High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	98.79 73	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

FOURS1					100.00 1	99.97 21	100.00 1	99.42 147	98.52 89	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 147	98.72 78	100.00 1	100.00 1	99.60 21

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 207	100.00 1	100.00 1	99.32 69	99.99 102	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	99.03 21	100.00 1	100.00 1	99.50 41	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 147	99.12 7	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 30	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 147	99.03 21	100.00 1	100.00 1	99.50 41	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	99.04 16	100.00 1	100.00 1	99.53 33	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					100.00 1	99.99 5	100.00 1	99.42 147	99.04 16	100.00 1	100.00 1	99.53 33

SR-MVS		99.68 43	99.58 49	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 174	100.00 1	100.00 1	98.99 104	99.99 102	100.00 1	100.00 1	100.00 1

GST-MVS		99.64 51	99.53 62	99.95 55	100.00 1	99.86 83	100.00 1	99.79 45	97.72 153	99.95 174	100.00 1	98.39 148	100.00 1	99.96 100	99.99 103	100.00 1

test_part2					100.00 1	99.99 5				100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 68	100.00 1	99.86 83	100.00 1	99.42 147	98.87 57	100.00 1	100.00 1	99.65 19	99.96 159	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 128	99.05 15	100.00 1	100.00 1	99.45 50	99.99 102	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TEST9					100.00 1	99.95 32	100.00 1	99.42 147	97.65 161	100.00 1	100.00 1	99.53 33	99.97 142

train_agg		99.71 33	99.63 41	99.97 35	100.00 1	99.95 32	100.00 1	99.42 147	97.70 155	100.00 1	100.00 1	99.51 37	99.97 142	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 57	100.00 1	99.42 147	97.70 155	100.00 1	100.00 1	99.51 37	99.98 133

agg_prior					100.00 1	99.88 78		99.42 147		100.00 1			99.97 142

HFP-MVS		99.74 25	99.67 30	99.96 46	100.00 1	99.89 71	100.00 1	99.76 49	97.95 136	100.00 1	100.00 1	99.31 71	100.00 1	99.99 71	100.00 1	100.00 1

region2R		99.72 29	99.64 37	99.97 35	100.00 1	99.90 64	100.00 1	99.74 55	97.86 142	100.00 1	100.00 1	99.19 86	100.00 1	99.99 71	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 91	100.00 1	99.64 120	99.98 265	99.44 119	98.35 104	99.99 123	100.00 1	99.04 101	99.96 159	99.98 86	100.00 1	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 81	100.00 1	100.00 1	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 299	96.06 328	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 81	100.00 1	66.97 464	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 81	100.00 1	99.75 101		99.73 56					99.97 142			100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 139	100.00 1	100.00 1	99.20 85	100.00 1	97.91 300	100.00 1	100.00 1

原ACMM1				99.93 72	100.00 1	99.80 94		99.66 63	98.18 113	100.00 1	100.00 1	99.43 55	100.00 1	99.50 210	100.00 1	100.00 1

MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 8	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 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 119	99.06 13	100.00 1	100.00 1	99.56 27	99.99 102	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 25	99.67 30	99.96 46	100.00 1	99.89 71	100.00 1	99.76 49	97.95 136	100.00 1	100.00 1	99.29 77	100.00 1	99.99 71	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 71	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 28	99.64 37	99.99 12	100.00 1	99.97 21	100.00 1	99.42 147	98.02 126	100.00 1	100.00 1	99.32 69	99.99 102	100.00 1	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 35	100.00 1	99.91 57	100.00 1	99.42 147	97.91 138	100.00 1	100.00 1	99.04 101	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS		99.59 66	99.50 68	99.89 84	100.00 1	99.70 113	100.00 1	99.42 147	97.46 190	100.00 1	100.00 1	98.60 138	99.96 159	99.99 71	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 11	100.00 1	100.00 1	99.39 64	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 61	99.07 11	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 46	99.58 49	99.95 55	100.00 1	99.84 88	100.00 1	99.42 147	97.77 150	100.00 1	100.00 1	99.07 95	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 77	99.38 80	99.85 98	100.00 1	99.54 135	100.00 1	99.42 147	97.58 175	99.98 131	100.00 1	97.43 191	100.00 1	99.99 71	100.00 1	100.00 1

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 128	99.99 123	100.00 1	99.72 14	100.00 1	99.96 100	100.00 1	100.00 1

PAPM_NR		99.74 25	99.66 33	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 141	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 28	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 114	100.00 1	100.00 1	99.51 37	100.00 1	100.00 1	100.00 1	100.00 1

PHI-MVS		99.50 75	99.39 79	99.82 106	100.00 1	99.45 154	100.00 1	99.94 22	96.38 295	100.00 1	100.00 1	98.18 152	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 111	99.25 101	99.44 185	100.00 1	98.32 263	100.00 1	99.86 38	98.04 125	100.00 1	100.00 1	96.10 227	100.00 1	99.55 203	99.73 165	100.00 1

MG-MVS		99.75 23	99.68 28	99.97 35	100.00 1	99.91 57	99.98 265	99.47 79	99.09 10	100.00 1	100.00 1	98.59 139	100.00 1	99.95 106	100.00 1	100.00 1

AdaColmap		99.44 85	99.26 99	99.95 55	100.00 1	99.86 83	99.70 354	99.99 13	98.53 87	99.90 189	100.00 1	95.34 239	100.00 1	99.92 111	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 166	100.00 1	98.79 222	100.00 1	99.54 71	98.58 86	99.96 144	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 144
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NormalMVS		99.47 81	99.48 73	99.43 188	99.99 49	98.55 239	99.94 294	99.28 279	98.39 96	100.00 1	100.00 1	98.44 145	99.98 133	99.36 216	99.92 133	99.75 260

lecture		99.64 51	99.53 62	99.98 23	99.99 49	99.93 47	100.00 1	99.47 79	98.53 87	100.00 1	100.00 1	97.88 163	100.00 1	99.98 86	99.92 133	100.00 1

MM		99.63 55	99.52 65	99.94 68	99.99 49	99.82 91	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 217	100.00 1	100.00 1	99.97 116	100.00 1

SR-MVS-dyc-post		99.63 55	99.52 65	99.97 35	99.99 49	99.91 57	100.00 1	99.42 147	97.62 166	100.00 1	100.00 1	98.65 135	99.99 102	99.99 71	100.00 1	100.00 1

RE-MVS-def			99.55 59		99.99 49	99.91 57	100.00 1	99.42 147	97.62 166	100.00 1	100.00 1	98.94 115		99.99 71	100.00 1	100.00 1

SF-MVS		99.66 48	99.57 52	99.95 55	99.99 49	99.85 86	100.00 1	99.42 147	97.67 158	100.00 1	100.00 1	99.05 98	99.99 102	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 35	99.99 49	99.90 64	100.00 1	99.79 45	97.97 132	99.97 137	100.00 1	98.97 109	100.00 1	99.94 108	100.00 1	100.00 1

9.14			99.57 52		99.99 49		100.00 1	99.42 147	97.54 178	100.00 1	100.00 1	99.15 90	99.99 102	100.00 1	100.00 1

save fliter					99.99 49	99.93 47	100.00 1	99.42 147	98.93 44

CS-MVS		99.33 105	99.27 95	99.50 174	99.99 49	99.00 208	100.00 1	99.13 365	97.26 210	99.96 144	100.00 1	97.79 170	99.64 256	99.64 183	99.67 171	99.87 202

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

SMA-MVS		99.69 39	99.59 47	99.98 23	99.99 49	99.93 47	100.00 1	99.43 128	97.50 186	100.00 1	100.00 1	99.43 55	100.00 1	100.00 1	100.00 1	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 147	98.91 49	100.00 1	100.00 1	99.22 83	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CHOSEN 280x420		99.85 3	99.87 1	99.80 117	99.99 49	99.97 21	99.97 272	99.98 16	98.96 34	100.00 1	100.00 1	99.96 4	99.42 302	100.00 1	100.00 1	100.00 1

CANet		99.40 89	99.24 105	99.89 84	99.99 49	99.76 100	100.00 1	99.73 56	98.40 95	99.78 216	100.00 1	95.28 240	99.96 159	100.00 1	99.99 103	99.96 133

MVS_0304		99.72 29	99.65 34	99.93 72	99.99 49	99.79 95	100.00 1	99.91 35	99.17 6	100.00 1	100.00 1	97.84 167	100.00 1	100.00 1	99.95 122	100.00 1

SPE-MVS-test		99.31 109	99.27 95	99.43 188	99.99 49	98.77 223	100.00 1	99.19 330	97.24 211	99.96 144	100.00 1	97.56 182	99.70 253	99.68 173	99.81 160	99.82 219

MTAPA		99.68 43	99.59 47	99.97 35	99.99 49	99.91 57	100.00 1	99.42 147	98.32 106	99.94 177	100.00 1	98.65 135	100.00 1	99.96 100	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 39	99.63 41	99.87 91	99.99 49	99.64 120	99.95 286	99.44 119	98.35 104	100.00 1	100.00 1	98.98 107	99.97 142	99.98 86	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 54	99.96 144	100.00 1	99.21 84	100.00 1	100.00 1	100.00 1	99.99 117

旧先验1					99.99 49	99.88 78		99.82 40			100.00 1	99.27 80			100.00 1	100.00 1

test222					99.99 49	99.90 64	100.00 1	99.69 62	97.66 159	100.00 1	100.00 1	99.30 76			100.00 1	100.00 1

testdata				99.66 150	99.99 49	98.97 212		99.73 56	97.96 135	100.00 1	100.00 1	99.42 59	100.00 1	99.28 226	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 147	99.01 26	100.00 1	100.00 1	99.33 66	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 49	99.55 59	99.97 35	99.99 49	99.91 57	100.00 1	99.48 78	97.54 178	100.00 1	100.00 1	98.97 109	99.99 102	99.98 86	100.00 1	100.00 1

MP-MVS		99.61 62	99.49 70	99.98 23	99.99 49	99.94 41	100.00 1	99.42 147	97.82 145	99.99 123	100.00 1	98.20 151	100.00 1	99.99 71	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 39	99.61 45	99.95 55	99.99 49	99.85 86	100.00 1	99.58 67	97.69 157	100.00 1	100.00 1	99.44 51	100.00 1	99.79 136	100.00 1	100.00 1

test12				99.95 55	99.99 49	99.89 71		99.42 147		100.00 1		99.24 82	99.97 142		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 62	99.69 22	99.35 204	99.99 49	98.06 283	100.00 1	99.36 226	99.83 2	100.00 1	100.00 1	98.95 113	99.99 102	100.00 1	99.11 191	100.00 1

HPM-MVS_fast		99.60 65	99.49 70	99.91 77	99.99 49	99.78 96	100.00 1	99.42 147	97.09 222	100.00 1	100.00 1	98.95 113	99.96 159	99.98 86	100.00 1	100.00 1

EPNet		99.62 60	99.69 22	99.42 191	99.99 49	98.37 256	100.00 1	99.89 37	98.83 63	100.00 1	100.00 1	98.97 109	100.00 1	99.90 114	99.61 178	99.89 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		99.68 43	99.58 49	99.97 35	99.99 49	99.96 24	100.00 1	99.42 147	97.53 181	100.00 1	100.00 1	99.27 80	99.97 142	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		99.28 116	99.35 88	99.05 237	99.99 49	97.15 329	100.00 1	99.47 79	97.44 194	99.42 247	100.00 1	97.83 169	100.00 1	99.99 71	100.00 1	100.00 1

PatchMatch-RL		99.02 155	98.78 167	99.74 133	99.99 49	99.29 172	100.00 1	100.00 1	98.38 98	99.89 192	99.81 296	93.14 290	99.99 102	97.85 302	99.98 113	99.95 139

ACMMP		99.65 49	99.57 52	99.89 84	99.99 49	99.66 118	99.75 342	99.73 56	98.16 114	99.75 220	100.00 1	98.90 120	100.00 1	99.96 100	99.88 144	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 51	99.64 37	99.67 147	99.99 49	99.07 196	100.00 1	99.44 119	98.30 107	99.90 189	100.00 1	99.18 87	99.99 102	99.91 113	100.00 1	99.94 144

DeepPCF-MVS	98.03 4	98.54 218	99.72 19	94.98 402	99.99 49	84.94 441	100.00 1	99.42 147	99.98 1	100.00 1	100.00 1	98.11 154	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 23	99.67 30	99.99 12	99.99 49	99.96 24	99.73 348	99.52 72	99.06 13	100.00 1	100.00 1	98.80 129	100.00 1	99.95 106	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		99.31 109	99.13 123	99.87 91	99.99 49	99.71 109	99.55 372	99.46 97	97.32 205	99.82 209	100.00 1	96.85 211	99.97 142	99.14 235	100.00 1	99.92 157

MP-MVS-pluss		99.61 62	99.50 68	99.97 35	99.98 88	99.92 54	100.00 1	99.42 147	97.53 181	99.77 217	100.00 1	98.77 130	100.00 1	99.99 71	100.00 1	99.99 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.67 46	99.57 52	99.97 35	99.98 88	99.92 54	100.00 1	99.42 147	97.83 143	100.00 1	100.00 1	98.89 121	100.00 1	99.98 86	100.00 1	100.00 1

PVSNet_BlendedMVS		98.71 195	98.62 191	98.98 244	99.98 88	99.60 124	100.00 1	100.00 1	97.23 213	100.00 1	99.03 388	96.57 219	99.99 102	100.00 1	94.75 336	97.35 409

PVSNet_Blended		99.48 79	99.36 86	99.83 104	99.98 88	99.60 124	100.00 1	100.00 1	97.79 148	100.00 1	100.00 1	96.57 219	99.99 102	100.00 1	99.88 144	99.90 172

reproduce_monomvs		98.61 206	98.54 200	98.82 252	99.97 92	99.28 174	100.00 1	99.33 247	98.51 90	97.87 356	99.24 372	99.98 3	99.45 298	99.02 244	92.93 353	97.74 346

114514_t		99.39 90	99.25 101	99.81 111	99.97 92	99.48 152	100.00 1	99.42 147	95.53 329	100.00 1	100.00 1	98.37 149	99.95 172	99.97 98	100.00 1	100.00 1

DP-MVS		98.86 182	98.54 200	99.81 111	99.97 92	99.45 154	99.52 376	99.40 200	94.35 365	98.36 325	100.00 1	96.13 226	99.97 142	99.12 238	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 121	99.97 92	99.37 165	99.96 279	99.94 22	98.48 91	100.00 1	100.00 1	98.92 118	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 29	99.65 34	99.91 77	99.97 92	99.72 108	100.00 1	99.47 79	98.43 94	99.88 194	100.00 1	99.14 91	100.00 1	99.97 98	100.00 1	100.00 1

MAR-MVS		99.49 77	99.36 86	99.89 84	99.97 92	99.66 118	99.74 343	99.95 19	97.89 139	100.00 1	100.00 1	96.71 216	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 18	99.69 22	99.98 23	99.96 98	99.93 47	100.00 1	99.42 147	98.81 69	100.00 1	100.00 1	98.98 107	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 18	99.69 22	99.98 23	99.96 98	99.94 41	100.00 1	99.42 147	98.82 65	100.00 1	100.00 1	98.99 104	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 18	99.69 22	99.98 23	99.96 98	99.94 41	100.00 1	99.42 147	98.82 65	100.00 1	100.00 1	98.99 104	100.00 1	100.00 1	100.00 1	100.00 1

MVS_111021_LR		99.70 36	99.65 34	99.88 89	99.96 98	99.70 113	100.00 1	99.97 17	98.96 34	100.00 1	100.00 1	97.93 159	99.95 172	99.99 71	100.00 1	100.00 1

HyFIR lowres test		99.32 107	99.24 105	99.58 166	99.95 102	99.26 177	100.00 1	99.99 13	96.72 258	99.29 260	99.91 273	99.49 43	99.47 292	99.74 151	98.08 253	100.00 1

MVS_111021_HR		99.71 33	99.63 41	99.93 72	99.95 102	99.83 90	100.00 1	100.00 1	98.89 53	100.00 1	100.00 1	97.85 165	99.95 172	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 117	99.38 80	98.96 245	99.95 102	97.06 333	100.00 1	99.40 200	98.83 63	99.88 194	100.00 1	97.01 201	99.86 208	99.47 211	99.84 155	99.97 127

TAPA-MVS	96.40 10	97.64 269	97.37 278	98.45 275	99.94 105	95.70 356	100.00 1	99.40 200	97.65 161	99.53 237	100.00 1	99.31 71	99.66 255	80.48 448	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	98.29 240	98.17 239	98.65 261	99.94 105	97.39 316	99.30 399	99.40 200	95.64 324	97.75 362	100.00 1	92.69 300	99.95 172	98.89 250	99.92 133	98.62 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.55 215	98.40 216	98.99 242	99.93 107	97.35 319	100.00 1	99.40 200	97.08 224	99.09 273	99.98 222	93.37 284	99.95 172	96.94 335	99.84 155	99.68 279

TestCases				98.99 242	99.93 107	97.35 319		99.40 200	97.08 224	99.09 273	99.98 222	93.37 284	99.95 172	96.94 335	99.84 155	99.68 279

DeepC-MVS	97.84 5	99.00 158	98.80 166	99.60 160	99.93 107	99.03 201	100.00 1	99.40 200	98.61 85	99.33 258	100.00 1	92.23 305	99.95 172	99.74 151	99.96 120	99.83 213
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		99.04 146	99.79 6	96.81 372	99.92 110	90.47 424	100.00 1	99.41 196	98.95 37	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 139

EPNet_dtu		98.53 219	98.23 237	99.43 188	99.92 110	99.01 205	99.96 279	99.47 79	98.80 70	99.96 144	99.96 251	98.56 140	99.30 310	87.78 433	99.68 169	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 60	99.56 57	99.82 106	99.92 110	99.45 154	100.00 1	99.78 47	98.92 46	99.73 224	100.00 1	97.70 174	100.00 1	99.93 110	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		98.90 179	98.63 190	99.70 142	99.92 110	99.25 179	100.00 1	99.37 220	95.71 323	99.40 253	100.00 1	96.58 218	99.95 172	96.80 343	99.94 126	99.91 161

DPM-MVS		99.63 55	99.51 67	100.00 1	99.90 114	100.00 1	100.00 1	99.43 128	99.00 27	100.00 1	100.00 1	99.58 26	100.00 1	97.64 312	100.00 1	100.00 1

CHOSEN 1792x2688		99.00 158	98.91 155	99.25 226	99.90 114	97.79 303	100.00 1	99.99 13	98.79 73	98.28 333	100.00 1	93.63 279	99.95 172	99.66 181	99.95 122	100.00 1

test2506		99.48 79	99.38 80	99.75 132	99.89 116	99.51 142	99.45 382	100.00 1	98.38 98	99.83 201	100.00 1	98.86 122	99.81 227	99.25 227	98.78 200	99.94 144

ECVR-MVS		98.43 226	98.14 240	99.32 215	99.89 116	98.21 272	99.46 380	100.00 1	98.38 98	99.47 244	100.00 1	87.91 369	99.80 231	99.35 219	98.78 200	99.94 144

test1111		98.42 228	98.12 241	99.29 218	99.88 118	98.15 274	99.46 380	100.00 1	98.36 102	99.42 247	100.00 1	87.91 369	99.79 232	99.31 224	98.78 200	99.94 144

CANet_DTU		99.02 155	98.90 158	99.41 192	99.88 118	98.71 228	100.00 1	99.29 273	98.84 61	100.00 1	100.00 1	94.02 275	100.00 1	98.08 291	99.96 120	99.52 289

dcpmvs_2		98.87 181	99.53 62	96.90 366	99.87 120	90.88 422	99.94 294	99.07 387	98.20 112	100.00 1	100.00 1	98.69 134	99.86 208	100.00 1	100.00 1	99.95 139

fmvsm_s_conf0.5_n_9		99.04 146	98.78 167	99.81 111	99.86 121	99.44 157	100.00 1	99.32 250	98.94 40	100.00 1	100.00 1	91.00 321	99.99 102	100.00 1	99.94 126	100.00 1

mvsany_test1		99.57 67	99.48 73	99.85 98	99.86 121	99.54 135	100.00 1	99.36 226	98.94 40	100.00 1	100.00 1	97.97 157	100.00 1	99.88 118	99.28 186	100.00 1

test_fmvsm_n_1920		99.55 70	99.49 70	99.73 136	99.85 123	99.19 187	100.00 1	99.41 196	98.87 57	100.00 1	100.00 1	97.34 194	100.00 1	99.98 86	99.90 140	100.00 1

fmvsm_s_conf0.5_n		99.21 128	99.01 136	99.83 104	99.84 124	99.53 137	100.00 1	99.38 216	98.29 108	100.00 1	100.00 1	93.62 280	99.99 102	99.99 71	99.93 130	99.98 120

test_fmvs1		98.37 233	98.04 250	99.34 206	99.84 124	98.07 281	100.00 1	99.00 407	98.85 59	100.00 1	100.00 1	85.11 397	99.96 159	99.69 172	99.88 144	100.00 1

PVSNet_0	93.57 19	96.41 328	95.74 345	98.41 278	99.84 124	95.22 366	100.00 1	100.00 1	98.08 123	97.55 371	99.78 303	84.40 400	100.00 1	100.00 1	81.99 437	100.00 1

fmvsm_l_conf0.5_n_9		99.35 98	99.15 120	99.95 55	99.83 127	99.84 88	100.00 1	99.30 265	98.92 46	100.00 1	100.00 1	94.32 270	100.00 1	100.00 1	99.93 130	100.00 1

fmvsm_s_conf0.5_n_8		99.34 102	99.14 122	99.91 77	99.83 127	99.74 104	100.00 1	99.38 216	98.94 40	100.00 1	100.00 1	94.25 272	99.99 102	100.00 1	99.91 138	100.00 1

fmvsm_l_conf0.5_n_a		99.63 55	99.55 59	99.86 94	99.83 127	99.58 128	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.85 165	99.99 102	100.00 1	99.94 126	100.00 1

SDMVSNet		98.49 223	98.08 246	99.73 136	99.82 130	99.53 137	99.99 238	99.45 105	97.62 166	99.38 255	99.86 281	90.06 340	99.88 205	99.92 111	96.61 301	99.79 252

sd_testset		97.81 263	97.48 272	98.79 256	99.82 130	96.80 338	99.32 395	99.45 105	97.62 166	99.38 255	99.86 281	85.56 395	99.77 238	99.72 157	96.61 301	99.79 252

cl22		98.23 245	98.11 242	98.58 268	99.82 130	99.01 205	100.00 1	99.28 279	96.92 238	98.33 329	99.21 375	98.09 156	98.97 330	98.72 260	92.61 356	97.76 313

test_yl		99.51 72	99.37 83	99.95 55	99.82 130	99.90 64	100.00 1	99.47 79	97.48 188	100.00 1	100.00 1	99.80 6	100.00 1	99.98 86	97.75 278	99.94 144

DCV-MVSNet		99.51 72	99.37 83	99.95 55	99.82 130	99.90 64	100.00 1	99.47 79	97.48 188	100.00 1	100.00 1	99.80 6	100.00 1	99.98 86	97.75 278	99.94 144

PVSNet_Blended_VisFu		99.33 105	99.18 118	99.78 126	99.82 130	99.49 148	100.00 1	99.95 19	97.36 199	99.63 232	100.00 1	96.45 223	99.95 172	99.79 136	99.65 174	99.89 178

fmvsm_s_conf0.5_n_5		99.00 158	98.70 181	99.88 89	99.81 136	99.64 120	100.00 1	99.26 299	98.78 76	99.97 137	100.00 1	90.65 328	99.99 102	100.00 1	99.89 141	99.99 117

fmvsm_s_conf0.5_n_4		98.98 166	98.74 173	99.68 146	99.81 136	99.50 144	100.00 1	99.26 299	98.91 49	100.00 1	100.00 1	90.87 325	99.97 142	99.99 71	99.81 160	99.57 286

fmvsm_l_conf0.5_n		99.63 55	99.56 57	99.86 94	99.81 136	99.59 126	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.92 160	99.99 102	100.00 1	99.95 122	100.00 1

ET-MVSNet_ETH3D		96.41 328	95.48 359	99.20 229	99.81 136	99.75 101	100.00 1	99.02 404	97.30 209	78.33 453	100.00 1	97.73 172	97.94 415	99.70 165	87.41 416	99.92 157

Anonymous202405211		97.87 259	97.53 271	98.90 248	99.81 136	96.70 341	99.35 393	99.46 97	92.98 399	98.83 296	99.99 214	90.63 330	100.00 1	99.70 165	97.03 292	100.00 1

thres100view900		99.25 123	99.01 136	99.95 55	99.81 136	99.87 80	100.00 1	99.94 22	97.13 219	99.83 201	99.96 251	97.01 201	100.00 1	99.59 197	97.85 268	99.98 120

tfpn200view9		99.26 119	99.03 134	99.96 46	99.81 136	99.89 71	100.00 1	99.94 22	97.23 213	99.83 201	99.96 251	97.04 197	100.00 1	99.59 197	97.85 268	99.98 120

VNet		99.04 146	98.75 171	99.90 81	99.81 136	99.75 101	99.50 378	99.47 79	98.36 102	100.00 1	99.99 214	94.66 260	100.00 1	99.90 114	97.09 291	99.96 133

thres600view7		99.24 126	99.00 139	99.95 55	99.81 136	99.87 80	100.00 1	99.94 22	97.13 219	99.83 201	99.96 251	97.01 201	100.00 1	99.54 206	97.77 277	99.97 127

thres400		99.26 119	99.03 134	99.95 55	99.81 136	99.89 71	100.00 1	99.94 22	97.23 213	99.83 201	99.96 251	97.04 197	100.00 1	99.59 197	97.85 268	99.97 127

thres200		99.27 117	99.04 133	99.96 46	99.81 136	99.90 64	100.00 1	99.94 22	97.31 207	99.83 201	99.96 251	97.04 197	100.00 1	99.62 190	97.88 266	99.98 120

WTY-MVS		99.54 71	99.40 78	99.95 55	99.81 136	99.93 47	100.00 1	100.00 1	97.98 130	99.84 198	100.00 1	98.94 115	99.98 133	99.86 122	98.21 241	99.94 144

HY-MVS	96.53 9	99.50 75	99.35 88	99.96 46	99.81 136	99.93 47	99.64 361	100.00 1	97.97 132	99.84 198	99.85 286	98.94 115	99.99 102	99.86 122	98.23 240	99.95 139

fmvsm_l_conf0.5_n_3		99.38 93	99.20 114	99.92 76	99.80 149	99.78 96	100.00 1	99.35 237	98.94 40	100.00 1	100.00 1	94.77 256	99.99 102	99.99 71	99.92 133	100.00 1

fmvsm_s_conf0.5_n_a		99.32 107	99.15 120	99.81 111	99.80 149	99.47 153	100.00 1	99.35 237	98.22 109	100.00 1	100.00 1	95.21 244	99.99 102	99.96 100	99.86 150	99.98 120

Anonymous20231211		96.29 337	95.70 347	98.07 307	99.80 149	97.49 312	99.15 421	99.40 200	89.11 426	97.75 362	99.45 359	88.93 358	98.98 328	98.26 287	89.47 398	97.73 353

LFMVS		97.42 282	96.62 303	99.81 111	99.80 149	99.50 144	99.16 419	99.56 70	94.48 361	100.00 1	100.00 1	79.35 423	100.00 1	99.89 116	97.37 287	99.94 144

RPSCF		97.37 284	98.24 234	94.76 405	99.80 149	84.57 442	99.99 238	99.05 397	94.95 346	99.82 209	100.00 1	94.03 274	100.00 1	98.15 290	98.38 223	99.70 277

SymmetryMVS		99.30 111	99.25 101	99.45 183	99.79 154	98.55 239	99.94 294	99.47 79	98.39 96	100.00 1	100.00 1	98.44 145	99.98 133	99.36 216	97.83 271	99.83 213

fmvsm_s_conf0.5_n_2		98.90 179	98.57 197	99.90 81	99.79 154	99.78 96	100.00 1	99.25 303	98.97 32	100.00 1	100.00 1	89.22 353	99.99 102	100.00 1	99.88 144	99.92 157

test_vis1_n_1920		97.77 265	97.24 286	99.34 206	99.79 154	98.04 285	100.00 1	99.25 303	98.88 54	100.00 1	100.00 1	77.52 429	100.00 1	99.88 118	99.85 153	100.00 1

h-mvs33		97.03 300	96.53 306	98.51 270	99.79 154	95.90 352	99.45 382	99.45 105	98.21 110	100.00 1	99.78 303	97.49 185	99.99 102	99.72 157	74.92 447	99.65 284

Anonymous20240529		96.93 305	96.22 322	99.05 237	99.79 154	97.30 323	99.16 419	99.47 79	88.51 429	98.69 302	100.00 1	83.50 408	100.00 1	99.83 128	97.02 293	99.83 213

PS-MVSNAJ		99.64 51	99.57 52	99.85 98	99.78 159	99.81 92	99.95 286	99.42 147	98.38 98	100.00 1	100.00 1	98.75 131	100.00 1	99.88 118	99.99 103	99.74 267

PCF-MVS	98.23 3	98.69 198	98.37 223	99.62 156	99.78 159	99.02 203	99.23 410	99.06 395	96.43 287	98.08 342	100.00 1	94.72 259	99.95 172	98.16 289	99.91 138	99.90 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+-dtu		98.51 222	98.86 160	97.47 340	99.77 161	94.21 392	100.00 1	98.94 412	97.61 170	99.91 187	98.75 406	95.89 229	99.51 285	99.36 216	99.48 182	98.68 306

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 162	99.95 32	100.00 1	99.42 147	98.69 79	100.00 1	100.00 1	99.52 36	99.99 102	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
sss		99.45 83	99.34 90	99.80 117	99.76 162	99.50 144	100.00 1	99.91 35	97.72 153	99.98 131	99.94 264	98.45 144	100.00 1	99.53 208	98.75 203	99.89 178

sasdasda		99.03 149	98.73 174	99.94 68	99.75 164	99.95 32	100.00 1	99.30 265	97.64 163	100.00 1	100.00 1	95.22 242	99.97 142	99.76 146	96.90 296	99.91 161

canonicalmvs		99.03 149	98.73 174	99.94 68	99.75 164	99.95 32	100.00 1	99.30 265	97.64 163	100.00 1	100.00 1	95.22 242	99.97 142	99.76 146	96.90 296	99.91 161

MGCFI-Net		99.01 157	98.70 181	99.93 72	99.74 166	99.94 41	100.00 1	99.29 273	97.60 173	100.00 1	100.00 1	95.10 248	99.96 159	99.74 151	96.85 298	99.91 161

UWE-MVS-28		99.29 114	99.23 108	99.48 179	99.73 167	98.86 217	100.00 1	99.43 128	96.97 233	99.99 123	99.83 289	99.43 55	99.77 238	99.35 219	98.31 232	99.80 246

alignmvs		99.38 93	99.21 110	99.91 77	99.73 167	99.92 54	100.00 1	99.51 76	97.61 170	100.00 1	100.00 1	99.06 96	99.93 189	99.83 128	97.12 290	99.90 172

baseline1		98.91 177	98.61 192	99.81 111	99.71 169	99.77 99	99.78 333	99.44 119	97.51 185	98.81 297	99.99 214	98.25 150	99.76 242	98.60 270	95.41 311	99.89 178

ab-mvs		98.42 228	98.02 252	99.61 158	99.71 169	99.00 208	99.10 427	99.64 64	96.70 264	99.04 279	99.81 296	90.64 329	99.98 133	99.64 183	97.93 263	99.84 209

testing3-2		99.45 83	99.31 91	99.86 94	99.70 171	99.73 106	100.00 1	99.47 79	97.46 190	99.97 137	99.97 234	99.48 47	100.00 1	99.78 142	97.99 257	99.85 207

fmvsm_s_conf0.5_n_3		98.99 162	98.69 183	99.89 84	99.70 171	99.69 115	100.00 1	99.39 213	98.93 44	100.00 1	100.00 1	90.20 336	99.99 102	100.00 1	99.95 122	100.00 1

xiu_mvs_v2_base		99.51 72	99.41 77	99.82 106	99.70 171	99.73 106	99.92 302	99.40 200	98.15 116	100.00 1	100.00 1	98.50 143	100.00 1	99.85 124	99.13 190	99.74 267

myMVS_eth3d28		99.41 88	99.28 93	99.80 117	99.69 174	99.53 137	100.00 1	99.43 128	97.12 221	99.98 131	99.97 234	99.41 61	100.00 1	99.81 135	98.07 254	99.88 191

test_cas_vis1_n_1920		98.63 204	98.25 231	99.77 129	99.69 174	99.32 169	100.00 1	99.31 259	98.84 61	99.96 144	100.00 1	87.42 376	99.99 102	99.14 235	99.86 150	100.00 1

tpmvs		98.59 209	98.38 221	99.23 227	99.69 174	97.90 295	99.31 398	99.47 79	94.52 359	99.68 229	99.28 370	97.64 177	99.89 197	97.71 310	98.17 246	99.89 178

Test_1112_low_res		98.83 185	98.60 194	99.51 171	99.69 174	98.75 224	99.99 238	99.14 359	96.81 246	98.84 294	99.06 382	97.45 188	99.89 197	98.66 262	97.75 278	99.89 178

1112_ss		98.91 177	98.71 179	99.51 171	99.69 174	98.75 224	99.99 238	99.15 352	96.82 245	98.84 294	100.00 1	97.45 188	99.89 197	98.66 262	97.75 278	99.89 178

SD_0403		97.92 258	98.43 212	96.39 380	99.68 179	89.74 429	99.92 302	99.34 244	96.75 252	99.39 254	99.93 269	93.54 283	99.51 285	99.11 239	98.21 241	99.92 157

MVSMamba_PlusPlus		99.39 90	99.25 101	99.80 117	99.68 179	99.59 126	99.99 238	99.30 265	96.66 269	99.96 144	99.97 234	97.89 162	99.92 192	99.76 146	100.00 1	99.90 172

test_fmvsmconf_n		99.56 68	99.46 76	99.86 94	99.68 179	99.58 128	100.00 1	99.31 259	98.92 46	99.88 194	100.00 1	97.35 193	99.99 102	99.98 86	99.99 103	100.00 1

test_fmvs1_n		97.43 281	96.86 294	99.15 231	99.68 179	97.48 313	99.99 238	98.98 410	98.82 65	100.00 1	100.00 1	74.85 436	99.96 159	99.67 176	99.70 168	100.00 1

balanced_conf03		99.43 86	99.28 93	99.85 98	99.68 179	99.68 116	99.97 272	99.28 279	97.03 228	99.96 144	99.97 234	97.90 161	99.93 189	99.77 144	100.00 1	99.94 144

hse-mvs2		96.79 308	96.38 314	98.04 317	99.68 179	95.54 359	99.81 326	99.42 147	98.21 110	100.00 1	99.80 299	97.49 185	99.46 297	99.72 157	73.27 450	99.12 301

AUN-MVS		96.26 339	95.67 351	98.06 311	99.68 179	95.60 358	99.82 325	99.42 147	96.78 249	99.88 194	99.80 299	94.84 254	99.47 292	97.48 319	73.29 449	99.12 301

mamv4		98.95 172	99.11 126	98.46 273	99.68 179	95.67 357	99.14 423	99.27 292	96.43 287	99.94 177	99.97 234	97.79 170	99.88 205	99.77 144	100.00 1	99.84 209

Vis-MVSNet		98.52 220	98.25 231	99.34 206	99.68 179	98.55 239	99.68 358	99.41 196	97.34 202	99.94 177	100.00 1	90.38 335	99.70 253	99.03 243	98.84 198	99.76 259
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_7		98.98 166	98.85 161	99.37 202	99.67 188	98.34 260	100.00 1	99.31 259	98.97 32	100.00 1	100.00 1	91.70 310	99.97 142	99.99 71	99.97 116	99.80 246

UWE-MVS		99.18 131	99.06 131	99.51 171	99.67 188	98.80 221	100.00 1	99.43 128	96.80 247	99.93 183	99.86 281	99.79 8	99.94 185	97.78 308	98.33 229	99.80 246

ETVMVS		99.16 134	98.98 142	99.69 143	99.67 188	99.56 130	100.00 1	99.45 105	96.36 297	99.98 131	99.95 258	98.65 135	99.64 256	99.11 239	97.63 285	99.88 191

testing222		99.14 136	98.94 150	99.73 136	99.67 188	99.51 142	100.00 1	99.43 128	96.90 241	99.99 123	99.90 275	98.55 141	99.86 208	98.85 252	97.18 289	99.81 228

xiu_mvs_v1_base_debu		99.35 98	99.21 110	99.79 121	99.67 188	99.71 109	99.78 333	99.36 226	98.13 118	100.00 1	100.00 1	97.00 204	100.00 1	99.83 128	99.07 192	99.66 281

xiu_mvs_v1_base		99.35 98	99.21 110	99.79 121	99.67 188	99.71 109	99.78 333	99.36 226	98.13 118	100.00 1	100.00 1	97.00 204	100.00 1	99.83 128	99.07 192	99.66 281

xiu_mvs_v1_base_debi		99.35 98	99.21 110	99.79 121	99.67 188	99.71 109	99.78 333	99.36 226	98.13 118	100.00 1	100.00 1	97.00 204	100.00 1	99.83 128	99.07 192	99.66 281

IB-MVS	96.24 12	97.54 276	96.95 291	99.33 212	99.67 188	98.10 279	100.00 1	99.47 79	97.42 196	99.26 261	99.69 315	98.83 126	99.89 197	99.43 212	78.77 445	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		98.55 215	98.53 202	98.62 263	99.66 196	96.16 348	100.00 1	99.44 119	93.93 375	99.81 213	99.98 222	97.58 178	99.81 227	98.08 291	98.28 236	99.89 178

miper_enhance_ethall		98.33 236	98.27 229	98.51 270	99.66 196	99.04 200	100.00 1	99.22 318	97.53 181	98.51 317	99.38 364	99.49 43	98.75 352	98.02 295	92.61 356	97.76 313

FE-MVS		99.16 134	98.99 141	99.66 150	99.65 198	99.18 189	99.58 369	99.43 128	95.24 341	99.91 187	99.59 340	99.37 65	99.97 142	98.31 282	99.81 160	99.83 213

RRT-MVS		98.75 192	98.52 203	99.44 185	99.65 198	98.57 238	99.90 308	99.08 382	96.51 283	99.96 144	99.95 258	92.59 301	99.96 159	99.60 195	99.45 184	99.81 228

MVSTER		98.58 211	98.52 203	98.77 257	99.65 198	99.68 116	100.00 1	99.29 273	95.63 325	98.65 305	99.80 299	99.78 9	98.88 342	98.59 271	95.31 315	97.73 353

FMVSNet3		97.30 287	96.95 291	98.37 281	99.65 198	99.25 179	99.71 352	99.28 279	94.23 366	98.53 314	98.91 399	93.30 286	98.11 401	95.31 369	93.60 344	97.73 353

cascas		98.43 226	98.07 248	99.50 174	99.65 198	99.02 203	100.00 1	99.22 318	94.21 368	99.72 225	99.98 222	92.03 308	99.93 189	99.68 173	98.12 251	99.54 288

IS-MVSNet		99.08 140	98.91 155	99.59 162	99.65 198	99.38 162	99.78 333	99.24 309	96.70 264	99.51 239	100.00 1	98.44 145	99.52 283	98.47 275	98.39 221	99.88 191

testing11		99.26 119	99.19 115	99.46 181	99.64 204	98.61 235	100.00 1	99.43 128	96.94 235	99.92 184	99.94 264	99.43 55	99.97 142	99.67 176	97.79 276	99.82 219

Vis-MVSNet (Re-imp)		98.99 162	98.89 159	99.29 218	99.64 204	98.89 216	99.98 265	99.31 259	96.74 255	99.48 241	100.00 1	98.11 154	99.10 319	98.39 278	98.34 226	99.89 178

WBMVS		98.19 247	98.10 245	98.47 272	99.63 206	99.03 201	100.00 1	99.32 250	95.46 336	98.39 324	99.40 363	99.69 17	98.61 362	98.64 265	92.39 361	97.76 313

UBG		99.36 97	99.27 95	99.63 154	99.63 206	99.01 205	100.00 1	99.43 128	96.99 231	100.00 1	99.92 270	99.69 17	99.99 102	99.74 151	98.06 255	99.88 191

test_vis1_rt		93.10 388	92.93 389	93.58 415	99.63 206	85.07 440	99.99 238	93.71 461	97.49 187	90.96 433	97.10 435	60.40 452	99.95 172	99.24 229	97.90 265	95.72 437

FA-MVS(test-final)		99.00 158	98.75 171	99.73 136	99.63 206	99.43 158	99.83 322	99.43 128	95.84 321	99.52 238	99.37 365	97.84 167	99.96 159	97.63 313	99.68 169	99.79 252

EIA-MVS		99.26 119	99.19 115	99.45 183	99.63 206	98.75 224	100.00 1	99.27 292	96.93 236	99.95 174	100.00 1	97.47 187	99.79 232	99.74 151	99.72 166	99.82 219

Fast-Effi-MVS+		98.40 231	98.02 252	99.55 170	99.63 206	99.06 198	100.00 1	99.15 352	95.07 343	99.42 247	99.95 258	93.26 287	99.73 249	97.44 320	98.24 239	99.87 202

TESTMET0.1,1		99.08 140	98.96 145	99.44 185	99.63 206	99.38 162	100.00 1	99.45 105	95.53 329	99.48 241	100.00 1	99.71 15	99.02 323	96.84 340	99.99 103	99.91 161

MVS_Test		98.93 175	98.65 187	99.77 129	99.62 213	99.50 144	99.99 238	99.19 330	95.52 331	99.96 144	99.86 281	96.54 221	99.98 133	98.65 264	98.48 214	99.82 219

BH-w/o		98.82 186	98.81 165	98.88 250	99.62 213	96.71 340	100.00 1	99.28 279	97.09 222	98.81 297	100.00 1	94.91 252	99.96 159	99.54 206	100.00 1	99.96 133

fmvsm_s_conf0.1_n_2		98.95 172	98.69 183	99.73 136	99.61 215	99.74 104	100.00 1	99.23 313	98.95 37	99.97 137	100.00 1	90.92 324	99.97 142	100.00 1	99.58 179	99.47 292

tpmrst		98.98 166	98.93 152	99.14 233	99.61 215	97.74 304	99.52 376	99.36 226	96.05 312	99.98 131	99.64 328	99.04 101	99.86 208	98.94 247	98.19 244	99.82 219

BH-RMVSNet		98.46 224	98.08 246	99.59 162	99.61 215	99.19 187	100.00 1	99.28 279	97.06 226	98.95 282	100.00 1	88.99 356	99.82 224	98.83 255	100.00 1	99.77 257

testing91		99.18 131	99.10 127	99.41 192	99.60 218	98.43 248	100.00 1	99.43 128	96.76 250	99.82 209	99.92 270	99.05 98	99.98 133	99.62 190	97.67 282	99.81 228

testing99		99.18 131	99.10 127	99.41 192	99.60 218	98.43 248	100.00 1	99.43 128	96.76 250	99.84 198	99.92 270	99.06 96	99.98 133	99.62 190	97.67 282	99.81 228

Effi-MVS+		98.58 211	98.24 234	99.61 158	99.60 218	99.26 177	97.85 449	99.10 376	96.22 307	99.97 137	99.89 276	93.75 277	99.77 238	99.43 212	98.34 226	99.81 228

EPMVS		99.25 123	99.13 123	99.60 160	99.60 218	99.20 186	99.60 367	100.00 1	96.93 236	99.92 184	99.36 366	99.05 98	99.71 252	98.77 257	98.94 197	99.90 172

thisisatest0530		99.37 96	99.27 95	99.69 143	99.59 222	99.41 160	100.00 1	99.46 97	96.46 286	99.90 189	100.00 1	99.44 51	99.85 215	98.97 246	99.58 179	99.80 246

thisisatest0515		99.42 87	99.31 91	99.74 133	99.59 222	99.55 132	100.00 1	99.46 97	96.65 270	99.92 184	100.00 1	99.44 51	99.85 215	99.09 241	99.63 177	99.81 228

XVG-OURS-SEG-HR		98.27 243	98.31 228	98.14 301	99.59 222	95.92 350	100.00 1	99.36 226	98.48 91	99.21 263	100.00 1	89.27 352	99.94 185	99.76 146	99.17 188	98.56 309

BH-untuned		98.64 201	98.65 187	98.60 265	99.59 222	96.17 347	100.00 1	99.28 279	96.67 268	98.41 322	100.00 1	94.52 263	99.83 221	99.41 214	100.00 1	99.81 228

TR-MVS		98.14 248	97.74 262	99.33 212	99.59 222	98.28 266	99.27 400	99.21 326	96.42 292	99.15 268	99.94 264	88.87 359	99.79 232	98.88 251	98.29 235	99.93 155

dongtai		98.29 240	98.25 231	98.42 277	99.58 227	95.86 353	100.00 1	99.44 119	93.46 388	99.69 228	99.97 234	97.53 183	99.51 285	96.28 353	98.27 238	99.89 178

ETV-MVS		99.34 102	99.24 105	99.64 153	99.58 227	99.33 168	100.00 1	99.25 303	97.57 176	99.96 144	100.00 1	97.44 190	99.79 232	99.70 165	99.65 174	99.81 228

Fast-Effi-MVS+-dtu		98.38 232	98.56 198	97.82 330	99.58 227	94.44 389	100.00 1	99.16 347	96.75 252	99.51 239	99.63 332	95.03 250	99.60 258	97.71 310	99.67 171	99.42 294

XVG-OURS		98.30 237	98.36 225	98.13 304	99.58 227	95.91 351	100.00 1	99.36 226	98.69 79	99.23 262	100.00 1	91.20 316	99.92 192	99.34 221	97.82 272	98.56 309

tttt0517		99.34 102	99.23 108	99.67 147	99.57 231	99.38 162	100.00 1	99.46 97	96.33 300	99.89 192	100.00 1	99.44 51	99.84 219	98.93 248	99.46 183	99.78 256

mvs_anonymous		98.80 187	98.60 194	99.38 201	99.57 231	99.24 181	100.00 1	99.21 326	95.87 316	98.92 286	99.82 293	96.39 224	99.03 322	99.13 237	98.50 212	99.88 191

mvsmamba		99.05 145	98.98 142	99.27 224	99.57 231	98.10 279	100.00 1	99.28 279	95.92 315	99.96 144	99.97 234	96.73 215	99.89 197	99.72 157	99.65 174	99.81 228

PMMVS		99.12 137	98.97 144	99.58 166	99.57 231	98.98 210	100.00 1	99.30 265	97.14 217	99.96 144	100.00 1	96.53 222	99.82 224	99.70 165	98.49 213	99.94 144

VortexMVS		98.23 245	98.11 242	98.59 266	99.56 235	99.37 165	99.95 286	99.03 403	96.47 285	98.69 302	99.55 348	95.91 228	98.66 357	99.01 245	94.80 335	97.73 353

guyue		99.21 128	99.07 130	99.62 156	99.55 236	99.29 172	100.00 1	99.32 250	97.66 159	99.96 144	100.00 1	95.84 231	99.84 219	99.63 188	99.67 171	99.75 260

dmvs_re		97.54 276	97.88 257	96.54 377	99.55 236	90.35 425	99.86 317	99.46 97	97.00 230	99.41 252	100.00 1	90.78 327	99.30 310	99.60 195	95.24 320	99.96 133

GG-mvs-BLEND				99.59 162	99.54 238	99.49 148	99.17 418	99.52 72		99.96 144	99.68 319	100.00 1	99.33 309	99.71 161	99.99 103	99.96 133

diffmvs		98.96 169	98.73 174	99.63 154	99.54 238	99.16 191	100.00 1	99.18 337	97.33 204	99.96 144	100.00 1	94.60 262	99.91 194	99.66 181	98.33 229	99.82 219
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		98.41 230	98.11 242	99.31 217	99.54 238	98.55 239	99.18 413	100.00 1	98.64 84	99.79 214	99.04 385	87.61 374	100.00 1	99.30 225	99.89 141	99.40 295
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
mamba_0408		98.63 204	98.40 216	99.34 206	99.53 241	98.52 244	99.24 403	99.16 347	96.43 287	98.95 282	99.98 222	94.47 264	99.76 242	99.21 233	98.62 205	99.75 260

SSM_04072		98.59 209	98.40 216	99.15 231	99.53 241	98.52 244	99.24 403	99.16 347	96.43 287	98.95 282	99.98 222	94.47 264	99.19 316	99.21 233	98.62 205	99.75 260

SSM_0407		98.72 193	98.52 203	99.33 212	99.53 241	98.52 244	99.88 314	99.15 352	96.53 279	98.95 282	100.00 1	94.38 267	99.72 250	99.64 183	98.62 205	99.75 260

SSM_0404		98.76 189	98.56 198	99.35 204	99.53 241	98.65 233	99.80 331	99.15 352	96.53 279	99.47 244	100.00 1	94.38 267	99.76 242	99.64 183	98.59 208	99.64 285

GDP-MVS		99.39 90	99.26 99	99.77 129	99.53 241	99.55 132	100.00 1	99.11 373	97.14 217	99.96 144	100.00 1	99.83 5	99.89 197	98.47 275	99.26 187	99.87 202

KD-MVS_2432*1600		94.15 377	93.08 386	97.35 345	99.53 241	97.83 301	99.63 363	99.19 330	92.88 401	96.29 397	97.68 430	98.84 124	96.70 430	89.73 423	63.92 454	97.53 400

miper_refine_blended		94.15 377	93.08 386	97.35 345	99.53 241	97.83 301	99.63 363	99.19 330	92.88 401	96.29 397	97.68 430	98.84 124	96.70 430	89.73 423	63.92 454	97.53 400

GBi-Net		96.07 351	95.80 341	96.89 367	99.53 241	94.87 370	99.18 413	99.27 292	93.71 377	98.53 314	98.81 403	84.23 402	98.07 406	95.31 369	93.60 344	97.72 360

test1		96.07 351	95.80 341	96.89 367	99.53 241	94.87 370	99.18 413	99.27 292	93.71 377	98.53 314	98.81 403	84.23 402	98.07 406	95.31 369	93.60 344	97.72 360

dp		98.72 193	98.61 192	99.03 240	99.53 241	97.39 316	99.45 382	99.39 213	95.62 326	99.94 177	99.52 352	98.83 126	99.82 224	96.77 346	98.42 218	99.89 178

FMVSNet2		96.22 341	95.60 353	98.06 311	99.53 241	98.33 261	99.45 382	99.27 292	93.71 377	98.03 346	98.84 402	84.23 402	98.10 404	93.97 388	93.40 347	97.73 353

MDTV_nov1_ep13			98.94 150		99.53 241	98.36 258	99.39 389	99.46 97	96.54 278	99.99 123	99.63 332	98.92 118	99.86 208	98.30 285	98.71 204

gm-plane-assit					99.52 253	97.26 325			95.86 318		100.00 1		99.43 300	98.76 258

UA-Net		99.06 143	98.83 162	99.74 133	99.52 253	99.40 161	99.08 430	99.45 105	97.64 163	99.83 201	100.00 1	95.80 232	99.94 185	98.35 280	99.80 163	99.88 191

VDD-MVS		96.58 320	95.99 331	98.34 283	99.52 253	95.33 364	99.18 413	99.38 216	96.64 271	99.77 217	100.00 1	72.51 441	100.00 1	100.00 1	96.94 295	99.70 277

casdiffmvs		98.65 200	98.38 221	99.46 181	99.52 253	98.74 227	100.00 1	99.15 352	96.91 239	99.05 278	100.00 1	92.75 296	99.83 221	99.70 165	98.38 223	99.81 228
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		98.69 198	98.45 210	99.41 192	99.52 253	98.67 231	100.00 1	99.17 346	97.03 228	99.13 269	100.00 1	93.17 288	99.74 247	99.70 165	98.34 226	99.81 228

viewmanbaseed2359cas		98.86 182	98.68 185	99.40 196	99.51 258	98.51 247	99.98 265	99.22 318	97.05 227	99.72 225	100.00 1	94.77 256	99.89 197	99.58 200	98.31 232	99.81 228

testing3		98.44 225	98.37 223	98.65 261	99.51 258	98.32 263	100.00 1	99.62 66	96.43 287	97.93 352	99.99 214	99.11 92	97.81 418	94.88 376	97.80 274	99.82 219

ADS-MVSNet2		98.28 242	98.51 206	97.62 336	99.51 258	95.03 369	99.24 403	99.41 196	95.52 331	99.96 144	99.70 312	97.57 180	97.94 415	97.11 331	98.54 210	99.88 191

ADS-MVSNet		98.70 197	98.51 206	99.28 221	99.51 258	98.39 253	99.24 403	99.44 119	95.52 331	99.96 144	99.70 312	97.57 180	99.58 264	97.11 331	98.54 210	99.88 191

EPP-MVSNet		99.10 139	99.00 139	99.40 196	99.51 258	98.68 230	99.92 302	99.43 128	95.47 335	99.65 231	100.00 1	99.51 37	99.76 242	99.53 208	98.00 256	99.75 260

Syy-MVS		96.17 345	96.57 305	95.00 400	99.50 263	87.37 437	100.00 1	99.57 68	96.23 304	98.07 343	100.00 1	92.41 304	97.81 418	85.34 438	97.96 260	99.82 219

myMVS_eth3d		98.52 220	98.51 206	98.53 269	99.50 263	97.98 288	100.00 1	99.57 68	96.23 304	98.07 343	100.00 1	99.09 94	97.81 418	96.17 354	97.96 260	99.82 219

casdiffmvs_mvg		98.64 201	98.39 219	99.40 196	99.50 263	98.60 236	100.00 1	99.22 318	96.85 243	99.10 272	100.00 1	92.75 296	99.78 237	99.71 161	98.35 225	99.81 228
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		98.05 253	97.76 261	98.92 247	99.50 263	97.10 332	99.77 338	99.30 265	90.20 423	99.72 225	98.71 407	97.71 173	99.86 208	96.75 347	98.20 243	99.81 228

miper_ehance_all_eth		97.81 263	97.66 268	98.23 292	99.49 267	98.37 256	99.99 238	99.11 373	94.78 349	98.25 337	99.21 375	98.18 152	98.57 370	97.35 326	92.61 356	97.76 313

PatchmatchNet		99.03 149	98.96 145	99.26 225	99.49 267	98.33 261	99.38 390	99.45 105	96.64 271	99.96 144	99.58 342	99.49 43	99.50 288	97.63 313	99.00 196	99.93 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
diffmvs_AUTHOR		98.92 176	98.73 174	99.49 178	99.48 269	98.81 220	99.94 294	99.14 359	97.24 211	99.96 144	100.00 1	94.85 253	99.87 207	99.67 176	98.31 232	99.79 252

BP-MVS1		99.56 68	99.48 73	99.79 121	99.48 269	99.61 123	100.00 1	99.32 250	97.34 202	99.94 177	100.00 1	99.74 13	99.89 197	99.75 150	99.72 166	99.87 202

miper_lstm_enhance		97.40 283	97.28 282	97.75 333	99.48 269	97.52 311	100.00 1	99.07 387	94.08 372	98.01 349	99.61 338	97.38 192	97.98 413	96.44 351	91.47 380	97.76 313

c3_l		97.58 273	97.42 274	98.06 311	99.48 269	98.16 273	99.96 279	99.10 376	94.54 358	98.13 341	99.20 377	97.87 164	98.25 392	97.28 327	91.20 383	97.75 324

CDS-MVSNet		98.96 169	98.95 149	99.01 241	99.48 269	98.36 258	99.93 300	99.37 220	96.79 248	99.31 259	99.83 289	99.77 11	98.91 336	98.07 293	97.98 258	99.77 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n		98.77 188	98.42 213	99.82 106	99.47 274	99.52 141	100.00 1	99.27 292	97.53 181	100.00 1	100.00 1	89.73 345	99.96 159	99.84 127	99.93 130	99.97 127

GeoE		98.06 252	97.65 269	99.29 218	99.47 274	98.41 250	100.00 1	99.19 330	94.85 348	98.88 289	100.00 1	91.21 315	99.59 260	97.02 333	98.19 244	99.88 191

cl____		97.54 276	97.32 280	98.18 297	99.47 274	98.14 276	100.00 1	99.10 376	94.16 371	97.60 369	99.63 332	97.52 184	98.65 359	96.47 348	91.97 369	97.76 313

viewmambaseed2359dif		98.57 213	98.34 227	99.28 221	99.46 277	98.23 269	100.00 1	99.16 347	96.26 303	99.11 271	100.00 1	93.12 291	99.79 232	99.61 193	98.33 229	99.80 246

KinetiMVS		98.61 206	98.26 230	99.65 152	99.46 277	99.24 181	99.96 279	99.44 119	97.54 178	99.99 123	99.99 214	90.83 326	99.95 172	97.18 329	99.92 133	99.75 260

AstraMVS		99.03 149	99.01 136	99.09 234	99.46 277	97.66 307	100.00 1	99.23 313	97.83 143	99.95 174	100.00 1	95.52 238	99.86 208	99.74 151	99.39 185	99.74 267

DIV-MVS_self_test		97.52 279	97.35 279	98.05 315	99.46 277	98.11 277	100.00 1	99.10 376	94.21 368	97.62 367	99.63 332	97.65 176	98.29 389	96.47 348	91.98 368	97.76 313

MVSFormer		98.94 174	98.82 163	99.28 221	99.45 281	99.49 148	100.00 1	99.13 365	95.46 336	99.97 137	100.00 1	96.76 212	98.59 367	98.63 267	100.00 1	99.74 267

lupinMVS		99.29 114	99.16 119	99.69 143	99.45 281	99.49 148	100.00 1	99.15 352	97.45 192	99.97 137	100.00 1	96.76 212	99.76 242	99.67 176	100.00 1	99.81 228

WB-MVSnew		97.02 302	97.24 286	96.37 382	99.44 283	97.36 318	100.00 1	99.43 128	96.12 311	99.35 257	99.89 276	93.60 281	98.42 382	88.91 432	98.39 221	93.33 447

TAMVS		98.76 189	98.73 174	98.86 251	99.44 283	97.69 305	99.57 370	99.34 244	96.57 276	99.12 270	99.81 296	98.83 126	99.16 317	97.97 299	97.91 264	99.73 276

tpm2		98.64 201	98.58 196	98.81 255	99.42 285	97.12 330	99.69 356	99.37 220	93.63 382	99.94 177	99.67 320	98.96 112	99.47 292	98.62 269	97.95 262	99.83 213

EC-MVSNet		99.19 130	99.09 129	99.48 179	99.42 285	99.07 196	100.00 1	99.21 326	96.95 234	99.96 144	100.00 1	96.88 210	99.48 290	99.64 183	99.79 164	99.88 191

eth_miper_zixun_eth		97.47 280	97.28 282	98.06 311	99.41 287	97.94 293	99.62 365	99.08 382	94.46 362	98.19 340	99.56 347	96.91 209	98.50 375	96.78 344	91.49 378	97.74 346

gg-mvs-nofinetune		96.95 304	96.10 326	99.50 174	99.41 287	99.36 167	99.07 432	99.52 72	83.69 442	99.96 144	83.60 461	100.00 1	99.20 315	99.68 173	99.99 103	99.96 133

SCA		98.30 237	97.98 254	99.23 227	99.41 287	98.25 268	99.99 238	99.45 105	96.91 239	99.76 219	99.58 342	89.65 347	99.54 277	98.31 282	98.79 199	99.91 161

CostFormer		98.84 184	98.77 169	99.04 239	99.41 287	97.58 310	99.67 359	99.35 237	94.66 354	99.96 144	99.36 366	99.28 79	99.74 247	99.41 214	97.81 273	99.81 228

IterMVS		96.76 310	96.46 311	97.63 334	99.41 287	96.89 335	99.99 238	99.13 365	94.74 352	97.59 370	99.66 322	89.63 349	98.28 390	95.71 361	92.31 363	97.72 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		96.72 313	96.42 313	97.62 336	99.40 292	96.83 337	99.99 238	99.14 359	94.65 355	97.55 371	99.72 307	89.65 347	98.31 388	95.62 365	92.05 366	97.73 353

test-LLR		99.03 149	98.91 155	99.40 196	99.40 292	99.28 174	100.00 1	99.45 105	96.70 264	99.42 247	99.12 378	99.31 71	99.01 324	96.82 341	99.99 103	99.91 161

test-mter		98.96 169	98.82 163	99.40 196	99.40 292	99.28 174	100.00 1	99.45 105	95.44 340	99.42 247	99.12 378	99.70 16	99.01 324	96.82 341	99.99 103	99.91 161

FMVSNet5		95.32 365	95.43 362	94.99 401	99.39 295	92.99 405	99.25 402	99.24 309	90.45 419	97.44 374	98.45 417	95.78 233	94.39 447	87.02 434	91.88 370	97.59 396

icg_test_0407_2		98.30 237	98.45 210	97.85 329	99.38 296	95.36 360	99.99 238	99.18 337	96.72 258	99.58 234	100.00 1	95.17 246	98.45 380	97.84 303	98.15 247	99.74 267

IMVS_0407		98.36 235	98.42 213	98.19 296	99.38 296	95.36 360	99.73 348	99.18 337	96.72 258	99.58 234	100.00 1	95.17 246	99.47 292	97.84 303	98.15 247	99.74 267

IMVS_0404		97.87 259	97.89 256	97.81 331	99.38 296	95.36 360	99.84 320	99.18 337	96.72 258	98.41 322	100.00 1	91.43 313	98.32 387	97.84 303	98.15 247	99.74 267

IMVS_0403		98.37 233	98.39 219	98.29 286	99.38 296	95.36 360	99.97 272	99.18 337	96.72 258	99.68 229	100.00 1	94.61 261	99.77 238	97.84 303	98.15 247	99.74 267

MIMVSNet		97.06 298	96.73 299	98.05 315	99.38 296	96.64 343	98.47 445	99.35 237	93.41 389	99.48 241	98.53 414	89.66 346	97.70 424	94.16 386	98.11 252	99.80 246

IterMVS-LS		97.56 274	97.44 273	97.92 326	99.38 296	97.90 295	99.89 312	99.10 376	94.41 363	98.32 330	99.54 351	97.21 195	98.11 401	97.50 318	91.62 375	97.75 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		94.12 379	92.73 393	98.29 286	99.33 302	95.95 349	99.38 390	99.19 330	74.54 453	98.26 336	86.34 457	86.07 389	99.06 321	91.60 409	99.87 149	99.85 207

CR-MVSNet		98.02 255	97.71 267	98.93 246	99.31 303	98.86 217	99.13 424	99.00 407	96.53 279	99.96 144	98.98 392	96.94 207	98.10 404	91.18 411	98.40 219	99.84 209

Patchmtry		96.81 307	96.37 315	98.14 301	99.31 303	98.55 239	98.91 435	99.00 407	90.45 419	97.92 353	98.98 392	96.94 207	98.12 399	94.27 383	91.53 377	97.75 324

RPMNet		95.26 368	93.82 377	99.56 169	99.31 303	98.86 217	99.13 424	99.42 147	79.82 447	99.96 144	95.13 443	95.69 235	99.98 133	77.54 453	98.40 219	99.84 209

Elysia		98.12 249	97.72 265	99.34 206	99.30 306	98.96 213	99.95 286	99.28 279	96.64 271	99.75 220	99.99 214	88.71 361	99.81 227	95.99 356	99.84 155	99.26 296

StellarMVS		98.12 249	97.72 265	99.34 206	99.30 306	98.96 213	99.95 286	99.28 279	96.64 271	99.75 220	99.99 214	88.71 361	99.81 227	95.99 356	99.84 155	99.26 296

tt0805		96.52 321	96.23 321	97.40 341	99.30 306	93.55 397	99.32 395	99.45 105	96.75 252	97.88 355	99.99 214	79.99 421	99.59 260	97.39 324	95.98 304	99.06 303

VDDNet		96.39 332	95.55 354	98.90 248	99.27 309	97.45 314	99.15 421	99.92 34	91.28 412	99.98 131	100.00 1	73.55 437	100.00 1	99.85 124	96.98 294	99.24 298

LCM-MVSNet-Re		96.52 321	97.21 288	94.44 406	99.27 309	85.80 439	99.85 319	96.61 456	95.98 313	92.75 429	98.48 416	93.97 276	97.55 425	99.58 200	98.43 217	99.98 120

baseline2		98.99 162	98.93 152	99.18 230	99.26 311	99.15 192	100.00 1	99.46 97	96.71 263	96.79 389	100.00 1	99.42 59	99.25 313	98.75 259	99.94 126	99.15 300

fmvsm_s_conf0.5_n_6		99.30 111	99.12 125	99.84 103	99.24 312	99.56 130	100.00 1	99.31 259	98.90 52	100.00 1	100.00 1	94.75 258	99.97 142	99.98 86	99.88 144	100.00 1

GA-MVS		97.72 267	97.27 284	99.06 235	99.24 312	97.93 294	100.00 1	99.24 309	95.80 322	98.99 281	99.64 328	89.77 344	99.36 305	95.12 373	97.62 286	99.89 178

APD_test1		93.07 389	94.14 375	89.85 423	99.18 314	72.49 451	99.76 340	98.90 417	92.86 403	96.35 396	99.94 264	75.56 434	99.91 194	86.73 435	97.98 258	97.15 414

jason		99.11 138	98.96 145	99.59 162	99.17 315	99.31 171	100.00 1	99.13 365	97.38 198	99.83 201	100.00 1	95.54 237	99.72 250	99.57 202	99.97 116	99.74 267
jason: jason.
test_vis1_n		96.69 315	95.81 339	99.32 215	99.14 316	97.98 288	99.97 272	98.98 410	98.45 93	100.00 1	100.00 1	66.44 450	99.99 102	99.78 142	99.57 181	100.00 1

tpm		98.24 244	98.22 238	98.32 285	99.13 317	95.79 354	99.53 375	99.12 371	95.20 342	99.96 144	99.36 366	97.58 178	99.28 312	97.41 322	96.67 299	99.88 191

EI-MVSNet		97.98 256	97.93 255	98.16 300	99.11 318	97.84 300	99.74 343	99.29 273	94.39 364	98.65 305	100.00 1	97.21 195	98.88 342	97.62 316	95.31 315	97.75 324

CVMVSNet		98.56 214	98.47 209	98.82 252	99.11 318	97.67 306	99.74 343	99.47 79	97.57 176	99.06 277	100.00 1	95.72 234	98.97 330	98.21 288	97.33 288	99.83 213

test0.0.03 1		98.12 249	98.03 251	98.39 279	99.11 318	98.07 281	100.00 1	99.93 30	96.70 264	96.91 385	99.95 258	99.31 71	98.19 394	91.93 406	98.44 216	98.91 304

fmvsm_s_conf0.1_n_a		98.71 195	98.36 225	99.78 126	99.09 321	99.42 159	100.00 1	99.26 299	97.42 196	100.00 1	100.00 1	89.78 343	99.96 159	99.82 133	99.85 153	99.97 127

Patchmatch-test		97.83 262	97.42 274	99.06 235	99.08 322	97.66 307	98.66 441	99.21 326	93.65 381	98.25 337	99.58 342	99.47 48	99.57 265	90.25 421	98.59 208	99.95 139

HQP-NCC					99.07 323		100.00 1		99.04 16	99.17 264

ACMP_Plane					99.07 323		100.00 1		99.04 16	99.17 264

NP-MVS					99.07 323	94.81 375					99.97 234

HQP-MVS		97.73 266	97.85 258	97.39 342	99.07 323	94.82 373	100.00 1	99.40 200	99.04 16	99.17 264	99.97 234	88.61 364	99.57 265	99.79 136	95.58 305	97.77 311

plane_prior6					99.06 327	94.80 376						88.58 366

ACMH+	96.20 13	96.49 326	96.33 318	97.00 360	99.06 327	93.80 395	99.81 326	99.31 259	97.32 205	95.89 407	99.97 234	82.62 412	99.54 277	98.34 281	94.63 338	97.65 386

PatchT		95.90 357	94.95 372	98.75 258	99.03 329	98.39 253	99.08 430	99.32 250	85.52 438	99.96 144	94.99 445	97.94 158	98.05 410	80.20 449	98.47 215	99.81 228

plane_prior1					99.02 330

QAPM		98.99 162	98.66 186	99.96 46	99.01 331	99.87 80	99.88 314	99.93 30	97.99 128	98.68 304	100.00 1	93.17 288	100.00 1	99.32 223	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 98	99.01 331	99.95 32	100.00 1	99.75 52	99.37 3	99.99 123	100.00 1	99.76 12	99.60 258	100.00 1	100.00 1	100.00 1

ACMM	97.17 6	97.37 284	97.40 276	97.29 349	99.01 331	94.64 384	100.00 1	99.25 303	98.07 124	98.44 321	99.98 222	87.38 377	99.55 274	99.25 227	95.19 323	97.69 374
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 269	97.74 262	97.36 344	99.01 331	94.76 381	100.00 1	99.34 244	99.30 4	99.00 280	99.97 234	87.49 375	99.57 265	99.96 100	95.58 305	97.75 324
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		99.22 127	98.96 145	99.98 23	99.00 335	99.95 32	99.24 403	99.94 22	98.14 117	98.88 289	100.00 1	95.63 236	100.00 1	99.85 124	100.00 1	100.00 1

HQP_MVS		97.71 268	97.82 260	97.37 343	99.00 335	94.80 376	100.00 1	99.40 200	99.00 27	99.08 275	99.97 234	88.58 366	99.55 274	99.79 136	95.57 309	97.76 313

plane_prior7					99.00 335	94.78 380

3Dnovator+	95.58 15	99.03 149	98.71 179	99.96 46	98.99 338	99.89 71	100.00 1	99.51 76	98.96 34	98.32 330	100.00 1	92.78 295	100.00 1	99.87 121	100.00 1	100.00 1

testgi		96.18 343	95.93 334	96.93 365	98.98 339	94.20 393	100.00 1	99.07 387	97.16 216	96.06 404	99.86 281	84.08 405	97.79 421	90.38 420	97.80 274	98.81 305

ACMP	97.00 8	97.19 290	97.16 290	97.27 352	98.97 340	94.58 388	100.00 1	99.32 250	97.97 132	97.45 373	99.98 222	85.79 393	99.56 269	99.70 165	95.24 320	97.67 380
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ITE_SJBPF				96.84 370	98.96 341	93.49 398		98.12 434	98.12 121	98.35 327	99.97 234	84.45 399	99.56 269	95.63 364	95.25 319	97.49 402

ACMH	96.25 11	96.77 309	96.62 303	97.21 353	98.96 341	94.43 390	99.64 361	99.33 247	97.43 195	96.55 394	99.97 234	83.52 407	99.54 277	99.07 242	95.13 327	97.66 381
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		95.17 370	95.23 365	95.01 399	98.95 343	88.99 433	99.99 238	97.77 445	97.79 148	98.58 310	99.70 312	73.36 438	99.34 308	95.88 358	95.03 330	96.70 423

test_fmvsmconf0.1_n		99.25 123	99.05 132	99.82 106	98.92 344	99.55 132	100.00 1	99.23 313	98.91 49	99.75 220	99.97 234	94.79 255	99.94 185	99.94 108	99.99 103	99.97 127

UniMVSNet_ETH3D		95.28 367	94.41 373	97.89 327	98.91 345	95.14 367	99.13 424	99.35 237	92.11 407	97.17 380	99.66 322	70.28 445	99.36 305	97.88 301	95.18 324	99.16 299

DeepMVS_CX				89.98 422	98.90 346	71.46 453		99.18 337	97.61 170	96.92 383	99.83 289	86.07 389	99.83 221	96.02 355	97.65 284	98.65 307

OpenMVS	95.20 17	98.76 189	98.41 215	99.78 126	98.89 347	99.81 92	99.99 238	99.76 49	98.02 126	98.02 348	100.00 1	91.44 312	100.00 1	99.63 188	99.97 116	99.55 287

test_fmvsmvis_n_1920		99.46 82	99.37 83	99.73 136	98.88 348	99.18 189	100.00 1	99.26 299	98.85 59	99.79 214	100.00 1	97.70 174	100.00 1	99.98 86	99.86 150	100.00 1

1314		99.38 93	99.19 115	99.96 46	98.88 348	99.89 71	99.24 403	99.93 30	98.88 54	98.79 299	100.00 1	97.02 200	100.00 1	100.00 1	100.00 1	100.00 1

JIA-IIPM		97.09 295	96.34 317	99.36 203	98.88 348	98.59 237	99.81 326	99.43 128	84.81 440	99.96 144	90.34 453	98.55 141	99.52 283	97.00 334	98.28 236	99.98 120

LuminaMVS		99.07 142	98.92 154	99.50 174	98.87 351	99.12 194	99.92 302	99.22 318	97.45 192	99.82 209	99.98 222	96.29 225	99.85 215	99.71 161	99.05 195	99.52 289

FMVSNet1		94.45 373	93.63 380	96.89 367	98.87 351	94.87 370	99.18 413	99.27 292	90.95 416	97.31 376	98.81 403	72.89 440	98.07 406	92.61 400	92.81 354	97.72 360

3Dnovator	95.63 14	99.06 143	98.76 170	99.96 46	98.86 353	99.90 64	99.98 265	99.93 30	98.95 37	98.49 319	100.00 1	92.91 293	100.00 1	99.71 161	100.00 1	100.00 1

LPG-MVS_test		97.31 286	97.32 280	97.28 350	98.85 354	94.60 385	100.00 1	99.37 220	97.35 200	98.85 292	99.98 222	86.66 383	99.56 269	99.55 203	95.26 317	97.70 369

LGP-MVS_train				97.28 350	98.85 354	94.60 385		99.37 220	97.35 200	98.85 292	99.98 222	86.66 383	99.56 269	99.55 203	95.26 317	97.70 369

MVStest1		94.27 375	93.30 384	97.19 354	98.83 356	97.18 328	99.93 300	98.79 422	86.80 435	84.88 450	99.04 385	94.32 270	98.25 392	90.55 417	86.57 424	96.12 433

D2MVS		97.63 272	97.83 259	97.05 357	98.83 356	94.60 385	100.00 1	99.82 40	96.89 242	98.28 333	99.03 388	94.05 273	99.47 292	98.58 272	94.97 333	97.09 415

mmtdpeth		94.58 372	94.18 374	95.81 392	98.82 358	91.09 421	99.99 238	98.61 427	96.38 295	100.00 1	97.23 434	76.52 432	99.85 215	99.82 133	80.22 441	96.48 426

dmvs_testset		93.27 386	95.48 359	86.65 429	98.74 359	68.42 458	99.92 302	98.91 415	96.19 309	93.28 426	100.00 1	91.06 320	91.67 454	89.64 425	91.54 376	99.86 206

TinyColmap		95.50 363	95.12 368	96.64 374	98.69 360	93.00 404	99.40 388	97.75 446	96.40 294	96.14 401	99.87 279	79.47 422	99.50 288	93.62 392	94.72 337	97.40 407

XXY-MVS		97.14 294	96.63 302	98.67 260	98.65 361	98.92 215	99.54 374	99.29 273	95.57 328	97.63 365	99.83 289	87.79 373	99.35 307	98.39 278	92.95 352	97.75 324

VPA-MVSNet		97.03 300	96.43 312	98.82 252	98.64 362	99.32 169	99.38 390	99.47 79	96.73 257	98.91 288	98.94 397	87.00 381	99.40 303	99.23 230	89.59 396	97.76 313

VPNet		96.41 328	95.76 344	98.33 284	98.61 363	98.30 265	99.48 379	99.45 105	96.98 232	98.87 291	99.88 278	81.57 415	98.93 334	99.22 232	87.82 413	97.76 313

USDC		95.90 357	95.70 347	96.50 378	98.60 364	92.56 411	100.00 1	98.30 430	97.77 150	96.92 383	99.94 264	81.25 418	99.45 298	93.54 393	94.96 334	97.49 402

tfpnnormal		96.36 333	95.69 350	98.37 281	98.55 365	98.71 228	99.69 356	99.45 105	93.16 397	96.69 393	99.71 309	88.44 368	98.99 327	94.17 384	91.38 381	97.41 406

LTVRE_ROB	95.29 16	96.32 336	96.10 326	96.99 361	98.55 365	93.88 394	99.45 382	99.28 279	94.50 360	96.46 395	99.52 352	84.86 398	99.48 290	97.26 328	95.03 330	97.59 396
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		97.95 257	97.73 264	98.62 263	98.53 367	99.24 181	100.00 1	99.43 128	96.74 255	97.87 356	99.82 293	95.27 241	98.89 339	98.78 256	93.07 350	97.74 346

UniMVSNet (Re)		97.29 288	96.85 295	98.59 266	98.49 368	99.13 193	100.00 1	99.42 147	96.52 282	98.24 339	98.90 400	94.93 251	98.89 339	97.54 317	87.61 414	97.75 324

WR-MVS		97.09 295	96.64 301	98.46 273	98.43 369	99.09 195	99.97 272	99.33 247	95.62 326	97.76 359	99.67 320	91.17 317	98.56 372	98.49 274	89.28 401	97.74 346

XVG-ACMP-BASELINE		96.60 319	96.52 308	96.84 370	98.41 370	93.29 402	99.99 238	99.32 250	97.76 152	98.51 317	99.29 369	81.95 414	99.54 277	98.40 277	95.03 330	97.68 376

UniMVSNet_NR-MVSNet		97.16 292	96.80 296	98.22 293	98.38 371	98.41 250	100.00 1	99.45 105	96.14 310	97.76 359	99.64 328	95.05 249	98.50 375	97.98 296	86.84 420	97.75 324

test_method		91.04 404	91.10 400	90.85 420	98.34 372	77.63 447	100.00 1	98.93 414	76.69 450	96.25 399	98.52 415	70.44 444	97.98 413	89.02 431	91.74 372	96.92 419

nrg030		97.64 269	97.27 284	98.75 258	98.34 372	99.53 137	100.00 1	99.22 318	96.21 308	98.27 335	99.95 258	94.40 266	98.98 328	99.23 230	89.78 395	97.75 324

TransMVSNet (Re)		94.78 371	93.72 378	97.93 325	98.34 372	97.88 297	99.23 410	97.98 441	91.60 410	94.55 417	99.71 309	87.89 371	98.36 385	89.30 428	84.92 428	97.56 398

DU-MVS		96.93 305	96.49 309	98.22 293	98.31 375	98.41 250	100.00 1	99.37 220	96.41 293	97.76 359	99.65 324	92.14 306	98.50 375	97.98 296	86.84 420	97.75 324

NR-MVSNet		96.63 317	96.04 329	98.38 280	98.31 375	98.98 210	99.22 412	99.35 237	95.87 316	94.43 420	99.65 324	92.73 298	98.40 383	96.78 344	88.05 411	97.75 324

tt0320		92.36 394	91.28 397	95.58 394	98.30 377	90.65 423	98.69 440	99.14 359	76.73 449	96.07 403	99.50 355	72.28 442	98.39 384	93.29 396	87.56 415	97.70 369

FC-MVSNet-test		97.84 261	97.63 270	98.45 275	98.30 377	99.05 199	100.00 1	99.43 128	96.63 275	97.61 368	99.82 293	95.19 245	98.57 370	98.64 265	93.05 351	97.73 353

SSC-MVS3.2		95.32 365	94.97 371	96.37 382	98.29 379	92.75 407	100.00 1	99.30 265	95.46 336	98.36 325	99.42 361	78.92 425	98.63 360	93.28 397	91.72 374	97.72 360

Baseline_NR-MVSNet		96.16 347	95.70 347	97.56 339	98.28 380	96.79 339	100.00 1	97.86 444	91.93 409	97.63 365	99.47 358	92.14 306	98.35 386	97.13 330	86.83 422	97.54 399

WR-MVS_H		96.73 311	96.32 319	97.95 322	98.26 381	97.88 297	99.72 351	99.43 128	95.06 344	96.99 382	98.68 409	93.02 292	98.53 373	97.43 321	88.33 410	97.43 405

LF4IMVS		96.19 342	96.18 323	96.23 386	98.26 381	92.09 413	100.00 1	97.89 443	97.82 145	97.94 351	99.87 279	82.71 411	99.38 304	97.41 322	93.71 343	97.20 412

pm-mvs1		95.76 359	95.01 369	98.00 319	98.23 383	97.45 314	99.24 403	99.04 400	93.13 398	95.93 406	99.72 307	86.28 387	98.84 344	95.62 365	87.92 412	97.72 360

MonoMVSNet		98.55 215	98.64 189	98.26 290	98.21 384	95.76 355	99.94 294	99.16 347	96.23 304	99.47 244	99.24 372	96.75 214	99.22 314	99.61 193	99.17 188	99.81 228

CP-MVSNet		96.73 311	96.25 320	98.18 297	98.21 384	98.67 231	99.77 338	99.32 250	95.06 344	97.20 379	99.65 324	90.10 338	98.19 394	98.06 294	88.90 405	97.66 381

v10		96.14 349	95.50 355	98.07 307	98.19 386	97.96 291	99.83 322	99.07 387	92.10 408	98.07 343	98.94 397	91.07 319	98.61 362	92.41 405	89.82 394	97.63 390

PS-CasMVS		96.34 335	95.78 343	98.03 318	98.18 387	98.27 267	99.71 352	99.32 250	94.75 350	96.82 388	99.65 324	86.98 382	98.15 396	97.74 309	88.85 406	97.66 381

V42		96.65 316	96.16 325	98.11 306	98.17 388	98.23 269	99.99 238	99.09 381	93.97 373	98.74 301	99.05 384	91.09 318	98.82 345	95.46 367	89.90 393	97.27 411

v8		96.35 334	95.73 346	98.21 295	98.11 389	98.23 269	99.94 294	99.07 387	92.66 405	98.29 332	99.00 391	91.46 311	98.77 350	94.17 384	88.83 407	97.62 392

OPM-MVS		97.21 289	97.18 289	97.32 347	98.08 390	94.66 382	100.00 1	99.28 279	98.65 83	98.92 286	99.98 222	86.03 391	99.56 269	98.28 286	95.41 311	97.72 360
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tt0320-xc		91.69 400	90.50 404	95.26 396	98.04 391	90.12 427	98.60 443	98.70 425	76.63 451	94.66 416	99.52 352	68.57 448	97.99 412	94.61 378	85.18 427	97.66 381

v2v482		96.70 314	96.18 323	98.27 288	98.04 391	98.39 253	100.00 1	99.13 365	94.19 370	98.58 310	99.08 381	90.48 333	98.67 356	95.69 362	90.44 391	97.75 324

v1192		96.18 343	95.49 357	98.26 290	98.01 393	98.15 274	99.99 238	99.08 382	93.36 391	98.54 313	98.97 395	89.47 350	98.89 339	91.15 412	90.82 386	97.75 324

TranMVSNet+NR-MVSNet		96.45 327	96.01 330	97.79 332	98.00 394	97.62 309	100.00 1	99.35 237	95.98 313	97.31 376	99.64 328	90.09 339	98.00 411	96.89 339	86.80 423	97.75 324

v1144		96.51 323	95.97 333	98.13 304	97.98 395	98.04 285	99.99 238	99.08 382	93.51 386	98.62 308	98.98 392	90.98 323	98.62 361	93.79 390	90.79 387	97.74 346

test_0402		94.35 374	93.70 379	96.32 384	97.92 396	93.60 396	99.61 366	98.85 419	88.19 432	94.68 415	99.48 357	80.01 420	98.58 369	89.39 427	95.15 326	96.77 421

v1240		95.96 355	95.25 364	98.07 307	97.91 397	97.87 299	99.96 279	99.07 387	93.24 395	98.64 307	98.96 396	88.98 357	98.61 362	89.58 426	90.92 385	97.75 324

v144192		96.40 331	95.81 339	98.17 299	97.89 398	98.11 277	99.99 238	99.06 395	93.39 390	98.75 300	99.09 380	90.43 334	98.66 357	93.10 398	90.55 390	97.75 324

v1921920		96.16 347	95.50 355	98.14 301	97.88 399	97.96 291	99.99 238	99.07 387	93.33 392	98.60 309	99.24 372	89.37 351	98.71 354	91.28 410	90.74 388	97.75 324

sc_t1		92.52 392	91.34 396	96.09 388	97.80 400	89.86 428	98.61 442	99.12 371	77.73 448	96.09 402	99.79 302	68.64 447	98.94 333	96.94 335	87.31 417	99.46 293

ttmdpeth		96.24 340	95.88 336	97.32 347	97.80 400	96.61 344	99.95 286	98.77 423	97.80 147	93.42 425	99.28 370	86.42 386	99.01 324	97.63 313	91.84 371	96.33 430

MS-PatchMatch		95.66 361	95.87 337	95.05 398	97.80 400	89.25 431	98.88 436	99.30 265	96.35 298	96.86 386	99.01 390	81.35 417	99.43 300	93.30 395	99.98 113	96.46 427

v148		96.29 337	95.84 338	97.63 334	97.74 403	96.53 345	100.00 1	99.07 387	93.52 385	98.01 349	99.42 361	91.22 314	98.60 365	96.37 352	87.22 419	97.75 324

PEN-MVS		96.01 354	95.48 359	97.58 338	97.74 403	97.26 325	99.90 308	99.29 273	94.55 357	96.79 389	99.55 348	87.38 377	97.84 417	96.92 338	87.24 418	97.65 386

pmmvs4		97.17 291	96.80 296	98.27 288	97.68 405	98.64 234	100.00 1	99.18 337	94.22 367	98.55 312	99.71 309	93.67 278	98.47 378	95.66 363	92.57 359	97.71 368

PS-MVSNAJss		98.03 254	98.06 249	97.94 323	97.63 406	97.33 322	99.89 312	99.23 313	96.27 302	98.03 346	99.59 340	98.75 131	98.78 347	98.52 273	94.61 339	97.70 369

UnsupCasMVSNet_eth		94.25 376	93.89 376	95.34 395	97.63 406	92.13 412	99.73 348	99.36 226	94.88 347	92.78 427	98.63 411	82.72 410	96.53 434	94.57 379	84.73 429	97.36 408

our_test_3		96.51 323	96.35 316	96.98 362	97.61 408	95.05 368	99.98 265	99.01 406	94.68 353	96.77 391	99.06 382	95.87 230	98.14 397	91.81 407	92.37 362	97.75 324

ppachtmachnet_test		96.17 345	95.89 335	97.02 359	97.61 408	95.24 365	99.99 238	99.24 309	93.31 393	96.71 392	99.62 336	94.34 269	98.07 406	89.87 422	92.30 364	97.75 324

EU-MVSNet		96.63 317	96.53 306	96.94 364	97.59 410	96.87 336	99.76 340	99.47 79	96.35 298	96.85 387	99.78 303	92.57 302	96.27 438	95.33 368	91.08 384	97.68 376

v7n		96.06 353	95.42 363	97.99 321	97.58 411	97.35 319	99.86 317	99.11 373	92.81 404	97.91 354	99.49 356	90.99 322	98.92 335	92.51 402	88.49 409	97.70 369

lessismore_v0				96.05 389	97.55 412	91.80 415		99.22 318		91.87 431	99.91 273	83.50 408	98.68 355	92.48 403	90.42 392	97.68 376

K. test v3		95.46 364	95.14 367	96.40 379	97.53 413	93.40 400	99.99 238	99.23 313	95.49 334	92.70 430	99.73 306	84.26 401	98.12 399	93.94 389	93.38 348	97.68 376

test_djsdf		97.55 275	97.38 277	98.07 307	97.50 414	97.99 287	100.00 1	99.13 365	95.46 336	98.47 320	99.85 286	92.01 309	98.59 367	98.63 267	95.36 313	97.62 392

Gipuma		84.73 415	83.50 420	88.40 426	97.50 414	82.21 444	88.87 455	99.05 397	65.81 455	85.71 446	90.49 452	53.70 453	96.31 436	78.64 451	91.74 372	86.67 454
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		95.52 362	94.99 370	97.08 356	97.49 416	96.45 346	100.00 1	99.25 303	93.82 376	96.17 400	99.57 346	87.81 372	97.18 426	94.57 379	86.26 426	97.62 392

OurMVSNet-221017-0		96.14 349	95.98 332	96.62 375	97.49 416	93.44 399	99.92 302	98.16 432	95.86 318	97.65 364	99.95 258	85.71 394	98.78 347	94.93 375	94.18 342	97.64 389

pmmvs6		93.64 382	92.87 390	95.94 391	97.47 418	91.41 418	98.92 434	99.02 404	87.84 433	95.01 412	99.61 338	77.24 431	98.77 350	94.33 382	86.41 425	97.63 390

pmmvs5		95.94 356	95.61 352	96.95 363	97.42 419	94.66 382	100.00 1	98.08 436	93.60 383	97.05 381	99.43 360	87.02 380	98.46 379	95.76 359	92.12 365	97.72 360

SixPastTwentyTwo		95.71 360	95.49 357	96.38 381	97.42 419	93.01 403	99.84 320	98.23 431	94.75 350	95.98 405	99.97 234	85.35 396	98.43 381	94.71 377	93.17 349	97.69 374

mvs_tets		97.00 303	96.69 300	97.94 323	97.41 421	97.27 324	99.60 367	99.18 337	96.51 283	97.35 375	99.69 315	86.53 385	98.91 336	98.84 253	95.09 329	97.65 386

jajsoiax		97.07 297	96.79 298	97.89 327	97.28 422	97.12 330	99.95 286	99.19 330	96.55 277	97.31 376	99.69 315	87.35 379	98.91 336	98.70 261	95.12 328	97.66 381

N_pmnet		91.88 398	93.37 383	87.40 428	97.24 423	66.33 461	99.90 308	91.05 464	89.77 425	95.65 408	98.58 413	90.05 341	98.11 401	85.39 437	92.72 355	97.75 324

anonymousdsp		97.16 292	96.88 293	98.00 319	97.08 424	98.06 283	99.81 326	99.15 352	94.58 356	97.84 358	99.62 336	90.49 332	98.60 365	97.98 296	95.32 314	97.33 410

test_fmvsmconf0.01_n		98.60 208	98.24 234	99.67 147	96.90 425	99.21 185	99.99 238	99.04 400	98.80 70	99.57 236	99.96 251	90.12 337	99.91 194	99.89 116	99.89 141	99.90 172

MDA-MVSNet-bldmvs		91.65 401	89.94 409	96.79 373	96.72 426	96.70 341	99.42 387	98.94 412	88.89 427	66.97 461	98.37 420	81.43 416	95.91 441	89.24 429	89.46 399	97.75 324

MDA-MVSNet_test_wron		92.61 391	91.09 401	97.19 354	96.71 427	97.26 325	100.00 1	99.14 359	88.61 428	67.90 459	98.32 422	89.03 355	96.57 433	90.47 419	89.59 396	97.74 346

new_pmnet		94.11 380	93.47 382	96.04 390	96.60 428	92.82 406	99.97 272	98.91 415	90.21 422	95.26 409	98.05 428	85.89 392	98.14 397	84.28 440	92.01 367	97.16 413

YYNet1		92.44 393	90.92 402	97.03 358	96.20 429	97.06 333	99.99 238	99.14 359	88.21 431	67.93 458	98.43 419	88.63 363	96.28 437	90.64 414	89.08 403	97.74 346

CMPMVS	66.12 22	90.65 405	92.04 395	86.46 430	96.18 430	66.87 460	98.03 448	99.38 216	83.38 443	85.49 447	99.55 348	77.59 428	98.80 346	94.44 381	94.31 341	93.72 445
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DSMNet-mixed		95.18 369	95.21 366	95.08 397	96.03 431	90.21 426	99.65 360	93.64 462	92.91 400	98.34 328	97.40 433	90.05 341	95.51 444	91.02 413	97.86 267	99.51 291

EGC-MVSNET		79.46 420	74.04 428	95.72 393	96.00 432	92.73 408	99.09 429	99.04 400	5.08 465	16.72 465	98.71 407	73.03 439	98.74 353	82.05 445	96.64 300	95.69 438

EG-PatchMatch MVS		92.94 390	92.49 394	94.29 410	95.87 433	87.07 438	99.07 432	98.11 435	93.19 396	88.98 439	98.66 410	70.89 443	99.08 320	92.43 404	95.21 322	96.72 422

testf1		84.40 416	84.79 418	83.23 434	95.71 434	58.71 467	98.79 438	97.75 446	81.58 444	84.94 448	98.07 426	45.33 458	97.73 422	77.09 455	83.85 431	93.24 448

APD_test2		84.40 416	84.79 418	83.23 434	95.71 434	58.71 467	98.79 438	97.75 446	81.58 444	84.94 448	98.07 426	45.33 458	97.73 422	77.09 455	83.85 431	93.24 448

MVP-Stereo		96.51 323	96.48 310	96.60 376	95.65 436	94.25 391	98.84 437	98.16 432	95.85 320	95.23 410	99.04 385	92.54 303	99.13 318	92.98 399	99.98 113	96.43 428
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		93.29 385	92.76 392	94.90 404	95.64 437	91.27 419	99.97 272	98.82 420	87.04 434	94.71 414	98.19 423	83.86 406	96.80 429	84.04 441	92.56 360	96.64 424

OpenMVS_ROB	88.34 20	91.89 397	91.12 399	94.19 412	95.55 438	87.63 436	99.26 401	98.03 438	86.61 437	90.65 437	96.82 437	70.14 446	98.78 347	86.54 436	96.50 303	96.15 431

UnsupCasMVSNet_bld		89.50 408	88.00 414	93.99 413	95.30 439	88.86 434	98.52 444	99.28 279	85.50 439	87.80 443	94.11 447	61.63 451	96.96 428	90.63 415	79.26 442	96.15 431

test20.03		93.11 387	92.85 391	93.88 414	95.19 440	91.83 414	100.00 1	98.87 418	93.68 380	92.76 428	98.88 401	89.20 354	92.71 452	77.88 452	89.19 402	97.09 415

Anonymous20231206		93.45 384	93.17 385	94.30 409	95.00 441	89.69 430	99.98 265	98.43 429	93.30 394	94.50 419	98.59 412	90.52 331	95.73 443	77.46 454	90.73 389	97.48 404

MIMVSNet1		91.96 395	91.20 398	94.23 411	94.94 442	91.69 416	99.34 394	99.22 318	88.23 430	94.18 421	98.45 417	75.52 435	93.41 451	79.37 450	91.49 378	97.60 395

KD-MVS_self_test		91.16 402	90.09 407	94.35 408	94.44 443	91.27 419	99.74 343	99.08 382	90.82 417	94.53 418	94.91 446	86.11 388	94.78 446	82.67 443	68.52 452	96.99 417

mvs5depth		93.81 381	93.00 388	96.23 386	94.25 444	93.33 401	97.43 451	98.07 437	93.47 387	94.15 422	99.58 342	77.52 429	98.97 330	93.64 391	88.92 404	96.39 429

CL-MVSNet_self_test		91.07 403	90.35 406	93.24 416	93.27 445	89.16 432	99.55 372	99.25 303	92.34 406	95.23 410	97.05 436	88.86 360	93.59 450	80.67 447	66.95 453	96.96 418

test_fmvs3		87.19 413	87.02 416	87.71 427	92.69 446	76.64 448	99.96 279	97.27 450	93.55 384	90.82 435	94.03 448	38.00 462	92.19 453	93.49 394	83.35 435	94.32 442

mvsany_test3		89.36 409	88.96 413	90.56 421	91.95 447	78.97 446	99.74 343	96.59 457	96.84 244	89.25 438	96.07 439	52.59 454	97.11 427	95.17 372	82.44 436	95.58 440

Patchmatch-RL test		93.49 383	93.63 380	93.05 417	91.78 448	83.41 443	98.21 447	96.95 453	91.58 411	91.05 432	97.64 432	99.40 63	95.83 442	94.11 387	81.95 438	99.91 161

PM-MVS		88.39 410	87.41 415	91.31 419	91.73 449	82.02 445	99.79 332	96.62 455	91.06 415	90.71 436	95.73 440	48.60 456	95.96 440	90.56 416	81.91 439	95.97 435

pmmvs-eth3d		91.73 399	90.67 403	94.92 403	91.63 450	92.71 409	99.90 308	98.54 428	91.19 413	88.08 441	95.50 441	79.31 424	96.13 439	90.55 417	81.32 440	95.91 436

WB-MVS		88.24 411	90.09 407	82.68 436	91.56 451	69.51 456	100.00 1	98.73 424	90.72 418	87.29 444	98.12 424	92.87 294	85.01 458	62.19 459	89.34 400	93.54 446

test_f		86.87 414	86.06 417	89.28 424	91.45 452	76.37 449	99.87 316	97.11 451	91.10 414	88.46 440	93.05 450	38.31 461	96.66 432	91.77 408	83.46 434	94.82 441

new-patchmatchnet		90.30 407	89.46 411	92.84 418	90.77 453	88.55 435	99.83 322	98.80 421	90.07 424	87.86 442	95.00 444	78.77 426	94.30 448	84.86 439	79.15 443	95.68 439

SSC-MVS		87.61 412	89.47 410	82.04 437	90.63 454	68.77 457	99.99 238	98.66 426	90.34 421	86.70 445	98.08 425	92.72 299	84.12 459	59.41 462	88.71 408	93.22 450

pmmvs3		90.62 406	89.36 412	94.40 407	90.53 455	91.49 417	100.00 1	96.73 454	84.21 441	93.65 424	96.65 438	82.56 413	94.83 445	82.28 444	77.62 446	96.89 420

test_vis3_rt		79.61 419	78.19 424	83.86 433	88.68 456	69.56 455	99.81 326	82.19 469	86.78 436	68.57 457	84.51 460	25.06 466	98.26 391	89.18 430	78.94 444	83.75 457

ambc				88.45 425	86.84 457	70.76 454	97.79 450	98.02 440		90.91 434	95.14 442	38.69 460	98.51 374	94.97 374	84.23 430	96.09 434

EMVS		69.88 427	69.09 430	72.24 445	84.70 458	65.82 462	99.96 279	87.08 468	49.82 462	71.51 456	84.74 459	49.30 455	75.32 462	50.97 464	43.71 460	75.59 460

E-PMN		70.72 426	70.06 429	72.69 444	83.92 459	65.48 463	99.95 286	92.72 463	49.88 461	72.30 455	86.26 458	47.17 457	77.43 461	53.83 463	44.49 459	75.17 461

PMMVS2		79.15 422	77.28 425	84.76 432	82.34 460	72.66 450	99.70 354	95.11 460	71.68 454	84.78 451	90.87 451	32.05 464	89.99 455	75.53 457	63.45 456	91.64 451

TDRefinement		91.93 396	90.48 405	96.27 385	81.60 461	92.65 410	99.10 427	97.61 449	93.96 374	93.77 423	99.85 286	80.03 419	99.53 282	97.82 307	70.59 451	96.63 425

test123		79.44 421	79.23 423	80.05 439	80.03 462	71.72 452	100.00 1	77.93 470	62.52 456	94.81 413	99.69 315	78.21 427	74.53 463	92.57 401	27.33 463	93.90 443

LCM-MVSNet		79.01 423	76.93 426	85.27 431	78.28 463	68.01 459	96.57 452	98.03 438	55.10 459	82.03 452	93.27 449	31.99 465	93.95 449	82.72 442	74.37 448	93.84 444

FPMVS		77.92 424	79.45 422	73.34 443	76.87 464	46.81 470	98.24 446	99.05 397	59.89 458	73.55 454	98.34 421	36.81 463	86.55 456	80.96 446	91.35 382	86.65 455

wuyk23d		28.28 431	29.73 435	23.92 447	75.89 465	32.61 472	66.50 458	12.88 471	16.09 464	14.59 466	16.59 465	12.35 468	32.36 466	39.36 465	13.36 464	6.79 462

MVE	68.59 21	67.22 428	64.68 432	74.84 440	74.67 466	62.32 465	95.84 453	90.87 465	50.98 460	58.72 462	81.05 462	12.20 470	78.95 460	61.06 461	56.75 457	83.24 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 429	63.44 433	73.88 442	61.14 467	63.45 464	95.68 454	87.18 466	79.93 446	47.35 463	80.68 463	22.35 467	72.33 465	61.24 460	35.42 461	85.88 456

testmvs		80.17 418	81.95 421	74.80 441	58.54 468	59.58 466	100.00 1	87.14 467	76.09 452	99.61 233	100.00 1	67.06 449	74.19 464	98.84 253	50.30 458	90.64 453

PMVS	60.66 23	65.98 430	65.05 431	68.75 446	55.06 469	38.40 471	88.19 456	96.98 452	48.30 463	44.82 464	88.52 455	12.22 469	86.49 457	67.58 458	83.79 433	81.35 459
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 425	74.26 427	80.43 438	52.91 470	53.67 469	87.42 457	97.98 441	61.80 457	67.04 460	100.00 1	76.43 433	96.40 435	96.47 348	28.26 462	91.23 452

mmdepth		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

monomultidepth		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

test_blank		0.07 435	0.09 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.79 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

eth-test2					0.00 471

eth-test					0.00 471

uanet_test		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

DCPMVS		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

cdsmvs_eth3d_5k		24.41 432	32.55 434	0.00 448	0.00 471	0.00 473	0.00 459	99.39 213	0.00 466	0.00 467	100.00 1	93.55 282	0.00 467	0.00 466	0.00 465	0.00 463

pcd_1.5k_mvsjas		8.24 434	10.99 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	98.75 131	0.00 467	0.00 466	0.00 465	0.00 463

sosnet-low-res		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

sosnet		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uncertanet		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

Regformer		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

ab-mvs-re		8.33 433	11.11 436	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uanet		0.01 436	0.02 439	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.14 467	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

WAC-MVS						97.98 288								95.74 360

PC_three_1452									98.80 70	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 147	99.03 21	100.00 1	100.00 1	99.56 27	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									98.79 73	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 161

sam_mvs1												99.29 77				99.91 161

sam_mvs												99.33 66

MTGPA								99.42 147

test_post1							99.32 395				88.24 456	99.33 66	99.59 260	98.31 282

test_post											89.05 454	99.49 43	99.59 260

patchmatchnet-post											97.79 429	99.41 61	99.54 277

MTMP							100.00 1	99.18 337

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 47	100.00 1

test_prior2							100.00 1		98.82 65	100.00 1	100.00 1	99.47 48		100.00 1	100.00 1

旧先验2							100.00 1		98.11 122	100.00 1			100.00 1	99.67 176

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 130				100.00 1	99.33 222		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 325

segment_acmp												99.55 29

testdata1							100.00 1		98.77 77

plane_prior5								99.40 200					99.55 274	99.79 136	95.57 309	97.76 313

plane_prior4											99.97 234

plane_prior3						94.79 379			99.03 21	99.08 275

plane_prior2							100.00 1		99.00 27

plane_prior						94.80 376	100.00 1		99.03 21						95.58 305

n2								0.00 472

nn								0.00 472

door-mid								96.32 458

test11								99.42 147

door								96.13 459

HQP5-MVS						94.82 373

BP-MVS														99.79 136

HQP4-MVS										99.17 264			99.57 265			97.77 311

HQP3-MVS								99.40 200							95.58 305

HQP2-MVS												88.61 364

MDTV_nov1_ep13_2view						99.24 181	99.56 371		96.31 301	99.96 144		98.86 122		98.92 249		99.89 178

ACMMP++_ref															94.58 340

ACMMP++															95.17 325

Test By Simon												99.10 93

High-resolution multi-view benchmark