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	99.42 141	98.79 67	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	99.42 141	98.52 81	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 141					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 141					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 141	98.72 71	100.00 1	100.00 1	99.60 21

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 192	100.00 1	100.00 1	99.32 66	99.99 98	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	99.42 141	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		99.42 141	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		99.42 141	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	99.42 141	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	99.42 141	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	97.59 162	100.00 1	100.00 1	98.99 101	99.99 98	100.00 1	100.00 1	100.00 1

GST-MVS		99.64 51	99.53 62	99.95 54	100.00 1	99.86 82	100.00 1	99.79 45	97.72 142	99.95 158	100.00 1	98.39 143	100.00 1	99.96 92	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 66	100.00 1	99.86 82	100.00 1	99.42 141	98.87 51	100.00 1	100.00 1	99.65 19	99.96 147	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	99.43 124	99.05 15	100.00 1	100.00 1	99.45 49	99.99 98	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	99.42 141	97.65 149	100.00 1	100.00 1	99.53 33	99.97 133

train_agg		99.71 33	99.63 41	99.97 34	100.00 1	99.95 32	100.00 1	99.42 141	97.70 144	100.00 1	100.00 1	99.51 37	99.97 133	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 56	100.00 1	99.42 141	97.70 144	100.00 1	100.00 1	99.51 37	99.98 126

agg_prior					100.00 1	99.88 77		99.42 141		100.00 1			99.97 133

HFP-MVS		99.74 25	99.67 30	99.96 45	100.00 1	99.89 70	100.00 1	99.76 49	97.95 126	100.00 1	100.00 1	99.31 68	100.00 1	99.99 67	100.00 1	100.00 1

region2R		99.72 29	99.64 37	99.97 34	100.00 1	99.90 63	100.00 1	99.74 55	97.86 132	100.00 1	100.00 1	99.19 83	100.00 1	99.99 67	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 87	100.00 1	99.64 116	99.98 249	99.44 116	98.35 94	99.99 114	100.00 1	99.04 98	99.96 147	99.98 80	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 74	100.00 1	100.00 1	99.16 85	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 267	96.06 296	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 74	100.00 1	66.97 428	99.16 85	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 78	100.00 1	99.75 99		99.73 56					99.97 133			100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 129	100.00 1	100.00 1	99.20 82	100.00 1	97.91 275	100.00 1	100.00 1

原ACMM1				99.93 70	100.00 1	99.80 92		99.66 63	98.18 103	100.00 1	100.00 1	99.43 54	100.00 1	99.50 192	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	99.44 116	99.06 13	100.00 1	100.00 1	99.56 27	99.99 98	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 45	100.00 1	99.89 70	100.00 1	99.76 49	97.95 126	100.00 1	100.00 1	99.29 74	100.00 1	99.99 67	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 68	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	99.42 141	98.02 116	100.00 1	100.00 1	99.32 66	99.99 98	100.00 1	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 34	100.00 1	99.91 56	100.00 1	99.42 141	97.91 128	100.00 1	100.00 1	99.04 98	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS		99.59 65	99.50 67	99.89 81	100.00 1	99.70 109	100.00 1	99.42 141	97.46 177	100.00 1	100.00 1	98.60 135	99.96 147	99.99 67	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 61	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 54	100.00 1	99.84 87	100.00 1	99.42 141	97.77 139	100.00 1	100.00 1	99.07 92	100.00 1	100.00 1	100.00 1	100.00 1

CPTT-MVS		99.49 76	99.38 78	99.85 93	100.00 1	99.54 129	100.00 1	99.42 141	97.58 163	99.98 120	100.00 1	97.43 185	100.00 1	99.99 67	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	97.99 118	99.99 114	100.00 1	99.72 14	100.00 1	99.96 92	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	97.87 131	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	98.16 104	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 74	99.39 77	99.82 100	100.00 1	99.45 146	100.00 1	99.94 22	96.38 264	100.00 1	100.00 1	98.18 147	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 105	99.25 97	99.44 170	100.00 1	98.32 238	100.00 1	99.86 38	98.04 115	100.00 1	100.00 1	96.10 220	100.00 1	99.55 185	99.73 153	100.00 1

MG-MVS		99.75 23	99.68 28	99.97 34	100.00 1	99.91 56	99.98 249	99.47 79	99.09 10	100.00 1	100.00 1	98.59 136	100.00 1	99.95 98	100.00 1	100.00 1

AdaColmap		99.44 82	99.26 95	99.95 54	100.00 1	99.86 82	99.70 323	99.99 13	98.53 80	99.90 172	100.00 1	95.34 229	100.00 1	99.92 103	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 155	100.00 1	98.79 205	100.00 1	99.54 71	98.58 79	99.96 130	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 138
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MM		99.63 54	99.52 64	99.94 66	99.99 49	99.82 89	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 211	100.00 1	100.00 1	99.97 116	100.00 1

SR-MVS-dyc-post		99.63 54	99.52 64	99.97 34	99.99 49	99.91 56	100.00 1	99.42 141	97.62 154	100.00 1	100.00 1	98.65 132	99.99 98	99.99 67	100.00 1	100.00 1

RE-MVS-def			99.55 59		99.99 49	99.91 56	100.00 1	99.42 141	97.62 154	100.00 1	100.00 1	98.94 112		99.99 67	100.00 1	100.00 1

SF-MVS		99.66 48	99.57 52	99.95 54	99.99 49	99.85 85	100.00 1	99.42 141	97.67 147	100.00 1	100.00 1	99.05 95	99.99 98	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 34	99.99 49	99.90 63	100.00 1	99.79 45	97.97 122	99.97 125	100.00 1	98.97 106	100.00 1	99.94 100	100.00 1	100.00 1

9.14			99.57 52		99.99 49		100.00 1	99.42 141	97.54 166	100.00 1	100.00 1	99.15 87	99.99 98	100.00 1	100.00 1

save fliter					99.99 49	99.93 47	100.00 1	99.42 141	98.93 41

CS-MVS		99.33 99	99.27 91	99.50 163	99.99 49	99.00 195	100.00 1	99.13 332	97.26 195	99.96 130	100.00 1	97.79 164	99.64 230	99.64 170	99.67 159	99.87 194

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 141					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	99.43 124	97.50 173	100.00 1	100.00 1	99.43 54	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	99.42 141	98.91 45	100.00 1	100.00 1	99.22 80	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	99.80 110	99.99 49	99.97 21	99.97 255	99.98 16	98.96 33	100.00 1	100.00 1	99.96 4	99.42 274	100.00 1	100.00 1	100.00 1

CANet		99.40 85	99.24 100	99.89 81	99.99 49	99.76 98	100.00 1	99.73 56	98.40 87	99.78 198	100.00 1	95.28 230	99.96 147	100.00 1	99.99 103	99.96 127

MVS_0304		99.72 29	99.65 34	99.93 70	99.99 49	99.79 93	100.00 1	99.91 35	99.17 6	100.00 1	100.00 1	97.84 161	100.00 1	100.00 1	99.95 121	100.00 1

SPE-MVS-test		99.31 103	99.27 91	99.43 173	99.99 49	98.77 206	100.00 1	99.19 308	97.24 196	99.96 130	100.00 1	97.56 176	99.70 227	99.68 161	99.81 149	99.82 209

MTAPA		99.68 43	99.59 47	99.97 34	99.99 49	99.91 56	100.00 1	99.42 141	98.32 96	99.94 160	100.00 1	98.65 132	100.00 1	99.96 92	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 39	99.63 41	99.87 87	99.99 49	99.64 116	99.95 267	99.44 116	98.35 94	100.00 1	100.00 1	98.98 104	99.97 133	99.98 80	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 48	99.96 130	100.00 1	99.21 81	100.00 1	100.00 1	100.00 1	99.99 112

旧先验1					99.99 49	99.88 77		99.82 40			100.00 1	99.27 77			100.00 1	100.00 1

test222					99.99 49	99.90 63	100.00 1	99.69 62	97.66 148	100.00 1	100.00 1	99.30 73			100.00 1	100.00 1

testdata				99.66 141	99.99 49	98.97 199		99.73 56	97.96 125	100.00 1	100.00 1	99.42 57	100.00 1	99.28 205	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	99.42 141	99.01 26	100.00 1	100.00 1	99.33 63	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 34	99.99 49	99.91 56	100.00 1	99.48 78	97.54 166	100.00 1	100.00 1	98.97 106	99.99 98	99.98 80	100.00 1	100.00 1

MP-MVS		99.61 61	99.49 69	99.98 23	99.99 49	99.94 41	100.00 1	99.42 141	97.82 134	99.99 114	100.00 1	98.20 146	100.00 1	99.99 67	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 54	99.99 49	99.85 85	100.00 1	99.58 67	97.69 146	100.00 1	100.00 1	99.44 50	100.00 1	99.79 127	100.00 1	100.00 1

test12				99.95 54	99.99 49	99.89 70		99.42 141		100.00 1		99.24 79	99.97 133		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 61	99.69 22	99.35 186	99.99 49	98.06 257	100.00 1	99.36 219	99.83 2	100.00 1	100.00 1	98.95 110	99.99 98	100.00 1	99.11 177	100.00 1

HPM-MVS_fast		99.60 64	99.49 69	99.91 75	99.99 49	99.78 94	100.00 1	99.42 141	97.09 205	100.00 1	100.00 1	98.95 110	99.96 147	99.98 80	100.00 1	100.00 1

EPNet		99.62 59	99.69 22	99.42 175	99.99 49	98.37 232	100.00 1	99.89 37	98.83 57	100.00 1	100.00 1	98.97 106	100.00 1	99.90 106	99.61 165	99.89 171
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 34	99.99 49	99.96 24	100.00 1	99.42 141	97.53 168	100.00 1	100.00 1	99.27 77	99.97 133	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		99.28 107	99.35 86	99.05 211	99.99 49	97.15 302	100.00 1	99.47 79	97.44 179	99.42 222	100.00 1	97.83 163	100.00 1	99.99 67	100.00 1	100.00 1

PatchMatch-RL		99.02 142	98.78 154	99.74 125	99.99 49	99.29 162	100.00 1	100.00 1	98.38 88	99.89 175	99.81 265	93.14 267	99.99 98	97.85 277	99.98 113	99.95 133

ACMMP		99.65 49	99.57 52	99.89 81	99.99 49	99.66 114	99.75 312	99.73 56	98.16 104	99.75 202	100.00 1	98.90 117	100.00 1	99.96 92	99.88 136	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	99.67 138	99.99 49	99.07 183	100.00 1	99.44 116	98.30 97	99.90 172	100.00 1	99.18 84	99.99 98	99.91 105	100.00 1	99.94 138

DeepPCF-MVS	98.03 4	98.54 194	99.72 19	94.98 366	99.99 49	84.94 405	100.00 1	99.42 141	99.98 1	100.00 1	100.00 1	98.11 149	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	99.73 318	99.52 72	99.06 13	100.00 1	100.00 1	98.80 126	100.00 1	99.95 98	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		99.31 103	99.13 115	99.87 87	99.99 49	99.71 105	99.55 341	99.46 94	97.32 190	99.82 192	100.00 1	96.85 205	99.97 133	99.14 212	100.00 1	99.92 151

MP-MVS-pluss		99.61 61	99.50 67	99.97 34	99.98 86	99.92 53	100.00 1	99.42 141	97.53 168	99.77 199	100.00 1	98.77 127	100.00 1	99.99 67	100.00 1	99.99 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.67 46	99.57 52	99.97 34	99.98 86	99.92 53	100.00 1	99.42 141	97.83 133	100.00 1	100.00 1	98.89 118	100.00 1	99.98 80	100.00 1	100.00 1

PVSNet_BlendedMVS		98.71 175	98.62 173	98.98 218	99.98 86	99.60 119	100.00 1	100.00 1	97.23 197	100.00 1	99.03 352	96.57 213	99.99 98	100.00 1	94.75 304	97.35 373

PVSNet_Blended		99.48 78	99.36 84	99.83 98	99.98 86	99.60 119	100.00 1	100.00 1	97.79 137	100.00 1	100.00 1	96.57 213	99.99 98	100.00 1	99.88 136	99.90 165

reproduce_monomvs		98.61 185	98.54 181	98.82 226	99.97 90	99.28 163	100.00 1	99.33 239	98.51 82	97.87 323	99.24 336	99.98 3	99.45 270	99.02 220	92.93 321	97.74 314

114514_t		99.39 86	99.25 97	99.81 105	99.97 90	99.48 144	100.00 1	99.42 141	95.53 297	100.00 1	100.00 1	98.37 144	99.95 160	99.97 90	100.00 1	100.00 1

DP-MVS		98.86 165	98.54 181	99.81 105	99.97 90	99.45 146	99.52 345	99.40 194	94.35 332	98.36 293	100.00 1	96.13 219	99.97 133	99.12 215	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 113	99.97 90	99.37 156	99.96 261	99.94 22	98.48 83	100.00 1	100.00 1	98.92 115	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 29	99.65 34	99.91 75	99.97 90	99.72 104	100.00 1	99.47 79	98.43 86	99.88 177	100.00 1	99.14 88	100.00 1	99.97 90	100.00 1	100.00 1

MAR-MVS		99.49 76	99.36 84	99.89 81	99.97 90	99.66 114	99.74 313	99.95 19	97.89 129	100.00 1	100.00 1	96.71 210	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 96	99.93 47	100.00 1	99.42 141	98.81 63	100.00 1	100.00 1	98.98 104	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 96	99.94 41	100.00 1	99.42 141	98.82 59	100.00 1	100.00 1	98.99 101	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 96	99.94 41	100.00 1	99.42 141	98.82 59	100.00 1	100.00 1	98.99 101	100.00 1	100.00 1	100.00 1	100.00 1

MVS_111021_LR		99.70 36	99.65 34	99.88 86	99.96 96	99.70 109	100.00 1	99.97 17	98.96 33	100.00 1	100.00 1	97.93 154	99.95 160	99.99 67	100.00 1	100.00 1

HyFIR lowres test		99.32 101	99.24 100	99.58 155	99.95 100	99.26 166	100.00 1	99.99 13	96.72 238	99.29 234	99.91 243	99.49 43	99.47 265	99.74 141	98.08 225	100.00 1

MVS_111021_HR		99.71 33	99.63 41	99.93 70	99.95 100	99.83 88	100.00 1	100.00 1	98.89 47	100.00 1	100.00 1	97.85 159	99.95 160	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 108	99.38 78	98.96 219	99.95 100	97.06 306	100.00 1	99.40 194	98.83 57	99.88 177	100.00 1	97.01 195	99.86 193	99.47 193	99.84 146	99.97 121

TAPA-MVS	96.40 10	97.64 237	97.37 246	98.45 248	99.94 103	95.70 329	100.00 1	99.40 194	97.65 149	99.53 213	100.00 1	99.31 68	99.66 229	80.48 412	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	98.29 213	98.17 211	98.65 235	99.94 103	97.39 289	99.30 368	99.40 194	95.64 292	97.75 329	100.00 1	92.69 276	99.95 160	98.89 225	99.92 130	98.62 276
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		98.55 191	98.40 193	98.99 216	99.93 105	97.35 292	100.00 1	99.40 194	97.08 207	99.09 246	99.98 198	93.37 261	99.95 160	96.94 305	99.84 146	99.68 253

TestCases				98.99 216	99.93 105	97.35 292		99.40 194	97.08 207	99.09 246	99.98 198	93.37 261	99.95 160	96.94 305	99.84 146	99.68 253

DeepC-MVS	97.84 5	99.00 145	98.80 153	99.60 149	99.93 105	99.03 188	100.00 1	99.40 194	98.61 78	99.33 232	100.00 1	92.23 281	99.95 160	99.74 141	99.96 119	99.83 204
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		99.04 135	99.79 6	96.81 341	99.92 108	90.47 391	100.00 1	99.41 190	98.95 36	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 133

EPNet_dtu		98.53 195	98.23 209	99.43 173	99.92 108	99.01 192	99.96 261	99.47 79	98.80 64	99.96 130	99.96 222	98.56 137	99.30 282	87.78 397	99.68 157	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		99.62 59	99.56 57	99.82 100	99.92 108	99.45 146	100.00 1	99.78 47	98.92 43	99.73 204	100.00 1	97.70 168	100.00 1	99.93 102	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		98.90 162	98.63 172	99.70 134	99.92 108	99.25 168	100.00 1	99.37 213	95.71 291	99.40 228	100.00 1	96.58 212	99.95 160	96.80 312	99.94 125	99.91 154

DPM-MVS		99.63 54	99.51 66	100.00 1	99.90 112	100.00 1	100.00 1	99.43 124	99.00 27	100.00 1	100.00 1	99.58 26	100.00 1	97.64 283	100.00 1	100.00 1

CHOSEN 1792x2688		99.00 145	98.91 143	99.25 202	99.90 112	97.79 277	100.00 1	99.99 13	98.79 67	98.28 300	100.00 1	93.63 257	99.95 160	99.66 168	99.95 121	100.00 1

test2506		99.48 78	99.38 78	99.75 124	99.89 114	99.51 135	99.45 351	100.00 1	98.38 88	99.83 184	100.00 1	98.86 119	99.81 209	99.25 206	98.78 185	99.94 138

ECVR-MVS		98.43 202	98.14 212	99.32 192	99.89 114	98.21 246	99.46 349	100.00 1	98.38 88	99.47 220	100.00 1	87.91 337	99.80 211	99.35 199	98.78 185	99.94 138

test1111		98.42 204	98.12 213	99.29 195	99.88 116	98.15 248	99.46 349	100.00 1	98.36 92	99.42 222	100.00 1	87.91 337	99.79 212	99.31 203	98.78 185	99.94 138

CANet_DTU		99.02 142	98.90 146	99.41 176	99.88 116	98.71 211	100.00 1	99.29 259	98.84 55	100.00 1	100.00 1	94.02 253	100.00 1	98.08 266	99.96 119	99.52 261

dcpmvs_2		98.87 164	99.53 62	96.90 335	99.87 118	90.88 390	99.94 272	99.07 353	98.20 102	100.00 1	100.00 1	98.69 131	99.86 193	100.00 1	100.00 1	99.95 133

mvsany_test1		99.57 66	99.48 72	99.85 93	99.86 119	99.54 129	100.00 1	99.36 219	98.94 39	100.00 1	100.00 1	97.97 152	100.00 1	99.88 110	99.28 172	100.00 1

test_fmvsm_n_1920		99.55 69	99.49 69	99.73 128	99.85 120	99.19 175	100.00 1	99.41 190	98.87 51	100.00 1	100.00 1	97.34 188	100.00 1	99.98 80	99.90 133	100.00 1

fmvsm_s_conf0.5_n		99.21 119	99.01 126	99.83 98	99.84 121	99.53 131	100.00 1	99.38 210	98.29 98	100.00 1	100.00 1	93.62 258	99.99 98	99.99 67	99.93 128	99.98 114

test_fmvs1		98.37 209	98.04 221	99.34 187	99.84 121	98.07 255	100.00 1	99.00 372	98.85 53	100.00 1	100.00 1	85.11 365	99.96 147	99.69 160	99.88 136	100.00 1

PVSNet_0	93.57 19	96.41 296	95.74 313	98.41 251	99.84 121	95.22 335	100.00 1	100.00 1	98.08 113	97.55 338	99.78 271	84.40 368	100.00 1	100.00 1	81.99 401	100.00 1

fmvsm_l_conf0.5_n_a		99.63 54	99.55 59	99.86 90	99.83 124	99.58 123	100.00 1	99.36 219	98.98 30	100.00 1	100.00 1	97.85 159	99.99 98	100.00 1	99.94 125	100.00 1

SDMVSNet		98.49 199	98.08 217	99.73 128	99.82 125	99.53 131	99.99 223	99.45 102	97.62 154	99.38 229	99.86 251	90.06 310	99.88 191	99.92 103	96.61 270	99.79 238

sd_testset		97.81 231	97.48 240	98.79 230	99.82 125	96.80 311	99.32 364	99.45 102	97.62 154	99.38 229	99.86 251	85.56 363	99.77 217	99.72 146	96.61 270	99.79 238

cl22		98.23 218	98.11 214	98.58 241	99.82 125	99.01 192	100.00 1	99.28 265	96.92 219	98.33 296	99.21 339	98.09 151	98.97 301	98.72 235	92.61 324	97.76 281

test_yl		99.51 71	99.37 81	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 175	100.00 1	100.00 1	99.80 6	100.00 1	99.98 80	97.75 247	99.94 138

DCV-MVSNet		99.51 71	99.37 81	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 175	100.00 1	100.00 1	99.80 6	100.00 1	99.98 80	97.75 247	99.94 138

PVSNet_Blended_VisFu		99.33 99	99.18 112	99.78 118	99.82 125	99.49 140	100.00 1	99.95 19	97.36 184	99.63 210	100.00 1	96.45 217	99.95 160	99.79 127	99.65 161	99.89 171

fmvsm_l_conf0.5_n		99.63 54	99.56 57	99.86 90	99.81 131	99.59 121	100.00 1	99.36 219	98.98 30	100.00 1	100.00 1	97.92 155	99.99 98	100.00 1	99.95 121	100.00 1

ET-MVSNet_ETH3D		96.41 296	95.48 327	99.20 205	99.81 131	99.75 99	100.00 1	99.02 369	97.30 194	78.33 417	100.00 1	97.73 166	97.94 379	99.70 153	87.41 382	99.92 151

Anonymous202405211		97.87 228	97.53 239	98.90 222	99.81 131	96.70 314	99.35 362	99.46 94	92.98 366	98.83 266	99.99 193	90.63 300	100.00 1	99.70 153	97.03 261	100.00 1

thres100view900		99.25 114	99.01 126	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 203	99.83 184	99.96 222	97.01 195	100.00 1	99.59 180	97.85 238	99.98 114

tfpn200view9		99.26 110	99.03 124	99.96 45	99.81 131	99.89 70	100.00 1	99.94 22	97.23 197	99.83 184	99.96 222	97.04 191	100.00 1	99.59 180	97.85 238	99.98 114

VNet		99.04 135	98.75 157	99.90 78	99.81 131	99.75 99	99.50 347	99.47 79	98.36 92	100.00 1	99.99 193	94.66 245	100.00 1	99.90 106	97.09 260	99.96 127

thres600view7		99.24 117	99.00 128	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 203	99.83 184	99.96 222	97.01 195	100.00 1	99.54 188	97.77 246	99.97 121

thres400		99.26 110	99.03 124	99.95 54	99.81 131	99.89 70	100.00 1	99.94 22	97.23 197	99.83 184	99.96 222	97.04 191	100.00 1	99.59 180	97.85 238	99.97 121

thres200		99.27 108	99.04 123	99.96 45	99.81 131	99.90 63	100.00 1	99.94 22	97.31 192	99.83 184	99.96 222	97.04 191	100.00 1	99.62 174	97.88 236	99.98 114

WTY-MVS		99.54 70	99.40 76	99.95 54	99.81 131	99.93 47	100.00 1	100.00 1	97.98 120	99.84 181	100.00 1	98.94 112	99.98 126	99.86 114	98.21 218	99.94 138

HY-MVS	96.53 9	99.50 74	99.35 86	99.96 45	99.81 131	99.93 47	99.64 330	100.00 1	97.97 122	99.84 181	99.85 256	98.94 112	99.99 98	99.86 114	98.23 217	99.95 133

fmvsm_l_conf0.5_n_3		99.38 89	99.20 108	99.92 74	99.80 142	99.78 94	100.00 1	99.35 230	98.94 39	100.00 1	100.00 1	94.77 243	99.99 98	99.99 67	99.92 130	100.00 1

fmvsm_s_conf0.5_n_a		99.32 101	99.15 114	99.81 105	99.80 142	99.47 145	100.00 1	99.35 230	98.22 99	100.00 1	100.00 1	95.21 234	99.99 98	99.96 92	99.86 141	99.98 114

Anonymous20231211		96.29 305	95.70 315	98.07 278	99.80 142	97.49 285	99.15 388	99.40 194	89.11 393	97.75 329	99.45 324	88.93 328	98.98 299	98.26 262	89.47 365	97.73 321

LFMVS		97.42 250	96.62 271	99.81 105	99.80 142	99.50 137	99.16 386	99.56 70	94.48 328	100.00 1	100.00 1	79.35 391	100.00 1	99.89 108	97.37 256	99.94 138

RPSCF		97.37 252	98.24 206	94.76 369	99.80 142	84.57 406	99.99 223	99.05 363	94.95 313	99.82 192	100.00 1	94.03 252	100.00 1	98.15 265	98.38 204	99.70 251

fmvsm_s_conf0.5_n_2		98.90 162	98.57 179	99.90 78	99.79 147	99.78 94	100.00 1	99.25 284	98.97 32	100.00 1	100.00 1	89.22 323	99.99 98	100.00 1	99.88 136	99.92 151

test_vis1_n_1920		97.77 233	97.24 254	99.34 187	99.79 147	98.04 259	100.00 1	99.25 284	98.88 48	100.00 1	100.00 1	77.52 396	100.00 1	99.88 110	99.85 144	100.00 1

h-mvs33		97.03 268	96.53 274	98.51 243	99.79 147	95.90 325	99.45 351	99.45 102	98.21 100	100.00 1	99.78 271	97.49 179	99.99 98	99.72 146	74.92 411	99.65 258

Anonymous20240529		96.93 273	96.22 290	99.05 211	99.79 147	97.30 296	99.16 386	99.47 79	88.51 396	98.69 272	100.00 1	83.50 376	100.00 1	99.83 120	97.02 262	99.83 204

PS-MVSNAJ		99.64 51	99.57 52	99.85 93	99.78 151	99.81 90	99.95 267	99.42 141	98.38 88	100.00 1	100.00 1	98.75 128	100.00 1	99.88 110	99.99 103	99.74 246

PCF-MVS	98.23 3	98.69 178	98.37 197	99.62 146	99.78 151	99.02 190	99.23 377	99.06 361	96.43 258	98.08 309	100.00 1	94.72 244	99.95 160	98.16 264	99.91 132	99.90 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+-dtu		98.51 198	98.86 148	97.47 309	99.77 153	94.21 361	100.00 1	98.94 377	97.61 158	99.91 170	98.75 370	95.89 221	99.51 259	99.36 198	99.48 169	98.68 274

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 154	99.95 32	100.00 1	99.42 141	98.69 72	100.00 1	100.00 1	99.52 36	99.99 98	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
sss		99.45 81	99.34 88	99.80 110	99.76 154	99.50 137	100.00 1	99.91 35	97.72 142	99.98 120	99.94 235	98.45 141	100.00 1	99.53 190	98.75 188	99.89 171

sasdasda		99.03 137	98.73 159	99.94 66	99.75 156	99.95 32	100.00 1	99.30 253	97.64 151	100.00 1	100.00 1	95.22 232	99.97 133	99.76 136	96.90 265	99.91 154

canonicalmvs		99.03 137	98.73 159	99.94 66	99.75 156	99.95 32	100.00 1	99.30 253	97.64 151	100.00 1	100.00 1	95.22 232	99.97 133	99.76 136	96.90 265	99.91 154

MGCFI-Net		99.01 144	98.70 165	99.93 70	99.74 158	99.94 41	100.00 1	99.29 259	97.60 161	100.00 1	100.00 1	95.10 236	99.96 147	99.74 141	96.85 267	99.91 154

alignmvs		99.38 89	99.21 104	99.91 75	99.73 159	99.92 53	100.00 1	99.51 76	97.61 158	100.00 1	100.00 1	99.06 93	99.93 176	99.83 120	97.12 259	99.90 165

baseline1		98.91 160	98.61 174	99.81 105	99.71 160	99.77 97	99.78 303	99.44 116	97.51 172	98.81 267	99.99 193	98.25 145	99.76 219	98.60 245	95.41 280	99.89 171

ab-mvs		98.42 204	98.02 223	99.61 147	99.71 160	99.00 195	99.10 394	99.64 64	96.70 240	99.04 252	99.81 265	90.64 299	99.98 126	99.64 170	97.93 233	99.84 200

fmvsm_s_conf0.5_n_3		98.99 148	98.69 166	99.89 81	99.70 162	99.69 111	100.00 1	99.39 207	98.93 41	100.00 1	100.00 1	90.20 306	99.99 98	100.00 1	99.95 121	100.00 1

xiu_mvs_v2_base		99.51 71	99.41 75	99.82 100	99.70 162	99.73 103	99.92 277	99.40 194	98.15 106	100.00 1	100.00 1	98.50 140	100.00 1	99.85 116	99.13 176	99.74 246

test_cas_vis1_n_1920		98.63 184	98.25 203	99.77 121	99.69 164	99.32 159	100.00 1	99.31 249	98.84 55	99.96 130	100.00 1	87.42 344	99.99 98	99.14 212	99.86 141	100.00 1

tpmvs		98.59 187	98.38 195	99.23 203	99.69 164	97.90 269	99.31 367	99.47 79	94.52 326	99.68 208	99.28 334	97.64 171	99.89 184	97.71 281	98.17 222	99.89 171

Test_1112_low_res		98.83 167	98.60 176	99.51 160	99.69 164	98.75 207	99.99 223	99.14 328	96.81 227	98.84 264	99.06 346	97.45 182	99.89 184	98.66 237	97.75 247	99.89 171

1112_ss		98.91 160	98.71 163	99.51 160	99.69 164	98.75 207	99.99 223	99.15 323	96.82 226	98.84 264	100.00 1	97.45 182	99.89 184	98.66 237	97.75 247	99.89 171

MVSMamba_PlusPlus		99.39 86	99.25 97	99.80 110	99.68 168	99.59 121	99.99 223	99.30 253	96.66 245	99.96 130	99.97 207	97.89 157	99.92 179	99.76 136	100.00 1	99.90 165

test_fmvsmconf_n		99.56 67	99.46 74	99.86 90	99.68 168	99.58 123	100.00 1	99.31 249	98.92 43	99.88 177	100.00 1	97.35 187	99.99 98	99.98 80	99.99 103	100.00 1

test_fmvs1_n		97.43 249	96.86 262	99.15 207	99.68 168	97.48 286	99.99 223	98.98 375	98.82 59	100.00 1	100.00 1	74.85 403	99.96 147	99.67 164	99.70 156	100.00 1

balanced_conf03		99.43 83	99.28 90	99.85 93	99.68 168	99.68 112	99.97 255	99.28 265	97.03 210	99.96 130	99.97 207	97.90 156	99.93 176	99.77 134	100.00 1	99.94 138

hse-mvs2		96.79 276	96.38 282	98.04 288	99.68 168	95.54 332	99.81 297	99.42 141	98.21 100	100.00 1	99.80 268	97.49 179	99.46 269	99.72 146	73.27 414	99.12 269

AUN-MVS		96.26 307	95.67 319	98.06 282	99.68 168	95.60 331	99.82 296	99.42 141	96.78 230	99.88 177	99.80 268	94.84 241	99.47 265	97.48 290	73.29 413	99.12 269

mamv4		98.95 156	99.11 117	98.46 246	99.68 168	95.67 330	99.14 390	99.27 275	96.43 258	99.94 160	99.97 207	97.79 164	99.88 191	99.77 134	100.00 1	99.84 200

Vis-MVSNet		98.52 196	98.25 203	99.34 187	99.68 168	98.55 221	99.68 327	99.41 190	97.34 187	99.94 160	100.00 1	90.38 305	99.70 227	99.03 219	98.84 183	99.76 244
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UWE-MVS		99.18 121	99.06 121	99.51 160	99.67 176	98.80 204	100.00 1	99.43 124	96.80 228	99.93 166	99.86 251	99.79 8	99.94 172	97.78 279	98.33 210	99.80 235

ETVMVS		99.16 124	98.98 131	99.69 135	99.67 176	99.56 125	100.00 1	99.45 102	96.36 266	99.98 120	99.95 229	98.65 132	99.64 230	99.11 216	97.63 254	99.88 184

testing222		99.14 126	98.94 139	99.73 128	99.67 176	99.51 135	100.00 1	99.43 124	96.90 222	99.99 114	99.90 245	98.55 138	99.86 193	98.85 227	97.18 258	99.81 218

xiu_mvs_v1_base_debu		99.35 94	99.21 104	99.79 113	99.67 176	99.71 105	99.78 303	99.36 219	98.13 108	100.00 1	100.00 1	97.00 198	100.00 1	99.83 120	99.07 178	99.66 255

xiu_mvs_v1_base		99.35 94	99.21 104	99.79 113	99.67 176	99.71 105	99.78 303	99.36 219	98.13 108	100.00 1	100.00 1	97.00 198	100.00 1	99.83 120	99.07 178	99.66 255

xiu_mvs_v1_base_debi		99.35 94	99.21 104	99.79 113	99.67 176	99.71 105	99.78 303	99.36 219	98.13 108	100.00 1	100.00 1	97.00 198	100.00 1	99.83 120	99.07 178	99.66 255

IB-MVS	96.24 12	97.54 244	96.95 259	99.33 190	99.67 176	98.10 253	100.00 1	99.47 79	97.42 181	99.26 235	99.69 283	98.83 123	99.89 184	99.43 194	78.77 409	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		98.55 191	98.53 183	98.62 237	99.66 183	96.16 321	100.00 1	99.44 116	93.93 342	99.81 195	99.98 198	97.58 172	99.81 209	98.08 266	98.28 213	99.89 171

miper_enhance_ethall		98.33 210	98.27 202	98.51 243	99.66 183	99.04 187	100.00 1	99.22 298	97.53 168	98.51 286	99.38 328	99.49 43	98.75 322	98.02 270	92.61 324	97.76 281

FE-MVS		99.16 124	98.99 130	99.66 141	99.65 185	99.18 177	99.58 338	99.43 124	95.24 308	99.91 170	99.59 308	99.37 62	99.97 133	98.31 257	99.81 149	99.83 204

RRT-MVS		98.75 173	98.52 184	99.44 170	99.65 185	98.57 220	99.90 281	99.08 348	96.51 255	99.96 130	99.95 229	92.59 277	99.96 147	99.60 178	99.45 171	99.81 218

MVSTER		98.58 188	98.52 184	98.77 231	99.65 185	99.68 112	100.00 1	99.29 259	95.63 293	98.65 274	99.80 268	99.78 9	98.88 312	98.59 246	95.31 284	97.73 321

FMVSNet3		97.30 255	96.95 259	98.37 254	99.65 185	99.25 168	99.71 321	99.28 265	94.23 333	98.53 283	98.91 363	93.30 263	98.11 366	95.31 336	93.60 312	97.73 321

cascas		98.43 202	98.07 219	99.50 163	99.65 185	99.02 190	100.00 1	99.22 298	94.21 335	99.72 205	99.98 198	92.03 284	99.93 176	99.68 161	98.12 223	99.54 260

IS-MVSNet		99.08 130	98.91 143	99.59 151	99.65 185	99.38 153	99.78 303	99.24 290	96.70 240	99.51 215	100.00 1	98.44 142	99.52 257	98.47 250	98.39 202	99.88 184

testing11		99.26 110	99.19 109	99.46 167	99.64 191	98.61 217	100.00 1	99.43 124	96.94 216	99.92 167	99.94 235	99.43 54	99.97 133	99.67 164	97.79 245	99.82 209

Vis-MVSNet (Re-imp)		98.99 148	98.89 147	99.29 195	99.64 191	98.89 201	99.98 249	99.31 249	96.74 235	99.48 217	100.00 1	98.11 149	99.10 290	98.39 253	98.34 207	99.89 171

WBMVS		98.19 219	98.10 216	98.47 245	99.63 193	99.03 188	100.00 1	99.32 242	95.46 304	98.39 292	99.40 327	99.69 17	98.61 330	98.64 240	92.39 329	97.76 281

UBG		99.36 93	99.27 91	99.63 144	99.63 193	99.01 192	100.00 1	99.43 124	96.99 213	100.00 1	99.92 240	99.69 17	99.99 98	99.74 141	98.06 226	99.88 184

test_vis1_rt		93.10 355	92.93 356	93.58 379	99.63 193	85.07 404	99.99 223	93.71 425	97.49 174	90.96 397	97.10 399	60.40 416	99.95 160	99.24 208	97.90 235	95.72 401

FA-MVS(test-final)		99.00 145	98.75 157	99.73 128	99.63 193	99.43 149	99.83 293	99.43 124	95.84 289	99.52 214	99.37 329	97.84 161	99.96 147	97.63 284	99.68 157	99.79 238

EIA-MVS		99.26 110	99.19 109	99.45 169	99.63 193	98.75 207	100.00 1	99.27 275	96.93 217	99.95 158	100.00 1	97.47 181	99.79 212	99.74 141	99.72 154	99.82 209

Fast-Effi-MVS+		98.40 207	98.02 223	99.55 159	99.63 193	99.06 185	100.00 1	99.15 323	95.07 310	99.42 222	99.95 229	93.26 264	99.73 224	97.44 291	98.24 216	99.87 194

TESTMET0.1,1		99.08 130	98.96 134	99.44 170	99.63 193	99.38 153	100.00 1	99.45 102	95.53 297	99.48 217	100.00 1	99.71 15	99.02 294	96.84 309	99.99 103	99.91 154

MVS_Test		98.93 159	98.65 169	99.77 121	99.62 200	99.50 137	99.99 223	99.19 308	95.52 299	99.96 130	99.86 251	96.54 215	99.98 126	98.65 239	98.48 195	99.82 209

BH-w/o		98.82 168	98.81 152	98.88 224	99.62 200	96.71 313	100.00 1	99.28 265	97.09 205	98.81 267	100.00 1	94.91 240	99.96 147	99.54 188	100.00 1	99.96 127

fmvsm_s_conf0.1_n_2		98.95 156	98.69 166	99.73 128	99.61 202	99.74 102	100.00 1	99.23 294	98.95 36	99.97 125	100.00 1	90.92 297	99.97 133	100.00 1	99.58 166	99.47 263

tpmrst		98.98 152	98.93 141	99.14 208	99.61 202	97.74 278	99.52 345	99.36 219	96.05 280	99.98 120	99.64 296	99.04 98	99.86 193	98.94 222	98.19 220	99.82 209

BH-RMVSNet		98.46 200	98.08 217	99.59 151	99.61 202	99.19 175	100.00 1	99.28 265	97.06 209	98.95 255	100.00 1	88.99 326	99.82 206	98.83 230	100.00 1	99.77 242

testing91		99.18 121	99.10 118	99.41 176	99.60 205	98.43 224	100.00 1	99.43 124	96.76 231	99.82 192	99.92 240	99.05 95	99.98 126	99.62 174	97.67 251	99.81 218

testing99		99.18 121	99.10 118	99.41 176	99.60 205	98.43 224	100.00 1	99.43 124	96.76 231	99.84 181	99.92 240	99.06 93	99.98 126	99.62 174	97.67 251	99.81 218

Effi-MVS+		98.58 188	98.24 206	99.61 147	99.60 205	99.26 166	97.85 413	99.10 342	96.22 275	99.97 125	99.89 246	93.75 255	99.77 217	99.43 194	98.34 207	99.81 218

EPMVS		99.25 114	99.13 115	99.60 149	99.60 205	99.20 174	99.60 336	100.00 1	96.93 217	99.92 167	99.36 330	99.05 95	99.71 226	98.77 232	98.94 182	99.90 165

thisisatest0530		99.37 92	99.27 91	99.69 135	99.59 209	99.41 151	100.00 1	99.46 94	96.46 257	99.90 172	100.00 1	99.44 50	99.85 199	98.97 221	99.58 166	99.80 235

thisisatest0515		99.42 84	99.31 89	99.74 125	99.59 209	99.55 126	100.00 1	99.46 94	96.65 246	99.92 167	100.00 1	99.44 50	99.85 199	99.09 217	99.63 164	99.81 218

XVG-OURS-SEG-HR		98.27 216	98.31 201	98.14 272	99.59 209	95.92 323	100.00 1	99.36 219	98.48 83	99.21 237	100.00 1	89.27 322	99.94 172	99.76 136	99.17 174	98.56 277

BH-untuned		98.64 181	98.65 169	98.60 239	99.59 209	96.17 320	100.00 1	99.28 265	96.67 244	98.41 291	100.00 1	94.52 247	99.83 203	99.41 196	100.00 1	99.81 218

TR-MVS		98.14 220	97.74 232	99.33 190	99.59 209	98.28 241	99.27 369	99.21 304	96.42 261	99.15 242	99.94 235	88.87 329	99.79 212	98.88 226	98.29 212	99.93 149

dongtai		98.29 213	98.25 203	98.42 250	99.58 214	95.86 326	100.00 1	99.44 116	93.46 355	99.69 207	99.97 207	97.53 177	99.51 259	96.28 322	98.27 215	99.89 171

ETV-MVS		99.34 97	99.24 100	99.64 143	99.58 214	99.33 158	100.00 1	99.25 284	97.57 164	99.96 130	100.00 1	97.44 184	99.79 212	99.70 153	99.65 161	99.81 218

Fast-Effi-MVS+-dtu		98.38 208	98.56 180	97.82 300	99.58 214	94.44 358	100.00 1	99.16 321	96.75 233	99.51 215	99.63 300	95.03 238	99.60 232	97.71 281	99.67 159	99.42 264

XVG-OURS		98.30 211	98.36 199	98.13 275	99.58 214	95.91 324	100.00 1	99.36 219	98.69 72	99.23 236	100.00 1	91.20 290	99.92 179	99.34 200	97.82 241	98.56 277

tttt0517		99.34 97	99.23 103	99.67 138	99.57 218	99.38 153	100.00 1	99.46 94	96.33 269	99.89 175	100.00 1	99.44 50	99.84 202	98.93 223	99.46 170	99.78 241

mvs_anonymous		98.80 169	98.60 176	99.38 184	99.57 218	99.24 170	100.00 1	99.21 304	95.87 284	98.92 256	99.82 262	96.39 218	99.03 293	99.13 214	98.50 193	99.88 184

mvsmamba		99.05 134	98.98 131	99.27 200	99.57 218	98.10 253	100.00 1	99.28 265	95.92 283	99.96 130	99.97 207	96.73 209	99.89 184	99.72 146	99.65 161	99.81 218

PMMVS		99.12 127	98.97 133	99.58 155	99.57 218	98.98 197	100.00 1	99.30 253	97.14 201	99.96 130	100.00 1	96.53 216	99.82 206	99.70 153	98.49 194	99.94 138

dmvs_re		97.54 244	97.88 227	96.54 346	99.55 222	90.35 392	99.86 289	99.46 94	97.00 212	99.41 227	100.00 1	90.78 298	99.30 282	99.60 178	95.24 289	99.96 127

GG-mvs-BLEND				99.59 151	99.54 223	99.49 140	99.17 385	99.52 72		99.96 130	99.68 287	100.00 1	99.33 281	99.71 150	99.99 103	99.96 127

diffmvs		98.96 153	98.73 159	99.63 144	99.54 223	99.16 179	100.00 1	99.18 315	97.33 189	99.96 130	100.00 1	94.60 246	99.91 181	99.66 168	98.33 210	99.82 209
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		98.41 206	98.11 214	99.31 194	99.54 223	98.55 221	99.18 380	100.00 1	98.64 77	99.79 196	99.04 349	87.61 342	100.00 1	99.30 204	99.89 134	99.40 265
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
GDP-MVS		99.39 86	99.26 95	99.77 121	99.53 226	99.55 126	100.00 1	99.11 339	97.14 201	99.96 130	100.00 1	99.83 5	99.89 184	98.47 250	99.26 173	99.87 194

KD-MVS_2432*1600		94.15 344	93.08 353	97.35 314	99.53 226	97.83 275	99.63 332	99.19 308	92.88 368	96.29 364	97.68 394	98.84 121	96.70 394	89.73 387	63.92 418	97.53 364

miper_refine_blended		94.15 344	93.08 353	97.35 314	99.53 226	97.83 275	99.63 332	99.19 308	92.88 368	96.29 364	97.68 394	98.84 121	96.70 394	89.73 387	63.92 418	97.53 364

GBi-Net		96.07 319	95.80 309	96.89 336	99.53 226	94.87 339	99.18 380	99.27 275	93.71 344	98.53 283	98.81 367	84.23 370	98.07 371	95.31 336	93.60 312	97.72 327

test1		96.07 319	95.80 309	96.89 336	99.53 226	94.87 339	99.18 380	99.27 275	93.71 344	98.53 283	98.81 367	84.23 370	98.07 371	95.31 336	93.60 312	97.72 327

dp		98.72 174	98.61 174	99.03 214	99.53 226	97.39 289	99.45 351	99.39 207	95.62 294	99.94 160	99.52 319	98.83 123	99.82 206	96.77 315	98.42 199	99.89 171

FMVSNet2		96.22 309	95.60 321	98.06 282	99.53 226	98.33 236	99.45 351	99.27 275	93.71 344	98.03 313	98.84 366	84.23 370	98.10 369	93.97 354	93.40 315	97.73 321

MDTV_nov1_ep13			98.94 139		99.53 226	98.36 234	99.39 358	99.46 94	96.54 252	99.99 114	99.63 300	98.92 115	99.86 193	98.30 260	98.71 189

gm-plane-assit					99.52 234	97.26 298			95.86 286		100.00 1		99.43 272	98.76 233

UA-Net		99.06 132	98.83 149	99.74 125	99.52 234	99.40 152	99.08 397	99.45 102	97.64 151	99.83 184	100.00 1	95.80 223	99.94 172	98.35 255	99.80 151	99.88 184

VDD-MVS		96.58 288	95.99 299	98.34 256	99.52 234	95.33 333	99.18 380	99.38 210	96.64 247	99.77 199	100.00 1	72.51 408	100.00 1	100.00 1	96.94 264	99.70 251

casdiffmvs		98.65 180	98.38 195	99.46 167	99.52 234	98.74 210	100.00 1	99.15 323	96.91 220	99.05 251	100.00 1	92.75 272	99.83 203	99.70 153	98.38 204	99.81 218
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		98.69 178	98.45 190	99.41 176	99.52 234	98.67 214	100.00 1	99.17 320	97.03 210	99.13 243	100.00 1	93.17 265	99.74 222	99.70 153	98.34 207	99.81 218

testing3		98.44 201	98.37 197	98.65 235	99.51 239	98.32 238	100.00 1	99.62 66	96.43 258	97.93 319	99.99 193	99.11 89	97.81 382	94.88 343	97.80 243	99.82 209

ADS-MVSNet2		98.28 215	98.51 186	97.62 305	99.51 239	95.03 338	99.24 372	99.41 190	95.52 299	99.96 130	99.70 280	97.57 174	97.94 379	97.11 301	98.54 191	99.88 184

ADS-MVSNet		98.70 177	98.51 186	99.28 198	99.51 239	98.39 229	99.24 372	99.44 116	95.52 299	99.96 130	99.70 280	97.57 174	99.58 238	97.11 301	98.54 191	99.88 184

EPP-MVSNet		99.10 129	99.00 128	99.40 180	99.51 239	98.68 213	99.92 277	99.43 124	95.47 303	99.65 209	100.00 1	99.51 37	99.76 219	99.53 190	98.00 227	99.75 245

Syy-MVS		96.17 313	96.57 273	95.00 364	99.50 243	87.37 401	100.00 1	99.57 68	96.23 272	98.07 310	100.00 1	92.41 280	97.81 382	85.34 402	97.96 230	99.82 209

myMVS_eth3d		98.52 196	98.51 186	98.53 242	99.50 243	97.98 262	100.00 1	99.57 68	96.23 272	98.07 310	100.00 1	99.09 91	97.81 382	96.17 323	97.96 230	99.82 209

casdiffmvs_mvg		98.64 181	98.39 194	99.40 180	99.50 243	98.60 218	100.00 1	99.22 298	96.85 224	99.10 245	100.00 1	92.75 272	99.78 216	99.71 150	98.35 206	99.81 218
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		98.05 223	97.76 231	98.92 221	99.50 243	97.10 305	99.77 308	99.30 253	90.20 390	99.72 205	98.71 371	97.71 167	99.86 193	96.75 316	98.20 219	99.81 218

miper_ehance_all_eth		97.81 231	97.66 236	98.23 264	99.49 247	98.37 232	99.99 223	99.11 339	94.78 316	98.25 304	99.21 339	98.18 147	98.57 338	97.35 297	92.61 324	97.76 281

PatchmatchNet		99.03 137	98.96 134	99.26 201	99.49 247	98.33 236	99.38 359	99.45 102	96.64 247	99.96 130	99.58 310	99.49 43	99.50 261	97.63 284	99.00 181	99.93 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
BP-MVS1		99.56 67	99.48 72	99.79 113	99.48 249	99.61 118	100.00 1	99.32 242	97.34 187	99.94 160	100.00 1	99.74 13	99.89 184	99.75 140	99.72 154	99.87 194

miper_lstm_enhance		97.40 251	97.28 250	97.75 302	99.48 249	97.52 284	100.00 1	99.07 353	94.08 339	98.01 316	99.61 306	97.38 186	97.98 377	96.44 320	91.47 347	97.76 281

c3_l		97.58 241	97.42 242	98.06 282	99.48 249	98.16 247	99.96 261	99.10 342	94.54 325	98.13 308	99.20 341	97.87 158	98.25 357	97.28 298	91.20 350	97.75 292

CDS-MVSNet		98.96 153	98.95 138	99.01 215	99.48 249	98.36 234	99.93 275	99.37 213	96.79 229	99.31 233	99.83 259	99.77 11	98.91 306	98.07 268	97.98 228	99.77 242
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.1_n		98.77 170	98.42 191	99.82 100	99.47 253	99.52 134	100.00 1	99.27 275	97.53 168	100.00 1	100.00 1	89.73 315	99.96 147	99.84 119	99.93 128	99.97 121

GeoE		98.06 222	97.65 237	99.29 195	99.47 253	98.41 226	100.00 1	99.19 308	94.85 315	98.88 259	100.00 1	91.21 289	99.59 234	97.02 303	98.19 220	99.88 184

cl____		97.54 244	97.32 248	98.18 268	99.47 253	98.14 250	100.00 1	99.10 342	94.16 338	97.60 336	99.63 300	97.52 178	98.65 328	96.47 317	91.97 337	97.76 281

DIV-MVS_self_test		97.52 247	97.35 247	98.05 286	99.46 256	98.11 251	100.00 1	99.10 342	94.21 335	97.62 334	99.63 300	97.65 170	98.29 354	96.47 317	91.98 336	97.76 281

MVSFormer		98.94 158	98.82 150	99.28 198	99.45 257	99.49 140	100.00 1	99.13 332	95.46 304	99.97 125	100.00 1	96.76 206	98.59 335	98.63 242	100.00 1	99.74 246

lupinMVS		99.29 106	99.16 113	99.69 135	99.45 257	99.49 140	100.00 1	99.15 323	97.45 178	99.97 125	100.00 1	96.76 206	99.76 219	99.67 164	100.00 1	99.81 218

WB-MVSnew		97.02 270	97.24 254	96.37 350	99.44 259	97.36 291	100.00 1	99.43 124	96.12 279	99.35 231	99.89 246	93.60 259	98.42 349	88.91 396	98.39 202	93.33 411

TAMVS		98.76 171	98.73 159	98.86 225	99.44 259	97.69 279	99.57 339	99.34 237	96.57 250	99.12 244	99.81 265	98.83 123	99.16 288	97.97 274	97.91 234	99.73 250

tpm2		98.64 181	98.58 178	98.81 229	99.42 261	97.12 303	99.69 325	99.37 213	93.63 349	99.94 160	99.67 288	98.96 109	99.47 265	98.62 244	97.95 232	99.83 204

EC-MVSNet		99.19 120	99.09 120	99.48 166	99.42 261	99.07 183	100.00 1	99.21 304	96.95 215	99.96 130	100.00 1	96.88 204	99.48 263	99.64 170	99.79 152	99.88 184

eth_miper_zixun_eth		97.47 248	97.28 250	98.06 282	99.41 263	97.94 267	99.62 334	99.08 348	94.46 329	98.19 307	99.56 315	96.91 203	98.50 343	96.78 313	91.49 345	97.74 314

gg-mvs-nofinetune		96.95 272	96.10 294	99.50 163	99.41 263	99.36 157	99.07 399	99.52 72	83.69 409	99.96 130	83.60 425	100.00 1	99.20 287	99.68 161	99.99 103	99.96 127

SCA		98.30 211	97.98 225	99.23 203	99.41 263	98.25 243	99.99 223	99.45 102	96.91 220	99.76 201	99.58 310	89.65 317	99.54 251	98.31 257	98.79 184	99.91 154

CostFormer		98.84 166	98.77 155	99.04 213	99.41 263	97.58 283	99.67 328	99.35 230	94.66 321	99.96 130	99.36 330	99.28 76	99.74 222	99.41 196	97.81 242	99.81 218

IterMVS		96.76 278	96.46 279	97.63 303	99.41 263	96.89 308	99.99 223	99.13 332	94.74 319	97.59 337	99.66 290	89.63 319	98.28 355	95.71 328	92.31 331	97.72 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		96.72 281	96.42 281	97.62 305	99.40 268	96.83 310	99.99 223	99.14 328	94.65 322	97.55 338	99.72 275	89.65 317	98.31 353	95.62 332	92.05 334	97.73 321

test-LLR		99.03 137	98.91 143	99.40 180	99.40 268	99.28 163	100.00 1	99.45 102	96.70 240	99.42 222	99.12 342	99.31 68	99.01 295	96.82 310	99.99 103	99.91 154

test-mter		98.96 153	98.82 150	99.40 180	99.40 268	99.28 163	100.00 1	99.45 102	95.44 307	99.42 222	99.12 342	99.70 16	99.01 295	96.82 310	99.99 103	99.91 154

FMVSNet5		95.32 333	95.43 330	94.99 365	99.39 271	92.99 374	99.25 371	99.24 290	90.45 386	97.44 341	98.45 381	95.78 224	94.39 411	87.02 398	91.88 338	97.59 360

MIMVSNet		97.06 266	96.73 267	98.05 286	99.38 272	96.64 316	98.47 409	99.35 230	93.41 356	99.48 217	98.53 378	89.66 316	97.70 388	94.16 352	98.11 224	99.80 235

IterMVS-LS		97.56 242	97.44 241	97.92 297	99.38 272	97.90 269	99.89 285	99.10 342	94.41 330	98.32 297	99.54 318	97.21 189	98.11 366	97.50 289	91.62 342	97.75 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		94.12 346	92.73 360	98.29 259	99.33 274	95.95 322	99.38 359	99.19 308	74.54 417	98.26 303	86.34 421	86.07 357	99.06 292	91.60 373	99.87 140	99.85 199

CR-MVSNet		98.02 225	97.71 235	98.93 220	99.31 275	98.86 202	99.13 391	99.00 372	96.53 253	99.96 130	98.98 356	96.94 201	98.10 369	91.18 375	98.40 200	99.84 200

Patchmtry		96.81 275	96.37 283	98.14 272	99.31 275	98.55 221	98.91 402	99.00 372	90.45 386	97.92 320	98.98 356	96.94 201	98.12 364	94.27 349	91.53 344	97.75 292

RPMNet		95.26 335	93.82 344	99.56 158	99.31 275	98.86 202	99.13 391	99.42 141	79.82 414	99.96 130	95.13 407	95.69 226	99.98 126	77.54 417	98.40 200	99.84 200

tt0805		96.52 289	96.23 289	97.40 310	99.30 278	93.55 366	99.32 364	99.45 102	96.75 233	97.88 322	99.99 193	79.99 389	99.59 234	97.39 295	95.98 273	99.06 271

VDDNet		96.39 300	95.55 322	98.90 222	99.27 279	97.45 287	99.15 388	99.92 34	91.28 379	99.98 120	100.00 1	73.55 404	100.00 1	99.85 116	96.98 263	99.24 266

LCM-MVSNet-Re		96.52 289	97.21 256	94.44 370	99.27 279	85.80 403	99.85 291	96.61 420	95.98 281	92.75 393	98.48 380	93.97 254	97.55 389	99.58 183	98.43 198	99.98 114

baseline2		98.99 148	98.93 141	99.18 206	99.26 281	99.15 180	100.00 1	99.46 94	96.71 239	96.79 356	100.00 1	99.42 57	99.25 285	98.75 234	99.94 125	99.15 268

GA-MVS		97.72 235	97.27 252	99.06 209	99.24 282	97.93 268	100.00 1	99.24 290	95.80 290	98.99 254	99.64 296	89.77 314	99.36 277	95.12 340	97.62 255	99.89 171

APD_test1		93.07 356	94.14 342	89.85 387	99.18 283	72.49 415	99.76 310	98.90 382	92.86 370	96.35 363	99.94 235	75.56 401	99.91 181	86.73 399	97.98 228	97.15 378

jason		99.11 128	98.96 134	99.59 151	99.17 284	99.31 161	100.00 1	99.13 332	97.38 183	99.83 184	100.00 1	95.54 228	99.72 225	99.57 184	99.97 116	99.74 246
jason: jason.
test_vis1_n		96.69 283	95.81 307	99.32 192	99.14 285	97.98 262	99.97 255	98.98 375	98.45 85	100.00 1	100.00 1	66.44 414	99.99 98	99.78 133	99.57 168	100.00 1

tpm		98.24 217	98.22 210	98.32 258	99.13 286	95.79 327	99.53 344	99.12 338	95.20 309	99.96 130	99.36 330	97.58 172	99.28 284	97.41 293	96.67 268	99.88 184

EI-MVSNet		97.98 226	97.93 226	98.16 271	99.11 287	97.84 274	99.74 313	99.29 259	94.39 331	98.65 274	100.00 1	97.21 189	98.88 312	97.62 287	95.31 284	97.75 292

CVMVSNet		98.56 190	98.47 189	98.82 226	99.11 287	97.67 280	99.74 313	99.47 79	97.57 164	99.06 250	100.00 1	95.72 225	98.97 301	98.21 263	97.33 257	99.83 204

test0.0.03 1		98.12 221	98.03 222	98.39 252	99.11 287	98.07 255	100.00 1	99.93 30	96.70 240	96.91 352	99.95 229	99.31 68	98.19 359	91.93 370	98.44 197	98.91 272

fmvsm_s_conf0.1_n_a		98.71 175	98.36 199	99.78 118	99.09 290	99.42 150	100.00 1	99.26 282	97.42 181	100.00 1	100.00 1	89.78 313	99.96 147	99.82 125	99.85 144	99.97 121

Patchmatch-test		97.83 230	97.42 242	99.06 209	99.08 291	97.66 281	98.66 407	99.21 304	93.65 348	98.25 304	99.58 310	99.47 47	99.57 239	90.25 385	98.59 190	99.95 133

HQP-NCC					99.07 292		100.00 1		99.04 16	99.17 238

ACMP_Plane					99.07 292		100.00 1		99.04 16	99.17 238

NP-MVS					99.07 292	94.81 344					99.97 207

HQP-MVS		97.73 234	97.85 228	97.39 311	99.07 292	94.82 342	100.00 1	99.40 194	99.04 16	99.17 238	99.97 207	88.61 332	99.57 239	99.79 127	95.58 274	97.77 279

plane_prior6					99.06 296	94.80 345						88.58 334

ACMH+	96.20 13	96.49 294	96.33 286	97.00 329	99.06 296	93.80 364	99.81 297	99.31 249	97.32 190	95.89 372	99.97 207	82.62 380	99.54 251	98.34 256	94.63 306	97.65 350

PatchT		95.90 325	94.95 339	98.75 232	99.03 298	98.39 229	99.08 397	99.32 242	85.52 405	99.96 130	94.99 409	97.94 153	98.05 375	80.20 413	98.47 196	99.81 218

plane_prior1					99.02 299

QAPM		98.99 148	98.66 168	99.96 45	99.01 300	99.87 79	99.88 287	99.93 30	97.99 118	98.68 273	100.00 1	93.17 265	100.00 1	99.32 202	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 93	99.01 300	99.95 32	100.00 1	99.75 52	99.37 3	99.99 114	100.00 1	99.76 12	99.60 232	100.00 1	100.00 1	100.00 1

ACMM	97.17 6	97.37 252	97.40 244	97.29 318	99.01 300	94.64 353	100.00 1	99.25 284	98.07 114	98.44 290	99.98 198	87.38 345	99.55 248	99.25 206	95.19 292	97.69 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 237	97.74 232	97.36 313	99.01 300	94.76 350	100.00 1	99.34 237	99.30 4	99.00 253	99.97 207	87.49 343	99.57 239	99.96 92	95.58 274	97.75 292
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		99.22 118	98.96 134	99.98 23	99.00 304	99.95 32	99.24 372	99.94 22	98.14 107	98.88 259	100.00 1	95.63 227	100.00 1	99.85 116	100.00 1	100.00 1

HQP_MVS		97.71 236	97.82 230	97.37 312	99.00 304	94.80 345	100.00 1	99.40 194	99.00 27	99.08 248	99.97 207	88.58 334	99.55 248	99.79 127	95.57 278	97.76 281

plane_prior7					99.00 304	94.78 349

3Dnovator+	95.58 15	99.03 137	98.71 163	99.96 45	98.99 307	99.89 70	100.00 1	99.51 76	98.96 33	98.32 297	100.00 1	92.78 271	100.00 1	99.87 113	100.00 1	100.00 1

testgi		96.18 311	95.93 302	96.93 334	98.98 308	94.20 362	100.00 1	99.07 353	97.16 200	96.06 369	99.86 251	84.08 373	97.79 385	90.38 384	97.80 243	98.81 273

ACMP	97.00 8	97.19 258	97.16 258	97.27 321	98.97 309	94.58 357	100.00 1	99.32 242	97.97 122	97.45 340	99.98 198	85.79 361	99.56 243	99.70 153	95.24 289	97.67 345
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ITE_SJBPF				96.84 339	98.96 310	93.49 367		98.12 398	98.12 111	98.35 294	99.97 207	84.45 367	99.56 243	95.63 331	95.25 288	97.49 366

ACMH	96.25 11	96.77 277	96.62 271	97.21 322	98.96 310	94.43 359	99.64 330	99.33 239	97.43 180	96.55 361	99.97 207	83.52 375	99.54 251	99.07 218	95.13 296	97.66 346
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		95.17 337	95.23 333	95.01 363	98.95 312	88.99 397	99.99 223	97.77 409	97.79 137	98.58 279	99.70 280	73.36 405	99.34 280	95.88 325	95.03 299	96.70 387

test_fmvsmconf0.1_n		99.25 114	99.05 122	99.82 100	98.92 313	99.55 126	100.00 1	99.23 294	98.91 45	99.75 202	99.97 207	94.79 242	99.94 172	99.94 100	99.99 103	99.97 121

UniMVSNet_ETH3D		95.28 334	94.41 340	97.89 298	98.91 314	95.14 336	99.13 391	99.35 230	92.11 374	97.17 347	99.66 290	70.28 411	99.36 277	97.88 276	95.18 293	99.16 267

DeepMVS_CX				89.98 386	98.90 315	71.46 417		99.18 315	97.61 158	96.92 350	99.83 259	86.07 357	99.83 203	96.02 324	97.65 253	98.65 275

OpenMVS	95.20 17	98.76 171	98.41 192	99.78 118	98.89 316	99.81 90	99.99 223	99.76 49	98.02 116	98.02 315	100.00 1	91.44 287	100.00 1	99.63 173	99.97 116	99.55 259

test_fmvsmvis_n_1920		99.46 80	99.37 81	99.73 128	98.88 317	99.18 177	100.00 1	99.26 282	98.85 53	99.79 196	100.00 1	97.70 168	100.00 1	99.98 80	99.86 141	100.00 1

1314		99.38 89	99.19 109	99.96 45	98.88 317	99.89 70	99.24 372	99.93 30	98.88 48	98.79 269	100.00 1	97.02 194	100.00 1	100.00 1	100.00 1	100.00 1

JIA-IIPM		97.09 263	96.34 285	99.36 185	98.88 317	98.59 219	99.81 297	99.43 124	84.81 407	99.96 130	90.34 417	98.55 138	99.52 257	97.00 304	98.28 213	99.98 114

FMVSNet1		94.45 340	93.63 347	96.89 336	98.87 320	94.87 339	99.18 380	99.27 275	90.95 383	97.31 343	98.81 367	72.89 407	98.07 371	92.61 364	92.81 322	97.72 327

3Dnovator	95.63 14	99.06 132	98.76 156	99.96 45	98.86 321	99.90 63	99.98 249	99.93 30	98.95 36	98.49 288	100.00 1	92.91 269	100.00 1	99.71 150	100.00 1	100.00 1

LPG-MVS_test		97.31 254	97.32 248	97.28 319	98.85 322	94.60 354	100.00 1	99.37 213	97.35 185	98.85 262	99.98 198	86.66 351	99.56 243	99.55 185	95.26 286	97.70 335

LGP-MVS_train				97.28 319	98.85 322	94.60 354		99.37 213	97.35 185	98.85 262	99.98 198	86.66 351	99.56 243	99.55 185	95.26 286	97.70 335

MVStest1		94.27 342	93.30 351	97.19 323	98.83 324	97.18 301	99.93 275	98.79 387	86.80 402	84.88 414	99.04 349	94.32 250	98.25 357	90.55 381	86.57 389	96.12 397

D2MVS		97.63 240	97.83 229	97.05 326	98.83 324	94.60 354	100.00 1	99.82 40	96.89 223	98.28 300	99.03 352	94.05 251	99.47 265	98.58 247	94.97 302	97.09 379

mmtdpeth		94.58 339	94.18 341	95.81 358	98.82 326	91.09 389	99.99 223	98.61 391	96.38 264	100.00 1	97.23 398	76.52 399	99.85 199	99.82 125	80.22 405	96.48 390

dmvs_testset		93.27 353	95.48 327	86.65 393	98.74 327	68.42 422	99.92 277	98.91 380	96.19 277	93.28 390	100.00 1	91.06 294	91.67 418	89.64 389	91.54 343	99.86 198

TinyColmap		95.50 331	95.12 336	96.64 343	98.69 328	93.00 373	99.40 357	97.75 410	96.40 263	96.14 368	99.87 249	79.47 390	99.50 261	93.62 358	94.72 305	97.40 371

XXY-MVS		97.14 262	96.63 270	98.67 234	98.65 329	98.92 200	99.54 343	99.29 259	95.57 296	97.63 332	99.83 259	87.79 341	99.35 279	98.39 253	92.95 320	97.75 292

VPA-MVSNet		97.03 268	96.43 280	98.82 226	98.64 330	99.32 159	99.38 359	99.47 79	96.73 237	98.91 258	98.94 361	87.00 349	99.40 275	99.23 209	89.59 363	97.76 281

VPNet		96.41 296	95.76 312	98.33 257	98.61 331	98.30 240	99.48 348	99.45 102	96.98 214	98.87 261	99.88 248	81.57 383	98.93 304	99.22 211	87.82 380	97.76 281

USDC		95.90 325	95.70 315	96.50 347	98.60 332	92.56 379	100.00 1	98.30 394	97.77 139	96.92 350	99.94 235	81.25 386	99.45 270	93.54 359	94.96 303	97.49 366

tfpnnormal		96.36 301	95.69 318	98.37 254	98.55 333	98.71 211	99.69 325	99.45 102	93.16 364	96.69 360	99.71 277	88.44 336	98.99 298	94.17 350	91.38 348	97.41 370

LTVRE_ROB	95.29 16	96.32 304	96.10 294	96.99 330	98.55 333	93.88 363	99.45 351	99.28 265	94.50 327	96.46 362	99.52 319	84.86 366	99.48 263	97.26 299	95.03 299	97.59 360
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		97.95 227	97.73 234	98.62 237	98.53 335	99.24 170	100.00 1	99.43 124	96.74 235	97.87 323	99.82 262	95.27 231	98.89 309	98.78 231	93.07 318	97.74 314

UniMVSNet (Re)		97.29 256	96.85 263	98.59 240	98.49 336	99.13 181	100.00 1	99.42 141	96.52 254	98.24 306	98.90 364	94.93 239	98.89 309	97.54 288	87.61 381	97.75 292

WR-MVS		97.09 263	96.64 269	98.46 246	98.43 337	99.09 182	99.97 255	99.33 239	95.62 294	97.76 326	99.67 288	91.17 291	98.56 340	98.49 249	89.28 368	97.74 314

XVG-ACMP-BASELINE		96.60 287	96.52 276	96.84 339	98.41 338	93.29 371	99.99 223	99.32 242	97.76 141	98.51 286	99.29 333	81.95 382	99.54 251	98.40 252	95.03 299	97.68 341

UniMVSNet_NR-MVSNet		97.16 260	96.80 264	98.22 265	98.38 339	98.41 226	100.00 1	99.45 102	96.14 278	97.76 326	99.64 296	95.05 237	98.50 343	97.98 271	86.84 385	97.75 292

test_method		91.04 368	91.10 365	90.85 384	98.34 340	77.63 411	100.00 1	98.93 379	76.69 415	96.25 366	98.52 379	70.44 410	97.98 377	89.02 395	91.74 340	96.92 383

nrg030		97.64 237	97.27 252	98.75 232	98.34 340	99.53 131	100.00 1	99.22 298	96.21 276	98.27 302	99.95 229	94.40 248	98.98 299	99.23 209	89.78 362	97.75 292

TransMVSNet (Re)		94.78 338	93.72 345	97.93 296	98.34 340	97.88 271	99.23 377	97.98 405	91.60 377	94.55 381	99.71 277	87.89 339	98.36 351	89.30 392	84.92 392	97.56 362

DU-MVS		96.93 273	96.49 277	98.22 265	98.31 343	98.41 226	100.00 1	99.37 213	96.41 262	97.76 326	99.65 292	92.14 282	98.50 343	97.98 271	86.84 385	97.75 292

NR-MVSNet		96.63 285	96.04 297	98.38 253	98.31 343	98.98 197	99.22 379	99.35 230	95.87 284	94.43 384	99.65 292	92.73 274	98.40 350	96.78 313	88.05 378	97.75 292

FC-MVSNet-test		97.84 229	97.63 238	98.45 248	98.30 345	99.05 186	100.00 1	99.43 124	96.63 249	97.61 335	99.82 262	95.19 235	98.57 338	98.64 240	93.05 319	97.73 321

Baseline_NR-MVSNet		96.16 315	95.70 315	97.56 308	98.28 346	96.79 312	100.00 1	97.86 408	91.93 376	97.63 332	99.47 323	92.14 282	98.35 352	97.13 300	86.83 387	97.54 363

WR-MVS_H		96.73 279	96.32 287	97.95 293	98.26 347	97.88 271	99.72 320	99.43 124	95.06 311	96.99 349	98.68 373	93.02 268	98.53 341	97.43 292	88.33 377	97.43 369

LF4IMVS		96.19 310	96.18 291	96.23 353	98.26 347	92.09 381	100.00 1	97.89 407	97.82 134	97.94 318	99.87 249	82.71 379	99.38 276	97.41 293	93.71 311	97.20 376

pm-mvs1		95.76 327	95.01 337	98.00 290	98.23 349	97.45 287	99.24 372	99.04 366	93.13 365	95.93 371	99.72 275	86.28 355	98.84 314	95.62 332	87.92 379	97.72 327

MonoMVSNet		98.55 191	98.64 171	98.26 262	98.21 350	95.76 328	99.94 272	99.16 321	96.23 272	99.47 220	99.24 336	96.75 208	99.22 286	99.61 177	99.17 174	99.81 218

CP-MVSNet		96.73 279	96.25 288	98.18 268	98.21 350	98.67 214	99.77 308	99.32 242	95.06 311	97.20 346	99.65 292	90.10 308	98.19 359	98.06 269	88.90 372	97.66 346

v10		96.14 317	95.50 323	98.07 278	98.19 352	97.96 265	99.83 293	99.07 353	92.10 375	98.07 310	98.94 361	91.07 293	98.61 330	92.41 369	89.82 361	97.63 354

PS-CasMVS		96.34 303	95.78 311	98.03 289	98.18 353	98.27 242	99.71 321	99.32 242	94.75 317	96.82 355	99.65 292	86.98 350	98.15 361	97.74 280	88.85 373	97.66 346

V42		96.65 284	96.16 293	98.11 277	98.17 354	98.23 244	99.99 223	99.09 347	93.97 340	98.74 271	99.05 348	91.09 292	98.82 315	95.46 334	89.90 360	97.27 375

v8		96.35 302	95.73 314	98.21 267	98.11 355	98.23 244	99.94 272	99.07 353	92.66 372	98.29 299	99.00 355	91.46 286	98.77 320	94.17 350	88.83 374	97.62 356

OPM-MVS		97.21 257	97.18 257	97.32 316	98.08 356	94.66 351	100.00 1	99.28 265	98.65 76	98.92 256	99.98 198	86.03 359	99.56 243	98.28 261	95.41 280	97.72 327
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v2v482		96.70 282	96.18 291	98.27 260	98.04 357	98.39 229	100.00 1	99.13 332	94.19 337	98.58 279	99.08 345	90.48 303	98.67 326	95.69 329	90.44 358	97.75 292

v1192		96.18 311	95.49 325	98.26 262	98.01 358	98.15 248	99.99 223	99.08 348	93.36 358	98.54 282	98.97 359	89.47 320	98.89 309	91.15 376	90.82 353	97.75 292

TranMVSNet+NR-MVSNet		96.45 295	96.01 298	97.79 301	98.00 359	97.62 282	100.00 1	99.35 230	95.98 281	97.31 343	99.64 296	90.09 309	98.00 376	96.89 308	86.80 388	97.75 292

v1144		96.51 291	95.97 301	98.13 275	97.98 360	98.04 259	99.99 223	99.08 348	93.51 353	98.62 277	98.98 356	90.98 296	98.62 329	93.79 356	90.79 354	97.74 314

test_0402		94.35 341	93.70 346	96.32 351	97.92 361	93.60 365	99.61 335	98.85 384	88.19 399	94.68 380	99.48 322	80.01 388	98.58 337	89.39 391	95.15 295	96.77 385

v1240		95.96 323	95.25 332	98.07 278	97.91 362	97.87 273	99.96 261	99.07 353	93.24 362	98.64 276	98.96 360	88.98 327	98.61 330	89.58 390	90.92 352	97.75 292

v144192		96.40 299	95.81 307	98.17 270	97.89 363	98.11 251	99.99 223	99.06 361	93.39 357	98.75 270	99.09 344	90.43 304	98.66 327	93.10 362	90.55 357	97.75 292

v1921920		96.16 315	95.50 323	98.14 272	97.88 364	97.96 265	99.99 223	99.07 353	93.33 359	98.60 278	99.24 336	89.37 321	98.71 324	91.28 374	90.74 355	97.75 292

ttmdpeth		96.24 308	95.88 304	97.32 316	97.80 365	96.61 317	99.95 267	98.77 388	97.80 136	93.42 389	99.28 334	86.42 354	99.01 295	97.63 284	91.84 339	96.33 394

MS-PatchMatch		95.66 329	95.87 305	95.05 362	97.80 365	89.25 395	98.88 403	99.30 253	96.35 267	96.86 353	99.01 354	81.35 385	99.43 272	93.30 361	99.98 113	96.46 391

v148		96.29 305	95.84 306	97.63 303	97.74 367	96.53 318	100.00 1	99.07 353	93.52 352	98.01 316	99.42 326	91.22 288	98.60 333	96.37 321	87.22 384	97.75 292

PEN-MVS		96.01 322	95.48 327	97.58 307	97.74 367	97.26 298	99.90 281	99.29 259	94.55 324	96.79 356	99.55 316	87.38 345	97.84 381	96.92 307	87.24 383	97.65 350

pmmvs4		97.17 259	96.80 264	98.27 260	97.68 369	98.64 216	100.00 1	99.18 315	94.22 334	98.55 281	99.71 277	93.67 256	98.47 346	95.66 330	92.57 327	97.71 334

PS-MVSNAJss		98.03 224	98.06 220	97.94 294	97.63 370	97.33 295	99.89 285	99.23 294	96.27 271	98.03 313	99.59 308	98.75 128	98.78 317	98.52 248	94.61 307	97.70 335

UnsupCasMVSNet_eth		94.25 343	93.89 343	95.34 360	97.63 370	92.13 380	99.73 318	99.36 219	94.88 314	92.78 391	98.63 375	82.72 378	96.53 398	94.57 345	84.73 393	97.36 372

our_test_3		96.51 291	96.35 284	96.98 331	97.61 372	95.05 337	99.98 249	99.01 371	94.68 320	96.77 358	99.06 346	95.87 222	98.14 362	91.81 371	92.37 330	97.75 292

ppachtmachnet_test		96.17 313	95.89 303	97.02 328	97.61 372	95.24 334	99.99 223	99.24 290	93.31 360	96.71 359	99.62 304	94.34 249	98.07 371	89.87 386	92.30 332	97.75 292

EU-MVSNet		96.63 285	96.53 274	96.94 333	97.59 374	96.87 309	99.76 310	99.47 79	96.35 267	96.85 354	99.78 271	92.57 278	96.27 402	95.33 335	91.08 351	97.68 341

v7n		96.06 321	95.42 331	97.99 292	97.58 375	97.35 292	99.86 289	99.11 339	92.81 371	97.91 321	99.49 321	90.99 295	98.92 305	92.51 366	88.49 376	97.70 335

lessismore_v0				96.05 355	97.55 376	91.80 383		99.22 298		91.87 395	99.91 243	83.50 376	98.68 325	92.48 367	90.42 359	97.68 341

K. test v3		95.46 332	95.14 335	96.40 348	97.53 377	93.40 369	99.99 223	99.23 294	95.49 302	92.70 394	99.73 274	84.26 369	98.12 364	93.94 355	93.38 316	97.68 341

test_djsdf		97.55 243	97.38 245	98.07 278	97.50 378	97.99 261	100.00 1	99.13 332	95.46 304	98.47 289	99.85 256	92.01 285	98.59 335	98.63 242	95.36 282	97.62 356

Gipuma		84.73 379	83.50 384	88.40 390	97.50 378	82.21 408	88.87 419	99.05 363	65.81 419	85.71 410	90.49 416	53.70 417	96.31 400	78.64 415	91.74 340	86.67 418
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		95.52 330	94.99 338	97.08 325	97.49 380	96.45 319	100.00 1	99.25 284	93.82 343	96.17 367	99.57 314	87.81 340	97.18 390	94.57 345	86.26 391	97.62 356

OurMVSNet-221017-0		96.14 317	95.98 300	96.62 344	97.49 380	93.44 368	99.92 277	98.16 396	95.86 286	97.65 331	99.95 229	85.71 362	98.78 317	94.93 342	94.18 310	97.64 353

pmmvs6		93.64 349	92.87 357	95.94 357	97.47 382	91.41 386	98.92 401	99.02 369	87.84 400	95.01 377	99.61 306	77.24 398	98.77 320	94.33 348	86.41 390	97.63 354

pmmvs5		95.94 324	95.61 320	96.95 332	97.42 383	94.66 351	100.00 1	98.08 400	93.60 350	97.05 348	99.43 325	87.02 348	98.46 347	95.76 326	92.12 333	97.72 327

SixPastTwentyTwo		95.71 328	95.49 325	96.38 349	97.42 383	93.01 372	99.84 292	98.23 395	94.75 317	95.98 370	99.97 207	85.35 364	98.43 348	94.71 344	93.17 317	97.69 339

mvs_tets		97.00 271	96.69 268	97.94 294	97.41 385	97.27 297	99.60 336	99.18 315	96.51 255	97.35 342	99.69 283	86.53 353	98.91 306	98.84 228	95.09 298	97.65 350

jajsoiax		97.07 265	96.79 266	97.89 298	97.28 386	97.12 303	99.95 267	99.19 308	96.55 251	97.31 343	99.69 283	87.35 347	98.91 306	98.70 236	95.12 297	97.66 346

N_pmnet		91.88 363	93.37 350	87.40 392	97.24 387	66.33 425	99.90 281	91.05 428	89.77 392	95.65 373	98.58 377	90.05 311	98.11 366	85.39 401	92.72 323	97.75 292

anonymousdsp		97.16 260	96.88 261	98.00 290	97.08 388	98.06 257	99.81 297	99.15 323	94.58 323	97.84 325	99.62 304	90.49 302	98.60 333	97.98 271	95.32 283	97.33 374

test_fmvsmconf0.01_n		98.60 186	98.24 206	99.67 138	96.90 389	99.21 173	99.99 223	99.04 366	98.80 64	99.57 212	99.96 222	90.12 307	99.91 181	99.89 108	99.89 134	99.90 165

MDA-MVSNet-bldmvs		91.65 365	89.94 373	96.79 342	96.72 390	96.70 314	99.42 356	98.94 377	88.89 394	66.97 425	98.37 384	81.43 384	95.91 405	89.24 393	89.46 366	97.75 292

MDA-MVSNet_test_wron		92.61 358	91.09 366	97.19 323	96.71 391	97.26 298	100.00 1	99.14 328	88.61 395	67.90 423	98.32 386	89.03 325	96.57 397	90.47 383	89.59 363	97.74 314

new_pmnet		94.11 347	93.47 349	96.04 356	96.60 392	92.82 375	99.97 255	98.91 380	90.21 389	95.26 374	98.05 392	85.89 360	98.14 362	84.28 404	92.01 335	97.16 377

YYNet1		92.44 359	90.92 367	97.03 327	96.20 393	97.06 306	99.99 223	99.14 328	88.21 398	67.93 422	98.43 383	88.63 331	96.28 401	90.64 378	89.08 370	97.74 314

CMPMVS	66.12 22	90.65 369	92.04 362	86.46 394	96.18 394	66.87 424	98.03 412	99.38 210	83.38 410	85.49 411	99.55 316	77.59 395	98.80 316	94.44 347	94.31 309	93.72 409
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DSMNet-mixed		95.18 336	95.21 334	95.08 361	96.03 395	90.21 393	99.65 329	93.64 426	92.91 367	98.34 295	97.40 397	90.05 311	95.51 408	91.02 377	97.86 237	99.51 262

EGC-MVSNET		79.46 384	74.04 392	95.72 359	96.00 396	92.73 376	99.09 396	99.04 366	5.08 429	16.72 429	98.71 371	73.03 406	98.74 323	82.05 409	96.64 269	95.69 402

EG-PatchMatch MVS		92.94 357	92.49 361	94.29 374	95.87 397	87.07 402	99.07 399	98.11 399	93.19 363	88.98 403	98.66 374	70.89 409	99.08 291	92.43 368	95.21 291	96.72 386

testf1		84.40 380	84.79 382	83.23 398	95.71 398	58.71 431	98.79 405	97.75 410	81.58 411	84.94 412	98.07 390	45.33 422	97.73 386	77.09 419	83.85 395	93.24 412

APD_test2		84.40 380	84.79 382	83.23 398	95.71 398	58.71 431	98.79 405	97.75 410	81.58 411	84.94 412	98.07 390	45.33 422	97.73 386	77.09 419	83.85 395	93.24 412

MVP-Stereo		96.51 291	96.48 278	96.60 345	95.65 400	94.25 360	98.84 404	98.16 396	95.85 288	95.23 375	99.04 349	92.54 279	99.13 289	92.98 363	99.98 113	96.43 392
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		93.29 352	92.76 359	94.90 368	95.64 401	91.27 387	99.97 255	98.82 385	87.04 401	94.71 379	98.19 387	83.86 374	96.80 393	84.04 405	92.56 328	96.64 388

OpenMVS_ROB	88.34 20	91.89 362	91.12 364	94.19 376	95.55 402	87.63 400	99.26 370	98.03 402	86.61 404	90.65 401	96.82 401	70.14 412	98.78 317	86.54 400	96.50 272	96.15 395

UnsupCasMVSNet_bld		89.50 372	88.00 378	93.99 377	95.30 403	88.86 398	98.52 408	99.28 265	85.50 406	87.80 407	94.11 411	61.63 415	96.96 392	90.63 379	79.26 406	96.15 395

test20.03		93.11 354	92.85 358	93.88 378	95.19 404	91.83 382	100.00 1	98.87 383	93.68 347	92.76 392	98.88 365	89.20 324	92.71 416	77.88 416	89.19 369	97.09 379

Anonymous20231206		93.45 351	93.17 352	94.30 373	95.00 405	89.69 394	99.98 249	98.43 393	93.30 361	94.50 383	98.59 376	90.52 301	95.73 407	77.46 418	90.73 356	97.48 368

MIMVSNet1		91.96 360	91.20 363	94.23 375	94.94 406	91.69 384	99.34 363	99.22 298	88.23 397	94.18 385	98.45 381	75.52 402	93.41 415	79.37 414	91.49 345	97.60 359

KD-MVS_self_test		91.16 366	90.09 371	94.35 372	94.44 407	91.27 387	99.74 313	99.08 348	90.82 384	94.53 382	94.91 410	86.11 356	94.78 410	82.67 407	68.52 416	96.99 381

mvs5depth		93.81 348	93.00 355	96.23 353	94.25 408	93.33 370	97.43 415	98.07 401	93.47 354	94.15 386	99.58 310	77.52 396	98.97 301	93.64 357	88.92 371	96.39 393

CL-MVSNet_self_test		91.07 367	90.35 370	93.24 380	93.27 409	89.16 396	99.55 341	99.25 284	92.34 373	95.23 375	97.05 400	88.86 330	93.59 414	80.67 411	66.95 417	96.96 382

test_fmvs3		87.19 377	87.02 380	87.71 391	92.69 410	76.64 412	99.96 261	97.27 414	93.55 351	90.82 399	94.03 412	38.00 426	92.19 417	93.49 360	83.35 399	94.32 406

mvsany_test3		89.36 373	88.96 377	90.56 385	91.95 411	78.97 410	99.74 313	96.59 421	96.84 225	89.25 402	96.07 403	52.59 418	97.11 391	95.17 339	82.44 400	95.58 404

Patchmatch-RL test		93.49 350	93.63 347	93.05 381	91.78 412	83.41 407	98.21 411	96.95 417	91.58 378	91.05 396	97.64 396	99.40 60	95.83 406	94.11 353	81.95 402	99.91 154

PM-MVS		88.39 374	87.41 379	91.31 383	91.73 413	82.02 409	99.79 302	96.62 419	91.06 382	90.71 400	95.73 404	48.60 420	95.96 404	90.56 380	81.91 403	95.97 399

pmmvs-eth3d		91.73 364	90.67 368	94.92 367	91.63 414	92.71 377	99.90 281	98.54 392	91.19 380	88.08 405	95.50 405	79.31 392	96.13 403	90.55 381	81.32 404	95.91 400

WB-MVS		88.24 375	90.09 371	82.68 400	91.56 415	69.51 420	100.00 1	98.73 389	90.72 385	87.29 408	98.12 388	92.87 270	85.01 422	62.19 423	89.34 367	93.54 410

test_f		86.87 378	86.06 381	89.28 388	91.45 416	76.37 413	99.87 288	97.11 415	91.10 381	88.46 404	93.05 414	38.31 425	96.66 396	91.77 372	83.46 398	94.82 405

new-patchmatchnet		90.30 371	89.46 375	92.84 382	90.77 417	88.55 399	99.83 293	98.80 386	90.07 391	87.86 406	95.00 408	78.77 393	94.30 412	84.86 403	79.15 407	95.68 403

SSC-MVS		87.61 376	89.47 374	82.04 401	90.63 418	68.77 421	99.99 223	98.66 390	90.34 388	86.70 409	98.08 389	92.72 275	84.12 423	59.41 426	88.71 375	93.22 414

pmmvs3		90.62 370	89.36 376	94.40 371	90.53 419	91.49 385	100.00 1	96.73 418	84.21 408	93.65 388	96.65 402	82.56 381	94.83 409	82.28 408	77.62 410	96.89 384

test_vis3_rt		79.61 383	78.19 388	83.86 397	88.68 420	69.56 419	99.81 297	82.19 433	86.78 403	68.57 421	84.51 424	25.06 430	98.26 356	89.18 394	78.94 408	83.75 421

ambc				88.45 389	86.84 421	70.76 418	97.79 414	98.02 404		90.91 398	95.14 406	38.69 424	98.51 342	94.97 341	84.23 394	96.09 398

EMVS		69.88 391	69.09 394	72.24 409	84.70 422	65.82 426	99.96 261	87.08 432	49.82 426	71.51 420	84.74 423	49.30 419	75.32 426	50.97 428	43.71 424	75.59 424

E-PMN		70.72 390	70.06 393	72.69 408	83.92 423	65.48 427	99.95 267	92.72 427	49.88 425	72.30 419	86.26 422	47.17 421	77.43 425	53.83 427	44.49 423	75.17 425

PMMVS2		79.15 386	77.28 389	84.76 396	82.34 424	72.66 414	99.70 323	95.11 424	71.68 418	84.78 415	90.87 415	32.05 428	89.99 419	75.53 421	63.45 420	91.64 415

TDRefinement		91.93 361	90.48 369	96.27 352	81.60 425	92.65 378	99.10 394	97.61 413	93.96 341	93.77 387	99.85 256	80.03 387	99.53 256	97.82 278	70.59 415	96.63 389

test123		79.44 385	79.23 387	80.05 403	80.03 426	71.72 416	100.00 1	77.93 434	62.52 420	94.81 378	99.69 283	78.21 394	74.53 427	92.57 365	27.33 427	93.90 407

LCM-MVSNet		79.01 387	76.93 390	85.27 395	78.28 427	68.01 423	96.57 416	98.03 402	55.10 423	82.03 416	93.27 413	31.99 429	93.95 413	82.72 406	74.37 412	93.84 408

FPMVS		77.92 388	79.45 386	73.34 407	76.87 428	46.81 434	98.24 410	99.05 363	59.89 422	73.55 418	98.34 385	36.81 427	86.55 420	80.96 410	91.35 349	86.65 419

wuyk23d		28.28 395	29.73 399	23.92 411	75.89 429	32.61 436	66.50 422	12.88 435	16.09 428	14.59 430	16.59 429	12.35 432	32.36 430	39.36 429	13.36 428	6.79 426

MVE	68.59 21	67.22 392	64.68 396	74.84 404	74.67 430	62.32 429	95.84 417	90.87 429	50.98 424	58.72 426	81.05 426	12.20 434	78.95 424	61.06 425	56.75 421	83.24 422
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 393	63.44 397	73.88 406	61.14 431	63.45 428	95.68 418	87.18 430	79.93 413	47.35 427	80.68 427	22.35 431	72.33 429	61.24 424	35.42 425	85.88 420

testmvs		80.17 382	81.95 385	74.80 405	58.54 432	59.58 430	100.00 1	87.14 431	76.09 416	99.61 211	100.00 1	67.06 413	74.19 428	98.84 228	50.30 422	90.64 417

PMVS	60.66 23	65.98 394	65.05 395	68.75 410	55.06 433	38.40 435	88.19 420	96.98 416	48.30 427	44.82 428	88.52 419	12.22 433	86.49 421	67.58 422	83.79 397	81.35 423
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 389	74.26 391	80.43 402	52.91 434	53.67 433	87.42 421	97.98 405	61.80 421	67.04 424	100.00 1	76.43 400	96.40 399	96.47 317	28.26 426	91.23 416

mmdepth		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.07 399	0.09 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.79 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

eth-test2					0.00 435

eth-test					0.00 435

uanet_test		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

cdsmvs_eth3d_5k		24.41 396	32.55 398	0.00 412	0.00 435	0.00 437	0.00 423	99.39 207	0.00 430	0.00 431	100.00 1	93.55 260	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		8.24 398	10.99 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	98.75 128	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

ab-mvs-re		8.33 397	11.11 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	100.00 1	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.01 400	0.02 403	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.14 431	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						97.98 262								95.74 327

PC_three_1452									98.80 64	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 141	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 67	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 154

sam_mvs1												99.29 74				99.91 154

sam_mvs												99.33 63

MTGPA								99.42 141

test_post1							99.32 364				88.24 420	99.33 63	99.59 234	98.31 257

test_post											89.05 418	99.49 43	99.59 234

patchmatchnet-post											97.79 393	99.41 59	99.54 251

MTMP							100.00 1	99.18 315

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 59	100.00 1	100.00 1	99.47 47		100.00 1	100.00 1

旧先验2							100.00 1		98.11 112	100.00 1			100.00 1	99.67 164

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 120				100.00 1	99.33 201		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 296

segment_acmp												99.55 29

testdata1							100.00 1		98.77 70

plane_prior5								99.40 194					99.55 248	99.79 127	95.57 278	97.76 281

plane_prior4											99.97 207

plane_prior3						94.79 348			99.03 21	99.08 248

plane_prior2							100.00 1		99.00 27

plane_prior						94.80 345	100.00 1		99.03 21						95.58 274

n2								0.00 436

nn								0.00 436

door-mid								96.32 422

test11								99.42 141

door								96.13 423

HQP5-MVS						94.82 342

BP-MVS														99.79 127

HQP4-MVS										99.17 238			99.57 239			97.77 279

HQP3-MVS								99.40 194							95.58 274

HQP2-MVS												88.61 332

MDTV_nov1_ep13_2view						99.24 170	99.56 340		96.31 270	99.96 130		98.86 119		98.92 224		99.89 171

ACMMP++_ref															94.58 308

ACMMP++															95.17 294

Test By Simon												99.10 90

High-resolution multi-view benchmark