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.

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

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

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 51	99.51 63	100.00 1	99.90 108	100.00 1	100.00 1	99.43 122	99.00 27	100.00 1	100.00 1	99.58 22	100.00 1	97.64 266	100.00 1	100.00 1

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 138	98.79 60	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

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

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 138	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 138	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 138	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 138	99.04 15	100.00 1	100.00 1	99.53 29	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 138					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 5	100.00 1	99.42 138	99.04 15	100.00 1	100.00 1	99.53 29

test_part2					100.00 1	99.99 5				100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 64	100.00 1	100.00 1	100.00 1	100.00 1

CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 10	100.00 1	100.00 1	99.39 57	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 61	99.07 10	100.00 1	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	100.00 1

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 182	100.00 1	100.00 1	99.32 62	99.99 94	100.00 1	100.00 1

DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 138	98.91 41	100.00 1	100.00 1	99.22 76	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 44	99.96 119	100.00 1	99.21 77	100.00 1	100.00 1	100.00 1	99.99 109

APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 116	99.06 12	100.00 1	100.00 1	99.56 23	99.99 94	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
FOURS1					100.00 1	99.97 21	100.00 1	99.42 138	98.52 74	100.00 1

CHOSEN 280x420		99.85 3	99.87 1	99.80 101	99.99 49	99.97 21	99.97 241	99.98 16	98.96 32	100.00 1	100.00 1	99.96 4	99.42 255	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 25	99.64 33	99.99 12	100.00 1	99.97 21	100.00 1	99.42 138	98.02 108	100.00 1	100.00 1	99.32 62	99.99 94	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 122	99.05 14	100.00 1	100.00 1	99.45 45	99.99 94	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 121	100.00 1	100.00 1	99.20 78	100.00 1	97.91 258	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 138	99.01 26	100.00 1	100.00 1	99.33 59	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 7	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

APD-MVS		99.68 40	99.58 46	99.97 31	99.99 49	99.96 24	100.00 1	99.42 138	97.53 159	100.00 1	100.00 1	99.27 73	99.97 125	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM_NR		99.74 22	99.66 30	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 123	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

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

DeepC-MVS_fast	98.92 1	99.75 20	99.67 27	99.99 12	99.99 49	99.96 24	99.73 299	99.52 72	99.06 12	100.00 1	100.00 1	98.80 119	100.00 1	99.95 91	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		99.68 40	99.58 46	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 153	100.00 1	100.00 1	98.99 97	99.99 94	100.00 1	100.00 1	100.00 1

TEST9					100.00 1	99.95 32	100.00 1	99.42 138	97.65 142	100.00 1	100.00 1	99.53 29	99.97 125

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

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

MVS		99.22 109	98.96 123	99.98 23	99.00 286	99.95 32	99.24 353	99.94 22	98.14 99	98.88 242	100.00 1	95.63 215	100.00 1	99.85 109	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 148	99.95 32	100.00 1	99.42 138	98.69 65	100.00 1	100.00 1	99.52 32	99.99 94	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 110	99.99 103	100.00 1	99.72 12	100.00 1	99.96 85	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 85	99.01 282	99.95 32	100.00 1	99.75 52	99.37 3	99.99 103	100.00 1	99.76 11	99.60 215	100.00 1	100.00 1	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 67	100.00 1	100.00 1	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 250	96.06 279	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 67	100.00 1	66.97 407	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

MP-MVS		99.61 58	99.49 66	99.98 23	99.99 49	99.94 40	100.00 1	99.42 138	97.82 128	99.99 103	100.00 1	98.20 139	100.00 1	99.99 61	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
save fliter					99.99 49	99.93 43	100.00 1	99.42 138	98.93 38

SMA-MVS		99.69 35	99.59 44	99.98 23	99.99 49	99.93 43	100.00 1	99.43 122	97.50 164	100.00 1	100.00 1	99.43 50	100.00 1	100.00 1	100.00 1	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test_prior4						99.93 43	100.00 1

WTY-MVS		99.54 66	99.40 72	99.95 51	99.81 127	99.93 43	100.00 1	100.00 1	97.98 112	99.84 164	100.00 1	98.94 105	99.98 118	99.86 107	98.21 202	99.94 135

HY-MVS	96.53 9	99.50 70	99.35 82	99.96 42	99.81 127	99.93 43	99.64 311	100.00 1	97.97 114	99.84 164	99.85 242	98.94 105	99.99 94	99.86 107	98.23 201	99.95 130

MP-MVS-pluss		99.61 58	99.50 64	99.97 31	99.98 85	99.92 48	100.00 1	99.42 138	97.53 159	99.77 181	100.00 1	98.77 120	100.00 1	99.99 61	100.00 1	99.99 109
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.67 43	99.57 49	99.97 31	99.98 85	99.92 48	100.00 1	99.42 138	97.83 127	100.00 1	100.00 1	98.89 111	100.00 1	99.98 73	100.00 1	100.00 1

alignmvs		99.38 82	99.21 96	99.91 68	99.73 151	99.92 48	100.00 1	99.51 76	97.61 150	100.00 1	100.00 1	99.06 89	99.93 166	99.83 113	97.12 242	99.90 158

SR-MVS-dyc-post		99.63 51	99.52 61	99.97 31	99.99 49	99.91 51	100.00 1	99.42 138	97.62 146	100.00 1	100.00 1	98.65 125	99.99 94	99.99 61	100.00 1	100.00 1

RE-MVS-def			99.55 56		99.99 49	99.91 51	100.00 1	99.42 138	97.62 146	100.00 1	100.00 1	98.94 105		99.99 61	100.00 1	100.00 1

MTAPA		99.68 40	99.59 44	99.97 31	99.99 49	99.91 51	100.00 1	99.42 138	98.32 88	99.94 145	100.00 1	98.65 125	100.00 1	99.96 85	100.00 1	100.00 1

test_8					100.00 1	99.91 51	100.00 1	99.42 138	97.70 137	100.00 1	100.00 1	99.51 33	99.98 118

APD-MVS_3200maxsize		99.65 46	99.55 56	99.97 31	99.99 49	99.91 51	100.00 1	99.48 78	97.54 157	100.00 1	100.00 1	98.97 99	99.99 94	99.98 73	100.00 1	100.00 1

mPP-MVS		99.69 35	99.60 43	99.97 31	100.00 1	99.91 51	100.00 1	99.42 138	97.91 120	100.00 1	100.00 1	99.04 94	100.00 1	100.00 1	100.00 1	100.00 1

MG-MVS		99.75 20	99.68 25	99.97 31	100.00 1	99.91 51	99.98 235	99.47 79	99.09 9	100.00 1	100.00 1	98.59 129	100.00 1	99.95 91	100.00 1	100.00 1

ZNCC-MVS		99.71 29	99.62 41	99.97 31	99.99 49	99.90 58	100.00 1	99.79 45	97.97 114	99.97 114	100.00 1	98.97 99	100.00 1	99.94 93	100.00 1	100.00 1

test_yl		99.51 67	99.37 77	99.95 51	99.82 121	99.90 58	100.00 1	99.47 79	97.48 166	100.00 1	100.00 1	99.80 5	100.00 1	99.98 73	97.75 230	99.94 135

DCV-MVSNet		99.51 67	99.37 77	99.95 51	99.82 121	99.90 58	100.00 1	99.47 79	97.48 166	100.00 1	100.00 1	99.80 5	100.00 1	99.98 73	97.75 230	99.94 135

region2R		99.72 26	99.64 33	99.97 31	100.00 1	99.90 58	100.00 1	99.74 55	97.86 124	100.00 1	100.00 1	99.19 79	100.00 1	99.99 61	100.00 1	100.00 1

test222					99.99 49	99.90 58	100.00 1	99.69 62	97.66 141	100.00 1	100.00 1	99.30 69			100.00 1	100.00 1

thres200		99.27 99	99.04 113	99.96 42	99.81 127	99.90 58	100.00 1	99.94 22	97.31 182	99.83 167	99.96 209	97.04 179	100.00 1	99.62 160	97.88 219	99.98 111

3Dnovator	95.63 14	99.06 123	98.76 146	99.96 42	98.86 303	99.90 58	99.98 235	99.93 30	98.95 35	98.49 271	100.00 1	92.91 253	100.00 1	99.71 136	100.00 1	100.00 1

tfpn200view9		99.26 101	99.03 114	99.96 42	99.81 127	99.89 65	100.00 1	99.94 22	97.23 187	99.83 167	99.96 209	97.04 179	100.00 1	99.59 164	97.85 221	99.98 111

HFP-MVS		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.31 64	100.00 1	99.99 61	100.00 1	100.00 1

1314		99.38 82	99.19 100	99.96 42	98.88 299	99.89 65	99.24 353	99.93 30	98.88 44	98.79 252	100.00 1	97.02 182	100.00 1	100.00 1	100.00 1	100.00 1

ACMMPR		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.29 70	100.00 1	99.99 61	100.00 1	100.00 1

thres400		99.26 101	99.03 114	99.95 51	99.81 127	99.89 65	100.00 1	99.94 22	97.23 187	99.83 167	99.96 209	97.04 179	100.00 1	99.59 164	97.85 221	99.97 118

test12				99.95 51	99.99 49	99.89 65		99.42 138		100.00 1		99.24 75	99.97 125		100.00 1	100.00 1

3Dnovator+	95.58 15	99.03 127	98.71 152	99.96 42	98.99 289	99.89 65	100.00 1	99.51 76	98.96 32	98.32 279	100.00 1	92.78 255	100.00 1	99.87 106	100.00 1	100.00 1

agg_prior					100.00 1	99.88 72		99.42 138		100.00 1			99.97 125

旧先验1					99.99 49	99.88 72		99.82 40			100.00 1	99.27 73			100.00 1	100.00 1

thres100view900		99.25 105	99.01 116	99.95 51	99.81 127	99.87 74	100.00 1	99.94 22	97.13 192	99.83 167	99.96 209	97.01 183	100.00 1	99.59 164	97.85 221	99.98 111

thres600view7		99.24 108	99.00 118	99.95 51	99.81 127	99.87 74	100.00 1	99.94 22	97.13 192	99.83 167	99.96 209	97.01 183	100.00 1	99.54 172	97.77 229	99.97 118

QAPM		98.99 136	98.66 154	99.96 42	99.01 282	99.87 74	99.88 268	99.93 30	97.99 110	98.68 256	100.00 1	93.17 249	100.00 1	99.32 186	100.00 1	100.00 1

GST-MVS		99.64 48	99.53 59	99.95 51	100.00 1	99.86 77	100.00 1	99.79 45	97.72 135	99.95 143	100.00 1	98.39 136	100.00 1	99.96 85	99.99 98	100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 63	100.00 1	99.86 77	100.00 1	99.42 138	98.87 47	100.00 1	100.00 1	99.65 15	99.96 137	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
AdaColmap		99.44 78	99.26 89	99.95 51	100.00 1	99.86 77	99.70 304	99.99 13	98.53 73	99.90 155	100.00 1	95.34 217	100.00 1	99.92 96	100.00 1	100.00 1

SF-MVS		99.66 45	99.57 49	99.95 51	99.99 49	99.85 80	100.00 1	99.42 138	97.67 140	100.00 1	100.00 1	99.05 91	99.99 94	100.00 1	100.00 1	100.00 1

PGM-MVS		99.69 35	99.61 42	99.95 51	99.99 49	99.85 80	100.00 1	99.58 67	97.69 139	100.00 1	100.00 1	99.44 46	100.00 1	99.79 119	100.00 1	100.00 1

CP-MVS		99.67 43	99.58 46	99.95 51	100.00 1	99.84 82	100.00 1	99.42 138	97.77 132	100.00 1	100.00 1	99.07 88	100.00 1	100.00 1	100.00 1	100.00 1

MVS_111021_HR		99.71 29	99.63 37	99.93 66	99.95 96	99.83 83	100.00 1	100.00 1	98.89 43	100.00 1	100.00 1	97.85 151	99.95 150	100.00 1	100.00 1	100.00 1

MM		99.63 51	99.52 61	99.94 63	99.99 49	99.82 84	100.00 1	99.97 17	99.11 7	100.00 1	100.00 1	96.65 197	100.00 1	100.00 1	99.97 111	100.00 1

PS-MVSNAJ		99.64 48	99.57 49	99.85 85	99.78 145	99.81 85	99.95 252	99.42 138	98.38 80	100.00 1	100.00 1	98.75 121	100.00 1	99.88 103	99.99 98	99.74 229

OpenMVS	95.20 17	98.76 155	98.41 173	99.78 107	98.89 298	99.81 85	99.99 211	99.76 49	98.02 108	98.02 297	100.00 1	91.44 270	100.00 1	99.63 159	99.97 111	99.55 242

原ACMM1				99.93 66	100.00 1	99.80 87		99.66 63	98.18 95	100.00 1	100.00 1	99.43 50	100.00 1	99.50 176	100.00 1	100.00 1

HPM-MVS_fast		99.60 61	99.49 66	99.91 68	99.99 49	99.78 88	100.00 1	99.42 138	97.09 194	100.00 1	100.00 1	98.95 103	99.96 137	99.98 73	100.00 1	100.00 1

baseline1		98.91 145	98.61 159	99.81 96	99.71 152	99.77 89	99.78 284	99.44 116	97.51 163	98.81 250	99.99 184	98.25 138	99.76 202	98.60 230	95.41 261	99.89 163

CANet		99.40 80	99.24 92	99.89 73	99.99 49	99.76 90	100.00 1	99.73 56	98.40 79	99.78 180	100.00 1	95.28 218	99.96 137	100.00 1	99.99 98	99.96 124

ET-MVSNet_ETH3D		96.41 279	95.48 309	99.20 191	99.81 127	99.75 91	100.00 1	99.02 350	97.30 184	78.33 396	100.00 1	97.73 156	97.94 358	99.70 139	87.41 363	99.92 147

test_prior				99.90 71	100.00 1	99.75 91		99.73 56					99.97 125			100.00 1

VNet		99.04 125	98.75 147	99.90 71	99.81 127	99.75 91	99.50 328	99.47 79	98.36 84	100.00 1	99.99 184	94.66 230	100.00 1	99.90 99	97.09 243	99.96 124

xiu_mvs_v2_base		99.51 67	99.41 71	99.82 91	99.70 154	99.73 94	99.92 259	99.40 188	98.15 98	100.00 1	100.00 1	98.50 133	100.00 1	99.85 109	99.13 162	99.74 229

CNLPA		99.72 26	99.65 31	99.91 68	99.97 89	99.72 95	100.00 1	99.47 79	98.43 78	99.88 160	100.00 1	99.14 84	100.00 1	99.97 83	100.00 1	100.00 1

xiu_mvs_v1_base_debu		99.35 85	99.21 96	99.79 103	99.67 164	99.71 96	99.78 284	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 238

xiu_mvs_v1_base		99.35 85	99.21 96	99.79 103	99.67 164	99.71 96	99.78 284	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 238

xiu_mvs_v1_base_debi		99.35 85	99.21 96	99.79 103	99.67 164	99.71 96	99.78 284	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 238

LS3D		99.31 94	99.13 106	99.87 78	99.99 49	99.71 96	99.55 322	99.46 94	97.32 180	99.82 175	100.00 1	96.85 193	99.97 125	99.14 198	100.00 1	99.92 147

HPM-MVS		99.59 62	99.50 64	99.89 73	100.00 1	99.70 100	100.00 1	99.42 138	97.46 168	100.00 1	100.00 1	98.60 128	99.96 137	99.99 61	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		99.70 32	99.65 31	99.88 77	99.96 94	99.70 100	100.00 1	99.97 17	98.96 32	100.00 1	100.00 1	97.93 147	99.95 150	99.99 61	100.00 1	100.00 1

MVSTER		98.58 171	98.52 166	98.77 216	99.65 174	99.68 102	100.00 1	99.29 246	95.63 277	98.65 257	99.80 254	99.78 8	98.88 290	98.59 231	95.31 266	97.73 302

ACMMP		99.65 46	99.57 49	99.89 73	99.99 49	99.66 103	99.75 293	99.73 56	98.16 96	99.75 184	100.00 1	98.90 110	100.00 1	99.96 85	99.88 129	100.00 1
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MAR-MVS		99.49 72	99.36 80	99.89 73	99.97 89	99.66 103	99.74 294	99.95 19	97.89 121	100.00 1	100.00 1	96.71 196	100.00 1	100.00 1	100.00 1	100.00 1
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 78	99.99 49	99.64 105	99.95 252	99.44 116	98.35 86	100.00 1	100.00 1	98.98 98	99.97 125	99.98 73	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 32	99.64 33	99.87 78	100.00 1	99.64 105	99.98 235	99.44 116	98.35 86	99.99 103	100.00 1	99.04 94	99.96 137	99.98 73	100.00 1	100.00 1

MVS_0304		99.69 35	99.63 37	99.86 81	99.96 94	99.63 107	100.00 1	99.92 34	99.03 20	99.97 114	100.00 1	97.87 149	99.96 137	100.00 1	99.96 114	100.00 1

PVSNet_BlendedMVS		98.71 159	98.62 158	98.98 204	99.98 85	99.60 108	100.00 1	100.00 1	97.23 187	100.00 1	99.03 332	96.57 199	99.99 94	100.00 1	94.75 288	97.35 356

PVSNet_Blended		99.48 74	99.36 80	99.83 89	99.98 85	99.60 108	100.00 1	100.00 1	97.79 130	100.00 1	100.00 1	96.57 199	99.99 94	100.00 1	99.88 129	99.90 158

fmvsm_l_conf0.5_n		99.63 51	99.56 54	99.86 81	99.81 127	99.59 110	100.00 1	99.36 212	98.98 30	100.00 1	100.00 1	97.92 148	99.99 94	100.00 1	99.95 117	100.00 1

fmvsm_l_conf0.5_n_a		99.63 51	99.55 56	99.86 81	99.83 120	99.58 111	100.00 1	99.36 212	98.98 30	100.00 1	100.00 1	97.85 151	99.99 94	100.00 1	99.94 119	100.00 1

test_fmvsmconf_n		99.56 64	99.46 70	99.86 81	99.68 159	99.58 111	100.00 1	99.31 238	98.92 39	99.88 160	100.00 1	97.35 175	99.99 94	99.98 73	99.99 98	100.00 1

ETVMVS		99.16 115	98.98 121	99.69 122	99.67 164	99.56 113	100.00 1	99.45 102	96.36 251	99.98 109	99.95 217	98.65 125	99.64 213	99.11 202	97.63 237	99.88 174

test_fmvsmconf0.1_n		99.25 105	99.05 112	99.82 91	98.92 295	99.55 114	100.00 1	99.23 277	98.91 41	99.75 184	99.97 197	94.79 228	99.94 162	99.94 93	99.99 98	99.97 118

thisisatest0515		99.42 79	99.31 85	99.74 113	99.59 195	99.55 114	100.00 1	99.46 94	96.65 233	99.92 150	100.00 1	99.44 46	99.85 184	99.09 203	99.63 154	99.81 204

mvsany_test1		99.57 63	99.48 69	99.85 85	99.86 115	99.54 116	100.00 1	99.36 212	98.94 37	100.00 1	100.00 1	97.97 145	100.00 1	99.88 103	99.28 160	100.00 1

CPTT-MVS		99.49 72	99.38 74	99.85 85	100.00 1	99.54 116	100.00 1	99.42 138	97.58 154	99.98 109	100.00 1	97.43 173	100.00 1	99.99 61	100.00 1	100.00 1

fmvsm_s_conf0.5_n		99.21 110	99.01 116	99.83 89	99.84 117	99.53 118	100.00 1	99.38 203	98.29 90	100.00 1	100.00 1	93.62 242	99.99 94	99.99 61	99.93 122	99.98 111

SDMVSNet		98.49 180	98.08 196	99.73 116	99.82 121	99.53 118	99.99 211	99.45 102	97.62 146	99.38 209	99.86 237	90.06 291	99.88 176	99.92 96	96.61 251	99.79 221

nrg030		97.64 219	97.27 234	98.75 217	98.34 320	99.53 118	100.00 1	99.22 280	96.21 260	98.27 284	99.95 217	94.40 233	98.98 278	99.23 194	89.78 344	97.75 273

fmvsm_s_conf0.1_n		98.77 154	98.42 172	99.82 91	99.47 235	99.52 121	100.00 1	99.27 260	97.53 159	100.00 1	100.00 1	89.73 296	99.96 137	99.84 112	99.93 122	99.97 118

testing222		99.14 117	98.94 128	99.73 116	99.67 164	99.51 122	100.00 1	99.43 122	96.90 209	99.99 103	99.90 231	98.55 131	99.86 178	98.85 213	97.18 241	99.81 204

test2506		99.48 74	99.38 74	99.75 112	99.89 110	99.51 122	99.45 332	100.00 1	98.38 80	99.83 167	100.00 1	98.86 112	99.81 193	99.25 191	98.78 171	99.94 135

LFMVS		97.42 233	96.62 254	99.81 96	99.80 138	99.50 124	99.16 367	99.56 70	94.48 311	100.00 1	100.00 1	79.35 372	100.00 1	99.89 101	97.37 239	99.94 135

MVS_Test		98.93 144	98.65 155	99.77 110	99.62 187	99.50 124	99.99 211	99.19 292	95.52 283	99.96 119	99.86 237	96.54 201	99.98 118	98.65 225	98.48 181	99.82 195

sss		99.45 77	99.34 84	99.80 101	99.76 148	99.50 124	100.00 1	99.91 36	97.72 135	99.98 109	99.94 222	98.45 134	100.00 1	99.53 174	98.75 174	99.89 163

GG-mvs-BLEND				99.59 139	99.54 207	99.49 127	99.17 366	99.52 72		99.96 119	99.68 273	100.00 1	99.33 262	99.71 136	99.99 98	99.96 124

MVSFormer		98.94 143	98.82 139	99.28 185	99.45 239	99.49 127	100.00 1	99.13 315	95.46 288	99.97 114	100.00 1	96.76 194	98.59 314	98.63 227	100.00 1	99.74 229

lupinMVS		99.29 97	99.16 104	99.69 122	99.45 239	99.49 127	100.00 1	99.15 306	97.45 169	99.97 114	100.00 1	96.76 194	99.76 202	99.67 150	100.00 1	99.81 204

PVSNet_Blended_VisFu		99.33 90	99.18 103	99.78 107	99.82 121	99.49 127	100.00 1	99.95 19	97.36 175	99.63 191	100.00 1	96.45 203	99.95 150	99.79 119	99.65 152	99.89 163

114514_t		99.39 81	99.25 90	99.81 96	99.97 89	99.48 131	100.00 1	99.42 138	95.53 281	100.00 1	100.00 1	98.37 137	99.95 150	99.97 83	100.00 1	100.00 1

fmvsm_s_conf0.5_n_a		99.32 92	99.15 105	99.81 96	99.80 138	99.47 132	100.00 1	99.35 223	98.22 91	100.00 1	100.00 1	95.21 221	99.99 94	99.96 85	99.86 133	99.98 111

DELS-MVS		99.62 56	99.56 54	99.82 91	99.92 104	99.45 133	100.00 1	99.78 47	98.92 39	99.73 186	100.00 1	97.70 158	100.00 1	99.93 95	100.00 1	100.00 1
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DP-MVS		98.86 149	98.54 165	99.81 96	99.97 89	99.45 133	99.52 326	99.40 188	94.35 315	98.36 275	100.00 1	96.13 205	99.97 125	99.12 201	100.00 1	100.00 1

PHI-MVS		99.50 70	99.39 73	99.82 91	100.00 1	99.45 133	100.00 1	99.94 22	96.38 250	100.00 1	100.00 1	98.18 140	100.00 1	100.00 1	100.00 1	100.00 1

FA-MVS(test-final)		99.00 133	98.75 147	99.73 116	99.63 182	99.43 136	99.83 274	99.43 122	95.84 272	99.52 195	99.37 313	97.84 153	99.96 137	97.63 267	99.68 148	99.79 221

fmvsm_s_conf0.1_n_a		98.71 159	98.36 180	99.78 107	99.09 272	99.42 137	100.00 1	99.26 266	97.42 172	100.00 1	100.00 1	89.78 294	99.96 137	99.82 118	99.85 136	99.97 118

thisisatest0530		99.37 84	99.27 86	99.69 122	99.59 195	99.41 138	100.00 1	99.46 94	96.46 243	99.90 155	100.00 1	99.44 46	99.85 184	98.97 206	99.58 156	99.80 218

UA-Net		99.06 123	98.83 138	99.74 113	99.52 217	99.40 139	99.08 377	99.45 102	97.64 144	99.83 167	100.00 1	95.80 210	99.94 162	98.35 239	99.80 143	99.88 174

tttt0517		99.34 88	99.23 95	99.67 125	99.57 203	99.38 140	100.00 1	99.46 94	96.33 254	99.89 158	100.00 1	99.44 46	99.84 186	98.93 208	99.46 159	99.78 224

TESTMET0.1,1		99.08 121	98.96 123	99.44 158	99.63 182	99.38 140	100.00 1	99.45 102	95.53 281	99.48 198	100.00 1	99.71 13	99.02 274	96.84 291	99.99 98	99.91 149

IS-MVSNet		99.08 121	98.91 132	99.59 139	99.65 174	99.38 140	99.78 284	99.24 273	96.70 227	99.51 196	100.00 1	98.44 135	99.52 240	98.47 235	98.39 188	99.88 174

API-MVS		99.72 26	99.70 21	99.79 103	99.97 89	99.37 143	99.96 246	99.94 22	98.48 75	100.00 1	100.00 1	98.92 108	100.00 1	100.00 1	100.00 1	100.00 1

gg-mvs-nofinetune		96.95 255	96.10 277	99.50 151	99.41 245	99.36 144	99.07 379	99.52 72	83.69 388	99.96 119	83.60 404	100.00 1	99.20 267	99.68 147	99.99 98	99.96 124

ETV-MVS		99.34 88	99.24 92	99.64 131	99.58 200	99.33 145	100.00 1	99.25 268	97.57 155	99.96 119	100.00 1	97.44 172	99.79 195	99.70 139	99.65 152	99.81 204

test_cas_vis1_n_1920		98.63 168	98.25 184	99.77 110	99.69 155	99.32 146	100.00 1	99.31 238	98.84 51	99.96 119	100.00 1	87.42 324	99.99 94	99.14 198	99.86 133	100.00 1

VPA-MVSNet		97.03 251	96.43 263	98.82 212	98.64 310	99.32 146	99.38 340	99.47 79	96.73 224	98.91 240	98.94 341	87.00 329	99.40 256	99.23 194	89.59 345	97.76 263

jason		99.11 119	98.96 123	99.59 139	99.17 266	99.31 148	100.00 1	99.13 315	97.38 174	99.83 167	100.00 1	95.54 216	99.72 208	99.57 168	99.97 111	99.74 229
jason: jason.
PatchMatch-RL		99.02 131	98.78 143	99.74 113	99.99 49	99.29 149	100.00 1	100.00 1	98.38 80	99.89 158	99.81 251	93.14 251	99.99 94	97.85 260	99.98 108	99.95 130

test-LLR		99.03 127	98.91 132	99.40 167	99.40 250	99.28 150	100.00 1	99.45 102	96.70 227	99.42 202	99.12 323	99.31 64	99.01 275	96.82 292	99.99 98	99.91 149

test-mter		98.96 140	98.82 139	99.40 167	99.40 250	99.28 150	100.00 1	99.45 102	95.44 290	99.42 202	99.12 323	99.70 14	99.01 275	96.82 292	99.99 98	99.91 149

Effi-MVS+		98.58 171	98.24 186	99.61 135	99.60 191	99.26 152	97.85 393	99.10 324	96.22 259	99.97 114	99.89 232	93.75 239	99.77 200	99.43 178	98.34 193	99.81 204

HyFIR lowres test		99.32 92	99.24 92	99.58 143	99.95 96	99.26 152	100.00 1	99.99 13	96.72 225	99.29 214	99.91 229	99.49 39	99.47 247	99.74 128	98.08 209	100.00 1

FMVSNet3		97.30 238	96.95 242	98.37 236	99.65 174	99.25 154	99.71 302	99.28 252	94.23 316	98.53 266	98.91 343	93.30 247	98.11 345	95.31 317	93.60 297	97.73 302

MSDG		98.90 147	98.63 157	99.70 121	99.92 104	99.25 154	100.00 1	99.37 206	95.71 275	99.40 208	100.00 1	96.58 198	99.95 150	96.80 294	99.94 119	99.91 149

FIs		97.95 208	97.73 216	98.62 222	98.53 315	99.24 156	100.00 1	99.43 122	96.74 222	97.87 306	99.82 248	95.27 219	98.89 287	98.78 217	93.07 303	97.74 296

mvs_anonymous		98.80 153	98.60 161	99.38 171	99.57 203	99.24 156	100.00 1	99.21 288	95.87 267	98.92 238	99.82 248	96.39 204	99.03 273	99.13 200	98.50 179	99.88 174

MDTV_nov1_ep13_2view						99.24 156	99.56 321		96.31 255	99.96 119		98.86 112		98.92 209		99.89 163

iter_conf05		98.73 157	98.77 144	98.60 223	99.65 174	99.22 159	100.00 1	99.22 280	96.68 231	98.98 235	99.97 197	99.99 3	98.84 292	99.29 189	95.11 280	97.75 273

test_fmvsmconf0.01_n		98.60 169	98.24 186	99.67 125	96.90 369	99.21 160	99.99 211	99.04 347	98.80 57	99.57 193	99.96 209	90.12 288	99.91 169	99.89 101	99.89 127	99.90 158

EPMVS		99.25 105	99.13 106	99.60 137	99.60 191	99.20 161	99.60 317	100.00 1	96.93 204	99.92 150	99.36 314	99.05 91	99.71 209	98.77 218	98.94 168	99.90 158

test_fmvsm_n_1920		99.55 65	99.49 66	99.73 116	99.85 116	99.19 162	100.00 1	99.41 184	98.87 47	100.00 1	100.00 1	97.34 176	100.00 1	99.98 73	99.90 126	100.00 1

BH-RMVSNet		98.46 181	98.08 196	99.59 139	99.61 189	99.19 162	100.00 1	99.28 252	97.06 198	98.95 236	100.00 1	88.99 306	99.82 190	98.83 216	100.00 1	99.77 225

test_fmvsmvis_n_1920		99.46 76	99.37 77	99.73 116	98.88 299	99.18 164	100.00 1	99.26 266	98.85 49	99.79 178	100.00 1	97.70 158	100.00 1	99.98 73	99.86 133	100.00 1

FE-MVS		99.16 115	98.99 120	99.66 128	99.65 174	99.18 164	99.58 319	99.43 122	95.24 291	99.91 153	99.59 294	99.37 58	99.97 125	98.31 241	99.81 141	99.83 190

diffmvs		98.96 140	98.73 149	99.63 132	99.54 207	99.16 166	100.00 1	99.18 299	97.33 179	99.96 119	100.00 1	94.60 231	99.91 169	99.66 154	98.33 196	99.82 195
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		98.99 136	98.93 130	99.18 192	99.26 263	99.15 167	100.00 1	99.46 94	96.71 226	96.79 338	100.00 1	99.42 53	99.25 266	98.75 220	99.94 119	99.15 250

UniMVSNet (Re)		97.29 239	96.85 246	98.59 225	98.49 316	99.13 168	100.00 1	99.42 138	96.52 241	98.24 288	98.90 344	94.93 225	98.89 287	97.54 270	87.61 362	97.75 273

WR-MVS		97.09 246	96.64 252	98.46 230	98.43 317	99.09 169	99.97 241	99.33 231	95.62 278	97.76 308	99.67 274	91.17 274	98.56 319	98.49 234	89.28 350	97.74 296

EC-MVSNet		99.19 111	99.09 110	99.48 154	99.42 243	99.07 170	100.00 1	99.21 288	96.95 202	99.96 119	100.00 1	96.88 192	99.48 245	99.64 156	99.79 144	99.88 174

F-COLMAP		99.64 48	99.64 33	99.67 125	99.99 49	99.07 170	100.00 1	99.44 116	98.30 89	99.90 155	100.00 1	99.18 80	99.99 94	99.91 98	100.00 1	99.94 135

iter_conf05_11		98.21 199	97.74 213	99.65 130	99.67 164	99.06 172	100.00 1	98.87 364	97.84 126	99.96 119	100.00 1	83.57 354	99.88 176	99.72 131	100.00 1	100.00 1

Fast-Effi-MVS+		98.40 188	98.02 203	99.55 147	99.63 182	99.06 172	100.00 1	99.15 306	95.07 293	99.42 202	99.95 217	93.26 248	99.73 207	97.44 273	98.24 200	99.87 183

FC-MVSNet-test		97.84 210	97.63 220	98.45 231	98.30 325	99.05 174	100.00 1	99.43 122	96.63 236	97.61 317	99.82 248	95.19 222	98.57 317	98.64 226	93.05 304	97.73 302

miper_enhance_ethall		98.33 191	98.27 183	98.51 228	99.66 173	99.04 175	100.00 1	99.22 280	97.53 159	98.51 269	99.38 312	99.49 39	98.75 301	98.02 253	92.61 308	97.76 263

DeepC-MVS	97.84 5	99.00 133	98.80 142	99.60 137	99.93 101	99.03 176	100.00 1	99.40 188	98.61 71	99.33 212	100.00 1	92.23 264	99.95 150	99.74 128	99.96 114	99.83 190
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
cascas		98.43 183	98.07 198	99.50 151	99.65 174	99.02 177	100.00 1	99.22 280	94.21 318	99.72 187	99.98 189	92.03 267	99.93 166	99.68 147	98.12 207	99.54 243

PCF-MVS	98.23 3	98.69 162	98.37 178	99.62 134	99.78 145	99.02 177	99.23 358	99.06 342	96.43 244	98.08 291	100.00 1	94.72 229	99.95 150	98.16 248	99.91 125	99.90 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		98.23 198	98.11 194	98.58 226	99.82 121	99.01 179	100.00 1	99.28 252	96.92 206	98.33 278	99.21 320	98.09 144	98.97 280	98.72 221	92.61 308	97.76 263

EPNet_dtu		98.53 176	98.23 189	99.43 160	99.92 104	99.01 179	99.96 246	99.47 79	98.80 57	99.96 119	99.96 209	98.56 130	99.30 263	87.78 376	99.68 148	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CS-MVS		99.33 90	99.27 86	99.50 151	99.99 49	99.00 181	100.00 1	99.13 315	97.26 185	99.96 119	100.00 1	97.79 155	99.64 213	99.64 156	99.67 150	99.87 183

ab-mvs		98.42 185	98.02 203	99.61 135	99.71 152	99.00 181	99.10 374	99.64 64	96.70 227	99.04 232	99.81 251	90.64 281	99.98 118	99.64 156	97.93 216	99.84 187

NR-MVSNet		96.63 268	96.04 280	98.38 235	98.31 323	98.98 183	99.22 360	99.35 223	95.87 267	94.43 366	99.65 278	92.73 258	98.40 330	96.78 295	88.05 359	97.75 273

PMMVS		99.12 118	98.97 122	99.58 143	99.57 203	98.98 183	100.00 1	99.30 242	97.14 191	99.96 119	100.00 1	96.53 202	99.82 190	99.70 139	98.49 180	99.94 135

testdata				99.66 128	99.99 49	98.97 185		99.73 56	97.96 117	100.00 1	100.00 1	99.42 53	100.00 1	99.28 190	100.00 1	100.00 1

bld_raw_dy_0_64		97.64 219	96.98 241	99.63 132	99.67 164	98.94 186	100.00 1	97.98 383	97.85 125	98.93 237	100.00 1	83.23 358	99.96 137	99.72 131	95.41 261	100.00 1

XXY-MVS		97.14 245	96.63 253	98.67 219	98.65 309	98.92 187	99.54 324	99.29 246	95.57 280	97.63 314	99.83 245	87.79 321	99.35 260	98.39 237	92.95 305	97.75 273

Vis-MVSNet (Re-imp)		98.99 136	98.89 136	99.29 182	99.64 180	98.89 188	99.98 235	99.31 238	96.74 222	99.48 198	100.00 1	98.11 142	99.10 270	98.39 237	98.34 193	99.89 163

CR-MVSNet		98.02 206	97.71 217	98.93 206	99.31 257	98.86 189	99.13 371	99.00 353	96.53 240	99.96 119	98.98 336	96.94 189	98.10 348	91.18 355	98.40 186	99.84 187

RPMNet		95.26 317	93.82 325	99.56 146	99.31 257	98.86 189	99.13 371	99.42 138	79.82 393	99.96 119	95.13 386	95.69 213	99.98 118	77.54 396	98.40 186	99.84 187

UWE-MVS		99.18 112	99.06 111	99.51 148	99.67 164	98.80 191	100.00 1	99.43 122	96.80 215	99.93 149	99.86 237	99.79 7	99.94 162	97.78 262	98.33 196	99.80 218

PLC	98.56 2	99.70 32	99.74 16	99.58 143	100.00 1	98.79 192	100.00 1	99.54 71	98.58 72	99.96 119	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CS-MVS-test		99.31 94	99.27 86	99.43 160	99.99 49	98.77 193	100.00 1	99.19 292	97.24 186	99.96 119	100.00 1	97.56 165	99.70 210	99.68 147	99.81 141	99.82 195

EIA-MVS		99.26 101	99.19 100	99.45 157	99.63 182	98.75 194	100.00 1	99.27 260	96.93 204	99.95 143	100.00 1	97.47 169	99.79 195	99.74 128	99.72 146	99.82 195

Test_1112_low_res		98.83 151	98.60 161	99.51 148	99.69 155	98.75 194	99.99 211	99.14 311	96.81 214	98.84 247	99.06 327	97.45 170	99.89 172	98.66 223	97.75 230	99.89 163

1112_ss		98.91 145	98.71 152	99.51 148	99.69 155	98.75 194	99.99 211	99.15 306	96.82 213	98.84 247	100.00 1	97.45 170	99.89 172	98.66 223	97.75 230	99.89 163

casdiffmvs		98.65 164	98.38 176	99.46 155	99.52 217	98.74 197	100.00 1	99.15 306	96.91 207	99.05 231	100.00 1	92.75 256	99.83 187	99.70 139	98.38 190	99.81 204
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tfpnnormal		96.36 284	95.69 300	98.37 236	98.55 313	98.71 198	99.69 306	99.45 102	93.16 344	96.69 342	99.71 263	88.44 316	98.99 277	94.17 331	91.38 330	97.41 353

CANet_DTU		99.02 131	98.90 135	99.41 163	99.88 112	98.71 198	100.00 1	99.29 246	98.84 51	100.00 1	100.00 1	94.02 237	100.00 1	98.08 250	99.96 114	99.52 244

EPP-MVSNet		99.10 120	99.00 118	99.40 167	99.51 222	98.68 200	99.92 259	99.43 122	95.47 287	99.65 190	100.00 1	99.51 33	99.76 202	99.53 174	98.00 210	99.75 228

CP-MVSNet		96.73 262	96.25 271	98.18 250	98.21 331	98.67 201	99.77 289	99.32 233	95.06 294	97.20 328	99.65 278	90.10 289	98.19 338	98.06 252	88.90 353	97.66 329

baseline		98.69 162	98.45 171	99.41 163	99.52 217	98.67 201	100.00 1	99.17 304	97.03 199	99.13 223	100.00 1	93.17 249	99.74 205	99.70 139	98.34 193	99.81 204

pmmvs4		97.17 242	96.80 247	98.27 243	97.68 349	98.64 203	100.00 1	99.18 299	94.22 317	98.55 264	99.71 263	93.67 240	98.47 326	95.66 311	92.57 311	97.71 317

testing11		99.26 101	99.19 100	99.46 155	99.64 180	98.61 204	100.00 1	99.43 122	96.94 203	99.92 150	99.94 222	99.43 50	99.97 125	99.67 150	97.79 228	99.82 195

casdiffmvs_mvg		98.64 165	98.39 175	99.40 167	99.50 226	98.60 205	100.00 1	99.22 280	96.85 211	99.10 225	100.00 1	92.75 256	99.78 199	99.71 136	98.35 192	99.81 204
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
JIA-IIPM		97.09 246	96.34 268	99.36 172	98.88 299	98.59 206	99.81 278	99.43 122	84.81 386	99.96 119	90.34 396	98.55 131	99.52 240	97.00 286	98.28 199	99.98 111

Patchmtry		96.81 258	96.37 266	98.14 254	99.31 257	98.55 207	98.91 382	99.00 353	90.45 366	97.92 303	98.98 336	96.94 189	98.12 343	94.27 330	91.53 326	97.75 273

UGNet		98.41 187	98.11 194	99.31 181	99.54 207	98.55 207	99.18 361	100.00 1	98.64 70	99.79 178	99.04 330	87.61 322	100.00 1	99.30 188	99.89 127	99.40 247
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
Vis-MVSNet		98.52 177	98.25 184	99.34 174	99.68 159	98.55 207	99.68 308	99.41 184	97.34 178	99.94 145	100.00 1	90.38 287	99.70 210	99.03 205	98.84 169	99.76 227
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvsmamba		98.13 201	98.06 199	98.32 240	98.22 330	98.50 210	100.00 1	99.22 280	96.41 247	98.91 240	99.96 209	95.69 213	98.73 303	99.19 197	94.95 287	97.73 302

testing91		99.18 112	99.10 108	99.41 163	99.60 191	98.43 211	100.00 1	99.43 122	96.76 218	99.82 175	99.92 227	99.05 91	99.98 118	99.62 160	97.67 234	99.81 204

testing99		99.18 112	99.10 108	99.41 163	99.60 191	98.43 211	100.00 1	99.43 122	96.76 218	99.84 164	99.92 227	99.06 89	99.98 118	99.62 160	97.67 234	99.81 204

GeoE		98.06 203	97.65 219	99.29 182	99.47 235	98.41 213	100.00 1	99.19 292	94.85 298	98.88 242	100.00 1	91.21 272	99.59 217	97.02 285	98.19 204	99.88 174

UniMVSNet_NR-MVSNet		97.16 243	96.80 247	98.22 247	98.38 319	98.41 213	100.00 1	99.45 102	96.14 262	97.76 308	99.64 282	95.05 223	98.50 323	97.98 254	86.84 366	97.75 273

DU-MVS		96.93 256	96.49 260	98.22 247	98.31 323	98.41 213	100.00 1	99.37 206	96.41 247	97.76 308	99.65 278	92.14 265	98.50 323	97.98 254	86.84 366	97.75 273

v2v482		96.70 265	96.18 274	98.27 243	98.04 337	98.39 216	100.00 1	99.13 315	94.19 320	98.58 262	99.08 326	90.48 285	98.67 306	95.69 310	90.44 340	97.75 273

ADS-MVSNet		98.70 161	98.51 167	99.28 185	99.51 222	98.39 216	99.24 353	99.44 116	95.52 283	99.96 119	99.70 266	97.57 163	99.58 221	97.11 283	98.54 177	99.88 174

PatchT		95.90 307	94.95 321	98.75 217	99.03 280	98.39 216	99.08 377	99.32 233	85.52 384	99.96 119	94.99 388	97.94 146	98.05 354	80.20 392	98.47 182	99.81 204

miper_ehance_all_eth		97.81 212	97.66 218	98.23 246	99.49 230	98.37 219	99.99 211	99.11 322	94.78 299	98.25 286	99.21 320	98.18 140	98.57 317	97.35 279	92.61 308	97.76 263

EPNet		99.62 56	99.69 22	99.42 162	99.99 49	98.37 219	100.00 1	99.89 37	98.83 53	100.00 1	100.00 1	98.97 99	100.00 1	99.90 99	99.61 155	99.89 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			98.94 128		99.53 210	98.36 221	99.39 339	99.46 94	96.54 239	99.99 103	99.63 286	98.92 108	99.86 178	98.30 244	98.71 175

CDS-MVSNet		98.96 140	98.95 127	99.01 201	99.48 232	98.36 221	99.93 258	99.37 206	96.79 216	99.31 213	99.83 245	99.77 10	98.91 284	98.07 251	97.98 211	99.77 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		96.22 291	95.60 303	98.06 264	99.53 210	98.33 223	99.45 332	99.27 260	93.71 326	98.03 295	98.84 346	84.23 349	98.10 348	93.97 335	93.40 300	97.73 302

PatchmatchNet		99.03 127	98.96 123	99.26 187	99.49 230	98.33 223	99.38 340	99.45 102	96.64 234	99.96 119	99.58 296	99.49 39	99.50 243	97.63 267	99.00 167	99.93 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3		98.44 182	98.37 178	98.65 220	99.51 222	98.32 225	100.00 1	99.62 66	96.43 244	97.93 302	99.99 184	99.11 85	97.81 361	94.88 324	97.80 226	99.82 195

PVSNet	94.91 18	99.30 96	99.25 90	99.44 158	100.00 1	98.32 225	100.00 1	99.86 38	98.04 107	100.00 1	100.00 1	96.10 206	100.00 1	99.55 169	99.73 145	100.00 1

VPNet		96.41 279	95.76 294	98.33 239	98.61 311	98.30 227	99.48 329	99.45 102	96.98 201	98.87 244	99.88 234	81.57 364	98.93 282	99.22 196	87.82 361	97.76 263

TR-MVS		98.14 200	97.74 213	99.33 177	99.59 195	98.28 228	99.27 350	99.21 288	96.42 246	99.15 222	99.94 222	88.87 309	99.79 195	98.88 211	98.29 198	99.93 145

PS-CasMVS		96.34 286	95.78 293	98.03 271	98.18 333	98.27 229	99.71 302	99.32 233	94.75 300	96.82 337	99.65 278	86.98 330	98.15 340	97.74 263	88.85 354	97.66 329

SCA		98.30 192	97.98 205	99.23 189	99.41 245	98.25 230	99.99 211	99.45 102	96.91 207	99.76 183	99.58 296	89.65 298	99.54 234	98.31 241	98.79 170	99.91 149

v8		96.35 285	95.73 296	98.21 249	98.11 335	98.23 231	99.94 256	99.07 334	92.66 352	98.29 281	99.00 335	91.46 269	98.77 299	94.17 331	88.83 355	97.62 339

V42		96.65 267	96.16 276	98.11 259	98.17 334	98.23 231	99.99 211	99.09 329	93.97 323	98.74 254	99.05 329	91.09 275	98.82 294	95.46 315	89.90 342	97.27 358

ECVR-MVS		98.43 183	98.14 192	99.32 179	99.89 110	98.21 233	99.46 330	100.00 1	98.38 80	99.47 201	100.00 1	87.91 317	99.80 194	99.35 183	98.78 171	99.94 135

c3_l		97.58 224	97.42 224	98.06 264	99.48 232	98.16 234	99.96 246	99.10 324	94.54 308	98.13 290	99.20 322	97.87 149	98.25 337	97.28 280	91.20 332	97.75 273

test1111		98.42 185	98.12 193	99.29 182	99.88 112	98.15 235	99.46 330	100.00 1	98.36 84	99.42 202	100.00 1	87.91 317	99.79 195	99.31 187	98.78 171	99.94 135

v1192		96.18 293	95.49 307	98.26 245	98.01 338	98.15 235	99.99 211	99.08 330	93.36 338	98.54 265	98.97 339	89.47 301	98.89 287	91.15 356	90.82 335	97.75 273

cl____		97.54 227	97.32 230	98.18 250	99.47 235	98.14 237	100.00 1	99.10 324	94.16 321	97.60 318	99.63 286	97.52 166	98.65 308	96.47 299	91.97 320	97.76 263

DIV-MVS_self_test		97.52 230	97.35 229	98.05 268	99.46 238	98.11 238	100.00 1	99.10 324	94.21 318	97.62 316	99.63 286	97.65 160	98.29 334	96.47 299	91.98 319	97.76 263

v144192		96.40 282	95.81 289	98.17 252	97.89 343	98.11 238	99.99 211	99.06 342	93.39 337	98.75 253	99.09 325	90.43 286	98.66 307	93.10 342	90.55 339	97.75 273

IB-MVS	96.24 12	97.54 227	96.95 242	99.33 177	99.67 164	98.10 240	100.00 1	99.47 79	97.42 172	99.26 215	99.69 269	98.83 116	99.89 172	99.43 178	78.77 388	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
test_fmvs1		98.37 190	98.04 201	99.34 174	99.84 117	98.07 241	100.00 1	99.00 353	98.85 49	100.00 1	100.00 1	85.11 344	99.96 137	99.69 146	99.88 129	100.00 1

test0.0.03 1		98.12 202	98.03 202	98.39 234	99.11 269	98.07 241	100.00 1	99.93 30	96.70 227	96.91 334	99.95 217	99.31 64	98.19 338	91.93 350	98.44 183	98.91 254

anonymousdsp		97.16 243	96.88 244	98.00 272	97.08 368	98.06 243	99.81 278	99.15 306	94.58 306	97.84 307	99.62 290	90.49 284	98.60 312	97.98 254	95.32 265	97.33 357

TSAR-MVS + GP.		99.61 58	99.69 22	99.35 173	99.99 49	98.06 243	100.00 1	99.36 212	99.83 2	100.00 1	100.00 1	98.95 103	99.99 94	100.00 1	99.11 163	100.00 1

test_vis1_n_1920		97.77 214	97.24 236	99.34 174	99.79 142	98.04 245	100.00 1	99.25 268	98.88 44	100.00 1	100.00 1	77.52 377	100.00 1	99.88 103	99.85 136	100.00 1

v1144		96.51 274	95.97 284	98.13 257	97.98 340	98.04 245	99.99 211	99.08 330	93.51 335	98.62 260	98.98 336	90.98 279	98.62 309	93.79 337	90.79 336	97.74 296

test_djsdf		97.55 226	97.38 227	98.07 260	97.50 358	97.99 247	100.00 1	99.13 315	95.46 288	98.47 272	99.85 242	92.01 268	98.59 314	98.63 227	95.36 264	97.62 339

WAC-MVS						97.98 248								95.74 308

myMVS_eth3d		98.52 177	98.51 167	98.53 227	99.50 226	97.98 248	100.00 1	99.57 68	96.23 257	98.07 292	100.00 1	99.09 87	97.81 361	96.17 304	97.96 213	99.82 195

test_vis1_n		96.69 266	95.81 289	99.32 179	99.14 267	97.98 248	99.97 241	98.98 356	98.45 77	100.00 1	100.00 1	66.44 393	99.99 94	99.78 125	99.57 157	100.00 1

v1921920		96.16 297	95.50 305	98.14 254	97.88 345	97.96 251	99.99 211	99.07 334	93.33 339	98.60 261	99.24 319	89.37 302	98.71 304	91.28 354	90.74 337	97.75 273

v10		96.14 299	95.50 305	98.07 260	98.19 332	97.96 251	99.83 274	99.07 334	92.10 355	98.07 292	98.94 341	91.07 276	98.61 310	92.41 349	89.82 343	97.63 337

eth_miper_zixun_eth		97.47 231	97.28 232	98.06 264	99.41 245	97.94 253	99.62 315	99.08 330	94.46 312	98.19 289	99.56 300	96.91 191	98.50 323	96.78 295	91.49 327	97.74 296

GA-MVS		97.72 217	97.27 234	99.06 195	99.24 264	97.93 254	100.00 1	99.24 273	95.80 273	98.99 234	99.64 282	89.77 295	99.36 258	95.12 321	97.62 238	99.89 163

tpmvs		98.59 170	98.38 176	99.23 189	99.69 155	97.90 255	99.31 348	99.47 79	94.52 309	99.68 189	99.28 318	97.64 161	99.89 172	97.71 264	98.17 206	99.89 163

IterMVS-LS		97.56 225	97.44 223	97.92 279	99.38 254	97.90 255	99.89 266	99.10 324	94.41 313	98.32 279	99.54 303	97.21 177	98.11 345	97.50 271	91.62 324	97.75 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		94.78 320	93.72 326	97.93 278	98.34 320	97.88 257	99.23 358	97.98 383	91.60 357	94.55 363	99.71 263	87.89 319	98.36 331	89.30 371	84.92 372	97.56 345

WR-MVS_H		96.73 262	96.32 270	97.95 275	98.26 327	97.88 257	99.72 301	99.43 122	95.06 294	96.99 331	98.68 353	93.02 252	98.53 321	97.43 274	88.33 358	97.43 352

v1240		95.96 305	95.25 314	98.07 260	97.91 342	97.87 259	99.96 246	99.07 334	93.24 342	98.64 259	98.96 340	88.98 307	98.61 310	89.58 369	90.92 334	97.75 273

EI-MVSNet		97.98 207	97.93 206	98.16 253	99.11 269	97.84 260	99.74 294	99.29 246	94.39 314	98.65 257	100.00 1	97.21 177	98.88 290	97.62 269	95.31 266	97.75 273

KD-MVS_2432*1600		94.15 324	93.08 333	97.35 297	99.53 210	97.83 261	99.63 313	99.19 292	92.88 348	96.29 346	97.68 374	98.84 114	96.70 373	89.73 366	63.92 397	97.53 347

miper_refine_blended		94.15 324	93.08 333	97.35 297	99.53 210	97.83 261	99.63 313	99.19 292	92.88 348	96.29 346	97.68 374	98.84 114	96.70 373	89.73 366	63.92 397	97.53 347

CHOSEN 1792x2688		99.00 133	98.91 132	99.25 188	99.90 108	97.79 263	100.00 1	99.99 13	98.79 60	98.28 282	100.00 1	93.63 241	99.95 150	99.66 154	99.95 117	100.00 1

tpmrst		98.98 139	98.93 130	99.14 194	99.61 189	97.74 264	99.52 326	99.36 212	96.05 264	99.98 109	99.64 282	99.04 94	99.86 178	98.94 207	98.19 204	99.82 195

TAMVS		98.76 155	98.73 149	98.86 211	99.44 241	97.69 265	99.57 320	99.34 229	96.57 237	99.12 224	99.81 251	98.83 116	99.16 268	97.97 257	97.91 217	99.73 233

CVMVSNet		98.56 173	98.47 170	98.82 212	99.11 269	97.67 266	99.74 294	99.47 79	97.57 155	99.06 230	100.00 1	95.72 212	98.97 280	98.21 247	97.33 240	99.83 190

Patchmatch-test		97.83 211	97.42 224	99.06 195	99.08 273	97.66 267	98.66 387	99.21 288	93.65 330	98.25 286	99.58 296	99.47 43	99.57 222	90.25 364	98.59 176	99.95 130

TranMVSNet+NR-MVSNet		96.45 278	96.01 281	97.79 283	98.00 339	97.62 268	100.00 1	99.35 223	95.98 265	97.31 325	99.64 282	90.09 290	98.00 355	96.89 290	86.80 369	97.75 273

CostFormer		98.84 150	98.77 144	99.04 199	99.41 245	97.58 269	99.67 309	99.35 223	94.66 304	99.96 119	99.36 314	99.28 72	99.74 205	99.41 180	97.81 225	99.81 204

miper_lstm_enhance		97.40 234	97.28 232	97.75 285	99.48 232	97.52 270	100.00 1	99.07 334	94.08 322	98.01 298	99.61 292	97.38 174	97.98 356	96.44 302	91.47 329	97.76 263

Anonymous20231211		96.29 288	95.70 297	98.07 260	99.80 138	97.49 271	99.15 369	99.40 188	89.11 373	97.75 311	99.45 309	88.93 308	98.98 278	98.26 246	89.47 347	97.73 302

test_fmvs1_n		97.43 232	96.86 245	99.15 193	99.68 159	97.48 272	99.99 211	98.98 356	98.82 55	100.00 1	100.00 1	74.85 382	99.96 137	99.67 150	99.70 147	100.00 1

pm-mvs1		95.76 309	95.01 319	98.00 272	98.23 329	97.45 273	99.24 353	99.04 347	93.13 345	95.93 353	99.72 261	86.28 334	98.84 292	95.62 313	87.92 360	97.72 309

VDDNet		96.39 283	95.55 304	98.90 208	99.27 261	97.45 273	99.15 369	99.92 34	91.28 359	99.98 109	100.00 1	73.55 383	100.00 1	99.85 109	96.98 246	99.24 248

dp		98.72 158	98.61 159	99.03 200	99.53 210	97.39 275	99.45 332	99.39 201	95.62 278	99.94 145	99.52 304	98.83 116	99.82 190	96.77 297	98.42 185	99.89 163

COLMAP_ROB	97.10 7	98.29 194	98.17 191	98.65 220	99.94 99	97.39 275	99.30 349	99.40 188	95.64 276	97.75 311	100.00 1	92.69 260	99.95 150	98.89 210	99.92 124	98.62 258
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WB-MVSnew		97.02 253	97.24 236	96.37 331	99.44 241	97.36 277	100.00 1	99.43 122	96.12 263	99.35 211	99.89 232	93.60 243	98.42 329	88.91 375	98.39 188	93.33 390

AllTest		98.55 174	98.40 174	98.99 202	99.93 101	97.35 278	100.00 1	99.40 188	97.08 196	99.09 226	99.98 189	93.37 245	99.95 150	96.94 287	99.84 138	99.68 236

TestCases				98.99 202	99.93 101	97.35 278		99.40 188	97.08 196	99.09 226	99.98 189	93.37 245	99.95 150	96.94 287	99.84 138	99.68 236

v7n		96.06 303	95.42 313	97.99 274	97.58 355	97.35 278	99.86 270	99.11 322	92.81 351	97.91 304	99.49 306	90.99 278	98.92 283	92.51 346	88.49 357	97.70 318

PS-MVSNAJss		98.03 205	98.06 199	97.94 276	97.63 350	97.33 281	99.89 266	99.23 277	96.27 256	98.03 295	99.59 294	98.75 121	98.78 296	98.52 233	94.61 291	97.70 318

Anonymous20240529		96.93 256	96.22 273	99.05 197	99.79 142	97.30 282	99.16 367	99.47 79	88.51 376	98.69 255	100.00 1	83.50 356	100.00 1	99.83 113	97.02 245	99.83 190

mvs_tets		97.00 254	96.69 251	97.94 276	97.41 365	97.27 283	99.60 317	99.18 299	96.51 242	97.35 324	99.69 269	86.53 333	98.91 284	98.84 214	95.09 281	97.65 333

gm-plane-assit					99.52 217	97.26 284			95.86 269		100.00 1		99.43 253	98.76 219

MDA-MVSNet_test_wron		92.61 337	91.09 345	97.19 305	96.71 371	97.26 284	100.00 1	99.14 311	88.61 375	67.90 402	98.32 366	89.03 305	96.57 376	90.47 362	89.59 345	97.74 296

PEN-MVS		96.01 304	95.48 309	97.58 290	97.74 347	97.26 284	99.90 263	99.29 246	94.55 307	96.79 338	99.55 301	87.38 325	97.84 360	96.92 289	87.24 364	97.65 333

CSCG		99.28 98	99.35 82	99.05 197	99.99 49	97.15 287	100.00 1	99.47 79	97.44 170	99.42 202	100.00 1	97.83 154	100.00 1	99.99 61	100.00 1	100.00 1

jajsoiax		97.07 248	96.79 249	97.89 280	97.28 366	97.12 288	99.95 252	99.19 292	96.55 238	97.31 325	99.69 269	87.35 327	98.91 284	98.70 222	95.12 279	97.66 329

tpm2		98.64 165	98.58 163	98.81 214	99.42 243	97.12 288	99.69 306	99.37 206	93.63 331	99.94 145	99.67 274	98.96 102	99.47 247	98.62 229	97.95 215	99.83 190

tpm cat1		98.05 204	97.76 211	98.92 207	99.50 226	97.10 290	99.77 289	99.30 242	90.20 370	99.72 187	98.71 351	97.71 157	99.86 178	96.75 298	98.20 203	99.81 204

RRT_MVS		97.77 214	97.76 211	97.78 284	97.89 343	97.06 291	100.00 1	99.29 246	95.74 274	98.00 300	99.97 197	95.94 207	98.55 320	98.87 212	94.18 294	97.72 309

YYNet1		92.44 338	90.92 346	97.03 308	96.20 373	97.06 291	99.99 211	99.14 311	88.21 378	67.93 401	98.43 363	88.63 311	96.28 380	90.64 358	89.08 352	97.74 296

OMC-MVS		99.27 99	99.38 74	98.96 205	99.95 96	97.06 291	100.00 1	99.40 188	98.83 53	99.88 160	100.00 1	97.01 183	99.86 178	99.47 177	99.84 138	99.97 118

IterMVS		96.76 261	96.46 262	97.63 286	99.41 245	96.89 294	99.99 211	99.13 315	94.74 302	97.59 319	99.66 276	89.63 300	98.28 335	95.71 309	92.31 314	97.72 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EU-MVSNet		96.63 268	96.53 257	96.94 314	97.59 354	96.87 295	99.76 291	99.47 79	96.35 252	96.85 336	99.78 257	92.57 261	96.27 381	95.33 316	91.08 333	97.68 324

IterMVS-SCA-FT		96.72 264	96.42 264	97.62 288	99.40 250	96.83 296	99.99 211	99.14 311	94.65 305	97.55 320	99.72 261	89.65 298	98.31 333	95.62 313	92.05 317	97.73 302

sd_testset		97.81 212	97.48 222	98.79 215	99.82 121	96.80 297	99.32 345	99.45 102	97.62 146	99.38 209	99.86 237	85.56 342	99.77 200	99.72 131	96.61 251	99.79 221

Baseline_NR-MVSNet		96.16 297	95.70 297	97.56 291	98.28 326	96.79 298	100.00 1	97.86 387	91.93 356	97.63 314	99.47 308	92.14 265	98.35 332	97.13 282	86.83 368	97.54 346

BH-w/o		98.82 152	98.81 141	98.88 210	99.62 187	96.71 299	100.00 1	99.28 252	97.09 194	98.81 250	100.00 1	94.91 226	99.96 137	99.54 172	100.00 1	99.96 124

Anonymous202405211		97.87 209	97.53 221	98.90 208	99.81 127	96.70 300	99.35 343	99.46 94	92.98 346	98.83 249	99.99 184	90.63 282	100.00 1	99.70 139	97.03 244	100.00 1

MDA-MVSNet-bldmvs		91.65 344	89.94 352	96.79 323	96.72 370	96.70 300	99.42 337	98.94 358	88.89 374	66.97 404	98.37 364	81.43 365	95.91 384	89.24 372	89.46 348	97.75 273

MIMVSNet		97.06 249	96.73 250	98.05 268	99.38 254	96.64 302	98.47 389	99.35 223	93.41 336	99.48 198	98.53 358	89.66 297	97.70 367	94.16 333	98.11 208	99.80 218

v148		96.29 288	95.84 288	97.63 286	97.74 347	96.53 303	100.00 1	99.07 334	93.52 334	98.01 298	99.42 311	91.22 271	98.60 312	96.37 303	87.22 365	97.75 273

DTE-MVSNet		95.52 312	94.99 320	97.08 306	97.49 360	96.45 304	100.00 1	99.25 268	93.82 325	96.17 349	99.57 299	87.81 320	97.18 369	94.57 326	86.26 371	97.62 339

BH-untuned		98.64 165	98.65 155	98.60 223	99.59 195	96.17 305	100.00 1	99.28 252	96.67 232	98.41 274	100.00 1	94.52 232	99.83 187	99.41 180	100.00 1	99.81 204

MVS-HIRNet		94.12 326	92.73 339	98.29 242	99.33 256	95.95 306	99.38 340	99.19 292	74.54 396	98.26 285	86.34 400	86.07 336	99.06 272	91.60 353	99.87 132	99.85 186

XVG-OURS-SEG-HR		98.27 196	98.31 182	98.14 254	99.59 195	95.92 307	100.00 1	99.36 212	98.48 75	99.21 217	100.00 1	89.27 303	99.94 162	99.76 126	99.17 161	98.56 259

XVG-OURS		98.30 192	98.36 180	98.13 257	99.58 200	95.91 308	100.00 1	99.36 212	98.69 65	99.23 216	100.00 1	91.20 273	99.92 168	99.34 184	97.82 224	98.56 259

h-mvs33		97.03 251	96.53 257	98.51 228	99.79 142	95.90 309	99.45 332	99.45 102	98.21 92	100.00 1	99.78 257	97.49 167	99.99 94	99.72 131	74.92 390	99.65 241

tpm		98.24 197	98.22 190	98.32 240	99.13 268	95.79 310	99.53 325	99.12 321	95.20 292	99.96 119	99.36 314	97.58 162	99.28 265	97.41 275	96.67 249	99.88 174

TAPA-MVS	96.40 10	97.64 219	97.37 228	98.45 231	99.94 99	95.70 311	100.00 1	99.40 188	97.65 142	99.53 194	100.00 1	99.31 64	99.66 212	80.48 391	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
AUN-MVS		96.26 290	95.67 301	98.06 264	99.68 159	95.60 312	99.82 277	99.42 138	96.78 217	99.88 160	99.80 254	94.84 227	99.47 247	97.48 272	73.29 392	99.12 251

hse-mvs2		96.79 259	96.38 265	98.04 270	99.68 159	95.54 313	99.81 278	99.42 138	98.21 92	100.00 1	99.80 254	97.49 167	99.46 251	99.72 131	73.27 393	99.12 251

VDD-MVS		96.58 271	95.99 282	98.34 238	99.52 217	95.33 314	99.18 361	99.38 203	96.64 234	99.77 181	100.00 1	72.51 387	100.00 1	100.00 1	96.94 247	99.70 234

ppachtmachnet_test		96.17 295	95.89 286	97.02 309	97.61 352	95.24 315	99.99 211	99.24 273	93.31 340	96.71 341	99.62 290	94.34 234	98.07 350	89.87 365	92.30 315	97.75 273

PVSNet_0	93.57 19	96.41 279	95.74 295	98.41 233	99.84 117	95.22 316	100.00 1	100.00 1	98.08 105	97.55 320	99.78 257	84.40 347	100.00 1	100.00 1	81.99 381	100.00 1

UniMVSNet_ETH3D		95.28 316	94.41 322	97.89 280	98.91 296	95.14 317	99.13 371	99.35 223	92.11 354	97.17 329	99.66 276	70.28 390	99.36 258	97.88 259	95.18 275	99.16 249

our_test_3		96.51 274	96.35 267	96.98 312	97.61 352	95.05 318	99.98 235	99.01 352	94.68 303	96.77 340	99.06 327	95.87 209	98.14 341	91.81 351	92.37 313	97.75 273

ADS-MVSNet2		98.28 195	98.51 167	97.62 288	99.51 222	95.03 319	99.24 353	99.41 184	95.52 283	99.96 119	99.70 266	97.57 163	97.94 358	97.11 283	98.54 177	99.88 174

GBi-Net		96.07 301	95.80 291	96.89 317	99.53 210	94.87 320	99.18 361	99.27 260	93.71 326	98.53 266	98.81 347	84.23 349	98.07 350	95.31 317	93.60 297	97.72 309

test1		96.07 301	95.80 291	96.89 317	99.53 210	94.87 320	99.18 361	99.27 260	93.71 326	98.53 266	98.81 347	84.23 349	98.07 350	95.31 317	93.60 297	97.72 309

FMVSNet1		94.45 321	93.63 328	96.89 317	98.87 302	94.87 320	99.18 361	99.27 260	90.95 363	97.31 325	98.81 347	72.89 386	98.07 350	92.61 344	92.81 306	97.72 309

HQP5-MVS						94.82 323

HQP-MVS		97.73 216	97.85 208	97.39 294	99.07 274	94.82 323	100.00 1	99.40 188	99.04 15	99.17 218	99.97 197	88.61 312	99.57 222	99.79 119	95.58 255	97.77 261

NP-MVS					99.07 274	94.81 325					99.97 197

HQP_MVS		97.71 218	97.82 210	97.37 295	99.00 286	94.80 326	100.00 1	99.40 188	99.00 27	99.08 228	99.97 197	88.58 314	99.55 231	99.79 119	95.57 259	97.76 263

plane_prior6					99.06 278	94.80 326						88.58 314

plane_prior						94.80 326	100.00 1		99.03 20						95.58 255

plane_prior3						94.79 329			99.03 20	99.08 228

plane_prior7					99.00 286	94.78 330

CLD-MVS		97.64 219	97.74 213	97.36 296	99.01 282	94.76 331	100.00 1	99.34 229	99.30 4	99.00 233	99.97 197	87.49 323	99.57 222	99.96 85	95.58 255	97.75 273
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		97.21 240	97.18 239	97.32 299	98.08 336	94.66 332	100.00 1	99.28 252	98.65 69	98.92 238	99.98 189	86.03 338	99.56 226	98.28 245	95.41 261	97.72 309
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs5		95.94 306	95.61 302	96.95 313	97.42 363	94.66 332	100.00 1	98.08 379	93.60 332	97.05 330	99.43 310	87.02 328	98.46 327	95.76 307	92.12 316	97.72 309

ACMM	97.17 6	97.37 235	97.40 226	97.29 300	99.01 282	94.64 334	100.00 1	99.25 268	98.07 106	98.44 273	99.98 189	87.38 325	99.55 231	99.25 191	95.19 274	97.69 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
D2MVS		97.63 223	97.83 209	97.05 307	98.83 306	94.60 335	100.00 1	99.82 40	96.89 210	98.28 282	99.03 332	94.05 235	99.47 247	98.58 232	94.97 285	97.09 362

LPG-MVS_test		97.31 237	97.32 230	97.28 301	98.85 304	94.60 335	100.00 1	99.37 206	97.35 176	98.85 245	99.98 189	86.66 331	99.56 226	99.55 169	95.26 268	97.70 318

LGP-MVS_train				97.28 301	98.85 304	94.60 335		99.37 206	97.35 176	98.85 245	99.98 189	86.66 331	99.56 226	99.55 169	95.26 268	97.70 318

ACMP	97.00 8	97.19 241	97.16 240	97.27 303	98.97 291	94.58 338	100.00 1	99.32 233	97.97 114	97.45 322	99.98 189	85.79 340	99.56 226	99.70 139	95.24 271	97.67 328
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Fast-Effi-MVS+-dtu		98.38 189	98.56 164	97.82 282	99.58 200	94.44 339	100.00 1	99.16 305	96.75 220	99.51 196	99.63 286	95.03 224	99.60 215	97.71 264	99.67 150	99.42 246

ACMH	96.25 11	96.77 260	96.62 254	97.21 304	98.96 292	94.43 340	99.64 311	99.33 231	97.43 171	96.55 343	99.97 197	83.52 355	99.54 234	99.07 204	95.13 278	97.66 329
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		96.51 274	96.48 261	96.60 326	95.65 380	94.25 341	98.84 384	98.16 375	95.85 271	95.23 357	99.04 330	92.54 262	99.13 269	92.98 343	99.98 108	96.43 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Effi-MVS+-dtu		98.51 179	98.86 137	97.47 292	99.77 147	94.21 342	100.00 1	98.94 358	97.61 150	99.91 153	98.75 350	95.89 208	99.51 242	99.36 182	99.48 158	98.68 256

testgi		96.18 293	95.93 285	96.93 315	98.98 290	94.20 343	100.00 1	99.07 334	97.16 190	96.06 351	99.86 237	84.08 352	97.79 364	90.38 363	97.80 226	98.81 255

LTVRE_ROB	95.29 16	96.32 287	96.10 277	96.99 311	98.55 313	93.88 344	99.45 332	99.28 252	94.50 310	96.46 344	99.52 304	84.86 345	99.48 245	97.26 281	95.03 282	97.59 343
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
ACMH+	96.20 13	96.49 277	96.33 269	97.00 310	99.06 278	93.80 345	99.81 278	99.31 238	97.32 180	95.89 354	99.97 197	82.62 361	99.54 234	98.34 240	94.63 290	97.65 333

test_0402		94.35 322	93.70 327	96.32 332	97.92 341	93.60 346	99.61 316	98.85 366	88.19 379	94.68 362	99.48 307	80.01 369	98.58 316	89.39 370	95.15 277	96.77 368

tt0805		96.52 272	96.23 272	97.40 293	99.30 260	93.55 347	99.32 345	99.45 102	96.75 220	97.88 305	99.99 184	79.99 370	99.59 217	97.39 277	95.98 254	99.06 253

ITE_SJBPF				96.84 320	98.96 292	93.49 348		98.12 377	98.12 103	98.35 276	99.97 197	84.45 346	99.56 226	95.63 312	95.25 270	97.49 349

OurMVSNet-221017-0		96.14 299	95.98 283	96.62 325	97.49 360	93.44 349	99.92 259	98.16 375	95.86 269	97.65 313	99.95 217	85.71 341	98.78 296	94.93 323	94.18 294	97.64 336

K. test v3		95.46 314	95.14 317	96.40 329	97.53 357	93.40 350	99.99 211	99.23 277	95.49 286	92.70 374	99.73 260	84.26 348	98.12 343	93.94 336	93.38 301	97.68 324

XVG-ACMP-BASELINE		96.60 270	96.52 259	96.84 320	98.41 318	93.29 351	99.99 211	99.32 233	97.76 134	98.51 269	99.29 317	81.95 363	99.54 234	98.40 236	95.03 282	97.68 324

SixPastTwentyTwo		95.71 310	95.49 307	96.38 330	97.42 363	93.01 352	99.84 273	98.23 374	94.75 300	95.98 352	99.97 197	85.35 343	98.43 328	94.71 325	93.17 302	97.69 322

TinyColmap		95.50 313	95.12 318	96.64 324	98.69 308	93.00 353	99.40 338	97.75 389	96.40 249	96.14 350	99.87 235	79.47 371	99.50 243	93.62 338	94.72 289	97.40 354

FMVSNet5		95.32 315	95.43 312	94.99 344	99.39 253	92.99 354	99.25 352	99.24 273	90.45 366	97.44 323	98.45 361	95.78 211	94.39 390	87.02 377	91.88 321	97.59 343

new_pmnet		94.11 327	93.47 330	96.04 336	96.60 372	92.82 355	99.97 241	98.91 361	90.21 369	95.26 356	98.05 372	85.89 339	98.14 341	84.28 383	92.01 318	97.16 360

EGC-MVSNET		79.46 363	74.04 371	95.72 338	96.00 376	92.73 356	99.09 376	99.04 347	5.08 408	16.72 408	98.71 351	73.03 385	98.74 302	82.05 388	96.64 250	95.69 381

pmmvs-eth3d		91.73 343	90.67 347	94.92 346	91.63 393	92.71 357	99.90 263	98.54 371	91.19 360	88.08 385	95.50 384	79.31 373	96.13 382	90.55 361	81.32 384	95.91 379

TDRefinement		91.93 340	90.48 348	96.27 333	81.60 404	92.65 358	99.10 374	97.61 392	93.96 324	93.77 368	99.85 242	80.03 368	99.53 239	97.82 261	70.59 394	96.63 372

USDC		95.90 307	95.70 297	96.50 328	98.60 312	92.56 359	100.00 1	98.30 373	97.77 132	96.92 332	99.94 222	81.25 367	99.45 252	93.54 339	94.96 286	97.49 349

UnsupCasMVSNet_eth		94.25 323	93.89 324	95.34 339	97.63 350	92.13 360	99.73 299	99.36 212	94.88 297	92.78 371	98.63 355	82.72 359	96.53 377	94.57 326	84.73 373	97.36 355

LF4IMVS		96.19 292	96.18 274	96.23 334	98.26 327	92.09 361	100.00 1	97.89 386	97.82 128	97.94 301	99.87 235	82.71 360	99.38 257	97.41 275	93.71 296	97.20 359

test20.03		93.11 333	92.85 337	93.88 357	95.19 384	91.83 362	100.00 1	98.87 364	93.68 329	92.76 372	98.88 345	89.20 304	92.71 395	77.88 395	89.19 351	97.09 362

lessismore_v0				96.05 335	97.55 356	91.80 363		99.22 280		91.87 375	99.91 229	83.50 356	98.68 305	92.48 347	90.42 341	97.68 324

MIMVSNet1		91.96 339	91.20 342	94.23 354	94.94 386	91.69 364	99.34 344	99.22 280	88.23 377	94.18 367	98.45 361	75.52 381	93.41 394	79.37 393	91.49 327	97.60 342

pmmvs3		90.62 349	89.36 355	94.40 350	90.53 398	91.49 365	100.00 1	96.73 397	84.21 387	93.65 369	96.65 381	82.56 362	94.83 388	82.28 387	77.62 389	96.89 367

pmmvs6		93.64 328	92.87 336	95.94 337	97.47 362	91.41 366	98.92 381	99.02 350	87.84 380	95.01 359	99.61 292	77.24 378	98.77 299	94.33 329	86.41 370	97.63 337

Anonymous20240521		93.29 331	92.76 338	94.90 347	95.64 381	91.27 367	99.97 241	98.82 367	87.04 381	94.71 361	98.19 367	83.86 353	96.80 372	84.04 384	92.56 312	96.64 371

KD-MVS_self_test		91.16 345	90.09 350	94.35 351	94.44 387	91.27 367	99.74 294	99.08 330	90.82 364	94.53 364	94.91 389	86.11 335	94.78 389	82.67 386	68.52 395	96.99 364

dcpmvs_2		98.87 148	99.53 59	96.90 316	99.87 114	90.88 369	99.94 256	99.07 334	98.20 94	100.00 1	100.00 1	98.69 124	99.86 178	100.00 1	100.00 1	99.95 130

patch_mono-2		99.04 125	99.79 6	96.81 322	99.92 104	90.47 370	100.00 1	99.41 184	98.95 35	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 130

dmvs_re		97.54 227	97.88 207	96.54 327	99.55 206	90.35 371	99.86 270	99.46 94	97.00 200	99.41 207	100.00 1	90.78 280	99.30 263	99.60 163	95.24 271	99.96 124

DSMNet-mixed		95.18 318	95.21 316	95.08 340	96.03 375	90.21 372	99.65 310	93.64 405	92.91 347	98.34 277	97.40 377	90.05 292	95.51 387	91.02 357	97.86 220	99.51 245

Anonymous20231206		93.45 330	93.17 332	94.30 352	95.00 385	89.69 373	99.98 235	98.43 372	93.30 341	94.50 365	98.59 356	90.52 283	95.73 386	77.46 397	90.73 338	97.48 351

MS-PatchMatch		95.66 311	95.87 287	95.05 341	97.80 346	89.25 374	98.88 383	99.30 242	96.35 252	96.86 335	99.01 334	81.35 366	99.43 253	93.30 341	99.98 108	96.46 373

CL-MVSNet_self_test		91.07 346	90.35 349	93.24 359	93.27 388	89.16 375	99.55 322	99.25 268	92.34 353	95.23 357	97.05 379	88.86 310	93.59 393	80.67 390	66.95 396	96.96 365

test_fmvs2		95.17 319	95.23 315	95.01 342	98.95 294	88.99 376	99.99 211	97.77 388	97.79 130	98.58 262	99.70 266	73.36 384	99.34 261	95.88 306	95.03 282	96.70 370

UnsupCasMVSNet_bld		89.50 351	88.00 357	93.99 356	95.30 383	88.86 377	98.52 388	99.28 252	85.50 385	87.80 387	94.11 390	61.63 394	96.96 371	90.63 359	79.26 385	96.15 375

new-patchmatchnet		90.30 350	89.46 354	92.84 361	90.77 396	88.55 378	99.83 274	98.80 368	90.07 371	87.86 386	95.00 387	78.77 374	94.30 391	84.86 382	79.15 386	95.68 382

OpenMVS_ROB	88.34 20	91.89 341	91.12 343	94.19 355	95.55 382	87.63 379	99.26 351	98.03 380	86.61 383	90.65 381	96.82 380	70.14 391	98.78 296	86.54 379	96.50 253	96.15 375

Syy-MVS		96.17 295	96.57 256	95.00 343	99.50 226	87.37 380	100.00 1	99.57 68	96.23 257	98.07 292	100.00 1	92.41 263	97.81 361	85.34 381	97.96 213	99.82 195

EG-PatchMatch MVS		92.94 336	92.49 340	94.29 353	95.87 377	87.07 381	99.07 379	98.11 378	93.19 343	88.98 383	98.66 354	70.89 388	99.08 271	92.43 348	95.21 273	96.72 369

LCM-MVSNet-Re		96.52 272	97.21 238	94.44 349	99.27 261	85.80 382	99.85 272	96.61 399	95.98 265	92.75 373	98.48 360	93.97 238	97.55 368	99.58 167	98.43 184	99.98 111

test_vis1_rt		93.10 334	92.93 335	93.58 358	99.63 182	85.07 383	99.99 211	93.71 404	97.49 165	90.96 377	97.10 378	60.40 395	99.95 150	99.24 193	97.90 218	95.72 380

DeepPCF-MVS	98.03 4	98.54 175	99.72 19	94.98 345	99.99 49	84.94 384	100.00 1	99.42 138	99.98 1	100.00 1	100.00 1	98.11 142	100.00 1	100.00 1	100.00 1	100.00 1

RPSCF		97.37 235	98.24 186	94.76 348	99.80 138	84.57 385	99.99 211	99.05 344	94.95 296	99.82 175	100.00 1	94.03 236	100.00 1	98.15 249	98.38 190	99.70 234

Patchmatch-RL test		93.49 329	93.63 328	93.05 360	91.78 391	83.41 386	98.21 391	96.95 396	91.58 358	91.05 376	97.64 376	99.40 56	95.83 385	94.11 334	81.95 382	99.91 149

Gipuma		84.73 358	83.50 363	88.40 369	97.50 358	82.21 387	88.87 398	99.05 344	65.81 398	85.71 390	90.49 395	53.70 396	96.31 379	78.64 394	91.74 322	86.67 397
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		88.39 353	87.41 358	91.31 362	91.73 392	82.02 388	99.79 283	96.62 398	91.06 362	90.71 380	95.73 383	48.60 399	95.96 383	90.56 360	81.91 383	95.97 378

mvsany_test3		89.36 352	88.96 356	90.56 364	91.95 390	78.97 389	99.74 294	96.59 400	96.84 212	89.25 382	96.07 382	52.59 397	97.11 370	95.17 320	82.44 380	95.58 383

test_method		91.04 347	91.10 344	90.85 363	98.34 320	77.63 390	100.00 1	98.93 360	76.69 394	96.25 348	98.52 359	70.44 389	97.98 356	89.02 374	91.74 322	96.92 366

test_fmvs3		87.19 356	87.02 359	87.71 370	92.69 389	76.64 391	99.96 246	97.27 393	93.55 333	90.82 379	94.03 391	38.00 405	92.19 396	93.49 340	83.35 379	94.32 385

test_f		86.87 357	86.06 360	89.28 367	91.45 395	76.37 392	99.87 269	97.11 394	91.10 361	88.46 384	93.05 393	38.31 404	96.66 375	91.77 352	83.46 378	94.82 384

PMMVS2		79.15 365	77.28 368	84.76 375	82.34 403	72.66 393	99.70 304	95.11 403	71.68 397	84.78 394	90.87 394	32.05 407	89.99 398	75.53 400	63.45 399	91.64 394

APD_test1		93.07 335	94.14 323	89.85 366	99.18 265	72.49 394	99.76 291	98.90 363	92.86 350	96.35 345	99.94 222	75.56 380	99.91 169	86.73 378	97.98 211	97.15 361

test123		79.44 364	79.23 366	80.05 382	80.03 405	71.72 395	100.00 1	77.93 413	62.52 399	94.81 360	99.69 269	78.21 375	74.53 406	92.57 345	27.33 406	93.90 386

DeepMVS_CX				89.98 365	98.90 297	71.46 396		99.18 299	97.61 150	96.92 332	99.83 245	86.07 336	99.83 187	96.02 305	97.65 236	98.65 257

ambc				88.45 368	86.84 400	70.76 397	97.79 394	98.02 382		90.91 378	95.14 385	38.69 403	98.51 322	94.97 322	84.23 374	96.09 377

test_vis3_rt		79.61 362	78.19 367	83.86 376	88.68 399	69.56 398	99.81 278	82.19 412	86.78 382	68.57 400	84.51 403	25.06 409	98.26 336	89.18 373	78.94 387	83.75 400

WB-MVS		88.24 354	90.09 350	82.68 379	91.56 394	69.51 399	100.00 1	98.73 369	90.72 365	87.29 388	98.12 368	92.87 254	85.01 401	62.19 402	89.34 349	93.54 389

SSC-MVS		87.61 355	89.47 353	82.04 380	90.63 397	68.77 400	99.99 211	98.66 370	90.34 368	86.70 389	98.08 369	92.72 259	84.12 402	59.41 405	88.71 356	93.22 393

dmvs_testset		93.27 332	95.48 309	86.65 372	98.74 307	68.42 401	99.92 259	98.91 361	96.19 261	93.28 370	100.00 1	91.06 277	91.67 397	89.64 368	91.54 325	99.86 185

LCM-MVSNet		79.01 366	76.93 369	85.27 374	78.28 406	68.01 402	96.57 395	98.03 380	55.10 402	82.03 395	93.27 392	31.99 408	93.95 392	82.72 385	74.37 391	93.84 387

CMPMVS	66.12 22	90.65 348	92.04 341	86.46 373	96.18 374	66.87 403	98.03 392	99.38 203	83.38 389	85.49 391	99.55 301	77.59 376	98.80 295	94.44 328	94.31 293	93.72 388
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		91.88 342	93.37 331	87.40 371	97.24 367	66.33 404	99.90 263	91.05 407	89.77 372	95.65 355	98.58 357	90.05 292	98.11 345	85.39 380	92.72 307	97.75 273

EMVS		69.88 370	69.09 373	72.24 388	84.70 401	65.82 405	99.96 246	87.08 411	49.82 405	71.51 399	84.74 402	49.30 398	75.32 405	50.97 407	43.71 403	75.59 403

E-PMN		70.72 369	70.06 372	72.69 387	83.92 402	65.48 406	99.95 252	92.72 406	49.88 404	72.30 398	86.26 401	47.17 400	77.43 404	53.83 406	44.49 402	75.17 404

ANet_high		66.05 372	63.44 376	73.88 385	61.14 410	63.45 407	95.68 397	87.18 409	79.93 392	47.35 406	80.68 406	22.35 410	72.33 408	61.24 403	35.42 404	85.88 399

MVE	68.59 21	67.22 371	64.68 375	74.84 383	74.67 409	62.32 408	95.84 396	90.87 408	50.98 403	58.72 405	81.05 405	12.20 413	78.95 403	61.06 404	56.75 400	83.24 401
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		80.17 361	81.95 364	74.80 384	58.54 411	59.58 409	100.00 1	87.14 410	76.09 395	99.61 192	100.00 1	67.06 392	74.19 407	98.84 214	50.30 401	90.64 396

testf1		84.40 359	84.79 361	83.23 377	95.71 378	58.71 410	98.79 385	97.75 389	81.58 390	84.94 392	98.07 370	45.33 401	97.73 365	77.09 398	83.85 375	93.24 391

APD_test2		84.40 359	84.79 361	83.23 377	95.71 378	58.71 410	98.79 385	97.75 389	81.58 390	84.94 392	98.07 370	45.33 401	97.73 365	77.09 398	83.85 375	93.24 391

tmp_tt		75.80 368	74.26 370	80.43 381	52.91 413	53.67 412	87.42 400	97.98 383	61.80 400	67.04 403	100.00 1	76.43 379	96.40 378	96.47 299	28.26 405	91.23 395

FPMVS		77.92 367	79.45 365	73.34 386	76.87 407	46.81 413	98.24 390	99.05 344	59.89 401	73.55 397	98.34 365	36.81 406	86.55 399	80.96 389	91.35 331	86.65 398

PMVS	60.66 23	65.98 373	65.05 374	68.75 389	55.06 412	38.40 414	88.19 399	96.98 395	48.30 406	44.82 407	88.52 398	12.22 412	86.49 400	67.58 401	83.79 377	81.35 402
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		28.28 374	29.73 378	23.92 390	75.89 408	32.61 415	66.50 401	12.88 414	16.09 407	14.59 409	16.59 408	12.35 411	32.36 409	39.36 408	13.36 407	6.79 405

test_blank		0.07 378	0.09 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.79 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		24.41 375	32.55 377	0.00 391	0.00 414	0.00 416	0.00 402	99.39 201	0.00 409	0.00 410	100.00 1	93.55 244	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		8.24 377	10.99 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	98.75 121	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.33 376	11.11 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	100.00 1	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.01 379	0.02 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.14 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

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

eth-test2					0.00 414

eth-test					0.00 414

test_241102_TWO								99.42 138	99.03 20	100.00 1	100.00 1	99.56 23	100.00 1	100.00 1	100.00 1	100.00 1

9.14			99.57 49		99.99 49		100.00 1	99.42 138	97.54 157	100.00 1	100.00 1	99.15 83	99.99 94	100.00 1	100.00 1

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

GSMVS																99.91 149

sam_mvs1												99.29 70				99.91 149

sam_mvs												99.33 59

MTGPA								99.42 138

test_post1							99.32 345				88.24 399	99.33 59	99.59 217	98.31 241

test_post											89.05 397	99.49 39	99.59 217

patchmatchnet-post											97.79 373	99.41 55	99.54 234

MTMP							100.00 1	99.18 299

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

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

旧先验2							100.00 1		98.11 104	100.00 1			100.00 1	99.67 150

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 112				100.00 1	99.33 185		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 278

segment_acmp												99.55 25

testdata1							100.00 1		98.77 63

plane_prior5								99.40 188					99.55 231	99.79 119	95.57 259	97.76 263

plane_prior4											99.97 197

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 281

n2								0.00 415

nn								0.00 415

door-mid								96.32 401

test11								99.42 138

door								96.13 402

HQP-NCC					99.07 274		100.00 1		99.04 15	99.17 218

ACMP_Plane					99.07 274		100.00 1		99.04 15	99.17 218

BP-MVS														99.79 119

HQP4-MVS										99.17 218			99.57 222			97.77 261

HQP3-MVS								99.40 188							95.58 255

HQP2-MVS												88.61 312

ACMMP++_ref															94.58 292

ACMMP++															95.17 276

Test By Simon												99.10 86

High-resolution multi-view benchmark