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.

test-260524					100.00 1	99.98 18		99.69 67		100.00 1		99.45 53	100.00 1	100.00 1	100.00 1

MED-MVS test				99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.10 12	99.99 129	100.00 1		100.00 1	100.00 1	100.00 1	100.00 1

MED-MVS		99.89 1	99.86 2	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.18 6	99.99 129	100.00 1	99.58 27	100.00 1	100.00 1	100.00 1	100.00 1

TestfortrainingZip a		99.85 5	99.81 6	99.99 13	100.00 1	99.98 18	100.00 1	99.95 19	99.18 6	100.00 1	100.00 1	99.45 53	99.99 107	99.68 182	99.99 107	100.00 1

DVP-MVS++		99.81 14	99.75 17	100.00 1	100.00 1	99.99 6	100.00 1	99.42 153	98.79 80	100.00 1	100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

FOURS1					100.00 1	99.97 27	100.00 1	99.42 153	98.52 96	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 153					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 153					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 6		99.42 153	98.72 85	100.00 1	100.00 1	99.60 21

ZD-MVS					100.00 1	99.98 18		99.80 48	97.31 216	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1

SED-MVS		99.83 10	99.77 12	100.00 1	100.00 1	99.99 6	100.00 1	99.42 153	99.03 25	100.00 1	100.00 1	99.50 43	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 6		99.42 153	99.12 9	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 32	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 6		99.42 153	99.03 25	100.00 1	100.00 1	99.50 43	100.00 1

DVP-MVS		99.83 10	99.78 10	100.00 1	100.00 1	99.99 6	100.00 1	99.42 153	99.04 20	100.00 1	100.00 1	99.53 35	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 6	100.00 1	99.42 153	99.04 20	100.00 1	100.00 1	99.53 35

SR-MVS		99.68 46	99.58 52	99.98 28	100.00 1	99.95 38	100.00 1	99.64 70	97.59 181	100.00 1	100.00 1	98.99 113	99.99 107	100.00 1	100.00 1	100.00 1

GST-MVS		99.64 54	99.53 65	99.95 61	100.00 1	99.86 90	100.00 1	99.79 50	97.72 160	99.95 183	100.00 1	98.39 157	100.00 1	99.96 106	99.99 107	100.00 1

test_part2					100.00 1	99.99 6				100.00 1

MSP-MVS		99.81 14	99.77 12	99.94 74	100.00 1	99.86 90	100.00 1	99.42 153	98.87 64	100.00 1	100.00 1	99.65 19	99.96 170	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 12	99.77 12	99.99 13	100.00 1	99.96 30	100.00 1	99.43 134	99.05 18	100.00 1	100.00 1	99.45 53	99.99 107	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 38	100.00 1	99.42 153	97.65 168	100.00 1	100.00 1	99.53 35	99.97 150

train_agg		99.71 36	99.63 44	99.97 40	100.00 1	99.95 38	100.00 1	99.42 153	97.70 162	100.00 1	100.00 1	99.51 39	99.97 150	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 64	100.00 1	99.42 153	97.70 162	100.00 1	100.00 1	99.51 39	99.98 141

agg_prior					100.00 1	99.88 85		99.42 153		100.00 1			99.97 150

HFP-MVS		99.74 28	99.67 33	99.96 52	100.00 1	99.89 78	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.31 76	100.00 1	99.99 77	100.00 1	100.00 1

region2R		99.72 32	99.64 40	99.97 40	100.00 1	99.90 71	100.00 1	99.74 60	97.86 149	100.00 1	100.00 1	99.19 91	100.00 1	99.99 77	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 39	99.64 40	99.87 97	100.00 1	99.64 128	99.98 299	99.44 125	98.35 111	99.99 129	100.00 1	99.04 110	99.96 170	99.98 92	100.00 1	100.00 1

XVS		99.79 17	99.73 20	99.98 28	100.00 1	99.94 47	100.00 1	99.75 57	98.67 88	100.00 1	100.00 1	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 333	96.06 362	99.98 28	100.00 1	99.94 47	100.00 1	99.75 57	98.67 88	100.00 1	66.97 550	99.16 94	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 87	100.00 1	99.75 109		99.73 61					99.97 150			100.00 1

新几何1				99.99 13	100.00 1	99.96 30		99.81 47	97.89 146	100.00 1	100.00 1	99.20 90	100.00 1	97.91 333	100.00 1	100.00 1

原ACMM1				99.93 78	100.00 1	99.80 102		99.66 69	98.18 120	100.00 1	100.00 1	99.43 60	100.00 1	99.50 232	100.00 1	100.00 1

MSLP-MVS++		99.89 1	99.85 3	99.99 13	100.00 1	99.96 30	100.00 1	99.95 19	99.11 10	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 9	99.78 10	99.99 13	100.00 1	99.98 18	100.00 1	99.44 125	99.06 16	100.00 1	100.00 1	99.56 29	99.99 107	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 28	99.67 33	99.96 52	100.00 1	99.89 78	100.00 1	99.76 54	97.95 143	100.00 1	100.00 1	99.29 82	100.00 1	99.99 77	100.00 1	100.00 1

MCST-MVS		99.85 5	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.73 61	99.19 5	100.00 1	100.00 1	99.31 76	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 31	99.64 40	99.99 13	100.00 1	99.97 27	100.00 1	99.42 153	98.02 133	100.00 1	100.00 1	99.32 74	99.99 107	100.00 1	100.00 1	100.00 1

mPP-MVS		99.69 42	99.60 49	99.97 40	100.00 1	99.91 64	100.00 1	99.42 153	97.91 145	100.00 1	100.00 1	99.04 110	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS		99.59 69	99.50 71	99.89 90	100.00 1	99.70 121	100.00 1	99.42 153	97.46 197	100.00 1	100.00 1	98.60 147	99.96 170	99.99 77	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 5	99.80 7	100.00 1	100.00 1	99.99 6	100.00 1	99.77 53	99.07 14	100.00 1	100.00 1	99.39 69	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 4	99.82 5	100.00 1	100.00 1	99.99 6	100.00 1	99.71 66	99.07 14	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

CP-MVS		99.67 49	99.58 52	99.95 61	100.00 1	99.84 96	100.00 1	99.42 153	97.77 157	100.00 1	100.00 1	99.07 104	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 80	99.38 83	99.85 104	100.00 1	99.54 143	100.00 1	99.42 153	97.58 182	99.98 139	100.00 1	97.43 199	100.00 1	99.99 77	100.00 1	100.00 1

DP-MVS Recon		99.76 21	99.69 25	99.98 28	100.00 1	99.95 38	100.00 1	99.52 78	97.99 135	99.99 129	100.00 1	99.72 14	100.00 1	99.96 106	100.00 1	100.00 1

PAPM_NR		99.74 28	99.66 36	99.99 13	100.00 1	99.96 30	100.00 1	99.47 85	97.87 148	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 21	99.68 31	99.99 13	100.00 1	99.96 30	100.00 1	99.47 85	98.16 121	100.00 1	100.00 1	99.51 39	100.00 1	100.00 1	100.00 1	100.00 1

PHI-MVS		99.50 78	99.39 82	99.82 112	100.00 1	99.45 162	100.00 1	99.94 27	96.38 326	100.00 1	100.00 1	98.18 161	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 114	99.25 104	99.44 197	100.00 1	98.32 287	100.00 1	99.86 43	98.04 132	100.00 1	100.00 1	96.10 235	100.00 1	99.55 221	99.73 173	100.00 1

MG-MVS		99.75 26	99.68 31	99.97 40	100.00 1	99.91 64	99.98 299	99.47 85	99.09 13	100.00 1	100.00 1	98.59 148	100.00 1	99.95 112	100.00 1	100.00 1

AdaColmap		99.44 88	99.26 102	99.95 61	100.00 1	99.86 90	99.70 401	99.99 13	98.53 94	99.90 201	100.00 1	95.34 248	100.00 1	99.92 117	100.00 1	100.00 1

PLC	98.56 2	99.70 39	99.74 19	99.58 173	100.00 1	98.79 235	100.00 1	99.54 77	98.58 93	99.96 152	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 154
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TestfortrainingZip				100.00 1	99.99 53	100.00 1	100.00 1	99.95 19	99.03 25	100.00 1	100.00 1	99.59 24	100.00 1		100.00 1	100.00 1

ME-MVS		99.87 3	99.83 4	99.99 13	99.99 53	99.98 18	100.00 1	99.95 19	99.05 18	100.00 1	100.00 1	99.50 43	100.00 1	100.00 1	100.00 1	100.00 1

NormalMVS		99.47 84	99.48 76	99.43 200	99.99 53	98.55 255	99.94 334	99.28 291	98.39 103	100.00 1	100.00 1	98.44 154	99.98 141	99.36 248	99.92 141	99.75 292

lecture		99.64 54	99.53 65	99.98 28	99.99 53	99.93 53	100.00 1	99.47 85	98.53 94	100.00 1	100.00 1	97.88 172	100.00 1	99.98 92	99.92 141	100.00 1

MM		99.63 58	99.52 68	99.94 74	99.99 53	99.82 99	100.00 1	99.97 17	99.11 10	100.00 1	100.00 1	96.65 225	100.00 1	100.00 1	99.97 122	100.00 1

SR-MVS-dyc-post		99.63 58	99.52 68	99.97 40	99.99 53	99.91 64	100.00 1	99.42 153	97.62 173	100.00 1	100.00 1	98.65 144	99.99 107	99.99 77	100.00 1	100.00 1

RE-MVS-def			99.55 62		99.99 53	99.91 64	100.00 1	99.42 153	97.62 173	100.00 1	100.00 1	98.94 124		99.99 77	100.00 1	100.00 1

SF-MVS		99.66 51	99.57 55	99.95 61	99.99 53	99.85 94	100.00 1	99.42 153	97.67 165	100.00 1	100.00 1	99.05 107	99.99 107	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 40	99.99 53	99.90 71	100.00 1	99.79 50	97.97 139	99.97 145	100.00 1	98.97 118	100.00 1	99.94 114	100.00 1	100.00 1

9.14			99.57 55		99.99 53		100.00 1	99.42 153	97.54 185	100.00 1	100.00 1	99.15 96	99.99 107	100.00 1	100.00 1

save fliter					99.99 53	99.93 53	100.00 1	99.42 153	98.93 49

CS-MVS		99.33 108	99.27 98	99.50 183	99.99 53	99.00 217	100.00 1	99.13 407	97.26 220	99.96 152	100.00 1	97.79 179	99.64 290	99.64 196	99.67 179	99.87 214

test_0728_SECOND				100.00 1	99.99 53	99.99 6	100.00 1	99.42 153					100.00 1	100.00 1	100.00 1	100.00 1

SMA-MVS		99.69 42	99.59 50	99.98 28	99.99 53	99.93 53	100.00 1	99.43 134	97.50 193	100.00 1	100.00 1	99.43 60	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 17	99.73 20	99.99 13	99.99 53	99.98 18	100.00 1	99.42 153	98.91 55	100.00 1	100.00 1	99.22 88	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 5	99.87 1	99.80 123	99.99 53	99.97 27	99.97 309	99.98 16	98.96 39	100.00 1	100.00 1	99.96 4	99.42 337	100.00 1	100.00 1	100.00 1

CANet		99.40 92	99.24 108	99.89 90	99.99 53	99.76 108	100.00 1	99.73 61	98.40 102	99.78 233	100.00 1	95.28 249	99.96 170	100.00 1	99.99 107	99.96 143

MGCNet		99.72 32	99.65 37	99.93 78	99.99 53	99.79 103	100.00 1	99.91 40	99.17 8	100.00 1	100.00 1	97.84 176	100.00 1	100.00 1	99.95 128	100.00 1

SPE-MVS-test		99.31 112	99.27 98	99.43 200	99.99 53	98.77 236	100.00 1	99.19 365	97.24 221	99.96 152	100.00 1	97.56 190	99.70 287	99.68 182	99.81 168	99.82 230

MTAPA		99.68 46	99.59 50	99.97 40	99.99 53	99.91 64	100.00 1	99.42 153	98.32 113	99.94 189	100.00 1	98.65 144	100.00 1	99.96 106	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 42	99.63 44	99.87 97	99.99 53	99.64 128	99.95 326	99.44 125	98.35 111	100.00 1	100.00 1	98.98 116	99.97 150	99.98 92	100.00 1	100.00 1

HPM-MVS++		99.82 12	99.76 15	99.99 13	99.99 53	99.98 18	100.00 1	99.83 44	98.88 61	99.96 152	100.00 1	99.21 89	100.00 1	100.00 1	100.00 1	99.99 124

旧先验1					99.99 53	99.88 85		99.82 45			100.00 1	99.27 85			100.00 1	100.00 1

test222					99.99 53	99.90 71	100.00 1	99.69 67	97.66 166	100.00 1	100.00 1	99.30 81			100.00 1	100.00 1

testdata				99.66 157	99.99 53	98.97 221		99.73 61	97.96 142	100.00 1	100.00 1	99.42 64	100.00 1	99.28 259	100.00 1	100.00 1

SD-MVS		99.81 14	99.75 17	99.99 13	99.99 53	99.96 30	100.00 1	99.42 153	99.01 31	100.00 1	100.00 1	99.33 71	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 52	99.55 62	99.97 40	99.99 53	99.91 64	100.00 1	99.48 84	97.54 185	100.00 1	100.00 1	98.97 118	99.99 107	99.98 92	100.00 1	100.00 1

MP-MVS		99.61 65	99.49 73	99.98 28	99.99 53	99.94 47	100.00 1	99.42 153	97.82 152	99.99 129	100.00 1	98.20 160	100.00 1	99.99 77	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 42	99.61 48	99.95 61	99.99 53	99.85 94	100.00 1	99.58 73	97.69 164	100.00 1	100.00 1	99.44 56	100.00 1	99.79 143	100.00 1	100.00 1

test12				99.95 61	99.99 53	99.89 78		99.42 153		100.00 1		99.24 87	99.97 150		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 65	99.69 25	99.35 221	99.99 53	98.06 314	100.00 1	99.36 235	99.83 2	100.00 1	100.00 1	98.95 122	99.99 107	100.00 1	99.11 199	100.00 1

HPM-MVS_fast		99.60 68	99.49 73	99.91 83	99.99 53	99.78 104	100.00 1	99.42 153	97.09 234	100.00 1	100.00 1	98.95 122	99.96 170	99.98 92	100.00 1	100.00 1

EPNet		99.62 63	99.69 25	99.42 203	99.99 53	98.37 277	100.00 1	99.89 42	98.83 70	100.00 1	100.00 1	98.97 118	100.00 1	99.90 120	99.61 186	99.89 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		99.68 46	99.58 52	99.97 40	99.99 53	99.96 30	100.00 1	99.42 153	97.53 188	100.00 1	100.00 1	99.27 85	99.97 150	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		99.28 119	99.35 91	99.05 265	99.99 53	97.15 361	100.00 1	99.47 85	97.44 201	99.42 275	100.00 1	97.83 178	100.00 1	99.99 77	100.00 1	100.00 1

PatchMatch-RL		99.02 159	98.78 170	99.74 140	99.99 53	99.29 180	100.00 1	100.00 1	98.38 105	99.89 204	99.81 327	93.14 317	99.99 107	97.85 335	99.98 118	99.95 149

ACMMP		99.65 52	99.57 55	99.89 90	99.99 53	99.66 126	99.75 389	99.73 61	98.16 121	99.75 237	100.00 1	98.90 129	100.00 1	99.96 106	99.88 152	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 54	99.64 40	99.67 154	99.99 53	99.07 205	100.00 1	99.44 125	98.30 114	99.90 201	100.00 1	99.18 92	99.99 107	99.91 119	100.00 1	99.94 154

DeepPCF-MVS	98.03 4	98.54 243	99.72 22	94.98 447	99.99 53	84.94 491	100.00 1	99.42 153	99.98 1	100.00 1	100.00 1	98.11 163	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 26	99.67 33	99.99 13	99.99 53	99.96 30	99.73 395	99.52 78	99.06 16	100.00 1	100.00 1	98.80 138	100.00 1	99.95 112	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		99.31 112	99.13 126	99.87 97	99.99 53	99.71 117	99.55 421	99.46 103	97.32 214	99.82 223	100.00 1	96.85 219	99.97 150	99.14 268	100.00 1	99.92 167

MP-MVS-pluss		99.61 65	99.50 71	99.97 40	99.98 94	99.92 60	100.00 1	99.42 153	97.53 188	99.77 234	100.00 1	98.77 139	100.00 1	99.99 77	100.00 1	99.99 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.67 49	99.57 55	99.97 40	99.98 94	99.92 60	100.00 1	99.42 153	97.83 150	100.00 1	100.00 1	98.89 130	100.00 1	99.98 92	100.00 1	100.00 1

PVSNet_BlendedMVS		98.71 210	98.62 200	98.98 273	99.98 94	99.60 132	100.00 1	100.00 1	97.23 223	100.00 1	99.03 427	96.57 227	99.99 107	100.00 1	94.75 369	97.35 455

PVSNet_Blended		99.48 82	99.36 89	99.83 110	99.98 94	99.60 132	100.00 1	100.00 1	97.79 155	100.00 1	100.00 1	96.57 227	99.99 107	100.00 1	99.88 152	99.90 182

reproduce_monomvs		98.61 229	98.54 214	98.82 284	99.97 98	99.28 182	100.00 1	99.33 256	98.51 97	97.87 400	99.24 411	99.98 3	99.45 333	99.02 277	92.93 388	97.74 386

114514_t		99.39 93	99.25 104	99.81 117	99.97 98	99.48 160	100.00 1	99.42 153	95.53 363	100.00 1	100.00 1	98.37 158	99.95 183	99.97 104	100.00 1	100.00 1

DP-MVS		98.86 190	98.54 214	99.81 117	99.97 98	99.45 162	99.52 425	99.40 206	94.35 402	98.36 369	100.00 1	96.13 234	99.97 150	99.12 271	100.00 1	100.00 1

API-MVS		99.72 32	99.70 24	99.79 128	99.97 98	99.37 173	99.96 317	99.94 27	98.48 98	100.00 1	100.00 1	98.92 127	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 32	99.65 37	99.91 83	99.97 98	99.72 116	100.00 1	99.47 85	98.43 101	99.88 207	100.00 1	99.14 97	100.00 1	99.97 104	100.00 1	100.00 1

MAR-MVS		99.49 80	99.36 89	99.89 90	99.97 98	99.66 126	99.74 390	99.95 19	97.89 146	100.00 1	100.00 1	96.71 224	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
reproduce_model		99.76 21	99.69 25	99.98 28	99.96 104	99.93 53	100.00 1	99.42 153	98.81 76	100.00 1	100.00 1	98.98 116	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 21	99.69 25	99.98 28	99.96 104	99.94 47	100.00 1	99.42 153	98.82 72	100.00 1	100.00 1	98.99 113	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 21	99.69 25	99.98 28	99.96 104	99.94 47	100.00 1	99.42 153	98.82 72	100.00 1	100.00 1	98.99 113	100.00 1	100.00 1	100.00 1	100.00 1

MVS_111021_LR		99.70 39	99.65 37	99.88 95	99.96 104	99.70 121	100.00 1	99.97 17	98.96 39	100.00 1	100.00 1	97.93 168	99.95 183	99.99 77	100.00 1	100.00 1

HyFIR lowres test		99.32 110	99.24 108	99.58 173	99.95 108	99.26 185	100.00 1	99.99 13	96.72 283	99.29 289	99.91 304	99.49 46	99.47 327	99.74 158	98.08 283	100.00 1

MVS_111021_HR		99.71 36	99.63 44	99.93 78	99.95 108	99.83 98	100.00 1	100.00 1	98.89 60	100.00 1	100.00 1	97.85 174	99.95 183	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 120	99.38 83	98.96 274	99.95 108	97.06 365	100.00 1	99.40 206	98.83 70	99.88 207	100.00 1	97.01 209	99.86 233	99.47 237	99.84 163	99.97 137

TAPA-MVS	96.40 10	97.64 298	97.37 307	98.45 307	99.94 111	95.70 392	100.00 1	99.40 206	97.65 168	99.53 263	100.00 1	99.31 76	99.66 289	80.48 504	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	98.29 266	98.17 264	98.65 294	99.94 111	97.39 348	99.30 449	99.40 206	95.64 358	97.75 406	100.00 1	92.69 329	99.95 183	98.89 283	99.92 141	98.62 343
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.55 240	98.40 239	98.99 271	99.93 113	97.35 351	100.00 1	99.40 206	97.08 236	99.09 305	99.98 251	93.37 307	99.95 183	96.94 368	99.84 163	99.68 311

TestCases				98.99 271	99.93 113	97.35 351		99.40 206	97.08 236	99.09 305	99.98 251	93.37 307	99.95 183	96.94 368	99.84 163	99.68 311

DeepC-MVS	97.84 5	99.00 162	98.80 168	99.60 167	99.93 113	99.03 210	100.00 1	99.40 206	98.61 92	99.33 286	100.00 1	92.23 336	99.95 183	99.74 158	99.96 126	99.83 224
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		99.04 150	99.79 9	96.81 414	99.92 116	90.47 471	100.00 1	99.41 202	98.95 42	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 149

EPNet_dtu		98.53 244	98.23 262	99.43 200	99.92 116	99.01 214	99.96 317	99.47 85	98.80 77	99.96 152	99.96 281	98.56 149	99.30 345	87.78 482	99.68 177	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 63	99.56 60	99.82 112	99.92 116	99.45 162	100.00 1	99.78 52	98.92 52	99.73 243	100.00 1	97.70 182	100.00 1	99.93 116	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		98.90 185	98.63 197	99.70 149	99.92 116	99.25 187	100.00 1	99.37 229	95.71 356	99.40 281	100.00 1	96.58 226	99.95 183	96.80 376	99.94 134	99.91 171

DPM-MVS		99.63 58	99.51 70	100.00 1	99.90 120	100.00 1	100.00 1	99.43 134	99.00 32	100.00 1	100.00 1	99.58 27	100.00 1	97.64 345	100.00 1	100.00 1

CHOSEN 1792x2688		99.00 162	98.91 157	99.25 254	99.90 120	97.79 335	100.00 1	99.99 13	98.79 80	98.28 377	100.00 1	93.63 300	99.95 183	99.66 193	99.95 128	100.00 1

test2506		99.48 82	99.38 83	99.75 139	99.89 122	99.51 150	99.45 432	100.00 1	98.38 105	99.83 214	100.00 1	98.86 131	99.81 253	99.25 260	98.78 208	99.94 154

ECVR-MVS		98.43 251	98.14 265	99.32 237	99.89 122	98.21 299	99.46 430	100.00 1	98.38 105	99.47 271	100.00 1	87.91 412	99.80 257	99.35 252	98.78 208	99.94 154

test1111		98.42 253	98.12 266	99.29 246	99.88 124	98.15 305	99.46 430	100.00 1	98.36 109	99.42 275	100.00 1	87.91 412	99.79 258	99.31 257	98.78 208	99.94 154

CANet_DTU		99.02 159	98.90 160	99.41 204	99.88 124	98.71 242	100.00 1	99.29 283	98.84 68	100.00 1	100.00 1	94.02 291	100.00 1	98.08 324	99.96 126	99.52 323

dcpmvs_2		98.87 189	99.53 65	96.90 402	99.87 126	90.88 467	99.94 334	99.07 429	98.20 119	100.00 1	100.00 1	98.69 143	99.86 233	100.00 1	100.00 1	99.95 149

fmvsm_s_conf0.5_n_9		99.04 150	98.78 170	99.81 117	99.86 127	99.44 165	100.00 1	99.32 259	98.94 45	100.00 1	100.00 1	91.00 354	99.99 107	100.00 1	99.94 134	100.00 1

mvsany_test1		99.57 70	99.48 76	99.85 104	99.86 127	99.54 143	100.00 1	99.36 235	98.94 45	100.00 1	100.00 1	97.97 166	100.00 1	99.88 124	99.28 194	100.00 1

fmvsm_s_conf0.5_n_11		98.92 180	98.63 197	99.80 123	99.85 129	99.86 90	100.00 1	99.24 322	98.91 55	100.00 1	100.00 1	89.69 388	99.99 107	100.00 1	99.98 118	99.54 321

test_fmvsm_n_1920		99.55 73	99.49 73	99.73 143	99.85 129	99.19 195	100.00 1	99.41 202	98.87 64	100.00 1	100.00 1	97.34 202	100.00 1	99.98 92	99.90 148	100.00 1

fmvsm_s_conf0.5_n_10		99.08 143	98.78 170	99.97 40	99.84 131	99.92 60	100.00 1	99.28 291	98.93 49	100.00 1	100.00 1	91.07 351	99.99 107	100.00 1	99.95 128	100.00 1

fmvsm_s_conf0.5_n		99.21 131	99.01 138	99.83 110	99.84 131	99.53 145	100.00 1	99.38 225	98.29 115	100.00 1	100.00 1	93.62 301	99.99 107	99.99 77	99.93 138	99.98 127

test_fmvs1		98.37 258	98.04 275	99.34 223	99.84 131	98.07 312	100.00 1	99.00 449	98.85 66	100.00 1	100.00 1	85.11 440	99.96 170	99.69 181	99.88 152	100.00 1

PVSNet_0	93.57 19	96.41 362	95.74 379	98.41 312	99.84 131	95.22 401	100.00 1	100.00 1	98.08 130	97.55 416	99.78 337	84.40 443	100.00 1	100.00 1	81.99 490	100.00 1

fmvsm_l_conf0.5_n_9		99.35 101	99.15 123	99.95 61	99.83 135	99.84 96	100.00 1	99.30 274	98.92 52	100.00 1	100.00 1	94.32 283	100.00 1	100.00 1	99.93 138	100.00 1

fmvsm_s_conf0.5_n_8		99.34 105	99.14 125	99.91 83	99.83 135	99.74 112	100.00 1	99.38 225	98.94 45	100.00 1	100.00 1	94.25 285	99.99 107	100.00 1	99.91 146	100.00 1

fmvsm_l_conf0.5_n_a		99.63 58	99.55 62	99.86 100	99.83 135	99.58 136	100.00 1	99.36 235	98.98 35	100.00 1	100.00 1	97.85 174	99.99 107	100.00 1	99.94 134	100.00 1

SDMVSNet		98.49 248	98.08 271	99.73 143	99.82 138	99.53 145	99.99 266	99.45 111	97.62 173	99.38 283	99.86 312	90.06 381	99.88 229	99.92 117	96.61 333	99.79 283

sd_testset		97.81 292	97.48 300	98.79 288	99.82 138	96.80 371	99.32 445	99.45 111	97.62 173	99.38 283	99.86 312	85.56 438	99.77 266	99.72 164	96.61 333	99.79 283

cl22		98.23 271	98.11 267	98.58 301	99.82 138	99.01 214	100.00 1	99.28 291	96.92 253	98.33 373	99.21 414	98.09 165	98.97 368	98.72 293	92.61 391	97.76 348

test_yl		99.51 75	99.37 86	99.95 61	99.82 138	99.90 71	100.00 1	99.47 85	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 92	97.75 308	99.94 154

DCV-MVSNet		99.51 75	99.37 86	99.95 61	99.82 138	99.90 71	100.00 1	99.47 85	97.48 195	100.00 1	100.00 1	99.80 6	100.00 1	99.98 92	97.75 308	99.94 154

PVSNet_Blended_VisFu		99.33 108	99.18 121	99.78 133	99.82 138	99.49 156	100.00 1	99.95 19	97.36 207	99.63 257	100.00 1	96.45 231	99.95 183	99.79 143	99.65 182	99.89 190

fmvsm_s_conf0.5_n_5		99.00 162	98.70 187	99.88 95	99.81 144	99.64 128	100.00 1	99.26 312	98.78 83	99.97 145	100.00 1	90.65 361	99.99 107	100.00 1	99.89 149	99.99 124

fmvsm_s_conf0.5_n_4		98.98 170	98.74 177	99.68 153	99.81 144	99.50 152	100.00 1	99.26 312	98.91 55	100.00 1	100.00 1	90.87 358	99.97 150	99.99 77	99.81 168	99.57 319

fmvsm_l_conf0.5_n		99.63 58	99.56 60	99.86 100	99.81 144	99.59 134	100.00 1	99.36 235	98.98 35	100.00 1	100.00 1	97.92 169	99.99 107	100.00 1	99.95 128	100.00 1

ET-MVSNet_ETH3D		96.41 362	95.48 393	99.20 257	99.81 144	99.75 109	100.00 1	99.02 446	97.30 218	78.33 516	100.00 1	97.73 180	97.94 467	99.70 172	87.41 457	99.92 167

Anonymous202405211		97.87 287	97.53 299	98.90 278	99.81 144	96.70 374	99.35 443	99.46 103	92.98 439	98.83 330	99.99 243	90.63 363	100.00 1	99.70 172	97.03 322	100.00 1

thres100view900		99.25 126	99.01 138	99.95 61	99.81 144	99.87 87	100.00 1	99.94 27	97.13 231	99.83 214	99.96 281	97.01 209	100.00 1	99.59 212	97.85 298	99.98 127

tfpn200view9		99.26 122	99.03 136	99.96 52	99.81 144	99.89 78	100.00 1	99.94 27	97.23 223	99.83 214	99.96 281	97.04 205	100.00 1	99.59 212	97.85 298	99.98 127

VNet		99.04 150	98.75 175	99.90 87	99.81 144	99.75 109	99.50 427	99.47 85	98.36 109	100.00 1	99.99 243	94.66 272	100.00 1	99.90 120	97.09 321	99.96 143

thres600view7		99.24 129	99.00 141	99.95 61	99.81 144	99.87 87	100.00 1	99.94 27	97.13 231	99.83 214	99.96 281	97.01 209	100.00 1	99.54 225	97.77 307	99.97 137

thres400		99.26 122	99.03 136	99.95 61	99.81 144	99.89 78	100.00 1	99.94 27	97.23 223	99.83 214	99.96 281	97.04 205	100.00 1	99.59 212	97.85 298	99.97 137

thres200		99.27 120	99.04 135	99.96 52	99.81 144	99.90 71	100.00 1	99.94 27	97.31 216	99.83 214	99.96 281	97.04 205	100.00 1	99.62 205	97.88 296	99.98 127

WTY-MVS		99.54 74	99.40 81	99.95 61	99.81 144	99.93 53	100.00 1	100.00 1	97.98 137	99.84 211	100.00 1	98.94 124	99.98 141	99.86 128	98.21 269	99.94 154

HY-MVS	96.53 9	99.50 78	99.35 91	99.96 52	99.81 144	99.93 53	99.64 408	100.00 1	97.97 139	99.84 211	99.85 317	98.94 124	99.99 107	99.86 128	98.23 268	99.95 149

fmvsm_l_conf0.5_n_3		99.38 96	99.20 117	99.92 82	99.80 157	99.78 104	100.00 1	99.35 246	98.94 45	100.00 1	100.00 1	94.77 267	99.99 107	99.99 77	99.92 141	100.00 1

fmvsm_s_conf0.5_n_a		99.32 110	99.15 123	99.81 117	99.80 157	99.47 161	100.00 1	99.35 246	98.22 116	100.00 1	100.00 1	95.21 254	99.99 107	99.96 106	99.86 158	99.98 127

Anonymous20231211		96.29 371	95.70 381	98.07 343	99.80 157	97.49 344	99.15 472	99.40 206	89.11 467	97.75 406	99.45 397	88.93 401	98.98 366	98.26 320	89.47 438	97.73 398

LFMVS		97.42 314	96.62 337	99.81 117	99.80 157	99.50 152	99.16 470	99.56 76	94.48 398	100.00 1	100.00 1	79.35 467	100.00 1	99.89 122	97.37 317	99.94 154

RPSCF		97.37 316	98.24 259	94.76 450	99.80 157	84.57 492	99.99 266	99.05 439	94.95 380	99.82 223	100.00 1	94.03 289	100.00 1	98.15 323	98.38 234	99.70 309

SymmetryMVS		99.30 114	99.25 104	99.45 194	99.79 162	98.55 255	99.94 334	99.47 85	98.39 103	100.00 1	100.00 1	98.44 154	99.98 141	99.36 248	97.83 301	99.83 224

fmvsm_s_conf0.5_n_2		98.90 185	98.57 209	99.90 87	99.79 162	99.78 104	100.00 1	99.25 316	98.97 37	100.00 1	100.00 1	89.22 396	99.99 107	100.00 1	99.88 152	99.92 167

test_vis1_n_1920		97.77 294	97.24 315	99.34 223	99.79 162	98.04 316	100.00 1	99.25 316	98.88 61	100.00 1	100.00 1	77.52 473	100.00 1	99.88 124	99.85 161	100.00 1

h-mvs33		97.03 334	96.53 340	98.51 303	99.79 162	95.90 388	99.45 432	99.45 111	98.21 117	100.00 1	99.78 337	97.49 193	99.99 107	99.72 164	74.92 507	99.65 316

Anonymous20240529		96.93 339	96.22 356	99.05 265	99.79 162	97.30 355	99.16 470	99.47 85	88.51 470	98.69 336	100.00 1	83.50 451	100.00 1	99.83 135	97.02 323	99.83 224

PS-MVSNAJ		99.64 54	99.57 55	99.85 104	99.78 167	99.81 100	99.95 326	99.42 153	98.38 105	100.00 1	100.00 1	98.75 140	100.00 1	99.88 124	99.99 107	99.74 299

PCF-MVS	98.23 3	98.69 215	98.37 246	99.62 163	99.78 167	99.02 212	99.23 461	99.06 437	96.43 319	98.08 386	100.00 1	94.72 270	99.95 183	98.16 322	99.91 146	99.90 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+-dtu		98.51 247	98.86 162	97.47 376	99.77 169	94.21 436	100.00 1	98.94 455	97.61 177	99.91 199	98.75 448	95.89 237	99.51 320	99.36 248	99.48 190	98.68 341

SteuartSystems-ACMMP		99.78 19	99.71 23	99.98 28	99.76 170	99.95 38	100.00 1	99.42 153	98.69 86	100.00 1	100.00 1	99.52 38	99.99 107	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
sss		99.45 86	99.34 93	99.80 123	99.76 170	99.50 152	100.00 1	99.91 40	97.72 160	99.98 139	99.94 295	98.45 153	100.00 1	99.53 228	98.75 211	99.89 190

sasdasda		99.03 153	98.73 179	99.94 74	99.75 172	99.95 38	100.00 1	99.30 274	97.64 170	100.00 1	100.00 1	95.22 252	99.97 150	99.76 153	96.90 326	99.91 171

canonicalmvs		99.03 153	98.73 179	99.94 74	99.75 172	99.95 38	100.00 1	99.30 274	97.64 170	100.00 1	100.00 1	95.22 252	99.97 150	99.76 153	96.90 326	99.91 171

MGCFI-Net		99.01 161	98.70 187	99.93 78	99.74 174	99.94 47	100.00 1	99.29 283	97.60 180	100.00 1	100.00 1	95.10 258	99.96 170	99.74 158	96.85 328	99.91 171

UWE-MVS-28		99.29 117	99.23 111	99.48 188	99.73 175	98.86 229	100.00 1	99.43 134	96.97 247	99.99 129	99.83 320	99.43 60	99.77 266	99.35 252	98.31 252	99.80 277

alignmvs		99.38 96	99.21 113	99.91 83	99.73 175	99.92 60	100.00 1	99.51 82	97.61 177	100.00 1	100.00 1	99.06 105	99.93 200	99.83 135	97.12 320	99.90 182

baseline1		98.91 183	98.61 201	99.81 117	99.71 177	99.77 107	99.78 380	99.44 125	97.51 192	98.81 331	99.99 243	98.25 159	99.76 271	98.60 303	95.41 344	99.89 190

ab-mvs		98.42 253	98.02 277	99.61 165	99.71 177	99.00 217	99.10 478	99.64 70	96.70 289	99.04 312	99.81 327	90.64 362	99.98 141	99.64 196	97.93 293	99.84 221

testing3-2		99.45 86	99.31 94	99.86 100	99.70 179	99.73 114	100.00 1	99.47 85	97.46 197	99.97 145	99.97 263	99.48 50	100.00 1	99.78 149	97.99 287	99.85 219

fmvsm_s_conf0.5_n_3		98.99 166	98.69 189	99.89 90	99.70 179	99.69 123	100.00 1	99.39 222	98.93 49	100.00 1	100.00 1	90.20 373	99.99 107	100.00 1	99.95 128	100.00 1

xiu_mvs_v2_base		99.51 75	99.41 80	99.82 112	99.70 179	99.73 114	99.92 342	99.40 206	98.15 123	100.00 1	100.00 1	98.50 152	100.00 1	99.85 131	99.13 198	99.74 299

myMVS_eth3d28		99.41 91	99.28 96	99.80 123	99.69 182	99.53 145	100.00 1	99.43 134	97.12 233	99.98 139	99.97 263	99.41 66	100.00 1	99.81 142	98.07 284	99.88 203

test_cas_vis1_n_1920		98.63 225	98.25 256	99.77 136	99.69 182	99.32 177	100.00 1	99.31 268	98.84 68	99.96 152	100.00 1	87.42 419	99.99 107	99.14 268	99.86 158	100.00 1

tpmvs		98.59 232	98.38 244	99.23 255	99.69 182	97.90 326	99.31 448	99.47 85	94.52 396	99.68 248	99.28 408	97.64 185	99.89 221	97.71 343	98.17 275	99.89 190

Test_1112_low_res		98.83 194	98.60 204	99.51 180	99.69 182	98.75 237	99.99 266	99.14 400	96.81 264	98.84 328	99.06 421	97.45 196	99.89 221	98.66 295	97.75 308	99.89 190

1112_ss		98.91 183	98.71 185	99.51 180	99.69 182	98.75 237	99.99 266	99.15 393	96.82 263	98.84 328	100.00 1	97.45 196	99.89 221	98.66 295	97.75 308	99.89 190

SD_0403		97.92 286	98.43 230	96.39 423	99.68 187	89.74 477	99.92 342	99.34 253	96.75 274	99.39 282	99.93 300	93.54 304	99.51 320	99.11 272	98.21 269	99.92 167

MVSMamba_PlusPlus		99.39 93	99.25 104	99.80 123	99.68 187	99.59 134	99.99 266	99.30 274	96.66 294	99.96 152	99.97 263	97.89 171	99.92 206	99.76 153	100.00 1	99.90 182

test_fmvsmconf_n		99.56 71	99.46 79	99.86 100	99.68 187	99.58 136	100.00 1	99.31 268	98.92 52	99.88 207	100.00 1	97.35 201	99.99 107	99.98 92	99.99 107	100.00 1

test_fmvs1_n		97.43 313	96.86 328	99.15 259	99.68 187	97.48 345	99.99 266	98.98 453	98.82 72	100.00 1	100.00 1	74.85 482	99.96 170	99.67 186	99.70 176	100.00 1

BridgeMVS		99.43 89	99.28 96	99.85 104	99.68 187	99.68 124	99.97 309	99.28 291	97.03 241	99.96 152	99.97 263	97.90 170	99.93 200	99.77 151	100.00 1	99.94 154

hse-mvs2		96.79 342	96.38 348	98.04 353	99.68 187	95.54 394	99.81 369	99.42 153	98.21 117	100.00 1	99.80 333	97.49 193	99.46 332	99.72 164	73.27 510	99.12 335

AUN-MVS		96.26 373	95.67 385	98.06 347	99.68 187	95.60 393	99.82 368	99.42 153	96.78 268	99.88 207	99.80 333	94.84 265	99.47 327	97.48 352	73.29 509	99.12 335

Vis-MVSNet		98.52 245	98.25 256	99.34 223	99.68 187	98.55 255	99.68 405	99.41 202	97.34 211	99.94 189	100.00 1	90.38 372	99.70 287	99.03 276	98.84 206	99.76 291
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_7		98.98 170	98.85 163	99.37 217	99.67 195	98.34 284	100.00 1	99.31 268	98.97 37	100.00 1	100.00 1	91.70 342	99.97 150	99.99 77	99.97 122	99.80 277

UWE-MVS		99.18 134	99.06 133	99.51 180	99.67 195	98.80 234	100.00 1	99.43 134	96.80 265	99.93 194	99.86 312	99.79 8	99.94 196	97.78 341	98.33 248	99.80 277

ETVMVS		99.16 137	98.98 144	99.69 150	99.67 195	99.56 138	100.00 1	99.45 111	96.36 328	99.98 139	99.95 289	98.65 144	99.64 290	99.11 272	97.63 315	99.88 203

testing222		99.14 139	98.94 152	99.73 143	99.67 195	99.51 150	100.00 1	99.43 134	96.90 256	99.99 129	99.90 306	98.55 150	99.86 233	98.85 285	97.18 319	99.81 246

xiu_mvs_v1_base_debu		99.35 101	99.21 113	99.79 128	99.67 195	99.71 117	99.78 380	99.36 235	98.13 125	100.00 1	100.00 1	97.00 212	100.00 1	99.83 135	99.07 200	99.66 313

xiu_mvs_v1_base		99.35 101	99.21 113	99.79 128	99.67 195	99.71 117	99.78 380	99.36 235	98.13 125	100.00 1	100.00 1	97.00 212	100.00 1	99.83 135	99.07 200	99.66 313

xiu_mvs_v1_base_debi		99.35 101	99.21 113	99.79 128	99.67 195	99.71 117	99.78 380	99.36 235	98.13 125	100.00 1	100.00 1	97.00 212	100.00 1	99.83 135	99.07 200	99.66 313

balanced_ft_v1		98.70 213	98.61 201	98.94 275	99.67 195	96.90 367	99.91 348	99.30 274	96.73 281	99.96 152	99.97 263	92.18 337	99.93 200	99.86 128	99.95 128	100.00 1

IB-MVS	96.24 12	97.54 308	96.95 325	99.33 231	99.67 195	98.10 310	100.00 1	99.47 85	97.42 203	99.26 290	99.69 350	98.83 135	99.89 221	99.43 244	78.77 504	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
kuosan		98.55 240	98.53 216	98.62 296	99.66 204	96.16 384	100.00 1	99.44 125	93.93 415	99.81 229	99.98 251	97.58 186	99.81 253	98.08 324	98.28 258	99.89 190

miper_enhance_ethall		98.33 261	98.27 254	98.51 303	99.66 204	99.04 209	100.00 1	99.22 332	97.53 188	98.51 359	99.38 402	99.49 46	98.75 390	98.02 328	92.61 391	97.76 348

FE-MVS		99.16 137	98.99 143	99.66 157	99.65 206	99.18 197	99.58 417	99.43 134	95.24 375	99.91 199	99.59 376	99.37 70	99.97 150	98.31 315	99.81 168	99.83 224

RRT-MVS		98.75 205	98.52 217	99.44 197	99.65 206	98.57 254	99.90 350	99.08 424	96.51 314	99.96 152	99.95 289	92.59 330	99.96 170	99.60 210	99.45 192	99.81 246

MVSTER		98.58 234	98.52 217	98.77 290	99.65 206	99.68 124	100.00 1	99.29 283	95.63 359	98.65 341	99.80 333	99.78 9	98.88 380	98.59 304	95.31 348	97.73 398

FMVSNet3		97.30 321	96.95 325	98.37 315	99.65 206	99.25 187	99.71 399	99.28 291	94.23 404	98.53 355	98.91 438	93.30 309	98.11 452	95.31 411	93.60 379	97.73 398

cascas		98.43 251	98.07 273	99.50 183	99.65 206	99.02 212	100.00 1	99.22 332	94.21 406	99.72 244	99.98 251	92.03 340	99.93 200	99.68 182	98.12 281	99.54 321

IS-MVSNet		99.08 143	98.91 157	99.59 169	99.65 206	99.38 170	99.78 380	99.24 322	96.70 289	99.51 265	100.00 1	98.44 154	99.52 318	98.47 308	98.39 230	99.88 203

testing11		99.26 122	99.19 118	99.46 190	99.64 212	98.61 251	100.00 1	99.43 134	96.94 250	99.92 196	99.94 295	99.43 60	99.97 150	99.67 186	97.79 306	99.82 230

Vis-MVSNet (Re-imp)		98.99 166	98.89 161	99.29 246	99.64 212	98.89 228	99.98 299	99.31 268	96.74 277	99.48 268	100.00 1	98.11 163	99.10 355	98.39 311	98.34 245	99.89 190

WBMVS		98.19 273	98.10 270	98.47 305	99.63 214	99.03 210	100.00 1	99.32 259	95.46 370	98.39 368	99.40 401	99.69 17	98.61 404	98.64 298	92.39 396	97.76 348

UBG		99.36 100	99.27 98	99.63 161	99.63 214	99.01 214	100.00 1	99.43 134	96.99 244	100.00 1	99.92 301	99.69 17	99.99 107	99.74 158	98.06 285	99.88 203

test_vis1_rt		93.10 431	92.93 427	93.58 463	99.63 214	85.07 490	99.99 266	93.71 524	97.49 194	90.96 482	97.10 487	60.40 504	99.95 183	99.24 262	97.90 295	95.72 494

FA-MVS(test-final)		99.00 162	98.75 175	99.73 143	99.63 214	99.43 166	99.83 365	99.43 134	95.84 354	99.52 264	99.37 403	97.84 176	99.96 170	97.63 346	99.68 177	99.79 283

EIA-MVS		99.26 122	99.19 118	99.45 194	99.63 214	98.75 237	100.00 1	99.27 306	96.93 251	99.95 183	100.00 1	97.47 195	99.79 258	99.74 158	99.72 174	99.82 230

Fast-Effi-MVS+		98.40 256	98.02 277	99.55 178	99.63 214	99.06 207	100.00 1	99.15 393	95.07 377	99.42 275	99.95 289	93.26 310	99.73 281	97.44 353	98.24 267	99.87 214

TESTMET0.1,1		99.08 143	98.96 147	99.44 197	99.63 214	99.38 170	100.00 1	99.45 111	95.53 363	99.48 268	100.00 1	99.71 15	99.02 360	96.84 373	99.99 107	99.91 171

MVS_Test		98.93 179	98.65 194	99.77 136	99.62 221	99.50 152	99.99 266	99.19 365	95.52 365	99.96 152	99.86 312	96.54 229	99.98 141	98.65 297	98.48 222	99.82 230

BH-w/o		98.82 195	98.81 167	98.88 280	99.62 221	96.71 373	100.00 1	99.28 291	97.09 234	98.81 331	100.00 1	94.91 263	99.96 170	99.54 225	100.00 1	99.96 143

fmvsm_s_conf0.1_n_2		98.95 176	98.69 189	99.73 143	99.61 223	99.74 112	100.00 1	99.23 327	98.95 42	99.97 145	100.00 1	90.92 357	99.97 150	100.00 1	99.58 187	99.47 326

tpmrst		98.98 170	98.93 154	99.14 261	99.61 223	97.74 336	99.52 425	99.36 235	96.05 345	99.98 139	99.64 364	99.04 110	99.86 233	98.94 280	98.19 272	99.82 230

BH-RMVSNet		98.46 249	98.08 271	99.59 169	99.61 223	99.19 195	100.00 1	99.28 291	97.06 238	98.95 316	100.00 1	88.99 399	99.82 249	98.83 288	100.00 1	99.77 289

testing91		99.18 134	99.10 129	99.41 204	99.60 226	98.43 267	100.00 1	99.43 134	96.76 271	99.82 223	99.92 301	99.05 107	99.98 141	99.62 205	97.67 312	99.81 246

testing99		99.18 134	99.10 129	99.41 204	99.60 226	98.43 267	100.00 1	99.43 134	96.76 271	99.84 211	99.92 301	99.06 105	99.98 141	99.62 205	97.67 312	99.81 246

Effi-MVS+		98.58 234	98.24 259	99.61 165	99.60 226	99.26 185	97.85 512	99.10 418	96.22 340	99.97 145	99.89 307	93.75 298	99.77 266	99.43 244	98.34 245	99.81 246

EPMVS		99.25 126	99.13 126	99.60 167	99.60 226	99.20 194	99.60 415	100.00 1	96.93 251	99.92 196	99.36 404	99.05 107	99.71 285	98.77 290	98.94 205	99.90 182

thisisatest0530		99.37 99	99.27 98	99.69 150	99.59 230	99.41 168	100.00 1	99.46 103	96.46 318	99.90 201	100.00 1	99.44 56	99.85 240	98.97 279	99.58 187	99.80 277

thisisatest0515		99.42 90	99.31 94	99.74 140	99.59 230	99.55 140	100.00 1	99.46 103	96.65 296	99.92 196	100.00 1	99.44 56	99.85 240	99.09 274	99.63 185	99.81 246

XVG-OURS-SEG-HR		98.27 269	98.31 252	98.14 337	99.59 230	95.92 386	100.00 1	99.36 235	98.48 98	99.21 294	100.00 1	89.27 395	99.94 196	99.76 153	99.17 196	98.56 344

BH-untuned		98.64 219	98.65 194	98.60 298	99.59 230	96.17 383	100.00 1	99.28 291	96.67 293	98.41 366	100.00 1	94.52 276	99.83 246	99.41 246	100.00 1	99.81 246

TR-MVS		98.14 274	97.74 290	99.33 231	99.59 230	98.28 291	99.27 450	99.21 351	96.42 323	99.15 299	99.94 295	88.87 402	99.79 258	98.88 284	98.29 255	99.93 165

dongtai		98.29 266	98.25 256	98.42 311	99.58 235	95.86 389	100.00 1	99.44 125	93.46 428	99.69 247	99.97 263	97.53 191	99.51 320	96.28 389	98.27 261	99.89 190

ETV-MVS		99.34 105	99.24 108	99.64 160	99.58 235	99.33 176	100.00 1	99.25 316	97.57 183	99.96 152	100.00 1	97.44 198	99.79 258	99.70 172	99.65 182	99.81 246

Fast-Effi-MVS+-dtu		98.38 257	98.56 212	97.82 366	99.58 235	94.44 429	100.00 1	99.16 388	96.75 274	99.51 265	99.63 368	95.03 260	99.60 292	97.71 343	99.67 179	99.42 328

XVG-OURS		98.30 263	98.36 248	98.13 340	99.58 235	95.91 387	100.00 1	99.36 235	98.69 86	99.23 293	100.00 1	91.20 348	99.92 206	99.34 254	97.82 302	98.56 344

E3new		98.95 176	98.80 168	99.41 204	99.57 239	98.50 264	100.00 1	99.22 332	96.84 261	99.89 204	100.00 1	95.70 243	99.93 200	99.57 218	98.39 230	99.82 230

viewcassd2359sk11		98.90 185	98.73 179	99.40 209	99.57 239	98.47 265	99.99 266	99.22 332	96.79 266	99.82 223	100.00 1	95.24 251	99.91 208	99.54 225	98.38 234	99.82 230

tttt0517		99.34 105	99.23 111	99.67 154	99.57 239	99.38 170	100.00 1	99.46 103	96.33 332	99.89 204	100.00 1	99.44 56	99.84 244	98.93 281	99.46 191	99.78 288

mvs_anonymous		98.80 197	98.60 204	99.38 216	99.57 239	99.24 189	100.00 1	99.21 351	95.87 349	98.92 320	99.82 324	96.39 232	99.03 359	99.13 270	98.50 220	99.88 203

mvsmamba		99.05 149	98.98 144	99.27 252	99.57 239	98.10 310	100.00 1	99.28 291	95.92 348	99.96 152	99.97 263	96.73 223	99.89 221	99.72 164	99.65 182	99.81 246

PMMVS		99.12 140	98.97 146	99.58 173	99.57 239	98.98 219	100.00 1	99.30 274	97.14 229	99.96 152	100.00 1	96.53 230	99.82 249	99.70 172	98.49 221	99.94 154

VortexMVS		98.23 271	98.11 267	98.59 299	99.56 245	99.37 173	99.95 326	99.03 445	96.47 317	98.69 336	99.55 384	95.91 236	98.66 395	99.01 278	94.80 368	97.73 398

viewdifsd2359ckpt13		98.72 206	98.52 217	99.34 223	99.55 246	98.46 266	99.99 266	99.22 332	96.50 316	99.05 310	100.00 1	94.54 275	99.73 281	99.46 240	98.35 241	99.81 246

guyue		99.21 131	99.07 132	99.62 163	99.55 246	99.29 180	100.00 1	99.32 259	97.66 166	99.96 152	100.00 1	95.84 239	99.84 244	99.63 203	99.67 179	99.75 292

dmvs_re		97.54 308	97.88 285	96.54 420	99.55 246	90.35 472	99.86 360	99.46 103	97.00 243	99.41 280	100.00 1	90.78 360	99.30 345	99.60 210	95.24 353	99.96 143

hybridcas		98.64 219	98.41 233	99.33 231	99.54 249	98.41 269	100.00 1	99.18 375	96.78 268	99.68 248	100.00 1	92.58 331	99.75 276	99.57 218	98.38 234	99.82 230

GG-mvs-BLEND				99.59 169	99.54 249	99.49 156	99.17 469	99.52 78		99.96 152	99.68 354	100.00 1	99.33 344	99.71 168	99.99 107	99.96 143

diffmvs		98.96 173	98.73 179	99.63 161	99.54 249	99.16 199	100.00 1	99.18 375	97.33 213	99.96 152	100.00 1	94.60 274	99.91 208	99.66 193	98.33 248	99.82 230
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		98.41 255	98.11 267	99.31 239	99.54 249	98.55 255	99.18 464	100.00 1	98.64 91	99.79 231	99.04 424	87.61 417	100.00 1	99.30 258	99.89 149	99.40 329
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
E2		98.77 199	98.57 209	99.37 217	99.53 253	98.38 276	99.98 299	99.22 332	96.77 270	99.75 237	100.00 1	94.03 289	99.91 208	99.53 228	98.35 241	99.82 230

viewdifsd2359ckpt09		98.78 198	98.60 204	99.31 239	99.53 253	98.37 277	100.00 1	99.20 361	96.85 259	99.32 287	100.00 1	94.68 271	99.74 277	99.46 240	98.36 239	99.81 246

mamba_0408		98.63 225	98.40 239	99.34 223	99.53 253	98.52 260	99.24 454	99.16 388	96.43 319	98.95 316	99.98 251	94.47 277	99.76 271	99.21 266	98.62 213	99.75 292

SSM_04072		98.59 232	98.40 239	99.15 259	99.53 253	98.52 260	99.24 454	99.16 388	96.43 319	98.95 316	99.98 251	94.47 277	99.19 352	99.21 266	98.62 213	99.75 292

SSM_0407		98.72 206	98.52 217	99.33 231	99.53 253	98.52 260	99.88 357	99.15 393	96.53 309	98.95 316	100.00 1	94.38 280	99.72 283	99.64 196	98.62 213	99.75 292

SSM_0404		98.76 202	98.56 212	99.35 221	99.53 253	98.65 249	99.80 374	99.15 393	96.53 309	99.47 271	100.00 1	94.38 280	99.76 271	99.64 196	98.59 216	99.64 317

GDP-MVS		99.39 93	99.26 102	99.77 136	99.53 253	99.55 140	100.00 1	99.11 415	97.14 229	99.96 152	100.00 1	99.83 5	99.89 221	98.47 308	99.26 195	99.87 214

KD-MVS_2432*1600		94.15 414	93.08 424	97.35 381	99.53 253	97.83 333	99.63 410	99.19 365	92.88 441	96.29 443	97.68 482	98.84 133	96.70 483	89.73 470	63.92 529	97.53 445

miper_refine_blended		94.15 414	93.08 424	97.35 381	99.53 253	97.83 333	99.63 410	99.19 365	92.88 441	96.29 443	97.68 482	98.84 133	96.70 483	89.73 470	63.92 529	97.53 445

GBi-Net		96.07 385	95.80 375	96.89 403	99.53 253	94.87 407	99.18 464	99.27 306	93.71 417	98.53 355	98.81 445	84.23 445	98.07 458	95.31 411	93.60 379	97.72 405

test1		96.07 385	95.80 375	96.89 403	99.53 253	94.87 407	99.18 464	99.27 306	93.71 417	98.53 355	98.81 445	84.23 445	98.07 458	95.31 411	93.60 379	97.72 405

dp		98.72 206	98.61 201	99.03 268	99.53 253	97.39 348	99.45 432	99.39 222	95.62 360	99.94 189	99.52 388	98.83 135	99.82 249	96.77 379	98.42 226	99.89 190

FMVSNet2		96.22 375	95.60 387	98.06 347	99.53 253	98.33 285	99.45 432	99.27 306	93.71 417	98.03 390	98.84 443	84.23 445	98.10 456	93.97 431	93.40 382	97.73 398

MDTV_nov1_ep13			98.94 152		99.53 253	98.36 280	99.39 439	99.46 103	96.54 308	99.99 129	99.63 368	98.92 127	99.86 233	98.30 318	98.71 212

hybridnocas07		98.85 192	98.63 197	99.53 179	99.52 267	98.95 224	100.00 1	99.19 365	97.15 228	99.93 194	100.00 1	93.83 297	99.82 249	99.67 186	98.38 234	99.82 230

hybrid		98.81 196	98.60 204	99.45 194	99.52 267	98.74 240	100.00 1	99.19 365	97.04 240	99.95 183	100.00 1	93.89 296	99.78 264	99.64 196	98.19 272	99.81 246

gm-plane-assit					99.52 267	97.26 357			95.86 351		100.00 1		99.43 335	98.76 291

UA-Net		99.06 147	98.83 164	99.74 140	99.52 267	99.40 169	99.08 481	99.45 111	97.64 170	99.83 214	100.00 1	95.80 240	99.94 196	98.35 313	99.80 171	99.88 203

VDD-MVS		96.58 354	95.99 365	98.34 317	99.52 267	95.33 399	99.18 464	99.38 225	96.64 297	99.77 234	100.00 1	72.51 488	100.00 1	100.00 1	96.94 325	99.70 309

casdiffmvs		98.65 218	98.38 244	99.46 190	99.52 267	98.74 240	100.00 1	99.15 393	96.91 254	99.05 310	100.00 1	92.75 325	99.83 246	99.70 172	98.38 234	99.81 246
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		98.69 215	98.45 228	99.41 204	99.52 267	98.67 246	100.00 1	99.17 385	97.03 241	99.13 300	100.00 1	93.17 313	99.74 277	99.70 172	98.34 245	99.81 246

onestephybrid01		98.89 188	98.67 192	99.56 176	99.51 274	99.08 204	100.00 1	99.20 361	97.30 218	99.95 183	100.00 1	94.04 288	99.79 258	99.77 151	98.29 255	99.81 246

E5new		98.63 225	98.41 233	99.31 239	99.51 274	98.21 299	99.79 375	99.21 351	96.62 302	99.67 254	100.00 1	93.15 315	99.91 208	99.46 240	98.26 263	99.81 246

E5		98.63 225	98.41 233	99.31 239	99.51 274	98.21 299	99.79 375	99.21 351	96.62 302	99.67 254	100.00 1	93.15 315	99.91 208	99.46 240	98.26 263	99.81 246

E4		98.68 217	98.46 227	99.33 231	99.51 274	98.27 293	99.96 317	99.21 351	96.66 294	99.68 248	100.00 1	93.38 306	99.91 208	99.49 234	98.27 261	99.81 246

viewmanbaseed2359cas		98.86 190	98.68 191	99.40 209	99.51 274	98.51 263	99.98 299	99.22 332	97.05 239	99.72 244	100.00 1	94.77 267	99.89 221	99.58 215	98.31 252	99.81 246

testing3		98.44 250	98.37 246	98.65 294	99.51 274	98.32 287	100.00 1	99.62 72	96.43 319	97.93 396	99.99 243	99.11 98	97.81 470	94.88 418	97.80 304	99.82 230

ADS-MVSNet2		98.28 268	98.51 222	97.62 372	99.51 274	95.03 404	99.24 454	99.41 202	95.52 365	99.96 152	99.70 347	97.57 188	97.94 467	97.11 364	98.54 218	99.88 203

ADS-MVSNet		98.70 213	98.51 222	99.28 249	99.51 274	98.39 273	99.24 454	99.44 125	95.52 365	99.96 152	99.70 347	97.57 188	99.58 298	97.11 364	98.54 218	99.88 203

EPP-MVSNet		99.10 142	99.00 141	99.40 209	99.51 274	98.68 245	99.92 342	99.43 134	95.47 369	99.65 256	100.00 1	99.51 39	99.76 271	99.53 228	98.00 286	99.75 292

Syy-MVS		96.17 379	96.57 339	95.00 445	99.50 283	87.37 485	100.00 1	99.57 74	96.23 337	98.07 387	100.00 1	92.41 335	97.81 470	85.34 489	97.96 290	99.82 230

myMVS_eth3d		98.52 245	98.51 222	98.53 302	99.50 283	97.98 319	100.00 1	99.57 74	96.23 337	98.07 387	100.00 1	99.09 100	97.81 470	96.17 390	97.96 290	99.82 230

casdiffmvs_mvg		98.64 219	98.39 242	99.40 209	99.50 283	98.60 252	100.00 1	99.22 332	96.85 259	99.10 303	100.00 1	92.75 325	99.78 264	99.71 168	98.35 241	99.81 246
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		98.05 279	97.76 289	98.92 277	99.50 283	97.10 364	99.77 385	99.30 274	90.20 464	99.72 244	98.71 449	97.71 181	99.86 233	96.75 380	98.20 271	99.81 246

viewmacassd2359aftdt		98.57 236	98.31 252	99.33 231	99.49 287	98.31 289	99.89 354	99.21 351	96.87 258	99.10 303	100.00 1	92.48 334	99.88 229	99.50 232	98.28 258	99.81 246

miper_ehance_all_eth		97.81 292	97.66 296	98.23 328	99.49 287	98.37 277	99.99 266	99.11 415	94.78 384	98.25 381	99.21 414	98.18 161	98.57 413	97.35 359	92.61 391	97.76 348

PatchmatchNet		99.03 153	98.96 147	99.26 253	99.49 287	98.33 285	99.38 440	99.45 111	96.64 297	99.96 152	99.58 378	99.49 46	99.50 323	97.63 346	99.00 204	99.93 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
diffmvs_AUTHOR		98.92 180	98.73 179	99.49 187	99.48 290	98.81 233	99.94 334	99.14 400	97.24 221	99.96 152	100.00 1	94.85 264	99.87 231	99.67 186	98.31 252	99.79 283

BP-MVS1		99.56 71	99.48 76	99.79 128	99.48 290	99.61 131	100.00 1	99.32 259	97.34 211	99.94 189	100.00 1	99.74 13	99.89 221	99.75 157	99.72 174	99.87 214

miper_lstm_enhance		97.40 315	97.28 311	97.75 369	99.48 290	97.52 343	100.00 1	99.07 429	94.08 412	98.01 393	99.61 374	97.38 200	97.98 465	96.44 385	91.47 416	97.76 348

c3_l		97.58 304	97.42 303	98.06 347	99.48 290	98.16 304	99.96 317	99.10 418	94.54 395	98.13 385	99.20 416	97.87 173	98.25 439	97.28 360	91.20 419	97.75 359

CDS-MVSNet		98.96 173	98.95 151	99.01 270	99.48 290	98.36 280	99.93 340	99.37 229	96.79 266	99.31 288	99.83 320	99.77 11	98.91 374	98.07 326	97.98 288	99.77 289
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Casviewmamba		98.71 210	98.47 225	99.46 190	99.47 295	98.70 244	100.00 1	99.17 385	96.97 247	99.45 274	100.00 1	93.04 319	99.87 231	99.67 186	98.41 227	99.81 246

E3		98.77 199	98.57 209	99.36 219	99.47 295	98.36 280	99.98 299	99.22 332	96.76 271	99.75 237	100.00 1	94.10 286	99.91 208	99.53 228	98.35 241	99.82 230

viewdifsd2359ckpt07		98.72 206	98.52 217	99.34 223	99.47 295	98.28 291	99.99 266	99.20 361	96.98 245	99.60 259	100.00 1	93.45 305	99.93 200	99.58 215	98.36 239	99.82 230

viewdifsd2359ckpt11		97.98 282	97.89 282	98.26 325	99.47 295	94.98 406	99.99 266	99.22 332	96.74 277	99.24 291	100.00 1	90.14 375	99.90 219	99.49 234	96.73 329	99.90 182

viewmsd2359difaftdt		97.98 282	97.89 282	98.27 322	99.47 295	94.99 405	99.99 266	99.22 332	96.74 277	99.24 291	100.00 1	90.14 375	99.90 219	99.49 234	96.73 329	99.90 182

fmvsm_s_conf0.1_n		98.77 199	98.42 231	99.82 112	99.47 295	99.52 149	100.00 1	99.27 306	97.53 188	100.00 1	100.00 1	89.73 386	99.96 170	99.84 134	99.93 138	99.97 137

GeoE		98.06 278	97.65 297	99.29 246	99.47 295	98.41 269	100.00 1	99.19 365	94.85 382	98.88 323	100.00 1	91.21 347	99.59 294	97.02 366	98.19 272	99.88 203

cl____		97.54 308	97.32 309	98.18 333	99.47 295	98.14 307	100.00 1	99.10 418	94.16 410	97.60 413	99.63 368	97.52 192	98.65 397	96.47 382	91.97 404	97.76 348

viewmamba		98.92 180	98.74 177	99.46 190	99.46 303	98.83 232	100.00 1	99.19 365	97.18 226	99.95 183	100.00 1	94.97 261	99.74 277	99.64 196	98.29 255	99.81 246

casdiffseed414692147		98.31 262	97.94 280	99.40 209	99.46 303	98.67 246	99.91 348	99.17 385	96.33 332	98.66 340	99.97 263	90.47 370	99.71 285	99.36 248	98.16 276	99.81 246

E6new		98.64 219	98.41 233	99.30 243	99.46 303	98.19 302	99.79 375	99.21 351	96.62 302	99.68 248	100.00 1	93.24 311	99.91 208	99.47 237	98.26 263	99.81 246

E6		98.64 219	98.41 233	99.30 243	99.46 303	98.19 302	99.79 375	99.21 351	96.62 302	99.68 248	100.00 1	93.24 311	99.91 208	99.47 237	98.26 263	99.81 246

viewmambaseed2359dif		98.57 236	98.34 250	99.28 249	99.46 303	98.23 296	100.00 1	99.16 388	96.26 336	99.11 302	100.00 1	93.12 318	99.79 258	99.61 208	98.33 248	99.80 277

KinetiMVS		98.61 229	98.26 255	99.65 159	99.46 303	99.24 189	99.96 317	99.44 125	97.54 185	99.99 129	99.99 243	90.83 359	99.95 183	97.18 362	99.92 141	99.75 292

AstraMVS		99.03 153	99.01 138	99.09 262	99.46 303	97.66 339	100.00 1	99.23 327	97.83 150	99.95 183	100.00 1	95.52 247	99.86 233	99.74 158	99.39 193	99.74 299

DIV-MVS_self_test		97.52 311	97.35 308	98.05 351	99.46 303	98.11 308	100.00 1	99.10 418	94.21 406	97.62 411	99.63 368	97.65 184	98.29 436	96.47 382	91.98 403	97.76 348

MVSFormer		98.94 178	98.82 165	99.28 249	99.45 311	99.49 156	100.00 1	99.13 407	95.46 370	99.97 145	100.00 1	96.76 220	98.59 409	98.63 300	100.00 1	99.74 299

lupinMVS		99.29 117	99.16 122	99.69 150	99.45 311	99.49 156	100.00 1	99.15 393	97.45 199	99.97 145	100.00 1	96.76 220	99.76 271	99.67 186	100.00 1	99.81 246

dtuplus		98.57 236	98.32 251	99.30 243	99.44 313	98.35 283	100.00 1	99.14 400	96.36 328	98.97 315	100.00 1	93.04 319	99.77 266	99.55 221	98.39 230	99.79 283

ALIKED-NN		82.28 478	81.49 482	84.63 496	99.44 313	67.26 526	97.36 519	90.47 531	62.09 519	81.26 514	95.45 501	59.17 505	93.89 507	63.93 528	84.26 475	92.75 516

WB-MVSnew		97.02 336	97.24 315	96.37 425	99.44 313	97.36 350	100.00 1	99.43 134	96.12 344	99.35 285	99.89 307	93.60 302	98.42 425	88.91 480	98.39 230	93.33 512

TAMVS		98.76 202	98.73 179	98.86 281	99.44 313	97.69 337	99.57 418	99.34 253	96.57 306	99.12 301	99.81 327	98.83 135	99.16 353	97.97 332	97.91 294	99.73 308

tpm2		98.64 219	98.58 208	98.81 287	99.42 317	97.12 362	99.69 403	99.37 229	93.63 422	99.94 189	99.67 355	98.96 121	99.47 327	98.62 302	97.95 292	99.83 224

EC-MVSNet		99.19 133	99.09 131	99.48 188	99.42 317	99.07 205	100.00 1	99.21 351	96.95 249	99.96 152	100.00 1	96.88 218	99.48 325	99.64 196	99.79 172	99.88 203

eth_miper_zixun_eth		97.47 312	97.28 311	98.06 347	99.41 319	97.94 324	99.62 413	99.08 424	94.46 399	98.19 384	99.56 383	96.91 217	98.50 418	96.78 377	91.49 414	97.74 386

gg-mvs-nofinetune		96.95 338	96.10 360	99.50 183	99.41 319	99.36 175	99.07 483	99.52 78	83.69 495	99.96 152	83.60 535	100.00 1	99.20 351	99.68 182	99.99 107	99.96 143

SCA		98.30 263	97.98 279	99.23 255	99.41 319	98.25 295	99.99 266	99.45 111	96.91 254	99.76 236	99.58 378	89.65 390	99.54 312	98.31 315	98.79 207	99.91 171

CostFormer		98.84 193	98.77 173	99.04 267	99.41 319	97.58 342	99.67 406	99.35 246	94.66 391	99.96 152	99.36 404	99.28 84	99.74 277	99.41 246	97.81 303	99.81 246

IterMVS		96.76 344	96.46 345	97.63 370	99.41 319	96.89 368	99.99 266	99.13 407	94.74 387	97.59 415	99.66 357	89.63 392	98.28 437	95.71 399	92.31 398	97.72 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		96.72 347	96.42 347	97.62 372	99.40 324	96.83 370	99.99 266	99.14 400	94.65 392	97.55 416	99.72 342	89.65 390	98.31 432	95.62 405	92.05 401	97.73 398

test-LLR		99.03 153	98.91 157	99.40 209	99.40 324	99.28 182	100.00 1	99.45 111	96.70 289	99.42 275	99.12 417	99.31 76	99.01 362	96.82 374	99.99 107	99.91 171

test-mter		98.96 173	98.82 165	99.40 209	99.40 324	99.28 182	100.00 1	99.45 111	95.44 374	99.42 275	99.12 417	99.70 16	99.01 362	96.82 374	99.99 107	99.91 171

FMVSNet5		95.32 400	95.43 396	94.99 446	99.39 327	92.99 449	99.25 453	99.24 322	90.45 460	97.44 419	98.45 466	95.78 241	94.39 503	87.02 484	91.88 405	97.59 441

icg_test_0407_2		98.30 263	98.45 228	97.85 365	99.38 328	95.36 395	99.99 266	99.18 375	96.72 283	99.58 260	100.00 1	95.17 256	98.45 423	97.84 336	98.15 277	99.74 299

IMVS_0407		98.36 260	98.42 231	98.19 332	99.38 328	95.36 395	99.73 395	99.18 375	96.72 283	99.58 260	100.00 1	95.17 256	99.47 327	97.84 336	98.15 277	99.74 299

IMVS_0404		97.87 287	97.89 282	97.81 367	99.38 328	95.36 395	99.84 363	99.18 375	96.72 283	98.41 366	100.00 1	91.43 345	98.32 431	97.84 336	98.15 277	99.74 299

IMVS_0403		98.37 258	98.39 242	98.29 320	99.38 328	95.36 395	99.97 309	99.18 375	96.72 283	99.68 248	100.00 1	94.61 273	99.77 266	97.84 336	98.15 277	99.74 299

MIMVSNet		97.06 332	96.73 333	98.05 351	99.38 328	96.64 376	98.47 503	99.35 246	93.41 429	99.48 268	98.53 462	89.66 389	97.70 476	94.16 429	98.11 282	99.80 277

IterMVS-LS		97.56 305	97.44 302	97.92 362	99.38 328	97.90 326	99.89 354	99.10 418	94.41 400	98.32 374	99.54 387	97.21 203	98.11 452	97.50 351	91.62 411	97.75 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		94.12 416	92.73 433	98.29 320	99.33 334	95.95 385	99.38 440	99.19 365	74.54 513	98.26 380	86.34 529	86.07 432	99.06 357	91.60 453	99.87 157	99.85 219

CR-MVSNet		98.02 281	97.71 295	98.93 276	99.31 335	98.86 229	99.13 474	99.00 449	96.53 309	99.96 152	98.98 431	96.94 215	98.10 456	91.18 456	98.40 228	99.84 221

Patchmtry		96.81 341	96.37 349	98.14 337	99.31 335	98.55 255	98.91 488	99.00 449	90.45 460	97.92 397	98.98 431	96.94 215	98.12 450	94.27 426	91.53 413	97.75 359

RPMNet		95.26 403	93.82 414	99.56 176	99.31 335	98.86 229	99.13 474	99.42 153	79.82 504	99.96 152	95.13 504	95.69 244	99.98 141	77.54 513	98.40 228	99.84 221

Elysia		98.12 275	97.72 293	99.34 223	99.30 338	98.96 222	99.95 326	99.28 291	96.64 297	99.75 237	99.99 243	88.71 404	99.81 253	95.99 392	99.84 163	99.26 330

StellarMVS		98.12 275	97.72 293	99.34 223	99.30 338	98.96 222	99.95 326	99.28 291	96.64 297	99.75 237	99.99 243	88.71 404	99.81 253	95.99 392	99.84 163	99.26 330

tt0805		96.52 355	96.23 355	97.40 377	99.30 338	93.55 441	99.32 445	99.45 111	96.75 274	97.88 399	99.99 243	79.99 465	99.59 294	97.39 357	95.98 337	99.06 337

ALIKED-MNN		79.54 484	78.11 489	83.80 501	99.29 341	66.55 528	97.70 515	90.37 533	57.60 524	74.96 521	92.30 515	53.12 518	93.57 510	58.80 533	78.89 503	91.27 518

VDDNet		96.39 366	95.55 388	98.90 278	99.27 342	97.45 346	99.15 472	99.92 39	91.28 452	99.98 139	100.00 1	73.55 484	100.00 1	99.85 131	96.98 324	99.24 332

LCM-MVSNet-Re		96.52 355	97.21 317	94.44 452	99.27 342	85.80 488	99.85 362	96.61 515	95.98 346	92.75 477	98.48 464	93.97 293	97.55 478	99.58 215	98.43 225	99.98 127

baseline2		98.99 166	98.93 154	99.18 258	99.26 344	99.15 200	100.00 1	99.46 103	96.71 288	96.79 435	100.00 1	99.42 64	99.25 348	98.75 292	99.94 134	99.15 334

fmvsm_s_conf0.5_n_6		99.30 114	99.12 128	99.84 109	99.24 345	99.56 138	100.00 1	99.31 268	98.90 59	100.00 1	100.00 1	94.75 269	99.97 150	99.98 92	99.88 152	100.00 1

GA-MVS		97.72 296	97.27 313	99.06 263	99.24 345	97.93 325	100.00 1	99.24 322	95.80 355	98.99 314	99.64 364	89.77 385	99.36 340	95.12 415	97.62 316	99.89 190

APD_test1		93.07 432	94.14 411	89.85 478	99.18 347	72.49 512	99.76 387	98.90 461	92.86 443	96.35 442	99.94 295	75.56 480	99.91 208	86.73 485	97.98 288	97.15 460

jason		99.11 141	98.96 147	99.59 169	99.17 348	99.31 179	100.00 1	99.13 407	97.38 206	99.83 214	100.00 1	95.54 246	99.72 283	99.57 218	99.97 122	99.74 299
jason: jason.
test_vis1_n		96.69 349	95.81 373	99.32 237	99.14 349	97.98 319	99.97 309	98.98 453	98.45 100	100.00 1	100.00 1	66.44 499	99.99 107	99.78 149	99.57 189	100.00 1

0.3-1-1-0.015		97.60 302	97.19 318	98.83 283	99.13 350	96.55 379	100.00 1	99.40 206	94.19 408	99.83 214	99.81 327	99.18 92	99.97 150	99.70 172	83.50 483	99.98 127

tpm		98.24 270	98.22 263	98.32 319	99.13 350	95.79 390	99.53 424	99.12 413	95.20 376	99.96 152	99.36 404	97.58 186	99.28 347	97.41 355	96.67 331	99.88 203

EI-MVSNet		97.98 282	97.93 281	98.16 336	99.11 352	97.84 332	99.74 390	99.29 283	94.39 401	98.65 341	100.00 1	97.21 203	98.88 380	97.62 349	95.31 348	97.75 359

CVMVSNet		98.56 239	98.47 225	98.82 284	99.11 352	97.67 338	99.74 390	99.47 85	97.57 183	99.06 309	100.00 1	95.72 242	98.97 368	98.21 321	97.33 318	99.83 224

test0.0.03 1		98.12 275	98.03 276	98.39 313	99.11 352	98.07 312	100.00 1	99.93 35	96.70 289	96.91 431	99.95 289	99.31 76	98.19 444	91.93 450	98.44 224	98.91 338

fmvsm_s_conf0.1_n_a		98.71 210	98.36 248	99.78 133	99.09 355	99.42 167	100.00 1	99.26 312	97.42 203	100.00 1	100.00 1	89.78 384	99.96 170	99.82 140	99.85 161	99.97 137

Patchmatch-test		97.83 291	97.42 303	99.06 263	99.08 356	97.66 339	98.66 497	99.21 351	93.65 421	98.25 381	99.58 378	99.47 51	99.57 300	90.25 466	98.59 216	99.95 149

HQP-NCC					99.07 357		100.00 1		99.04 20	99.17 295

ACMP_Plane					99.07 357		100.00 1		99.04 20	99.17 295

NP-MVS					99.07 357	94.81 412					99.97 263

HQP-MVS		97.73 295	97.85 286	97.39 378	99.07 357	94.82 410	100.00 1	99.40 206	99.04 20	99.17 295	99.97 263	88.61 407	99.57 300	99.79 143	95.58 338	97.77 346

plane_prior6					99.06 361	94.80 413						88.58 409

ACMH+	96.20 13	96.49 360	96.33 352	97.00 396	99.06 361	93.80 439	99.81 369	99.31 268	97.32 214	95.89 453	99.97 263	82.62 455	99.54 312	98.34 314	94.63 371	97.65 431

0.4-1-1-0.2		97.60 302	97.18 319	98.86 281	99.05 363	96.62 377	100.00 1	99.40 206	94.24 403	99.82 223	99.81 327	99.09 100	99.97 150	99.70 172	83.50 483	99.98 127

PatchT		95.90 391	94.95 408	98.75 291	99.03 364	98.39 273	99.08 481	99.32 259	85.52 489	99.96 152	94.99 507	97.94 167	98.05 462	80.20 505	98.47 223	99.81 246

plane_prior1					99.02 365

0.4-1-1-0.1		97.56 305	97.15 322	98.79 288	99.01 366	96.44 382	100.00 1	99.40 206	94.11 411	99.81 229	99.81 327	99.09 100	99.97 150	99.65 195	83.48 485	99.98 127

QAPM		98.99 166	98.66 193	99.96 52	99.01 366	99.87 87	99.88 357	99.93 35	97.99 135	98.68 338	100.00 1	93.17 313	100.00 1	99.32 256	100.00 1	100.00 1

PAPM		99.78 19	99.76 15	99.85 104	99.01 366	99.95 38	100.00 1	99.75 57	99.37 3	99.99 129	100.00 1	99.76 12	99.60 292	100.00 1	100.00 1	100.00 1

ACMM	97.17 6	97.37 316	97.40 305	97.29 385	99.01 366	94.64 421	100.00 1	99.25 316	98.07 131	98.44 365	99.98 251	87.38 420	99.55 309	99.25 260	95.19 356	97.69 419
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 298	97.74 290	97.36 380	99.01 366	94.76 418	100.00 1	99.34 253	99.30 4	99.00 313	99.97 263	87.49 418	99.57 300	99.96 106	95.58 338	97.75 359
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		99.22 130	98.96 147	99.98 28	99.00 371	99.95 38	99.24 454	99.94 27	98.14 124	98.88 323	100.00 1	95.63 245	100.00 1	99.85 131	100.00 1	100.00 1

HQP_MVS		97.71 297	97.82 288	97.37 379	99.00 371	94.80 413	100.00 1	99.40 206	99.00 32	99.08 307	99.97 263	88.58 409	99.55 309	99.79 143	95.57 342	97.76 348

plane_prior7					99.00 371	94.78 417

3Dnovator+	95.58 15	99.03 153	98.71 185	99.96 52	98.99 374	99.89 78	100.00 1	99.51 82	98.96 39	98.32 374	100.00 1	92.78 324	100.00 1	99.87 127	100.00 1	100.00 1

SP-NN		83.33 475	82.73 477	85.13 493	98.98 375	65.96 530	97.92 511	95.13 521	56.43 526	83.71 509	90.52 517	58.27 506	91.69 517	71.99 520	91.66 410	97.74 386

testgi		96.18 377	95.93 368	96.93 401	98.98 375	94.20 437	100.00 1	99.07 429	97.16 227	96.06 450	99.86 312	84.08 448	97.79 473	90.38 465	97.80 304	98.81 339

ACMP	97.00 8	97.19 324	97.16 321	97.27 388	98.97 377	94.58 426	100.00 1	99.32 259	97.97 139	97.45 418	99.98 251	85.79 436	99.56 304	99.70 172	95.24 353	97.67 425
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ITE_SJBPF				96.84 406	98.96 378	93.49 442		98.12 483	98.12 128	98.35 371	99.97 263	84.45 442	99.56 304	95.63 404	95.25 352	97.49 447

ACMH	96.25 11	96.77 343	96.62 337	97.21 389	98.96 378	94.43 430	99.64 408	99.33 256	97.43 202	96.55 440	99.97 263	83.52 450	99.54 312	99.07 275	95.13 360	97.66 426
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		95.17 405	95.23 401	95.01 444	98.95 380	88.99 481	99.99 266	97.77 497	97.79 155	98.58 347	99.70 347	73.36 485	99.34 343	95.88 394	95.03 363	96.70 473

test_fmvsmconf0.1_n		99.25 126	99.05 134	99.82 112	98.92 381	99.55 140	100.00 1	99.23 327	98.91 55	99.75 237	99.97 263	94.79 266	99.94 196	99.94 114	99.99 107	99.97 137

UniMVSNet_ETH3D		95.28 402	94.41 409	97.89 363	98.91 382	95.14 402	99.13 474	99.35 246	92.11 447	97.17 426	99.66 357	70.28 493	99.36 340	97.88 334	95.18 357	99.16 333

DeepMVS_CX				89.98 477	98.90 383	71.46 514		99.18 375	97.61 177	96.92 429	99.83 320	86.07 432	99.83 246	96.02 391	97.65 314	98.65 342

OpenMVS	95.20 17	98.76 202	98.41 233	99.78 133	98.89 384	99.81 100	99.99 266	99.76 54	98.02 133	98.02 392	100.00 1	91.44 344	100.00 1	99.63 203	99.97 122	99.55 320

test_fmvsmvis_n_1920		99.46 85	99.37 86	99.73 143	98.88 385	99.18 197	100.00 1	99.26 312	98.85 66	99.79 231	100.00 1	97.70 182	100.00 1	99.98 92	99.86 158	100.00 1

1314		99.38 96	99.19 118	99.96 52	98.88 385	99.89 78	99.24 454	99.93 35	98.88 61	98.79 333	100.00 1	97.02 208	100.00 1	100.00 1	100.00 1	100.00 1

JIA-IIPM		97.09 329	96.34 351	99.36 219	98.88 385	98.59 253	99.81 369	99.43 134	84.81 492	99.96 152	90.34 521	98.55 150	99.52 318	97.00 367	98.28 258	99.98 127

LuminaMVS		99.07 146	98.92 156	99.50 183	98.87 388	99.12 202	99.92 342	99.22 332	97.45 199	99.82 223	99.98 251	96.29 233	99.85 240	99.71 168	99.05 203	99.52 323

FMVSNet1		94.45 410	93.63 417	96.89 403	98.87 388	94.87 407	99.18 464	99.27 306	90.95 456	97.31 421	98.81 445	72.89 487	98.07 458	92.61 444	92.81 389	97.72 405

3Dnovator	95.63 14	99.06 147	98.76 174	99.96 52	98.86 390	99.90 71	99.98 299	99.93 35	98.95 42	98.49 361	100.00 1	92.91 322	100.00 1	99.71 168	100.00 1	100.00 1

LPG-MVS_test		97.31 320	97.32 309	97.28 386	98.85 391	94.60 423	100.00 1	99.37 229	97.35 208	98.85 326	99.98 251	86.66 426	99.56 304	99.55 221	95.26 350	97.70 414

LGP-MVS_train				97.28 386	98.85 391	94.60 423		99.37 229	97.35 208	98.85 326	99.98 251	86.66 426	99.56 304	99.55 221	95.26 350	97.70 414

MVStest1		94.27 412	93.30 421	97.19 390	98.83 393	97.18 360	99.93 340	98.79 468	86.80 485	84.88 506	99.04 424	94.32 283	98.25 439	90.55 462	86.57 468	96.12 488

D2MVS		97.63 301	97.83 287	97.05 393	98.83 393	94.60 423	100.00 1	99.82 45	96.89 257	98.28 377	99.03 427	94.05 287	99.47 327	98.58 305	94.97 366	97.09 461

mmtdpeth		94.58 408	94.18 410	95.81 435	98.82 395	91.09 466	99.99 266	98.61 474	96.38 326	100.00 1	97.23 486	76.52 477	99.85 240	99.82 140	80.22 498	96.48 478

dmvs_testset		93.27 429	95.48 393	86.65 488	98.74 396	68.42 522	99.92 342	98.91 459	96.19 342	93.28 474	100.00 1	91.06 353	91.67 518	89.64 472	91.54 412	99.86 218

TinyColmap		95.50 398	95.12 404	96.64 417	98.69 397	93.00 448	99.40 438	97.75 498	96.40 325	96.14 447	99.87 310	79.47 466	99.50 323	93.62 435	94.72 370	97.40 453

XXY-MVS		97.14 328	96.63 336	98.67 293	98.65 398	98.92 225	99.54 423	99.29 283	95.57 362	97.63 409	99.83 320	87.79 416	99.35 342	98.39 311	92.95 387	97.75 359

VPA-MVSNet		97.03 334	96.43 346	98.82 284	98.64 399	99.32 177	99.38 440	99.47 85	96.73 281	98.91 322	98.94 436	87.00 424	99.40 338	99.23 263	89.59 435	97.76 348

VPNet		96.41 362	95.76 378	98.33 318	98.61 400	98.30 290	99.48 428	99.45 111	96.98 245	98.87 325	99.88 309	81.57 459	98.93 372	99.22 265	87.82 454	97.76 348

USDC		95.90 391	95.70 381	96.50 421	98.60 401	92.56 455	100.00 1	98.30 478	97.77 157	96.92 429	99.94 295	81.25 462	99.45 333	93.54 436	94.96 367	97.49 447

ArgMatch-SfM		93.74 421	93.14 423	95.54 439	98.57 402	90.54 470	99.97 309	98.86 464	97.35 208	97.60 413	99.66 357	71.88 490	99.02 360	90.18 467	84.16 477	97.07 463

tfpnnormal		96.36 367	95.69 384	98.37 315	98.55 403	98.71 242	99.69 403	99.45 111	93.16 437	96.69 439	99.71 344	88.44 411	98.99 365	94.17 427	91.38 417	97.41 452

LTVRE_ROB	95.29 16	96.32 370	96.10 360	96.99 397	98.55 403	93.88 438	99.45 432	99.28 291	94.50 397	96.46 441	99.52 388	84.86 441	99.48 325	97.26 361	95.03 363	97.59 441
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		97.95 285	97.73 292	98.62 296	98.53 405	99.24 189	100.00 1	99.43 134	96.74 277	97.87 400	99.82 324	95.27 250	98.89 377	98.78 289	93.07 385	97.74 386

UniMVSNet (Re)		97.29 322	96.85 329	98.59 299	98.49 406	99.13 201	100.00 1	99.42 153	96.52 313	98.24 383	98.90 439	94.93 262	98.89 377	97.54 350	87.61 455	97.75 359

dtuonly		97.85 289	97.46 301	99.02 269	98.44 407	97.89 328	99.99 266	97.62 501	96.53 309	99.49 267	99.96 281	94.01 292	99.58 298	92.75 443	98.32 251	99.59 318

WR-MVS		97.09 329	96.64 335	98.46 306	98.43 408	99.09 203	99.97 309	99.33 256	95.62 360	97.76 403	99.67 355	91.17 349	98.56 415	98.49 307	89.28 441	97.74 386

XVG-ACMP-BASELINE		96.60 353	96.52 342	96.84 406	98.41 409	93.29 446	99.99 266	99.32 259	97.76 159	98.51 359	99.29 407	81.95 458	99.54 312	98.40 310	95.03 363	97.68 421

UniMVSNet_NR-MVSNet		97.16 326	96.80 330	98.22 329	98.38 410	98.41 269	100.00 1	99.45 111	96.14 343	97.76 403	99.64 364	95.05 259	98.50 418	97.98 329	86.84 464	97.75 359

test_method		91.04 450	91.10 445	90.85 474	98.34 411	77.63 504	100.00 1	98.93 457	76.69 507	96.25 445	98.52 463	70.44 492	97.98 465	89.02 479	91.74 407	96.92 467

nrg030		97.64 298	97.27 313	98.75 291	98.34 411	99.53 145	100.00 1	99.22 332	96.21 341	98.27 379	99.95 289	94.40 279	98.98 366	99.23 263	89.78 434	97.75 359

TransMVSNet (Re)		94.78 407	93.72 415	97.93 361	98.34 411	97.88 329	99.23 461	97.98 491	91.60 450	94.55 464	99.71 344	87.89 414	98.36 429	89.30 476	84.92 473	97.56 443

ALIKED-LG		80.86 481	79.70 485	84.33 497	98.33 414	69.33 518	97.59 516	90.14 534	65.38 517	76.03 519	94.87 509	54.78 516	93.65 508	57.59 534	82.61 488	90.01 521

DU-MVS		96.93 339	96.49 343	98.22 329	98.31 415	98.41 269	100.00 1	99.37 229	96.41 324	97.76 403	99.65 360	92.14 338	98.50 418	97.98 329	86.84 464	97.75 359

NR-MVSNet		96.63 351	96.04 363	98.38 314	98.31 415	98.98 219	99.22 463	99.35 246	95.87 349	94.43 467	99.65 360	92.73 327	98.40 426	96.78 377	88.05 451	97.75 359

tt0320		92.36 438	91.28 442	95.58 438	98.30 417	90.65 469	98.69 496	99.14 400	76.73 506	96.07 449	99.50 391	72.28 489	98.39 427	93.29 439	87.56 456	97.70 414

FC-MVSNet-test		97.84 290	97.63 298	98.45 307	98.30 417	99.05 208	100.00 1	99.43 134	96.63 301	97.61 412	99.82 324	95.19 255	98.57 413	98.64 298	93.05 386	97.73 398

SSC-MVS3.2		95.32 400	94.97 407	96.37 425	98.29 419	92.75 451	100.00 1	99.30 274	95.46 370	98.36 369	99.42 399	78.92 469	98.63 401	93.28 440	91.72 409	97.72 405

Baseline_NR-MVSNet		96.16 381	95.70 381	97.56 375	98.28 420	96.79 372	100.00 1	97.86 495	91.93 449	97.63 409	99.47 394	92.14 338	98.35 430	97.13 363	86.83 466	97.54 444

dtuonlycased		95.07 406	95.43 396	93.98 460	98.26 421	85.63 489	99.98 299	98.92 458	94.83 383	94.13 470	99.47 394	82.60 456	97.61 477	94.66 420	96.01 336	98.70 340

SP-MNN		81.80 479	81.08 483	83.94 499	98.26 421	64.81 533	98.20 507	93.56 526	55.15 527	77.43 517	90.43 519	56.33 513	90.69 523	70.11 524	90.27 431	96.32 483

WR-MVS_H		96.73 345	96.32 353	97.95 358	98.26 421	97.88 329	99.72 398	99.43 134	95.06 378	96.99 428	98.68 451	93.02 321	98.53 416	97.43 354	88.33 450	97.43 451

LF4IMVS		96.19 376	96.18 357	96.23 429	98.26 421	92.09 458	100.00 1	97.89 494	97.82 152	97.94 395	99.87 310	82.71 454	99.38 339	97.41 355	93.71 378	97.20 458

pm-mvs1		95.76 393	95.01 405	98.00 355	98.23 425	97.45 346	99.24 454	99.04 442	93.13 438	95.93 452	99.72 342	86.28 430	98.84 382	95.62 405	87.92 452	97.72 405

MonoMVSNet		98.55 240	98.64 196	98.26 325	98.21 426	95.76 391	99.94 334	99.16 388	96.23 337	99.47 271	99.24 411	96.75 222	99.22 349	99.61 208	99.17 196	99.81 246

CP-MVSNet		96.73 345	96.25 354	98.18 333	98.21 426	98.67 246	99.77 385	99.32 259	95.06 378	97.20 425	99.65 360	90.10 379	98.19 444	98.06 327	88.90 445	97.66 426

v10		96.14 383	95.50 389	98.07 343	98.19 428	97.96 322	99.83 365	99.07 429	92.10 448	98.07 387	98.94 436	91.07 351	98.61 404	92.41 449	89.82 433	97.63 435

PS-CasMVS		96.34 369	95.78 377	98.03 354	98.18 429	98.27 293	99.71 399	99.32 259	94.75 385	96.82 434	99.65 360	86.98 425	98.15 446	97.74 342	88.85 446	97.66 426

V42		96.65 350	96.16 359	98.11 342	98.17 430	98.23 296	99.99 266	99.09 423	93.97 413	98.74 335	99.05 423	91.09 350	98.82 383	95.46 409	89.90 432	97.27 457

ArgMatch-Sym		94.50 409	94.12 412	95.63 437	98.16 431	90.84 468	100.00 1	99.00 449	97.42 203	97.22 424	99.76 340	73.91 483	99.05 358	91.22 455	90.43 429	97.01 464

v8		96.35 368	95.73 380	98.21 331	98.11 432	98.23 296	99.94 334	99.07 429	92.66 445	98.29 376	99.00 430	91.46 343	98.77 388	94.17 427	88.83 447	97.62 437

OPM-MVS		97.21 323	97.18 319	97.32 383	98.08 433	94.66 419	100.00 1	99.28 291	98.65 90	98.92 320	99.98 251	86.03 434	99.56 304	98.28 319	95.41 344	97.72 405
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tt0320-xc		91.69 445	90.50 449	95.26 441	98.04 434	90.12 475	98.60 500	98.70 471	76.63 508	94.66 463	99.52 388	68.57 496	97.99 464	94.61 421	85.18 472	97.66 426

v2v482		96.70 348	96.18 357	98.27 322	98.04 434	98.39 273	100.00 1	99.13 407	94.19 408	98.58 347	99.08 420	90.48 366	98.67 394	95.69 400	90.44 428	97.75 359

SP-SuperGlue		82.71 477	81.92 479	85.07 494	98.02 436	67.96 525	98.10 508	95.26 520	57.79 523	82.47 511	90.37 520	57.02 510	91.04 520	70.34 523	87.92 452	96.23 484

v1192		96.18 377	95.49 391	98.26 325	98.01 437	98.15 305	99.99 266	99.08 424	93.36 431	98.54 350	98.97 434	89.47 393	98.89 377	91.15 457	90.82 422	97.75 359

TranMVSNet+NR-MVSNet		96.45 361	96.01 364	97.79 368	98.00 438	97.62 341	100.00 1	99.35 246	95.98 346	97.31 421	99.64 364	90.09 380	98.00 463	96.89 372	86.80 467	97.75 359

v1144		96.51 357	95.97 367	98.13 340	97.98 439	98.04 316	99.99 266	99.08 424	93.51 426	98.62 344	98.98 431	90.98 356	98.62 403	93.79 433	90.79 423	97.74 386

test_0402		94.35 411	93.70 416	96.32 427	97.92 440	93.60 440	99.61 414	98.85 465	88.19 474	94.68 462	99.48 393	80.01 464	98.58 412	89.39 475	95.15 359	96.77 469

v1240		95.96 389	95.25 400	98.07 343	97.91 441	97.87 331	99.96 317	99.07 429	93.24 435	98.64 343	98.96 435	88.98 400	98.61 404	89.58 474	90.92 421	97.75 359

v144192		96.40 365	95.81 373	98.17 335	97.89 442	98.11 308	99.99 266	99.06 437	93.39 430	98.75 334	99.09 419	90.43 371	98.66 395	93.10 441	90.55 426	97.75 359

v1921920		96.16 381	95.50 389	98.14 337	97.88 443	97.96 322	99.99 266	99.07 429	93.33 432	98.60 345	99.24 411	89.37 394	98.71 392	91.28 454	90.74 424	97.75 359

usedtu_dtu_shiyan1		97.34 318	96.97 323	98.43 309	97.82 444	98.91 226	100.00 1	99.29 283	94.70 388	98.46 363	98.89 440	93.95 294	98.64 399	95.86 396	93.75 376	97.74 386

FE-MVSNET3		97.34 318	96.97 323	98.43 309	97.82 444	98.91 226	100.00 1	99.29 283	94.70 388	98.46 363	98.89 440	93.95 294	98.64 399	95.88 394	93.75 376	97.74 386

sc_t1		92.52 436	91.34 441	96.09 431	97.80 446	89.86 476	98.61 499	99.12 413	77.73 505	96.09 448	99.79 336	68.64 495	98.94 371	96.94 368	87.31 459	99.46 327

ttmdpeth		96.24 374	95.88 370	97.32 383	97.80 446	96.61 378	99.95 326	98.77 469	97.80 154	93.42 473	99.28 408	86.42 429	99.01 362	97.63 346	91.84 406	96.33 482

MS-PatchMatch		95.66 396	95.87 371	95.05 443	97.80 446	89.25 479	98.88 489	99.30 274	96.35 330	96.86 432	99.01 429	81.35 461	99.43 335	93.30 438	99.98 118	96.46 479

v148		96.29 371	95.84 372	97.63 370	97.74 449	96.53 380	100.00 1	99.07 429	93.52 425	98.01 393	99.42 399	91.22 346	98.60 407	96.37 386	87.22 462	97.75 359

PEN-MVS		96.01 388	95.48 393	97.58 374	97.74 449	97.26 357	99.90 350	99.29 283	94.55 394	96.79 435	99.55 384	87.38 420	97.84 469	96.92 371	87.24 461	97.65 431

SP-LightGlue		82.73 476	81.92 479	85.19 492	97.73 451	68.40 523	98.05 509	94.51 523	56.95 525	82.72 510	90.14 523	58.20 507	90.97 521	71.57 521	87.38 458	96.20 485

DenseAffine		90.43 453	89.28 460	93.87 462	97.71 452	86.21 487	99.13 474	98.10 485	87.86 475	90.15 487	98.43 469	60.76 503	98.65 397	84.48 492	86.90 463	96.74 470

pmmvs4		97.17 325	96.80 330	98.27 322	97.68 453	98.64 250	100.00 1	99.18 375	94.22 405	98.55 349	99.71 344	93.67 299	98.47 421	95.66 403	92.57 394	97.71 413

PS-MVSNAJss		98.03 280	98.06 274	97.94 359	97.63 454	97.33 354	99.89 354	99.23 327	96.27 335	98.03 390	99.59 376	98.75 140	98.78 385	98.52 306	94.61 372	97.70 414

UnsupCasMVSNet_eth		94.25 413	93.89 413	95.34 440	97.63 454	92.13 457	99.73 395	99.36 235	94.88 381	92.78 475	98.63 453	82.72 453	96.53 487	94.57 422	84.73 474	97.36 454

our_test_3		96.51 357	96.35 350	96.98 398	97.61 456	95.05 403	99.98 299	99.01 448	94.68 390	96.77 437	99.06 421	95.87 238	98.14 448	91.81 451	92.37 397	97.75 359

ppachtmachnet_test		96.17 379	95.89 369	97.02 395	97.61 456	95.24 400	99.99 266	99.24 322	93.31 433	96.71 438	99.62 372	94.34 282	98.07 458	89.87 469	92.30 399	97.75 359

EU-MVSNet		96.63 351	96.53 340	96.94 400	97.59 458	96.87 369	99.76 387	99.47 85	96.35 330	96.85 433	99.78 337	92.57 332	96.27 491	95.33 410	91.08 420	97.68 421

v7n		96.06 387	95.42 398	97.99 357	97.58 459	97.35 351	99.86 360	99.11 415	92.81 444	97.91 398	99.49 392	90.99 355	98.92 373	92.51 446	88.49 449	97.70 414

lessismore_v0				96.05 432	97.55 460	91.80 460		99.22 332		91.87 479	99.91 304	83.50 451	98.68 393	92.48 447	90.42 430	97.68 421

K. test v3		95.46 399	95.14 403	96.40 422	97.53 461	93.40 444	99.99 266	99.23 327	95.49 368	92.70 478	99.73 341	84.26 444	98.12 450	93.94 432	93.38 383	97.68 421

test_djsdf		97.55 307	97.38 306	98.07 343	97.50 462	97.99 318	100.00 1	99.13 407	95.46 370	98.47 362	99.85 317	92.01 341	98.59 409	98.63 300	95.36 346	97.62 437

Gipuma		84.73 471	83.50 475	88.40 484	97.50 462	82.21 499	88.87 535	99.05 439	65.81 516	85.71 502	90.49 518	53.70 517	96.31 489	78.64 508	91.74 407	86.67 523
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		95.52 397	94.99 406	97.08 392	97.49 464	96.45 381	100.00 1	99.25 316	93.82 416	96.17 446	99.57 382	87.81 415	97.18 479	94.57 422	86.26 470	97.62 437

OurMVSNet-221017-0		96.14 383	95.98 366	96.62 418	97.49 464	93.44 443	99.92 342	98.16 481	95.86 351	97.65 408	99.95 289	85.71 437	98.78 385	94.93 417	94.18 375	97.64 434

RoMa-SfM		90.39 454	89.63 456	92.66 469	97.47 466	83.18 496	98.81 491	98.21 480	85.44 491	89.21 489	99.46 396	63.72 500	98.30 435	87.11 483	87.25 460	96.51 477

pmmvs6		93.64 425	92.87 428	95.94 434	97.47 466	91.41 463	98.92 487	99.02 446	87.84 476	95.01 459	99.61 374	77.24 475	98.77 388	94.33 425	86.41 469	97.63 435

pmmvs5		95.94 390	95.61 386	96.95 399	97.42 468	94.66 419	100.00 1	98.08 486	93.60 423	97.05 427	99.43 398	87.02 423	98.46 422	95.76 397	92.12 400	97.72 405

SixPastTwentyTwo		95.71 395	95.49 391	96.38 424	97.42 468	93.01 447	99.84 363	98.23 479	94.75 385	95.98 451	99.97 263	85.35 439	98.43 424	94.71 419	93.17 384	97.69 419

mvs_tets		97.00 337	96.69 334	97.94 359	97.41 470	97.27 356	99.60 415	99.18 375	96.51 314	97.35 420	99.69 350	86.53 428	98.91 374	98.84 286	95.09 362	97.65 431

DKM		88.67 459	87.74 464	91.44 472	97.38 471	82.60 497	98.95 486	97.94 493	87.54 477	87.00 498	98.48 464	55.08 514	95.81 496	86.05 487	81.29 497	95.91 492

jajsoiax		97.07 331	96.79 332	97.89 363	97.28 472	97.12 362	99.95 326	99.19 365	96.55 307	97.31 421	99.69 350	87.35 422	98.91 374	98.70 294	95.12 361	97.66 426

N_pmnet		91.88 443	93.37 420	87.40 486	97.24 473	66.33 529	99.90 350	91.05 529	89.77 466	95.65 454	98.58 456	90.05 382	98.11 452	85.39 488	92.72 390	97.75 359

anonymousdsp		97.16 326	96.88 327	98.00 355	97.08 474	98.06 314	99.81 369	99.15 393	94.58 393	97.84 402	99.62 372	90.49 365	98.60 407	97.98 329	95.32 347	97.33 456

test_fmvsmconf0.01_n		98.60 231	98.24 259	99.67 154	96.90 475	99.21 193	99.99 266	99.04 442	98.80 77	99.57 262	99.96 281	90.12 378	99.91 208	99.89 122	99.89 149	99.90 182

MDA-MVSNet-bldmvs		91.65 446	89.94 455	96.79 415	96.72 476	96.70 374	99.42 437	98.94 455	88.89 468	66.97 534	98.37 471	81.43 460	95.91 494	89.24 477	89.46 439	97.75 359

DKM-HiRes		87.00 466	86.38 469	88.84 482	96.71 477	79.05 501	98.73 495	97.57 504	84.56 493	84.00 508	98.23 474	52.90 519	92.48 514	84.95 490	79.77 499	95.00 501

MDA-MVSNet_test_wron		92.61 435	91.09 446	97.19 390	96.71 477	97.26 357	100.00 1	99.14 400	88.61 469	67.90 532	98.32 473	89.03 398	96.57 486	90.47 464	89.59 435	97.74 386

MASt3R-SfM		91.92 441	92.47 438	90.28 476	96.64 479	75.61 508	99.63 410	98.31 477	95.70 357	95.42 455	98.84 443	67.34 497	99.22 349	89.92 468	90.47 427	96.01 490

new_pmnet		94.11 417	93.47 419	96.04 433	96.60 480	92.82 450	99.97 309	98.91 459	90.21 463	95.26 456	98.05 480	85.89 435	98.14 448	84.28 493	92.01 402	97.16 459

YYNet1		92.44 437	90.92 447	97.03 394	96.20 481	97.06 365	99.99 266	99.14 400	88.21 473	67.93 531	98.43 469	88.63 406	96.28 490	90.64 459	89.08 443	97.74 386

LoFTR		88.61 460	87.13 466	93.06 465	96.18 482	83.87 493	99.48 428	97.21 506	86.37 488	82.32 512	96.66 491	58.07 509	98.59 409	81.76 500	86.15 471	96.72 471

CMPMVS	66.12 22	90.65 451	92.04 439	86.46 489	96.18 482	66.87 527	98.03 510	99.38 225	83.38 496	85.49 503	99.55 384	77.59 472	98.80 384	94.44 424	94.31 374	93.72 510
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MatchFormer		86.71 468	84.75 474	92.57 470	96.14 484	82.52 498	99.27 450	97.86 495	80.17 502	78.74 515	96.16 495	54.81 515	98.63 401	75.87 517	83.75 482	96.56 476

DSMNet-mixed		95.18 404	95.21 402	95.08 442	96.03 485	90.21 474	99.65 407	93.64 525	92.91 440	98.34 372	97.40 485	90.05 382	95.51 499	91.02 458	97.86 297	99.51 325

SIFT-NN		67.52 498	68.28 500	65.25 519	96.00 486	45.92 544	93.38 527	80.01 542	43.05 536	69.06 529	85.13 531	39.13 529	85.13 530	32.15 538	76.58 506	64.70 535

EGC-MVSNET		79.46 485	74.04 495	95.72 436	96.00 486	92.73 452	99.09 480	99.04 442	5.08 551	16.72 552	98.71 449	73.03 486	98.74 391	82.05 499	96.64 332	95.69 495

EG-PatchMatch MVS		92.94 433	92.49 437	94.29 456	95.87 488	87.07 486	99.07 483	98.11 484	93.19 436	88.98 490	98.66 452	70.89 491	99.08 356	92.43 448	95.21 355	96.72 471

RoMa-HiRes		87.37 464	86.72 468	89.32 480	95.81 489	78.25 503	98.63 498	97.01 508	82.18 498	86.32 500	99.25 410	56.48 512	94.79 501	83.17 495	81.62 493	94.91 503

testf1		84.40 472	84.79 472	83.23 502	95.71 490	58.71 538	98.79 492	97.75 498	81.58 499	84.94 504	98.07 478	45.33 526	97.73 474	77.09 515	83.85 479	93.24 513

APD_test2		84.40 472	84.79 472	83.23 502	95.71 490	58.71 538	98.79 492	97.75 498	81.58 499	84.94 504	98.07 478	45.33 526	97.73 474	77.09 515	83.85 479	93.24 513

MVP-Stereo		96.51 357	96.48 344	96.60 419	95.65 492	94.25 435	98.84 490	98.16 481	95.85 353	95.23 457	99.04 424	92.54 333	99.13 354	92.98 442	99.98 118	96.43 480
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		93.29 428	92.76 430	94.90 449	95.64 493	91.27 464	99.97 309	98.82 466	87.04 482	94.71 461	98.19 475	83.86 449	96.80 482	84.04 494	92.56 395	96.64 474

OpenMVS_ROB	88.34 20	91.89 442	91.12 444	94.19 458	95.55 494	87.63 484	99.26 452	98.03 488	86.61 487	90.65 486	96.82 489	70.14 494	98.78 385	86.54 486	96.50 335	96.15 486

blend_shiyan4		95.76 393	95.40 399	96.82 412	95.50 495	94.40 431	100.00 1	99.22 332	87.12 480	98.67 339	98.59 454	99.09 100	98.31 432	96.31 387	84.14 478	97.75 359

UnsupCasMVSNet_bld		89.50 456	88.00 463	93.99 459	95.30 496	88.86 482	98.52 502	99.28 291	85.50 490	87.80 496	94.11 510	61.63 501	96.96 481	90.63 460	79.26 500	96.15 486

wanda-best-256-512		93.76 419	92.74 431	96.84 406	95.22 497	94.54 427	100.00 1	99.22 332	87.22 478	98.54 350	98.56 457	90.48 366	98.22 441	95.67 401	69.73 518	97.75 359

FE-blended-shiyan7		93.76 419	92.74 431	96.84 406	95.22 497	94.54 427	100.00 1	99.22 332	87.22 478	98.54 350	98.56 457	90.48 366	98.22 441	95.67 401	69.73 518	97.75 359

usedtu_blend_shiyan5		92.75 434	91.39 440	96.82 412	95.22 497	94.40 431	99.05 485	98.64 473	75.98 512	98.54 350	98.56 457	90.48 366	98.31 432	96.31 387	69.73 518	97.75 359

test20.03		93.11 430	92.85 429	93.88 461	95.19 500	91.83 459	100.00 1	98.87 462	93.68 420	92.76 476	98.88 442	89.20 397	92.71 513	77.88 512	89.19 442	97.09 461

blended_shiyan8		93.73 422	92.69 434	96.84 406	95.17 501	94.40 431	100.00 1	99.20 361	87.05 481	98.60 345	98.54 461	90.15 374	98.39 427	95.54 408	69.93 517	97.74 386

blended_shiyan6		93.70 424	92.67 436	96.78 416	95.17 501	94.38 434	100.00 1	99.22 332	87.03 483	98.54 350	98.56 457	90.14 375	98.22 441	95.62 405	69.73 518	97.75 359

Anonymous20231206		93.45 427	93.17 422	94.30 455	95.00 503	89.69 478	99.98 299	98.43 476	93.30 434	94.50 466	98.59 454	90.52 364	95.73 497	77.46 514	90.73 425	97.48 450

MIMVSNet1		91.96 439	91.20 443	94.23 457	94.94 504	91.69 461	99.34 444	99.22 332	88.23 471	94.18 468	98.45 466	75.52 481	93.41 511	79.37 506	91.49 414	97.60 440

gbinet_0.2-2-1-0.02		93.73 422	92.69 434	96.84 406	94.91 505	94.62 422	100.00 1	99.28 291	87.02 484	98.53 355	98.45 466	89.72 387	98.15 446	96.65 381	69.64 522	97.74 386

KD-MVS_self_test		91.16 447	90.09 452	94.35 454	94.44 506	91.27 464	99.74 390	99.08 424	90.82 457	94.53 465	94.91 508	86.11 431	94.78 502	82.67 497	68.52 523	96.99 465

mvs5depth		93.81 418	93.00 426	96.23 429	94.25 507	93.33 445	97.43 518	98.07 487	93.47 427	94.15 469	99.58 378	77.52 473	98.97 368	93.64 434	88.92 444	96.39 481

SP-DiffGlue		85.17 470	85.16 471	85.22 491	93.54 508	69.16 519	97.83 513	95.33 519	60.61 521	86.04 501	92.86 514	61.04 502	90.90 522	89.62 473	89.57 437	95.59 498

CL-MVSNet_self_test		91.07 449	90.35 451	93.24 464	93.27 509	89.16 480	99.55 421	99.25 316	92.34 446	95.23 457	97.05 488	88.86 403	93.59 509	80.67 503	66.95 528	96.96 466

XFeat-NN		75.54 493	76.00 493	74.19 513	93.25 510	52.63 542	95.93 524	81.98 541	46.32 534	75.32 520	90.27 522	56.80 511	85.05 531	71.26 522	72.85 511	84.87 526

test_fmvs3		87.19 465	87.02 467	87.71 485	92.69 511	76.64 505	99.96 317	97.27 505	93.55 424	90.82 484	94.03 511	38.00 532	92.19 515	93.49 437	83.35 487	94.32 507

PMatch-SfM		81.57 480	79.80 484	86.88 487	92.36 512	73.86 510	97.50 517	92.66 528	80.39 501	73.10 524	96.35 493	33.54 538	91.86 516	81.28 501	71.01 515	94.92 502

ELoFTR		83.63 474	81.67 481	89.53 479	92.30 513	75.98 507	98.27 504	96.74 512	83.38 496	74.05 522	95.78 497	43.66 528	98.11 452	78.01 510	72.80 512	94.48 506

SIFT-MNN		64.77 502	65.11 502	63.77 520	92.18 514	44.02 546	91.93 529	78.84 543	41.80 538	61.69 536	84.03 534	33.92 537	81.69 535	29.20 543	72.39 513	65.59 534

mvsany_test3		89.36 458	88.96 461	90.56 475	91.95 515	78.97 502	99.74 390	96.59 516	96.84 261	89.25 488	96.07 496	52.59 520	97.11 480	95.17 414	82.44 489	95.58 499

Patchmatch-RL test		93.49 426	93.63 417	93.05 466	91.78 516	83.41 494	98.21 506	96.95 510	91.58 451	91.05 481	97.64 484	99.40 68	95.83 495	94.11 430	81.95 491	99.91 171

PM-MVS		88.39 461	87.41 465	91.31 473	91.73 517	82.02 500	99.79 375	96.62 514	91.06 455	90.71 485	95.73 498	48.60 523	95.96 493	90.56 461	81.91 492	95.97 491

pmmvs-eth3d		91.73 444	90.67 448	94.92 448	91.63 518	92.71 453	99.90 350	98.54 475	91.19 453	88.08 494	95.50 499	79.31 468	96.13 492	90.55 462	81.32 496	95.91 492

WB-MVS		88.24 462	90.09 452	82.68 504	91.56 519	69.51 517	100.00 1	98.73 470	90.72 459	87.29 497	98.12 476	92.87 323	85.01 532	62.19 529	89.34 440	93.54 511

test_f		86.87 467	86.06 470	89.28 481	91.45 520	76.37 506	99.87 359	97.11 507	91.10 454	88.46 492	93.05 513	38.31 531	96.66 485	91.77 452	83.46 486	94.82 504

new-patchmatchnet		90.30 455	89.46 458	92.84 468	90.77 521	88.55 483	99.83 365	98.80 467	90.07 465	87.86 495	95.00 506	78.77 470	94.30 504	84.86 491	79.15 501	95.68 496

FE-MVSNET2		91.15 448	90.00 454	94.58 451	90.74 522	92.52 456	99.56 419	98.87 462	90.82 457	88.96 491	95.40 502	76.26 479	95.56 498	87.84 481	81.59 494	95.66 497

SSC-MVS		87.61 463	89.47 457	82.04 505	90.63 523	68.77 521	99.99 266	98.66 472	90.34 462	86.70 499	98.08 477	92.72 328	84.12 533	59.41 532	88.71 448	93.22 515

PMatch-Up-SfM		79.27 487	77.62 490	84.22 498	90.58 524	69.08 520	96.98 520	90.47 531	76.44 509	71.47 527	96.27 494	30.15 543	88.77 525	78.74 507	67.46 524	94.81 505

pmmvs3		90.62 452	89.36 459	94.40 453	90.53 525	91.49 462	100.00 1	96.73 513	84.21 494	93.65 472	96.65 492	82.56 457	94.83 500	82.28 498	77.62 505	96.89 468

SIFT-NCM-Cal		59.75 505	59.15 508	61.53 523	90.12 526	43.18 549	91.26 530	70.04 548	40.34 542	38.39 547	81.51 543	27.19 544	79.90 536	26.25 548	67.30 526	61.50 539

XFeat-MNN		73.39 494	73.10 497	74.25 512	89.63 527	53.35 541	96.25 523	84.01 538	43.66 535	69.74 528	89.91 524	52.56 521	85.32 529	64.72 527	67.44 525	84.08 528

PDCNetPlus		75.87 491	73.92 496	81.72 506	89.55 528	74.48 509	98.59 501	62.34 550	72.19 514	76.04 518	95.03 505	47.66 524	86.31 528	77.97 511	45.88 535	84.35 527

FE-MVSNET		89.50 456	88.33 462	93.00 467	88.89 529	90.24 473	99.96 317	96.86 511	88.23 471	88.46 492	95.47 500	77.03 476	93.37 512	78.54 509	81.56 495	95.39 500

SIFT-NN-NCMNet		64.49 503	64.92 504	63.20 521	88.84 530	44.41 545	92.37 528	78.67 544	41.90 537	62.62 535	83.27 536	34.31 535	81.88 534	30.88 539	71.40 514	63.31 537

SIFT-ConvMatch		56.83 508	55.72 511	60.16 525	88.80 531	43.02 550	88.55 536	64.15 549	40.75 541	45.84 542	83.12 538	27.00 545	77.01 542	28.36 544	34.89 541	60.45 541

test_vis3_rt		79.61 483	78.19 488	83.86 500	88.68 532	69.56 516	99.81 369	82.19 540	86.78 486	68.57 530	84.51 533	25.06 548	98.26 438	89.18 478	78.94 502	83.75 529

usedtu_dtu_shiyan2		85.34 469	83.22 476	91.71 471	88.10 533	83.34 495	98.75 494	97.59 503	76.21 510	91.11 480	96.80 490	58.14 508	94.30 504	75.00 519	67.24 527	97.49 447

SIFT-CM-Cal		53.99 510	52.89 513	57.28 528	87.31 534	41.77 552	86.71 541	54.86 554	39.82 546	45.09 543	82.10 542	25.89 547	71.72 548	27.27 546	26.97 546	58.36 543

SIFT-NN-CMatch		60.63 504	60.17 507	62.02 522	86.89 535	43.32 548	90.70 532	71.03 546	41.60 540	61.16 537	83.16 537	33.45 539	78.31 539	30.28 540	43.26 538	64.44 536

ambc				88.45 483	86.84 536	70.76 515	97.79 514	98.02 490		90.91 483	95.14 503	38.69 530	98.51 417	94.97 416	84.23 476	96.09 489

SIFT-UM-Cal		51.73 511	50.25 514	56.15 529	85.87 537	41.10 554	88.21 537	50.44 555	39.83 545	33.54 549	82.23 540	23.59 549	71.25 549	27.05 547	21.52 548	56.10 545

SIFT-UMatch		55.48 509	53.92 512	60.16 525	85.84 538	42.45 551	89.09 534	61.68 552	39.97 544	41.34 546	82.92 539	26.90 546	77.66 540	27.36 545	30.17 543	60.37 542

EMVS		69.88 497	69.09 499	72.24 517	84.70 539	65.82 531	99.96 317	87.08 537	49.82 532	71.51 526	84.74 532	49.30 522	75.32 543	50.97 536	43.71 537	75.59 532

GLUNet-SfM		70.22 496	66.87 501	80.24 508	84.13 540	61.64 536	96.72 521	82.62 539	51.83 529	60.24 538	88.02 528	36.12 534	91.44 519	67.32 526	34.86 542	87.65 522

E-PMN		70.72 495	70.06 498	72.69 516	83.92 541	65.48 532	99.95 326	92.72 527	49.88 531	72.30 525	86.26 530	47.17 525	77.43 541	53.83 535	44.49 536	75.17 533

SIFT-NN-UMatch		59.27 506	58.65 509	61.13 524	83.27 542	43.66 547	91.00 531	70.69 547	41.78 539	44.38 545	82.21 541	34.17 536	79.10 537	30.07 541	50.25 534	60.64 540

PMMVS2		79.15 488	77.28 491	84.76 495	82.34 543	72.66 511	99.70 401	95.11 522	71.68 515	84.78 507	90.87 516	32.05 541	89.99 524	75.53 518	63.45 531	91.64 517

SIFT-NN-PointCN		57.34 507	56.95 510	58.53 527	82.11 544	41.35 553	90.36 533	61.72 551	40.01 543	54.78 540	80.99 545	32.74 540	72.39 546	29.64 542	40.16 539	61.83 538

SIFT-PCN-Cal		47.97 513	47.56 516	49.20 531	81.85 545	33.99 557	86.00 542	49.11 556	36.44 548	32.13 550	77.60 547	22.63 550	62.04 550	23.11 549	19.17 549	51.55 546

TDRefinement		91.93 440	90.48 450	96.27 428	81.60 546	92.65 454	99.10 478	97.61 502	93.96 414	93.77 471	99.85 317	80.03 463	99.53 317	97.82 340	70.59 516	96.63 475

SIFT-PointCN		49.44 512	48.89 515	51.12 530	81.24 547	34.25 556	87.16 540	56.78 553	36.95 547	33.84 548	76.32 548	20.17 552	61.65 551	21.99 550	25.53 547	57.46 544

test123		79.44 486	79.23 487	80.05 509	80.03 548	71.72 513	100.00 1	77.93 545	62.52 518	94.81 460	99.69 350	78.21 471	74.53 544	92.57 445	27.33 545	93.90 508

LCM-MVSNet		79.01 489	76.93 492	85.27 490	78.28 549	68.01 524	96.57 522	98.03 488	55.10 528	82.03 513	93.27 512	31.99 542	93.95 506	82.72 496	74.37 508	93.84 509

FPMVS		77.92 490	79.45 486	73.34 515	76.87 550	46.81 543	98.24 505	99.05 439	59.89 522	73.55 523	98.34 472	36.81 533	86.55 526	80.96 502	91.35 418	86.65 524

SIFT-NCMNet		41.74 514	41.17 517	43.45 532	76.48 551	31.10 559	80.74 543	30.14 557	35.07 549	28.33 551	71.87 549	16.32 553	52.56 552	19.72 551	11.82 551	46.67 547

wuyk23d		28.28 515	29.73 519	23.92 533	75.89 552	32.61 558	66.50 544	12.88 558	16.09 550	14.59 553	16.59 551	12.35 554	32.36 553	39.36 537	13.36 550	6.79 548

MVE	68.59 21	67.22 499	64.68 505	74.84 510	74.67 553	62.32 535	95.84 525	90.87 530	50.98 530	58.72 539	81.05 544	12.20 556	78.95 538	61.06 531	56.75 532	83.24 530
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 500	63.44 506	73.88 514	61.14 554	63.45 534	95.68 526	87.18 535	79.93 503	47.35 541	80.68 546	22.35 551	72.33 547	61.24 530	35.42 540	85.88 525

testmvs		80.17 482	81.95 478	74.80 511	58.54 555	59.58 537	100.00 1	87.14 536	76.09 511	99.61 258	100.00 1	67.06 498	74.19 545	98.84 286	50.30 533	90.64 520

PMVS	60.66 23	65.98 501	65.05 503	68.75 518	55.06 556	38.40 555	88.19 538	96.98 509	48.30 533	44.82 544	88.52 526	12.22 555	86.49 527	67.58 525	83.79 481	81.35 531
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 492	74.26 494	80.43 507	52.91 557	53.67 540	87.42 539	97.98 491	61.80 520	67.04 533	100.00 1	76.43 478	96.40 488	96.47 382	28.26 544	91.23 519

mmdepth		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.07 519	0.09 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.79 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

eth-test2					0.00 558

eth-test					0.00 558

uanet_test		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

cdsmvs_eth3d_5k		24.41 516	32.55 518	0.00 534	0.00 558	0.00 560	0.00 545	99.39 222	0.00 552	0.00 554	100.00 1	93.55 303	0.00 554	0.00 552	0.00 552	0.00 549

pcd_1.5k_mvsjas		8.24 518	10.99 521	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	98.75 140	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		8.33 517	11.11 520	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	100.00 1	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.01 520	0.02 523	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.14 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

WAC-MVS						97.98 319								95.74 398

PC_three_1452									98.80 77	100.00 1	100.00 1	99.54 32	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 153	99.03 25	100.00 1	100.00 1	99.56 29	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									98.79 80	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 171

sam_mvs1												99.29 82				99.91 171

sam_mvs												99.33 71

MTGPA								99.42 153

test_post1							99.32 445				88.24 527	99.33 71	99.59 294	98.31 315

test_post											89.05 525	99.49 46	99.59 294

patchmatchnet-post											97.79 481	99.41 66	99.54 312

MTMP							100.00 1	99.18 375

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 53	100.00 1

test_prior2							100.00 1		98.82 72	100.00 1	100.00 1	99.47 51		100.00 1	100.00 1

旧先验2							100.00 1		98.11 129	100.00 1			100.00 1	99.67 186

新几何2							100.00 1

无先验							100.00 1	99.80 48	97.98 137				100.00 1	99.33 255		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 358

segment_acmp												99.55 31

testdata1							100.00 1		98.77 84

plane_prior5								99.40 206					99.55 309	99.79 143	95.57 342	97.76 348

plane_prior4											99.97 263

plane_prior3						94.79 416			99.03 25	99.08 307

plane_prior2							100.00 1		99.00 32

plane_prior						94.80 413	100.00 1		99.03 25						95.58 338

n2								0.00 559

nn								0.00 559

door-mid								96.32 517

test11								99.42 153

door								96.13 518

HQP5-MVS						94.82 410

BP-MVS														99.79 143

HQP4-MVS										99.17 295			99.57 300			97.77 346

HQP3-MVS								99.40 206							95.58 338

HQP2-MVS												88.61 407

MDTV_nov1_ep13_2view						99.24 189	99.56 419		96.31 334	99.96 152		98.86 131		98.92 282		99.89 190

ACMMP++_ref															94.58 373

ACMMP++															95.17 358

Test By Simon												99.10 99

High-resolution multi-view benchmark