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.

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 34	99.86 20	99.61 74	99.56 130	99.63 41	99.48 3	99.98 8	99.83 76	98.75 58	99.99 4	99.97 1	99.96 13	99.94 13

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 34	99.84 32	99.63 71	99.56 130	99.63 41	99.47 4	99.98 8	99.82 85	98.75 58	99.99 4	99.97 1	99.97 7	99.94 13

test_fmvsmconf_n		99.70 3	99.64 4	99.87 16	99.80 53	99.66 60	99.48 189	99.64 38	99.45 8	99.92 20	99.92 17	98.62 73	99.99 4	99.96 8	99.99 1	99.96 7

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 59	99.84 32	99.44 103	99.58 117	99.69 18	99.43 11	99.98 8	99.91 23	98.62 73	100.00 1	99.97 1	99.95 18	99.90 19

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 65	99.89 4	99.75 42	99.56 74	99.02 46	99.88 28	99.85 61	99.18 10	99.96 34	99.22 77	99.92 30	99.90 19
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 62	99.38 228	99.37 109	99.58 117	99.62 43	99.41 13	99.87 33	99.92 17	98.81 47	100.00 1	99.97 1	99.93 27	99.94 13

reproduce_model		99.63 7	99.54 11	99.90 5	99.78 58	99.88 8	99.56 130	99.55 82	99.15 25	99.90 23	99.90 30	99.00 22	99.97 22	99.11 87	99.91 37	99.86 35

fmvsm_l_conf0.5_n_3		99.61 8	99.51 16	99.92 1	99.84 32	99.82 25	99.54 148	99.66 28	99.46 7	99.98 8	99.89 35	97.27 129	99.99 4	99.97 1	99.95 18	99.95 9

reproduce-ours		99.61 8	99.52 12	99.90 5	99.76 69	99.88 8	99.52 158	99.54 91	99.13 28	99.89 25	99.89 35	98.96 25	99.96 34	99.04 95	99.90 46	99.85 39

our_new_method		99.61 8	99.52 12	99.90 5	99.76 69	99.88 8	99.52 158	99.54 91	99.13 28	99.89 25	99.89 35	98.96 25	99.96 34	99.04 95	99.90 46	99.85 39

SED-MVS		99.61 8	99.52 12	99.88 10	99.84 32	99.90 2	99.60 102	99.48 165	99.08 41	99.91 21	99.81 99	99.20 7	99.96 34	98.91 113	99.85 78	99.79 80

DVP-MVS++		99.59 12	99.50 17	99.88 10	99.51 180	99.88 8	99.87 8	99.51 123	98.99 53	99.88 28	99.81 99	99.27 5	99.96 34	98.85 126	99.80 106	99.81 67

TSAR-MVS + MP.		99.58 13	99.50 17	99.81 50	99.91 1	99.66 60	99.63 90	99.39 234	98.91 66	99.78 58	99.85 61	99.36 2	99.94 76	98.84 129	99.88 60	99.82 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet-UG-set		99.58 13	99.57 8	99.64 87	99.78 58	99.14 143	99.60 102	99.45 206	99.01 48	99.90 23	99.83 76	98.98 24	99.93 94	99.59 33	99.95 18	99.86 35

EI-MVSNet-Vis-set		99.58 13	99.56 10	99.64 87	99.78 58	99.15 142	99.61 101	99.45 206	99.01 48	99.89 25	99.82 85	99.01 18	99.92 106	99.56 37	99.95 18	99.85 39

DVP-MVS		99.57 16	99.47 21	99.88 10	99.85 26	99.89 4	99.57 124	99.37 250	99.10 35	99.81 47	99.80 112	98.94 32	99.96 34	98.93 110	99.86 71	99.81 67
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
fmvsm_s_conf0.5_n_a		99.56 17	99.47 21	99.85 34	99.83 40	99.64 70	99.52 158	99.65 35	99.10 35	99.98 8	99.92 17	97.35 125	99.96 34	99.94 12	99.92 30	99.95 9

test_fmvsmconf0.1_n		99.55 18	99.45 25	99.86 27	99.44 210	99.65 64	99.50 174	99.61 50	99.45 8	99.87 33	99.92 17	97.31 126	99.97 22	99.95 10	99.99 1	99.97 4

SteuartSystems-ACMMP		99.54 19	99.42 26	99.87 16	99.82 43	99.81 29	99.59 109	99.51 123	98.62 93	99.79 53	99.83 76	99.28 4	99.97 22	98.48 180	99.90 46	99.84 45
Skip Steuart: Steuart Systems R&D Blog.
XVS		99.53 20	99.42 26	99.87 16	99.85 26	99.83 19	99.69 60	99.68 20	98.98 56	99.37 174	99.74 151	98.81 47	99.94 76	98.79 137	99.86 71	99.84 45

MTAPA		99.52 21	99.39 33	99.89 8	99.90 4	99.86 16	99.66 75	99.47 186	98.79 78	99.68 87	99.81 99	98.43 86	99.97 22	98.88 116	99.90 46	99.83 55

fmvsm_s_conf0.5_n		99.51 22	99.40 31	99.85 34	99.84 32	99.65 64	99.51 167	99.67 23	99.13 28	99.98 8	99.92 17	96.60 153	99.96 34	99.95 10	99.96 13	99.95 9

HPM-MVS_fast		99.51 22	99.40 31	99.85 34	99.91 1	99.79 34	99.76 37	99.56 74	97.72 204	99.76 68	99.75 146	99.13 12	99.92 106	99.07 93	99.92 30	99.85 39

mvsany_test1		99.50 24	99.46 24	99.62 94	99.61 149	99.09 148	98.94 357	99.48 165	99.10 35	99.96 18	99.91 23	98.85 42	99.96 34	99.72 23	99.58 149	99.82 60

CS-MVS		99.50 24	99.48 19	99.54 108	99.76 69	99.42 105	99.90 1	99.55 82	98.56 98	99.78 58	99.70 166	98.65 71	99.79 203	99.65 29	99.78 115	99.41 213

SPE-MVS-test		99.49 26	99.48 19	99.54 108	99.78 58	99.30 121	99.89 2	99.58 65	98.56 98	99.73 74	99.69 176	98.55 78	99.82 188	99.69 25	99.85 78	99.48 192

HFP-MVS		99.49 26	99.37 37	99.86 27	99.87 15	99.80 31	99.66 75	99.67 23	98.15 146	99.68 87	99.69 176	99.06 16	99.96 34	98.69 149	99.87 63	99.84 45

ACMMPR		99.49 26	99.36 39	99.86 27	99.87 15	99.79 34	99.66 75	99.67 23	98.15 146	99.67 91	99.69 176	98.95 30	99.96 34	98.69 149	99.87 63	99.84 45

DeepC-MVS_fast	98.69 1	99.49 26	99.39 33	99.77 62	99.63 139	99.59 77	99.36 246	99.46 195	99.07 43	99.79 53	99.82 85	98.85 42	99.92 106	98.68 151	99.87 63	99.82 60
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		99.48 30	99.35 41	99.87 16	99.88 11	99.80 31	99.65 81	99.66 28	98.13 151	99.66 96	99.68 183	98.96 25	99.96 34	98.62 158	99.87 63	99.84 45

APD-MVS_3200maxsize		99.48 30	99.35 41	99.85 34	99.76 69	99.83 19	99.63 90	99.54 91	98.36 119	99.79 53	99.82 85	98.86 41	99.95 65	98.62 158	99.81 102	99.78 86

DELS-MVS		99.48 30	99.42 26	99.65 81	99.72 98	99.40 108	99.05 329	99.66 28	99.14 27	99.57 128	99.80 112	98.46 84	99.94 76	99.57 36	99.84 86	99.60 156
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
ZNCC-MVS		99.47 33	99.33 45	99.87 16	99.87 15	99.81 29	99.64 84	99.67 23	98.08 161	99.55 133	99.64 202	98.91 37	99.96 34	98.72 144	99.90 46	99.82 60

ACMMP_NAP		99.47 33	99.34 43	99.88 10	99.87 15	99.86 16	99.47 196	99.48 165	98.05 168	99.76 68	99.86 56	98.82 46	99.93 94	98.82 136	99.91 37	99.84 45

MVSMamba_PlusPlus		99.46 35	99.41 30	99.64 87	99.68 116	99.50 95	99.75 42	99.50 143	98.27 129	99.87 33	99.92 17	98.09 104	99.94 76	99.65 29	99.95 18	99.47 198

balanced_conf03		99.46 35	99.39 33	99.67 76	99.55 168	99.58 82	99.74 46	99.51 123	98.42 112	99.87 33	99.84 71	98.05 107	99.91 118	99.58 35	99.94 25	99.52 179

DPE-MVS		99.46 35	99.32 47	99.91 3	99.78 58	99.88 8	99.36 246	99.51 123	98.73 85	99.88 28	99.84 71	98.72 64	99.96 34	98.16 210	99.87 63	99.88 28
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.46 35	99.47 21	99.44 140	99.60 154	99.16 138	99.41 222	99.71 13	98.98 56	99.45 149	99.78 131	99.19 9	99.54 275	99.28 71	99.84 86	99.63 149

SR-MVS-dyc-post		99.45 39	99.31 53	99.85 34	99.76 69	99.82 25	99.63 90	99.52 109	98.38 115	99.76 68	99.82 85	98.53 79	99.95 65	98.61 161	99.81 102	99.77 88

PGM-MVS		99.45 39	99.31 53	99.86 27	99.87 15	99.78 40	99.58 117	99.65 35	97.84 190	99.71 81	99.80 112	99.12 13	99.97 22	98.33 196	99.87 63	99.83 55

CP-MVS		99.45 39	99.32 47	99.85 34	99.83 40	99.75 44	99.69 60	99.52 109	98.07 162	99.53 136	99.63 208	98.93 36	99.97 22	98.74 141	99.91 37	99.83 55

ACMMP		99.45 39	99.32 47	99.82 47	99.89 8	99.67 58	99.62 95	99.69 18	98.12 152	99.63 111	99.84 71	98.73 63	99.96 34	98.55 176	99.83 95	99.81 67
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
SMA-MVS		99.44 43	99.30 55	99.85 34	99.73 94	99.83 19	99.56 130	99.47 186	97.45 237	99.78 58	99.82 85	99.18 10	99.91 118	98.79 137	99.89 57	99.81 67
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
mPP-MVS		99.44 43	99.30 55	99.86 27	99.88 11	99.79 34	99.69 60	99.48 165	98.12 152	99.50 141	99.75 146	98.78 51	99.97 22	98.57 170	99.89 57	99.83 55

EC-MVSNet		99.44 43	99.39 33	99.58 101	99.56 164	99.49 96	99.88 4	99.58 65	98.38 115	99.73 74	99.69 176	98.20 99	99.70 241	99.64 31	99.82 99	99.54 172

SR-MVS		99.43 46	99.29 59	99.86 27	99.75 79	99.83 19	99.59 109	99.62 43	98.21 139	99.73 74	99.79 124	98.68 67	99.96 34	98.44 186	99.77 118	99.79 80

MCST-MVS		99.43 46	99.30 55	99.82 47	99.79 56	99.74 47	99.29 267	99.40 231	98.79 78	99.52 138	99.62 213	98.91 37	99.90 130	98.64 155	99.75 123	99.82 60

MSP-MVS		99.42 48	99.27 64	99.88 10	99.89 8	99.80 31	99.67 69	99.50 143	98.70 87	99.77 62	99.49 259	98.21 98	99.95 65	98.46 184	99.77 118	99.88 28
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
UA-Net		99.42 48	99.29 59	99.80 53	99.62 145	99.55 85	99.50 174	99.70 15	98.79 78	99.77 62	99.96 1	97.45 120	99.96 34	98.92 112	99.90 46	99.89 22

HPM-MVS		99.42 48	99.28 61	99.83 46	99.90 4	99.72 48	99.81 20	99.54 91	97.59 218	99.68 87	99.63 208	98.91 37	99.94 76	98.58 167	99.91 37	99.84 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 48	99.30 55	99.78 59	99.62 145	99.71 50	99.26 286	99.52 109	98.82 73	99.39 170	99.71 162	98.96 25	99.85 161	98.59 166	99.80 106	99.77 88

SD-MVS		99.41 52	99.52 12	99.05 195	99.74 87	99.68 55	99.46 199	99.52 109	99.11 34	99.88 28	99.91 23	99.43 1	97.70 405	98.72 144	99.93 27	99.77 88
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
MVS_111021_LR		99.41 52	99.33 45	99.65 81	99.77 65	99.51 94	98.94 357	99.85 6	98.82 73	99.65 103	99.74 151	98.51 81	99.80 200	98.83 132	99.89 57	99.64 144

MVS_111021_HR		99.41 52	99.32 47	99.66 77	99.72 98	99.47 100	98.95 355	99.85 6	98.82 73	99.54 134	99.73 157	98.51 81	99.74 219	98.91 113	99.88 60	99.77 88

MM		99.40 55	99.28 61	99.74 68	99.67 118	99.31 119	99.52 158	98.87 358	99.55 1	99.74 72	99.80 112	96.47 159	99.98 14	99.97 1	99.97 7	99.94 13

GST-MVS		99.40 55	99.24 69	99.85 34	99.86 20	99.79 34	99.60 102	99.67 23	97.97 175	99.63 111	99.68 183	98.52 80	99.95 65	98.38 189	99.86 71	99.81 67

HPM-MVS++		99.39 57	99.23 71	99.87 16	99.75 79	99.84 18	99.43 212	99.51 123	98.68 90	99.27 198	99.53 246	98.64 72	99.96 34	98.44 186	99.80 106	99.79 80

SF-MVS		99.38 58	99.24 69	99.79 56	99.79 56	99.68 55	99.57 124	99.54 91	97.82 195	99.71 81	99.80 112	98.95 30	99.93 94	98.19 206	99.84 86	99.74 98

fmvsm_s_conf0.5_n_3		99.37 59	99.20 74	99.87 16	99.75 79	99.70 52	99.48 189	99.66 28	99.45 8	99.99 2	99.93 10	94.64 239	99.97 22	99.94 12	99.97 7	99.95 9

fmvsm_s_conf0.1_n_2		99.37 59	99.22 72	99.81 50	99.77 65	99.75 44	99.46 199	99.60 56	99.47 4	99.98 8	99.94 6	94.98 212	99.95 65	99.97 1	99.79 113	99.73 103

MP-MVS-pluss		99.37 59	99.20 74	99.88 10	99.90 4	99.87 15	99.30 262	99.52 109	97.18 263	99.60 121	99.79 124	98.79 50	99.95 65	98.83 132	99.91 37	99.83 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 62	99.36 39	99.36 149	99.67 118	98.61 210	99.07 324	99.33 270	99.00 51	99.82 46	99.81 99	99.06 16	99.84 168	99.09 91	99.42 160	99.65 137

PVSNet_Blended_VisFu		99.36 62	99.28 61	99.61 95	99.86 20	99.07 153	99.47 196	99.93 2	97.66 213	99.71 81	99.86 56	97.73 115	99.96 34	99.47 52	99.82 99	99.79 80

NCCC		99.34 64	99.19 76	99.79 56	99.61 149	99.65 64	99.30 262	99.48 165	98.86 68	99.21 212	99.63 208	98.72 64	99.90 130	98.25 202	99.63 144	99.80 76

mamv4		99.33 65	99.42 26	99.07 191	99.67 118	97.73 266	99.42 219	99.60 56	98.15 146	99.94 19	99.91 23	98.42 88	99.94 76	99.72 23	99.96 13	99.54 172

MP-MVS		99.33 65	99.15 79	99.87 16	99.88 11	99.82 25	99.66 75	99.46 195	98.09 157	99.48 145	99.74 151	98.29 95	99.96 34	97.93 228	99.87 63	99.82 60
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_2		99.32 67	99.13 81	99.89 8	99.80 53	99.77 41	99.44 207	99.58 65	99.47 4	99.99 2	99.93 10	94.04 263	99.96 34	99.96 8	99.93 27	99.93 18

PS-MVSNAJ		99.32 67	99.32 47	99.30 163	99.57 160	98.94 175	98.97 351	99.46 195	98.92 65	99.71 81	99.24 327	99.01 18	99.98 14	99.35 59	99.66 139	98.97 263

CSCG		99.32 67	99.32 47	99.32 157	99.85 26	98.29 235	99.71 55	99.66 28	98.11 154	99.41 163	99.80 112	98.37 92	99.96 34	98.99 101	99.96 13	99.72 110

PHI-MVS		99.30 70	99.17 78	99.70 74	99.56 164	99.52 93	99.58 117	99.80 8	97.12 269	99.62 115	99.73 157	98.58 75	99.90 130	98.61 161	99.91 37	99.68 127

DeepC-MVS	98.35 2	99.30 70	99.19 76	99.64 87	99.82 43	99.23 131	99.62 95	99.55 82	98.94 62	99.63 111	99.95 3	95.82 185	99.94 76	99.37 58	99.97 7	99.73 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n		99.29 72	99.10 85	99.86 27	99.70 108	99.65 64	99.53 157	99.62 43	98.74 84	99.99 2	99.95 3	94.53 246	99.94 76	99.89 16	99.96 13	99.97 4

xiu_mvs_v1_base_debu		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 314	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 248

xiu_mvs_v1_base		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 314	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 248

xiu_mvs_v1_base_debi		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 314	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 248

APD-MVS		99.27 76	99.08 89	99.84 45	99.75 79	99.79 34	99.50 174	99.50 143	97.16 265	99.77 62	99.82 85	98.78 51	99.94 76	97.56 267	99.86 71	99.80 76
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 76	99.12 83	99.74 68	99.18 281	99.75 44	99.56 130	99.57 69	98.45 108	99.49 144	99.85 61	97.77 114	99.94 76	98.33 196	99.84 86	99.52 179

fmvsm_s_conf0.1_n_a		99.26 78	99.06 91	99.85 34	99.52 177	99.62 72	99.54 148	99.62 43	98.69 88	99.99 2	99.96 1	94.47 248	99.94 76	99.88 17	99.92 30	99.98 2

patch_mono-2		99.26 78	99.62 5	98.16 310	99.81 47	94.59 378	99.52 158	99.64 38	99.33 17	99.73 74	99.90 30	99.00 22	99.99 4	99.69 25	99.98 4	99.89 22

ETV-MVS		99.26 78	99.21 73	99.40 143	99.46 203	99.30 121	99.56 130	99.52 109	98.52 102	99.44 154	99.27 323	98.41 90	99.86 155	99.10 90	99.59 148	99.04 255

xiu_mvs_v2_base		99.26 78	99.25 68	99.29 166	99.53 172	98.91 179	99.02 337	99.45 206	98.80 77	99.71 81	99.26 325	98.94 32	99.98 14	99.34 64	99.23 175	98.98 262

CANet		99.25 82	99.14 80	99.59 98	99.41 218	99.16 138	99.35 251	99.57 69	98.82 73	99.51 140	99.61 217	96.46 160	99.95 65	99.59 33	99.98 4	99.65 137

3Dnovator	97.25 9	99.24 83	99.05 92	99.81 50	99.12 297	99.66 60	99.84 12	99.74 10	99.09 40	98.92 266	99.90 30	95.94 179	99.98 14	98.95 106	99.92 30	99.79 80

dcpmvs_2		99.23 84	99.58 7	98.16 310	99.83 40	94.68 376	99.76 37	99.52 109	99.07 43	99.98 8	99.88 43	98.56 77	99.93 94	99.67 27	99.98 4	99.87 33

test_fmvsmconf0.01_n		99.22 85	99.03 96	99.79 56	98.42 384	99.48 98	99.55 144	99.51 123	99.39 14	99.78 58	99.93 10	94.80 223	99.95 65	99.93 14	99.95 18	99.94 13

CHOSEN 1792x2688		99.19 86	99.10 85	99.45 136	99.89 8	98.52 220	99.39 234	99.94 1	98.73 85	99.11 231	99.89 35	95.50 195	99.94 76	99.50 45	99.97 7	99.89 22

F-COLMAP		99.19 86	99.04 94	99.64 87	99.78 58	99.27 126	99.42 219	99.54 91	97.29 254	99.41 163	99.59 222	98.42 88	99.93 94	98.19 206	99.69 134	99.73 103

EIA-MVS		99.18 88	99.09 88	99.45 136	99.49 193	99.18 135	99.67 69	99.53 104	97.66 213	99.40 168	99.44 275	98.10 103	99.81 193	98.94 107	99.62 145	99.35 222

3Dnovator+	97.12 13	99.18 88	98.97 110	99.82 47	99.17 289	99.68 55	99.81 20	99.51 123	99.20 22	98.72 293	99.89 35	95.68 190	99.97 22	98.86 124	99.86 71	99.81 67

MVSFormer		99.17 90	99.12 83	99.29 166	99.51 180	98.94 175	99.88 4	99.46 195	97.55 224	99.80 51	99.65 196	97.39 121	99.28 316	99.03 97	99.85 78	99.65 137

sss		99.17 90	99.05 92	99.53 116	99.62 145	98.97 165	99.36 246	99.62 43	97.83 191	99.67 91	99.65 196	97.37 124	99.95 65	99.19 79	99.19 178	99.68 127

test_cas_vis1_n_1920		99.16 92	99.01 104	99.61 95	99.81 47	98.86 185	99.65 81	99.64 38	99.39 14	99.97 17	99.94 6	93.20 285	99.98 14	99.55 38	99.91 37	99.99 1

DP-MVS		99.16 92	98.95 116	99.78 59	99.77 65	99.53 90	99.41 222	99.50 143	97.03 281	99.04 247	99.88 43	97.39 121	99.92 106	98.66 153	99.90 46	99.87 33

MVS_0304		99.15 94	98.96 114	99.73 71	98.92 332	99.37 109	99.37 241	96.92 410	99.51 2	99.66 96	99.78 131	96.69 150	99.97 22	99.84 19	99.97 7	99.84 45

casdiffmvs_mvg		99.15 94	99.02 100	99.55 107	99.66 128	99.09 148	99.64 84	99.56 74	98.26 131	99.45 149	99.87 52	96.03 174	99.81 193	99.54 39	99.15 182	99.73 103
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		99.15 94	99.02 100	99.53 116	99.66 128	99.14 143	99.72 52	99.48 165	98.35 120	99.42 159	99.84 71	96.07 172	99.79 203	99.51 44	99.14 183	99.67 130

diffmvs		99.14 97	99.02 100	99.51 124	99.61 149	98.96 169	99.28 272	99.49 153	98.46 107	99.72 79	99.71 162	96.50 158	99.88 147	99.31 67	99.11 185	99.67 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		99.14 97	98.99 106	99.59 98	99.58 158	99.41 107	99.16 305	99.44 214	98.45 108	99.19 218	99.49 259	98.08 105	99.89 142	97.73 250	99.75 123	99.48 192

CDPH-MVS		99.13 99	98.91 121	99.80 53	99.75 79	99.71 50	99.15 308	99.41 225	96.60 313	99.60 121	99.55 237	98.83 45	99.90 130	97.48 274	99.83 95	99.78 86

casdiffmvs		99.13 99	98.98 109	99.56 105	99.65 134	99.16 138	99.56 130	99.50 143	98.33 123	99.41 163	99.86 56	95.92 180	99.83 181	99.45 54	99.16 179	99.70 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jason		99.13 99	99.03 96	99.45 136	99.46 203	98.87 182	99.12 314	99.26 298	98.03 171	99.79 53	99.65 196	97.02 139	99.85 161	99.02 99	99.90 46	99.65 137
jason: jason.
lupinMVS		99.13 99	99.01 104	99.46 135	99.51 180	98.94 175	99.05 329	99.16 315	97.86 185	99.80 51	99.56 234	97.39 121	99.86 155	98.94 107	99.85 78	99.58 164

EPP-MVSNet		99.13 99	98.99 106	99.53 116	99.65 134	99.06 154	99.81 20	99.33 270	97.43 241	99.60 121	99.88 43	97.14 132	99.84 168	99.13 85	98.94 199	99.69 123

MG-MVS		99.13 99	99.02 100	99.45 136	99.57 160	98.63 207	99.07 324	99.34 263	98.99 53	99.61 118	99.82 85	97.98 109	99.87 152	97.00 304	99.80 106	99.85 39

BP-MVS1		99.12 105	98.94 118	99.65 81	99.51 180	99.30 121	99.67 69	98.92 346	98.48 105	99.84 39	99.69 176	94.96 213	99.92 106	99.62 32	99.79 113	99.71 119

CHOSEN 280x420		99.12 105	99.13 81	99.08 190	99.66 128	97.89 259	98.43 398	99.71 13	98.88 67	99.62 115	99.76 143	96.63 152	99.70 241	99.46 53	99.99 1	99.66 133

DP-MVS Recon		99.12 105	98.95 116	99.65 81	99.74 87	99.70 52	99.27 277	99.57 69	96.40 329	99.42 159	99.68 183	98.75 58	99.80 200	97.98 225	99.72 129	99.44 208

Vis-MVSNet		99.12 105	98.97 110	99.56 105	99.78 58	99.10 147	99.68 66	99.66 28	98.49 104	99.86 37	99.87 52	94.77 228	99.84 168	99.19 79	99.41 161	99.74 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 105	99.08 89	99.24 175	99.46 203	98.55 214	99.51 167	99.46 195	98.09 157	99.45 149	99.82 85	98.34 93	99.51 277	98.70 146	98.93 200	99.67 130

SDMVSNet		99.11 110	98.90 122	99.75 65	99.81 47	99.59 77	99.81 20	99.65 35	98.78 81	99.64 108	99.88 43	94.56 242	99.93 94	99.67 27	98.26 242	99.72 110

VNet		99.11 110	98.90 122	99.73 71	99.52 177	99.56 83	99.41 222	99.39 234	99.01 48	99.74 72	99.78 131	95.56 193	99.92 106	99.52 43	98.18 249	99.72 110

CPTT-MVS		99.11 110	98.90 122	99.74 68	99.80 53	99.46 101	99.59 109	99.49 153	97.03 281	99.63 111	99.69 176	97.27 129	99.96 34	97.82 239	99.84 86	99.81 67

HyFIR lowres test		99.11 110	98.92 119	99.65 81	99.90 4	99.37 109	99.02 337	99.91 3	97.67 212	99.59 124	99.75 146	95.90 182	99.73 225	99.53 41	99.02 196	99.86 35

MVS_Test		99.10 114	98.97 110	99.48 130	99.49 193	99.14 143	99.67 69	99.34 263	97.31 252	99.58 125	99.76 143	97.65 117	99.82 188	98.87 119	99.07 191	99.46 203

CDS-MVSNet		99.09 115	99.03 96	99.25 173	99.42 213	98.73 198	99.45 201	99.46 195	98.11 154	99.46 148	99.77 139	98.01 108	99.37 299	98.70 146	98.92 202	99.66 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GDP-MVS		99.08 116	98.89 125	99.64 87	99.53 172	99.34 113	99.64 84	99.48 165	98.32 124	99.77 62	99.66 194	95.14 209	99.93 94	98.97 105	99.50 155	99.64 144

PVSNet_Blended		99.08 116	98.97 110	99.42 141	99.76 69	98.79 194	98.78 373	99.91 3	96.74 298	99.67 91	99.49 259	97.53 118	99.88 147	98.98 102	99.85 78	99.60 156

OMC-MVS		99.08 116	99.04 94	99.20 179	99.67 118	98.22 239	99.28 272	99.52 109	98.07 162	99.66 96	99.81 99	97.79 113	99.78 208	97.79 241	99.81 102	99.60 156

mvsmamba		99.06 119	98.96 114	99.36 149	99.47 201	98.64 206	99.70 56	99.05 330	97.61 217	99.65 103	99.83 76	96.54 156	99.92 106	99.19 79	99.62 145	99.51 186

WTY-MVS		99.06 119	98.88 127	99.61 95	99.62 145	99.16 138	99.37 241	99.56 74	98.04 169	99.53 136	99.62 213	96.84 144	99.94 76	98.85 126	98.49 229	99.72 110

IS-MVSNet		99.05 121	98.87 128	99.57 103	99.73 94	99.32 115	99.75 42	99.20 310	98.02 172	99.56 129	99.86 56	96.54 156	99.67 249	98.09 213	99.13 184	99.73 103

PAPM_NR		99.04 122	98.84 134	99.66 77	99.74 87	99.44 103	99.39 234	99.38 242	97.70 208	99.28 193	99.28 320	98.34 93	99.85 161	96.96 308	99.45 158	99.69 123

API-MVS		99.04 122	99.03 96	99.06 193	99.40 223	99.31 119	99.55 144	99.56 74	98.54 100	99.33 184	99.39 291	98.76 55	99.78 208	96.98 306	99.78 115	98.07 382

mvs_anonymous		99.03 124	98.99 106	99.16 183	99.38 228	98.52 220	99.51 167	99.38 242	97.79 196	99.38 172	99.81 99	97.30 127	99.45 282	99.35 59	98.99 197	99.51 186

sasdasda		99.02 125	98.86 130	99.51 124	99.42 213	99.32 115	99.80 25	99.48 165	98.63 91	99.31 186	98.81 370	97.09 134	99.75 217	99.27 73	97.90 260	99.47 198

train_agg		99.02 125	98.77 141	99.77 62	99.67 118	99.65 64	99.05 329	99.41 225	96.28 333	98.95 262	99.49 259	98.76 55	99.91 118	97.63 258	99.72 129	99.75 94

canonicalmvs		99.02 125	98.86 130	99.51 124	99.42 213	99.32 115	99.80 25	99.48 165	98.63 91	99.31 186	98.81 370	97.09 134	99.75 217	99.27 73	97.90 260	99.47 198

PLC	97.94 4	99.02 125	98.85 132	99.53 116	99.66 128	99.01 160	99.24 290	99.52 109	96.85 293	99.27 198	99.48 265	98.25 97	99.91 118	97.76 246	99.62 145	99.65 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MGCFI-Net		99.01 129	98.85 132	99.50 129	99.42 213	99.26 127	99.82 16	99.48 165	98.60 95	99.28 193	98.81 370	97.04 138	99.76 214	99.29 70	97.87 263	99.47 198

AdaColmap		99.01 129	98.80 137	99.66 77	99.56 164	99.54 87	99.18 303	99.70 15	98.18 144	99.35 180	99.63 208	96.32 165	99.90 130	97.48 274	99.77 118	99.55 170

1112_ss		98.98 131	98.77 141	99.59 98	99.68 116	99.02 158	99.25 288	99.48 165	97.23 260	99.13 227	99.58 226	96.93 143	99.90 130	98.87 119	98.78 213	99.84 45

MSDG		98.98 131	98.80 137	99.53 116	99.76 69	99.19 133	98.75 376	99.55 82	97.25 257	99.47 146	99.77 139	97.82 112	99.87 152	96.93 311	99.90 46	99.54 172

CANet_DTU		98.97 133	98.87 128	99.25 173	99.33 240	98.42 232	99.08 323	99.30 288	99.16 24	99.43 156	99.75 146	95.27 203	99.97 22	98.56 173	99.95 18	99.36 221

DPM-MVS		98.95 134	98.71 147	99.66 77	99.63 139	99.55 85	98.64 387	99.10 321	97.93 178	99.42 159	99.55 237	98.67 69	99.80 200	95.80 343	99.68 137	99.61 153

114514_t		98.93 135	98.67 151	99.72 73	99.85 26	99.53 90	99.62 95	99.59 61	92.65 398	99.71 81	99.78 131	98.06 106	99.90 130	98.84 129	99.91 37	99.74 98

PS-MVSNAJss		98.92 136	98.92 119	98.90 220	98.78 351	98.53 216	99.78 32	99.54 91	98.07 162	99.00 254	99.76 143	99.01 18	99.37 299	99.13 85	97.23 302	98.81 272

RRT-MVS		98.91 137	98.75 143	99.39 147	99.46 203	98.61 210	99.76 37	99.50 143	98.06 166	99.81 47	99.88 43	93.91 270	99.94 76	99.11 87	99.27 173	99.61 153

Test_1112_low_res		98.89 138	98.66 154	99.57 103	99.69 112	98.95 172	99.03 334	99.47 186	96.98 283	99.15 225	99.23 328	96.77 147	99.89 142	98.83 132	98.78 213	99.86 35

test_fmvs1		98.88 139	98.79 140	99.16 183	99.69 112	97.61 275	99.55 144	99.49 153	99.32 18	99.98 8	99.91 23	91.41 333	99.96 34	99.82 20	99.92 30	99.90 19

AllTest		98.87 140	98.72 145	99.31 158	99.86 20	98.48 226	99.56 130	99.61 50	97.85 188	99.36 177	99.85 61	95.95 177	99.85 161	96.66 324	99.83 95	99.59 160

UGNet		98.87 140	98.69 149	99.40 143	99.22 272	98.72 199	99.44 207	99.68 20	99.24 21	99.18 222	99.42 279	92.74 295	99.96 34	99.34 64	99.94 25	99.53 178
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 (Re-imp)		98.87 140	98.72 145	99.31 158	99.71 103	98.88 181	99.80 25	99.44 214	97.91 180	99.36 177	99.78 131	95.49 196	99.43 291	97.91 229	99.11 185	99.62 151

test_yl		98.86 143	98.63 156	99.54 108	99.49 193	99.18 135	99.50 174	99.07 327	98.22 137	99.61 118	99.51 253	95.37 199	99.84 168	98.60 164	98.33 236	99.59 160

DCV-MVSNet		98.86 143	98.63 156	99.54 108	99.49 193	99.18 135	99.50 174	99.07 327	98.22 137	99.61 118	99.51 253	95.37 199	99.84 168	98.60 164	98.33 236	99.59 160

EPNet		98.86 143	98.71 147	99.30 163	97.20 404	98.18 240	99.62 95	98.91 351	99.28 20	98.63 312	99.81 99	95.96 176	99.99 4	99.24 76	99.72 129	99.73 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 143	98.80 137	99.03 197	99.76 69	98.79 194	99.28 272	99.91 3	97.42 243	99.67 91	99.37 296	97.53 118	99.88 147	98.98 102	97.29 300	98.42 360

ab-mvs		98.86 143	98.63 156	99.54 108	99.64 136	99.19 133	99.44 207	99.54 91	97.77 199	99.30 189	99.81 99	94.20 256	99.93 94	99.17 83	98.82 210	99.49 191

MAR-MVS		98.86 143	98.63 156	99.54 108	99.37 231	99.66 60	99.45 201	99.54 91	96.61 310	99.01 250	99.40 287	97.09 134	99.86 155	97.68 257	99.53 153	99.10 243
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
COLMAP_ROB	97.56 6	98.86 143	98.75 143	99.17 182	99.88 11	98.53 216	99.34 254	99.59 61	97.55 224	98.70 300	99.89 35	95.83 184	99.90 130	98.10 212	99.90 46	99.08 248
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		98.85 150	98.62 161	99.53 116	99.61 149	99.08 151	99.80 25	99.51 123	97.10 273	99.31 186	99.78 131	95.23 207	99.77 210	98.21 204	99.03 194	99.75 94

HY-MVS	97.30 7	98.85 150	98.64 155	99.47 133	99.42 213	99.08 151	99.62 95	99.36 251	97.39 246	99.28 193	99.68 183	96.44 162	99.92 106	98.37 191	98.22 244	99.40 215

PVSNet	96.02 17	98.85 150	98.84 134	98.89 223	99.73 94	97.28 284	98.32 404	99.60 56	97.86 185	99.50 141	99.57 231	96.75 148	99.86 155	98.56 173	99.70 133	99.54 172

PatchMatch-RL		98.84 153	98.62 161	99.52 122	99.71 103	99.28 124	99.06 327	99.77 9	97.74 203	99.50 141	99.53 246	95.41 197	99.84 168	97.17 298	99.64 142	99.44 208

Effi-MVS+		98.81 154	98.59 167	99.48 130	99.46 203	99.12 146	98.08 411	99.50 143	97.50 232	99.38 172	99.41 283	96.37 164	99.81 193	99.11 87	98.54 226	99.51 186

alignmvs		98.81 154	98.56 170	99.58 101	99.43 211	99.42 105	99.51 167	98.96 341	98.61 94	99.35 180	98.92 365	94.78 225	99.77 210	99.35 59	98.11 254	99.54 172

DeepPCF-MVS	98.18 3	98.81 154	99.37 37	97.12 363	99.60 154	91.75 403	98.61 388	99.44 214	99.35 16	99.83 45	99.85 61	98.70 66	99.81 193	99.02 99	99.91 37	99.81 67

PMMVS		98.80 157	98.62 161	99.34 151	99.27 258	98.70 200	98.76 375	99.31 284	97.34 249	99.21 212	99.07 344	97.20 131	99.82 188	98.56 173	98.87 205	99.52 179

Effi-MVS+-dtu		98.78 158	98.89 125	98.47 279	99.33 240	96.91 313	99.57 124	99.30 288	98.47 106	99.41 163	98.99 355	96.78 146	99.74 219	98.73 143	99.38 162	98.74 285

FIs		98.78 158	98.63 156	99.23 177	99.18 281	99.54 87	99.83 15	99.59 61	98.28 127	98.79 287	99.81 99	96.75 148	99.37 299	99.08 92	96.38 318	98.78 274

Fast-Effi-MVS+-dtu		98.77 160	98.83 136	98.60 258	99.41 218	96.99 307	99.52 158	99.49 153	98.11 154	99.24 204	99.34 306	96.96 142	99.79 203	97.95 227	99.45 158	99.02 258

sd_testset		98.75 161	98.57 168	99.29 166	99.81 47	98.26 237	99.56 130	99.62 43	98.78 81	99.64 108	99.88 43	92.02 317	99.88 147	99.54 39	98.26 242	99.72 110

FA-MVS(test-final)		98.75 161	98.53 172	99.41 142	99.55 168	99.05 156	99.80 25	99.01 335	96.59 315	99.58 125	99.59 222	95.39 198	99.90 130	97.78 242	99.49 156	99.28 230

FC-MVSNet-test		98.75 161	98.62 161	99.15 187	99.08 308	99.45 102	99.86 11	99.60 56	98.23 136	98.70 300	99.82 85	96.80 145	99.22 328	99.07 93	96.38 318	98.79 273

XVG-OURS		98.73 164	98.68 150	98.88 225	99.70 108	97.73 266	98.92 359	99.55 82	98.52 102	99.45 149	99.84 71	95.27 203	99.91 118	98.08 217	98.84 208	99.00 259

Fast-Effi-MVS+		98.70 165	98.43 176	99.51 124	99.51 180	99.28 124	99.52 158	99.47 186	96.11 349	99.01 250	99.34 306	96.20 169	99.84 168	97.88 231	98.82 210	99.39 216

XVG-OURS-SEG-HR		98.69 166	98.62 161	98.89 223	99.71 103	97.74 265	99.12 314	99.54 91	98.44 111	99.42 159	99.71 162	94.20 256	99.92 106	98.54 177	98.90 204	99.00 259

1314		98.68 167	98.54 171	99.11 189	98.89 335	98.65 204	99.27 277	99.49 153	96.89 291	97.99 351	99.56 234	97.72 116	99.83 181	97.74 249	99.27 173	98.84 271

EI-MVSNet		98.67 168	98.67 151	98.68 254	99.35 235	97.97 252	99.50 174	99.38 242	96.93 290	99.20 215	99.83 76	97.87 110	99.36 303	98.38 189	97.56 279	98.71 289

test_djsdf		98.67 168	98.57 168	98.98 203	98.70 365	98.91 179	99.88 4	99.46 195	97.55 224	99.22 209	99.88 43	95.73 188	99.28 316	99.03 97	97.62 274	98.75 282

QAPM		98.67 168	98.30 186	99.80 53	99.20 275	99.67 58	99.77 34	99.72 11	94.74 376	98.73 292	99.90 30	95.78 186	99.98 14	96.96 308	99.88 60	99.76 93

nrg030		98.64 171	98.42 177	99.28 170	99.05 314	99.69 54	99.81 20	99.46 195	98.04 169	99.01 250	99.82 85	96.69 150	99.38 296	99.34 64	94.59 362	98.78 274

test_vis1_n_1920		98.63 172	98.40 179	99.31 158	99.86 20	97.94 258	99.67 69	99.62 43	99.43 11	99.99 2	99.91 23	87.29 383	100.00 1	99.92 15	99.92 30	99.98 2

PAPR		98.63 172	98.34 182	99.51 124	99.40 223	99.03 157	98.80 371	99.36 251	96.33 330	99.00 254	99.12 342	98.46 84	99.84 168	95.23 358	99.37 169	99.66 133

CVMVSNet		98.57 174	98.67 151	98.30 299	99.35 235	95.59 354	99.50 174	99.55 82	98.60 95	99.39 170	99.83 76	94.48 247	99.45 282	98.75 140	98.56 224	99.85 39

MVSTER		98.49 175	98.32 184	99.00 201	99.35 235	99.02 158	99.54 148	99.38 242	97.41 244	99.20 215	99.73 157	93.86 272	99.36 303	98.87 119	97.56 279	98.62 331

FE-MVS		98.48 176	98.17 191	99.40 143	99.54 171	98.96 169	99.68 66	98.81 365	95.54 360	99.62 115	99.70 166	93.82 273	99.93 94	97.35 285	99.46 157	99.32 227

OpenMVS	96.50 16	98.47 177	98.12 198	99.52 122	99.04 315	99.53 90	99.82 16	99.72 11	94.56 379	98.08 346	99.88 43	94.73 231	99.98 14	97.47 276	99.76 121	99.06 254

IterMVS-LS		98.46 178	98.42 177	98.58 262	99.59 156	98.00 250	99.37 241	99.43 220	96.94 289	99.07 239	99.59 222	97.87 110	99.03 356	98.32 198	95.62 340	98.71 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 179	98.28 187	98.94 210	98.50 381	98.96 169	99.77 34	99.50 143	97.07 275	98.87 275	99.77 139	94.76 229	99.28 316	98.66 153	97.60 275	98.57 346

jajsoiax		98.43 180	98.28 187	98.88 225	98.60 375	98.43 230	99.82 16	99.53 104	98.19 141	98.63 312	99.80 112	93.22 284	99.44 287	99.22 77	97.50 286	98.77 278

tttt0517		98.42 181	98.14 195	99.28 170	99.66 128	98.38 233	99.74 46	96.85 411	97.68 210	99.79 53	99.74 151	91.39 334	99.89 142	98.83 132	99.56 150	99.57 167

BH-untuned		98.42 181	98.36 180	98.59 259	99.49 193	96.70 321	99.27 277	99.13 319	97.24 259	98.80 285	99.38 293	95.75 187	99.74 219	97.07 302	99.16 179	99.33 226

test_fmvs1_n		98.41 183	98.14 195	99.21 178	99.82 43	97.71 271	99.74 46	99.49 153	99.32 18	99.99 2	99.95 3	85.32 394	99.97 22	99.82 20	99.84 86	99.96 7

D2MVS		98.41 183	98.50 173	98.15 313	99.26 260	96.62 327	99.40 230	99.61 50	97.71 205	98.98 257	99.36 299	96.04 173	99.67 249	98.70 146	97.41 296	98.15 378

BH-RMVSNet		98.41 183	98.08 204	99.40 143	99.41 218	98.83 190	99.30 262	98.77 369	97.70 208	98.94 264	99.65 196	92.91 291	99.74 219	96.52 328	99.55 152	99.64 144

mvs_tets		98.40 186	98.23 189	98.91 218	98.67 368	98.51 222	99.66 75	99.53 104	98.19 141	98.65 309	99.81 99	92.75 293	99.44 287	99.31 67	97.48 290	98.77 278

MonoMVSNet		98.38 187	98.47 175	98.12 315	98.59 377	96.19 344	99.72 52	98.79 368	97.89 182	99.44 154	99.52 249	96.13 170	98.90 377	98.64 155	97.54 281	99.28 230

XXY-MVS		98.38 187	98.09 203	99.24 175	99.26 260	99.32 115	99.56 130	99.55 82	97.45 237	98.71 294	99.83 76	93.23 282	99.63 266	98.88 116	96.32 320	98.76 280

ACMM	97.58 5	98.37 189	98.34 182	98.48 274	99.41 218	97.10 294	99.56 130	99.45 206	98.53 101	99.04 247	99.85 61	93.00 287	99.71 235	98.74 141	97.45 291	98.64 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest0530		98.35 190	98.03 210	99.31 158	99.63 139	98.56 213	99.54 148	96.75 413	97.53 228	99.73 74	99.65 196	91.25 338	99.89 142	98.62 158	99.56 150	99.48 192

tpmrst		98.33 191	98.48 174	97.90 331	99.16 291	94.78 374	99.31 260	99.11 320	97.27 255	99.45 149	99.59 222	95.33 201	99.84 168	98.48 180	98.61 218	99.09 247

baseline1		98.31 192	97.95 219	99.38 148	99.50 191	98.74 197	99.59 109	98.93 343	98.41 113	99.14 226	99.60 220	94.59 240	99.79 203	98.48 180	93.29 381	99.61 153

PatchmatchNet		98.31 192	98.36 180	98.19 308	99.16 291	95.32 364	99.27 277	98.92 346	97.37 247	99.37 174	99.58 226	94.90 218	99.70 241	97.43 280	99.21 176	99.54 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 194	97.98 215	99.26 172	99.57 160	98.16 241	99.41 222	98.55 387	96.03 354	99.19 218	99.74 151	91.87 320	99.92 106	99.16 84	98.29 241	99.70 121

VPA-MVSNet		98.29 195	97.95 219	99.30 163	99.16 291	99.54 87	99.50 174	99.58 65	98.27 129	99.35 180	99.37 296	92.53 305	99.65 257	99.35 59	94.46 363	98.72 287

UniMVSNet (Re)		98.29 195	98.00 213	99.13 188	99.00 319	99.36 112	99.49 185	99.51 123	97.95 176	98.97 259	99.13 339	96.30 166	99.38 296	98.36 193	93.34 380	98.66 318

HQP_MVS		98.27 197	98.22 190	98.44 285	99.29 253	96.97 309	99.39 234	99.47 186	98.97 59	99.11 231	99.61 217	92.71 298	99.69 246	97.78 242	97.63 272	98.67 310

UniMVSNet_NR-MVSNet		98.22 198	97.97 216	98.96 206	98.92 332	98.98 162	99.48 189	99.53 104	97.76 200	98.71 294	99.46 272	96.43 163	99.22 328	98.57 170	92.87 387	98.69 298

LPG-MVS_test		98.22 198	98.13 197	98.49 272	99.33 240	97.05 300	99.58 117	99.55 82	97.46 234	99.24 204	99.83 76	92.58 303	99.72 229	98.09 213	97.51 284	98.68 303

RPSCF		98.22 198	98.62 161	96.99 365	99.82 43	91.58 404	99.72 52	99.44 214	96.61 310	99.66 96	99.89 35	95.92 180	99.82 188	97.46 277	99.10 188	99.57 167

ADS-MVSNet		98.20 201	98.08 204	98.56 266	99.33 240	96.48 332	99.23 293	99.15 316	96.24 337	99.10 234	99.67 189	94.11 260	99.71 235	96.81 316	99.05 192	99.48 192

OPM-MVS		98.19 202	98.10 200	98.45 282	98.88 336	97.07 298	99.28 272	99.38 242	98.57 97	99.22 209	99.81 99	92.12 315	99.66 252	98.08 217	97.54 281	98.61 340
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SCA		98.19 202	98.16 192	98.27 305	99.30 249	95.55 355	99.07 324	98.97 339	97.57 221	99.43 156	99.57 231	92.72 296	99.74 219	97.58 262	99.20 177	99.52 179

miper_ehance_all_eth		98.18 204	98.10 200	98.41 288	99.23 268	97.72 268	98.72 379	99.31 284	96.60 313	98.88 272	99.29 318	97.29 128	99.13 342	97.60 260	95.99 329	98.38 365

CR-MVSNet		98.17 205	97.93 222	98.87 229	99.18 281	98.49 224	99.22 297	99.33 270	96.96 285	99.56 129	99.38 293	94.33 252	99.00 361	94.83 365	98.58 221	99.14 240

miper_enhance_ethall		98.16 206	98.08 204	98.41 288	98.96 328	97.72 268	98.45 397	99.32 280	96.95 287	98.97 259	99.17 334	97.06 137	99.22 328	97.86 234	95.99 329	98.29 369

CLD-MVS		98.16 206	98.10 200	98.33 295	99.29 253	96.82 318	98.75 376	99.44 214	97.83 191	99.13 227	99.55 237	92.92 289	99.67 249	98.32 198	97.69 270	98.48 352
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		98.14 208	97.79 234	99.19 180	99.50 191	98.50 223	98.61 388	96.82 412	96.95 287	99.54 134	99.43 277	91.66 329	99.86 155	98.08 217	99.51 154	99.22 237

pmmvs4		98.13 209	97.90 224	98.81 241	98.61 374	98.87 182	98.99 345	99.21 309	96.44 325	99.06 244	99.58 226	95.90 182	99.11 347	97.18 297	96.11 325	98.46 357

WR-MVS_H		98.13 209	97.87 229	98.90 220	99.02 317	98.84 187	99.70 56	99.59 61	97.27 255	98.40 328	99.19 333	95.53 194	99.23 324	98.34 195	93.78 377	98.61 340

c3_l		98.12 211	98.04 209	98.38 292	99.30 249	97.69 272	98.81 370	99.33 270	96.67 303	98.83 281	99.34 306	97.11 133	98.99 362	97.58 262	95.34 347	98.48 352

ACMH	97.28 8	98.10 212	97.99 214	98.44 285	99.41 218	96.96 311	99.60 102	99.56 74	98.09 157	98.15 344	99.91 23	90.87 342	99.70 241	98.88 116	97.45 291	98.67 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		98.09 213	97.68 250	99.34 151	99.66 128	98.44 229	99.40 230	99.43 220	93.67 386	99.22 209	99.89 35	90.23 350	99.93 94	99.26 75	98.33 236	99.66 133

CP-MVSNet		98.09 213	97.78 237	99.01 199	98.97 327	99.24 130	99.67 69	99.46 195	97.25 257	98.48 325	99.64 202	93.79 274	99.06 352	98.63 157	94.10 371	98.74 285

dmvs_re		98.08 215	98.16 192	97.85 334	99.55 168	94.67 377	99.70 56	98.92 346	98.15 146	99.06 244	99.35 302	93.67 278	99.25 321	97.77 245	97.25 301	99.64 144

DU-MVS		98.08 215	97.79 234	98.96 206	98.87 339	98.98 162	99.41 222	99.45 206	97.87 184	98.71 294	99.50 256	94.82 221	99.22 328	98.57 170	92.87 387	98.68 303

v2v482		98.06 217	97.77 239	98.92 214	98.90 334	98.82 191	99.57 124	99.36 251	96.65 305	99.19 218	99.35 302	94.20 256	99.25 321	97.72 252	94.97 355	98.69 298

V42		98.06 217	97.79 234	98.86 232	98.98 325	98.84 187	99.69 60	99.34 263	96.53 317	99.30 189	99.37 296	94.67 236	99.32 311	97.57 266	94.66 360	98.42 360

test-LLR		98.06 217	97.90 224	98.55 268	98.79 348	97.10 294	98.67 382	97.75 402	97.34 249	98.61 315	98.85 367	94.45 249	99.45 282	97.25 289	99.38 162	99.10 243

WR-MVS		98.06 217	97.73 246	99.06 193	98.86 342	99.25 129	99.19 301	99.35 258	97.30 253	98.66 303	99.43 277	93.94 267	99.21 333	98.58 167	94.28 367	98.71 289

ACMP	97.20 11	98.06 217	97.94 221	98.45 282	99.37 231	97.01 305	99.44 207	99.49 153	97.54 227	98.45 326	99.79 124	91.95 319	99.72 229	97.91 229	97.49 289	98.62 331
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		98.05 222	97.96 217	98.33 295	99.26 260	97.38 281	98.56 393	99.31 284	96.65 305	98.88 272	99.52 249	96.58 154	99.12 346	97.39 282	95.53 344	98.47 354

test1111		98.04 223	98.11 199	97.83 337	99.74 87	93.82 387	99.58 117	95.40 420	99.12 33	99.65 103	99.93 10	90.73 343	99.84 168	99.43 55	99.38 162	99.82 60

ECVR-MVS		98.04 223	98.05 208	98.00 323	99.74 87	94.37 382	99.59 109	94.98 421	99.13 28	99.66 96	99.93 10	90.67 344	99.84 168	99.40 56	99.38 162	99.80 76

EPNet_dtu		98.03 225	97.96 217	98.23 306	98.27 386	95.54 357	99.23 293	98.75 370	99.02 46	97.82 358	99.71 162	96.11 171	99.48 278	93.04 386	99.65 141	99.69 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 225	97.76 243	98.84 236	99.39 226	98.98 162	99.40 230	99.38 242	96.67 303	99.07 239	99.28 320	92.93 288	98.98 363	97.10 299	96.65 311	98.56 347

ADS-MVSNet2		98.02 227	98.07 207	97.87 333	99.33 240	95.19 367	99.23 293	99.08 324	96.24 337	99.10 234	99.67 189	94.11 260	98.93 374	96.81 316	99.05 192	99.48 192

HQP-MVS		98.02 227	97.90 224	98.37 293	99.19 278	96.83 316	98.98 348	99.39 234	98.24 133	98.66 303	99.40 287	92.47 307	99.64 260	97.19 295	97.58 277	98.64 322

LTVRE_ROB	97.16 12	98.02 227	97.90 224	98.40 290	99.23 268	96.80 319	99.70 56	99.60 56	97.12 269	98.18 343	99.70 166	91.73 325	99.72 229	98.39 188	97.45 291	98.68 303
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
cl____		98.01 230	97.84 232	98.55 268	99.25 264	97.97 252	98.71 380	99.34 263	96.47 324	98.59 318	99.54 242	95.65 191	99.21 333	97.21 291	95.77 335	98.46 357

DIV-MVS_self_test		98.01 230	97.85 231	98.48 274	99.24 266	97.95 256	98.71 380	99.35 258	96.50 318	98.60 317	99.54 242	95.72 189	99.03 356	97.21 291	95.77 335	98.46 357

miper_lstm_enhance		98.00 232	97.91 223	98.28 304	99.34 239	97.43 279	98.88 363	99.36 251	96.48 322	98.80 285	99.55 237	95.98 175	98.91 375	97.27 288	95.50 345	98.51 350

BH-w/o		98.00 232	97.89 228	98.32 297	99.35 235	96.20 343	99.01 342	98.90 353	96.42 327	98.38 329	99.00 353	95.26 205	99.72 229	96.06 336	98.61 218	99.03 256

v1144		97.98 234	97.69 249	98.85 235	98.87 339	98.66 203	99.54 148	99.35 258	96.27 335	99.23 208	99.35 302	94.67 236	99.23 324	96.73 319	95.16 351	98.68 303

EU-MVSNet		97.98 234	98.03 210	97.81 340	98.72 362	96.65 326	99.66 75	99.66 28	98.09 157	98.35 331	99.82 85	95.25 206	98.01 398	97.41 281	95.30 348	98.78 274

tpmvs		97.98 234	98.02 212	97.84 336	99.04 315	94.73 375	99.31 260	99.20 310	96.10 353	98.76 290	99.42 279	94.94 214	99.81 193	96.97 307	98.45 230	98.97 263

tt0805		97.97 237	97.77 239	98.57 263	99.59 156	96.61 328	99.45 201	99.08 324	98.21 139	98.88 272	99.80 112	88.66 368	99.70 241	98.58 167	97.72 269	99.39 216

NR-MVSNet		97.97 237	97.61 259	99.02 198	98.87 339	99.26 127	99.47 196	99.42 222	97.63 215	97.08 376	99.50 256	95.07 211	99.13 342	97.86 234	93.59 378	98.68 303

v8		97.95 239	97.63 257	98.93 212	98.95 329	98.81 193	99.80 25	99.41 225	96.03 354	99.10 234	99.42 279	94.92 217	99.30 314	96.94 310	94.08 372	98.66 318

Patchmatch-test		97.93 240	97.65 253	98.77 246	99.18 281	97.07 298	99.03 334	99.14 318	96.16 344	98.74 291	99.57 231	94.56 242	99.72 229	93.36 382	99.11 185	99.52 179

PS-CasMVS		97.93 240	97.59 261	98.95 208	98.99 322	99.06 154	99.68 66	99.52 109	97.13 267	98.31 333	99.68 183	92.44 311	99.05 353	98.51 178	94.08 372	98.75 282

TranMVSNet+NR-MVSNet		97.93 240	97.66 252	98.76 247	98.78 351	98.62 208	99.65 81	99.49 153	97.76 200	98.49 324	99.60 220	94.23 255	98.97 370	98.00 224	92.90 385	98.70 294

test_vis1_n		97.92 243	97.44 282	99.34 151	99.53 172	98.08 246	99.74 46	99.49 153	99.15 25	100.00 1	99.94 6	79.51 413	99.98 14	99.88 17	99.76 121	99.97 4

v144192		97.92 243	97.60 260	98.87 229	98.83 346	98.65 204	99.55 144	99.34 263	96.20 340	99.32 185	99.40 287	94.36 251	99.26 320	96.37 333	95.03 354	98.70 294

ACMH+	97.24 10	97.92 243	97.78 237	98.32 297	99.46 203	96.68 325	99.56 130	99.54 91	98.41 113	97.79 360	99.87 52	90.18 351	99.66 252	98.05 221	97.18 305	98.62 331

LFMVS		97.90 246	97.35 294	99.54 108	99.52 177	99.01 160	99.39 234	98.24 394	97.10 273	99.65 103	99.79 124	84.79 397	99.91 118	99.28 71	98.38 233	99.69 123

reproduce_monomvs		97.89 247	97.87 229	97.96 327	99.51 180	95.45 360	99.60 102	99.25 300	99.17 23	98.85 280	99.49 259	89.29 360	99.64 260	99.35 59	96.31 321	98.78 274

Anonymous20231211		97.88 248	97.54 265	98.90 220	99.71 103	98.53 216	99.48 189	99.57 69	94.16 382	98.81 283	99.68 183	93.23 282	99.42 292	98.84 129	94.42 365	98.76 280

OurMVSNet-221017-0		97.88 248	97.77 239	98.19 308	98.71 364	96.53 330	99.88 4	99.00 336	97.79 196	98.78 288	99.94 6	91.68 326	99.35 306	97.21 291	96.99 309	98.69 298

v7n		97.87 250	97.52 266	98.92 214	98.76 358	98.58 212	99.84 12	99.46 195	96.20 340	98.91 267	99.70 166	94.89 219	99.44 287	96.03 337	93.89 375	98.75 282

baseline2		97.87 250	97.55 262	98.82 238	99.18 281	98.02 249	99.41 222	96.58 417	96.97 284	96.51 383	99.17 334	93.43 279	99.57 271	97.71 253	99.03 194	98.86 269

thres600view7		97.86 252	97.51 268	98.92 214	99.72 98	97.95 256	99.59 109	98.74 373	97.94 177	99.27 198	98.62 378	91.75 323	99.86 155	93.73 378	98.19 248	98.96 265

UBG		97.85 253	97.48 271	98.95 208	99.25 264	97.64 273	99.24 290	98.74 373	97.90 181	98.64 310	98.20 394	88.65 369	99.81 193	98.27 201	98.40 231	99.42 210

cl22		97.85 253	97.64 256	98.48 274	99.09 305	97.87 260	98.60 390	99.33 270	97.11 272	98.87 275	99.22 329	92.38 312	99.17 337	98.21 204	95.99 329	98.42 360

v10		97.85 253	97.52 266	98.86 232	98.99 322	98.67 202	99.75 42	99.41 225	95.70 358	98.98 257	99.41 283	94.75 230	99.23 324	96.01 339	94.63 361	98.67 310

GA-MVS		97.85 253	97.47 274	99.00 201	99.38 228	97.99 251	98.57 391	99.15 316	97.04 280	98.90 269	99.30 316	89.83 354	99.38 296	96.70 321	98.33 236	99.62 151

tfpnnormal		97.84 257	97.47 274	98.98 203	99.20 275	99.22 132	99.64 84	99.61 50	96.32 331	98.27 337	99.70 166	93.35 281	99.44 287	95.69 346	95.40 346	98.27 370

VPNet		97.84 257	97.44 282	99.01 199	99.21 273	98.94 175	99.48 189	99.57 69	98.38 115	99.28 193	99.73 157	88.89 363	99.39 294	99.19 79	93.27 382	98.71 289

LCM-MVSNet-Re		97.83 259	98.15 194	96.87 371	99.30 249	92.25 401	99.59 109	98.26 392	97.43 241	96.20 387	99.13 339	96.27 167	98.73 384	98.17 209	98.99 197	99.64 144

XVG-ACMP-BASELINE		97.83 259	97.71 248	98.20 307	99.11 299	96.33 337	99.41 222	99.52 109	98.06 166	99.05 246	99.50 256	89.64 357	99.73 225	97.73 250	97.38 298	98.53 348

IterMVS		97.83 259	97.77 239	98.02 320	99.58 158	96.27 340	99.02 337	99.48 165	97.22 261	98.71 294	99.70 166	92.75 293	99.13 342	97.46 277	96.00 328	98.67 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.82 262	97.75 244	98.06 317	99.57 160	96.36 336	99.02 337	99.49 153	97.18 263	98.71 294	99.72 161	92.72 296	99.14 339	97.44 279	95.86 334	98.67 310

EPMVS		97.82 262	97.65 253	98.35 294	98.88 336	95.98 347	99.49 185	94.71 423	97.57 221	99.26 202	99.48 265	92.46 310	99.71 235	97.87 233	99.08 190	99.35 222

MVP-Stereo		97.81 264	97.75 244	97.99 324	97.53 397	96.60 329	98.96 352	98.85 360	97.22 261	97.23 371	99.36 299	95.28 202	99.46 281	95.51 350	99.78 115	97.92 395
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 264	97.44 282	98.91 218	98.88 336	98.68 201	99.51 167	99.34 263	96.18 342	99.20 215	99.34 306	94.03 264	99.36 303	95.32 356	95.18 350	98.69 298

ttmdpeth		97.80 266	97.63 257	98.29 300	98.77 356	97.38 281	99.64 84	99.36 251	98.78 81	96.30 386	99.58 226	92.34 314	99.39 294	98.36 193	95.58 341	98.10 380

v1921920		97.80 266	97.45 277	98.84 236	98.80 347	98.53 216	99.52 158	99.34 263	96.15 346	99.24 204	99.47 268	93.98 266	99.29 315	95.40 354	95.13 352	98.69 298

v148		97.79 268	97.55 262	98.50 271	98.74 359	97.72 268	99.54 148	99.33 270	96.26 336	98.90 269	99.51 253	94.68 235	99.14 339	97.83 238	93.15 384	98.63 329

thres400		97.77 269	97.38 290	98.92 214	99.69 112	97.96 254	99.50 174	98.73 379	97.83 191	99.17 223	98.45 384	91.67 327	99.83 181	93.22 383	98.18 249	98.96 265

thres100view900		97.76 270	97.45 277	98.69 253	99.72 98	97.86 262	99.59 109	98.74 373	97.93 178	99.26 202	98.62 378	91.75 323	99.83 181	93.22 383	98.18 249	98.37 366

PEN-MVS		97.76 270	97.44 282	98.72 249	98.77 356	98.54 215	99.78 32	99.51 123	97.06 277	98.29 336	99.64 202	92.63 302	98.89 378	98.09 213	93.16 383	98.72 287

Baseline_NR-MVSNet		97.76 270	97.45 277	98.68 254	99.09 305	98.29 235	99.41 222	98.85 360	95.65 359	98.63 312	99.67 189	94.82 221	99.10 349	98.07 220	92.89 386	98.64 322

TR-MVS		97.76 270	97.41 288	98.82 238	99.06 311	97.87 260	98.87 365	98.56 386	96.63 309	98.68 302	99.22 329	92.49 306	99.65 257	95.40 354	97.79 267	98.95 267

Patchmtry		97.75 274	97.40 289	98.81 241	99.10 302	98.87 182	99.11 320	99.33 270	94.83 374	98.81 283	99.38 293	94.33 252	99.02 358	96.10 335	95.57 342	98.53 348

dp		97.75 274	97.80 233	97.59 351	99.10 302	93.71 390	99.32 257	98.88 356	96.48 322	99.08 238	99.55 237	92.67 301	99.82 188	96.52 328	98.58 221	99.24 236

WBMVS		97.74 276	97.50 269	98.46 280	99.24 266	97.43 279	99.21 299	99.42 222	97.45 237	98.96 261	99.41 283	88.83 364	99.23 324	98.94 107	96.02 326	98.71 289

TAPA-MVS	97.07 15	97.74 276	97.34 297	98.94 210	99.70 108	97.53 276	99.25 288	99.51 123	91.90 400	99.30 189	99.63 208	98.78 51	99.64 260	88.09 409	99.87 63	99.65 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 278	97.35 294	98.88 225	99.47 201	97.12 293	99.34 254	98.85 360	98.19 141	99.67 91	99.85 61	82.98 404	99.92 106	99.49 49	98.32 240	99.60 156

MIMVSNet		97.73 278	97.45 277	98.57 263	99.45 209	97.50 277	99.02 337	98.98 338	96.11 349	99.41 163	99.14 338	90.28 346	98.74 383	95.74 344	98.93 200	99.47 198

tfpn200view9		97.72 280	97.38 290	98.72 249	99.69 112	97.96 254	99.50 174	98.73 379	97.83 191	99.17 223	98.45 384	91.67 327	99.83 181	93.22 383	98.18 249	98.37 366

CostFormer		97.72 280	97.73 246	97.71 345	99.15 295	94.02 386	99.54 148	99.02 334	94.67 377	99.04 247	99.35 302	92.35 313	99.77 210	98.50 179	97.94 259	99.34 225

FMVSNet2		97.72 280	97.36 292	98.80 243	99.51 180	98.84 187	99.45 201	99.42 222	96.49 319	98.86 279	99.29 318	90.26 347	98.98 363	96.44 330	96.56 314	98.58 345

test0.0.03 1		97.71 283	97.42 287	98.56 266	98.41 385	97.82 263	98.78 373	98.63 384	97.34 249	98.05 350	98.98 357	94.45 249	98.98 363	95.04 361	97.15 306	98.89 268

h-mvs33		97.70 284	97.28 305	98.97 205	99.70 108	97.27 285	99.36 246	99.45 206	98.94 62	99.66 96	99.64 202	94.93 215	99.99 4	99.48 50	84.36 412	99.65 137

v1240		97.69 285	97.32 300	98.79 244	98.85 343	98.43 230	99.48 189	99.36 251	96.11 349	99.27 198	99.36 299	93.76 276	99.24 323	94.46 368	95.23 349	98.70 294

cascas		97.69 285	97.43 286	98.48 274	98.60 375	97.30 283	98.18 409	99.39 234	92.96 394	98.41 327	98.78 374	93.77 275	99.27 319	98.16 210	98.61 218	98.86 269

pm-mvs1		97.68 287	97.28 305	98.88 225	99.06 311	98.62 208	99.50 174	99.45 206	96.32 331	97.87 356	99.79 124	92.47 307	99.35 306	97.54 269	93.54 379	98.67 310

GBi-Net		97.68 287	97.48 271	98.29 300	99.51 180	97.26 287	99.43 212	99.48 165	96.49 319	99.07 239	99.32 313	90.26 347	98.98 363	97.10 299	96.65 311	98.62 331

test1		97.68 287	97.48 271	98.29 300	99.51 180	97.26 287	99.43 212	99.48 165	96.49 319	99.07 239	99.32 313	90.26 347	98.98 363	97.10 299	96.65 311	98.62 331

tpm		97.67 290	97.55 262	98.03 318	99.02 317	95.01 370	99.43 212	98.54 388	96.44 325	99.12 229	99.34 306	91.83 322	99.60 269	97.75 248	96.46 316	99.48 192

PCF-MVS	97.08 14	97.66 291	97.06 317	99.47 133	99.61 149	99.09 148	98.04 412	99.25 300	91.24 403	98.51 322	99.70 166	94.55 244	99.91 118	92.76 391	99.85 78	99.42 210
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		97.65 292	97.65 253	97.63 348	98.78 351	97.62 274	99.13 311	98.33 391	97.36 248	99.07 239	98.94 361	95.64 192	99.15 338	92.95 387	98.68 217	96.12 414

our_test_3		97.65 292	97.68 250	97.55 352	98.62 372	94.97 371	98.84 367	99.30 288	96.83 296	98.19 342	99.34 306	97.01 140	99.02 358	95.00 362	96.01 327	98.64 322

testgi		97.65 292	97.50 269	98.13 314	99.36 234	96.45 333	99.42 219	99.48 165	97.76 200	97.87 356	99.45 274	91.09 339	98.81 380	94.53 367	98.52 227	99.13 242

thres200		97.61 295	97.28 305	98.62 257	99.64 136	98.03 248	99.26 286	98.74 373	97.68 210	99.09 237	98.32 390	91.66 329	99.81 193	92.88 388	98.22 244	98.03 385

PAPM		97.59 296	97.09 316	99.07 191	99.06 311	98.26 237	98.30 405	99.10 321	94.88 372	98.08 346	99.34 306	96.27 167	99.64 260	89.87 402	98.92 202	99.31 228

UWE-MVS		97.58 297	97.29 304	98.48 274	99.09 305	96.25 341	99.01 342	96.61 416	97.86 185	99.19 218	99.01 352	88.72 365	99.90 130	97.38 283	98.69 216	99.28 230

VDDNet		97.55 298	97.02 318	99.16 183	99.49 193	98.12 245	99.38 239	99.30 288	95.35 362	99.68 87	99.90 30	82.62 406	99.93 94	99.31 67	98.13 253	99.42 210

TESTMET0.1,1		97.55 298	97.27 308	98.40 290	98.93 330	96.53 330	98.67 382	97.61 405	96.96 285	98.64 310	99.28 320	88.63 371	99.45 282	97.30 287	99.38 162	99.21 238

pmmvs5		97.52 300	97.30 302	98.16 310	98.57 378	96.73 320	99.27 277	98.90 353	96.14 347	98.37 330	99.53 246	91.54 332	99.14 339	97.51 271	95.87 333	98.63 329

LF4IMVS		97.52 300	97.46 276	97.70 346	98.98 325	95.55 355	99.29 267	98.82 363	98.07 162	98.66 303	99.64 202	89.97 352	99.61 268	97.01 303	96.68 310	97.94 393

DTE-MVSNet		97.51 302	97.19 311	98.46 280	98.63 371	98.13 244	99.84 12	99.48 165	96.68 302	97.97 353	99.67 189	92.92 289	98.56 387	96.88 315	92.60 391	98.70 294

testing11		97.50 303	97.10 315	98.71 251	99.20 275	96.91 313	99.29 267	98.82 363	97.89 182	98.21 341	98.40 386	85.63 391	99.83 181	98.45 185	98.04 256	99.37 220

ETVMVS		97.50 303	96.90 322	99.29 166	99.23 268	98.78 196	99.32 257	98.90 353	97.52 230	98.56 319	98.09 400	84.72 398	99.69 246	97.86 234	97.88 262	99.39 216

hse-mvs2		97.50 303	97.14 312	98.59 259	99.49 193	97.05 300	99.28 272	99.22 306	98.94 62	99.66 96	99.42 279	94.93 215	99.65 257	99.48 50	83.80 414	99.08 248

SixPastTwentyTwo		97.50 303	97.33 299	98.03 318	98.65 369	96.23 342	99.77 34	98.68 382	97.14 266	97.90 354	99.93 10	90.45 345	99.18 336	97.00 304	96.43 317	98.67 310

JIA-IIPM		97.50 303	97.02 318	98.93 212	98.73 360	97.80 264	99.30 262	98.97 339	91.73 401	98.91 267	94.86 416	95.10 210	99.71 235	97.58 262	97.98 257	99.28 230

ppachtmachnet_test		97.49 308	97.45 277	97.61 350	98.62 372	95.24 365	98.80 371	99.46 195	96.11 349	98.22 340	99.62 213	96.45 161	98.97 370	93.77 376	95.97 332	98.61 340

test-mter		97.49 308	97.13 314	98.55 268	98.79 348	97.10 294	98.67 382	97.75 402	96.65 305	98.61 315	98.85 367	88.23 375	99.45 282	97.25 289	99.38 162	99.10 243

testing91		97.44 310	97.02 318	98.71 251	99.18 281	96.89 315	99.19 301	99.04 331	97.78 198	98.31 333	98.29 391	85.41 393	99.85 161	98.01 223	97.95 258	99.39 216

tpm2		97.44 310	97.34 297	97.74 344	99.15 295	94.36 383	99.45 201	98.94 342	93.45 391	98.90 269	99.44 275	91.35 335	99.59 270	97.31 286	98.07 255	99.29 229

tpm cat1		97.39 312	97.36 292	97.50 354	99.17 289	93.73 389	99.43 212	99.31 284	91.27 402	98.71 294	99.08 343	94.31 254	99.77 210	96.41 332	98.50 228	99.00 259

UWE-MVS-28		97.36 313	97.24 309	97.75 342	98.84 345	94.44 380	99.24 290	97.58 406	97.98 174	99.00 254	99.00 353	91.35 335	99.53 276	93.75 377	98.39 232	99.27 234

testing99		97.36 313	96.94 321	98.63 256	99.18 281	96.70 321	99.30 262	98.93 343	97.71 205	98.23 338	98.26 392	84.92 396	99.84 168	98.04 222	97.85 265	99.35 222

USDC		97.34 315	97.20 310	97.75 342	99.07 309	95.20 366	98.51 395	99.04 331	97.99 173	98.31 333	99.86 56	89.02 361	99.55 274	95.67 348	97.36 299	98.49 351

UniMVSNet_ETH3D		97.32 316	96.81 324	98.87 229	99.40 223	97.46 278	99.51 167	99.53 104	95.86 357	98.54 321	99.77 139	82.44 407	99.66 252	98.68 151	97.52 283	99.50 190

testing3		97.28 317	96.76 326	98.82 238	99.37 231	98.07 247	99.45 201	99.36 251	97.56 223	97.89 355	98.95 360	83.70 402	98.82 379	96.03 337	98.56 224	99.58 164

MVS		97.28 317	96.55 330	99.48 130	98.78 351	98.95 172	99.27 277	99.39 234	83.53 416	98.08 346	99.54 242	96.97 141	99.87 152	94.23 372	99.16 179	99.63 149

test_fmvs2		97.25 319	97.30 302	97.09 364	99.43 211	93.31 395	99.73 50	98.87 358	98.83 72	99.28 193	99.80 112	84.45 399	99.66 252	97.88 231	97.45 291	98.30 368

DSMNet-mixed		97.25 319	97.35 294	96.95 368	97.84 392	93.61 393	99.57 124	96.63 415	96.13 348	98.87 275	98.61 380	94.59 240	97.70 405	95.08 360	98.86 206	99.55 170

MS-PatchMatch		97.24 321	97.32 300	96.99 365	98.45 383	93.51 394	98.82 369	99.32 280	97.41 244	98.13 345	99.30 316	88.99 362	99.56 272	95.68 347	99.80 106	97.90 396

testing222		97.16 322	96.50 331	99.16 183	99.16 291	98.47 228	99.27 277	98.66 383	97.71 205	98.23 338	98.15 395	82.28 409	99.84 168	97.36 284	97.66 271	99.18 239

TransMVSNet (Re)		97.15 323	96.58 329	98.86 232	99.12 297	98.85 186	99.49 185	98.91 351	95.48 361	97.16 374	99.80 112	93.38 280	99.11 347	94.16 374	91.73 393	98.62 331

TinyColmap		97.12 324	96.89 323	97.83 337	99.07 309	95.52 358	98.57 391	98.74 373	97.58 220	97.81 359	99.79 124	88.16 376	99.56 272	95.10 359	97.21 303	98.39 364

K. test v3		97.10 325	96.79 325	98.01 321	98.72 362	96.33 337	99.87 8	97.05 409	97.59 218	96.16 388	99.80 112	88.71 366	99.04 354	96.69 322	96.55 315	98.65 320

Syy-MVS		97.09 326	97.14 312	96.95 368	99.00 319	92.73 399	99.29 267	99.39 234	97.06 277	97.41 365	98.15 395	93.92 269	98.68 385	91.71 395	98.34 234	99.45 206

PatchT		97.03 327	96.44 333	98.79 244	98.99 322	98.34 234	99.16 305	99.07 327	92.13 399	99.52 138	97.31 409	94.54 245	98.98 363	88.54 407	98.73 215	99.03 256

mmtdpeth		96.95 328	96.71 327	97.67 347	99.33 240	94.90 373	99.89 2	99.28 294	98.15 146	99.72 79	98.57 381	86.56 386	99.90 130	99.82 20	89.02 405	98.20 375

myMVS_eth3d		96.89 329	96.37 334	98.43 287	99.00 319	97.16 291	99.29 267	99.39 234	97.06 277	97.41 365	98.15 395	83.46 403	98.68 385	95.27 357	98.34 234	99.45 206

AUN-MVS		96.88 330	96.31 336	98.59 259	99.48 200	97.04 303	99.27 277	99.22 306	97.44 240	98.51 322	99.41 283	91.97 318	99.66 252	97.71 253	83.83 413	99.07 253

FMVSNet1		96.84 331	96.36 335	98.29 300	99.32 247	97.26 287	99.43 212	99.48 165	95.11 366	98.55 320	99.32 313	83.95 401	98.98 363	95.81 342	96.26 322	98.62 331

test2506		96.81 332	96.65 328	97.29 359	99.74 87	92.21 402	99.60 102	85.06 433	99.13 28	99.77 62	99.93 10	87.82 381	99.85 161	99.38 57	99.38 162	99.80 76

RPMNet		96.72 333	95.90 346	99.19 180	99.18 281	98.49 224	99.22 297	99.52 109	88.72 412	99.56 129	97.38 406	94.08 262	99.95 65	86.87 414	98.58 221	99.14 240

mvs5depth		96.66 334	96.22 338	97.97 325	97.00 408	96.28 339	98.66 385	99.03 333	96.61 310	96.93 380	99.79 124	87.20 384	99.47 279	96.65 326	94.13 370	98.16 377

test_0402		96.64 335	96.24 337	97.85 334	98.85 343	96.43 334	99.44 207	99.26 298	93.52 388	96.98 378	99.52 249	88.52 372	99.20 335	92.58 393	97.50 286	97.93 394

X-MVStestdata		96.55 336	95.45 355	99.87 16	99.85 26	99.83 19	99.69 60	99.68 20	98.98 56	99.37 174	64.01 429	98.81 47	99.94 76	98.79 137	99.86 71	99.84 45

pmmvs6		96.53 337	96.09 342	97.82 339	98.69 366	95.47 359	99.37 241	99.47 186	93.46 390	97.41 365	99.78 131	87.06 385	99.33 309	96.92 313	92.70 389	98.65 320

ET-MVSNet_ETH3D		96.49 338	95.64 352	99.05 195	99.53 172	98.82 191	98.84 367	97.51 407	97.63 215	84.77 416	99.21 332	92.09 316	98.91 375	98.98 102	92.21 392	99.41 213

UnsupCasMVSNet_eth		96.44 339	96.12 340	97.40 356	98.65 369	95.65 352	99.36 246	99.51 123	97.13 267	96.04 390	98.99 355	88.40 373	98.17 394	96.71 320	90.27 401	98.40 363

FMVSNet5		96.43 340	96.19 339	97.15 360	99.11 299	95.89 349	99.32 257	99.52 109	94.47 381	98.34 332	99.07 344	87.54 382	97.07 410	92.61 392	95.72 338	98.47 354

new_pmnet		96.38 341	96.03 343	97.41 355	98.13 389	95.16 369	99.05 329	99.20 310	93.94 383	97.39 368	98.79 373	91.61 331	99.04 354	90.43 400	95.77 335	98.05 384

Anonymous20231206		96.22 342	96.03 343	96.79 373	97.31 402	94.14 385	99.63 90	99.08 324	96.17 343	97.04 377	99.06 346	93.94 267	97.76 404	86.96 413	95.06 353	98.47 354

IB-MVS	95.67 18	96.22 342	95.44 356	98.57 263	99.21 273	96.70 321	98.65 386	97.74 404	96.71 300	97.27 370	98.54 382	86.03 388	99.92 106	98.47 183	86.30 410	99.10 243
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
Anonymous20240521		96.20 344	95.89 347	97.13 362	97.72 396	94.96 372	99.79 31	99.29 292	93.01 393	97.20 373	99.03 349	89.69 356	98.36 391	91.16 398	96.13 324	98.07 382

gg-mvs-nofinetune		96.17 345	95.32 357	98.73 248	98.79 348	98.14 243	99.38 239	94.09 424	91.07 405	98.07 349	91.04 422	89.62 358	99.35 306	96.75 318	99.09 189	98.68 303

test20.03		96.12 346	95.96 345	96.63 374	97.44 398	95.45 360	99.51 167	99.38 242	96.55 316	96.16 388	99.25 326	93.76 276	96.17 415	87.35 412	94.22 368	98.27 370

PVSNet_0	94.43 19	96.09 347	95.47 354	97.94 328	99.31 248	94.34 384	97.81 413	99.70 15	97.12 269	97.46 364	98.75 375	89.71 355	99.79 203	97.69 256	81.69 416	99.68 127

MVStest1		96.08 348	95.48 353	97.89 332	98.93 330	96.70 321	99.56 130	99.35 258	92.69 397	91.81 411	99.46 272	89.90 353	98.96 372	95.00 362	92.61 390	98.00 389

EG-PatchMatch MVS		95.97 349	95.69 350	96.81 372	97.78 393	92.79 398	99.16 305	98.93 343	96.16 344	94.08 401	99.22 329	82.72 405	99.47 279	95.67 348	97.50 286	98.17 376

APD_test1		95.87 350	96.49 332	94.00 385	99.53 172	84.01 414	99.54 148	99.32 280	95.91 356	97.99 351	99.85 61	85.49 392	99.88 147	91.96 394	98.84 208	98.12 379

Patchmatch-RL test		95.84 351	95.81 349	95.95 380	95.61 413	90.57 406	98.24 406	98.39 390	95.10 368	95.20 395	98.67 377	94.78 225	97.77 403	96.28 334	90.02 402	99.51 186

test_vis1_rt		95.81 352	95.65 351	96.32 378	99.67 118	91.35 405	99.49 185	96.74 414	98.25 132	95.24 393	98.10 399	74.96 414	99.90 130	99.53 41	98.85 207	97.70 399

MVS-HIRNet		95.75 353	95.16 358	97.51 353	99.30 249	93.69 391	98.88 363	95.78 418	85.09 415	98.78 288	92.65 418	91.29 337	99.37 299	94.85 364	99.85 78	99.46 203

MIMVSNet1		95.51 354	95.04 359	96.92 370	97.38 399	95.60 353	99.52 158	99.50 143	93.65 387	96.97 379	99.17 334	85.28 395	96.56 414	88.36 408	95.55 343	98.60 343

MDA-MVSNet_test_wron		95.45 355	94.60 362	98.01 321	98.16 388	97.21 290	99.11 320	99.24 303	93.49 389	80.73 422	98.98 357	93.02 286	98.18 393	94.22 373	94.45 364	98.64 322

TDRefinement		95.42 356	94.57 363	97.97 325	89.83 426	96.11 346	99.48 189	98.75 370	96.74 298	96.68 382	99.88 43	88.65 369	99.71 235	98.37 191	82.74 415	98.09 381

YYNet1		95.36 357	94.51 364	97.92 329	97.89 391	97.10 294	99.10 322	99.23 304	93.26 392	80.77 421	99.04 348	92.81 292	98.02 397	94.30 369	94.18 369	98.64 322

pmmvs-eth3d		95.34 358	94.73 361	97.15 360	95.53 415	95.94 348	99.35 251	99.10 321	95.13 364	93.55 403	97.54 404	88.15 377	97.91 400	94.58 366	89.69 404	97.61 400

dmvs_testset		95.02 359	96.12 340	91.72 394	99.10 302	80.43 422	99.58 117	97.87 401	97.47 233	95.22 394	98.82 369	93.99 265	95.18 419	88.09 409	94.91 358	99.56 169

KD-MVS_self_test		95.00 360	94.34 365	96.96 367	97.07 407	95.39 363	99.56 130	99.44 214	95.11 366	97.13 375	97.32 408	91.86 321	97.27 409	90.35 401	81.23 417	98.23 374

MDA-MVSNet-bldmvs		94.96 361	93.98 368	97.92 329	98.24 387	97.27 285	99.15 308	99.33 270	93.80 385	80.09 423	99.03 349	88.31 374	97.86 402	93.49 381	94.36 366	98.62 331

N_pmnet		94.95 362	95.83 348	92.31 392	98.47 382	79.33 424	99.12 314	92.81 430	93.87 384	97.68 361	99.13 339	93.87 271	99.01 360	91.38 397	96.19 323	98.59 344

KD-MVS_2432*1600		94.62 363	93.72 371	97.31 357	97.19 405	95.82 350	98.34 401	99.20 310	95.00 370	97.57 362	98.35 388	87.95 378	98.10 395	92.87 389	77.00 420	98.01 386

miper_refine_blended		94.62 363	93.72 371	97.31 357	97.19 405	95.82 350	98.34 401	99.20 310	95.00 370	97.57 362	98.35 388	87.95 378	98.10 395	92.87 389	77.00 420	98.01 386

CL-MVSNet_self_test		94.49 365	93.97 369	96.08 379	96.16 410	93.67 392	98.33 403	99.38 242	95.13 364	97.33 369	98.15 395	92.69 300	96.57 413	88.67 406	79.87 418	97.99 390

new-patchmatchnet		94.48 366	94.08 367	95.67 381	95.08 418	92.41 400	99.18 303	99.28 294	94.55 380	93.49 404	97.37 407	87.86 380	97.01 411	91.57 396	88.36 406	97.61 400

OpenMVS_ROB	92.34 20	94.38 367	93.70 373	96.41 377	97.38 399	93.17 396	99.06 327	98.75 370	86.58 413	94.84 399	98.26 392	81.53 410	99.32 311	89.01 405	97.87 263	96.76 407

CMPMVS	69.68 23	94.13 368	94.90 360	91.84 393	97.24 403	80.01 423	98.52 394	99.48 165	89.01 410	91.99 410	99.67 189	85.67 390	99.13 342	95.44 352	97.03 308	96.39 411
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 369	93.25 375	96.60 375	94.76 420	94.49 379	98.92 359	98.18 397	89.66 406	96.48 384	98.06 401	86.28 387	97.33 408	89.68 403	87.20 409	97.97 392

mvsany_test3		93.77 370	93.45 374	94.74 383	95.78 412	88.01 409	99.64 84	98.25 393	98.28 127	94.31 400	97.97 402	68.89 417	98.51 389	97.50 272	90.37 400	97.71 397

UnsupCasMVSNet_bld		93.53 371	92.51 377	96.58 376	97.38 399	93.82 387	98.24 406	99.48 165	91.10 404	93.10 405	96.66 411	74.89 415	98.37 390	94.03 375	87.71 408	97.56 402

dongtai		93.26 372	92.93 376	94.25 384	99.39 226	85.68 412	97.68 415	93.27 426	92.87 395	96.85 381	99.39 291	82.33 408	97.48 407	76.78 420	97.80 266	99.58 164

WB-MVS		93.10 373	94.10 366	90.12 399	95.51 417	81.88 419	99.73 50	99.27 297	95.05 369	93.09 406	98.91 366	94.70 234	91.89 423	76.62 421	94.02 374	96.58 409

PM-MVS		92.96 374	92.23 378	95.14 382	95.61 413	89.98 408	99.37 241	98.21 395	94.80 375	95.04 398	97.69 403	65.06 418	97.90 401	94.30 369	89.98 403	97.54 403

SSC-MVS		92.73 375	93.73 370	89.72 400	95.02 419	81.38 420	99.76 37	99.23 304	94.87 373	92.80 407	98.93 362	94.71 233	91.37 424	74.49 423	93.80 376	96.42 410

test_fmvs3		92.10 376	91.77 379	93.08 390	96.19 409	86.25 410	99.82 16	98.62 385	96.65 305	95.19 396	96.90 410	55.05 425	95.93 417	96.63 327	90.92 399	97.06 406

test_f		91.90 377	91.26 381	93.84 386	95.52 416	85.92 411	99.69 60	98.53 389	95.31 363	93.87 402	96.37 413	55.33 424	98.27 392	95.70 345	90.98 398	97.32 405

test_method		91.10 378	91.36 380	90.31 398	95.85 411	73.72 431	94.89 419	99.25 300	68.39 422	95.82 391	99.02 351	80.50 412	98.95 373	93.64 379	94.89 359	98.25 372

Gipuma		90.99 379	90.15 384	93.51 387	98.73 360	90.12 407	93.98 420	99.45 206	79.32 418	92.28 408	94.91 415	69.61 416	97.98 399	87.42 411	95.67 339	92.45 418
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		90.92 380	90.11 385	93.34 388	98.78 351	85.59 413	98.15 410	93.16 428	89.37 409	92.07 409	98.38 387	81.48 411	95.19 418	62.54 427	97.04 307	99.25 235

testf1		90.42 381	90.68 382	89.65 401	97.78 393	73.97 429	99.13 311	98.81 365	89.62 407	91.80 412	98.93 362	62.23 421	98.80 381	86.61 415	91.17 395	96.19 412

APD_test2		90.42 381	90.68 382	89.65 401	97.78 393	73.97 429	99.13 311	98.81 365	89.62 407	91.80 412	98.93 362	62.23 421	98.80 381	86.61 415	91.17 395	96.19 412

test_vis3_rt		87.04 383	85.81 386	90.73 397	93.99 421	81.96 418	99.76 37	90.23 432	92.81 396	81.35 420	91.56 420	40.06 429	99.07 351	94.27 371	88.23 407	91.15 420

PMMVS2		86.87 384	85.37 388	91.35 396	90.21 425	83.80 415	98.89 362	97.45 408	83.13 417	91.67 414	95.03 414	48.49 427	94.70 420	85.86 417	77.62 419	95.54 415

LCM-MVSNet		86.80 385	85.22 389	91.53 395	87.81 427	80.96 421	98.23 408	98.99 337	71.05 420	90.13 415	96.51 412	48.45 428	96.88 412	90.51 399	85.30 411	96.76 407

FPMVS		84.93 386	85.65 387	82.75 407	86.77 428	63.39 433	98.35 400	98.92 346	74.11 419	83.39 418	98.98 357	50.85 426	92.40 422	84.54 418	94.97 355	92.46 417

EGC-MVSNET		82.80 387	77.86 393	97.62 349	97.91 390	96.12 345	99.33 256	99.28 294	8.40 430	25.05 431	99.27 323	84.11 400	99.33 309	89.20 404	98.22 244	97.42 404

tmp_tt		82.80 387	81.52 390	86.66 403	66.61 433	68.44 432	92.79 422	97.92 399	68.96 421	80.04 424	99.85 61	85.77 389	96.15 416	97.86 234	43.89 426	95.39 416

E-PMN		80.61 389	79.88 391	82.81 406	90.75 424	76.38 427	97.69 414	95.76 419	66.44 424	83.52 417	92.25 419	62.54 420	87.16 426	68.53 425	61.40 423	84.89 424

EMVS		80.02 390	79.22 392	82.43 408	91.19 423	76.40 426	97.55 417	92.49 431	66.36 425	83.01 419	91.27 421	64.63 419	85.79 427	65.82 426	60.65 424	85.08 423

ANet_high		77.30 391	74.86 395	84.62 405	75.88 431	77.61 425	97.63 416	93.15 429	88.81 411	64.27 426	89.29 423	36.51 430	83.93 428	75.89 422	52.31 425	92.33 419

MVE	76.82 21	76.91 392	74.31 396	84.70 404	85.38 430	76.05 428	96.88 418	93.17 427	67.39 423	71.28 425	89.01 424	21.66 435	87.69 425	71.74 424	72.29 422	90.35 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 393	74.97 394	79.01 409	70.98 432	55.18 434	93.37 421	98.21 395	65.08 426	61.78 427	93.83 417	21.74 434	92.53 421	78.59 419	91.12 397	89.34 422
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 394	41.29 399	36.84 410	86.18 429	49.12 435	79.73 423	22.81 435	27.64 427	25.46 430	28.45 430	21.98 433	48.89 429	55.80 428	23.56 429	12.51 427

testmvs		39.17 395	43.78 397	25.37 412	36.04 435	16.84 437	98.36 399	26.56 434	20.06 428	38.51 429	67.32 425	29.64 432	15.30 431	37.59 429	39.90 427	43.98 426

test123		39.01 396	42.50 398	28.53 411	39.17 434	20.91 436	98.75 376	19.17 436	19.83 429	38.57 428	66.67 426	33.16 431	15.42 430	37.50 430	29.66 428	49.26 425

cdsmvs_eth3d_5k		24.64 397	32.85 400	0.00 413	0.00 436	0.00 438	0.00 424	99.51 123	0.00 431	0.00 432	99.56 234	96.58 154	0.00 432	0.00 431	0.00 430	0.00 428

ab-mvs-re		8.30 398	11.06 401	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	99.58 226	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

pcd_1.5k_mvsjas		8.27 399	11.03 402	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	99.01 18	0.00 432	0.00 431	0.00 430	0.00 428

test_blank		0.13 400	0.17 403	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	1.57 431	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

mmdepth		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

monomultidepth		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

uanet_test		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

DCPMVS		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

sosnet-low-res		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

sosnet		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

uncertanet		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

Regformer		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

uanet		0.02 401	0.03 404	0.00 413	0.00 436	0.00 438	0.00 424	0.00 437	0.00 431	0.00 432	0.27 432	0.00 436	0.00 432	0.00 431	0.00 430	0.00 428

WAC-MVS						97.16 291								95.47 351

FOURS1					99.91 1	99.93 1	99.87 8	99.56 74	99.10 35	99.81 47

MSC_two_6792asdad				99.87 16	99.51 180	99.76 42		99.33 270					99.96 34	98.87 119	99.84 86	99.89 22

PC_three_1452									98.18 144	99.84 39	99.70 166	99.31 3	98.52 388	98.30 200	99.80 106	99.81 67

No_MVS				99.87 16	99.51 180	99.76 42		99.33 270					99.96 34	98.87 119	99.84 86	99.89 22

test_one_0601					99.81 47	99.88 8		99.49 153	98.97 59	99.65 103	99.81 99	99.09 14

eth-test2					0.00 436

eth-test					0.00 436

ZD-MVS					99.71 103	99.79 34		99.61 50	96.84 294	99.56 129	99.54 242	98.58 75	99.96 34	96.93 311	99.75 123

RE-MVS-def			99.34 43		99.76 69	99.82 25	99.63 90	99.52 109	98.38 115	99.76 68	99.82 85	98.75 58		98.61 161	99.81 102	99.77 88

IU-MVS					99.84 32	99.88 8		99.32 280	98.30 126	99.84 39				98.86 124	99.85 78	99.89 22

OPU-MVS				99.64 87	99.56 164	99.72 48	99.60 102				99.70 166	99.27 5	99.42 292	98.24 203	99.80 106	99.79 80

test_241102_TWO								99.48 165	99.08 41	99.88 28	99.81 99	98.94 32	99.96 34	98.91 113	99.84 86	99.88 28

test_241102_ONE					99.84 32	99.90 2		99.48 165	99.07 43	99.91 21	99.74 151	99.20 7	99.76 214

9.14			99.10 85		99.72 98		99.40 230	99.51 123	97.53 228	99.64 108	99.78 131	98.84 44	99.91 118	97.63 258	99.82 99

save fliter					99.76 69	99.59 77	99.14 310	99.40 231	99.00 51

test_0728_THIRD									98.99 53	99.81 47	99.80 112	99.09 14	99.96 34	98.85 126	99.90 46	99.88 28

test_0728_SECOND				99.91 3	99.84 32	99.89 4	99.57 124	99.51 123					99.96 34	98.93 110	99.86 71	99.88 28

test0726					99.85 26	99.89 4	99.62 95	99.50 143	99.10 35	99.86 37	99.82 85	98.94 32

GSMVS																99.52 179

test_part2					99.81 47	99.83 19				99.77 62

sam_mvs1												94.86 220				99.52 179

sam_mvs												94.72 232

ambc				93.06 391	92.68 422	82.36 416	98.47 396	98.73 379		95.09 397	97.41 405	55.55 423	99.10 349	96.42 331	91.32 394	97.71 397

MTGPA								99.47 186

test_post1							99.23 293				65.14 428	94.18 259	99.71 235	97.58 262

test_post											65.99 427	94.65 238	99.73 225

patchmatchnet-post											98.70 376	94.79 224	99.74 219

GG-mvs-BLEND				98.45 282	98.55 379	98.16 241	99.43 212	93.68 425		97.23 371	98.46 383	89.30 359	99.22 328	95.43 353	98.22 244	97.98 391

MTMP							99.54 148	98.88 356

gm-plane-assit					98.54 380	92.96 397			94.65 378		99.15 337		99.64 260	97.56 267

test9_res														97.49 273	99.72 129	99.75 94

TEST9					99.67 118	99.65 64	99.05 329	99.41 225	96.22 339	98.95 262	99.49 259	98.77 54	99.91 118

test_8					99.67 118	99.61 74	99.03 334	99.41 225	96.28 333	98.93 265	99.48 265	98.76 55	99.91 118

agg_prior2														97.21 291	99.73 128	99.75 94

agg_prior					99.67 118	99.62 72		99.40 231		98.87 275			99.91 118

TestCases				99.31 158	99.86 20	98.48 226		99.61 50	97.85 188	99.36 177	99.85 61	95.95 177	99.85 161	96.66 324	99.83 95	99.59 160

test_prior4						99.56 83	98.99 345

test_prior2							98.96 352		98.34 121	99.01 250	99.52 249	98.68 67		97.96 226	99.74 126

test_prior				99.68 75	99.67 118	99.48 98		99.56 74					99.83 181			99.74 98

旧先验2							98.96 352		96.70 301	99.47 146			99.94 76	98.19 206

新几何2							99.01 342

新几何1				99.75 65	99.75 79	99.59 77		99.54 91	96.76 297	99.29 192	99.64 202	98.43 86	99.94 76	96.92 313	99.66 139	99.72 110

旧先验1					99.74 87	99.59 77		99.54 91			99.69 176	98.47 83			99.68 137	99.73 103

无先验							98.99 345	99.51 123	96.89 291				99.93 94	97.53 270		99.72 110

原ACMM2							98.95 355

原ACMM1				99.65 81	99.73 94	99.33 114		99.47 186	97.46 234	99.12 229	99.66 194	98.67 69	99.91 118	97.70 255	99.69 134	99.71 119

test222					99.75 79	99.49 96	98.91 361	99.49 153	96.42 327	99.34 183	99.65 196	98.28 96			99.69 134	99.72 110

testdata2													99.95 65	96.67 323

segment_acmp												98.96 25

testdata				99.54 108	99.75 79	98.95 172		99.51 123	97.07 275	99.43 156	99.70 166	98.87 40	99.94 76	97.76 246	99.64 142	99.72 110

testdata1							98.85 366		98.32 124

test12				99.75 65	99.64 136	99.61 74		99.29 292		99.21 212		98.38 91	99.89 142		99.74 126	99.74 98

plane_prior7					99.29 253	97.03 304

plane_prior6					99.27 258	96.98 308						92.71 298

plane_prior5								99.47 186					99.69 246	97.78 242	97.63 272	98.67 310

plane_prior4											99.61 217

plane_prior3						97.00 306			98.69 88	99.11 231

plane_prior2							99.39 234		98.97 59

plane_prior1					99.26 260

plane_prior						96.97 309	99.21 299		98.45 108						97.60 275

n2								0.00 437

nn								0.00 437

door-mid								98.05 398

lessismore_v0				97.79 341	98.69 366	95.44 362		94.75 422		95.71 392	99.87 52	88.69 367	99.32 311	95.89 340	94.93 357	98.62 331

LGP-MVS_train				98.49 272	99.33 240	97.05 300		99.55 82	97.46 234	99.24 204	99.83 76	92.58 303	99.72 229	98.09 213	97.51 284	98.68 303

test11								99.35 258

door								97.92 399

HQP5-MVS						96.83 316

HQP-NCC					99.19 278		98.98 348		98.24 133	98.66 303

ACMP_Plane					99.19 278		98.98 348		98.24 133	98.66 303

BP-MVS														97.19 295

HQP4-MVS										98.66 303			99.64 260			98.64 322

HQP3-MVS								99.39 234							97.58 277

HQP2-MVS												92.47 307

NP-MVS					99.23 268	96.92 312					99.40 287

MDTV_nov1_ep13_2view						95.18 368	99.35 251		96.84 294	99.58 125		95.19 208		97.82 239		99.46 203

MDTV_nov1_ep13			98.32 184		99.11 299	94.44 380	99.27 277	98.74 373	97.51 231	99.40 168	99.62 213	94.78 225	99.76 214	97.59 261	98.81 212

ACMMP++_ref															97.19 304

ACMMP++															97.43 295

Test By Simon												98.75 58

ITE_SJBPF				98.08 316	99.29 253	96.37 335		98.92 346	98.34 121	98.83 281	99.75 146	91.09 339	99.62 267	95.82 341	97.40 297	98.25 372

DeepMVS_CX				93.34 388	99.29 253	82.27 417		99.22 306	85.15 414	96.33 385	99.05 347	90.97 341	99.73 225	93.57 380	97.77 268	98.01 386

High-resolution multi-view benchmark