81 217

thres600view7		99.24 108	99.00 111	99.95 56	99.81 118	99.87 77	100.00 1	99.94 18	97.13 169	99.83 147	99.96 182	97.01 169	100.00 1	99.54 146	97.77 201	99.97 110

test_yl		99.51 73	99.37 79	99.95 56	99.82 114	99.90 57	100.00 1	99.47 75	97.48 143	100.00 1	100.00 1	99.80 4	100.00 1	99.98 71	97.75 202	99.94 121

DCV-MVSNet		99.51 73	99.37 79	99.95 56	99.82 114	99.90 57	100.00 1	99.47 75	97.48 143	100.00 1	100.00 1	99.80 4	100.00 1	99.98 71	97.75 202	99.94 121

Test_1112_low_res		98.83 142	98.60 150	99.51 136	99.69 143	98.75 176	99.99 186	99.14 274	96.81 185	98.84 211	99.06 285	97.45 157	99.89 151	98.66 191	97.75 202	99.89 147

1112_ss		98.91 136	98.71 141	99.51 136	99.69 143	98.75 176	99.99 186	99.15 269	96.82 184	98.84 211	100.00 1	97.45 157	99.89 151	98.66 191	97.75 202	99.89 147

DeepMVS_CX				89.98 320	98.90 256	71.46 343		99.18 262	97.61 127	96.92 292	99.83 209	86.07 298	99.83 163	96.02 268	97.65 206	98.65 219

GA-MVS		97.72 192	97.27 208	99.06 169	99.24 228	97.93 224	100.00 1	99.24 245	95.80 231	98.99 202	99.64 242	89.77 263	99.36 222	95.12 282	97.62 207	99.89 147

LFMVS		97.42 205	96.62 224	99.81 98	99.80 128	99.50 119	99.16 315	99.56 65	94.48 268	100.00 1	100.00 1	79.35 328	100.00 1	99.89 94	97.37 208	99.94 121

CVMVSNet		98.56 158	98.47 158	98.82 187	99.11 231	97.67 234	99.74 252	99.47 75	97.57 134	99.06 198	100.00 1	95.72 196	98.97 242	98.21 215	97.33 209	99.83 170

alignmvs		99.38 86	99.21 97	99.91 77	99.73 139	99.92 48	100.00 1	99.51 72	97.61 127	100.00 1	100.00 1	99.06 88	99.93 146	99.83 102	97.12 210	99.90 141

VNet		99.04 118	98.75 137	99.90 80	99.81 118	99.75 96	99.50 281	99.47 75	98.36 68	100.00 1	99.99 162	94.66 210	100.00 1	99.90 92	97.09 211	99.96 113

Anonymous202405211		97.87 187	97.53 196	98.90 183	99.81 118	96.70 264	99.35 292	99.46 90	92.98 303	98.83 213	99.99 162	90.63 253	100.00 1	99.70 121	97.03 212	100.00 1

Anonymous20240529		96.93 226	96.22 239	99.05 171	99.79 131	97.30 248	99.16 315	99.47 75	88.51 325	98.69 220	100.00 1	83.50 314	100.00 1	99.83 102	97.02 213	99.83 170

VDDNet		96.39 250	95.55 268	98.90 183	99.27 225	97.45 240	99.15 317	99.92 30	91.28 312	99.98 98	100.00 1	73.55 336	100.00 1	99.85 99	96.98 214	99.24 213

VDD-MVS		96.58 239	95.99 248	98.34 212	99.52 190	95.33 276	99.18 309	99.38 185	96.64 199	99.77 158	100.00 1	72.51 338	100.00 1	100.00 1	96.94 215	99.70 200

canonicalmvs		99.03 119	98.73 138	99.94 71	99.75 138	99.95 28	100.00 1	99.30 219	97.64 125	100.00 1	100.00 1	95.22 203	99.97 113	99.76 114	96.90 216	99.91 132

tpm		98.24 178	98.22 174	98.32 214	99.13 230	95.79 274	99.53 278	99.12 284	95.20 249	99.96 106	99.36 272	97.58 152	99.28 227	97.41 240	96.67 217	99.88 158

OpenMVS_ROB	88.34 20	91.89 300	91.12 301	94.19 314	95.55 331	87.63 333	99.26 298	98.03 332	86.61 330	90.65 331	96.82 328	70.14 341	98.78 259	86.54 330	96.50 218	96.15 330

plane_prior						94.80 288	100.00 1		99.03 19						95.58 219

HQP3-MVS								99.40 169							95.58 219

HQP-MVS		97.73 191	97.85 186	97.39 263	99.07 235	94.82 285	100.00 1	99.40 169	99.04 14	99.17 187	99.97 173	88.61 278	99.57 189	99.79 108	95.58 219	97.77 223

CLD-MVS		97.64 194	97.74 190	97.36 265	99.01 243	94.76 293	100.00 1	99.34 207	99.30 4	99.00 201	99.97 173	87.49 287	99.57 189	99.96 80	95.58 219	97.75 235
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		97.71 193	97.82 188	97.37 264	99.00 247	94.80 288	100.00 1	99.40 169	99.00 25	99.08 196	99.97 173	88.58 280	99.55 198	99.79 108	95.57 223	97.76 225

plane_prior5								99.40 169					99.55 198	99.79 108	95.57 223	97.76 225

OPM-MVS		97.21 212	97.18 211	97.32 266	98.08 291	94.66 294	100.00 1	99.28 228	98.65 53	98.92 204	99.98 165	86.03 300	99.56 193	98.28 213	95.41 225	97.72 271

baseline1		98.91 136	98.61 148	99.81 98	99.71 140	99.77 93	99.78 243	99.44 106	97.51 141	98.81 214	99.99 162	98.25 134	99.76 174	98.60 198	95.41 225	99.89 147

test_djsdf		97.55 200	97.38 201	98.07 233	97.50 312	97.99 219	100.00 1	99.13 279	95.46 246	98.47 236	99.85 206	92.01 242	98.59 277	98.63 195	95.36 227	97.62 302

anonymousdsp		97.16 215	96.88 215	98.00 243	97.08 322	98.06 216	99.81 239	99.15 269	94.58 262	97.84 267	99.62 250	90.49 255	98.60 275	97.98 222	95.32 228	97.33 318

EI-MVSNet		97.98 185	97.93 185	98.16 226	99.11 231	97.84 230	99.74 252	99.29 223	94.39 271	98.65 222	100.00 1	97.21 163	98.88 252	97.62 235	95.31 229	97.75 235

MVSTER		98.58 156	98.52 155	98.77 190	99.65 152	99.68 108	100.00 1	99.29 223	95.63 235	98.65 222	99.80 218	99.78 6	98.88 252	98.59 199	95.31 229	97.73 265

LPG-MVS_test		97.31 209	97.32 204	97.28 268	98.85 262	94.60 297	100.00 1	99.37 188	97.35 154	98.85 209	99.98 165	86.66 294	99.56 193	99.55 143	95.26 231	97.70 279

LGP-MVS_train				97.28 268	98.85 262	94.60 297		99.37 188	97.35 154	98.85 209	99.98 165	86.66 294	99.56 193	99.55 143	95.26 231	97.70 279

ITE_SJBPF				96.84 286	98.96 253	93.49 309		98.12 329	98.12 82	98.35 240	99.97 173	84.45 306	99.56 193	95.63 273	95.25 233	97.49 310

ACMP	97.00 8	97.19 213	97.16 212	97.27 270	98.97 252	94.58 300	100.00 1	99.32 211	97.97 94	97.45 282	99.98 165	85.79 302	99.56 193	99.70 121	95.24 234	97.67 290
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EG-PatchMatch MVS		92.94 295	92.49 298	94.29 312	95.87 329	87.07 334	99.07 326	98.11 330	93.19 300	88.98 332	98.66 311	70.89 339	99.08 233	92.43 307	95.21 235	96.72 326

ACMM	97.17 6	97.37 207	97.40 200	97.29 267	99.01 243	94.64 296	100.00 1	99.25 242	98.07 85	98.44 237	99.98 165	87.38 288	99.55 198	99.25 164	95.19 236	97.69 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D		95.28 281	94.41 285	97.89 251	98.91 255	95.14 279	99.13 319	99.35 202	92.11 307	97.17 289	99.66 236	70.28 340	99.36 222	97.88 227	95.18 237	99.16 214

ACMMP++															95.17 238

test_0402		94.35 287	93.70 289	96.32 295	97.92 296	93.60 308	99.61 271	98.85 321	88.19 328	94.68 316	99.48 266	80.01 326	98.58 279	89.39 325	95.15 239	96.77 325

ACMH	96.25 11	96.77 229	96.62 224	97.21 271	98.96 253	94.43 302	99.64 268	99.33 209	97.43 149	96.55 303	99.97 173	83.52 313	99.54 201	99.07 173	95.13 240	97.66 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		97.07 220	96.79 219	97.89 251	97.28 320	97.12 254	99.95 218	99.19 259	96.55 203	97.31 285	99.69 229	87.35 290	98.91 246	98.70 190	95.12 241	97.66 291

mvs_tets		97.00 224	96.69 221	97.94 247	97.41 319	97.27 249	99.60 272	99.18 262	96.51 207	97.35 284	99.69 229	86.53 296	98.91 246	98.84 182	95.09 242	97.65 296

XVG-ACMP-BASELINE		96.60 238	96.52 227	96.84 286	98.41 275	93.29 312	99.99 186	99.32 211	97.76 113	98.51 233	99.29 275	81.95 320	99.54 201	98.40 205	95.03 243	97.68 285

LTVRE_ROB	95.29 16	96.32 254	96.10 243	96.99 278	98.55 270	93.88 306	99.45 283	99.28 228	94.50 267	96.46 304	99.52 263	84.86 305	99.48 212	97.26 245	95.03 243	97.59 306
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
D2MVS		97.63 197	97.83 187	97.05 274	98.83 264	94.60 297	100.00 1	99.82 36	96.89 183	98.28 246	99.03 290	94.05 215	99.47 213	98.58 200	94.97 245	97.09 322

USDC		95.90 272	95.70 262	96.50 292	98.60 269	92.56 319	100.00 1	98.30 325	97.77 111	96.92 292	99.94 193	81.25 324	99.45 216	93.54 299	94.96 246	97.49 310

PVSNet_BlendedMVS		98.71 148	98.62 147	98.98 178	99.98 88	99.60 114	100.00 1	100.00 1	97.23 162	100.00 1	99.03 290	96.57 183	99.99 89	100.00 1	94.75 247	97.35 317

TinyColmap		95.50 278	95.12 280	96.64 289	98.69 265	93.00 314	99.40 287	97.75 339	96.40 212	96.14 306	99.87 202	79.47 327	99.50 210	93.62 298	94.72 248	97.40 315

ACMH+	96.20 13	96.49 244	96.33 236	97.00 277	99.06 239	93.80 307	99.81 239	99.31 216	97.32 158	95.89 310	99.97 173	82.62 318	99.54 201	98.34 209	94.63 249	97.65 296

PS-MVSNAJss		98.03 183	98.06 180	97.94 247	97.63 304	97.33 247	99.89 230	99.23 249	96.27 218	98.03 258	99.59 254	98.75 120	98.78 259	98.52 201	94.61 250	97.70 279

ACMMP++_ref															94.58 251

RRT_MVS		98.48 164	98.41 160	98.70 193	99.64 157	99.67 109	100.00 1	99.10 287	96.26 219	98.75 217	99.96 182	99.04 92	98.75 265	98.42 204	94.51 252	97.68 285

CMPMVS	66.12 22	90.65 305	92.04 299	86.46 324	96.18 327	66.87 346	98.03 338	99.38 185	83.38 336	85.49 337	99.55 260	77.59 332	98.80 258	94.44 288	94.31 253	93.72 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OurMVSNet-221017-0		96.14 264	95.98 249	96.62 290	97.49 314	93.44 310	99.92 224	98.16 327	95.86 228	97.65 273	99.95 189	85.71 303	98.78 259	94.93 284	94.18 254	97.64 299

LF4IMVS		96.19 258	96.18 240	96.23 297	98.26 283	92.09 321	100.00 1	97.89 337	97.82 108	97.94 263	99.87 202	82.71 317	99.38 221	97.41 240	93.71 255	97.20 320

GBi-Net		96.07 266	95.80 256	96.89 283	99.53 185	94.87 282	99.18 309	99.27 236	93.71 283	98.53 230	98.81 305	84.23 309	98.07 311	95.31 278	93.60 256	97.72 271

test1		96.07 266	95.80 256	96.89 283	99.53 185	94.87 282	99.18 309	99.27 236	93.71 283	98.53 230	98.81 305	84.23 309	98.07 311	95.31 278	93.60 256	97.72 271

FMVSNet3		97.30 210	96.95 213	98.37 209	99.65 152	99.25 145	99.71 258	99.28 228	94.23 273	98.53 230	98.91 301	93.30 225	98.11 305	95.31 278	93.60 256	97.73 265

RRT_test8_iter05		98.32 172	98.23 172	98.60 197	99.65 152	99.23 150	100.00 1	99.09 293	95.65 233	98.07 256	99.93 196	99.25 71	98.82 255	98.90 179	93.50 259	97.75 235

FMVSNet2		96.22 257	95.60 267	98.06 237	99.53 185	98.33 200	99.45 283	99.27 236	93.71 283	98.03 258	98.84 304	84.23 309	98.10 308	93.97 295	93.40 260	97.73 265

K. test v3		95.46 279	95.14 279	96.40 293	97.53 311	93.40 311	99.99 186	99.23 249	95.49 244	92.70 326	99.73 221	84.26 308	98.12 303	93.94 296	93.38 261	97.68 285

SixPastTwentyTwo		95.71 275	95.49 271	96.38 294	97.42 317	93.01 313	99.84 236	98.23 326	94.75 256	95.98 308	99.97 173	85.35 304	98.43 290	94.71 285	93.17 262	97.69 283

FIs		97.95 186	97.73 192	98.62 196	98.53 272	99.24 147	100.00 1	99.43 116	96.74 188	97.87 266	99.82 212	95.27 202	98.89 249	98.78 185	93.07 263	97.74 258

FC-MVSNet-test		97.84 188	97.63 195	98.45 204	98.30 281	99.05 160	100.00 1	99.43 116	96.63 201	97.61 277	99.82 212	95.19 204	98.57 280	98.64 194	93.05 264	97.73 265

XXY-MVS		97.14 217	96.63 223	98.67 194	98.65 266	98.92 171	99.54 277	99.29 223	95.57 238	97.63 274	99.83 209	87.79 285	99.35 224	98.39 206	92.95 265	97.75 235

FMVSNet1		94.45 286	93.63 290	96.89 283	98.87 260	94.87 282	99.18 309	99.27 236	90.95 315	97.31 285	98.81 305	72.89 337	98.07 311	92.61 303	92.81 266	97.72 271

N_pmnet		91.88 301	93.37 293	87.40 323	97.24 321	66.33 347	99.90 227	91.05 351	89.77 321	95.65 311	98.58 314	90.05 261	98.11 305	85.39 331	92.72 267	97.75 235

cl-mvsnet2		98.23 179	98.11 176	98.58 201	99.82 114	99.01 165	100.00 1	99.28 228	96.92 180	98.33 242	99.21 278	98.09 141	98.97 242	98.72 189	92.61 268	97.76 225

miper_ehance_all_eth		97.81 190	97.66 194	98.23 219	99.49 199	98.37 196	99.99 186	99.11 285	94.78 255	98.25 250	99.21 278	98.18 136	98.57 280	97.35 243	92.61 268	97.76 225

miper_enhance_ethall		98.33 171	98.27 168	98.51 202	99.66 151	99.04 161	100.00 1	99.22 251	97.53 138	98.51 233	99.38 271	99.49 35	98.75 265	98.02 221	92.61 268	97.76 225

pmmvs4		97.17 214	96.80 217	98.27 216	97.68 303	98.64 185	100.00 1	99.18 262	94.22 274	98.55 228	99.71 224	93.67 220	98.47 288	95.66 272	92.57 271	97.71 278

testing_2		90.79 304	88.26 309	98.35 211	89.11 341	98.56 187	99.70 260	99.14 274	93.70 286	73.80 341	94.06 337	55.19 345	98.76 264	97.17 246	92.50 272	97.74 258

our_test_3		96.51 241	96.35 234	96.98 279	97.61 306	95.05 280	99.98 204	99.01 313	94.68 259	96.77 300	99.06 285	95.87 193	98.14 301	91.81 310	92.37 273	97.75 235

IterMVS		96.76 230	96.46 230	97.63 256	99.41 210	96.89 259	99.99 186	99.13 279	94.74 258	97.59 279	99.66 236	89.63 267	98.28 296	95.71 270	92.31 274	97.72 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ppachtmachnet_test		96.17 261	95.89 252	97.02 276	97.61 306	95.24 277	99.99 186	99.24 245	93.31 297	96.71 301	99.62 250	94.34 214	98.07 311	89.87 323	92.30 275	97.75 235

pmmvs5		95.94 271	95.61 266	96.95 280	97.42 317	94.66 294	100.00 1	98.08 331	93.60 290	97.05 290	99.43 269	87.02 291	98.46 289	95.76 269	92.12 276	97.72 271

IterMVS-SCA-FT		96.72 233	96.42 232	97.62 258	99.40 215	96.83 261	99.99 186	99.14 274	94.65 261	97.55 280	99.72 222	89.65 265	98.31 294	95.62 274	92.05 277	97.73 265

new_pmnet		94.11 290	93.47 292	96.04 299	96.60 325	92.82 316	99.97 210	98.91 319	90.21 318	95.26 312	98.05 324	85.89 301	98.14 301	84.28 333	92.01 278	97.16 321

cl-mvsnet1		97.52 203	97.35 203	98.05 240	99.46 205	98.11 212	100.00 1	99.10 287	94.21 275	97.62 276	99.63 246	97.65 150	98.29 295	96.47 263	91.98 279	97.76 225

cl-mvsnet_		97.54 201	97.32 204	98.18 223	99.47 204	98.14 211	100.00 1	99.10 287	94.16 278	97.60 278	99.63 246	97.52 155	98.65 271	96.47 263	91.97 280	97.76 225

FMVSNet5		95.32 280	95.43 275	94.99 305	99.39 218	92.99 315	99.25 299	99.24 245	90.45 316	97.44 283	98.45 317	95.78 195	94.39 339	87.02 329	91.88 281	97.59 306

Gipuma		84.73 310	83.50 312	88.40 322	97.50 312	82.21 339	88.87 344	99.05 307	65.81 342	85.71 336	90.49 340	53.70 346	96.31 329	78.64 340	91.74 282	86.67 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
IterMVS-LS		97.56 199	97.44 197	97.92 250	99.38 219	97.90 225	99.89 230	99.10 287	94.41 270	98.32 243	99.54 262	97.21 163	98.11 305	97.50 237	91.62 283	97.75 235
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		96.81 228	96.37 233	98.14 227	99.31 222	98.55 188	98.91 329	99.00 314	90.45 316	97.92 264	98.98 294	96.94 175	98.12 303	94.27 290	91.53 284	97.75 235

eth_miper_zixun_eth		97.47 204	97.28 206	98.06 237	99.41 210	97.94 223	99.62 270	99.08 295	94.46 269	98.19 253	99.56 259	96.91 177	98.50 285	96.78 259	91.49 285	97.74 258

MIMVSNet1		91.96 298	91.20 300	94.23 313	94.94 335	91.69 324	99.34 293	99.22 251	88.23 326	94.18 320	98.45 317	75.52 335	93.41 342	79.37 339	91.49 285	97.60 305

miper_lstm_enhance		97.40 206	97.28 206	97.75 255	99.48 201	97.52 238	100.00 1	99.07 298	94.08 279	98.01 261	99.61 252	97.38 161	97.98 317	96.44 266	91.47 287	97.76 225

tfpnnormal		96.36 251	95.69 265	98.37 209	98.55 270	98.71 180	99.69 263	99.45 97	93.16 301	96.69 302	99.71 224	88.44 282	98.99 239	94.17 291	91.38 288	97.41 314

FPMVS		77.92 315	79.45 314	73.34 332	76.87 349	46.81 354	98.24 336	99.05 307	59.89 345	73.55 342	98.34 321	36.81 351	86.55 345	80.96 336	91.35 289	86.65 343

cl_fuxian		97.58 198	97.42 198	98.06 237	99.48 201	98.16 209	99.96 213	99.10 287	94.54 264	98.13 254	99.20 280	97.87 144	98.25 297	97.28 244	91.20 290	97.75 235

EU-MVSNet		96.63 236	96.53 226	96.94 281	97.59 308	96.87 260	99.76 250	99.47 75	96.35 214	96.85 296	99.78 219	92.57 236	96.27 331	95.33 277	91.08 291	97.68 285

v1240		95.96 270	95.25 277	98.07 233	97.91 297	97.87 229	99.96 213	99.07 298	93.24 299	98.64 224	98.96 298	88.98 274	98.61 273	89.58 324	90.92 292	97.75 235

v1192		96.18 259	95.49 271	98.26 218	98.01 293	98.15 210	99.99 186	99.08 295	93.36 295	98.54 229	98.97 297	89.47 268	98.89 249	91.15 314	90.82 293	97.75 235

v1144		96.51 241	95.97 250	98.13 230	97.98 295	98.04 218	99.99 186	99.08 295	93.51 292	98.62 225	98.98 294	90.98 251	98.62 272	93.79 297	90.79 294	97.74 258

v1921920		96.16 262	95.50 269	98.14 227	97.88 299	97.96 221	99.99 186	99.07 298	93.33 296	98.60 226	99.24 277	89.37 269	98.71 267	91.28 312	90.74 295	97.75 235

Anonymous20231206		93.45 293	93.17 294	94.30 311	95.00 334	89.69 329	99.98 204	98.43 324	93.30 298	94.50 318	98.59 313	90.52 254	95.73 336	77.46 343	90.73 296	97.48 312

v144192		96.40 249	95.81 255	98.17 225	97.89 298	98.11 212	99.99 186	99.06 305	93.39 294	98.75 217	99.09 283	90.43 257	98.66 270	93.10 301	90.55 297	97.75 235

v2v482		96.70 234	96.18 240	98.27 216	98.04 292	98.39 193	100.00 1	99.13 279	94.19 277	98.58 227	99.08 284	90.48 256	98.67 269	95.69 271	90.44 298	97.75 235

lessismore_v0				96.05 298	97.55 310	91.80 323		99.22 251		91.87 327	99.91 198	83.50 314	98.68 268	92.48 306	90.42 299	97.68 285

V42		96.65 235	96.16 242	98.11 232	98.17 289	98.23 207	99.99 186	99.09 293	93.97 280	98.74 219	99.05 287	91.09 248	98.82 255	95.46 276	89.90 300	97.27 319

v10		96.14 264	95.50 269	98.07 233	98.19 287	97.96 221	99.83 237	99.07 298	92.10 308	98.07 256	98.94 299	91.07 249	98.61 273	92.41 308	89.82 301	97.63 300

nrg030		97.64 194	97.27 208	98.75 191	98.34 277	99.53 118	100.00 1	99.22 251	96.21 221	98.27 248	99.95 189	94.40 213	98.98 240	99.23 165	89.78 302	97.75 235

VPA-MVSNet		97.03 223	96.43 231	98.82 187	98.64 267	99.32 138	99.38 289	99.47 75	96.73 190	98.91 206	98.94 299	87.00 292	99.40 220	99.23 165	89.59 303	97.76 225

MDA-MVSNet_test_wron		92.61 296	91.09 302	97.19 272	96.71 324	97.26 250	100.00 1	99.14 274	88.61 324	67.90 346	98.32 322	89.03 272	96.57 326	90.47 320	89.59 303	97.74 258

Anonymous20231211		96.29 255	95.70 262	98.07 233	99.80 128	97.49 239	99.15 317	99.40 169	89.11 322	97.75 271	99.45 268	88.93 275	98.98 240	98.26 214	89.47 305	97.73 265

MDA-MVSNet-bldmvs		91.65 303	89.94 306	96.79 288	96.72 323	96.70 264	99.42 286	98.94 316	88.89 323	66.97 348	98.37 320	81.43 322	95.91 334	89.24 327	89.46 306	97.75 235

WR-MVS		97.09 218	96.64 222	98.46 203	98.43 274	99.09 157	99.97 210	99.33 209	95.62 236	97.76 268	99.67 234	91.17 247	98.56 282	98.49 202	89.28 307	97.74 258

test20.03		93.11 294	92.85 296	93.88 316	95.19 333	91.83 322	100.00 1	98.87 320	93.68 287	92.76 324	98.88 303	89.20 271	92.71 343	77.88 341	89.19 308	97.09 322

YYNet1		92.44 297	90.92 303	97.03 275	96.20 326	97.06 257	99.99 186	99.14 274	88.21 327	67.93 345	98.43 319	88.63 277	96.28 330	90.64 316	89.08 309	97.74 258

CP-MVSNet		96.73 231	96.25 238	98.18 223	98.21 286	98.67 183	99.77 248	99.32 211	95.06 251	97.20 288	99.65 238	90.10 259	98.19 298	98.06 220	88.90 310	97.66 291

PS-CasMVS		96.34 253	95.78 258	98.03 242	98.18 288	98.27 205	99.71 258	99.32 211	94.75 256	96.82 297	99.65 238	86.98 293	98.15 300	97.74 230	88.85 311	97.66 291

v8		96.35 252	95.73 261	98.21 222	98.11 290	98.23 207	99.94 222	99.07 298	92.66 306	98.29 245	99.00 293	91.46 243	98.77 262	94.17 291	88.83 312	97.62 302

v7n		96.06 268	95.42 276	97.99 245	97.58 309	97.35 244	99.86 234	99.11 285	92.81 305	97.91 265	99.49 265	90.99 250	98.92 245	92.51 305	88.49 313	97.70 279

WR-MVS_H		96.73 231	96.32 237	97.95 246	98.26 283	97.88 227	99.72 257	99.43 116	95.06 251	96.99 291	98.68 310	93.02 230	98.53 283	97.43 239	88.33 314	97.43 313

NR-MVSNet		96.63 236	96.04 246	98.38 208	98.31 279	98.98 168	99.22 308	99.35 202	95.87 226	94.43 319	99.65 238	92.73 234	98.40 291	96.78 259	88.05 315	97.75 235

pm-mvs1		95.76 274	95.01 281	98.00 243	98.23 285	97.45 240	99.24 300	99.04 310	93.13 302	95.93 309	99.72 222	86.28 297	98.84 254	95.62 274	87.92 316	97.72 271

VPNet		96.41 246	95.76 259	98.33 213	98.61 268	98.30 203	99.48 282	99.45 97	96.98 177	98.87 208	99.88 201	81.57 321	98.93 244	99.22 167	87.82 317	97.76 225

UniMVSNet (Re)		97.29 211	96.85 216	98.59 200	98.49 273	99.13 156	100.00 1	99.42 124	96.52 206	98.24 252	98.90 302	94.93 207	98.89 249	97.54 236	87.61 318	97.75 235

ET-MVSNet_ETH3D		96.41 246	95.48 273	99.20 166	99.81 118	99.75 96	100.00 1	99.02 311	97.30 161	78.33 340	100.00 1	97.73 147	97.94 318	99.70 121	87.41 319	99.92 130

PEN-MVS		96.01 269	95.48 273	97.58 260	97.74 301	97.26 250	99.90 227	99.29 223	94.55 263	96.79 298	99.55 260	87.38 288	97.84 320	96.92 253	87.24 320	97.65 296

v148		96.29 255	95.84 254	97.63 256	97.74 301	96.53 267	100.00 1	99.07 298	93.52 291	98.01 261	99.42 270	91.22 245	98.60 275	96.37 267	87.22 321	97.75 235

UniMVSNet_NR-MVSNet		97.16 215	96.80 217	98.22 220	98.38 276	98.41 191	100.00 1	99.45 97	96.14 222	97.76 268	99.64 242	95.05 205	98.50 285	97.98 222	86.84 322	97.75 235

DU-MVS		96.93 226	96.49 228	98.22 220	98.31 279	98.41 191	100.00 1	99.37 188	96.41 211	97.76 268	99.65 238	92.14 239	98.50 285	97.98 222	86.84 322	97.75 235

Baseline_NR-MVSNet		96.16 262	95.70 262	97.56 261	98.28 282	96.79 262	100.00 1	97.86 338	91.93 309	97.63 274	99.47 267	92.14 239	98.35 293	97.13 247	86.83 324	97.54 309

TranMVSNet+NR-MVSNet		96.45 245	96.01 247	97.79 254	98.00 294	97.62 236	100.00 1	99.35 202	95.98 224	97.31 285	99.64 242	90.09 260	98.00 316	96.89 254	86.80 325	97.75 235

pmmvs6		93.64 291	92.87 295	95.94 300	97.47 316	91.41 326	98.92 328	99.02 311	87.84 329	95.01 314	99.61 252	77.24 333	98.77 262	94.33 289	86.41 326	97.63 300

DTE-MVSNet		95.52 277	94.99 282	97.08 273	97.49 314	96.45 268	100.00 1	99.25 242	93.82 282	96.17 305	99.57 258	87.81 284	97.18 324	94.57 286	86.26 327	97.62 302

TransMVSNet (Re)		94.78 285	93.72 288	97.93 249	98.34 277	97.88 227	99.23 306	97.98 335	91.60 310	94.55 317	99.71 224	87.89 283	98.36 292	89.30 326	84.92 328	97.56 308

UnsupCasMVSNet_eth		94.25 288	93.89 286	95.34 302	97.63 304	92.13 320	99.73 255	99.36 194	94.88 254	92.78 323	98.63 312	82.72 316	96.53 327	94.57 286	84.73 329	97.36 316

ambc				88.45 321	86.84 342	70.76 344	97.79 340	98.02 334		90.91 329	95.14 332	38.69 350	98.51 284	94.97 283	84.23 330	96.09 332

PMVS	60.66 23	65.98 321	65.05 321	68.75 335	55.06 354	38.40 355	88.19 345	96.98 341	48.30 350	44.82 351	88.52 343	12.22 356	86.49 346	67.58 345	83.79 331	81.35 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_0	93.57 19	96.41 246	95.74 260	98.41 206	99.84 113	95.22 278	100.00 1	100.00 1	98.08 84	97.55 280	99.78 219	84.40 307	100.00 1	100.00 1	81.99 332	100.00 1

Patchmatch-RL test		93.49 292	93.63 290	93.05 317	91.78 336	83.41 338	98.21 337	96.95 342	91.58 311	91.05 328	97.64 326	99.40 52	95.83 335	94.11 294	81.95 333	99.91 132

PM-MVS		88.39 309	87.41 311	91.31 319	91.73 337	82.02 340	99.79 242	96.62 344	91.06 314	90.71 330	95.73 330	48.60 348	95.96 333	90.56 318	81.91 334	95.97 333

pmmvs-eth3d		91.73 302	90.67 304	94.92 307	91.63 338	92.71 317	99.90 227	98.54 323	91.19 313	88.08 333	95.50 331	79.31 329	96.13 332	90.55 319	81.32 335	95.91 334

UnsupCasMVSNet_bld		89.50 308	88.00 310	93.99 315	95.30 332	88.86 331	98.52 334	99.28 228	85.50 332	87.80 335	94.11 336	61.63 344	96.96 325	90.63 317	79.26 336	96.15 330

new-patchmatchnet		90.30 307	89.46 307	92.84 318	90.77 339	88.55 332	99.83 237	98.80 322	90.07 320	87.86 334	95.00 334	78.77 330	94.30 340	84.86 332	79.15 337	95.68 335

IB-MVS	96.24 12	97.54 201	96.95 213	99.33 155	99.67 147	98.10 214	100.00 1	99.47 75	97.42 150	99.26 184	99.69 229	98.83 115	99.89 151	99.43 152	78.77 338	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
pmmvs3		90.62 306	89.36 308	94.40 310	90.53 340	91.49 325	100.00 1	96.73 343	84.21 334	93.65 322	96.65 329	82.56 319	94.83 338	82.28 335	77.62 339	96.89 324

LCM-MVSNet		79.01 314	76.93 317	85.27 325	78.28 348	68.01 345	96.57 341	98.03 332	55.10 346	82.03 339	93.27 338	31.99 353	93.95 341	82.72 334	74.37 340	93.84 337

TDRefinement		91.93 299	90.48 305	96.27 296	81.60 346	92.65 318	99.10 322	97.61 340	93.96 281	93.77 321	99.85 206	80.03 325	99.53 206	97.82 229	70.59 341	96.63 327

PMMVS2		79.15 313	77.28 316	84.76 326	82.34 345	72.66 341	99.70 260	95.11 348	71.68 341	84.78 338	90.87 339	32.05 352	89.99 344	75.53 344	63.45 342	91.64 339

MVE	68.59 21	67.22 319	64.68 322	74.84 329	74.67 351	62.32 351	95.84 342	90.87 352	50.98 347	58.72 349	81.05 349	12.20 357	78.95 347	61.06 347	56.75 343	83.24 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		80.17 311	81.95 313	74.80 330	58.54 353	59.58 352	100.00 1	87.14 354	76.09 339	99.61 169	100.00 1	67.06 343	74.19 351	98.84 182	50.30 344	90.64 341

E-PMN		70.72 317	70.06 319	72.69 333	83.92 344	65.48 349	99.95 218	92.72 350	49.88 348	72.30 343	86.26 346	47.17 349	77.43 348	53.83 348	44.49 345	75.17 348

EMVS		69.88 318	69.09 320	72.24 334	84.70 343	65.82 348	99.96 213	87.08 355	49.82 349	71.51 344	84.74 347	49.30 347	75.32 349	50.97 349	43.71 346	75.59 347

ANet_high		66.05 320	63.44 323	73.88 331	61.14 352	63.45 350	95.68 343	87.18 353	79.93 337	47.35 350	80.68 350	22.35 354	72.33 352	61.24 346	35.42 347	85.88 344

tmp_tt		75.80 316	74.26 318	80.43 327	52.91 355	53.67 353	87.42 346	97.98 335	61.80 344	67.04 347	100.00 1	76.43 334	96.40 328	96.47 263	28.26 348	91.23 340

test123		79.44 312	79.23 315	80.05 328	80.03 347	71.72 342	100.00 1	77.93 356	62.52 343	94.81 315	99.69 229	78.21 331	74.53 350	92.57 304	27.33 349	93.90 336

wuyk23d		28.28 322	29.73 325	23.92 336	75.89 350	32.61 356	66.50 347	12.88 357	16.09 351	14.59 352	16.59 352	12.35 355	32.36 353	39.36 350	13.36 350	6.79 349

uanet_test		0.01 326	0.02 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_part1				0.00 337		0.00 357	0.00 348	99.42 124				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

cdsmvs_eth3d_5k		24.41 323	32.55 324	0.00 337	0.00 356	0.00 357	0.00 348	99.39 182	0.00 352	0.00 353	100.00 1	93.55 222	0.00 354	0.00 351	0.00 351	0.00 350

pcd_1.5k_mvsjas		8.24 325	10.99 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	98.75 120	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.01 326	0.02 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.01 326	0.02 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.01 326	0.02 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.01 326	0.02 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		8.33 324	11.11 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	100.00 1	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.01 326	0.02 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.14 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_241102_ONE					100.00 1	99.99 3		99.42 124	99.03 19	100.00 1	100.00 1	99.50 33	100.00 1

save fliter					99.99 52	99.93 41	100.00 1	99.42 124	98.93 32

test0726					100.00 1	99.99 3	100.00 1	99.42 124	99.04 14	100.00 1	100.00 1	99.53 23

GSMVS																99.91 132

test_part2					100.00 1	99.99 3				100.00 1

sam_mvs1												99.29 66				99.91 132

sam_mvs												99.33 55

MTGPA								99.42 124

test_post1							99.32 294				88.24 344	99.33 55	99.59 186	98.31 210

test_post											89.05 342	99.49 35	99.59 186

patchmatchnet-post											97.79 325	99.41 51	99.54 201

MTMP							100.00 1	99.18 262

gm-plane-assit					99.52 190	97.26 250			95.86 228		100.00 1		99.43 217	98.76 187

TEST9					100.00 1	99.95 28	100.00 1	99.42 124	97.65 123	100.00 1	100.00 1	99.53 23	99.97 113

test_8					100.00 1	99.91 51	100.00 1	99.42 124	97.70 118	100.00 1	100.00 1	99.51 28	99.98 109

agg_prior					100.00 1	99.88 74		99.42 124		100.00 1			99.97 113

test_prior4						99.93 41	100.00 1

test_prior				99.90 80	100.00 1	99.75 96		99.73 53					99.97 113			100.00 1

旧先验2							100.00 1		98.11 83	100.00 1			100.00 1	99.67 130

新几何2							100.00 1

无先验							100.00 1	99.80 39	97.98 92				100.00 1	99.33 159		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 242

segment_acmp												99.55 21

testdata1							100.00 1		98.77 48

plane_prior7					99.00 247	94.78 292

plane_prior6					99.06 239	94.80 288						88.58 280

plane_prior4											99.97 173

plane_prior3						94.79 291			99.03 19	99.08 196

plane_prior2							100.00 1		99.00 25

plane_prior1					99.02 242

n2								0.00 358

nn								0.00 358

door-mid								96.32 346

test11								99.42 124

door								96.13 347

HQP5-MVS						94.82 285

HQP-NCC					99.07 235		100.00 1		99.04 14	99.17 187

ACMP_Plane					99.07 235		100.00 1		99.04 14	99.17 187

BP-MVS														99.79 108

HQP4-MVS										99.17 187			99.57 189			97.77 223

HQP2-MVS												88.61 278

NP-MVS					99.07 235	94.81 287					99.97 173

MDTV_nov1_ep13_2view						99.24 147	99.56 275		96.31 217	99.96 106		98.86 114		98.92 178		99.89 147

Test By Simon												99.10 86

High-resolution multi-view benchmark