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	99.42 138	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	99.42 138	98.52 74	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 138					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 138					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 138	98.72 64	100.00 1	100.00 1	99.60 17

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 183	100.00 1	100.00 1	99.32 62	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	99.42 138	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		99.42 138	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		99.42 138	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	99.42 138	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	99.42 138	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	97.59 154	100.00 1	100.00 1	98.99 97	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 78	100.00 1	99.79 45	97.72 135	99.95 144	100.00 1	98.39 136	100.00 1	99.96 85	99.99 98	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 78	100.00 1	99.42 138	98.87 47	100.00 1	100.00 1	99.65 15	99.96 138	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	99.43 122	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	99.42 138	97.65 142	100.00 1	100.00 1	99.53 29	99.97 125

train_agg		99.71 29	99.63 37	99.97 31	100.00 1	99.95 32	100.00 1	99.42 138	97.70 137	100.00 1	100.00 1	99.51 33	99.97 125	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 52	100.00 1	99.42 138	97.70 137	100.00 1	100.00 1	99.51 33	99.98 118

agg_prior					100.00 1	99.88 73		99.42 138		100.00 1			99.97 125

HFP-MVS		99.74 22	99.67 27	99.96 42	100.00 1	99.89 66	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.31 64	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 59	100.00 1	99.74 55	97.86 124	100.00 1	100.00 1	99.19 79	100.00 1	99.99 61	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 32	99.64 33	99.87 79	100.00 1	99.64 106	99.98 236	99.44 116	98.35 86	99.99 104	100.00 1	99.04 94	99.96 138	99.98 73	100.00 1	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 67	100.00 1	100.00 1	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 251	96.06 280	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 67	100.00 1	66.97 408	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 72	100.00 1	99.75 92		99.73 56					99.97 125			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 78	100.00 1	97.91 259	100.00 1	100.00 1

原ACMM1				99.93 67	100.00 1	99.80 88		99.66 63	98.18 95	100.00 1	100.00 1	99.43 50	100.00 1	99.50 177	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	99.44 116	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 66	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.29 70	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 64	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	99.42 138	98.02 108	100.00 1	100.00 1	99.32 62	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 52	100.00 1	99.42 138	97.91 120	100.00 1	100.00 1	99.04 94	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS		99.59 62	99.50 64	99.89 74	100.00 1	99.70 101	100.00 1	99.42 138	97.46 169	100.00 1	100.00 1	98.60 128	99.96 138	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 57	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 83	100.00 1	99.42 138	97.77 132	100.00 1	100.00 1	99.07 88	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 72	99.38 74	99.85 86	100.00 1	99.54 117	100.00 1	99.42 138	97.58 155	99.98 110	100.00 1	97.43 173	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	99.99 104	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 70	99.39 73	99.82 92	100.00 1	99.45 134	100.00 1	99.94 22	96.38 251	100.00 1	100.00 1	98.18 140	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 96	99.25 90	99.44 159	100.00 1	98.32 226	100.00 1	99.86 38	98.04 107	100.00 1	100.00 1	96.10 206	100.00 1	99.55 170	99.73 145	100.00 1

MG-MVS		99.75 20	99.68 25	99.97 31	100.00 1	99.91 52	99.98 236	99.47 79	99.09 9	100.00 1	100.00 1	98.59 129	100.00 1	99.95 91	100.00 1	100.00 1

AdaColmap		99.44 78	99.26 89	99.95 51	100.00 1	99.86 78	99.70 305	99.99 13	98.53 73	99.90 156	100.00 1	95.34 217	100.00 1	99.92 96	100.00 1	100.00 1

PLC	98.56 2	99.70 32	99.74 16	99.58 144	100.00 1	98.79 193	100.00 1	99.54 71	98.58 72	99.96 120	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MM		99.63 51	99.52 61	99.94 63	99.99 49	99.82 85	100.00 1	99.97 17	99.11 7	100.00 1	100.00 1	96.65 197	100.00 1	100.00 1	99.97 111	100.00 1

SR-MVS-dyc-post		99.63 51	99.52 61	99.97 31	99.99 49	99.91 52	100.00 1	99.42 138	97.62 147	100.00 1	100.00 1	98.65 125	99.99 94	99.99 61	100.00 1	100.00 1

RE-MVS-def			99.55 56		99.99 49	99.91 52	100.00 1	99.42 138	97.62 147	100.00 1	100.00 1	98.94 105		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 81	100.00 1	99.42 138	97.67 140	100.00 1	100.00 1	99.05 91	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 59	100.00 1	99.79 45	97.97 114	99.97 115	100.00 1	98.97 99	100.00 1	99.94 93	100.00 1	100.00 1

9.14			99.57 49		99.99 49		100.00 1	99.42 138	97.54 158	100.00 1	100.00 1	99.15 83	99.99 94	100.00 1	100.00 1

save fliter					99.99 49	99.93 44	100.00 1	99.42 138	98.93 38

CS-MVS		99.33 90	99.27 86	99.50 152	99.99 49	99.00 182	100.00 1	99.13 316	97.26 186	99.96 120	100.00 1	97.79 155	99.64 214	99.64 157	99.67 150	99.87 184

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 138					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 44	100.00 1	99.43 122	97.50 165	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	99.42 138	98.91 41	100.00 1	100.00 1	99.22 76	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	99.80 102	99.99 49	99.97 21	99.97 242	99.98 16	98.96 32	100.00 1	100.00 1	99.96 4	99.42 256	100.00 1	100.00 1	100.00 1

CANet		99.40 80	99.24 92	99.89 74	99.99 49	99.76 91	100.00 1	99.73 56	98.40 79	99.78 181	100.00 1	95.28 218	99.96 138	100.00 1	99.99 98	99.96 124

CS-MVS-test		99.31 94	99.27 86	99.43 161	99.99 49	98.77 194	100.00 1	99.19 293	97.24 187	99.96 120	100.00 1	97.56 165	99.70 211	99.68 148	99.81 141	99.82 196

MTAPA		99.68 40	99.59 44	99.97 31	99.99 49	99.91 52	100.00 1	99.42 138	98.32 88	99.94 146	100.00 1	98.65 125	100.00 1	99.96 85	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 79	99.99 49	99.64 106	99.95 253	99.44 116	98.35 86	100.00 1	100.00 1	98.98 98	99.97 125	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	99.96 120	100.00 1	99.21 77	100.00 1	100.00 1	100.00 1	99.99 109

旧先验1					99.99 49	99.88 73		99.82 40			100.00 1	99.27 73			100.00 1	100.00 1

test222					99.99 49	99.90 59	100.00 1	99.69 62	97.66 141	100.00 1	100.00 1	99.30 69			100.00 1	100.00 1

testdata				99.66 129	99.99 49	98.97 186		99.73 56	97.96 117	100.00 1	100.00 1	99.42 53	100.00 1	99.28 191	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	99.42 138	99.01 26	100.00 1	100.00 1	99.33 59	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 52	100.00 1	99.48 78	97.54 158	100.00 1	100.00 1	98.97 99	99.99 94	99.98 73	100.00 1	100.00 1

MP-MVS		99.61 58	99.49 66	99.98 23	99.99 49	99.94 41	100.00 1	99.42 138	97.82 128	99.99 104	100.00 1	98.20 139	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 81	100.00 1	99.58 67	97.69 139	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 66		99.42 138		100.00 1		99.24 75	99.97 125		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 58	99.69 22	99.35 174	99.99 49	98.06 244	100.00 1	99.36 212	99.83 2	100.00 1	100.00 1	98.95 103	99.99 94	100.00 1	99.11 163	100.00 1

HPM-MVS_fast		99.60 61	99.49 66	99.91 69	99.99 49	99.78 89	100.00 1	99.42 138	97.09 195	100.00 1	100.00 1	98.95 103	99.96 138	99.98 73	100.00 1	100.00 1

EPNet		99.62 56	99.69 22	99.42 163	99.99 49	98.37 220	100.00 1	99.89 37	98.83 53	100.00 1	100.00 1	98.97 99	100.00 1	99.90 99	99.61 155	99.89 164
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	99.42 138	97.53 160	100.00 1	100.00 1	99.27 73	99.97 125	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 98	99.35 82	99.05 198	99.99 49	97.15 288	100.00 1	99.47 79	97.44 171	99.42 203	100.00 1	97.83 154	100.00 1	99.99 61	100.00 1	100.00 1

PatchMatch-RL		99.02 132	98.78 143	99.74 114	99.99 49	99.29 150	100.00 1	100.00 1	98.38 80	99.89 159	99.81 252	93.14 252	99.99 94	97.85 261	99.98 108	99.95 130

ACMMP		99.65 46	99.57 49	99.89 74	99.99 49	99.66 104	99.75 294	99.73 56	98.16 96	99.75 185	100.00 1	98.90 110	100.00 1	99.96 85	99.88 129	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	99.67 126	99.99 49	99.07 171	100.00 1	99.44 116	98.30 89	99.90 156	100.00 1	99.18 80	99.99 94	99.91 98	100.00 1	99.94 135

DeepPCF-MVS	98.03 4	98.54 176	99.72 19	94.98 346	99.99 49	84.94 385	100.00 1	99.42 138	99.98 1	100.00 1	100.00 1	98.11 142	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	99.73 300	99.52 72	99.06 12	100.00 1	100.00 1	98.80 119	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 94	99.13 106	99.87 79	99.99 49	99.71 97	99.55 323	99.46 94	97.32 181	99.82 176	100.00 1	96.85 193	99.97 125	99.14 199	100.00 1	99.92 147

MP-MVS-pluss		99.61 58	99.50 64	99.97 31	99.98 85	99.92 49	100.00 1	99.42 138	97.53 160	99.77 182	100.00 1	98.77 120	100.00 1	99.99 61	100.00 1	99.99 109
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.67 43	99.57 49	99.97 31	99.98 85	99.92 49	100.00 1	99.42 138	97.83 127	100.00 1	100.00 1	98.89 111	100.00 1	99.98 73	100.00 1	100.00 1

PVSNet_BlendedMVS		98.71 160	98.62 159	98.98 205	99.98 85	99.60 109	100.00 1	100.00 1	97.23 188	100.00 1	99.03 333	96.57 199	99.99 94	100.00 1	94.75 289	97.35 357

PVSNet_Blended		99.48 74	99.36 80	99.83 90	99.98 85	99.60 109	100.00 1	100.00 1	97.79 130	100.00 1	100.00 1	96.57 199	99.99 94	100.00 1	99.88 129	99.90 159

114514_t		99.39 81	99.25 90	99.81 97	99.97 89	99.48 132	100.00 1	99.42 138	95.53 282	100.00 1	100.00 1	98.37 137	99.95 151	99.97 83	100.00 1	100.00 1

DP-MVS		98.86 150	98.54 166	99.81 97	99.97 89	99.45 134	99.52 327	99.40 188	94.35 316	98.36 276	100.00 1	96.13 205	99.97 125	99.12 202	100.00 1	100.00 1

API-MVS		99.72 26	99.70 21	99.79 104	99.97 89	99.37 144	99.96 247	99.94 22	98.48 75	100.00 1	100.00 1	98.92 108	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 26	99.65 31	99.91 69	99.97 89	99.72 96	100.00 1	99.47 79	98.43 78	99.88 161	100.00 1	99.14 84	100.00 1	99.97 83	100.00 1	100.00 1

MAR-MVS		99.49 72	99.36 80	99.89 74	99.97 89	99.66 104	99.74 295	99.95 19	97.89 121	100.00 1	100.00 1	96.71 196	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 82	99.96 94	99.63 108	100.00 1	99.92 34	99.03 20	99.97 115	100.00 1	97.87 149	99.96 138	100.00 1	99.96 114	100.00 1

MVS_111021_LR		99.70 32	99.65 31	99.88 78	99.96 94	99.70 101	100.00 1	99.97 17	98.96 32	100.00 1	100.00 1	97.93 147	99.95 151	99.99 61	100.00 1	100.00 1

HyFIR lowres test		99.32 92	99.24 92	99.58 144	99.95 96	99.26 153	100.00 1	99.99 13	96.72 226	99.29 215	99.91 230	99.49 39	99.47 248	99.74 129	98.08 209	100.00 1

MVS_111021_HR		99.71 29	99.63 37	99.93 67	99.95 96	99.83 84	100.00 1	100.00 1	98.89 43	100.00 1	100.00 1	97.85 151	99.95 151	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 99	99.38 74	98.96 206	99.95 96	97.06 292	100.00 1	99.40 188	98.83 53	99.88 161	100.00 1	97.01 183	99.86 179	99.47 178	99.84 138	99.97 118

TAPA-MVS	96.40 10	97.64 220	97.37 229	98.45 232	99.94 99	95.70 312	100.00 1	99.40 188	97.65 142	99.53 195	100.00 1	99.31 64	99.66 213	80.48 392	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	98.29 195	98.17 192	98.65 221	99.94 99	97.39 276	99.30 350	99.40 188	95.64 277	97.75 312	100.00 1	92.69 261	99.95 151	98.89 211	99.92 124	98.62 259
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.55 175	98.40 175	98.99 203	99.93 101	97.35 279	100.00 1	99.40 188	97.08 197	99.09 227	99.98 190	93.37 246	99.95 151	96.94 288	99.84 138	99.68 237

TestCases				98.99 203	99.93 101	97.35 279		99.40 188	97.08 197	99.09 227	99.98 190	93.37 246	99.95 151	96.94 288	99.84 138	99.68 237

DeepC-MVS	97.84 5	99.00 134	98.80 142	99.60 138	99.93 101	99.03 177	100.00 1	99.40 188	98.61 71	99.33 213	100.00 1	92.23 265	99.95 151	99.74 129	99.96 114	99.83 191
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		99.04 125	99.79 6	96.81 323	99.92 104	90.47 371	100.00 1	99.41 184	98.95 35	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 130

EPNet_dtu		98.53 177	98.23 190	99.43 161	99.92 104	99.01 180	99.96 247	99.47 79	98.80 57	99.96 120	99.96 210	98.56 130	99.30 264	87.78 377	99.68 148	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 56	99.56 54	99.82 92	99.92 104	99.45 134	100.00 1	99.78 47	98.92 39	99.73 187	100.00 1	97.70 158	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		98.90 148	98.63 158	99.70 122	99.92 104	99.25 155	100.00 1	99.37 206	95.71 276	99.40 209	100.00 1	96.58 198	99.95 151	96.80 295	99.94 119	99.91 149

DPM-MVS		99.63 51	99.51 63	100.00 1	99.90 108	100.00 1	100.00 1	99.43 122	99.00 27	100.00 1	100.00 1	99.58 22	100.00 1	97.64 267	100.00 1	100.00 1

CHOSEN 1792x2688		99.00 134	98.91 132	99.25 189	99.90 108	97.79 264	100.00 1	99.99 13	98.79 60	98.28 283	100.00 1	93.63 242	99.95 151	99.66 155	99.95 117	100.00 1

test2506		99.48 74	99.38 74	99.75 113	99.89 110	99.51 123	99.45 333	100.00 1	98.38 80	99.83 168	100.00 1	98.86 112	99.81 194	99.25 192	98.78 171	99.94 135

ECVR-MVS		98.43 184	98.14 193	99.32 180	99.89 110	98.21 234	99.46 331	100.00 1	98.38 80	99.47 202	100.00 1	87.91 318	99.80 195	99.35 184	98.78 171	99.94 135

test1111		98.42 186	98.12 194	99.29 183	99.88 112	98.15 236	99.46 331	100.00 1	98.36 84	99.42 203	100.00 1	87.91 318	99.79 196	99.31 188	98.78 171	99.94 135

CANet_DTU		99.02 132	98.90 135	99.41 164	99.88 112	98.71 199	100.00 1	99.29 247	98.84 51	100.00 1	100.00 1	94.02 238	100.00 1	98.08 251	99.96 114	99.52 245

dcpmvs_2		98.87 149	99.53 59	96.90 317	99.87 114	90.88 370	99.94 257	99.07 335	98.20 94	100.00 1	100.00 1	98.69 124	99.86 179	100.00 1	100.00 1	99.95 130

mvsany_test1		99.57 63	99.48 69	99.85 86	99.86 115	99.54 117	100.00 1	99.36 212	98.94 37	100.00 1	100.00 1	97.97 145	100.00 1	99.88 103	99.28 160	100.00 1

test_fmvsm_n_1920		99.55 65	99.49 66	99.73 117	99.85 116	99.19 163	100.00 1	99.41 184	98.87 47	100.00 1	100.00 1	97.34 176	100.00 1	99.98 73	99.90 126	100.00 1

fmvsm_s_conf0.5_n		99.21 110	99.01 116	99.83 90	99.84 117	99.53 119	100.00 1	99.38 203	98.29 90	100.00 1	100.00 1	93.62 243	99.99 94	99.99 61	99.93 122	99.98 111

test_fmvs1		98.37 191	98.04 202	99.34 175	99.84 117	98.07 242	100.00 1	99.00 354	98.85 49	100.00 1	100.00 1	85.11 345	99.96 138	99.69 147	99.88 129	100.00 1

PVSNet_0	93.57 19	96.41 280	95.74 296	98.41 234	99.84 117	95.22 317	100.00 1	100.00 1	98.08 105	97.55 321	99.78 258	84.40 348	100.00 1	100.00 1	81.99 382	100.00 1

fmvsm_l_conf0.5_n_a		99.63 51	99.55 56	99.86 82	99.83 120	99.58 112	100.00 1	99.36 212	98.98 30	100.00 1	100.00 1	97.85 151	99.99 94	100.00 1	99.94 119	100.00 1

SDMVSNet		98.49 181	98.08 197	99.73 117	99.82 121	99.53 119	99.99 212	99.45 102	97.62 147	99.38 210	99.86 238	90.06 292	99.88 177	99.92 96	96.61 252	99.79 222

sd_testset		97.81 213	97.48 223	98.79 216	99.82 121	96.80 298	99.32 346	99.45 102	97.62 147	99.38 210	99.86 238	85.56 343	99.77 201	99.72 132	96.61 252	99.79 222

cl22		98.23 199	98.11 195	98.58 227	99.82 121	99.01 180	100.00 1	99.28 253	96.92 207	98.33 279	99.21 321	98.09 144	98.97 281	98.72 222	92.61 309	97.76 264

test_yl		99.51 67	99.37 77	99.95 51	99.82 121	99.90 59	100.00 1	99.47 79	97.48 167	100.00 1	100.00 1	99.80 5	100.00 1	99.98 73	97.75 230	99.94 135

DCV-MVSNet		99.51 67	99.37 77	99.95 51	99.82 121	99.90 59	100.00 1	99.47 79	97.48 167	100.00 1	100.00 1	99.80 5	100.00 1	99.98 73	97.75 230	99.94 135

PVSNet_Blended_VisFu		99.33 90	99.18 103	99.78 108	99.82 121	99.49 128	100.00 1	99.95 19	97.36 176	99.63 192	100.00 1	96.45 203	99.95 151	99.79 119	99.65 152	99.89 164

fmvsm_l_conf0.5_n		99.63 51	99.56 54	99.86 82	99.81 127	99.59 111	100.00 1	99.36 212	98.98 30	100.00 1	100.00 1	97.92 148	99.99 94	100.00 1	99.95 117	100.00 1

ET-MVSNet_ETH3D		96.41 280	95.48 310	99.20 192	99.81 127	99.75 92	100.00 1	99.02 351	97.30 185	78.33 397	100.00 1	97.73 156	97.94 359	99.70 140	87.41 364	99.92 147

Anonymous202405211		97.87 210	97.53 222	98.90 209	99.81 127	96.70 301	99.35 344	99.46 94	92.98 347	98.83 250	99.99 185	90.63 283	100.00 1	99.70 140	97.03 244	100.00 1

thres100view900		99.25 105	99.01 116	99.95 51	99.81 127	99.87 75	100.00 1	99.94 22	97.13 193	99.83 168	99.96 210	97.01 183	100.00 1	99.59 165	97.85 221	99.98 111

tfpn200view9		99.26 101	99.03 114	99.96 42	99.81 127	99.89 66	100.00 1	99.94 22	97.23 188	99.83 168	99.96 210	97.04 179	100.00 1	99.59 165	97.85 221	99.98 111

VNet		99.04 125	98.75 147	99.90 72	99.81 127	99.75 92	99.50 329	99.47 79	98.36 84	100.00 1	99.99 185	94.66 231	100.00 1	99.90 99	97.09 243	99.96 124

thres600view7		99.24 108	99.00 118	99.95 51	99.81 127	99.87 75	100.00 1	99.94 22	97.13 193	99.83 168	99.96 210	97.01 183	100.00 1	99.54 173	97.77 229	99.97 118

thres400		99.26 101	99.03 114	99.95 51	99.81 127	99.89 66	100.00 1	99.94 22	97.23 188	99.83 168	99.96 210	97.04 179	100.00 1	99.59 165	97.85 221	99.97 118

thres200		99.27 99	99.04 113	99.96 42	99.81 127	99.90 59	100.00 1	99.94 22	97.31 183	99.83 168	99.96 210	97.04 179	100.00 1	99.62 161	97.88 219	99.98 111

WTY-MVS		99.54 66	99.40 72	99.95 51	99.81 127	99.93 44	100.00 1	100.00 1	97.98 112	99.84 165	100.00 1	98.94 105	99.98 118	99.86 107	98.21 202	99.94 135

HY-MVS	96.53 9	99.50 70	99.35 82	99.96 42	99.81 127	99.93 44	99.64 312	100.00 1	97.97 114	99.84 165	99.85 243	98.94 105	99.99 94	99.86 107	98.23 201	99.95 130

fmvsm_s_conf0.5_n_a		99.32 92	99.15 105	99.81 97	99.80 138	99.47 133	100.00 1	99.35 223	98.22 91	100.00 1	100.00 1	95.21 222	99.99 94	99.96 85	99.86 133	99.98 111

Anonymous20231211		96.29 289	95.70 298	98.07 261	99.80 138	97.49 272	99.15 370	99.40 188	89.11 374	97.75 312	99.45 310	88.93 309	98.98 279	98.26 247	89.47 348	97.73 303

LFMVS		97.42 234	96.62 255	99.81 97	99.80 138	99.50 125	99.16 368	99.56 70	94.48 312	100.00 1	100.00 1	79.35 373	100.00 1	99.89 101	97.37 239	99.94 135

RPSCF		97.37 236	98.24 187	94.76 349	99.80 138	84.57 386	99.99 212	99.05 345	94.95 297	99.82 176	100.00 1	94.03 237	100.00 1	98.15 250	98.38 190	99.70 235

test_vis1_n_1920		97.77 215	97.24 237	99.34 175	99.79 142	98.04 246	100.00 1	99.25 269	98.88 44	100.00 1	100.00 1	77.52 378	100.00 1	99.88 103	99.85 136	100.00 1

h-mvs33		97.03 252	96.53 258	98.51 229	99.79 142	95.90 310	99.45 333	99.45 102	98.21 92	100.00 1	99.78 258	97.49 167	99.99 94	99.72 132	74.92 391	99.65 242

Anonymous20240529		96.93 257	96.22 274	99.05 198	99.79 142	97.30 283	99.16 368	99.47 79	88.51 377	98.69 256	100.00 1	83.50 357	100.00 1	99.83 113	97.02 245	99.83 191

PS-MVSNAJ		99.64 48	99.57 49	99.85 86	99.78 145	99.81 86	99.95 253	99.42 138	98.38 80	100.00 1	100.00 1	98.75 121	100.00 1	99.88 103	99.99 98	99.74 230

PCF-MVS	98.23 3	98.69 163	98.37 179	99.62 135	99.78 145	99.02 178	99.23 359	99.06 343	96.43 245	98.08 292	100.00 1	94.72 230	99.95 151	98.16 249	99.91 125	99.90 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+-dtu		98.51 180	98.86 137	97.47 293	99.77 147	94.21 343	100.00 1	98.94 359	97.61 151	99.91 154	98.75 351	95.89 208	99.51 243	99.36 183	99.48 158	98.68 257

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 148	99.95 32	100.00 1	99.42 138	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 77	99.34 84	99.80 102	99.76 148	99.50 125	100.00 1	99.91 36	97.72 135	99.98 110	99.94 223	98.45 134	100.00 1	99.53 175	98.75 174	99.89 164

sasdasda		99.03 127	98.73 149	99.94 63	99.75 150	99.95 32	100.00 1	99.30 242	97.64 144	100.00 1	100.00 1	95.22 220	99.97 125	99.76 126	96.90 248	99.91 149

canonicalmvs		99.03 127	98.73 149	99.94 63	99.75 150	99.95 32	100.00 1	99.30 242	97.64 144	100.00 1	100.00 1	95.22 220	99.97 125	99.76 126	96.90 248	99.91 149

alignmvs		99.38 82	99.21 96	99.91 69	99.73 152	99.92 49	100.00 1	99.51 76	97.61 151	100.00 1	100.00 1	99.06 89	99.93 167	99.83 113	97.12 242	99.90 159

baseline1		98.91 146	98.61 160	99.81 97	99.71 153	99.77 90	99.78 285	99.44 116	97.51 164	98.81 251	99.99 185	98.25 138	99.76 203	98.60 231	95.41 262	99.89 164

ab-mvs		98.42 186	98.02 204	99.61 136	99.71 153	99.00 182	99.10 375	99.64 64	96.70 228	99.04 233	99.81 252	90.64 282	99.98 118	99.64 157	97.93 216	99.84 188

xiu_mvs_v2_base		99.51 67	99.41 71	99.82 92	99.70 155	99.73 95	99.92 260	99.40 188	98.15 98	100.00 1	100.00 1	98.50 133	100.00 1	99.85 109	99.13 162	99.74 230

test_cas_vis1_n_1920		98.63 169	98.25 185	99.77 111	99.69 156	99.32 147	100.00 1	99.31 238	98.84 51	99.96 120	100.00 1	87.42 325	99.99 94	99.14 199	99.86 133	100.00 1

tpmvs		98.59 171	98.38 177	99.23 190	99.69 156	97.90 256	99.31 349	99.47 79	94.52 310	99.68 190	99.28 319	97.64 161	99.89 173	97.71 265	98.17 206	99.89 164

Test_1112_low_res		98.83 152	98.60 162	99.51 149	99.69 156	98.75 195	99.99 212	99.14 312	96.81 215	98.84 248	99.06 328	97.45 170	99.89 173	98.66 224	97.75 230	99.89 164

1112_ss		98.91 146	98.71 153	99.51 149	99.69 156	98.75 195	99.99 212	99.15 307	96.82 214	98.84 248	100.00 1	97.45 170	99.89 173	98.66 224	97.75 230	99.89 164

test_fmvsmconf_n		99.56 64	99.46 70	99.86 82	99.68 160	99.58 112	100.00 1	99.31 238	98.92 39	99.88 161	100.00 1	97.35 175	99.99 94	99.98 73	99.99 98	100.00 1

test_fmvs1_n		97.43 233	96.86 246	99.15 194	99.68 160	97.48 273	99.99 212	98.98 357	98.82 55	100.00 1	100.00 1	74.85 383	99.96 138	99.67 151	99.70 147	100.00 1

hse-mvs2		96.79 260	96.38 266	98.04 271	99.68 160	95.54 314	99.81 279	99.42 138	98.21 92	100.00 1	99.80 255	97.49 167	99.46 252	99.72 132	73.27 394	99.12 252

AUN-MVS		96.26 291	95.67 302	98.06 265	99.68 160	95.60 313	99.82 278	99.42 138	96.78 218	99.88 161	99.80 255	94.84 228	99.47 248	97.48 273	73.29 393	99.12 252

Vis-MVSNet		98.52 178	98.25 185	99.34 175	99.68 160	98.55 208	99.68 309	99.41 184	97.34 179	99.94 146	100.00 1	90.38 288	99.70 211	99.03 206	98.84 169	99.76 228
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UWE-MVS		99.18 112	99.06 111	99.51 149	99.67 165	98.80 192	100.00 1	99.43 122	96.80 216	99.93 150	99.86 238	99.79 7	99.94 163	97.78 263	98.33 196	99.80 219

ETVMVS		99.16 115	98.98 121	99.69 123	99.67 165	99.56 114	100.00 1	99.45 102	96.36 252	99.98 110	99.95 218	98.65 125	99.64 214	99.11 203	97.63 237	99.88 175

testing222		99.14 117	98.94 128	99.73 117	99.67 165	99.51 123	100.00 1	99.43 122	96.90 210	99.99 104	99.90 232	98.55 131	99.86 179	98.85 214	97.18 241	99.81 205

iter_conf05_11		98.21 200	97.74 214	99.65 131	99.67 165	99.06 173	100.00 1	98.87 365	97.84 126	99.96 120	100.00 1	83.57 355	99.88 177	99.72 132	100.00 1	100.00 1

bld_raw_dy_0_64		97.64 220	96.98 242	99.63 133	99.67 165	98.94 187	100.00 1	97.98 384	97.85 125	98.93 238	100.00 1	83.23 359	99.96 138	99.72 132	95.41 262	100.00 1

xiu_mvs_v1_base_debu		99.35 85	99.21 96	99.79 104	99.67 165	99.71 97	99.78 285	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 239

xiu_mvs_v1_base		99.35 85	99.21 96	99.79 104	99.67 165	99.71 97	99.78 285	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 239

xiu_mvs_v1_base_debi		99.35 85	99.21 96	99.79 104	99.67 165	99.71 97	99.78 285	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 239

IB-MVS	96.24 12	97.54 228	96.95 243	99.33 178	99.67 165	98.10 241	100.00 1	99.47 79	97.42 173	99.26 216	99.69 270	98.83 116	99.89 173	99.43 179	78.77 389	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		98.33 192	98.27 184	98.51 229	99.66 174	99.04 176	100.00 1	99.22 281	97.53 160	98.51 270	99.38 313	99.49 39	98.75 302	98.02 254	92.61 309	97.76 264

FE-MVS		99.16 115	98.99 120	99.66 129	99.65 175	99.18 165	99.58 320	99.43 122	95.24 292	99.91 154	99.59 295	99.37 58	99.97 125	98.31 242	99.81 141	99.83 191

iter_conf05		98.73 158	98.77 144	98.60 224	99.65 175	99.22 160	100.00 1	99.22 281	96.68 232	98.98 236	99.97 198	99.99 3	98.84 293	99.29 190	95.11 281	97.75 274

MVSTER		98.58 172	98.52 167	98.77 217	99.65 175	99.68 103	100.00 1	99.29 247	95.63 278	98.65 258	99.80 255	99.78 8	98.88 291	98.59 232	95.31 267	97.73 303

FMVSNet3		97.30 239	96.95 243	98.37 237	99.65 175	99.25 155	99.71 303	99.28 253	94.23 317	98.53 267	98.91 344	93.30 248	98.11 346	95.31 318	93.60 298	97.73 303

cascas		98.43 184	98.07 199	99.50 152	99.65 175	99.02 178	100.00 1	99.22 281	94.21 319	99.72 188	99.98 190	92.03 268	99.93 167	99.68 148	98.12 207	99.54 244

IS-MVSNet		99.08 121	98.91 132	99.59 140	99.65 175	99.38 141	99.78 285	99.24 274	96.70 228	99.51 197	100.00 1	98.44 135	99.52 241	98.47 236	98.39 188	99.88 175

testing11		99.26 101	99.19 100	99.46 156	99.64 181	98.61 205	100.00 1	99.43 122	96.94 204	99.92 151	99.94 223	99.43 50	99.97 125	99.67 151	97.79 228	99.82 196

Vis-MVSNet (Re-imp)		98.99 137	98.89 136	99.29 183	99.64 181	98.89 189	99.98 236	99.31 238	96.74 223	99.48 199	100.00 1	98.11 142	99.10 271	98.39 238	98.34 193	99.89 164

test_vis1_rt		93.10 335	92.93 336	93.58 359	99.63 183	85.07 384	99.99 212	93.71 405	97.49 166	90.96 378	97.10 379	60.40 396	99.95 151	99.24 194	97.90 218	95.72 381

FA-MVS(test-final)		99.00 134	98.75 147	99.73 117	99.63 183	99.43 137	99.83 275	99.43 122	95.84 273	99.52 196	99.37 314	97.84 153	99.96 138	97.63 268	99.68 148	99.79 222

EIA-MVS		99.26 101	99.19 100	99.45 158	99.63 183	98.75 195	100.00 1	99.27 261	96.93 205	99.95 144	100.00 1	97.47 169	99.79 196	99.74 129	99.72 146	99.82 196

Fast-Effi-MVS+		98.40 189	98.02 204	99.55 148	99.63 183	99.06 173	100.00 1	99.15 307	95.07 294	99.42 203	99.95 218	93.26 249	99.73 208	97.44 274	98.24 200	99.87 184

TESTMET0.1,1		99.08 121	98.96 123	99.44 159	99.63 183	99.38 141	100.00 1	99.45 102	95.53 282	99.48 199	100.00 1	99.71 13	99.02 275	96.84 292	99.99 98	99.91 149

MVS_Test		98.93 145	98.65 156	99.77 111	99.62 188	99.50 125	99.99 212	99.19 293	95.52 284	99.96 120	99.86 238	96.54 201	99.98 118	98.65 226	98.48 181	99.82 196

BH-w/o		98.82 153	98.81 141	98.88 211	99.62 188	96.71 300	100.00 1	99.28 253	97.09 195	98.81 251	100.00 1	94.91 227	99.96 138	99.54 173	100.00 1	99.96 124

tpmrst		98.98 140	98.93 130	99.14 195	99.61 190	97.74 265	99.52 327	99.36 212	96.05 265	99.98 110	99.64 283	99.04 94	99.86 179	98.94 208	98.19 204	99.82 196

BH-RMVSNet		98.46 182	98.08 197	99.59 140	99.61 190	99.19 163	100.00 1	99.28 253	97.06 199	98.95 237	100.00 1	88.99 307	99.82 191	98.83 217	100.00 1	99.77 226

testing91		99.18 112	99.10 108	99.41 164	99.60 192	98.43 212	100.00 1	99.43 122	96.76 219	99.82 176	99.92 228	99.05 91	99.98 118	99.62 161	97.67 234	99.81 205

testing99		99.18 112	99.10 108	99.41 164	99.60 192	98.43 212	100.00 1	99.43 122	96.76 219	99.84 165	99.92 228	99.06 89	99.98 118	99.62 161	97.67 234	99.81 205

Effi-MVS+		98.58 172	98.24 187	99.61 136	99.60 192	99.26 153	97.85 394	99.10 325	96.22 260	99.97 115	99.89 233	93.75 240	99.77 201	99.43 179	98.34 193	99.81 205

EPMVS		99.25 105	99.13 106	99.60 138	99.60 192	99.20 162	99.60 318	100.00 1	96.93 205	99.92 151	99.36 315	99.05 91	99.71 210	98.77 219	98.94 168	99.90 159

thisisatest0530		99.37 84	99.27 86	99.69 123	99.59 196	99.41 139	100.00 1	99.46 94	96.46 244	99.90 156	100.00 1	99.44 46	99.85 185	98.97 207	99.58 156	99.80 219

thisisatest0515		99.42 79	99.31 85	99.74 114	99.59 196	99.55 115	100.00 1	99.46 94	96.65 234	99.92 151	100.00 1	99.44 46	99.85 185	99.09 204	99.63 154	99.81 205

XVG-OURS-SEG-HR		98.27 197	98.31 183	98.14 255	99.59 196	95.92 308	100.00 1	99.36 212	98.48 75	99.21 218	100.00 1	89.27 304	99.94 163	99.76 126	99.17 161	98.56 260

BH-untuned		98.64 166	98.65 156	98.60 224	99.59 196	96.17 306	100.00 1	99.28 253	96.67 233	98.41 275	100.00 1	94.52 233	99.83 188	99.41 181	100.00 1	99.81 205

TR-MVS		98.14 201	97.74 214	99.33 178	99.59 196	98.28 229	99.27 351	99.21 289	96.42 247	99.15 223	99.94 223	88.87 310	99.79 196	98.88 212	98.29 198	99.93 145

ETV-MVS		99.34 88	99.24 92	99.64 132	99.58 201	99.33 146	100.00 1	99.25 269	97.57 156	99.96 120	100.00 1	97.44 172	99.79 196	99.70 140	99.65 152	99.81 205

Fast-Effi-MVS+-dtu		98.38 190	98.56 165	97.82 283	99.58 201	94.44 340	100.00 1	99.16 306	96.75 221	99.51 197	99.63 287	95.03 225	99.60 216	97.71 265	99.67 150	99.42 247

XVG-OURS		98.30 193	98.36 181	98.13 258	99.58 201	95.91 309	100.00 1	99.36 212	98.69 65	99.23 217	100.00 1	91.20 274	99.92 169	99.34 185	97.82 224	98.56 260

tttt0517		99.34 88	99.23 95	99.67 126	99.57 204	99.38 141	100.00 1	99.46 94	96.33 255	99.89 159	100.00 1	99.44 46	99.84 187	98.93 209	99.46 159	99.78 225

mvs_anonymous		98.80 154	98.60 162	99.38 172	99.57 204	99.24 157	100.00 1	99.21 289	95.87 268	98.92 239	99.82 249	96.39 204	99.03 274	99.13 201	98.50 179	99.88 175

PMMVS		99.12 118	98.97 122	99.58 144	99.57 204	98.98 184	100.00 1	99.30 242	97.14 192	99.96 120	100.00 1	96.53 202	99.82 191	99.70 140	98.49 180	99.94 135

dmvs_re		97.54 228	97.88 208	96.54 328	99.55 207	90.35 372	99.86 271	99.46 94	97.00 201	99.41 208	100.00 1	90.78 281	99.30 264	99.60 164	95.24 272	99.96 124

GG-mvs-BLEND				99.59 140	99.54 208	99.49 128	99.17 367	99.52 72		99.96 120	99.68 274	100.00 1	99.33 263	99.71 137	99.99 98	99.96 124

diffmvs		98.96 141	98.73 149	99.63 133	99.54 208	99.16 167	100.00 1	99.18 300	97.33 180	99.96 120	100.00 1	94.60 232	99.91 170	99.66 155	98.33 196	99.82 196
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		98.41 188	98.11 195	99.31 182	99.54 208	98.55 208	99.18 362	100.00 1	98.64 70	99.79 179	99.04 331	87.61 323	100.00 1	99.30 189	99.89 127	99.40 248
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		94.15 325	93.08 334	97.35 298	99.53 211	97.83 262	99.63 314	99.19 293	92.88 349	96.29 347	97.68 375	98.84 114	96.70 374	89.73 367	63.92 398	97.53 348

miper_refine_blended		94.15 325	93.08 334	97.35 298	99.53 211	97.83 262	99.63 314	99.19 293	92.88 349	96.29 347	97.68 375	98.84 114	96.70 374	89.73 367	63.92 398	97.53 348

GBi-Net		96.07 302	95.80 292	96.89 318	99.53 211	94.87 321	99.18 362	99.27 261	93.71 327	98.53 267	98.81 348	84.23 350	98.07 351	95.31 318	93.60 298	97.72 310

test1		96.07 302	95.80 292	96.89 318	99.53 211	94.87 321	99.18 362	99.27 261	93.71 327	98.53 267	98.81 348	84.23 350	98.07 351	95.31 318	93.60 298	97.72 310

dp		98.72 159	98.61 160	99.03 201	99.53 211	97.39 276	99.45 333	99.39 201	95.62 279	99.94 146	99.52 305	98.83 116	99.82 191	96.77 298	98.42 185	99.89 164

FMVSNet2		96.22 292	95.60 304	98.06 265	99.53 211	98.33 224	99.45 333	99.27 261	93.71 327	98.03 296	98.84 347	84.23 350	98.10 349	93.97 336	93.40 301	97.73 303

MDTV_nov1_ep13			98.94 128		99.53 211	98.36 222	99.39 340	99.46 94	96.54 240	99.99 104	99.63 287	98.92 108	99.86 179	98.30 245	98.71 175

gm-plane-assit					99.52 218	97.26 285			95.86 270		100.00 1		99.43 254	98.76 220

UA-Net		99.06 123	98.83 138	99.74 114	99.52 218	99.40 140	99.08 378	99.45 102	97.64 144	99.83 168	100.00 1	95.80 210	99.94 163	98.35 240	99.80 143	99.88 175

VDD-MVS		96.58 272	95.99 283	98.34 239	99.52 218	95.33 315	99.18 362	99.38 203	96.64 235	99.77 182	100.00 1	72.51 388	100.00 1	100.00 1	96.94 247	99.70 235

casdiffmvs		98.65 165	98.38 177	99.46 156	99.52 218	98.74 198	100.00 1	99.15 307	96.91 208	99.05 232	100.00 1	92.75 257	99.83 188	99.70 140	98.38 190	99.81 205
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		98.69 163	98.45 172	99.41 164	99.52 218	98.67 202	100.00 1	99.17 305	97.03 200	99.13 224	100.00 1	93.17 250	99.74 206	99.70 140	98.34 193	99.81 205

testing3		98.44 183	98.37 179	98.65 221	99.51 223	98.32 226	100.00 1	99.62 66	96.43 245	97.93 303	99.99 185	99.11 85	97.81 362	94.88 325	97.80 226	99.82 196

ADS-MVSNet2		98.28 196	98.51 168	97.62 289	99.51 223	95.03 320	99.24 354	99.41 184	95.52 284	99.96 120	99.70 267	97.57 163	97.94 359	97.11 284	98.54 177	99.88 175

ADS-MVSNet		98.70 162	98.51 168	99.28 186	99.51 223	98.39 217	99.24 354	99.44 116	95.52 284	99.96 120	99.70 267	97.57 163	99.58 222	97.11 284	98.54 177	99.88 175

EPP-MVSNet		99.10 120	99.00 118	99.40 168	99.51 223	98.68 201	99.92 260	99.43 122	95.47 288	99.65 191	100.00 1	99.51 33	99.76 203	99.53 175	98.00 210	99.75 229

Syy-MVS		96.17 296	96.57 257	95.00 344	99.50 227	87.37 381	100.00 1	99.57 68	96.23 258	98.07 293	100.00 1	92.41 264	97.81 362	85.34 382	97.96 213	99.82 196

myMVS_eth3d		98.52 178	98.51 168	98.53 228	99.50 227	97.98 249	100.00 1	99.57 68	96.23 258	98.07 293	100.00 1	99.09 87	97.81 362	96.17 305	97.96 213	99.82 196

casdiffmvs_mvg		98.64 166	98.39 176	99.40 168	99.50 227	98.60 206	100.00 1	99.22 281	96.85 212	99.10 226	100.00 1	92.75 257	99.78 200	99.71 137	98.35 192	99.81 205
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		98.05 205	97.76 212	98.92 208	99.50 227	97.10 291	99.77 290	99.30 242	90.20 371	99.72 188	98.71 352	97.71 157	99.86 179	96.75 299	98.20 203	99.81 205

miper_ehance_all_eth		97.81 213	97.66 219	98.23 247	99.49 231	98.37 220	99.99 212	99.11 323	94.78 300	98.25 287	99.21 321	98.18 140	98.57 318	97.35 280	92.61 309	97.76 264

PatchmatchNet		99.03 127	98.96 123	99.26 188	99.49 231	98.33 224	99.38 341	99.45 102	96.64 235	99.96 120	99.58 297	99.49 39	99.50 244	97.63 268	99.00 167	99.93 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_lstm_enhance		97.40 235	97.28 233	97.75 286	99.48 233	97.52 271	100.00 1	99.07 335	94.08 323	98.01 299	99.61 293	97.38 174	97.98 357	96.44 303	91.47 330	97.76 264

c3_l		97.58 225	97.42 225	98.06 265	99.48 233	98.16 235	99.96 247	99.10 325	94.54 309	98.13 291	99.20 323	97.87 149	98.25 338	97.28 281	91.20 333	97.75 274

CDS-MVSNet		98.96 141	98.95 127	99.01 202	99.48 233	98.36 222	99.93 259	99.37 206	96.79 217	99.31 214	99.83 246	99.77 10	98.91 285	98.07 252	97.98 211	99.77 226
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n		98.77 155	98.42 173	99.82 92	99.47 236	99.52 122	100.00 1	99.27 261	97.53 160	100.00 1	100.00 1	89.73 297	99.96 138	99.84 112	99.93 122	99.97 118

GeoE		98.06 204	97.65 220	99.29 183	99.47 236	98.41 214	100.00 1	99.19 293	94.85 299	98.88 243	100.00 1	91.21 273	99.59 218	97.02 286	98.19 204	99.88 175

cl____		97.54 228	97.32 231	98.18 251	99.47 236	98.14 238	100.00 1	99.10 325	94.16 322	97.60 319	99.63 287	97.52 166	98.65 309	96.47 300	91.97 321	97.76 264

DIV-MVS_self_test		97.52 231	97.35 230	98.05 269	99.46 239	98.11 239	100.00 1	99.10 325	94.21 319	97.62 317	99.63 287	97.65 160	98.29 335	96.47 300	91.98 320	97.76 264

MVSFormer		98.94 144	98.82 139	99.28 186	99.45 240	99.49 128	100.00 1	99.13 316	95.46 289	99.97 115	100.00 1	96.76 194	98.59 315	98.63 228	100.00 1	99.74 230

lupinMVS		99.29 97	99.16 104	99.69 123	99.45 240	99.49 128	100.00 1	99.15 307	97.45 170	99.97 115	100.00 1	96.76 194	99.76 203	99.67 151	100.00 1	99.81 205

WB-MVSnew		97.02 254	97.24 237	96.37 332	99.44 242	97.36 278	100.00 1	99.43 122	96.12 264	99.35 212	99.89 233	93.60 244	98.42 330	88.91 376	98.39 188	93.33 391

TAMVS		98.76 156	98.73 149	98.86 212	99.44 242	97.69 266	99.57 321	99.34 229	96.57 238	99.12 225	99.81 252	98.83 116	99.16 269	97.97 258	97.91 217	99.73 234

tpm2		98.64 166	98.58 164	98.81 215	99.42 244	97.12 289	99.69 307	99.37 206	93.63 332	99.94 146	99.67 275	98.96 102	99.47 248	98.62 230	97.95 215	99.83 191

EC-MVSNet		99.19 111	99.09 110	99.48 155	99.42 244	99.07 171	100.00 1	99.21 289	96.95 203	99.96 120	100.00 1	96.88 192	99.48 246	99.64 157	99.79 144	99.88 175

eth_miper_zixun_eth		97.47 232	97.28 233	98.06 265	99.41 246	97.94 254	99.62 316	99.08 331	94.46 313	98.19 290	99.56 301	96.91 191	98.50 324	96.78 296	91.49 328	97.74 297

gg-mvs-nofinetune		96.95 256	96.10 278	99.50 152	99.41 246	99.36 145	99.07 380	99.52 72	83.69 389	99.96 120	83.60 405	100.00 1	99.20 268	99.68 148	99.99 98	99.96 124

SCA		98.30 193	97.98 206	99.23 190	99.41 246	98.25 231	99.99 212	99.45 102	96.91 208	99.76 184	99.58 297	89.65 299	99.54 235	98.31 242	98.79 170	99.91 149

CostFormer		98.84 151	98.77 144	99.04 200	99.41 246	97.58 270	99.67 310	99.35 223	94.66 305	99.96 120	99.36 315	99.28 72	99.74 206	99.41 181	97.81 225	99.81 205

IterMVS		96.76 262	96.46 263	97.63 287	99.41 246	96.89 295	99.99 212	99.13 316	94.74 303	97.59 320	99.66 277	89.63 301	98.28 336	95.71 310	92.31 315	97.72 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		96.72 265	96.42 265	97.62 289	99.40 251	96.83 297	99.99 212	99.14 312	94.65 306	97.55 321	99.72 262	89.65 299	98.31 334	95.62 314	92.05 318	97.73 303

test-LLR		99.03 127	98.91 132	99.40 168	99.40 251	99.28 151	100.00 1	99.45 102	96.70 228	99.42 203	99.12 324	99.31 64	99.01 276	96.82 293	99.99 98	99.91 149

test-mter		98.96 141	98.82 139	99.40 168	99.40 251	99.28 151	100.00 1	99.45 102	95.44 291	99.42 203	99.12 324	99.70 14	99.01 276	96.82 293	99.99 98	99.91 149

FMVSNet5		95.32 316	95.43 313	94.99 345	99.39 254	92.99 355	99.25 353	99.24 274	90.45 367	97.44 324	98.45 362	95.78 211	94.39 391	87.02 378	91.88 322	97.59 344

MIMVSNet		97.06 250	96.73 251	98.05 269	99.38 255	96.64 303	98.47 390	99.35 223	93.41 337	99.48 199	98.53 359	89.66 298	97.70 368	94.16 334	98.11 208	99.80 219

IterMVS-LS		97.56 226	97.44 224	97.92 280	99.38 255	97.90 256	99.89 267	99.10 325	94.41 314	98.32 280	99.54 304	97.21 177	98.11 346	97.50 272	91.62 325	97.75 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		94.12 327	92.73 340	98.29 243	99.33 257	95.95 307	99.38 341	99.19 293	74.54 397	98.26 286	86.34 401	86.07 337	99.06 273	91.60 354	99.87 132	99.85 187

CR-MVSNet		98.02 207	97.71 218	98.93 207	99.31 258	98.86 190	99.13 372	99.00 354	96.53 241	99.96 120	98.98 337	96.94 189	98.10 349	91.18 356	98.40 186	99.84 188

Patchmtry		96.81 259	96.37 267	98.14 255	99.31 258	98.55 208	98.91 383	99.00 354	90.45 367	97.92 304	98.98 337	96.94 189	98.12 344	94.27 331	91.53 327	97.75 274

RPMNet		95.26 318	93.82 326	99.56 147	99.31 258	98.86 190	99.13 372	99.42 138	79.82 394	99.96 120	95.13 387	95.69 213	99.98 118	77.54 397	98.40 186	99.84 188

tt0805		96.52 273	96.23 273	97.40 294	99.30 261	93.55 348	99.32 346	99.45 102	96.75 221	97.88 306	99.99 185	79.99 371	99.59 218	97.39 278	95.98 255	99.06 254

VDDNet		96.39 284	95.55 305	98.90 209	99.27 262	97.45 274	99.15 370	99.92 34	91.28 360	99.98 110	100.00 1	73.55 384	100.00 1	99.85 109	96.98 246	99.24 249

LCM-MVSNet-Re		96.52 273	97.21 239	94.44 350	99.27 262	85.80 383	99.85 273	96.61 400	95.98 266	92.75 374	98.48 361	93.97 239	97.55 369	99.58 168	98.43 184	99.98 111

baseline2		98.99 137	98.93 130	99.18 193	99.26 264	99.15 168	100.00 1	99.46 94	96.71 227	96.79 339	100.00 1	99.42 53	99.25 267	98.75 221	99.94 119	99.15 251

GA-MVS		97.72 218	97.27 235	99.06 196	99.24 265	97.93 255	100.00 1	99.24 274	95.80 274	98.99 235	99.64 283	89.77 296	99.36 259	95.12 322	97.62 238	99.89 164

APD_test1		93.07 336	94.14 324	89.85 367	99.18 266	72.49 395	99.76 292	98.90 364	92.86 351	96.35 346	99.94 223	75.56 381	99.91 170	86.73 379	97.98 211	97.15 362

jason		99.11 119	98.96 123	99.59 140	99.17 267	99.31 149	100.00 1	99.13 316	97.38 175	99.83 168	100.00 1	95.54 216	99.72 209	99.57 169	99.97 111	99.74 230
jason: jason.
test_vis1_n		96.69 267	95.81 290	99.32 180	99.14 268	97.98 249	99.97 242	98.98 357	98.45 77	100.00 1	100.00 1	66.44 394	99.99 94	99.78 125	99.57 157	100.00 1

tpm		98.24 198	98.22 191	98.32 241	99.13 269	95.79 311	99.53 326	99.12 322	95.20 293	99.96 120	99.36 315	97.58 162	99.28 266	97.41 276	96.67 250	99.88 175

EI-MVSNet		97.98 208	97.93 207	98.16 254	99.11 270	97.84 261	99.74 295	99.29 247	94.39 315	98.65 258	100.00 1	97.21 177	98.88 291	97.62 270	95.31 267	97.75 274

CVMVSNet		98.56 174	98.47 171	98.82 213	99.11 270	97.67 267	99.74 295	99.47 79	97.57 156	99.06 231	100.00 1	95.72 212	98.97 281	98.21 248	97.33 240	99.83 191

test0.0.03 1		98.12 203	98.03 203	98.39 235	99.11 270	98.07 242	100.00 1	99.93 30	96.70 228	96.91 335	99.95 218	99.31 64	98.19 339	91.93 351	98.44 183	98.91 255

fmvsm_s_conf0.1_n_a		98.71 160	98.36 181	99.78 108	99.09 273	99.42 138	100.00 1	99.26 267	97.42 173	100.00 1	100.00 1	89.78 295	99.96 138	99.82 118	99.85 136	99.97 118

Patchmatch-test		97.83 212	97.42 225	99.06 196	99.08 274	97.66 268	98.66 388	99.21 289	93.65 331	98.25 287	99.58 297	99.47 43	99.57 223	90.25 365	98.59 176	99.95 130

HQP-NCC					99.07 275		100.00 1		99.04 15	99.17 219

ACMP_Plane					99.07 275		100.00 1		99.04 15	99.17 219

NP-MVS					99.07 275	94.81 326					99.97 198

HQP-MVS		97.73 217	97.85 209	97.39 295	99.07 275	94.82 324	100.00 1	99.40 188	99.04 15	99.17 219	99.97 198	88.61 313	99.57 223	99.79 119	95.58 256	97.77 262

plane_prior6					99.06 279	94.80 327						88.58 315

ACMH+	96.20 13	96.49 278	96.33 270	97.00 311	99.06 279	93.80 346	99.81 279	99.31 238	97.32 181	95.89 355	99.97 198	82.62 362	99.54 235	98.34 241	94.63 291	97.65 334

PatchT		95.90 308	94.95 322	98.75 218	99.03 281	98.39 217	99.08 378	99.32 233	85.52 385	99.96 120	94.99 389	97.94 146	98.05 355	80.20 393	98.47 182	99.81 205

plane_prior1					99.02 282

QAPM		98.99 137	98.66 155	99.96 42	99.01 283	99.87 75	99.88 269	99.93 30	97.99 110	98.68 257	100.00 1	93.17 250	100.00 1	99.32 187	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 86	99.01 283	99.95 32	100.00 1	99.75 52	99.37 3	99.99 104	100.00 1	99.76 11	99.60 216	100.00 1	100.00 1	100.00 1

ACMM	97.17 6	97.37 236	97.40 227	97.29 301	99.01 283	94.64 335	100.00 1	99.25 269	98.07 106	98.44 274	99.98 190	87.38 326	99.55 232	99.25 192	95.19 275	97.69 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 220	97.74 214	97.36 297	99.01 283	94.76 332	100.00 1	99.34 229	99.30 4	99.00 234	99.97 198	87.49 324	99.57 223	99.96 85	95.58 256	97.75 274
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		99.22 109	98.96 123	99.98 23	99.00 287	99.95 32	99.24 354	99.94 22	98.14 99	98.88 243	100.00 1	95.63 215	100.00 1	99.85 109	100.00 1	100.00 1

HQP_MVS		97.71 219	97.82 211	97.37 296	99.00 287	94.80 327	100.00 1	99.40 188	99.00 27	99.08 229	99.97 198	88.58 315	99.55 232	99.79 119	95.57 260	97.76 264

plane_prior7					99.00 287	94.78 331

3Dnovator+	95.58 15	99.03 127	98.71 153	99.96 42	98.99 290	99.89 66	100.00 1	99.51 76	98.96 32	98.32 280	100.00 1	92.78 256	100.00 1	99.87 106	100.00 1	100.00 1

testgi		96.18 294	95.93 286	96.93 316	98.98 291	94.20 344	100.00 1	99.07 335	97.16 191	96.06 352	99.86 238	84.08 353	97.79 365	90.38 364	97.80 226	98.81 256

ACMP	97.00 8	97.19 242	97.16 241	97.27 304	98.97 292	94.58 339	100.00 1	99.32 233	97.97 114	97.45 323	99.98 190	85.79 341	99.56 227	99.70 140	95.24 272	97.67 329
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ITE_SJBPF				96.84 321	98.96 293	93.49 349		98.12 378	98.12 103	98.35 277	99.97 198	84.45 347	99.56 227	95.63 313	95.25 271	97.49 350

ACMH	96.25 11	96.77 261	96.62 255	97.21 305	98.96 293	94.43 341	99.64 312	99.33 231	97.43 172	96.55 344	99.97 198	83.52 356	99.54 235	99.07 205	95.13 279	97.66 330
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		95.17 320	95.23 316	95.01 343	98.95 295	88.99 377	99.99 212	97.77 389	97.79 130	98.58 263	99.70 267	73.36 385	99.34 262	95.88 307	95.03 283	96.70 371

test_fmvsmconf0.1_n		99.25 105	99.05 112	99.82 92	98.92 296	99.55 115	100.00 1	99.23 278	98.91 41	99.75 185	99.97 198	94.79 229	99.94 163	99.94 93	99.99 98	99.97 118

UniMVSNet_ETH3D		95.28 317	94.41 323	97.89 281	98.91 297	95.14 318	99.13 372	99.35 223	92.11 355	97.17 330	99.66 277	70.28 391	99.36 259	97.88 260	95.18 276	99.16 250

DeepMVS_CX				89.98 366	98.90 298	71.46 397		99.18 300	97.61 151	96.92 333	99.83 246	86.07 337	99.83 188	96.02 306	97.65 236	98.65 258

OpenMVS	95.20 17	98.76 156	98.41 174	99.78 108	98.89 299	99.81 86	99.99 212	99.76 49	98.02 108	98.02 298	100.00 1	91.44 271	100.00 1	99.63 160	99.97 111	99.55 243

test_fmvsmvis_n_1920		99.46 76	99.37 77	99.73 117	98.88 300	99.18 165	100.00 1	99.26 267	98.85 49	99.79 179	100.00 1	97.70 158	100.00 1	99.98 73	99.86 133	100.00 1

1314		99.38 82	99.19 100	99.96 42	98.88 300	99.89 66	99.24 354	99.93 30	98.88 44	98.79 253	100.00 1	97.02 182	100.00 1	100.00 1	100.00 1	100.00 1

JIA-IIPM		97.09 247	96.34 269	99.36 173	98.88 300	98.59 207	99.81 279	99.43 122	84.81 387	99.96 120	90.34 397	98.55 131	99.52 241	97.00 287	98.28 199	99.98 111

FMVSNet1		94.45 322	93.63 329	96.89 318	98.87 303	94.87 321	99.18 362	99.27 261	90.95 364	97.31 326	98.81 348	72.89 387	98.07 351	92.61 345	92.81 307	97.72 310

3Dnovator	95.63 14	99.06 123	98.76 146	99.96 42	98.86 304	99.90 59	99.98 236	99.93 30	98.95 35	98.49 272	100.00 1	92.91 254	100.00 1	99.71 137	100.00 1	100.00 1

LPG-MVS_test		97.31 238	97.32 231	97.28 302	98.85 305	94.60 336	100.00 1	99.37 206	97.35 177	98.85 246	99.98 190	86.66 332	99.56 227	99.55 170	95.26 269	97.70 319

LGP-MVS_train				97.28 302	98.85 305	94.60 336		99.37 206	97.35 177	98.85 246	99.98 190	86.66 332	99.56 227	99.55 170	95.26 269	97.70 319

D2MVS		97.63 224	97.83 210	97.05 308	98.83 307	94.60 336	100.00 1	99.82 40	96.89 211	98.28 283	99.03 333	94.05 236	99.47 248	98.58 233	94.97 286	97.09 363

dmvs_testset		93.27 333	95.48 310	86.65 373	98.74 308	68.42 402	99.92 260	98.91 362	96.19 262	93.28 371	100.00 1	91.06 278	91.67 398	89.64 369	91.54 326	99.86 186

TinyColmap		95.50 314	95.12 319	96.64 325	98.69 309	93.00 354	99.40 339	97.75 390	96.40 250	96.14 351	99.87 236	79.47 372	99.50 244	93.62 339	94.72 290	97.40 355

XXY-MVS		97.14 246	96.63 254	98.67 220	98.65 310	98.92 188	99.54 325	99.29 247	95.57 281	97.63 315	99.83 246	87.79 322	99.35 261	98.39 238	92.95 306	97.75 274

VPA-MVSNet		97.03 252	96.43 264	98.82 213	98.64 311	99.32 147	99.38 341	99.47 79	96.73 225	98.91 241	98.94 342	87.00 330	99.40 257	99.23 195	89.59 346	97.76 264

VPNet		96.41 280	95.76 295	98.33 240	98.61 312	98.30 228	99.48 330	99.45 102	96.98 202	98.87 245	99.88 235	81.57 365	98.93 283	99.22 197	87.82 362	97.76 264

USDC		95.90 308	95.70 298	96.50 329	98.60 313	92.56 360	100.00 1	98.30 374	97.77 132	96.92 333	99.94 223	81.25 368	99.45 253	93.54 340	94.96 287	97.49 350

tfpnnormal		96.36 285	95.69 301	98.37 237	98.55 314	98.71 199	99.69 307	99.45 102	93.16 345	96.69 343	99.71 264	88.44 317	98.99 278	94.17 332	91.38 331	97.41 354

LTVRE_ROB	95.29 16	96.32 288	96.10 278	96.99 312	98.55 314	93.88 345	99.45 333	99.28 253	94.50 311	96.46 345	99.52 305	84.86 346	99.48 246	97.26 282	95.03 283	97.59 344
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		97.95 209	97.73 217	98.62 223	98.53 316	99.24 157	100.00 1	99.43 122	96.74 223	97.87 307	99.82 249	95.27 219	98.89 288	98.78 218	93.07 304	97.74 297

UniMVSNet (Re)		97.29 240	96.85 247	98.59 226	98.49 317	99.13 169	100.00 1	99.42 138	96.52 242	98.24 289	98.90 345	94.93 226	98.89 288	97.54 271	87.61 363	97.75 274

WR-MVS		97.09 247	96.64 253	98.46 231	98.43 318	99.09 170	99.97 242	99.33 231	95.62 279	97.76 309	99.67 275	91.17 275	98.56 320	98.49 235	89.28 351	97.74 297

XVG-ACMP-BASELINE		96.60 271	96.52 260	96.84 321	98.41 319	93.29 352	99.99 212	99.32 233	97.76 134	98.51 270	99.29 318	81.95 364	99.54 235	98.40 237	95.03 283	97.68 325

UniMVSNet_NR-MVSNet		97.16 244	96.80 248	98.22 248	98.38 320	98.41 214	100.00 1	99.45 102	96.14 263	97.76 309	99.64 283	95.05 224	98.50 324	97.98 255	86.84 367	97.75 274

test_method		91.04 348	91.10 345	90.85 364	98.34 321	77.63 391	100.00 1	98.93 361	76.69 395	96.25 349	98.52 360	70.44 390	97.98 357	89.02 375	91.74 323	96.92 367

nrg030		97.64 220	97.27 235	98.75 218	98.34 321	99.53 119	100.00 1	99.22 281	96.21 261	98.27 285	99.95 218	94.40 234	98.98 279	99.23 195	89.78 345	97.75 274

TransMVSNet (Re)		94.78 321	93.72 327	97.93 279	98.34 321	97.88 258	99.23 359	97.98 384	91.60 358	94.55 364	99.71 264	87.89 320	98.36 332	89.30 372	84.92 373	97.56 346

DU-MVS		96.93 257	96.49 261	98.22 248	98.31 324	98.41 214	100.00 1	99.37 206	96.41 248	97.76 309	99.65 279	92.14 266	98.50 324	97.98 255	86.84 367	97.75 274

NR-MVSNet		96.63 269	96.04 281	98.38 236	98.31 324	98.98 184	99.22 361	99.35 223	95.87 268	94.43 367	99.65 279	92.73 259	98.40 331	96.78 296	88.05 360	97.75 274

FC-MVSNet-test		97.84 211	97.63 221	98.45 232	98.30 326	99.05 175	100.00 1	99.43 122	96.63 237	97.61 318	99.82 249	95.19 223	98.57 318	98.64 227	93.05 305	97.73 303

Baseline_NR-MVSNet		96.16 298	95.70 298	97.56 292	98.28 327	96.79 299	100.00 1	97.86 388	91.93 357	97.63 315	99.47 309	92.14 266	98.35 333	97.13 283	86.83 369	97.54 347

WR-MVS_H		96.73 263	96.32 271	97.95 276	98.26 328	97.88 258	99.72 302	99.43 122	95.06 295	96.99 332	98.68 354	93.02 253	98.53 322	97.43 275	88.33 359	97.43 353

LF4IMVS		96.19 293	96.18 275	96.23 335	98.26 328	92.09 362	100.00 1	97.89 387	97.82 128	97.94 302	99.87 236	82.71 361	99.38 258	97.41 276	93.71 297	97.20 360

pm-mvs1		95.76 310	95.01 320	98.00 273	98.23 330	97.45 274	99.24 354	99.04 348	93.13 346	95.93 354	99.72 262	86.28 335	98.84 293	95.62 314	87.92 361	97.72 310

mvsmamba		98.13 202	98.06 200	98.32 241	98.22 331	98.50 211	100.00 1	99.22 281	96.41 248	98.91 241	99.96 210	95.69 213	98.73 304	99.19 198	94.95 288	97.73 303

CP-MVSNet		96.73 263	96.25 272	98.18 251	98.21 332	98.67 202	99.77 290	99.32 233	95.06 295	97.20 329	99.65 279	90.10 290	98.19 339	98.06 253	88.90 354	97.66 330

v10		96.14 300	95.50 306	98.07 261	98.19 333	97.96 252	99.83 275	99.07 335	92.10 356	98.07 293	98.94 342	91.07 277	98.61 311	92.41 350	89.82 344	97.63 338

PS-CasMVS		96.34 287	95.78 294	98.03 272	98.18 334	98.27 230	99.71 303	99.32 233	94.75 301	96.82 338	99.65 279	86.98 331	98.15 341	97.74 264	88.85 355	97.66 330

V42		96.65 268	96.16 277	98.11 260	98.17 335	98.23 232	99.99 212	99.09 330	93.97 324	98.74 255	99.05 330	91.09 276	98.82 295	95.46 316	89.90 343	97.27 359

v8		96.35 286	95.73 297	98.21 250	98.11 336	98.23 232	99.94 257	99.07 335	92.66 353	98.29 282	99.00 336	91.46 270	98.77 300	94.17 332	88.83 356	97.62 340

OPM-MVS		97.21 241	97.18 240	97.32 300	98.08 337	94.66 333	100.00 1	99.28 253	98.65 69	98.92 239	99.98 190	86.03 339	99.56 227	98.28 246	95.41 262	97.72 310
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v2v482		96.70 266	96.18 275	98.27 244	98.04 338	98.39 217	100.00 1	99.13 316	94.19 321	98.58 263	99.08 327	90.48 286	98.67 307	95.69 311	90.44 341	97.75 274

v1192		96.18 294	95.49 308	98.26 246	98.01 339	98.15 236	99.99 212	99.08 331	93.36 339	98.54 266	98.97 340	89.47 302	98.89 288	91.15 357	90.82 336	97.75 274

TranMVSNet+NR-MVSNet		96.45 279	96.01 282	97.79 284	98.00 340	97.62 269	100.00 1	99.35 223	95.98 266	97.31 326	99.64 283	90.09 291	98.00 356	96.89 291	86.80 370	97.75 274

v1144		96.51 275	95.97 285	98.13 258	97.98 341	98.04 246	99.99 212	99.08 331	93.51 336	98.62 261	98.98 337	90.98 280	98.62 310	93.79 338	90.79 337	97.74 297

test_0402		94.35 323	93.70 328	96.32 333	97.92 342	93.60 347	99.61 317	98.85 367	88.19 380	94.68 363	99.48 308	80.01 370	98.58 317	89.39 371	95.15 278	96.77 369

v1240		95.96 306	95.25 315	98.07 261	97.91 343	97.87 260	99.96 247	99.07 335	93.24 343	98.64 260	98.96 341	88.98 308	98.61 311	89.58 370	90.92 335	97.75 274

v144192		96.40 283	95.81 290	98.17 253	97.89 344	98.11 239	99.99 212	99.06 343	93.39 338	98.75 254	99.09 326	90.43 287	98.66 308	93.10 343	90.55 340	97.75 274

RRT_MVS		97.77 215	97.76 212	97.78 285	97.89 344	97.06 292	100.00 1	99.29 247	95.74 275	98.00 301	99.97 198	95.94 207	98.55 321	98.87 213	94.18 295	97.72 310

v1921920		96.16 298	95.50 306	98.14 255	97.88 346	97.96 252	99.99 212	99.07 335	93.33 340	98.60 262	99.24 320	89.37 303	98.71 305	91.28 355	90.74 338	97.75 274

MS-PatchMatch		95.66 312	95.87 288	95.05 342	97.80 347	89.25 375	98.88 384	99.30 242	96.35 253	96.86 336	99.01 335	81.35 367	99.43 254	93.30 342	99.98 108	96.46 374

v148		96.29 289	95.84 289	97.63 287	97.74 348	96.53 304	100.00 1	99.07 335	93.52 335	98.01 299	99.42 312	91.22 272	98.60 313	96.37 304	87.22 366	97.75 274

PEN-MVS		96.01 305	95.48 310	97.58 291	97.74 348	97.26 285	99.90 264	99.29 247	94.55 308	96.79 339	99.55 302	87.38 326	97.84 361	96.92 290	87.24 365	97.65 334

pmmvs4		97.17 243	96.80 248	98.27 244	97.68 350	98.64 204	100.00 1	99.18 300	94.22 318	98.55 265	99.71 264	93.67 241	98.47 327	95.66 312	92.57 312	97.71 318

PS-MVSNAJss		98.03 206	98.06 200	97.94 277	97.63 351	97.33 282	99.89 267	99.23 278	96.27 257	98.03 296	99.59 295	98.75 121	98.78 297	98.52 234	94.61 292	97.70 319

UnsupCasMVSNet_eth		94.25 324	93.89 325	95.34 340	97.63 351	92.13 361	99.73 300	99.36 212	94.88 298	92.78 372	98.63 356	82.72 360	96.53 378	94.57 327	84.73 374	97.36 356

our_test_3		96.51 275	96.35 268	96.98 313	97.61 353	95.05 319	99.98 236	99.01 353	94.68 304	96.77 341	99.06 328	95.87 209	98.14 342	91.81 352	92.37 314	97.75 274

ppachtmachnet_test		96.17 296	95.89 287	97.02 310	97.61 353	95.24 316	99.99 212	99.24 274	93.31 341	96.71 342	99.62 291	94.34 235	98.07 351	89.87 366	92.30 316	97.75 274

EU-MVSNet		96.63 269	96.53 258	96.94 315	97.59 355	96.87 296	99.76 292	99.47 79	96.35 253	96.85 337	99.78 258	92.57 262	96.27 382	95.33 317	91.08 334	97.68 325

v7n		96.06 304	95.42 314	97.99 275	97.58 356	97.35 279	99.86 271	99.11 323	92.81 352	97.91 305	99.49 307	90.99 279	98.92 284	92.51 347	88.49 358	97.70 319

lessismore_v0				96.05 336	97.55 357	91.80 364		99.22 281		91.87 376	99.91 230	83.50 357	98.68 306	92.48 348	90.42 342	97.68 325

K. test v3		95.46 315	95.14 318	96.40 330	97.53 358	93.40 351	99.99 212	99.23 278	95.49 287	92.70 375	99.73 261	84.26 349	98.12 344	93.94 337	93.38 302	97.68 325

test_djsdf		97.55 227	97.38 228	98.07 261	97.50 359	97.99 248	100.00 1	99.13 316	95.46 289	98.47 273	99.85 243	92.01 269	98.59 315	98.63 228	95.36 265	97.62 340

Gipuma		84.73 359	83.50 364	88.40 370	97.50 359	82.21 388	88.87 399	99.05 345	65.81 399	85.71 391	90.49 396	53.70 397	96.31 380	78.64 395	91.74 323	86.67 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		95.52 313	94.99 321	97.08 307	97.49 361	96.45 305	100.00 1	99.25 269	93.82 326	96.17 350	99.57 300	87.81 321	97.18 370	94.57 327	86.26 372	97.62 340

OurMVSNet-221017-0		96.14 300	95.98 284	96.62 326	97.49 361	93.44 350	99.92 260	98.16 376	95.86 270	97.65 314	99.95 218	85.71 342	98.78 297	94.93 324	94.18 295	97.64 337

pmmvs6		93.64 329	92.87 337	95.94 338	97.47 363	91.41 367	98.92 382	99.02 351	87.84 381	95.01 360	99.61 293	77.24 379	98.77 300	94.33 330	86.41 371	97.63 338

pmmvs5		95.94 307	95.61 303	96.95 314	97.42 364	94.66 333	100.00 1	98.08 380	93.60 333	97.05 331	99.43 311	87.02 329	98.46 328	95.76 308	92.12 317	97.72 310

SixPastTwentyTwo		95.71 311	95.49 308	96.38 331	97.42 364	93.01 353	99.84 274	98.23 375	94.75 301	95.98 353	99.97 198	85.35 344	98.43 329	94.71 326	93.17 303	97.69 323

mvs_tets		97.00 255	96.69 252	97.94 277	97.41 366	97.27 284	99.60 318	99.18 300	96.51 243	97.35 325	99.69 270	86.53 334	98.91 285	98.84 215	95.09 282	97.65 334

jajsoiax		97.07 249	96.79 250	97.89 281	97.28 367	97.12 289	99.95 253	99.19 293	96.55 239	97.31 326	99.69 270	87.35 328	98.91 285	98.70 223	95.12 280	97.66 330

N_pmnet		91.88 343	93.37 332	87.40 372	97.24 368	66.33 405	99.90 264	91.05 408	89.77 373	95.65 356	98.58 358	90.05 293	98.11 346	85.39 381	92.72 308	97.75 274

anonymousdsp		97.16 244	96.88 245	98.00 273	97.08 369	98.06 244	99.81 279	99.15 307	94.58 307	97.84 308	99.62 291	90.49 285	98.60 313	97.98 255	95.32 266	97.33 358

test_fmvsmconf0.01_n		98.60 170	98.24 187	99.67 126	96.90 370	99.21 161	99.99 212	99.04 348	98.80 57	99.57 194	99.96 210	90.12 289	99.91 170	99.89 101	99.89 127	99.90 159

MDA-MVSNet-bldmvs		91.65 345	89.94 353	96.79 324	96.72 371	96.70 301	99.42 338	98.94 359	88.89 375	66.97 405	98.37 365	81.43 366	95.91 385	89.24 373	89.46 349	97.75 274

MDA-MVSNet_test_wron		92.61 338	91.09 346	97.19 306	96.71 372	97.26 285	100.00 1	99.14 312	88.61 376	67.90 403	98.32 367	89.03 306	96.57 377	90.47 363	89.59 346	97.74 297

new_pmnet		94.11 328	93.47 331	96.04 337	96.60 373	92.82 356	99.97 242	98.91 362	90.21 370	95.26 357	98.05 373	85.89 340	98.14 342	84.28 384	92.01 319	97.16 361

YYNet1		92.44 339	90.92 347	97.03 309	96.20 374	97.06 292	99.99 212	99.14 312	88.21 379	67.93 402	98.43 364	88.63 312	96.28 381	90.64 359	89.08 353	97.74 297

CMPMVS	66.12 22	90.65 349	92.04 342	86.46 374	96.18 375	66.87 404	98.03 393	99.38 203	83.38 390	85.49 392	99.55 302	77.59 377	98.80 296	94.44 329	94.31 294	93.72 389
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DSMNet-mixed		95.18 319	95.21 317	95.08 341	96.03 376	90.21 373	99.65 311	93.64 406	92.91 348	98.34 278	97.40 378	90.05 293	95.51 388	91.02 358	97.86 220	99.51 246

EGC-MVSNET		79.46 364	74.04 372	95.72 339	96.00 377	92.73 357	99.09 377	99.04 348	5.08 409	16.72 409	98.71 352	73.03 386	98.74 303	82.05 389	96.64 251	95.69 382

EG-PatchMatch MVS		92.94 337	92.49 341	94.29 354	95.87 378	87.07 382	99.07 380	98.11 379	93.19 344	88.98 384	98.66 355	70.89 389	99.08 272	92.43 349	95.21 274	96.72 370

testf1		84.40 360	84.79 362	83.23 378	95.71 379	58.71 411	98.79 386	97.75 390	81.58 391	84.94 393	98.07 371	45.33 402	97.73 366	77.09 399	83.85 376	93.24 392

APD_test2		84.40 360	84.79 362	83.23 378	95.71 379	58.71 411	98.79 386	97.75 390	81.58 391	84.94 393	98.07 371	45.33 402	97.73 366	77.09 399	83.85 376	93.24 392

MVP-Stereo		96.51 275	96.48 262	96.60 327	95.65 381	94.25 342	98.84 385	98.16 376	95.85 272	95.23 358	99.04 331	92.54 263	99.13 270	92.98 344	99.98 108	96.43 375
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		93.29 332	92.76 339	94.90 348	95.64 382	91.27 368	99.97 242	98.82 368	87.04 382	94.71 362	98.19 368	83.86 354	96.80 373	84.04 385	92.56 313	96.64 372

OpenMVS_ROB	88.34 20	91.89 342	91.12 344	94.19 356	95.55 383	87.63 380	99.26 352	98.03 381	86.61 384	90.65 382	96.82 381	70.14 392	98.78 297	86.54 380	96.50 254	96.15 376

UnsupCasMVSNet_bld		89.50 352	88.00 358	93.99 357	95.30 384	88.86 378	98.52 389	99.28 253	85.50 386	87.80 388	94.11 391	61.63 395	96.96 372	90.63 360	79.26 386	96.15 376

test20.03		93.11 334	92.85 338	93.88 358	95.19 385	91.83 363	100.00 1	98.87 365	93.68 330	92.76 373	98.88 346	89.20 305	92.71 396	77.88 396	89.19 352	97.09 363

Anonymous20231206		93.45 331	93.17 333	94.30 353	95.00 386	89.69 374	99.98 236	98.43 373	93.30 342	94.50 366	98.59 357	90.52 284	95.73 387	77.46 398	90.73 339	97.48 352

MIMVSNet1		91.96 340	91.20 343	94.23 355	94.94 387	91.69 365	99.34 345	99.22 281	88.23 378	94.18 368	98.45 362	75.52 382	93.41 395	79.37 394	91.49 328	97.60 343

KD-MVS_self_test		91.16 346	90.09 351	94.35 352	94.44 388	91.27 368	99.74 295	99.08 331	90.82 365	94.53 365	94.91 390	86.11 336	94.78 390	82.67 387	68.52 396	96.99 365

CL-MVSNet_self_test		91.07 347	90.35 350	93.24 360	93.27 389	89.16 376	99.55 323	99.25 269	92.34 354	95.23 358	97.05 380	88.86 311	93.59 394	80.67 391	66.95 397	96.96 366

test_fmvs3		87.19 357	87.02 360	87.71 371	92.69 390	76.64 392	99.96 247	97.27 394	93.55 334	90.82 380	94.03 392	38.00 406	92.19 397	93.49 341	83.35 380	94.32 386

mvsany_test3		89.36 353	88.96 357	90.56 365	91.95 391	78.97 390	99.74 295	96.59 401	96.84 213	89.25 383	96.07 383	52.59 398	97.11 371	95.17 321	82.44 381	95.58 384

Patchmatch-RL test		93.49 330	93.63 329	93.05 361	91.78 392	83.41 387	98.21 392	96.95 397	91.58 359	91.05 377	97.64 377	99.40 56	95.83 386	94.11 335	81.95 383	99.91 149

PM-MVS		88.39 354	87.41 359	91.31 363	91.73 393	82.02 389	99.79 284	96.62 399	91.06 363	90.71 381	95.73 384	48.60 400	95.96 384	90.56 361	81.91 384	95.97 379

pmmvs-eth3d		91.73 344	90.67 348	94.92 347	91.63 394	92.71 358	99.90 264	98.54 372	91.19 361	88.08 386	95.50 385	79.31 374	96.13 383	90.55 362	81.32 385	95.91 380

WB-MVS		88.24 355	90.09 351	82.68 380	91.56 395	69.51 400	100.00 1	98.73 370	90.72 366	87.29 389	98.12 369	92.87 255	85.01 402	62.19 403	89.34 350	93.54 390

test_f		86.87 358	86.06 361	89.28 368	91.45 396	76.37 393	99.87 270	97.11 395	91.10 362	88.46 385	93.05 394	38.31 405	96.66 376	91.77 353	83.46 379	94.82 385

new-patchmatchnet		90.30 351	89.46 355	92.84 362	90.77 397	88.55 379	99.83 275	98.80 369	90.07 372	87.86 387	95.00 388	78.77 375	94.30 392	84.86 383	79.15 387	95.68 383

SSC-MVS		87.61 356	89.47 354	82.04 381	90.63 398	68.77 401	99.99 212	98.66 371	90.34 369	86.70 390	98.08 370	92.72 260	84.12 403	59.41 406	88.71 357	93.22 394

pmmvs3		90.62 350	89.36 356	94.40 351	90.53 399	91.49 366	100.00 1	96.73 398	84.21 388	93.65 370	96.65 382	82.56 363	94.83 389	82.28 388	77.62 390	96.89 368

test_vis3_rt		79.61 363	78.19 368	83.86 377	88.68 400	69.56 399	99.81 279	82.19 413	86.78 383	68.57 401	84.51 404	25.06 410	98.26 337	89.18 374	78.94 388	83.75 401

ambc				88.45 369	86.84 401	70.76 398	97.79 395	98.02 383		90.91 379	95.14 386	38.69 404	98.51 323	94.97 323	84.23 375	96.09 378

EMVS		69.88 371	69.09 374	72.24 389	84.70 402	65.82 406	99.96 247	87.08 412	49.82 406	71.51 400	84.74 403	49.30 399	75.32 406	50.97 408	43.71 404	75.59 404

E-PMN		70.72 370	70.06 373	72.69 388	83.92 403	65.48 407	99.95 253	92.72 407	49.88 405	72.30 399	86.26 402	47.17 401	77.43 405	53.83 407	44.49 403	75.17 405

PMMVS2		79.15 366	77.28 369	84.76 376	82.34 404	72.66 394	99.70 305	95.11 404	71.68 398	84.78 395	90.87 395	32.05 408	89.99 399	75.53 401	63.45 400	91.64 395

TDRefinement		91.93 341	90.48 349	96.27 334	81.60 405	92.65 359	99.10 375	97.61 393	93.96 325	93.77 369	99.85 243	80.03 369	99.53 240	97.82 262	70.59 395	96.63 373

test123		79.44 365	79.23 367	80.05 383	80.03 406	71.72 396	100.00 1	77.93 414	62.52 400	94.81 361	99.69 270	78.21 376	74.53 407	92.57 346	27.33 407	93.90 387

LCM-MVSNet		79.01 367	76.93 370	85.27 375	78.28 407	68.01 403	96.57 396	98.03 381	55.10 403	82.03 396	93.27 393	31.99 409	93.95 393	82.72 386	74.37 392	93.84 388

FPMVS		77.92 368	79.45 366	73.34 387	76.87 408	46.81 414	98.24 391	99.05 345	59.89 402	73.55 398	98.34 366	36.81 407	86.55 400	80.96 390	91.35 332	86.65 399

wuyk23d		28.28 375	29.73 379	23.92 391	75.89 409	32.61 416	66.50 402	12.88 415	16.09 408	14.59 410	16.59 409	12.35 412	32.36 410	39.36 409	13.36 408	6.79 406

MVE	68.59 21	67.22 372	64.68 376	74.84 384	74.67 410	62.32 409	95.84 397	90.87 409	50.98 404	58.72 406	81.05 406	12.20 414	78.95 404	61.06 405	56.75 401	83.24 402
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 373	63.44 377	73.88 386	61.14 411	63.45 408	95.68 398	87.18 410	79.93 393	47.35 407	80.68 407	22.35 411	72.33 409	61.24 404	35.42 405	85.88 400

testmvs		80.17 362	81.95 365	74.80 385	58.54 412	59.58 410	100.00 1	87.14 411	76.09 396	99.61 193	100.00 1	67.06 393	74.19 408	98.84 215	50.30 402	90.64 397

PMVS	60.66 23	65.98 374	65.05 375	68.75 390	55.06 413	38.40 415	88.19 400	96.98 396	48.30 407	44.82 408	88.52 399	12.22 413	86.49 401	67.58 402	83.79 378	81.35 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 369	74.26 371	80.43 382	52.91 414	53.67 413	87.42 401	97.98 384	61.80 401	67.04 404	100.00 1	76.43 380	96.40 379	96.47 300	28.26 406	91.23 396

test_blank		0.07 379	0.09 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.79 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

eth-test2					0.00 415

eth-test					0.00 415

uanet_test		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

cdsmvs_eth3d_5k		24.41 376	32.55 378	0.00 392	0.00 415	0.00 417	0.00 403	99.39 201	0.00 410	0.00 411	100.00 1	93.55 245	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		8.24 378	10.99 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	98.75 121	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

ab-mvs-re		8.33 377	11.11 380	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		0.01 380	0.02 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.14 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						97.98 249								95.74 309

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								99.42 138	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																99.91 149

sam_mvs1												99.29 70				99.91 149

sam_mvs												99.33 59

MTGPA								99.42 138

test_post1							99.32 346				88.24 400	99.33 59	99.59 218	98.31 242

test_post											89.05 398	99.49 39	99.59 218

patchmatchnet-post											97.79 374	99.41 55	99.54 235

MTMP							100.00 1	99.18 300

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 44	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	99.67 151

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 112				100.00 1	99.33 186		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 279

segment_acmp												99.55 25

testdata1							100.00 1		98.77 63

plane_prior5								99.40 188					99.55 232	99.79 119	95.57 260	97.76 264

plane_prior4											99.97 198

plane_prior3						94.79 330			99.03 20	99.08 229

plane_prior2							100.00 1		99.00 27

plane_prior						94.80 327	100.00 1		99.03 20						95.58 256

n2								0.00 416

nn								0.00 416

door-mid								96.32 402

test11								99.42 138

door								96.13 403

HQP5-MVS						94.82 324

BP-MVS														99.79 119

HQP4-MVS										99.17 219			99.57 223			97.77 262

HQP3-MVS								99.40 188							95.58 256

HQP2-MVS												88.61 313

MDTV_nov1_ep13_2view						99.24 157	99.56 322		96.31 256	99.96 120		98.86 112		98.92 210		99.89 164

ACMMP++_ref															94.58 293

ACMMP++															95.17 277

Test By Simon												99.10 86

High-resolution multi-view benchmark