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.

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 133	98.79 60	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

FOURS1					100.00 1	99.97 21	100.00 1	99.42 133	98.52 74	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 133					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 133					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 133	98.72 64	100.00 1	100.00 1	99.60 17

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 180	100.00 1	100.00 1	99.32 61	99.99 94	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 133	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 133	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 26	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 133	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 133	99.04 15	100.00 1	100.00 1	99.53 29	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					100.00 1	99.99 5	100.00 1	99.42 133	99.04 15	100.00 1	100.00 1	99.53 29

SR-MVS		99.68 40	99.58 46	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 151	100.00 1	100.00 1	98.99 94	99.99 94	100.00 1	100.00 1	100.00 1

GST-MVS		99.64 48	99.53 59	99.95 51	100.00 1	99.86 77	100.00 1	99.79 45	97.72 133	99.95 141	100.00 1	98.39 132	100.00 1	99.96 85	99.99 97	100.00 1

test_part2					100.00 1	99.99 5				100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 63	100.00 1	99.86 77	100.00 1	99.42 133	98.87 47	100.00 1	100.00 1	99.65 15	99.96 134	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 121	99.05 14	100.00 1	100.00 1	99.45 45	99.99 94	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
TEST9					100.00 1	99.95 32	100.00 1	99.42 133	97.65 140	100.00 1	100.00 1	99.53 29	99.97 123

train_agg		99.71 29	99.63 37	99.97 31	100.00 1	99.95 32	100.00 1	99.42 133	97.70 135	100.00 1	100.00 1	99.51 33	99.97 123	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 51	100.00 1	99.42 133	97.70 135	100.00 1	100.00 1	99.51 33	99.98 118

agg_prior					100.00 1	99.88 72		99.42 133		100.00 1			99.97 123

HFP-MVS		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.31 63	100.00 1	99.99 61	100.00 1	100.00 1

region2R		99.72 26	99.64 33	99.97 31	100.00 1	99.90 58	100.00 1	99.74 55	97.86 124	100.00 1	100.00 1	99.19 78	100.00 1	99.99 61	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 32	99.64 33	99.87 78	100.00 1	99.64 105	99.98 229	99.44 115	98.35 86	99.99 103	100.00 1	99.04 91	99.96 134	99.98 73	100.00 1	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 67	100.00 1	100.00 1	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 244	96.06 273	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 67	100.00 1	66.97 402	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 71	100.00 1	99.75 91		99.73 56					99.97 123			100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 121	100.00 1	100.00 1	99.20 77	100.00 1	97.91 254	100.00 1	100.00 1

原ACMM1				99.93 66	100.00 1	99.80 87		99.66 63	98.18 95	100.00 1	100.00 1	99.43 50	100.00 1	99.50 171	100.00 1	100.00 1

MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 7	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 115	99.06 12	100.00 1	100.00 1	99.56 23	99.99 94	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.29 69	100.00 1	99.99 61	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 63	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 25	99.64 33	99.99 12	100.00 1	99.97 21	100.00 1	99.42 133	98.02 108	100.00 1	100.00 1	99.32 61	99.99 94	100.00 1	100.00 1	100.00 1

mPP-MVS		99.69 35	99.60 43	99.97 31	100.00 1	99.91 51	100.00 1	99.42 133	97.91 120	100.00 1	100.00 1	99.04 91	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS		99.59 61	99.50 63	99.89 73	100.00 1	99.70 100	100.00 1	99.42 133	97.46 166	100.00 1	100.00 1	98.60 124	99.96 134	99.99 61	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
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 10	100.00 1	100.00 1	99.39 56	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 5	100.00 1	99.71 61	99.07 10	100.00 1	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	100.00 1

CP-MVS		99.67 43	99.58 46	99.95 51	100.00 1	99.84 82	100.00 1	99.42 133	97.77 130	100.00 1	100.00 1	99.07 87	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 71	99.38 73	99.85 85	100.00 1	99.54 115	100.00 1	99.42 133	97.58 152	99.98 109	100.00 1	97.43 169	100.00 1	99.99 61	100.00 1	100.00 1

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 110	99.99 103	100.00 1	99.72 12	100.00 1	99.96 85	100.00 1	100.00 1

PAPM_NR		99.74 22	99.66 30	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 123	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 25	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 96	100.00 1	100.00 1	99.51 33	100.00 1	100.00 1	100.00 1	100.00 1

PHI-MVS		99.50 69	99.39 72	99.82 91	100.00 1	99.45 132	100.00 1	99.94 22	96.38 245	100.00 1	100.00 1	98.18 136	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 95	99.25 89	99.44 153	100.00 1	98.32 219	100.00 1	99.86 38	98.04 107	100.00 1	100.00 1	96.10 202	100.00 1	99.55 164	99.73 144	100.00 1

MG-MVS		99.75 20	99.68 25	99.97 31	100.00 1	99.91 51	99.98 229	99.47 79	99.09 9	100.00 1	100.00 1	98.59 125	100.00 1	99.95 91	100.00 1	100.00 1

AdaColmap		99.44 77	99.26 88	99.95 51	100.00 1	99.86 77	99.70 299	99.99 13	98.53 73	99.90 151	100.00 1	95.34 213	100.00 1	99.92 96	100.00 1	100.00 1

PLC	98.56 2	99.70 32	99.74 16	99.58 140	100.00 1	98.79 189	100.00 1	99.54 71	98.58 72	99.96 118	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 133
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SR-MVS-dyc-post		99.63 51	99.52 61	99.97 31	99.99 49	99.91 51	100.00 1	99.42 133	97.62 144	100.00 1	100.00 1	98.65 122	99.99 94	99.99 61	100.00 1	100.00 1

RE-MVS-def			99.55 56		99.99 49	99.91 51	100.00 1	99.42 133	97.62 144	100.00 1	100.00 1	98.94 102		99.99 61	100.00 1	100.00 1

SF-MVS		99.66 45	99.57 49	99.95 51	99.99 49	99.85 80	100.00 1	99.42 133	97.67 138	100.00 1	100.00 1	99.05 89	99.99 94	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 29	99.62 41	99.97 31	99.99 49	99.90 58	100.00 1	99.79 45	97.97 114	99.97 113	100.00 1	98.97 96	100.00 1	99.94 93	100.00 1	100.00 1

9.14			99.57 49		99.99 49		100.00 1	99.42 133	97.54 155	100.00 1	100.00 1	99.15 82	99.99 94	100.00 1	100.00 1

save fliter					99.99 49	99.93 43	100.00 1	99.42 133	98.93 38

CS-MVS		99.33 89	99.27 85	99.50 147	99.99 49	99.00 180	100.00 1	99.13 311	97.26 183	99.96 118	100.00 1	97.79 151	99.64 207	99.64 153	99.67 149	99.87 180

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 133					100.00 1	100.00 1	100.00 1	100.00 1

SMA-MVS		99.69 35	99.59 44	99.98 23	99.99 49	99.93 43	100.00 1	99.43 121	97.50 162	100.00 1	100.00 1	99.43 50	100.00 1	100.00 1	100.00 1	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 133	98.91 41	100.00 1	100.00 1	99.22 75	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CHOSEN 280x420		99.85 3	99.87 1	99.80 101	99.99 49	99.97 21	99.97 235	99.98 16	98.96 32	100.00 1	100.00 1	99.96 5	99.42 248	100.00 1	100.00 1	100.00 1

CANet		99.40 79	99.24 91	99.89 73	99.99 49	99.76 90	100.00 1	99.73 56	98.40 79	99.78 174	100.00 1	95.28 214	99.96 134	100.00 1	99.99 97	99.96 122

CS-MVS-test		99.31 93	99.27 85	99.43 155	99.99 49	98.77 190	100.00 1	99.19 288	97.24 184	99.96 118	100.00 1	97.56 161	99.70 204	99.68 145	99.81 140	99.82 192

MTAPA		99.68 40	99.59 44	99.97 31	99.99 49	99.91 51	100.00 1	99.42 133	98.32 88	99.94 143	100.00 1	98.65 122	100.00 1	99.96 85	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 78	99.99 49	99.64 105	99.95 246	99.44 115	98.35 86	100.00 1	100.00 1	98.98 95	99.97 123	99.98 73	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 44	99.96 118	100.00 1	99.21 76	100.00 1	100.00 1	100.00 1	99.99 107

旧先验1					99.99 49	99.88 72		99.82 40			100.00 1	99.27 72			100.00 1	100.00 1

test222					99.99 49	99.90 58	100.00 1	99.69 62	97.66 139	100.00 1	100.00 1	99.30 68			100.00 1	100.00 1

testdata				99.66 127	99.99 49	98.97 184		99.73 56	97.96 117	100.00 1	100.00 1	99.42 52	100.00 1	99.28 185	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 133	99.01 26	100.00 1	100.00 1	99.33 58	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
APD-MVS_3200maxsize		99.65 46	99.55 56	99.97 31	99.99 49	99.91 51	100.00 1	99.48 78	97.54 155	100.00 1	100.00 1	98.97 96	99.99 94	99.98 73	100.00 1	100.00 1

MP-MVS		99.61 57	99.49 65	99.98 23	99.99 49	99.94 40	100.00 1	99.42 133	97.82 126	99.99 103	100.00 1	98.20 135	100.00 1	99.99 61	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 35	99.61 42	99.95 51	99.99 49	99.85 80	100.00 1	99.58 67	97.69 137	100.00 1	100.00 1	99.44 46	100.00 1	99.79 119	100.00 1	100.00 1

test12				99.95 51	99.99 49	99.89 65		99.42 133		100.00 1		99.24 74	99.97 123		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 57	99.69 22	99.35 166	99.99 49	98.06 238	100.00 1	99.36 207	99.83 2	100.00 1	100.00 1	98.95 100	99.99 94	100.00 1	99.11 162	100.00 1

HPM-MVS_fast		99.60 60	99.49 65	99.91 68	99.99 49	99.78 88	100.00 1	99.42 133	97.09 192	100.00 1	100.00 1	98.95 100	99.96 134	99.98 73	100.00 1	100.00 1

EPNet		99.62 55	99.69 22	99.42 157	99.99 49	98.37 213	100.00 1	99.89 37	98.83 53	100.00 1	100.00 1	98.97 96	100.00 1	99.90 99	99.61 154	99.89 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		99.68 40	99.58 46	99.97 31	99.99 49	99.96 24	100.00 1	99.42 133	97.53 157	100.00 1	100.00 1	99.27 72	99.97 123	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
CSCG		99.28 97	99.35 81	99.05 190	99.99 49	97.15 282	100.00 1	99.47 79	97.44 168	99.42 196	100.00 1	97.83 150	100.00 1	99.99 61	100.00 1	100.00 1

PatchMatch-RL		99.02 125	98.78 137	99.74 113	99.99 49	99.29 148	100.00 1	100.00 1	98.38 80	99.89 154	99.81 246	93.14 248	99.99 94	97.85 256	99.98 107	99.95 128

ACMMP		99.65 46	99.57 49	99.89 73	99.99 49	99.66 103	99.75 288	99.73 56	98.16 96	99.75 178	100.00 1	98.90 107	100.00 1	99.96 85	99.88 128	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
F-COLMAP		99.64 48	99.64 33	99.67 124	99.99 49	99.07 170	100.00 1	99.44 115	98.30 89	99.90 151	100.00 1	99.18 79	99.99 94	99.91 98	100.00 1	99.94 133

DeepPCF-MVS	98.03 4	98.54 170	99.72 19	94.98 340	99.99 49	84.94 379	100.00 1	99.42 133	99.98 1	100.00 1	100.00 1	98.11 138	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 20	99.67 27	99.99 12	99.99 49	99.96 24	99.73 294	99.52 72	99.06 12	100.00 1	100.00 1	98.80 116	100.00 1	99.95 91	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
LS3D		99.31 93	99.13 104	99.87 78	99.99 49	99.71 96	99.55 317	99.46 94	97.32 178	99.82 170	100.00 1	96.85 189	99.97 123	99.14 194	100.00 1	99.92 145

MP-MVS-pluss		99.61 57	99.50 63	99.97 31	99.98 84	99.92 48	100.00 1	99.42 133	97.53 157	99.77 175	100.00 1	98.77 117	100.00 1	99.99 61	100.00 1	99.99 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.67 43	99.57 49	99.97 31	99.98 84	99.92 48	100.00 1	99.42 133	97.83 125	100.00 1	100.00 1	98.89 108	100.00 1	99.98 73	100.00 1	100.00 1

PVSNet_BlendedMVS		98.71 154	98.62 153	98.98 197	99.98 84	99.60 108	100.00 1	100.00 1	97.23 185	100.00 1	99.03 327	96.57 195	99.99 94	100.00 1	94.75 282	97.35 351

PVSNet_Blended		99.48 73	99.36 79	99.83 89	99.98 84	99.60 108	100.00 1	100.00 1	97.79 128	100.00 1	100.00 1	96.57 195	99.99 94	100.00 1	99.88 128	99.90 156

114514_t		99.39 80	99.25 89	99.81 96	99.97 88	99.48 130	100.00 1	99.42 133	95.53 276	100.00 1	100.00 1	98.37 133	99.95 146	99.97 83	100.00 1	100.00 1

DP-MVS		98.86 143	98.54 160	99.81 96	99.97 88	99.45 132	99.52 321	99.40 183	94.35 310	98.36 270	100.00 1	96.13 201	99.97 123	99.12 197	100.00 1	100.00 1

API-MVS		99.72 26	99.70 21	99.79 103	99.97 88	99.37 142	99.96 240	99.94 22	98.48 75	100.00 1	100.00 1	98.92 105	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 26	99.65 31	99.91 68	99.97 88	99.72 95	100.00 1	99.47 79	98.43 78	99.88 156	100.00 1	99.14 83	100.00 1	99.97 83	100.00 1	100.00 1

MAR-MVS		99.49 71	99.36 79	99.89 73	99.97 88	99.66 103	99.74 289	99.95 19	97.89 121	100.00 1	100.00 1	96.71 192	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
MVS_0304		99.69 35	99.63 37	99.86 81	99.96 93	99.63 107	100.00 1	99.92 34	99.03 20	99.97 113	100.00 1	97.87 145	99.96 134	100.00 1	99.96 113	100.00 1

MVS_111021_LR		99.70 32	99.65 31	99.88 77	99.96 93	99.70 100	100.00 1	99.97 17	98.96 32	100.00 1	100.00 1	97.93 143	99.95 146	99.99 61	100.00 1	100.00 1

HyFIR lowres test		99.32 91	99.24 91	99.58 140	99.95 95	99.26 151	100.00 1	99.99 13	96.72 219	99.29 208	99.91 225	99.49 39	99.47 240	99.74 128	98.08 207	100.00 1

MVS_111021_HR		99.71 29	99.63 37	99.93 66	99.95 95	99.83 83	100.00 1	100.00 1	98.89 43	100.00 1	100.00 1	97.85 147	99.95 146	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 98	99.38 73	98.96 198	99.95 95	97.06 286	100.00 1	99.40 183	98.83 53	99.88 156	100.00 1	97.01 179	99.86 172	99.47 172	99.84 137	99.97 116

TAPA-MVS	96.40 10	97.64 214	97.37 223	98.45 225	99.94 98	95.70 306	100.00 1	99.40 183	97.65 140	99.53 188	100.00 1	99.31 63	99.66 206	80.48 386	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
COLMAP_ROB	97.10 7	98.29 189	98.17 186	98.65 213	99.94 98	97.39 270	99.30 344	99.40 183	95.64 271	97.75 306	100.00 1	92.69 257	99.95 146	98.89 205	99.92 123	98.62 251
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.55 169	98.40 169	98.99 195	99.93 100	97.35 273	100.00 1	99.40 183	97.08 194	99.09 220	99.98 187	93.37 241	99.95 146	96.94 282	99.84 137	99.68 229

TestCases				98.99 195	99.93 100	97.35 273		99.40 183	97.08 194	99.09 220	99.98 187	93.37 241	99.95 146	96.94 282	99.84 137	99.68 229

DeepC-MVS	97.84 5	99.00 127	98.80 136	99.60 134	99.93 100	99.03 175	100.00 1	99.40 183	98.61 71	99.33 206	100.00 1	92.23 261	99.95 146	99.74 128	99.96 113	99.83 187
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
patch_mono-2		99.04 119	99.79 6	96.81 317	99.92 103	90.47 365	100.00 1	99.41 179	98.95 35	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 128

EPNet_dtu		98.53 171	98.23 184	99.43 155	99.92 103	99.01 178	99.96 240	99.47 79	98.80 57	99.96 118	99.96 209	98.56 126	99.30 256	87.78 371	99.68 147	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 55	99.56 54	99.82 91	99.92 103	99.45 132	100.00 1	99.78 47	98.92 39	99.73 180	100.00 1	97.70 154	100.00 1	99.93 95	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
MSDG		98.90 141	98.63 152	99.70 121	99.92 103	99.25 153	100.00 1	99.37 201	95.71 270	99.40 202	100.00 1	96.58 194	99.95 146	96.80 289	99.94 118	99.91 147

DPM-MVS		99.63 51	99.51 62	100.00 1	99.90 107	100.00 1	100.00 1	99.43 121	99.00 27	100.00 1	100.00 1	99.58 22	100.00 1	97.64 261	100.00 1	100.00 1

CHOSEN 1792x2688		99.00 127	98.91 126	99.25 181	99.90 107	97.79 258	100.00 1	99.99 13	98.79 60	98.28 277	100.00 1	93.63 237	99.95 146	99.66 151	99.95 116	100.00 1

test2506		99.48 73	99.38 73	99.75 112	99.89 109	99.51 121	99.45 327	100.00 1	98.38 80	99.83 162	100.00 1	98.86 109	99.81 187	99.25 186	98.78 170	99.94 133

ECVR-MVS		98.43 178	98.14 187	99.32 172	99.89 109	98.21 227	99.46 325	100.00 1	98.38 80	99.47 195	100.00 1	87.91 314	99.80 188	99.35 178	98.78 170	99.94 133

test1111		98.42 180	98.12 188	99.29 175	99.88 111	98.15 230	99.46 325	100.00 1	98.36 84	99.42 196	100.00 1	87.91 314	99.79 189	99.31 182	98.78 170	99.94 133

CANet_DTU		99.02 125	98.90 129	99.41 158	99.88 111	98.71 195	100.00 1	99.29 241	98.84 51	100.00 1	100.00 1	94.02 233	100.00 1	98.08 246	99.96 113	99.52 237

dcpmvs_2		98.87 142	99.53 59	96.90 311	99.87 113	90.88 364	99.94 251	99.07 331	98.20 94	100.00 1	100.00 1	98.69 121	99.86 172	100.00 1	100.00 1	99.95 128

mvsany_test1		99.57 62	99.48 68	99.85 85	99.86 114	99.54 115	100.00 1	99.36 207	98.94 37	100.00 1	100.00 1	97.97 141	100.00 1	99.88 103	99.28 159	100.00 1

test_fmvsm_n_1920		99.55 64	99.49 65	99.73 116	99.85 115	99.19 161	100.00 1	99.41 179	98.87 47	100.00 1	100.00 1	97.34 172	100.00 1	99.98 73	99.90 125	100.00 1

fmvsm_s_conf0.5_n		99.21 108	99.01 111	99.83 89	99.84 116	99.53 117	100.00 1	99.38 198	98.29 90	100.00 1	100.00 1	93.62 238	99.99 94	99.99 61	99.93 121	99.98 109

test_fmvs1		98.37 185	98.04 196	99.34 167	99.84 116	98.07 236	100.00 1	99.00 350	98.85 49	100.00 1	100.00 1	85.11 341	99.96 134	99.69 144	99.88 128	100.00 1

PVSNet_0	93.57 19	96.41 273	95.74 289	98.41 227	99.84 116	95.22 311	100.00 1	100.00 1	98.08 105	97.55 315	99.78 252	84.40 344	100.00 1	100.00 1	81.99 376	100.00 1

fmvsm_l_conf0.5_n_a		99.63 51	99.55 56	99.86 81	99.83 119	99.58 111	100.00 1	99.36 207	98.98 30	100.00 1	100.00 1	97.85 147	99.99 94	100.00 1	99.94 118	100.00 1

SDMVSNet		98.49 175	98.08 191	99.73 116	99.82 120	99.53 117	99.99 205	99.45 102	97.62 144	99.38 203	99.86 233	90.06 288	99.88 171	99.92 96	96.61 245	99.79 214

sd_testset		97.81 206	97.48 217	98.79 208	99.82 120	96.80 292	99.32 340	99.45 102	97.62 144	99.38 203	99.86 233	85.56 339	99.77 194	99.72 131	96.61 245	99.79 214

cl22		98.23 193	98.11 189	98.58 220	99.82 120	99.01 178	100.00 1	99.28 247	96.92 203	98.33 273	99.21 315	98.09 140	98.97 273	98.72 216	92.61 303	97.76 256

test_yl		99.51 66	99.37 76	99.95 51	99.82 120	99.90 58	100.00 1	99.47 79	97.48 164	100.00 1	100.00 1	99.80 6	100.00 1	99.98 73	97.75 227	99.94 133

DCV-MVSNet		99.51 66	99.37 76	99.95 51	99.82 120	99.90 58	100.00 1	99.47 79	97.48 164	100.00 1	100.00 1	99.80 6	100.00 1	99.98 73	97.75 227	99.94 133

PVSNet_Blended_VisFu		99.33 89	99.18 101	99.78 107	99.82 120	99.49 126	100.00 1	99.95 19	97.36 173	99.63 185	100.00 1	96.45 199	99.95 146	99.79 119	99.65 151	99.89 161

fmvsm_l_conf0.5_n		99.63 51	99.56 54	99.86 81	99.81 126	99.59 110	100.00 1	99.36 207	98.98 30	100.00 1	100.00 1	97.92 144	99.99 94	100.00 1	99.95 116	100.00 1

ET-MVSNet_ETH3D		96.41 273	95.48 303	99.20 184	99.81 126	99.75 91	100.00 1	99.02 347	97.30 182	78.33 391	100.00 1	97.73 152	97.94 353	99.70 137	87.41 358	99.92 145

Anonymous202405211		97.87 203	97.53 216	98.90 201	99.81 126	96.70 295	99.35 338	99.46 94	92.98 341	98.83 243	99.99 182	90.63 279	100.00 1	99.70 137	97.03 238	100.00 1

thres100view900		99.25 103	99.01 111	99.95 51	99.81 126	99.87 74	100.00 1	99.94 22	97.13 190	99.83 162	99.96 209	97.01 179	100.00 1	99.59 159	97.85 219	99.98 109

tfpn200view9		99.26 100	99.03 109	99.96 42	99.81 126	99.89 65	100.00 1	99.94 22	97.23 185	99.83 162	99.96 209	97.04 175	100.00 1	99.59 159	97.85 219	99.98 109

VNet		99.04 119	98.75 142	99.90 71	99.81 126	99.75 91	99.50 323	99.47 79	98.36 84	100.00 1	99.99 182	94.66 226	100.00 1	99.90 99	97.09 237	99.96 122

thres600view7		99.24 106	99.00 113	99.95 51	99.81 126	99.87 74	100.00 1	99.94 22	97.13 190	99.83 162	99.96 209	97.01 179	100.00 1	99.54 167	97.77 226	99.97 116

thres400		99.26 100	99.03 109	99.95 51	99.81 126	99.89 65	100.00 1	99.94 22	97.23 185	99.83 162	99.96 209	97.04 175	100.00 1	99.59 159	97.85 219	99.97 116

thres200		99.27 98	99.04 108	99.96 42	99.81 126	99.90 58	100.00 1	99.94 22	97.31 180	99.83 162	99.96 209	97.04 175	100.00 1	99.62 157	97.88 217	99.98 109

WTY-MVS		99.54 65	99.40 71	99.95 51	99.81 126	99.93 43	100.00 1	100.00 1	97.98 112	99.84 160	100.00 1	98.94 102	99.98 118	99.86 107	98.21 200	99.94 133

HY-MVS	96.53 9	99.50 69	99.35 81	99.96 42	99.81 126	99.93 43	99.64 306	100.00 1	97.97 114	99.84 160	99.85 237	98.94 102	99.99 94	99.86 107	98.23 199	99.95 128

fmvsm_s_conf0.5_n_a		99.32 91	99.15 103	99.81 96	99.80 137	99.47 131	100.00 1	99.35 218	98.22 91	100.00 1	100.00 1	95.21 217	99.99 94	99.96 85	99.86 132	99.98 109

Anonymous20231211		96.29 282	95.70 291	98.07 255	99.80 137	97.49 266	99.15 364	99.40 183	89.11 368	97.75 306	99.45 304	88.93 305	98.98 271	98.26 242	89.47 342	97.73 296

LFMVS		97.42 227	96.62 248	99.81 96	99.80 137	99.50 123	99.16 362	99.56 70	94.48 306	100.00 1	100.00 1	79.35 367	100.00 1	99.89 101	97.37 233	99.94 133

RPSCF		97.37 229	98.24 181	94.76 343	99.80 137	84.57 380	99.99 205	99.05 341	94.95 291	99.82 170	100.00 1	94.03 232	100.00 1	98.15 245	98.38 189	99.70 227

test_vis1_n_1920		97.77 208	97.24 231	99.34 167	99.79 141	98.04 240	100.00 1	99.25 263	98.88 44	100.00 1	100.00 1	77.52 372	100.00 1	99.88 103	99.85 135	100.00 1

h-mvs33		97.03 245	96.53 251	98.51 222	99.79 141	95.90 304	99.45 327	99.45 102	98.21 92	100.00 1	99.78 252	97.49 163	99.99 94	99.72 131	74.92 385	99.65 234

Anonymous20240529		96.93 250	96.22 267	99.05 190	99.79 141	97.30 277	99.16 362	99.47 79	88.51 371	98.69 250	100.00 1	83.50 352	100.00 1	99.83 113	97.02 239	99.83 187

PS-MVSNAJ		99.64 48	99.57 49	99.85 85	99.78 144	99.81 85	99.95 246	99.42 133	98.38 80	100.00 1	100.00 1	98.75 118	100.00 1	99.88 103	99.99 97	99.74 222

PCF-MVS	98.23 3	98.69 157	98.37 173	99.62 131	99.78 144	99.02 176	99.23 353	99.06 339	96.43 239	98.08 286	100.00 1	94.72 225	99.95 146	98.16 244	99.91 124	99.90 156
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+-dtu		98.51 174	98.86 131	97.47 287	99.77 146	94.21 337	100.00 1	98.94 355	97.61 148	99.91 149	98.75 345	95.89 204	99.51 235	99.36 177	99.48 157	98.68 249

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 147	99.95 32	100.00 1	99.42 133	98.69 65	100.00 1	100.00 1	99.52 32	99.99 94	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
sss		99.45 76	99.34 83	99.80 101	99.76 147	99.50 123	100.00 1	99.91 36	97.72 133	99.98 109	99.94 221	98.45 130	100.00 1	99.53 169	98.75 173	99.89 161

canonicalmvs		99.03 121	98.73 144	99.94 63	99.75 149	99.95 32	100.00 1	99.30 237	97.64 142	100.00 1	100.00 1	95.22 216	99.97 123	99.76 126	96.90 242	99.91 147

alignmvs		99.38 81	99.21 95	99.91 68	99.73 150	99.92 48	100.00 1	99.51 76	97.61 148	100.00 1	100.00 1	99.06 88	99.93 161	99.83 113	97.12 236	99.90 156

baseline1		98.91 139	98.61 154	99.81 96	99.71 151	99.77 89	99.78 279	99.44 115	97.51 161	98.81 244	99.99 182	98.25 134	99.76 196	98.60 226	95.41 255	99.89 161

ab-mvs		98.42 180	98.02 198	99.61 132	99.71 151	99.00 180	99.10 369	99.64 64	96.70 221	99.04 226	99.81 246	90.64 278	99.98 118	99.64 153	97.93 214	99.84 184

xiu_mvs_v2_base		99.51 66	99.41 70	99.82 91	99.70 153	99.73 94	99.92 254	99.40 183	98.15 98	100.00 1	100.00 1	98.50 129	100.00 1	99.85 109	99.13 161	99.74 222

test_cas_vis1_n_1920		98.63 163	98.25 179	99.77 110	99.69 154	99.32 145	100.00 1	99.31 233	98.84 51	99.96 118	100.00 1	87.42 321	99.99 94	99.14 194	99.86 132	100.00 1

tpmvs		98.59 165	98.38 171	99.23 182	99.69 154	97.90 250	99.31 343	99.47 79	94.52 304	99.68 183	99.28 313	97.64 157	99.89 167	97.71 259	98.17 204	99.89 161

Test_1112_low_res		98.83 145	98.60 156	99.51 145	99.69 154	98.75 191	99.99 205	99.14 307	96.81 211	98.84 241	99.06 322	97.45 166	99.89 167	98.66 218	97.75 227	99.89 161

1112_ss		98.91 139	98.71 147	99.51 145	99.69 154	98.75 191	99.99 205	99.15 302	96.82 210	98.84 241	100.00 1	97.45 166	99.89 167	98.66 218	97.75 227	99.89 161

test_fmvsmconf_n		99.56 63	99.46 69	99.86 81	99.68 158	99.58 111	100.00 1	99.31 233	98.92 39	99.88 156	100.00 1	97.35 171	99.99 94	99.98 73	99.99 97	100.00 1

test_fmvs1_n		97.43 226	96.86 239	99.15 186	99.68 158	97.48 267	99.99 205	98.98 353	98.82 55	100.00 1	100.00 1	74.85 377	99.96 134	99.67 148	99.70 146	100.00 1

hse-mvs2		96.79 253	96.38 259	98.04 265	99.68 158	95.54 308	99.81 273	99.42 133	98.21 92	100.00 1	99.80 249	97.49 163	99.46 244	99.72 131	73.27 388	99.12 244

AUN-MVS		96.26 284	95.67 295	98.06 259	99.68 158	95.60 307	99.82 272	99.42 133	96.78 213	99.88 156	99.80 249	94.84 223	99.47 240	97.48 267	73.29 387	99.12 244

Vis-MVSNet		98.52 172	98.25 179	99.34 167	99.68 158	98.55 203	99.68 303	99.41 179	97.34 176	99.94 143	100.00 1	90.38 284	99.70 204	99.03 200	98.84 168	99.76 220
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testing222		99.14 111	98.94 122	99.73 116	99.67 163	99.51 121	100.00 1	99.43 121	96.90 206	99.99 103	99.90 227	98.55 127	99.86 172	98.85 208	97.18 235	99.81 200

xiu_mvs_v1_base_debu		99.35 84	99.21 95	99.79 103	99.67 163	99.71 96	99.78 279	99.36 207	98.13 100	100.00 1	100.00 1	97.00 182	100.00 1	99.83 113	99.07 163	99.66 231

xiu_mvs_v1_base		99.35 84	99.21 95	99.79 103	99.67 163	99.71 96	99.78 279	99.36 207	98.13 100	100.00 1	100.00 1	97.00 182	100.00 1	99.83 113	99.07 163	99.66 231

xiu_mvs_v1_base_debi		99.35 84	99.21 95	99.79 103	99.67 163	99.71 96	99.78 279	99.36 207	98.13 100	100.00 1	100.00 1	97.00 182	100.00 1	99.83 113	99.07 163	99.66 231

IB-MVS	96.24 12	97.54 221	96.95 236	99.33 170	99.67 163	98.10 235	100.00 1	99.47 79	97.42 170	99.26 209	99.69 264	98.83 113	99.89 167	99.43 173	78.77 383	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
miper_enhance_ethall		98.33 186	98.27 178	98.51 222	99.66 168	99.04 174	100.00 1	99.22 275	97.53 157	98.51 264	99.38 307	99.49 39	98.75 295	98.02 249	92.61 303	97.76 256

FE-MVS		99.16 110	98.99 115	99.66 127	99.65 169	99.18 163	99.58 314	99.43 121	95.24 286	99.91 149	99.59 289	99.37 57	99.97 123	98.31 237	99.81 140	99.83 187

iter_conf05		98.73 151	98.77 138	98.60 216	99.65 169	99.22 158	100.00 1	99.22 275	96.68 225	98.98 230	99.97 195	99.99 3	98.84 285	99.29 184	95.11 274	97.75 267

MVSTER		98.58 166	98.52 161	98.77 209	99.65 169	99.68 102	100.00 1	99.29 241	95.63 272	98.65 252	99.80 249	99.78 8	98.88 283	98.59 227	95.31 259	97.73 296

FMVSNet3		97.30 232	96.95 236	98.37 230	99.65 169	99.25 153	99.71 297	99.28 247	94.23 311	98.53 261	98.91 338	93.30 243	98.11 340	95.31 312	93.60 292	97.73 296

cascas		98.43 178	98.07 193	99.50 147	99.65 169	99.02 176	100.00 1	99.22 275	94.21 313	99.72 181	99.98 187	92.03 264	99.93 161	99.68 145	98.12 205	99.54 236

IS-MVSNet		99.08 115	98.91 126	99.59 136	99.65 169	99.38 139	99.78 279	99.24 268	96.70 221	99.51 190	100.00 1	98.44 131	99.52 233	98.47 231	98.39 187	99.88 172

iter_conf_final		98.72 152	98.76 140	98.59 218	99.64 175	99.17 165	100.00 1	99.22 275	96.63 230	99.02 227	99.97 195	99.98 4	98.84 285	99.22 191	95.18 268	97.76 256

Vis-MVSNet (Re-imp)		98.99 130	98.89 130	99.29 175	99.64 175	98.89 186	99.98 229	99.31 233	96.74 216	99.48 192	100.00 1	98.11 138	99.10 263	98.39 233	98.34 192	99.89 161

test_vis1_rt		93.10 328	92.93 329	93.58 353	99.63 177	85.07 378	99.99 205	93.71 399	97.49 163	90.96 372	97.10 373	60.40 390	99.95 146	99.24 188	97.90 216	95.72 375

FA-MVS(test-final)		99.00 127	98.75 142	99.73 116	99.63 177	99.43 135	99.83 269	99.43 121	95.84 267	99.52 189	99.37 308	97.84 149	99.96 134	97.63 262	99.68 147	99.79 214

EIA-MVS		99.26 100	99.19 99	99.45 152	99.63 177	98.75 191	100.00 1	99.27 255	96.93 201	99.95 141	100.00 1	97.47 165	99.79 189	99.74 128	99.72 145	99.82 192

Fast-Effi-MVS+		98.40 183	98.02 198	99.55 144	99.63 177	99.06 172	100.00 1	99.15 302	95.07 288	99.42 196	99.95 217	93.26 244	99.73 201	97.44 268	98.24 198	99.87 180

TESTMET0.1,1		99.08 115	98.96 117	99.44 153	99.63 177	99.38 139	100.00 1	99.45 102	95.53 276	99.48 192	100.00 1	99.71 13	99.02 267	96.84 286	99.99 97	99.91 147

MVS_Test		98.93 138	98.65 150	99.77 110	99.62 182	99.50 123	99.99 205	99.19 288	95.52 278	99.96 118	99.86 233	96.54 197	99.98 118	98.65 220	98.48 180	99.82 192

BH-w/o		98.82 146	98.81 135	98.88 203	99.62 182	96.71 294	100.00 1	99.28 247	97.09 192	98.81 244	100.00 1	94.91 222	99.96 134	99.54 167	100.00 1	99.96 122

tpmrst		98.98 133	98.93 124	99.14 187	99.61 184	97.74 259	99.52 321	99.36 207	96.05 258	99.98 109	99.64 277	99.04 91	99.86 172	98.94 202	98.19 202	99.82 192

BH-RMVSNet		98.46 176	98.08 191	99.59 136	99.61 184	99.19 161	100.00 1	99.28 247	97.06 196	98.95 231	100.00 1	88.99 303	99.82 184	98.83 211	100.00 1	99.77 218

Effi-MVS+		98.58 166	98.24 181	99.61 132	99.60 186	99.26 151	97.85 388	99.10 321	96.22 253	99.97 113	99.89 228	93.75 235	99.77 194	99.43 173	98.34 192	99.81 200

EPMVS		99.25 103	99.13 104	99.60 134	99.60 186	99.20 160	99.60 312	100.00 1	96.93 201	99.92 147	99.36 309	99.05 89	99.71 203	98.77 213	98.94 167	99.90 156

thisisatest0530		99.37 83	99.27 85	99.69 122	99.59 188	99.41 137	100.00 1	99.46 94	96.46 238	99.90 151	100.00 1	99.44 46	99.85 178	98.97 201	99.58 155	99.80 212

thisisatest0515		99.42 78	99.31 84	99.74 113	99.59 188	99.55 113	100.00 1	99.46 94	96.65 227	99.92 147	100.00 1	99.44 46	99.85 178	99.09 198	99.63 153	99.81 200

XVG-OURS-SEG-HR		98.27 191	98.31 177	98.14 249	99.59 188	95.92 302	100.00 1	99.36 207	98.48 75	99.21 211	100.00 1	89.27 300	99.94 158	99.76 126	99.17 160	98.56 252

BH-untuned		98.64 160	98.65 150	98.60 216	99.59 188	96.17 300	100.00 1	99.28 247	96.67 226	98.41 269	100.00 1	94.52 228	99.83 181	99.41 175	100.00 1	99.81 200

TR-MVS		98.14 194	97.74 208	99.33 170	99.59 188	98.28 222	99.27 345	99.21 284	96.42 241	99.15 216	99.94 221	88.87 306	99.79 189	98.88 206	98.29 196	99.93 143

ETV-MVS		99.34 87	99.24 91	99.64 129	99.58 193	99.33 144	100.00 1	99.25 263	97.57 153	99.96 118	100.00 1	97.44 168	99.79 189	99.70 137	99.65 151	99.81 200

Fast-Effi-MVS+-dtu		98.38 184	98.56 159	97.82 277	99.58 193	94.44 334	100.00 1	99.16 301	96.75 214	99.51 190	99.63 281	95.03 220	99.60 208	97.71 259	99.67 149	99.42 239

XVG-OURS		98.30 187	98.36 175	98.13 252	99.58 193	95.91 303	100.00 1	99.36 207	98.69 65	99.23 210	100.00 1	91.20 270	99.92 163	99.34 179	97.82 222	98.56 252

tttt0517		99.34 87	99.23 94	99.67 124	99.57 196	99.38 139	100.00 1	99.46 94	96.33 248	99.89 154	100.00 1	99.44 46	99.84 180	98.93 203	99.46 158	99.78 217

mvs_anonymous		98.80 147	98.60 156	99.38 164	99.57 196	99.24 155	100.00 1	99.21 284	95.87 262	98.92 232	99.82 243	96.39 200	99.03 266	99.13 196	98.50 178	99.88 172

PMMVS		99.12 112	98.97 116	99.58 140	99.57 196	98.98 182	100.00 1	99.30 237	97.14 189	99.96 118	100.00 1	96.53 198	99.82 184	99.70 137	98.49 179	99.94 133

dmvs_re		97.54 221	97.88 202	96.54 322	99.55 199	90.35 366	99.86 265	99.46 94	97.00 198	99.41 201	100.00 1	90.78 277	99.30 256	99.60 158	95.24 264	99.96 122

GG-mvs-BLEND				99.59 136	99.54 200	99.49 126	99.17 361	99.52 72		99.96 118	99.68 268	100.00 1	99.33 255	99.71 134	99.99 97	99.96 122

diffmvs		98.96 134	98.73 144	99.63 130	99.54 200	99.16 166	100.00 1	99.18 295	97.33 177	99.96 118	100.00 1	94.60 227	99.91 164	99.66 151	98.33 195	99.82 192
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UGNet		98.41 182	98.11 189	99.31 174	99.54 200	98.55 203	99.18 356	100.00 1	98.64 70	99.79 172	99.04 325	87.61 319	100.00 1	99.30 183	99.89 126	99.40 240
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
KD-MVS_2432*1600		94.15 318	93.08 327	97.35 292	99.53 203	97.83 256	99.63 308	99.19 288	92.88 343	96.29 341	97.68 369	98.84 111	96.70 368	89.73 361	63.92 392	97.53 342

miper_refine_blended		94.15 318	93.08 327	97.35 292	99.53 203	97.83 256	99.63 308	99.19 288	92.88 343	96.29 341	97.68 369	98.84 111	96.70 368	89.73 361	63.92 392	97.53 342

GBi-Net		96.07 295	95.80 285	96.89 312	99.53 203	94.87 315	99.18 356	99.27 255	93.71 321	98.53 261	98.81 342	84.23 346	98.07 345	95.31 312	93.60 292	97.72 303

test1		96.07 295	95.80 285	96.89 312	99.53 203	94.87 315	99.18 356	99.27 255	93.71 321	98.53 261	98.81 342	84.23 346	98.07 345	95.31 312	93.60 292	97.72 303

dp		98.72 152	98.61 154	99.03 193	99.53 203	97.39 270	99.45 327	99.39 196	95.62 273	99.94 143	99.52 299	98.83 113	99.82 184	96.77 292	98.42 184	99.89 161

FMVSNet2		96.22 285	95.60 297	98.06 259	99.53 203	98.33 217	99.45 327	99.27 255	93.71 321	98.03 290	98.84 341	84.23 346	98.10 343	93.97 330	93.40 295	97.73 296

MDTV_nov1_ep13			98.94 122		99.53 203	98.36 215	99.39 334	99.46 94	96.54 234	99.99 103	99.63 281	98.92 105	99.86 172	98.30 240	98.71 174

gm-plane-assit					99.52 210	97.26 279			95.86 264		100.00 1		99.43 246	98.76 214

UA-Net		99.06 117	98.83 132	99.74 113	99.52 210	99.40 138	99.08 372	99.45 102	97.64 142	99.83 162	100.00 1	95.80 206	99.94 158	98.35 235	99.80 142	99.88 172

VDD-MVS		96.58 265	95.99 276	98.34 232	99.52 210	95.33 309	99.18 356	99.38 198	96.64 228	99.77 175	100.00 1	72.51 382	100.00 1	100.00 1	96.94 241	99.70 227

casdiffmvs		98.65 159	98.38 171	99.46 151	99.52 210	98.74 194	100.00 1	99.15 302	96.91 204	99.05 225	100.00 1	92.75 253	99.83 181	99.70 137	98.38 189	99.81 200
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		98.69 157	98.45 166	99.41 158	99.52 210	98.67 198	100.00 1	99.17 300	97.03 197	99.13 217	100.00 1	93.17 246	99.74 199	99.70 137	98.34 192	99.81 200

testing3		98.44 177	98.37 173	98.65 213	99.51 215	98.32 219	100.00 1	99.62 66	96.43 239	97.93 297	99.99 182	99.11 84	97.81 356	94.88 319	97.80 224	99.82 192

ADS-MVSNet2		98.28 190	98.51 162	97.62 283	99.51 215	95.03 314	99.24 348	99.41 179	95.52 278	99.96 118	99.70 261	97.57 159	97.94 353	97.11 278	98.54 176	99.88 172

ADS-MVSNet		98.70 156	98.51 162	99.28 178	99.51 215	98.39 210	99.24 348	99.44 115	95.52 278	99.96 118	99.70 261	97.57 159	99.58 214	97.11 278	98.54 176	99.88 172

EPP-MVSNet		99.10 114	99.00 113	99.40 160	99.51 215	98.68 197	99.92 254	99.43 121	95.47 282	99.65 184	100.00 1	99.51 33	99.76 196	99.53 169	98.00 208	99.75 221

Syy-MVS		96.17 289	96.57 250	95.00 338	99.50 219	87.37 375	100.00 1	99.57 68	96.23 251	98.07 287	100.00 1	92.41 260	97.81 356	85.34 376	97.96 211	99.82 192

myMVS_eth3d		98.52 172	98.51 162	98.53 221	99.50 219	97.98 243	100.00 1	99.57 68	96.23 251	98.07 287	100.00 1	99.09 86	97.81 356	96.17 299	97.96 211	99.82 192

casdiffmvs_mvg		98.64 160	98.39 170	99.40 160	99.50 219	98.60 201	100.00 1	99.22 275	96.85 208	99.10 219	100.00 1	92.75 253	99.78 193	99.71 134	98.35 191	99.81 200
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpm cat1		98.05 198	97.76 206	98.92 200	99.50 219	97.10 285	99.77 284	99.30 237	90.20 365	99.72 181	98.71 346	97.71 153	99.86 172	96.75 293	98.20 201	99.81 200

miper_ehance_all_eth		97.81 206	97.66 212	98.23 240	99.49 223	98.37 213	99.99 205	99.11 319	94.78 294	98.25 281	99.21 315	98.18 136	98.57 312	97.35 274	92.61 303	97.76 256

PatchmatchNet		99.03 121	98.96 117	99.26 180	99.49 223	98.33 217	99.38 335	99.45 102	96.64 228	99.96 118	99.58 291	99.49 39	99.50 236	97.63 262	99.00 166	99.93 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_lstm_enhance		97.40 228	97.28 227	97.75 280	99.48 225	97.52 265	100.00 1	99.07 331	94.08 317	98.01 293	99.61 287	97.38 170	97.98 351	96.44 297	91.47 324	97.76 256

c3_l		97.58 218	97.42 219	98.06 259	99.48 225	98.16 228	99.96 240	99.10 321	94.54 303	98.13 285	99.20 317	97.87 145	98.25 332	97.28 275	91.20 327	97.75 267

CDS-MVSNet		98.96 134	98.95 121	99.01 194	99.48 225	98.36 215	99.93 253	99.37 201	96.79 212	99.31 207	99.83 240	99.77 10	98.91 277	98.07 247	97.98 209	99.77 218
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n		98.77 148	98.42 167	99.82 91	99.47 228	99.52 120	100.00 1	99.27 255	97.53 157	100.00 1	100.00 1	89.73 293	99.96 134	99.84 112	99.93 121	99.97 116

GeoE		98.06 197	97.65 213	99.29 175	99.47 228	98.41 207	100.00 1	99.19 288	94.85 293	98.88 236	100.00 1	91.21 269	99.59 210	97.02 280	98.19 202	99.88 172

cl____		97.54 221	97.32 225	98.18 244	99.47 228	98.14 232	100.00 1	99.10 321	94.16 316	97.60 313	99.63 281	97.52 162	98.65 302	96.47 294	91.97 315	97.76 256

DIV-MVS_self_test		97.52 224	97.35 224	98.05 263	99.46 231	98.11 233	100.00 1	99.10 321	94.21 313	97.62 311	99.63 281	97.65 156	98.29 329	96.47 294	91.98 314	97.76 256

MVSFormer		98.94 137	98.82 133	99.28 178	99.45 232	99.49 126	100.00 1	99.13 311	95.46 283	99.97 113	100.00 1	96.76 190	98.59 309	98.63 223	100.00 1	99.74 222

lupinMVS		99.29 96	99.16 102	99.69 122	99.45 232	99.49 126	100.00 1	99.15 302	97.45 167	99.97 113	100.00 1	96.76 190	99.76 196	99.67 148	100.00 1	99.81 200

WB-MVSnew		97.02 247	97.24 231	96.37 326	99.44 234	97.36 272	100.00 1	99.43 121	96.12 257	99.35 205	99.89 228	93.60 239	98.42 324	88.91 370	98.39 187	93.33 385

TAMVS		98.76 149	98.73 144	98.86 204	99.44 234	97.69 260	99.57 315	99.34 224	96.57 232	99.12 218	99.81 246	98.83 113	99.16 261	97.97 253	97.91 215	99.73 226

tpm2		98.64 160	98.58 158	98.81 207	99.42 236	97.12 283	99.69 301	99.37 201	93.63 326	99.94 143	99.67 269	98.96 99	99.47 240	98.62 225	97.95 213	99.83 187

EC-MVSNet		99.19 109	99.09 106	99.48 150	99.42 236	99.07 170	100.00 1	99.21 284	96.95 200	99.96 118	100.00 1	96.88 188	99.48 238	99.64 153	99.79 143	99.88 172

eth_miper_zixun_eth		97.47 225	97.28 227	98.06 259	99.41 238	97.94 248	99.62 310	99.08 327	94.46 307	98.19 284	99.56 295	96.91 187	98.50 318	96.78 290	91.49 322	97.74 290

gg-mvs-nofinetune		96.95 249	96.10 271	99.50 147	99.41 238	99.36 143	99.07 374	99.52 72	83.69 383	99.96 118	83.60 399	100.00 1	99.20 260	99.68 145	99.99 97	99.96 122

SCA		98.30 187	97.98 200	99.23 182	99.41 238	98.25 224	99.99 205	99.45 102	96.91 204	99.76 177	99.58 291	89.65 295	99.54 227	98.31 237	98.79 169	99.91 147

CostFormer		98.84 144	98.77 138	99.04 192	99.41 238	97.58 264	99.67 304	99.35 218	94.66 299	99.96 118	99.36 309	99.28 71	99.74 199	99.41 175	97.81 223	99.81 200

IterMVS		96.76 255	96.46 256	97.63 281	99.41 238	96.89 289	99.99 205	99.13 311	94.74 297	97.59 314	99.66 271	89.63 297	98.28 330	95.71 304	92.31 309	97.72 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		96.72 258	96.42 258	97.62 283	99.40 243	96.83 291	99.99 205	99.14 307	94.65 300	97.55 315	99.72 256	89.65 295	98.31 328	95.62 308	92.05 312	97.73 296

test-LLR		99.03 121	98.91 126	99.40 160	99.40 243	99.28 149	100.00 1	99.45 102	96.70 221	99.42 196	99.12 318	99.31 63	99.01 268	96.82 287	99.99 97	99.91 147

test-mter		98.96 134	98.82 133	99.40 160	99.40 243	99.28 149	100.00 1	99.45 102	95.44 285	99.42 196	99.12 318	99.70 14	99.01 268	96.82 287	99.99 97	99.91 147

FMVSNet5		95.32 309	95.43 306	94.99 339	99.39 246	92.99 349	99.25 347	99.24 268	90.45 361	97.44 318	98.45 356	95.78 207	94.39 385	87.02 372	91.88 316	97.59 338

MIMVSNet		97.06 243	96.73 244	98.05 263	99.38 247	96.64 297	98.47 384	99.35 218	93.41 331	99.48 192	98.53 353	89.66 294	97.70 362	94.16 328	98.11 206	99.80 212

IterMVS-LS		97.56 219	97.44 218	97.92 274	99.38 247	97.90 250	99.89 261	99.10 321	94.41 308	98.32 274	99.54 298	97.21 173	98.11 340	97.50 266	91.62 319	97.75 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		94.12 320	92.73 333	98.29 236	99.33 249	95.95 301	99.38 335	99.19 288	74.54 391	98.26 280	86.34 395	86.07 333	99.06 265	91.60 348	99.87 131	99.85 183

CR-MVSNet		98.02 200	97.71 211	98.93 199	99.31 250	98.86 187	99.13 366	99.00 350	96.53 235	99.96 118	98.98 331	96.94 185	98.10 343	91.18 350	98.40 185	99.84 184

Patchmtry		96.81 252	96.37 260	98.14 249	99.31 250	98.55 203	98.91 377	99.00 350	90.45 361	97.92 298	98.98 331	96.94 185	98.12 338	94.27 325	91.53 321	97.75 267

RPMNet		95.26 311	93.82 319	99.56 143	99.31 250	98.86 187	99.13 366	99.42 133	79.82 388	99.96 118	95.13 381	95.69 209	99.98 118	77.54 391	98.40 185	99.84 184

tt0805		96.52 266	96.23 266	97.40 288	99.30 253	93.55 342	99.32 340	99.45 102	96.75 214	97.88 300	99.99 182	79.99 365	99.59 210	97.39 272	95.98 248	99.06 246

VDDNet		96.39 277	95.55 298	98.90 201	99.27 254	97.45 268	99.15 364	99.92 34	91.28 354	99.98 109	100.00 1	73.55 378	100.00 1	99.85 109	96.98 240	99.24 241

LCM-MVSNet-Re		96.52 266	97.21 233	94.44 344	99.27 254	85.80 377	99.85 267	96.61 394	95.98 259	92.75 368	98.48 355	93.97 234	97.55 363	99.58 162	98.43 183	99.98 109

baseline2		98.99 130	98.93 124	99.18 185	99.26 256	99.15 167	100.00 1	99.46 94	96.71 220	96.79 333	100.00 1	99.42 52	99.25 259	98.75 215	99.94 118	99.15 243

GA-MVS		97.72 211	97.27 229	99.06 188	99.24 257	97.93 249	100.00 1	99.24 268	95.80 268	98.99 229	99.64 277	89.77 292	99.36 251	95.12 316	97.62 232	99.89 161

APD_test1		93.07 329	94.14 317	89.85 361	99.18 258	72.49 389	99.76 286	98.90 360	92.86 345	96.35 340	99.94 221	75.56 375	99.91 164	86.73 373	97.98 209	97.15 356

jason		99.11 113	98.96 117	99.59 136	99.17 259	99.31 147	100.00 1	99.13 311	97.38 172	99.83 162	100.00 1	95.54 212	99.72 202	99.57 163	99.97 110	99.74 222
jason: jason.
test_vis1_n		96.69 260	95.81 283	99.32 172	99.14 260	97.98 243	99.97 235	98.98 353	98.45 77	100.00 1	100.00 1	66.44 388	99.99 94	99.78 125	99.57 156	100.00 1

tpm		98.24 192	98.22 185	98.32 234	99.13 261	95.79 305	99.53 320	99.12 317	95.20 287	99.96 118	99.36 309	97.58 158	99.28 258	97.41 270	96.67 243	99.88 172

EI-MVSNet		97.98 201	97.93 201	98.16 247	99.11 262	97.84 255	99.74 289	99.29 241	94.39 309	98.65 252	100.00 1	97.21 173	98.88 283	97.62 264	95.31 259	97.75 267

CVMVSNet		98.56 168	98.47 165	98.82 205	99.11 262	97.67 261	99.74 289	99.47 79	97.57 153	99.06 224	100.00 1	95.72 208	98.97 273	98.21 243	97.33 234	99.83 187

test0.0.03 1		98.12 196	98.03 197	98.39 228	99.11 262	98.07 236	100.00 1	99.93 30	96.70 221	96.91 329	99.95 217	99.31 63	98.19 333	91.93 345	98.44 182	98.91 247

fmvsm_s_conf0.1_n_a		98.71 154	98.36 175	99.78 107	99.09 265	99.42 136	100.00 1	99.26 261	97.42 170	100.00 1	100.00 1	89.78 291	99.96 134	99.82 118	99.85 135	99.97 116

Patchmatch-test		97.83 205	97.42 219	99.06 188	99.08 266	97.66 262	98.66 382	99.21 284	93.65 325	98.25 281	99.58 291	99.47 43	99.57 215	90.25 359	98.59 175	99.95 128

HQP-NCC					99.07 267		100.00 1		99.04 15	99.17 212

ACMP_Plane					99.07 267		100.00 1		99.04 15	99.17 212

NP-MVS					99.07 267	94.81 320					99.97 195

HQP-MVS		97.73 210	97.85 203	97.39 289	99.07 267	94.82 318	100.00 1	99.40 183	99.04 15	99.17 212	99.97 195	88.61 309	99.57 215	99.79 119	95.58 249	97.77 254

plane_prior6					99.06 271	94.80 321						88.58 311

ACMH+	96.20 13	96.49 271	96.33 263	97.00 305	99.06 271	93.80 340	99.81 273	99.31 233	97.32 178	95.89 349	99.97 195	82.62 356	99.54 227	98.34 236	94.63 285	97.65 328

PatchT		95.90 301	94.95 315	98.75 210	99.03 273	98.39 210	99.08 372	99.32 228	85.52 379	99.96 118	94.99 383	97.94 142	98.05 349	80.20 387	98.47 181	99.81 200

plane_prior1					99.02 274

QAPM		98.99 130	98.66 149	99.96 42	99.01 275	99.87 74	99.88 263	99.93 30	97.99 110	98.68 251	100.00 1	93.17 246	100.00 1	99.32 181	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 85	99.01 275	99.95 32	100.00 1	99.75 52	99.37 3	99.99 103	100.00 1	99.76 11	99.60 208	100.00 1	100.00 1	100.00 1

ACMM	97.17 6	97.37 229	97.40 221	97.29 295	99.01 275	94.64 329	100.00 1	99.25 263	98.07 106	98.44 268	99.98 187	87.38 322	99.55 224	99.25 186	95.19 267	97.69 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 214	97.74 208	97.36 291	99.01 275	94.76 326	100.00 1	99.34 224	99.30 4	99.00 228	99.97 195	87.49 320	99.57 215	99.96 85	95.58 249	97.75 267
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		99.22 107	98.96 117	99.98 23	99.00 279	99.95 32	99.24 348	99.94 22	98.14 99	98.88 236	100.00 1	95.63 211	100.00 1	99.85 109	100.00 1	100.00 1

HQP_MVS		97.71 212	97.82 205	97.37 290	99.00 279	94.80 321	100.00 1	99.40 183	99.00 27	99.08 222	99.97 195	88.58 311	99.55 224	99.79 119	95.57 253	97.76 256

plane_prior7					99.00 279	94.78 325

3Dnovator+	95.58 15	99.03 121	98.71 147	99.96 42	98.99 282	99.89 65	100.00 1	99.51 76	98.96 32	98.32 274	100.00 1	92.78 252	100.00 1	99.87 106	100.00 1	100.00 1

testgi		96.18 287	95.93 279	96.93 310	98.98 283	94.20 338	100.00 1	99.07 331	97.16 188	96.06 346	99.86 233	84.08 349	97.79 359	90.38 358	97.80 224	98.81 248

ACMP	97.00 8	97.19 235	97.16 235	97.27 298	98.97 284	94.58 333	100.00 1	99.32 228	97.97 114	97.45 317	99.98 187	85.79 337	99.56 219	99.70 137	95.24 264	97.67 322
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ITE_SJBPF				96.84 315	98.96 285	93.49 343		98.12 373	98.12 103	98.35 271	99.97 195	84.45 343	99.56 219	95.63 307	95.25 263	97.49 344

ACMH	96.25 11	96.77 254	96.62 248	97.21 299	98.96 285	94.43 335	99.64 306	99.33 226	97.43 169	96.55 338	99.97 195	83.52 351	99.54 227	99.07 199	95.13 272	97.66 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		95.17 313	95.23 309	95.01 337	98.95 287	88.99 371	99.99 205	97.77 383	97.79 128	98.58 257	99.70 261	73.36 379	99.34 254	95.88 301	95.03 276	96.70 365

test_fmvsmconf0.1_n		99.25 103	99.05 107	99.82 91	98.92 288	99.55 113	100.00 1	99.23 272	98.91 41	99.75 178	99.97 195	94.79 224	99.94 158	99.94 93	99.99 97	99.97 116

UniMVSNet_ETH3D		95.28 310	94.41 316	97.89 275	98.91 289	95.14 312	99.13 366	99.35 218	92.11 349	97.17 324	99.66 271	70.28 385	99.36 251	97.88 255	95.18 268	99.16 242

DeepMVS_CX				89.98 360	98.90 290	71.46 391		99.18 295	97.61 148	96.92 327	99.83 240	86.07 333	99.83 181	96.02 300	97.65 231	98.65 250

OpenMVS	95.20 17	98.76 149	98.41 168	99.78 107	98.89 291	99.81 85	99.99 205	99.76 49	98.02 108	98.02 292	100.00 1	91.44 267	100.00 1	99.63 156	99.97 110	99.55 235

test_fmvsmvis_n_1920		99.46 75	99.37 76	99.73 116	98.88 292	99.18 163	100.00 1	99.26 261	98.85 49	99.79 172	100.00 1	97.70 154	100.00 1	99.98 73	99.86 132	100.00 1

1314		99.38 81	99.19 99	99.96 42	98.88 292	99.89 65	99.24 348	99.93 30	98.88 44	98.79 246	100.00 1	97.02 178	100.00 1	100.00 1	100.00 1	100.00 1

JIA-IIPM		97.09 240	96.34 262	99.36 165	98.88 292	98.59 202	99.81 273	99.43 121	84.81 381	99.96 118	90.34 391	98.55 127	99.52 233	97.00 281	98.28 197	99.98 109

FMVSNet1		94.45 315	93.63 322	96.89 312	98.87 295	94.87 315	99.18 356	99.27 255	90.95 358	97.31 320	98.81 342	72.89 381	98.07 345	92.61 339	92.81 301	97.72 303

3Dnovator	95.63 14	99.06 117	98.76 140	99.96 42	98.86 296	99.90 58	99.98 229	99.93 30	98.95 35	98.49 266	100.00 1	92.91 250	100.00 1	99.71 134	100.00 1	100.00 1

LPG-MVS_test		97.31 231	97.32 225	97.28 296	98.85 297	94.60 330	100.00 1	99.37 201	97.35 174	98.85 239	99.98 187	86.66 328	99.56 219	99.55 164	95.26 261	97.70 312

LGP-MVS_train				97.28 296	98.85 297	94.60 330		99.37 201	97.35 174	98.85 239	99.98 187	86.66 328	99.56 219	99.55 164	95.26 261	97.70 312

D2MVS		97.63 217	97.83 204	97.05 302	98.83 299	94.60 330	100.00 1	99.82 40	96.89 207	98.28 277	99.03 327	94.05 231	99.47 240	98.58 228	94.97 279	97.09 357

dmvs_testset		93.27 326	95.48 303	86.65 367	98.74 300	68.42 396	99.92 254	98.91 358	96.19 255	93.28 365	100.00 1	91.06 274	91.67 392	89.64 363	91.54 320	99.86 182

TinyColmap		95.50 307	95.12 312	96.64 319	98.69 301	93.00 348	99.40 333	97.75 384	96.40 244	96.14 345	99.87 231	79.47 366	99.50 236	93.62 333	94.72 283	97.40 349

XXY-MVS		97.14 239	96.63 247	98.67 212	98.65 302	98.92 185	99.54 319	99.29 241	95.57 275	97.63 309	99.83 240	87.79 318	99.35 253	98.39 233	92.95 300	97.75 267

VPA-MVSNet		97.03 245	96.43 257	98.82 205	98.64 303	99.32 145	99.38 335	99.47 79	96.73 218	98.91 234	98.94 336	87.00 326	99.40 249	99.23 189	89.59 340	97.76 256

VPNet		96.41 273	95.76 288	98.33 233	98.61 304	98.30 221	99.48 324	99.45 102	96.98 199	98.87 238	99.88 230	81.57 359	98.93 275	99.22 191	87.82 356	97.76 256

USDC		95.90 301	95.70 291	96.50 323	98.60 305	92.56 354	100.00 1	98.30 369	97.77 130	96.92 327	99.94 221	81.25 362	99.45 245	93.54 334	94.96 280	97.49 344

tfpnnormal		96.36 278	95.69 294	98.37 230	98.55 306	98.71 195	99.69 301	99.45 102	93.16 339	96.69 337	99.71 258	88.44 313	98.99 270	94.17 326	91.38 325	97.41 348

LTVRE_ROB	95.29 16	96.32 281	96.10 271	96.99 306	98.55 306	93.88 339	99.45 327	99.28 247	94.50 305	96.46 339	99.52 299	84.86 342	99.48 238	97.26 276	95.03 276	97.59 338
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
FIs		97.95 202	97.73 210	98.62 215	98.53 308	99.24 155	100.00 1	99.43 121	96.74 216	97.87 301	99.82 243	95.27 215	98.89 280	98.78 212	93.07 298	97.74 290

UniMVSNet (Re)		97.29 233	96.85 240	98.59 218	98.49 309	99.13 168	100.00 1	99.42 133	96.52 236	98.24 283	98.90 339	94.93 221	98.89 280	97.54 265	87.61 357	97.75 267

WR-MVS		97.09 240	96.64 246	98.46 224	98.43 310	99.09 169	99.97 235	99.33 226	95.62 273	97.76 303	99.67 269	91.17 271	98.56 314	98.49 230	89.28 345	97.74 290

XVG-ACMP-BASELINE		96.60 264	96.52 253	96.84 315	98.41 311	93.29 346	99.99 205	99.32 228	97.76 132	98.51 264	99.29 312	81.95 358	99.54 227	98.40 232	95.03 276	97.68 318

UniMVSNet_NR-MVSNet		97.16 237	96.80 241	98.22 241	98.38 312	98.41 207	100.00 1	99.45 102	96.14 256	97.76 303	99.64 277	95.05 219	98.50 318	97.98 250	86.84 361	97.75 267

test_method		91.04 341	91.10 338	90.85 358	98.34 313	77.63 385	100.00 1	98.93 357	76.69 389	96.25 343	98.52 354	70.44 384	97.98 351	89.02 369	91.74 317	96.92 361

nrg030		97.64 214	97.27 229	98.75 210	98.34 313	99.53 117	100.00 1	99.22 275	96.21 254	98.27 279	99.95 217	94.40 229	98.98 271	99.23 189	89.78 339	97.75 267

TransMVSNet (Re)		94.78 314	93.72 320	97.93 273	98.34 313	97.88 252	99.23 353	97.98 379	91.60 352	94.55 358	99.71 258	87.89 316	98.36 326	89.30 366	84.92 367	97.56 340

DU-MVS		96.93 250	96.49 254	98.22 241	98.31 316	98.41 207	100.00 1	99.37 201	96.41 242	97.76 303	99.65 273	92.14 262	98.50 318	97.98 250	86.84 361	97.75 267

NR-MVSNet		96.63 262	96.04 274	98.38 229	98.31 316	98.98 182	99.22 355	99.35 218	95.87 262	94.43 361	99.65 273	92.73 255	98.40 325	96.78 290	88.05 354	97.75 267

FC-MVSNet-test		97.84 204	97.63 214	98.45 225	98.30 318	99.05 173	100.00 1	99.43 121	96.63 230	97.61 312	99.82 243	95.19 218	98.57 312	98.64 221	93.05 299	97.73 296

Baseline_NR-MVSNet		96.16 291	95.70 291	97.56 286	98.28 319	96.79 293	100.00 1	97.86 382	91.93 351	97.63 309	99.47 303	92.14 262	98.35 327	97.13 277	86.83 363	97.54 341

WR-MVS_H		96.73 256	96.32 264	97.95 270	98.26 320	97.88 252	99.72 296	99.43 121	95.06 289	96.99 326	98.68 348	93.02 249	98.53 316	97.43 269	88.33 353	97.43 347

LF4IMVS		96.19 286	96.18 268	96.23 329	98.26 320	92.09 356	100.00 1	97.89 381	97.82 126	97.94 296	99.87 231	82.71 355	99.38 250	97.41 270	93.71 291	97.20 354

pm-mvs1		95.76 303	95.01 313	98.00 267	98.23 322	97.45 268	99.24 348	99.04 344	93.13 340	95.93 348	99.72 256	86.28 331	98.84 285	95.62 308	87.92 355	97.72 303

mvsmamba		98.13 195	98.06 194	98.32 234	98.22 323	98.50 206	100.00 1	99.22 275	96.41 242	98.91 234	99.96 209	95.69 209	98.73 297	99.19 193	94.95 281	97.73 296

CP-MVSNet		96.73 256	96.25 265	98.18 244	98.21 324	98.67 198	99.77 284	99.32 228	95.06 289	97.20 323	99.65 273	90.10 286	98.19 333	98.06 248	88.90 348	97.66 323

v10		96.14 293	95.50 299	98.07 255	98.19 325	97.96 246	99.83 269	99.07 331	92.10 350	98.07 287	98.94 336	91.07 273	98.61 305	92.41 344	89.82 338	97.63 332

PS-CasMVS		96.34 280	95.78 287	98.03 266	98.18 326	98.27 223	99.71 297	99.32 228	94.75 295	96.82 332	99.65 273	86.98 327	98.15 335	97.74 258	88.85 349	97.66 323

V42		96.65 261	96.16 270	98.11 254	98.17 327	98.23 225	99.99 205	99.09 326	93.97 318	98.74 248	99.05 324	91.09 272	98.82 288	95.46 310	89.90 337	97.27 353

v8		96.35 279	95.73 290	98.21 243	98.11 328	98.23 225	99.94 251	99.07 331	92.66 347	98.29 276	99.00 330	91.46 266	98.77 293	94.17 326	88.83 350	97.62 334

OPM-MVS		97.21 234	97.18 234	97.32 294	98.08 329	94.66 327	100.00 1	99.28 247	98.65 69	98.92 232	99.98 187	86.03 335	99.56 219	98.28 241	95.41 255	97.72 303
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v2v482		96.70 259	96.18 268	98.27 237	98.04 330	98.39 210	100.00 1	99.13 311	94.19 315	98.58 257	99.08 321	90.48 282	98.67 300	95.69 305	90.44 335	97.75 267

v1192		96.18 287	95.49 301	98.26 239	98.01 331	98.15 230	99.99 205	99.08 327	93.36 333	98.54 260	98.97 334	89.47 298	98.89 280	91.15 351	90.82 330	97.75 267

TranMVSNet+NR-MVSNet		96.45 272	96.01 275	97.79 278	98.00 332	97.62 263	100.00 1	99.35 218	95.98 259	97.31 320	99.64 277	90.09 287	98.00 350	96.89 285	86.80 364	97.75 267

v1144		96.51 268	95.97 278	98.13 252	97.98 333	98.04 240	99.99 205	99.08 327	93.51 330	98.62 255	98.98 331	90.98 276	98.62 304	93.79 332	90.79 331	97.74 290

test_0402		94.35 316	93.70 321	96.32 327	97.92 334	93.60 341	99.61 311	98.85 362	88.19 374	94.68 357	99.48 302	80.01 364	98.58 311	89.39 365	95.15 271	96.77 363

v1240		95.96 299	95.25 308	98.07 255	97.91 335	97.87 254	99.96 240	99.07 331	93.24 337	98.64 254	98.96 335	88.98 304	98.61 305	89.58 364	90.92 329	97.75 267

v144192		96.40 276	95.81 283	98.17 246	97.89 336	98.11 233	99.99 205	99.06 339	93.39 332	98.75 247	99.09 320	90.43 283	98.66 301	93.10 337	90.55 334	97.75 267

RRT_MVS		97.77 208	97.76 206	97.78 279	97.89 336	97.06 286	100.00 1	99.29 241	95.74 269	98.00 295	99.97 195	95.94 203	98.55 315	98.87 207	94.18 289	97.72 303

v1921920		96.16 291	95.50 299	98.14 249	97.88 338	97.96 246	99.99 205	99.07 331	93.33 334	98.60 256	99.24 314	89.37 299	98.71 298	91.28 349	90.74 332	97.75 267

bld_raw_dy_0_64		97.71 212	97.56 215	98.15 248	97.83 339	98.16 228	99.95 246	99.12 317	95.95 261	98.73 249	99.97 195	93.19 245	98.63 303	98.64 221	94.69 284	97.66 323

MS-PatchMatch		95.66 305	95.87 281	95.05 336	97.80 340	89.25 369	98.88 378	99.30 237	96.35 246	96.86 330	99.01 329	81.35 361	99.43 246	93.30 336	99.98 107	96.46 368

v148		96.29 282	95.84 282	97.63 281	97.74 341	96.53 298	100.00 1	99.07 331	93.52 329	98.01 293	99.42 306	91.22 268	98.60 307	96.37 298	87.22 360	97.75 267

PEN-MVS		96.01 298	95.48 303	97.58 285	97.74 341	97.26 279	99.90 258	99.29 241	94.55 302	96.79 333	99.55 296	87.38 322	97.84 355	96.92 284	87.24 359	97.65 328

pmmvs4		97.17 236	96.80 241	98.27 237	97.68 343	98.64 200	100.00 1	99.18 295	94.22 312	98.55 259	99.71 258	93.67 236	98.47 321	95.66 306	92.57 306	97.71 311

PS-MVSNAJss		98.03 199	98.06 194	97.94 271	97.63 344	97.33 276	99.89 261	99.23 272	96.27 250	98.03 290	99.59 289	98.75 118	98.78 290	98.52 229	94.61 286	97.70 312

UnsupCasMVSNet_eth		94.25 317	93.89 318	95.34 334	97.63 344	92.13 355	99.73 294	99.36 207	94.88 292	92.78 366	98.63 350	82.72 354	96.53 372	94.57 321	84.73 368	97.36 350

our_test_3		96.51 268	96.35 261	96.98 307	97.61 346	95.05 313	99.98 229	99.01 349	94.68 298	96.77 335	99.06 322	95.87 205	98.14 336	91.81 346	92.37 308	97.75 267

ppachtmachnet_test		96.17 289	95.89 280	97.02 304	97.61 346	95.24 310	99.99 205	99.24 268	93.31 335	96.71 336	99.62 285	94.34 230	98.07 345	89.87 360	92.30 310	97.75 267

EU-MVSNet		96.63 262	96.53 251	96.94 309	97.59 348	96.87 290	99.76 286	99.47 79	96.35 246	96.85 331	99.78 252	92.57 258	96.27 376	95.33 311	91.08 328	97.68 318

v7n		96.06 297	95.42 307	97.99 269	97.58 349	97.35 273	99.86 265	99.11 319	92.81 346	97.91 299	99.49 301	90.99 275	98.92 276	92.51 341	88.49 352	97.70 312

lessismore_v0				96.05 330	97.55 350	91.80 358		99.22 275		91.87 370	99.91 225	83.50 352	98.68 299	92.48 342	90.42 336	97.68 318

K. test v3		95.46 308	95.14 311	96.40 324	97.53 351	93.40 345	99.99 205	99.23 272	95.49 281	92.70 369	99.73 255	84.26 345	98.12 338	93.94 331	93.38 296	97.68 318

test_djsdf		97.55 220	97.38 222	98.07 255	97.50 352	97.99 242	100.00 1	99.13 311	95.46 283	98.47 267	99.85 237	92.01 265	98.59 309	98.63 223	95.36 257	97.62 334

Gipuma		84.73 352	83.50 357	88.40 364	97.50 352	82.21 382	88.87 393	99.05 341	65.81 393	85.71 385	90.49 390	53.70 391	96.31 374	78.64 389	91.74 317	86.67 392
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		95.52 306	94.99 314	97.08 301	97.49 354	96.45 299	100.00 1	99.25 263	93.82 320	96.17 344	99.57 294	87.81 317	97.18 364	94.57 321	86.26 366	97.62 334

OurMVSNet-221017-0		96.14 293	95.98 277	96.62 320	97.49 354	93.44 344	99.92 254	98.16 371	95.86 264	97.65 308	99.95 217	85.71 338	98.78 290	94.93 318	94.18 289	97.64 331

pmmvs6		93.64 322	92.87 330	95.94 332	97.47 356	91.41 361	98.92 376	99.02 347	87.84 375	95.01 354	99.61 287	77.24 373	98.77 293	94.33 324	86.41 365	97.63 332

pmmvs5		95.94 300	95.61 296	96.95 308	97.42 357	94.66 327	100.00 1	98.08 375	93.60 327	97.05 325	99.43 305	87.02 325	98.46 322	95.76 302	92.12 311	97.72 303

SixPastTwentyTwo		95.71 304	95.49 301	96.38 325	97.42 357	93.01 347	99.84 268	98.23 370	94.75 295	95.98 347	99.97 195	85.35 340	98.43 323	94.71 320	93.17 297	97.69 316

mvs_tets		97.00 248	96.69 245	97.94 271	97.41 359	97.27 278	99.60 312	99.18 295	96.51 237	97.35 319	99.69 264	86.53 330	98.91 277	98.84 209	95.09 275	97.65 328

jajsoiax		97.07 242	96.79 243	97.89 275	97.28 360	97.12 283	99.95 246	99.19 288	96.55 233	97.31 320	99.69 264	87.35 324	98.91 277	98.70 217	95.12 273	97.66 323

N_pmnet		91.88 336	93.37 325	87.40 366	97.24 361	66.33 399	99.90 258	91.05 402	89.77 367	95.65 350	98.58 352	90.05 289	98.11 340	85.39 375	92.72 302	97.75 267

anonymousdsp		97.16 237	96.88 238	98.00 267	97.08 362	98.06 238	99.81 273	99.15 302	94.58 301	97.84 302	99.62 285	90.49 281	98.60 307	97.98 250	95.32 258	97.33 352

test_fmvsmconf0.01_n		98.60 164	98.24 181	99.67 124	96.90 363	99.21 159	99.99 205	99.04 344	98.80 57	99.57 187	99.96 209	90.12 285	99.91 164	99.89 101	99.89 126	99.90 156

MDA-MVSNet-bldmvs		91.65 338	89.94 346	96.79 318	96.72 364	96.70 295	99.42 332	98.94 355	88.89 369	66.97 399	98.37 359	81.43 360	95.91 379	89.24 367	89.46 343	97.75 267

MDA-MVSNet_test_wron		92.61 331	91.09 339	97.19 300	96.71 365	97.26 279	100.00 1	99.14 307	88.61 370	67.90 397	98.32 361	89.03 302	96.57 371	90.47 357	89.59 340	97.74 290

new_pmnet		94.11 321	93.47 324	96.04 331	96.60 366	92.82 350	99.97 235	98.91 358	90.21 364	95.26 351	98.05 367	85.89 336	98.14 336	84.28 378	92.01 313	97.16 355

YYNet1		92.44 332	90.92 340	97.03 303	96.20 367	97.06 286	99.99 205	99.14 307	88.21 373	67.93 396	98.43 358	88.63 308	96.28 375	90.64 353	89.08 347	97.74 290

CMPMVS	66.12 22	90.65 342	92.04 335	86.46 368	96.18 368	66.87 398	98.03 387	99.38 198	83.38 384	85.49 386	99.55 296	77.59 371	98.80 289	94.44 323	94.31 288	93.72 383
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DSMNet-mixed		95.18 312	95.21 310	95.08 335	96.03 369	90.21 367	99.65 305	93.64 400	92.91 342	98.34 272	97.40 372	90.05 289	95.51 382	91.02 352	97.86 218	99.51 238

EGC-MVSNET		79.46 357	74.04 365	95.72 333	96.00 370	92.73 351	99.09 371	99.04 344	5.08 403	16.72 403	98.71 346	73.03 380	98.74 296	82.05 383	96.64 244	95.69 376

EG-PatchMatch MVS		92.94 330	92.49 334	94.29 348	95.87 371	87.07 376	99.07 374	98.11 374	93.19 338	88.98 378	98.66 349	70.89 383	99.08 264	92.43 343	95.21 266	96.72 364

testf1		84.40 353	84.79 355	83.23 372	95.71 372	58.71 405	98.79 380	97.75 384	81.58 385	84.94 387	98.07 365	45.33 396	97.73 360	77.09 393	83.85 370	93.24 386

APD_test2		84.40 353	84.79 355	83.23 372	95.71 372	58.71 405	98.79 380	97.75 384	81.58 385	84.94 387	98.07 365	45.33 396	97.73 360	77.09 393	83.85 370	93.24 386

MVP-Stereo		96.51 268	96.48 255	96.60 321	95.65 374	94.25 336	98.84 379	98.16 371	95.85 266	95.23 352	99.04 325	92.54 259	99.13 262	92.98 338	99.98 107	96.43 369
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		93.29 325	92.76 332	94.90 342	95.64 375	91.27 362	99.97 235	98.82 363	87.04 376	94.71 356	98.19 362	83.86 350	96.80 367	84.04 379	92.56 307	96.64 366

OpenMVS_ROB	88.34 20	91.89 335	91.12 337	94.19 350	95.55 376	87.63 374	99.26 346	98.03 376	86.61 378	90.65 376	96.82 375	70.14 386	98.78 290	86.54 374	96.50 247	96.15 370

UnsupCasMVSNet_bld		89.50 345	88.00 351	93.99 351	95.30 377	88.86 372	98.52 383	99.28 247	85.50 380	87.80 382	94.11 385	61.63 389	96.96 366	90.63 354	79.26 380	96.15 370

test20.03		93.11 327	92.85 331	93.88 352	95.19 378	91.83 357	100.00 1	98.87 361	93.68 324	92.76 367	98.88 340	89.20 301	92.71 390	77.88 390	89.19 346	97.09 357

Anonymous20231206		93.45 324	93.17 326	94.30 347	95.00 379	89.69 368	99.98 229	98.43 368	93.30 336	94.50 360	98.59 351	90.52 280	95.73 381	77.46 392	90.73 333	97.48 346

MIMVSNet1		91.96 333	91.20 336	94.23 349	94.94 380	91.69 359	99.34 339	99.22 275	88.23 372	94.18 362	98.45 356	75.52 376	93.41 389	79.37 388	91.49 322	97.60 337

KD-MVS_self_test		91.16 339	90.09 344	94.35 346	94.44 381	91.27 362	99.74 289	99.08 327	90.82 359	94.53 359	94.91 384	86.11 332	94.78 384	82.67 381	68.52 390	96.99 359

CL-MVSNet_self_test		91.07 340	90.35 343	93.24 354	93.27 382	89.16 370	99.55 317	99.25 263	92.34 348	95.23 352	97.05 374	88.86 307	93.59 388	80.67 385	66.95 391	96.96 360

test_fmvs3		87.19 350	87.02 353	87.71 365	92.69 383	76.64 386	99.96 240	97.27 388	93.55 328	90.82 374	94.03 386	38.00 400	92.19 391	93.49 335	83.35 374	94.32 380

mvsany_test3		89.36 346	88.96 350	90.56 359	91.95 384	78.97 384	99.74 289	96.59 395	96.84 209	89.25 377	96.07 377	52.59 392	97.11 365	95.17 315	82.44 375	95.58 378

Patchmatch-RL test		93.49 323	93.63 322	93.05 355	91.78 385	83.41 381	98.21 386	96.95 391	91.58 353	91.05 371	97.64 371	99.40 55	95.83 380	94.11 329	81.95 377	99.91 147

PM-MVS		88.39 347	87.41 352	91.31 357	91.73 386	82.02 383	99.79 278	96.62 393	91.06 357	90.71 375	95.73 378	48.60 394	95.96 378	90.56 355	81.91 378	95.97 373

pmmvs-eth3d		91.73 337	90.67 341	94.92 341	91.63 387	92.71 352	99.90 258	98.54 367	91.19 355	88.08 380	95.50 379	79.31 368	96.13 377	90.55 356	81.32 379	95.91 374

WB-MVS		88.24 348	90.09 344	82.68 374	91.56 388	69.51 394	100.00 1	98.73 365	90.72 360	87.29 383	98.12 363	92.87 251	85.01 396	62.19 397	89.34 344	93.54 384

test_f		86.87 351	86.06 354	89.28 362	91.45 389	76.37 387	99.87 264	97.11 389	91.10 356	88.46 379	93.05 388	38.31 399	96.66 370	91.77 347	83.46 373	94.82 379

new-patchmatchnet		90.30 344	89.46 348	92.84 356	90.77 390	88.55 373	99.83 269	98.80 364	90.07 366	87.86 381	95.00 382	78.77 369	94.30 386	84.86 377	79.15 381	95.68 377

SSC-MVS		87.61 349	89.47 347	82.04 375	90.63 391	68.77 395	99.99 205	98.66 366	90.34 363	86.70 384	98.08 364	92.72 256	84.12 397	59.41 400	88.71 351	93.22 388

pmmvs3		90.62 343	89.36 349	94.40 345	90.53 392	91.49 360	100.00 1	96.73 392	84.21 382	93.65 364	96.65 376	82.56 357	94.83 383	82.28 382	77.62 384	96.89 362

test_vis3_rt		79.61 356	78.19 361	83.86 371	88.68 393	69.56 393	99.81 273	82.19 407	86.78 377	68.57 395	84.51 398	25.06 404	98.26 331	89.18 368	78.94 382	83.75 395

ambc				88.45 363	86.84 394	70.76 392	97.79 389	98.02 378		90.91 373	95.14 380	38.69 398	98.51 317	94.97 317	84.23 369	96.09 372

EMVS		69.88 364	69.09 367	72.24 383	84.70 395	65.82 400	99.96 240	87.08 406	49.82 400	71.51 394	84.74 397	49.30 393	75.32 400	50.97 402	43.71 398	75.59 398

E-PMN		70.72 363	70.06 366	72.69 382	83.92 396	65.48 401	99.95 246	92.72 401	49.88 399	72.30 393	86.26 396	47.17 395	77.43 399	53.83 401	44.49 397	75.17 399

PMMVS2		79.15 359	77.28 362	84.76 370	82.34 397	72.66 388	99.70 299	95.11 398	71.68 392	84.78 389	90.87 389	32.05 402	89.99 393	75.53 395	63.45 394	91.64 389

TDRefinement		91.93 334	90.48 342	96.27 328	81.60 398	92.65 353	99.10 369	97.61 387	93.96 319	93.77 363	99.85 237	80.03 363	99.53 232	97.82 257	70.59 389	96.63 367

test123		79.44 358	79.23 360	80.05 377	80.03 399	71.72 390	100.00 1	77.93 408	62.52 394	94.81 355	99.69 264	78.21 370	74.53 401	92.57 340	27.33 401	93.90 381

LCM-MVSNet		79.01 360	76.93 363	85.27 369	78.28 400	68.01 397	96.57 390	98.03 376	55.10 397	82.03 390	93.27 387	31.99 403	93.95 387	82.72 380	74.37 386	93.84 382

FPMVS		77.92 361	79.45 359	73.34 381	76.87 401	46.81 408	98.24 385	99.05 341	59.89 396	73.55 392	98.34 360	36.81 401	86.55 394	80.96 384	91.35 326	86.65 393

wuyk23d		28.28 368	29.73 372	23.92 385	75.89 402	32.61 410	66.50 396	12.88 409	16.09 402	14.59 404	16.59 403	12.35 406	32.36 404	39.36 403	13.36 402	6.79 400

MVE	68.59 21	67.22 365	64.68 369	74.84 378	74.67 403	62.32 403	95.84 391	90.87 403	50.98 398	58.72 400	81.05 400	12.20 408	78.95 398	61.06 399	56.75 395	83.24 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 366	63.44 370	73.88 380	61.14 404	63.45 402	95.68 392	87.18 404	79.93 387	47.35 401	80.68 401	22.35 405	72.33 403	61.24 398	35.42 399	85.88 394

testmvs		80.17 355	81.95 358	74.80 379	58.54 405	59.58 404	100.00 1	87.14 405	76.09 390	99.61 186	100.00 1	67.06 387	74.19 402	98.84 209	50.30 396	90.64 391

PMVS	60.66 23	65.98 367	65.05 368	68.75 384	55.06 406	38.40 409	88.19 394	96.98 390	48.30 401	44.82 402	88.52 393	12.22 407	86.49 395	67.58 396	83.79 372	81.35 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 362	74.26 364	80.43 376	52.91 407	53.67 407	87.42 395	97.98 379	61.80 395	67.04 398	100.00 1	76.43 374	96.40 373	96.47 294	28.26 400	91.23 390

test_blank		0.07 372	0.09 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.79 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

eth-test2					0.00 408

eth-test					0.00 408

uanet_test		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

cdsmvs_eth3d_5k		24.41 369	32.55 371	0.00 386	0.00 408	0.00 411	0.00 397	99.39 196	0.00 404	0.00 405	100.00 1	93.55 240	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		8.24 371	10.99 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	98.75 118	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs-re		8.33 370	11.11 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	100.00 1	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.01 373	0.02 376	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.14 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				99.94 63		99.82 84	100.00 1	99.97 17	99.11 7	100.00 1	100.00 1	96.65 193	100.00 1	100.00 1	99.97 110	100.00 1

WAC-MVS						97.98 243								95.74 303

PC_three_1452									98.80 57	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 133	99.03 20	100.00 1	100.00 1	99.56 23	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									98.79 60	100.00 1	100.00 1	99.61 16	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 147

sam_mvs1												99.29 69				99.91 147

sam_mvs												99.33 58

MTGPA								99.42 133

test_post1							99.32 340				88.24 394	99.33 58	99.59 210	98.31 237

test_post											89.05 392	99.49 39	99.59 210

patchmatchnet-post											97.79 368	99.41 54	99.54 227

MTMP							100.00 1	99.18 295

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

test_prior4						99.93 43	100.00 1

test_prior2							100.00 1		98.82 55	100.00 1	100.00 1	99.47 43		100.00 1	100.00 1

旧先验2							100.00 1		98.11 104	100.00 1			100.00 1	99.67 148

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 112				100.00 1	99.33 180		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 273

segment_acmp												99.55 25

testdata1							100.00 1		98.77 63

plane_prior5								99.40 183					99.55 224	99.79 119	95.57 253	97.76 256

plane_prior4											99.97 195

plane_prior3						94.79 324			99.03 20	99.08 222

plane_prior2							100.00 1		99.00 27

plane_prior						94.80 321	100.00 1		99.03 20						95.58 249

n2								0.00 410

nn								0.00 410

door-mid								96.32 396

test11								99.42 133

door								96.13 397

HQP5-MVS						94.82 318

BP-MVS														99.79 119

HQP4-MVS										99.17 212			99.57 215			97.77 254

HQP3-MVS								99.40 183							95.58 249

HQP2-MVS												88.61 309

MDTV_nov1_ep13_2view						99.24 155	99.56 316		96.31 249	99.96 118		98.86 109		98.92 204		99.89 161

ACMMP++_ref															94.58 287

ACMMP++															95.17 270

Test By Simon												99.10 85

High-resolution multi-view benchmark