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 28	99.86 20	99.61 67	99.56 122	99.63 39	99.48 3	99.98 6	99.83 67	98.75 55	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 28	99.84 32	99.63 64	99.56 122	99.63 39	99.47 4	99.98 6	99.82 75	98.75 55	99.99 4	99.97 1	99.97 7	99.94 11

test_fmvsmconf_n		99.70 3	99.64 4	99.87 11	99.80 52	99.66 53	99.48 178	99.64 36	99.45 5	99.92 15	99.92 14	98.62 70	99.99 4	99.96 7	99.99 1	99.96 7

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 94	99.58 109	99.69 18	99.43 7	99.98 6	99.91 20	98.62 70	100.00 1	99.97 1	99.95 16	99.90 17

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 41	99.56 69	99.02 38	99.88 20	99.85 53	99.18 10	99.96 30	99.22 69	99.92 25	99.90 17
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 55	99.38 212	99.37 100	99.58 109	99.62 41	99.41 9	99.87 25	99.92 14	98.81 44	100.00 1	99.97 1	99.93 23	99.94 11

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 95	99.48 155	99.08 33	99.91 16	99.81 89	99.20 7	99.96 30	98.91 100	99.85 70	99.79 74

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 170	99.88 8	99.87 9	99.51 115	98.99 45	99.88 20	99.81 89	99.27 5	99.96 30	98.85 113	99.80 98	99.81 61

TSAR-MVS + MP.		99.58 9	99.50 13	99.81 44	99.91 1	99.66 53	99.63 82	99.39 222	98.91 58	99.78 47	99.85 53	99.36 2	99.94 69	98.84 116	99.88 52	99.82 54
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 9	99.57 8	99.64 78	99.78 56	99.14 132	99.60 95	99.45 194	99.01 40	99.90 18	99.83 67	98.98 23	99.93 84	99.59 25	99.95 16	99.86 33

EI-MVSNet-Vis-set		99.58 9	99.56 10	99.64 78	99.78 56	99.15 131	99.61 94	99.45 194	99.01 40	99.89 19	99.82 75	99.01 18	99.92 95	99.56 28	99.95 16	99.85 36

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 116	99.37 238	99.10 27	99.81 37	99.80 102	98.94 29	99.96 30	98.93 97	99.86 63	99.81 61
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 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 148	99.65 33	99.10 27	99.98 6	99.92 14	97.35 120	99.96 30	99.94 10	99.92 25	99.95 9

test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 196	99.65 57	99.50 163	99.61 48	99.45 5	99.87 25	99.92 14	97.31 121	99.97 21	99.95 8	99.99 1	99.97 4

SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 101	99.51 115	98.62 84	99.79 42	99.83 67	99.28 4	99.97 21	98.48 166	99.90 40	99.84 40
Skip Steuart: Steuart Systems R&D Blog.
XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 161	99.74 141	98.81 44	99.94 69	98.79 124	99.86 63	99.84 40

MTAPA		99.52 17	99.39 27	99.89 4	99.90 4	99.86 13	99.66 69	99.47 174	98.79 70	99.68 74	99.81 89	98.43 83	99.97 21	98.88 103	99.90 40	99.83 49

fmvsm_s_conf0.5_n		99.51 18	99.40 25	99.85 28	99.84 32	99.65 57	99.51 156	99.67 23	99.13 22	99.98 6	99.92 14	96.60 147	99.96 30	99.95 8	99.96 12	99.95 9

HPM-MVS_fast		99.51 18	99.40 25	99.85 28	99.91 1	99.79 30	99.76 37	99.56 69	97.72 189	99.76 56	99.75 136	99.13 12	99.92 95	99.07 82	99.92 25	99.85 36

mvsany_test1		99.50 20	99.46 20	99.62 83	99.61 141	99.09 138	98.94 339	99.48 155	99.10 27	99.96 14	99.91 20	98.85 39	99.96 30	99.72 18	99.58 138	99.82 54

CS-MVS		99.50 20	99.48 15	99.54 97	99.76 65	99.42 96	99.90 1	99.55 77	98.56 88	99.78 47	99.70 156	98.65 68	99.79 189	99.65 23	99.78 105	99.41 196

CS-MVS-test		99.49 22	99.48 15	99.54 97	99.78 56	99.30 110	99.89 2	99.58 61	98.56 88	99.73 62	99.69 166	98.55 75	99.82 175	99.69 19	99.85 70	99.48 178

HFP-MVS		99.49 22	99.37 30	99.86 21	99.87 15	99.80 27	99.66 69	99.67 23	98.15 135	99.68 74	99.69 166	99.06 16	99.96 30	98.69 136	99.87 55	99.84 40

ACMMPR		99.49 22	99.36 32	99.86 21	99.87 15	99.79 30	99.66 69	99.67 23	98.15 135	99.67 78	99.69 166	98.95 27	99.96 30	98.69 136	99.87 55	99.84 40

DeepC-MVS_fast	98.69 1	99.49 22	99.39 27	99.77 55	99.63 131	99.59 70	99.36 230	99.46 183	99.07 35	99.79 42	99.82 75	98.85 39	99.92 95	98.68 138	99.87 55	99.82 54
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 26	99.35 34	99.87 11	99.88 11	99.80 27	99.65 75	99.66 28	98.13 139	99.66 83	99.68 172	98.96 24	99.96 30	98.62 144	99.87 55	99.84 40

APD-MVS_3200maxsize		99.48 26	99.35 34	99.85 28	99.76 65	99.83 16	99.63 82	99.54 85	98.36 109	99.79 42	99.82 75	98.86 38	99.95 59	98.62 144	99.81 94	99.78 80

DELS-MVS		99.48 26	99.42 22	99.65 73	99.72 91	99.40 99	99.05 310	99.66 28	99.14 21	99.57 114	99.80 102	98.46 81	99.94 69	99.57 27	99.84 78	99.60 146
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 29	99.33 38	99.87 11	99.87 15	99.81 25	99.64 78	99.67 23	98.08 149	99.55 119	99.64 190	98.91 34	99.96 30	98.72 131	99.90 40	99.82 54

ACMMP_NAP		99.47 29	99.34 36	99.88 5	99.87 15	99.86 13	99.47 184	99.48 155	98.05 155	99.76 56	99.86 48	98.82 43	99.93 84	98.82 123	99.91 32	99.84 40

DPE-MVS		99.46 31	99.32 40	99.91 2	99.78 56	99.88 8	99.36 230	99.51 115	98.73 76	99.88 20	99.84 63	98.72 61	99.96 30	98.16 194	99.87 55	99.88 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.46 31	99.47 17	99.44 128	99.60 146	99.16 126	99.41 207	99.71 13	98.98 48	99.45 135	99.78 120	99.19 9	99.54 259	99.28 62	99.84 78	99.63 140

SR-MVS-dyc-post		99.45 33	99.31 47	99.85 28	99.76 65	99.82 22	99.63 82	99.52 101	98.38 105	99.76 56	99.82 75	98.53 76	99.95 59	98.61 147	99.81 94	99.77 82

PGM-MVS		99.45 33	99.31 47	99.86 21	99.87 15	99.78 36	99.58 109	99.65 33	97.84 174	99.71 68	99.80 102	99.12 13	99.97 21	98.33 181	99.87 55	99.83 49

CP-MVS		99.45 33	99.32 40	99.85 28	99.83 39	99.75 39	99.69 55	99.52 101	98.07 150	99.53 122	99.63 196	98.93 33	99.97 21	98.74 128	99.91 32	99.83 49

ACMMP		99.45 33	99.32 40	99.82 41	99.89 8	99.67 51	99.62 87	99.69 18	98.12 140	99.63 96	99.84 63	98.73 60	99.96 30	98.55 162	99.83 87	99.81 61
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 37	99.30 49	99.85 28	99.73 87	99.83 16	99.56 122	99.47 174	97.45 221	99.78 47	99.82 75	99.18 10	99.91 105	98.79 124	99.89 49	99.81 61
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 37	99.30 49	99.86 21	99.88 11	99.79 30	99.69 55	99.48 155	98.12 140	99.50 127	99.75 136	98.78 48	99.97 21	98.57 156	99.89 49	99.83 49

EC-MVSNet		99.44 37	99.39 27	99.58 90	99.56 156	99.49 87	99.88 4	99.58 61	98.38 105	99.73 62	99.69 166	98.20 95	99.70 226	99.64 24	99.82 91	99.54 161

SR-MVS		99.43 40	99.29 53	99.86 21	99.75 73	99.83 16	99.59 101	99.62 41	98.21 127	99.73 62	99.79 114	98.68 64	99.96 30	98.44 172	99.77 108	99.79 74

MCST-MVS		99.43 40	99.30 49	99.82 41	99.79 54	99.74 41	99.29 251	99.40 219	98.79 70	99.52 124	99.62 201	98.91 34	99.90 116	98.64 142	99.75 113	99.82 54

MVS_0304		99.42 42	99.32 40	99.72 65	99.70 101	99.27 114	99.52 148	97.57 388	99.51 2	99.82 35	99.78 120	98.09 100	99.96 30	99.97 1	99.97 7	99.94 11

MSP-MVS		99.42 42	99.27 58	99.88 5	99.89 8	99.80 27	99.67 64	99.50 135	98.70 78	99.77 51	99.49 246	98.21 94	99.95 59	98.46 170	99.77 108	99.88 26
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 42	99.29 53	99.80 46	99.62 137	99.55 77	99.50 163	99.70 15	98.79 70	99.77 51	99.96 1	97.45 115	99.96 30	98.92 99	99.90 40	99.89 20

HPM-MVS		99.42 42	99.28 55	99.83 40	99.90 4	99.72 42	99.81 20	99.54 85	97.59 202	99.68 74	99.63 196	98.91 34	99.94 69	98.58 153	99.91 32	99.84 40
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 42	99.30 49	99.78 52	99.62 137	99.71 44	99.26 270	99.52 101	98.82 65	99.39 157	99.71 152	98.96 24	99.85 148	98.59 152	99.80 98	99.77 82

SD-MVS		99.41 47	99.52 11	99.05 183	99.74 80	99.68 48	99.46 187	99.52 101	99.11 26	99.88 20	99.91 20	99.43 1	97.70 386	98.72 131	99.93 23	99.77 82
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 47	99.33 38	99.65 73	99.77 62	99.51 86	98.94 339	99.85 6	98.82 65	99.65 89	99.74 141	98.51 78	99.80 186	98.83 119	99.89 49	99.64 136

MVS_111021_HR		99.41 47	99.32 40	99.66 69	99.72 91	99.47 91	98.95 337	99.85 6	98.82 65	99.54 120	99.73 147	98.51 78	99.74 204	98.91 100	99.88 52	99.77 82

MM		99.40 50	99.28 55	99.74 61	99.67 111	99.31 108	99.52 148	98.87 341	99.55 1	99.74 60	99.80 102	96.47 152	99.98 13	99.97 1	99.97 7	99.94 11

GST-MVS		99.40 50	99.24 63	99.85 28	99.86 20	99.79 30	99.60 95	99.67 23	97.97 161	99.63 96	99.68 172	98.52 77	99.95 59	98.38 175	99.86 63	99.81 61

HPM-MVS++		99.39 52	99.23 65	99.87 11	99.75 73	99.84 15	99.43 198	99.51 115	98.68 81	99.27 185	99.53 233	98.64 69	99.96 30	98.44 172	99.80 98	99.79 74

SF-MVS		99.38 53	99.24 63	99.79 49	99.79 54	99.68 48	99.57 116	99.54 85	97.82 179	99.71 68	99.80 102	98.95 27	99.93 84	98.19 190	99.84 78	99.74 92

MP-MVS-pluss		99.37 54	99.20 67	99.88 5	99.90 4	99.87 12	99.30 246	99.52 101	97.18 246	99.60 107	99.79 114	98.79 47	99.95 59	98.83 119	99.91 32	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		99.36 55	99.36 32	99.36 138	99.67 111	98.61 200	99.07 305	99.33 256	99.00 43	99.82 35	99.81 89	99.06 16	99.84 155	99.09 80	99.42 148	99.65 129

PVSNet_Blended_VisFu		99.36 55	99.28 55	99.61 84	99.86 20	99.07 144	99.47 184	99.93 2	97.66 198	99.71 68	99.86 48	97.73 110	99.96 30	99.47 43	99.82 91	99.79 74

NCCC		99.34 57	99.19 68	99.79 49	99.61 141	99.65 57	99.30 246	99.48 155	98.86 60	99.21 199	99.63 196	98.72 61	99.90 116	98.25 186	99.63 134	99.80 70

MP-MVS		99.33 58	99.15 71	99.87 11	99.88 11	99.82 22	99.66 69	99.46 183	98.09 145	99.48 131	99.74 141	98.29 91	99.96 30	97.93 212	99.87 55	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PS-MVSNAJ		99.32 59	99.32 40	99.30 151	99.57 152	98.94 166	98.97 332	99.46 183	98.92 57	99.71 68	99.24 311	99.01 18	99.98 13	99.35 51	99.66 129	98.97 245

CSCG		99.32 59	99.32 40	99.32 145	99.85 26	98.29 226	99.71 51	99.66 28	98.11 142	99.41 148	99.80 102	98.37 88	99.96 30	98.99 90	99.96 12	99.72 103

PHI-MVS		99.30 61	99.17 70	99.70 67	99.56 156	99.52 85	99.58 109	99.80 8	97.12 252	99.62 101	99.73 147	98.58 72	99.90 116	98.61 147	99.91 32	99.68 119

DeepC-MVS	98.35 2	99.30 61	99.19 68	99.64 78	99.82 42	99.23 119	99.62 87	99.55 77	98.94 54	99.63 96	99.95 3	95.82 177	99.94 69	99.37 50	99.97 7	99.73 97
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 63	99.10 76	99.86 21	99.70 101	99.65 57	99.53 147	99.62 41	98.74 75	99.99 2	99.95 3	94.53 234	99.94 69	99.89 13	99.96 12	99.97 4

xiu_mvs_v1_base_debu		99.29 63	99.27 58	99.34 139	99.63 131	98.97 156	99.12 295	99.51 115	98.86 60	99.84 29	99.47 254	98.18 96	99.99 4	99.50 36	99.31 158	99.08 230

xiu_mvs_v1_base		99.29 63	99.27 58	99.34 139	99.63 131	98.97 156	99.12 295	99.51 115	98.86 60	99.84 29	99.47 254	98.18 96	99.99 4	99.50 36	99.31 158	99.08 230

xiu_mvs_v1_base_debi		99.29 63	99.27 58	99.34 139	99.63 131	98.97 156	99.12 295	99.51 115	98.86 60	99.84 29	99.47 254	98.18 96	99.99 4	99.50 36	99.31 158	99.08 230

APD-MVS		99.27 67	99.08 80	99.84 39	99.75 73	99.79 30	99.50 163	99.50 135	97.16 248	99.77 51	99.82 75	98.78 48	99.94 69	97.56 251	99.86 63	99.80 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 67	99.12 74	99.74 61	99.18 265	99.75 39	99.56 122	99.57 64	98.45 99	99.49 130	99.85 53	97.77 109	99.94 69	98.33 181	99.84 78	99.52 167

fmvsm_s_conf0.1_n_a		99.26 69	99.06 82	99.85 28	99.52 167	99.62 65	99.54 139	99.62 41	98.69 79	99.99 2	99.96 1	94.47 236	99.94 69	99.88 14	99.92 25	99.98 2

patch_mono-2		99.26 69	99.62 5	98.16 297	99.81 46	94.59 360	99.52 148	99.64 36	99.33 13	99.73 62	99.90 26	99.00 22	99.99 4	99.69 19	99.98 4	99.89 20

ETV-MVS		99.26 69	99.21 66	99.40 132	99.46 190	99.30 110	99.56 122	99.52 101	98.52 93	99.44 140	99.27 307	98.41 86	99.86 142	99.10 79	99.59 137	99.04 237

xiu_mvs_v2_base		99.26 69	99.25 62	99.29 154	99.53 163	98.91 170	99.02 318	99.45 194	98.80 69	99.71 68	99.26 309	98.94 29	99.98 13	99.34 55	99.23 162	98.98 244

CANet		99.25 73	99.14 72	99.59 87	99.41 203	99.16 126	99.35 235	99.57 64	98.82 65	99.51 126	99.61 205	96.46 153	99.95 59	99.59 25	99.98 4	99.65 129

3Dnovator	97.25 9	99.24 74	99.05 83	99.81 44	99.12 281	99.66 53	99.84 13	99.74 10	99.09 32	98.92 252	99.90 26	95.94 171	99.98 13	98.95 94	99.92 25	99.79 74

dcpmvs_2		99.23 75	99.58 7	98.16 297	99.83 39	94.68 358	99.76 37	99.52 101	99.07 35	99.98 6	99.88 36	98.56 74	99.93 84	99.67 21	99.98 4	99.87 31

test_fmvsmconf0.01_n		99.22 76	99.03 87	99.79 49	98.42 364	99.48 89	99.55 134	99.51 115	99.39 10	99.78 47	99.93 9	94.80 212	99.95 59	99.93 11	99.95 16	99.94 11

CHOSEN 1792x2688		99.19 77	99.10 76	99.45 124	99.89 8	98.52 210	99.39 219	99.94 1	98.73 76	99.11 218	99.89 30	95.50 187	99.94 69	99.50 36	99.97 7	99.89 20

F-COLMAP		99.19 77	99.04 85	99.64 78	99.78 56	99.27 114	99.42 205	99.54 85	97.29 237	99.41 148	99.59 210	98.42 85	99.93 84	98.19 190	99.69 124	99.73 97

EIA-MVS		99.18 79	99.09 79	99.45 124	99.49 181	99.18 123	99.67 64	99.53 96	97.66 198	99.40 153	99.44 261	98.10 99	99.81 180	98.94 95	99.62 135	99.35 205

3Dnovator+	97.12 13	99.18 79	98.97 101	99.82 41	99.17 273	99.68 48	99.81 20	99.51 115	99.20 18	98.72 278	99.89 30	95.68 182	99.97 21	98.86 111	99.86 63	99.81 61

MVSFormer		99.17 81	99.12 74	99.29 154	99.51 170	98.94 166	99.88 4	99.46 183	97.55 208	99.80 40	99.65 184	97.39 116	99.28 299	99.03 84	99.85 70	99.65 129

sss		99.17 81	99.05 83	99.53 105	99.62 137	98.97 156	99.36 230	99.62 41	97.83 175	99.67 78	99.65 184	97.37 119	99.95 59	99.19 71	99.19 165	99.68 119

test_cas_vis1_n_1920		99.16 83	99.01 95	99.61 84	99.81 46	98.86 176	99.65 75	99.64 36	99.39 10	99.97 13	99.94 6	93.20 272	99.98 13	99.55 29	99.91 32	99.99 1

DP-MVS		99.16 83	98.95 105	99.78 52	99.77 62	99.53 82	99.41 207	99.50 135	97.03 264	99.04 234	99.88 36	97.39 116	99.92 95	98.66 140	99.90 40	99.87 31

casdiffmvs_mvg		99.15 85	99.02 91	99.55 96	99.66 120	99.09 138	99.64 78	99.56 69	98.26 119	99.45 135	99.87 44	96.03 166	99.81 180	99.54 30	99.15 169	99.73 97
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 85	99.02 91	99.53 105	99.66 120	99.14 132	99.72 49	99.48 155	98.35 110	99.42 144	99.84 63	96.07 164	99.79 189	99.51 35	99.14 170	99.67 122

diffmvs		99.14 87	99.02 91	99.51 113	99.61 141	98.96 160	99.28 256	99.49 143	98.46 98	99.72 67	99.71 152	96.50 151	99.88 133	99.31 58	99.11 172	99.67 122
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 87	98.99 97	99.59 87	99.58 150	99.41 98	99.16 286	99.44 202	98.45 99	99.19 205	99.49 246	98.08 101	99.89 127	97.73 234	99.75 113	99.48 178

CDPH-MVS		99.13 89	98.91 109	99.80 46	99.75 73	99.71 44	99.15 289	99.41 213	96.60 295	99.60 107	99.55 224	98.83 42	99.90 116	97.48 258	99.83 87	99.78 80

casdiffmvs		99.13 89	98.98 100	99.56 94	99.65 126	99.16 126	99.56 122	99.50 135	98.33 113	99.41 148	99.86 48	95.92 172	99.83 168	99.45 45	99.16 166	99.70 113
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 89	99.03 87	99.45 124	99.46 190	98.87 173	99.12 295	99.26 284	98.03 158	99.79 42	99.65 184	97.02 133	99.85 148	99.02 86	99.90 40	99.65 129
jason: jason.
lupinMVS		99.13 89	99.01 95	99.46 123	99.51 170	98.94 166	99.05 310	99.16 300	97.86 169	99.80 40	99.56 221	97.39 116	99.86 142	98.94 95	99.85 70	99.58 154

EPP-MVSNet		99.13 89	98.99 97	99.53 105	99.65 126	99.06 145	99.81 20	99.33 256	97.43 224	99.60 107	99.88 36	97.14 126	99.84 155	99.13 76	98.94 186	99.69 115

MG-MVS		99.13 89	99.02 91	99.45 124	99.57 152	98.63 197	99.07 305	99.34 249	98.99 45	99.61 104	99.82 75	97.98 104	99.87 138	97.00 288	99.80 98	99.85 36

CHOSEN 280x420		99.12 95	99.13 73	99.08 178	99.66 120	97.89 250	98.43 379	99.71 13	98.88 59	99.62 101	99.76 133	96.63 146	99.70 226	99.46 44	99.99 1	99.66 125

DP-MVS Recon		99.12 95	98.95 105	99.65 73	99.74 80	99.70 46	99.27 261	99.57 64	96.40 311	99.42 144	99.68 172	98.75 55	99.80 186	97.98 209	99.72 119	99.44 192

Vis-MVSNet		99.12 95	98.97 101	99.56 94	99.78 56	99.10 137	99.68 61	99.66 28	98.49 96	99.86 27	99.87 44	94.77 217	99.84 155	99.19 71	99.41 149	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 95	99.08 80	99.24 163	99.46 190	98.55 204	99.51 156	99.46 183	98.09 145	99.45 135	99.82 75	98.34 89	99.51 260	98.70 133	98.93 187	99.67 122

SDMVSNet		99.11 99	98.90 110	99.75 58	99.81 46	99.59 70	99.81 20	99.65 33	98.78 73	99.64 93	99.88 36	94.56 230	99.93 84	99.67 21	98.26 227	99.72 103

VNet		99.11 99	98.90 110	99.73 64	99.52 167	99.56 75	99.41 207	99.39 222	99.01 40	99.74 60	99.78 120	95.56 185	99.92 95	99.52 34	98.18 234	99.72 103

CPTT-MVS		99.11 99	98.90 110	99.74 61	99.80 52	99.46 92	99.59 101	99.49 143	97.03 264	99.63 96	99.69 166	97.27 124	99.96 30	97.82 223	99.84 78	99.81 61

HyFIR lowres test		99.11 99	98.92 107	99.65 73	99.90 4	99.37 100	99.02 318	99.91 3	97.67 197	99.59 110	99.75 136	95.90 174	99.73 210	99.53 32	99.02 183	99.86 33

MVS_Test		99.10 103	98.97 101	99.48 118	99.49 181	99.14 132	99.67 64	99.34 249	97.31 235	99.58 111	99.76 133	97.65 112	99.82 175	98.87 106	99.07 178	99.46 187

CDS-MVSNet		99.09 104	99.03 87	99.25 161	99.42 199	98.73 189	99.45 188	99.46 183	98.11 142	99.46 134	99.77 128	98.01 103	99.37 281	98.70 133	98.92 189	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		99.08 105	98.97 101	99.42 129	99.76 65	98.79 185	98.78 355	99.91 3	96.74 281	99.67 78	99.49 246	97.53 113	99.88 133	98.98 91	99.85 70	99.60 146

OMC-MVS		99.08 105	99.04 85	99.20 167	99.67 111	98.22 230	99.28 256	99.52 101	98.07 150	99.66 83	99.81 89	97.79 108	99.78 194	97.79 225	99.81 94	99.60 146

WTY-MVS		99.06 107	98.88 114	99.61 84	99.62 137	99.16 126	99.37 226	99.56 69	98.04 156	99.53 122	99.62 201	96.84 139	99.94 69	98.85 113	98.49 216	99.72 103

IS-MVSNet		99.05 108	98.87 115	99.57 92	99.73 87	99.32 104	99.75 41	99.20 295	98.02 159	99.56 115	99.86 48	96.54 150	99.67 234	98.09 197	99.13 171	99.73 97

PAPM_NR		99.04 109	98.84 121	99.66 69	99.74 80	99.44 94	99.39 219	99.38 230	97.70 193	99.28 181	99.28 304	98.34 89	99.85 148	96.96 292	99.45 146	99.69 115

API-MVS		99.04 109	99.03 87	99.06 181	99.40 208	99.31 108	99.55 134	99.56 69	98.54 90	99.33 172	99.39 276	98.76 52	99.78 194	96.98 290	99.78 105	98.07 362

mvs_anonymous		99.03 111	98.99 97	99.16 171	99.38 212	98.52 210	99.51 156	99.38 230	97.79 180	99.38 159	99.81 89	97.30 122	99.45 264	99.35 51	98.99 184	99.51 173

sasdasda		99.02 112	98.86 118	99.51 113	99.42 199	99.32 104	99.80 25	99.48 155	98.63 82	99.31 174	98.81 353	97.09 129	99.75 202	99.27 65	97.90 245	99.47 184

train_agg		99.02 112	98.77 128	99.77 55	99.67 111	99.65 57	99.05 310	99.41 213	96.28 315	98.95 247	99.49 246	98.76 52	99.91 105	97.63 242	99.72 119	99.75 88

canonicalmvs		99.02 112	98.86 118	99.51 113	99.42 199	99.32 104	99.80 25	99.48 155	98.63 82	99.31 174	98.81 353	97.09 129	99.75 202	99.27 65	97.90 245	99.47 184

PLC	97.94 4	99.02 112	98.85 120	99.53 105	99.66 120	99.01 151	99.24 274	99.52 101	96.85 276	99.27 185	99.48 251	98.25 93	99.91 105	97.76 230	99.62 135	99.65 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
AdaColmap		99.01 116	98.80 124	99.66 69	99.56 156	99.54 79	99.18 284	99.70 15	98.18 133	99.35 168	99.63 196	96.32 158	99.90 116	97.48 258	99.77 108	99.55 159

1112_ss		98.98 117	98.77 128	99.59 87	99.68 110	99.02 149	99.25 272	99.48 155	97.23 243	99.13 214	99.58 214	96.93 138	99.90 116	98.87 106	98.78 200	99.84 40

MSDG		98.98 117	98.80 124	99.53 105	99.76 65	99.19 121	98.75 358	99.55 77	97.25 240	99.47 132	99.77 128	97.82 107	99.87 138	96.93 295	99.90 40	99.54 161

CANet_DTU		98.97 119	98.87 115	99.25 161	99.33 227	98.42 223	99.08 304	99.30 274	99.16 19	99.43 141	99.75 136	95.27 195	99.97 21	98.56 159	99.95 16	99.36 204

DPM-MVS		98.95 120	98.71 133	99.66 69	99.63 131	99.55 77	98.64 368	99.10 306	97.93 164	99.42 144	99.55 224	98.67 66	99.80 186	95.80 326	99.68 127	99.61 144

114514_t		98.93 121	98.67 137	99.72 65	99.85 26	99.53 82	99.62 87	99.59 57	92.65 378	99.71 68	99.78 120	98.06 102	99.90 116	98.84 116	99.91 32	99.74 92

PS-MVSNAJss		98.92 122	98.92 107	98.90 207	98.78 334	98.53 206	99.78 32	99.54 85	98.07 150	99.00 241	99.76 133	99.01 18	99.37 281	99.13 76	97.23 287	98.81 254

mvsmamba		98.92 122	98.87 115	99.08 178	99.07 293	99.16 126	99.88 4	99.51 115	98.15 135	99.40 153	99.89 30	97.12 127	99.33 291	99.38 48	97.40 281	98.73 269

Test_1112_low_res		98.89 124	98.66 140	99.57 92	99.69 106	98.95 163	99.03 315	99.47 174	96.98 266	99.15 212	99.23 312	96.77 142	99.89 127	98.83 119	98.78 200	99.86 33

test_fmvs1		98.88 125	98.79 127	99.16 171	99.69 106	97.61 264	99.55 134	99.49 143	99.32 14	99.98 6	99.91 20	91.41 319	99.96 30	99.82 16	99.92 25	99.90 17

AllTest		98.87 126	98.72 131	99.31 146	99.86 20	98.48 216	99.56 122	99.61 48	97.85 172	99.36 165	99.85 53	95.95 169	99.85 148	96.66 308	99.83 87	99.59 150

UGNet		98.87 126	98.69 135	99.40 132	99.22 256	98.72 190	99.44 194	99.68 20	99.24 17	99.18 209	99.42 265	92.74 282	99.96 30	99.34 55	99.94 22	99.53 166
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)		98.87 126	98.72 131	99.31 146	99.71 96	98.88 172	99.80 25	99.44 202	97.91 166	99.36 165	99.78 120	95.49 188	99.43 273	97.91 213	99.11 172	99.62 142

test_yl		98.86 129	98.63 143	99.54 97	99.49 181	99.18 123	99.50 163	99.07 312	98.22 125	99.61 104	99.51 239	95.37 191	99.84 155	98.60 150	98.33 221	99.59 150

DCV-MVSNet		98.86 129	98.63 143	99.54 97	99.49 181	99.18 123	99.50 163	99.07 312	98.22 125	99.61 104	99.51 239	95.37 191	99.84 155	98.60 150	98.33 221	99.59 150

EPNet		98.86 129	98.71 133	99.30 151	97.20 384	98.18 231	99.62 87	98.91 334	99.28 16	98.63 296	99.81 89	95.96 168	99.99 4	99.24 68	99.72 119	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 129	98.80 124	99.03 185	99.76 65	98.79 185	99.28 256	99.91 3	97.42 226	99.67 78	99.37 280	97.53 113	99.88 133	98.98 91	97.29 285	98.42 343

ab-mvs		98.86 129	98.63 143	99.54 97	99.64 128	99.19 121	99.44 194	99.54 85	97.77 183	99.30 177	99.81 89	94.20 244	99.93 84	99.17 74	98.82 197	99.49 177

MAR-MVS		98.86 129	98.63 143	99.54 97	99.37 215	99.66 53	99.45 188	99.54 85	96.61 293	99.01 237	99.40 272	97.09 129	99.86 142	97.68 241	99.53 142	99.10 225
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	98.86 129	98.75 130	99.17 170	99.88 11	98.53 206	99.34 238	99.59 57	97.55 208	98.70 285	99.89 30	95.83 176	99.90 116	98.10 196	99.90 40	99.08 230
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		98.85 136	98.62 148	99.53 105	99.61 141	99.08 142	99.80 25	99.51 115	97.10 256	99.31 174	99.78 120	95.23 199	99.77 196	98.21 188	99.03 181	99.75 88

HY-MVS	97.30 7	98.85 136	98.64 142	99.47 121	99.42 199	99.08 142	99.62 87	99.36 239	97.39 229	99.28 181	99.68 172	96.44 155	99.92 95	98.37 177	98.22 229	99.40 198

PVSNet	96.02 17	98.85 136	98.84 121	98.89 210	99.73 87	97.28 271	98.32 385	99.60 54	97.86 169	99.50 127	99.57 218	96.75 143	99.86 142	98.56 159	99.70 123	99.54 161

PatchMatch-RL		98.84 139	98.62 148	99.52 111	99.71 96	99.28 112	99.06 308	99.77 9	97.74 188	99.50 127	99.53 233	95.41 189	99.84 155	97.17 282	99.64 132	99.44 192

Effi-MVS+		98.81 140	98.59 154	99.48 118	99.46 190	99.12 136	98.08 391	99.50 135	97.50 216	99.38 159	99.41 269	96.37 157	99.81 180	99.11 78	98.54 213	99.51 173

alignmvs		98.81 140	98.56 157	99.58 90	99.43 197	99.42 96	99.51 156	98.96 324	98.61 85	99.35 168	98.92 348	94.78 214	99.77 196	99.35 51	98.11 239	99.54 161

DeepPCF-MVS	98.18 3	98.81 140	99.37 30	97.12 344	99.60 146	91.75 384	98.61 369	99.44 202	99.35 12	99.83 34	99.85 53	98.70 63	99.81 180	99.02 86	99.91 32	99.81 61

PMMVS		98.80 143	98.62 148	99.34 139	99.27 244	98.70 191	98.76 357	99.31 270	97.34 232	99.21 199	99.07 328	97.20 125	99.82 175	98.56 159	98.87 192	99.52 167

Effi-MVS+-dtu		98.78 144	98.89 113	98.47 268	99.33 227	96.91 300	99.57 116	99.30 274	98.47 97	99.41 148	98.99 338	96.78 141	99.74 204	98.73 130	99.38 150	98.74 267

FIs		98.78 144	98.63 143	99.23 165	99.18 265	99.54 79	99.83 16	99.59 57	98.28 116	98.79 272	99.81 89	96.75 143	99.37 281	99.08 81	96.38 303	98.78 257

Fast-Effi-MVS+-dtu		98.77 146	98.83 123	98.60 247	99.41 203	96.99 294	99.52 148	99.49 143	98.11 142	99.24 191	99.34 290	96.96 137	99.79 189	97.95 211	99.45 146	99.02 240

sd_testset		98.75 147	98.57 155	99.29 154	99.81 46	98.26 228	99.56 122	99.62 41	98.78 73	99.64 93	99.88 36	92.02 303	99.88 133	99.54 30	98.26 227	99.72 103

FA-MVS(test-final)		98.75 147	98.53 159	99.41 130	99.55 160	99.05 147	99.80 25	99.01 318	96.59 297	99.58 111	99.59 210	95.39 190	99.90 116	97.78 226	99.49 144	99.28 213

FC-MVSNet-test		98.75 147	98.62 148	99.15 175	99.08 292	99.45 93	99.86 12	99.60 54	98.23 124	98.70 285	99.82 75	96.80 140	99.22 311	99.07 82	96.38 303	98.79 256

XVG-OURS		98.73 150	98.68 136	98.88 212	99.70 101	97.73 257	98.92 341	99.55 77	98.52 93	99.45 135	99.84 63	95.27 195	99.91 105	98.08 201	98.84 195	99.00 241

Fast-Effi-MVS+		98.70 151	98.43 163	99.51 113	99.51 170	99.28 112	99.52 148	99.47 174	96.11 331	99.01 237	99.34 290	96.20 162	99.84 155	97.88 215	98.82 197	99.39 199

RRT_MVS		98.70 151	98.66 140	98.83 226	98.90 317	98.45 219	99.89 2	99.28 280	97.76 184	98.94 249	99.92 14	96.98 135	99.25 304	99.28 62	97.00 293	98.80 255

XVG-OURS-SEG-HR		98.69 153	98.62 148	98.89 210	99.71 96	97.74 256	99.12 295	99.54 85	98.44 102	99.42 144	99.71 152	94.20 244	99.92 95	98.54 163	98.90 191	99.00 241

1314		98.68 154	98.54 158	99.11 177	98.89 319	98.65 195	99.27 261	99.49 143	96.89 274	97.99 335	99.56 221	97.72 111	99.83 168	97.74 233	99.27 161	98.84 253

EI-MVSNet		98.67 155	98.67 137	98.68 243	99.35 221	97.97 243	99.50 163	99.38 230	96.93 273	99.20 202	99.83 67	97.87 105	99.36 285	98.38 175	97.56 263	98.71 272

test_djsdf		98.67 155	98.57 155	98.98 191	98.70 346	98.91 170	99.88 4	99.46 183	97.55 208	99.22 196	99.88 36	95.73 180	99.28 299	99.03 84	97.62 258	98.75 264

QAPM		98.67 155	98.30 173	99.80 46	99.20 259	99.67 51	99.77 34	99.72 11	94.74 358	98.73 277	99.90 26	95.78 178	99.98 13	96.96 292	99.88 52	99.76 87

nrg030		98.64 158	98.42 164	99.28 158	99.05 299	99.69 47	99.81 20	99.46 183	98.04 156	99.01 237	99.82 75	96.69 145	99.38 277	99.34 55	94.59 344	98.78 257

test_vis1_n_1920		98.63 159	98.40 166	99.31 146	99.86 20	97.94 249	99.67 64	99.62 41	99.43 7	99.99 2	99.91 20	87.29 366	100.00 1	99.92 12	99.92 25	99.98 2

PAPR		98.63 159	98.34 169	99.51 113	99.40 208	99.03 148	98.80 353	99.36 239	96.33 312	99.00 241	99.12 326	98.46 81	99.84 155	95.23 341	99.37 157	99.66 125

CVMVSNet		98.57 161	98.67 137	98.30 287	99.35 221	95.59 338	99.50 163	99.55 77	98.60 86	99.39 157	99.83 67	94.48 235	99.45 264	98.75 127	98.56 211	99.85 36

iter_conf05		98.55 162	98.44 162	98.87 216	99.34 225	98.60 201	99.55 134	99.42 210	98.21 127	99.37 161	99.77 128	93.55 265	99.38 277	99.30 61	97.48 273	98.63 311

MVSTER		98.49 163	98.32 171	99.00 189	99.35 221	99.02 149	99.54 139	99.38 230	97.41 227	99.20 202	99.73 147	93.86 258	99.36 285	98.87 106	97.56 263	98.62 314

FE-MVS		98.48 164	98.17 178	99.40 132	99.54 162	98.96 160	99.68 61	98.81 348	95.54 342	99.62 101	99.70 156	93.82 259	99.93 84	97.35 269	99.46 145	99.32 210

OpenMVS	96.50 16	98.47 165	98.12 185	99.52 111	99.04 300	99.53 82	99.82 17	99.72 11	94.56 361	98.08 330	99.88 36	94.73 220	99.98 13	97.47 260	99.76 111	99.06 236

IterMVS-LS		98.46 166	98.42 164	98.58 251	99.59 148	98.00 241	99.37 226	99.43 208	96.94 272	99.07 226	99.59 210	97.87 105	99.03 339	98.32 183	95.62 323	98.71 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 167	98.28 174	98.94 197	98.50 361	98.96 160	99.77 34	99.50 135	97.07 258	98.87 261	99.77 128	94.76 218	99.28 299	98.66 140	97.60 259	98.57 329

jajsoiax		98.43 168	98.28 174	98.88 212	98.60 356	98.43 221	99.82 17	99.53 96	98.19 130	98.63 296	99.80 102	93.22 271	99.44 269	99.22 69	97.50 269	98.77 260

tttt0517		98.42 169	98.14 182	99.28 158	99.66 120	98.38 224	99.74 44	96.85 392	97.68 195	99.79 42	99.74 141	91.39 320	99.89 127	98.83 119	99.56 139	99.57 156

BH-untuned		98.42 169	98.36 167	98.59 248	99.49 181	96.70 308	99.27 261	99.13 304	97.24 242	98.80 270	99.38 277	95.75 179	99.74 204	97.07 286	99.16 166	99.33 209

test_fmvs1_n		98.41 171	98.14 182	99.21 166	99.82 42	97.71 261	99.74 44	99.49 143	99.32 14	99.99 2	99.95 3	85.32 375	99.97 21	99.82 16	99.84 78	99.96 7

D2MVS		98.41 171	98.50 160	98.15 300	99.26 246	96.62 313	99.40 215	99.61 48	97.71 190	98.98 243	99.36 283	96.04 165	99.67 234	98.70 133	97.41 280	98.15 359

BH-RMVSNet		98.41 171	98.08 191	99.40 132	99.41 203	98.83 181	99.30 246	98.77 351	97.70 193	98.94 249	99.65 184	92.91 278	99.74 204	96.52 311	99.55 141	99.64 136

mvs_tets		98.40 174	98.23 176	98.91 205	98.67 349	98.51 212	99.66 69	99.53 96	98.19 130	98.65 294	99.81 89	92.75 280	99.44 269	99.31 58	97.48 273	98.77 260

XXY-MVS		98.38 175	98.09 190	99.24 163	99.26 246	99.32 104	99.56 122	99.55 77	97.45 221	98.71 279	99.83 67	93.23 269	99.63 250	98.88 103	96.32 305	98.76 262

ACMM	97.58 5	98.37 176	98.34 169	98.48 263	99.41 203	97.10 281	99.56 122	99.45 194	98.53 91	99.04 234	99.85 53	93.00 274	99.71 220	98.74 128	97.45 275	98.64 304
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
iter_conf05_11		98.35 177	97.99 201	99.41 130	99.37 215	99.13 135	98.96 333	98.23 376	98.50 95	99.63 96	99.46 258	88.83 348	99.87 138	99.00 88	99.95 16	99.23 217

thisisatest0530		98.35 177	98.03 197	99.31 146	99.63 131	98.56 203	99.54 139	96.75 394	97.53 212	99.73 62	99.65 184	91.25 323	99.89 127	98.62 144	99.56 139	99.48 178

tpmrst		98.33 179	98.48 161	97.90 315	99.16 275	94.78 356	99.31 244	99.11 305	97.27 238	99.45 135	99.59 210	95.33 193	99.84 155	98.48 166	98.61 205	99.09 229

baseline1		98.31 180	97.95 207	99.38 137	99.50 179	98.74 188	99.59 101	98.93 327	98.41 103	99.14 213	99.60 208	94.59 228	99.79 189	98.48 166	93.29 362	99.61 144

PatchmatchNet		98.31 180	98.36 167	98.19 295	99.16 275	95.32 347	99.27 261	98.92 330	97.37 230	99.37 161	99.58 214	94.90 207	99.70 226	97.43 264	99.21 163	99.54 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 182	97.98 203	99.26 160	99.57 152	98.16 232	99.41 207	98.55 368	96.03 336	99.19 205	99.74 141	91.87 306	99.92 95	99.16 75	98.29 226	99.70 113

VPA-MVSNet		98.29 183	97.95 207	99.30 151	99.16 275	99.54 79	99.50 163	99.58 61	98.27 118	99.35 168	99.37 280	92.53 292	99.65 242	99.35 51	94.46 345	98.72 270

UniMVSNet (Re)		98.29 183	98.00 200	99.13 176	99.00 304	99.36 102	99.49 174	99.51 115	97.95 162	98.97 245	99.13 323	96.30 159	99.38 277	98.36 179	93.34 361	98.66 300

HQP_MVS		98.27 185	98.22 177	98.44 273	99.29 239	96.97 296	99.39 219	99.47 174	98.97 51	99.11 218	99.61 205	92.71 285	99.69 231	97.78 226	97.63 256	98.67 292

bld_raw_dy_0_64		98.26 186	97.88 217	99.40 132	99.37 215	99.09 138	99.62 87	98.94 325	98.53 91	99.40 153	99.51 239	88.93 346	99.89 127	99.00 88	97.64 255	99.23 217

UniMVSNet_NR-MVSNet		98.22 187	97.97 204	98.96 194	98.92 316	98.98 153	99.48 178	99.53 96	97.76 184	98.71 279	99.46 258	96.43 156	99.22 311	98.57 156	92.87 368	98.69 280

LPG-MVS_test		98.22 187	98.13 184	98.49 261	99.33 227	97.05 287	99.58 109	99.55 77	97.46 218	99.24 191	99.83 67	92.58 290	99.72 214	98.09 197	97.51 267	98.68 285

RPSCF		98.22 187	98.62 148	96.99 346	99.82 42	91.58 385	99.72 49	99.44 202	96.61 293	99.66 83	99.89 30	95.92 172	99.82 175	97.46 261	99.10 175	99.57 156

ADS-MVSNet		98.20 190	98.08 191	98.56 255	99.33 227	96.48 318	99.23 275	99.15 301	96.24 319	99.10 221	99.67 178	94.11 248	99.71 220	96.81 300	99.05 179	99.48 178

OPM-MVS		98.19 191	98.10 187	98.45 270	98.88 320	97.07 285	99.28 256	99.38 230	98.57 87	99.22 196	99.81 89	92.12 301	99.66 237	98.08 201	97.54 265	98.61 323
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SCA		98.19 191	98.16 179	98.27 292	99.30 235	95.55 339	99.07 305	98.97 322	97.57 205	99.43 141	99.57 218	92.72 283	99.74 204	97.58 246	99.20 164	99.52 167

miper_ehance_all_eth		98.18 193	98.10 187	98.41 276	99.23 252	97.72 258	98.72 361	99.31 270	96.60 295	98.88 258	99.29 302	97.29 123	99.13 325	97.60 244	95.99 312	98.38 348

CR-MVSNet		98.17 194	97.93 210	98.87 216	99.18 265	98.49 214	99.22 279	99.33 256	96.96 268	99.56 115	99.38 277	94.33 240	99.00 344	94.83 347	98.58 208	99.14 222

miper_enhance_ethall		98.16 195	98.08 191	98.41 276	98.96 313	97.72 258	98.45 378	99.32 266	96.95 270	98.97 245	99.17 318	97.06 132	99.22 311	97.86 218	95.99 312	98.29 352

CLD-MVS		98.16 195	98.10 187	98.33 283	99.29 239	96.82 305	98.75 358	99.44 202	97.83 175	99.13 214	99.55 224	92.92 276	99.67 234	98.32 183	97.69 253	98.48 335
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		98.14 197	97.79 222	99.19 168	99.50 179	98.50 213	98.61 369	96.82 393	96.95 270	99.54 120	99.43 263	91.66 315	99.86 142	98.08 201	99.51 143	99.22 219

pmmvs4		98.13 198	97.90 212	98.81 230	98.61 355	98.87 173	98.99 326	99.21 294	96.44 307	99.06 231	99.58 214	95.90 174	99.11 330	97.18 281	96.11 309	98.46 340

WR-MVS_H		98.13 198	97.87 218	98.90 207	99.02 302	98.84 178	99.70 52	99.59 57	97.27 238	98.40 312	99.19 317	95.53 186	99.23 308	98.34 180	93.78 358	98.61 323

c3_l		98.12 200	98.04 196	98.38 280	99.30 235	97.69 262	98.81 352	99.33 256	96.67 286	98.83 266	99.34 290	97.11 128	98.99 345	97.58 246	95.34 329	98.48 335

ACMH	97.28 8	98.10 201	97.99 201	98.44 273	99.41 203	96.96 298	99.60 95	99.56 69	98.09 145	98.15 328	99.91 20	90.87 327	99.70 226	98.88 103	97.45 275	98.67 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		98.09 202	97.68 238	99.34 139	99.66 120	98.44 220	99.40 215	99.43 208	93.67 368	99.22 196	99.89 30	90.23 335	99.93 84	99.26 67	98.33 221	99.66 125

CP-MVSNet		98.09 202	97.78 225	99.01 187	98.97 312	99.24 118	99.67 64	99.46 183	97.25 240	98.48 309	99.64 190	93.79 260	99.06 335	98.63 143	94.10 352	98.74 267

dmvs_re		98.08 204	98.16 179	97.85 317	99.55 160	94.67 359	99.70 52	98.92 330	98.15 135	99.06 231	99.35 286	93.67 264	99.25 304	97.77 229	97.25 286	99.64 136

DU-MVS		98.08 204	97.79 222	98.96 194	98.87 323	98.98 153	99.41 207	99.45 194	97.87 168	98.71 279	99.50 243	94.82 210	99.22 311	98.57 156	92.87 368	98.68 285

v2v482		98.06 206	97.77 227	98.92 201	98.90 317	98.82 182	99.57 116	99.36 239	96.65 288	99.19 205	99.35 286	94.20 244	99.25 304	97.72 236	94.97 337	98.69 280

V42		98.06 206	97.79 222	98.86 220	98.98 310	98.84 178	99.69 55	99.34 249	96.53 299	99.30 177	99.37 280	94.67 225	99.32 294	97.57 250	94.66 342	98.42 343

test-LLR		98.06 206	97.90 212	98.55 257	98.79 331	97.10 281	98.67 364	97.75 384	97.34 232	98.61 299	98.85 350	94.45 237	99.45 264	97.25 273	99.38 150	99.10 225

WR-MVS		98.06 206	97.73 234	99.06 181	98.86 326	99.25 117	99.19 282	99.35 245	97.30 236	98.66 288	99.43 263	93.94 254	99.21 316	98.58 153	94.28 349	98.71 272

ACMP	97.20 11	98.06 206	97.94 209	98.45 270	99.37 215	97.01 292	99.44 194	99.49 143	97.54 211	98.45 310	99.79 114	91.95 305	99.72 214	97.91 213	97.49 272	98.62 314
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		98.05 211	97.96 205	98.33 283	99.26 246	97.38 269	98.56 374	99.31 270	96.65 288	98.88 258	99.52 236	96.58 148	99.12 329	97.39 266	95.53 326	98.47 337

test1111		98.04 212	98.11 186	97.83 320	99.74 80	93.82 368	99.58 109	95.40 401	99.12 25	99.65 89	99.93 9	90.73 328	99.84 155	99.43 46	99.38 150	99.82 54

ECVR-MVS		98.04 212	98.05 195	98.00 309	99.74 80	94.37 363	99.59 101	94.98 402	99.13 22	99.66 83	99.93 9	90.67 329	99.84 155	99.40 47	99.38 150	99.80 70

EPNet_dtu		98.03 214	97.96 205	98.23 293	98.27 366	95.54 341	99.23 275	98.75 352	99.02 38	97.82 342	99.71 152	96.11 163	99.48 261	93.04 367	99.65 131	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 214	97.76 231	98.84 224	99.39 211	98.98 153	99.40 215	99.38 230	96.67 286	99.07 226	99.28 304	92.93 275	98.98 346	97.10 283	96.65 296	98.56 330

ADS-MVSNet2		98.02 216	98.07 194	97.87 316	99.33 227	95.19 350	99.23 275	99.08 309	96.24 319	99.10 221	99.67 178	94.11 248	98.93 356	96.81 300	99.05 179	99.48 178

HQP-MVS		98.02 216	97.90 212	98.37 281	99.19 262	96.83 303	98.98 329	99.39 222	98.24 121	98.66 288	99.40 272	92.47 294	99.64 245	97.19 279	97.58 261	98.64 304

LTVRE_ROB	97.16 12	98.02 216	97.90 212	98.40 278	99.23 252	96.80 306	99.70 52	99.60 54	97.12 252	98.18 327	99.70 156	91.73 311	99.72 214	98.39 174	97.45 275	98.68 285
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____		98.01 219	97.84 220	98.55 257	99.25 250	97.97 243	98.71 362	99.34 249	96.47 306	98.59 302	99.54 229	95.65 183	99.21 316	97.21 275	95.77 318	98.46 340

DIV-MVS_self_test		98.01 219	97.85 219	98.48 263	99.24 251	97.95 247	98.71 362	99.35 245	96.50 300	98.60 301	99.54 229	95.72 181	99.03 339	97.21 275	95.77 318	98.46 340

miper_lstm_enhance		98.00 221	97.91 211	98.28 291	99.34 225	97.43 268	98.88 345	99.36 239	96.48 304	98.80 270	99.55 224	95.98 167	98.91 357	97.27 272	95.50 327	98.51 333

BH-w/o		98.00 221	97.89 216	98.32 285	99.35 221	96.20 328	99.01 323	98.90 336	96.42 309	98.38 313	99.00 337	95.26 197	99.72 214	96.06 319	98.61 205	99.03 238

v1144		97.98 223	97.69 237	98.85 223	98.87 323	98.66 194	99.54 139	99.35 245	96.27 317	99.23 195	99.35 286	94.67 225	99.23 308	96.73 303	95.16 333	98.68 285

EU-MVSNet		97.98 223	98.03 197	97.81 323	98.72 343	96.65 312	99.66 69	99.66 28	98.09 145	98.35 315	99.82 75	95.25 198	98.01 379	97.41 265	95.30 330	98.78 257

tpmvs		97.98 223	98.02 199	97.84 319	99.04 300	94.73 357	99.31 244	99.20 295	96.10 335	98.76 275	99.42 265	94.94 203	99.81 180	96.97 291	98.45 217	98.97 245

tt0805		97.97 226	97.77 227	98.57 252	99.59 148	96.61 314	99.45 188	99.08 309	98.21 127	98.88 258	99.80 102	88.66 352	99.70 226	98.58 153	97.72 252	99.39 199

NR-MVSNet		97.97 226	97.61 246	99.02 186	98.87 323	99.26 116	99.47 184	99.42 210	97.63 200	97.08 360	99.50 243	95.07 202	99.13 325	97.86 218	93.59 359	98.68 285

v8		97.95 228	97.63 245	98.93 199	98.95 314	98.81 184	99.80 25	99.41 213	96.03 336	99.10 221	99.42 265	94.92 206	99.30 297	96.94 294	94.08 353	98.66 300

Patchmatch-test		97.93 229	97.65 241	98.77 235	99.18 265	97.07 285	99.03 315	99.14 303	96.16 326	98.74 276	99.57 218	94.56 230	99.72 214	93.36 363	99.11 172	99.52 167

PS-CasMVS		97.93 229	97.59 248	98.95 196	98.99 307	99.06 145	99.68 61	99.52 101	97.13 250	98.31 317	99.68 172	92.44 298	99.05 336	98.51 164	94.08 353	98.75 264

TranMVSNet+NR-MVSNet		97.93 229	97.66 240	98.76 236	98.78 334	98.62 198	99.65 75	99.49 143	97.76 184	98.49 308	99.60 208	94.23 243	98.97 353	98.00 208	92.90 366	98.70 276

test_vis1_n		97.92 232	97.44 267	99.34 139	99.53 163	98.08 237	99.74 44	99.49 143	99.15 20	100.00 1	99.94 6	79.51 392	99.98 13	99.88 14	99.76 111	99.97 4

v144192		97.92 232	97.60 247	98.87 216	98.83 329	98.65 195	99.55 134	99.34 249	96.20 322	99.32 173	99.40 272	94.36 239	99.26 303	96.37 316	95.03 336	98.70 276

ACMH+	97.24 10	97.92 232	97.78 225	98.32 285	99.46 190	96.68 311	99.56 122	99.54 85	98.41 103	97.79 344	99.87 44	90.18 336	99.66 237	98.05 205	97.18 290	98.62 314

LFMVS		97.90 235	97.35 279	99.54 97	99.52 167	99.01 151	99.39 219	98.24 375	97.10 256	99.65 89	99.79 114	84.79 378	99.91 105	99.28 62	98.38 218	99.69 115

Anonymous20231211		97.88 236	97.54 252	98.90 207	99.71 96	98.53 206	99.48 178	99.57 64	94.16 364	98.81 268	99.68 172	93.23 269	99.42 274	98.84 116	94.42 347	98.76 262

OurMVSNet-221017-0		97.88 236	97.77 227	98.19 295	98.71 345	96.53 316	99.88 4	99.00 319	97.79 180	98.78 273	99.94 6	91.68 312	99.35 288	97.21 275	96.99 294	98.69 280

v7n		97.87 238	97.52 253	98.92 201	98.76 339	98.58 202	99.84 13	99.46 183	96.20 322	98.91 253	99.70 156	94.89 208	99.44 269	96.03 320	93.89 356	98.75 264

baseline2		97.87 238	97.55 249	98.82 227	99.18 265	98.02 240	99.41 207	96.58 398	96.97 267	96.51 365	99.17 318	93.43 266	99.57 255	97.71 237	99.03 181	98.86 251

thres600view7		97.86 240	97.51 255	98.92 201	99.72 91	97.95 247	99.59 101	98.74 355	97.94 163	99.27 185	98.62 360	91.75 309	99.86 142	93.73 359	98.19 233	98.96 247

cl22		97.85 241	97.64 244	98.48 263	99.09 289	97.87 251	98.60 371	99.33 256	97.11 255	98.87 261	99.22 313	92.38 299	99.17 320	98.21 188	95.99 312	98.42 343

v10		97.85 241	97.52 253	98.86 220	98.99 307	98.67 193	99.75 41	99.41 213	95.70 340	98.98 243	99.41 269	94.75 219	99.23 308	96.01 322	94.63 343	98.67 292

GA-MVS		97.85 241	97.47 259	99.00 189	99.38 212	97.99 242	98.57 372	99.15 301	97.04 263	98.90 255	99.30 300	89.83 338	99.38 277	96.70 305	98.33 221	99.62 142

tfpnnormal		97.84 244	97.47 259	98.98 191	99.20 259	99.22 120	99.64 78	99.61 48	96.32 313	98.27 321	99.70 156	93.35 268	99.44 269	95.69 329	95.40 328	98.27 353

VPNet		97.84 244	97.44 267	99.01 187	99.21 257	98.94 166	99.48 178	99.57 64	98.38 105	99.28 181	99.73 147	88.89 347	99.39 276	99.19 71	93.27 363	98.71 272

LCM-MVSNet-Re		97.83 246	98.15 181	96.87 352	99.30 235	92.25 382	99.59 101	98.26 373	97.43 224	96.20 368	99.13 323	96.27 160	98.73 365	98.17 193	98.99 184	99.64 136

XVG-ACMP-BASELINE		97.83 246	97.71 236	98.20 294	99.11 283	96.33 323	99.41 207	99.52 101	98.06 154	99.05 233	99.50 243	89.64 341	99.73 210	97.73 234	97.38 283	98.53 331

IterMVS		97.83 246	97.77 227	98.02 306	99.58 150	96.27 325	99.02 318	99.48 155	97.22 244	98.71 279	99.70 156	92.75 280	99.13 325	97.46 261	96.00 311	98.67 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.82 249	97.75 232	98.06 303	99.57 152	96.36 322	99.02 318	99.49 143	97.18 246	98.71 279	99.72 151	92.72 283	99.14 322	97.44 263	95.86 317	98.67 292

EPMVS		97.82 249	97.65 241	98.35 282	98.88 320	95.98 331	99.49 174	94.71 404	97.57 205	99.26 189	99.48 251	92.46 297	99.71 220	97.87 217	99.08 177	99.35 205

MVP-Stereo		97.81 251	97.75 232	97.99 310	97.53 377	96.60 315	98.96 333	98.85 343	97.22 244	97.23 355	99.36 283	95.28 194	99.46 263	95.51 333	99.78 105	97.92 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 251	97.44 267	98.91 205	98.88 320	98.68 192	99.51 156	99.34 249	96.18 324	99.20 202	99.34 290	94.03 251	99.36 285	95.32 339	95.18 332	98.69 280

v1921920		97.80 253	97.45 262	98.84 224	98.80 330	98.53 206	99.52 148	99.34 249	96.15 328	99.24 191	99.47 254	93.98 253	99.29 298	95.40 337	95.13 334	98.69 280

v148		97.79 254	97.55 249	98.50 260	98.74 340	97.72 258	99.54 139	99.33 256	96.26 318	98.90 255	99.51 239	94.68 224	99.14 322	97.83 222	93.15 365	98.63 311

thres400		97.77 255	97.38 275	98.92 201	99.69 106	97.96 245	99.50 163	98.73 360	97.83 175	99.17 210	98.45 365	91.67 313	99.83 168	93.22 364	98.18 234	98.96 247

thres100view900		97.76 256	97.45 262	98.69 242	99.72 91	97.86 253	99.59 101	98.74 355	97.93 164	99.26 189	98.62 360	91.75 309	99.83 168	93.22 364	98.18 234	98.37 349

PEN-MVS		97.76 256	97.44 267	98.72 238	98.77 338	98.54 205	99.78 32	99.51 115	97.06 260	98.29 320	99.64 190	92.63 289	98.89 359	98.09 197	93.16 364	98.72 270

Baseline_NR-MVSNet		97.76 256	97.45 262	98.68 243	99.09 289	98.29 226	99.41 207	98.85 343	95.65 341	98.63 296	99.67 178	94.82 210	99.10 332	98.07 204	92.89 367	98.64 304

TR-MVS		97.76 256	97.41 273	98.82 227	99.06 296	97.87 251	98.87 347	98.56 367	96.63 292	98.68 287	99.22 313	92.49 293	99.65 242	95.40 337	97.79 250	98.95 249

Patchmtry		97.75 260	97.40 274	98.81 230	99.10 286	98.87 173	99.11 301	99.33 256	94.83 356	98.81 268	99.38 277	94.33 240	99.02 341	96.10 318	95.57 324	98.53 331

dp		97.75 260	97.80 221	97.59 332	99.10 286	93.71 371	99.32 241	98.88 339	96.48 304	99.08 225	99.55 224	92.67 288	99.82 175	96.52 311	98.58 208	99.24 216

TAPA-MVS	97.07 15	97.74 262	97.34 282	98.94 197	99.70 101	97.53 265	99.25 272	99.51 115	91.90 380	99.30 177	99.63 196	98.78 48	99.64 245	88.09 390	99.87 55	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 263	97.35 279	98.88 212	99.47 189	97.12 280	99.34 238	98.85 343	98.19 130	99.67 78	99.85 53	82.98 385	99.92 95	99.49 40	98.32 225	99.60 146

MIMVSNet		97.73 263	97.45 262	98.57 252	99.45 195	97.50 266	99.02 318	98.98 321	96.11 331	99.41 148	99.14 322	90.28 331	98.74 364	95.74 327	98.93 187	99.47 184

tfpn200view9		97.72 265	97.38 275	98.72 238	99.69 106	97.96 245	99.50 163	98.73 360	97.83 175	99.17 210	98.45 365	91.67 313	99.83 168	93.22 364	98.18 234	98.37 349

CostFormer		97.72 265	97.73 234	97.71 327	99.15 279	94.02 367	99.54 139	99.02 317	94.67 359	99.04 234	99.35 286	92.35 300	99.77 196	98.50 165	97.94 244	99.34 208

FMVSNet2		97.72 265	97.36 277	98.80 232	99.51 170	98.84 178	99.45 188	99.42 210	96.49 301	98.86 265	99.29 302	90.26 332	98.98 346	96.44 313	96.56 299	98.58 328

test0.0.03 1		97.71 268	97.42 272	98.56 255	98.41 365	97.82 254	98.78 355	98.63 365	97.34 232	98.05 334	98.98 340	94.45 237	98.98 346	95.04 344	97.15 291	98.89 250

h-mvs33		97.70 269	97.28 290	98.97 193	99.70 101	97.27 272	99.36 230	99.45 194	98.94 54	99.66 83	99.64 190	94.93 204	99.99 4	99.48 41	84.36 391	99.65 129

v1240		97.69 270	97.32 285	98.79 233	98.85 327	98.43 221	99.48 178	99.36 239	96.11 331	99.27 185	99.36 283	93.76 262	99.24 307	94.46 350	95.23 331	98.70 276

cascas		97.69 270	97.43 271	98.48 263	98.60 356	97.30 270	98.18 390	99.39 222	92.96 376	98.41 311	98.78 356	93.77 261	99.27 302	98.16 194	98.61 205	98.86 251

pm-mvs1		97.68 272	97.28 290	98.88 212	99.06 296	98.62 198	99.50 163	99.45 194	96.32 313	97.87 340	99.79 114	92.47 294	99.35 288	97.54 253	93.54 360	98.67 292

GBi-Net		97.68 272	97.48 257	98.29 288	99.51 170	97.26 274	99.43 198	99.48 155	96.49 301	99.07 226	99.32 297	90.26 332	98.98 346	97.10 283	96.65 296	98.62 314

test1		97.68 272	97.48 257	98.29 288	99.51 170	97.26 274	99.43 198	99.48 155	96.49 301	99.07 226	99.32 297	90.26 332	98.98 346	97.10 283	96.65 296	98.62 314

tpm		97.67 275	97.55 249	98.03 304	99.02 302	95.01 353	99.43 198	98.54 369	96.44 307	99.12 216	99.34 290	91.83 308	99.60 253	97.75 232	96.46 301	99.48 178

PCF-MVS	97.08 14	97.66 276	97.06 301	99.47 121	99.61 141	99.09 138	98.04 392	99.25 286	91.24 383	98.51 306	99.70 156	94.55 232	99.91 105	92.76 372	99.85 70	99.42 194
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		97.65 277	97.65 241	97.63 329	98.78 334	97.62 263	99.13 292	98.33 372	97.36 231	99.07 226	98.94 344	95.64 184	99.15 321	92.95 368	98.68 204	96.12 393

our_test_3		97.65 277	97.68 238	97.55 333	98.62 353	94.97 354	98.84 349	99.30 274	96.83 279	98.19 326	99.34 290	97.01 134	99.02 341	95.00 345	96.01 310	98.64 304

testgi		97.65 277	97.50 256	98.13 301	99.36 220	96.45 319	99.42 205	99.48 155	97.76 184	97.87 340	99.45 260	91.09 324	98.81 361	94.53 349	98.52 214	99.13 224

thres200		97.61 280	97.28 290	98.62 246	99.64 128	98.03 239	99.26 270	98.74 355	97.68 195	99.09 224	98.32 370	91.66 315	99.81 180	92.88 369	98.22 229	98.03 365

PAPM		97.59 281	97.09 300	99.07 180	99.06 296	98.26 228	98.30 386	99.10 306	94.88 354	98.08 330	99.34 290	96.27 160	99.64 245	89.87 383	98.92 189	99.31 211

UWE-MVS		97.58 282	97.29 289	98.48 263	99.09 289	96.25 326	99.01 323	96.61 397	97.86 169	99.19 205	99.01 336	88.72 349	99.90 116	97.38 267	98.69 203	99.28 213

VDDNet		97.55 283	97.02 302	99.16 171	99.49 181	98.12 236	99.38 224	99.30 274	95.35 344	99.68 74	99.90 26	82.62 387	99.93 84	99.31 58	98.13 238	99.42 194

TESTMET0.1,1		97.55 283	97.27 293	98.40 278	98.93 315	96.53 316	98.67 364	97.61 387	96.96 268	98.64 295	99.28 304	88.63 354	99.45 264	97.30 271	99.38 150	99.21 220

pmmvs5		97.52 285	97.30 287	98.16 297	98.57 358	96.73 307	99.27 261	98.90 336	96.14 329	98.37 314	99.53 233	91.54 318	99.14 322	97.51 255	95.87 316	98.63 311

LF4IMVS		97.52 285	97.46 261	97.70 328	98.98 310	95.55 339	99.29 251	98.82 346	98.07 150	98.66 288	99.64 190	89.97 337	99.61 252	97.01 287	96.68 295	97.94 372

DTE-MVSNet		97.51 287	97.19 295	98.46 269	98.63 352	98.13 235	99.84 13	99.48 155	96.68 285	97.97 337	99.67 178	92.92 276	98.56 368	96.88 299	92.60 371	98.70 276

testing11		97.50 288	97.10 299	98.71 240	99.20 259	96.91 300	99.29 251	98.82 346	97.89 167	98.21 325	98.40 367	85.63 372	99.83 168	98.45 171	98.04 241	99.37 203

ETVMVS		97.50 288	96.90 306	99.29 154	99.23 252	98.78 187	99.32 241	98.90 336	97.52 214	98.56 303	98.09 379	84.72 379	99.69 231	97.86 218	97.88 247	99.39 199

hse-mvs2		97.50 288	97.14 296	98.59 248	99.49 181	97.05 287	99.28 256	99.22 291	98.94 54	99.66 83	99.42 265	94.93 204	99.65 242	99.48 41	83.80 393	99.08 230

SixPastTwentyTwo		97.50 288	97.33 284	98.03 304	98.65 350	96.23 327	99.77 34	98.68 363	97.14 249	97.90 338	99.93 9	90.45 330	99.18 319	97.00 288	96.43 302	98.67 292

JIA-IIPM		97.50 288	97.02 302	98.93 199	98.73 341	97.80 255	99.30 246	98.97 322	91.73 381	98.91 253	94.86 395	95.10 201	99.71 220	97.58 246	97.98 242	99.28 213

ppachtmachnet_test		97.49 293	97.45 262	97.61 331	98.62 353	95.24 348	98.80 353	99.46 183	96.11 331	98.22 324	99.62 201	96.45 154	98.97 353	93.77 358	95.97 315	98.61 323

test-mter		97.49 293	97.13 298	98.55 257	98.79 331	97.10 281	98.67 364	97.75 384	96.65 288	98.61 299	98.85 350	88.23 358	99.45 264	97.25 273	99.38 150	99.10 225

testing91		97.44 295	97.02 302	98.71 240	99.18 265	96.89 302	99.19 282	99.04 315	97.78 182	98.31 317	98.29 371	85.41 374	99.85 148	98.01 207	97.95 243	99.39 199

tpm2		97.44 295	97.34 282	97.74 326	99.15 279	94.36 364	99.45 188	98.94 325	93.45 373	98.90 255	99.44 261	91.35 321	99.59 254	97.31 270	98.07 240	99.29 212

tpm cat1		97.39 297	97.36 277	97.50 335	99.17 273	93.73 370	99.43 198	99.31 270	91.27 382	98.71 279	99.08 327	94.31 242	99.77 196	96.41 315	98.50 215	99.00 241

testing99		97.36 298	96.94 305	98.63 245	99.18 265	96.70 308	99.30 246	98.93 327	97.71 190	98.23 322	98.26 372	84.92 377	99.84 155	98.04 206	97.85 249	99.35 205

USDC		97.34 299	97.20 294	97.75 325	99.07 293	95.20 349	98.51 376	99.04 315	97.99 160	98.31 317	99.86 48	89.02 344	99.55 258	95.67 331	97.36 284	98.49 334

UniMVSNet_ETH3D		97.32 300	96.81 308	98.87 216	99.40 208	97.46 267	99.51 156	99.53 96	95.86 339	98.54 305	99.77 128	82.44 388	99.66 237	98.68 138	97.52 266	99.50 176

testing3		97.28 301	96.76 310	98.82 227	99.37 215	98.07 238	99.45 188	99.36 239	97.56 207	97.89 339	98.95 343	83.70 383	98.82 360	96.03 320	98.56 211	99.58 154

MVS		97.28 301	96.55 313	99.48 118	98.78 334	98.95 163	99.27 261	99.39 222	83.53 395	98.08 330	99.54 229	96.97 136	99.87 138	94.23 354	99.16 166	99.63 140

test_fmvs2		97.25 303	97.30 287	97.09 345	99.43 197	93.31 376	99.73 47	98.87 341	98.83 64	99.28 181	99.80 102	84.45 380	99.66 237	97.88 215	97.45 275	98.30 351

DSMNet-mixed		97.25 303	97.35 279	96.95 349	97.84 372	93.61 374	99.57 116	96.63 396	96.13 330	98.87 261	98.61 362	94.59 228	97.70 386	95.08 343	98.86 193	99.55 159

MS-PatchMatch		97.24 305	97.32 285	96.99 346	98.45 363	93.51 375	98.82 351	99.32 266	97.41 227	98.13 329	99.30 300	88.99 345	99.56 256	95.68 330	99.80 98	97.90 375

testing222		97.16 306	96.50 314	99.16 171	99.16 275	98.47 218	99.27 261	98.66 364	97.71 190	98.23 322	98.15 374	82.28 389	99.84 155	97.36 268	97.66 254	99.18 221

TransMVSNet (Re)		97.15 307	96.58 312	98.86 220	99.12 281	98.85 177	99.49 174	98.91 334	95.48 343	97.16 358	99.80 102	93.38 267	99.11 330	94.16 356	91.73 373	98.62 314

TinyColmap		97.12 308	96.89 307	97.83 320	99.07 293	95.52 342	98.57 372	98.74 355	97.58 204	97.81 343	99.79 114	88.16 359	99.56 256	95.10 342	97.21 288	98.39 347

K. test v3		97.10 309	96.79 309	98.01 307	98.72 343	96.33 323	99.87 9	97.05 391	97.59 202	96.16 369	99.80 102	88.71 350	99.04 337	96.69 306	96.55 300	98.65 302

Syy-MVS		97.09 310	97.14 296	96.95 349	99.00 304	92.73 380	99.29 251	99.39 222	97.06 260	97.41 349	98.15 374	93.92 256	98.68 366	91.71 376	98.34 219	99.45 190

PatchT		97.03 311	96.44 316	98.79 233	98.99 307	98.34 225	99.16 286	99.07 312	92.13 379	99.52 124	97.31 388	94.54 233	98.98 346	88.54 388	98.73 202	99.03 238

myMVS_eth3d		96.89 312	96.37 317	98.43 275	99.00 304	97.16 278	99.29 251	99.39 222	97.06 260	97.41 349	98.15 374	83.46 384	98.68 366	95.27 340	98.34 219	99.45 190

AUN-MVS		96.88 313	96.31 319	98.59 248	99.48 188	97.04 290	99.27 261	99.22 291	97.44 223	98.51 306	99.41 269	91.97 304	99.66 237	97.71 237	83.83 392	99.07 235

FMVSNet1		96.84 314	96.36 318	98.29 288	99.32 233	97.26 274	99.43 198	99.48 155	95.11 348	98.55 304	99.32 297	83.95 382	98.98 346	95.81 325	96.26 306	98.62 314

test2506		96.81 315	96.65 311	97.29 340	99.74 80	92.21 383	99.60 95	85.06 412	99.13 22	99.77 51	99.93 9	87.82 364	99.85 148	99.38 48	99.38 150	99.80 70

RPMNet		96.72 316	95.90 328	99.19 168	99.18 265	98.49 214	99.22 279	99.52 101	88.72 391	99.56 115	97.38 385	94.08 250	99.95 59	86.87 395	98.58 208	99.14 222

test_0402		96.64 317	96.24 320	97.85 317	98.85 327	96.43 320	99.44 194	99.26 284	93.52 370	96.98 362	99.52 236	88.52 355	99.20 318	92.58 374	97.50 269	97.93 373

X-MVStestdata		96.55 318	95.45 336	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 161	64.01 408	98.81 44	99.94 69	98.79 124	99.86 63	99.84 40

pmmvs6		96.53 319	96.09 324	97.82 322	98.69 347	95.47 343	99.37 226	99.47 174	93.46 372	97.41 349	99.78 120	87.06 367	99.33 291	96.92 297	92.70 370	98.65 302

ET-MVSNet_ETH3D		96.49 320	95.64 334	99.05 183	99.53 163	98.82 182	98.84 349	97.51 389	97.63 200	84.77 395	99.21 316	92.09 302	98.91 357	98.98 91	92.21 372	99.41 196

UnsupCasMVSNet_eth		96.44 321	96.12 322	97.40 337	98.65 350	95.65 336	99.36 230	99.51 115	97.13 250	96.04 371	98.99 338	88.40 356	98.17 375	96.71 304	90.27 381	98.40 346

FMVSNet5		96.43 322	96.19 321	97.15 341	99.11 283	95.89 333	99.32 241	99.52 101	94.47 363	98.34 316	99.07 328	87.54 365	97.07 390	92.61 373	95.72 321	98.47 337

new_pmnet		96.38 323	96.03 325	97.41 336	98.13 369	95.16 352	99.05 310	99.20 295	93.94 365	97.39 352	98.79 355	91.61 317	99.04 337	90.43 381	95.77 318	98.05 364

Anonymous20231206		96.22 324	96.03 325	96.79 354	97.31 382	94.14 366	99.63 82	99.08 309	96.17 325	97.04 361	99.06 330	93.94 254	97.76 385	86.96 394	95.06 335	98.47 337

IB-MVS	95.67 18	96.22 324	95.44 337	98.57 252	99.21 257	96.70 308	98.65 367	97.74 386	96.71 283	97.27 354	98.54 363	86.03 369	99.92 95	98.47 169	86.30 389	99.10 225
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		96.20 326	95.89 329	97.13 343	97.72 376	94.96 355	99.79 31	99.29 278	93.01 375	97.20 357	99.03 333	89.69 340	98.36 372	91.16 379	96.13 308	98.07 362

gg-mvs-nofinetune		96.17 327	95.32 338	98.73 237	98.79 331	98.14 234	99.38 224	94.09 405	91.07 385	98.07 333	91.04 401	89.62 342	99.35 288	96.75 302	99.09 176	98.68 285

test20.03		96.12 328	95.96 327	96.63 355	97.44 378	95.45 344	99.51 156	99.38 230	96.55 298	96.16 369	99.25 310	93.76 262	96.17 395	87.35 393	94.22 350	98.27 353

PVSNet_0	94.43 19	96.09 329	95.47 335	97.94 312	99.31 234	94.34 365	97.81 393	99.70 15	97.12 252	97.46 348	98.75 357	89.71 339	99.79 189	97.69 240	81.69 395	99.68 119

EG-PatchMatch MVS		95.97 330	95.69 332	96.81 353	97.78 373	92.79 379	99.16 286	98.93 327	96.16 326	94.08 382	99.22 313	82.72 386	99.47 262	95.67 331	97.50 269	98.17 358

APD_test1		95.87 331	96.49 315	94.00 365	99.53 163	84.01 393	99.54 139	99.32 266	95.91 338	97.99 335	99.85 53	85.49 373	99.88 133	91.96 375	98.84 195	98.12 360

Patchmatch-RL test		95.84 332	95.81 331	95.95 361	95.61 392	90.57 387	98.24 387	98.39 371	95.10 350	95.20 376	98.67 359	94.78 214	97.77 384	96.28 317	90.02 382	99.51 173

test_vis1_rt		95.81 333	95.65 333	96.32 359	99.67 111	91.35 386	99.49 174	96.74 395	98.25 120	95.24 374	98.10 378	74.96 393	99.90 116	99.53 32	98.85 194	97.70 378

MVS-HIRNet		95.75 334	95.16 339	97.51 334	99.30 235	93.69 372	98.88 345	95.78 399	85.09 394	98.78 273	92.65 397	91.29 322	99.37 281	94.85 346	99.85 70	99.46 187

MIMVSNet1		95.51 335	95.04 340	96.92 351	97.38 379	95.60 337	99.52 148	99.50 135	93.65 369	96.97 363	99.17 318	85.28 376	96.56 394	88.36 389	95.55 325	98.60 326

MDA-MVSNet_test_wron		95.45 336	94.60 343	98.01 307	98.16 368	97.21 277	99.11 301	99.24 288	93.49 371	80.73 401	98.98 340	93.02 273	98.18 374	94.22 355	94.45 346	98.64 304

TDRefinement		95.42 337	94.57 344	97.97 311	89.83 405	96.11 330	99.48 178	98.75 352	96.74 281	96.68 364	99.88 36	88.65 353	99.71 220	98.37 177	82.74 394	98.09 361

YYNet1		95.36 338	94.51 345	97.92 313	97.89 371	97.10 281	99.10 303	99.23 289	93.26 374	80.77 400	99.04 332	92.81 279	98.02 378	94.30 351	94.18 351	98.64 304

pmmvs-eth3d		95.34 339	94.73 342	97.15 341	95.53 394	95.94 332	99.35 235	99.10 306	95.13 346	93.55 384	97.54 383	88.15 360	97.91 381	94.58 348	89.69 384	97.61 379

dmvs_testset		95.02 340	96.12 322	91.72 373	99.10 286	80.43 401	99.58 109	97.87 383	97.47 217	95.22 375	98.82 352	93.99 252	95.18 398	88.09 390	94.91 340	99.56 158

KD-MVS_self_test		95.00 341	94.34 346	96.96 348	97.07 387	95.39 346	99.56 122	99.44 202	95.11 348	97.13 359	97.32 387	91.86 307	97.27 389	90.35 382	81.23 396	98.23 357

MDA-MVSNet-bldmvs		94.96 342	93.98 349	97.92 313	98.24 367	97.27 272	99.15 289	99.33 256	93.80 367	80.09 402	99.03 333	88.31 357	97.86 383	93.49 362	94.36 348	98.62 314

N_pmnet		94.95 343	95.83 330	92.31 371	98.47 362	79.33 403	99.12 295	92.81 409	93.87 366	97.68 345	99.13 323	93.87 257	99.01 343	91.38 378	96.19 307	98.59 327

KD-MVS_2432*1600		94.62 344	93.72 352	97.31 338	97.19 385	95.82 334	98.34 382	99.20 295	95.00 352	97.57 346	98.35 368	87.95 361	98.10 376	92.87 370	77.00 399	98.01 366

miper_refine_blended		94.62 344	93.72 352	97.31 338	97.19 385	95.82 334	98.34 382	99.20 295	95.00 352	97.57 346	98.35 368	87.95 361	98.10 376	92.87 370	77.00 399	98.01 366

CL-MVSNet_self_test		94.49 346	93.97 350	96.08 360	96.16 389	93.67 373	98.33 384	99.38 230	95.13 346	97.33 353	98.15 374	92.69 287	96.57 393	88.67 387	79.87 397	97.99 369

new-patchmatchnet		94.48 347	94.08 348	95.67 362	95.08 397	92.41 381	99.18 284	99.28 280	94.55 362	93.49 385	97.37 386	87.86 363	97.01 391	91.57 377	88.36 385	97.61 379

OpenMVS_ROB	92.34 20	94.38 348	93.70 354	96.41 358	97.38 379	93.17 377	99.06 308	98.75 352	86.58 392	94.84 380	98.26 372	81.53 390	99.32 294	89.01 386	97.87 248	96.76 386

CMPMVS	69.68 23	94.13 349	94.90 341	91.84 372	97.24 383	80.01 402	98.52 375	99.48 155	89.01 389	91.99 390	99.67 178	85.67 371	99.13 325	95.44 335	97.03 292	96.39 390
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 350	93.25 356	96.60 356	94.76 399	94.49 361	98.92 341	98.18 379	89.66 386	96.48 366	98.06 380	86.28 368	97.33 388	89.68 384	87.20 388	97.97 371

mvsany_test3		93.77 351	93.45 355	94.74 364	95.78 391	88.01 390	99.64 78	98.25 374	98.28 116	94.31 381	97.97 381	68.89 396	98.51 370	97.50 256	90.37 380	97.71 376

UnsupCasMVSNet_bld		93.53 352	92.51 357	96.58 357	97.38 379	93.82 368	98.24 387	99.48 155	91.10 384	93.10 386	96.66 390	74.89 394	98.37 371	94.03 357	87.71 387	97.56 381

WB-MVS		93.10 353	94.10 347	90.12 378	95.51 396	81.88 398	99.73 47	99.27 283	95.05 351	93.09 387	98.91 349	94.70 223	91.89 402	76.62 401	94.02 355	96.58 388

PM-MVS		92.96 354	92.23 358	95.14 363	95.61 392	89.98 389	99.37 226	98.21 377	94.80 357	95.04 379	97.69 382	65.06 397	97.90 382	94.30 351	89.98 383	97.54 382

SSC-MVS		92.73 355	93.73 351	89.72 379	95.02 398	81.38 399	99.76 37	99.23 289	94.87 355	92.80 388	98.93 345	94.71 222	91.37 403	74.49 403	93.80 357	96.42 389

test_fmvs3		92.10 356	91.77 359	93.08 369	96.19 388	86.25 391	99.82 17	98.62 366	96.65 288	95.19 377	96.90 389	55.05 404	95.93 397	96.63 310	90.92 379	97.06 385

test_f		91.90 357	91.26 361	93.84 366	95.52 395	85.92 392	99.69 55	98.53 370	95.31 345	93.87 383	96.37 392	55.33 403	98.27 373	95.70 328	90.98 378	97.32 384

test_method		91.10 358	91.36 360	90.31 377	95.85 390	73.72 410	94.89 398	99.25 286	68.39 401	95.82 372	99.02 335	80.50 391	98.95 355	93.64 360	94.89 341	98.25 355

Gipuma		90.99 359	90.15 364	93.51 367	98.73 341	90.12 388	93.98 399	99.45 194	79.32 397	92.28 389	94.91 394	69.61 395	97.98 380	87.42 392	95.67 322	92.45 397
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		90.42 360	90.68 362	89.65 380	97.78 373	73.97 408	99.13 292	98.81 348	89.62 387	91.80 391	98.93 345	62.23 400	98.80 362	86.61 396	91.17 375	96.19 391

APD_test2		90.42 360	90.68 362	89.65 380	97.78 373	73.97 408	99.13 292	98.81 348	89.62 387	91.80 391	98.93 345	62.23 400	98.80 362	86.61 396	91.17 375	96.19 391

test_vis3_rt		87.04 362	85.81 365	90.73 376	93.99 400	81.96 397	99.76 37	90.23 411	92.81 377	81.35 399	91.56 399	40.06 408	99.07 334	94.27 353	88.23 386	91.15 399

PMMVS2		86.87 363	85.37 367	91.35 375	90.21 404	83.80 394	98.89 344	97.45 390	83.13 396	91.67 393	95.03 393	48.49 406	94.70 399	85.86 398	77.62 398	95.54 394

LCM-MVSNet		86.80 364	85.22 368	91.53 374	87.81 406	80.96 400	98.23 389	98.99 320	71.05 399	90.13 394	96.51 391	48.45 407	96.88 392	90.51 380	85.30 390	96.76 386

FPMVS		84.93 365	85.65 366	82.75 386	86.77 407	63.39 412	98.35 381	98.92 330	74.11 398	83.39 397	98.98 340	50.85 405	92.40 401	84.54 399	94.97 337	92.46 396

EGC-MVSNET		82.80 366	77.86 372	97.62 330	97.91 370	96.12 329	99.33 240	99.28 280	8.40 409	25.05 410	99.27 307	84.11 381	99.33 291	89.20 385	98.22 229	97.42 383

tmp_tt		82.80 366	81.52 369	86.66 382	66.61 412	68.44 411	92.79 401	97.92 381	68.96 400	80.04 403	99.85 53	85.77 370	96.15 396	97.86 218	43.89 405	95.39 395

E-PMN		80.61 368	79.88 370	82.81 385	90.75 403	76.38 406	97.69 394	95.76 400	66.44 403	83.52 396	92.25 398	62.54 399	87.16 405	68.53 405	61.40 402	84.89 403

EMVS		80.02 369	79.22 371	82.43 387	91.19 402	76.40 405	97.55 396	92.49 410	66.36 404	83.01 398	91.27 400	64.63 398	85.79 406	65.82 406	60.65 403	85.08 402

ANet_high		77.30 370	74.86 374	84.62 384	75.88 410	77.61 404	97.63 395	93.15 408	88.81 390	64.27 405	89.29 402	36.51 409	83.93 407	75.89 402	52.31 404	92.33 398

MVE	76.82 21	76.91 371	74.31 375	84.70 383	85.38 409	76.05 407	96.88 397	93.17 407	67.39 402	71.28 404	89.01 403	21.66 414	87.69 404	71.74 404	72.29 401	90.35 400
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 372	74.97 373	79.01 388	70.98 411	55.18 413	93.37 400	98.21 377	65.08 405	61.78 406	93.83 396	21.74 413	92.53 400	78.59 400	91.12 377	89.34 401
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 373	41.29 378	36.84 389	86.18 408	49.12 414	79.73 402	22.81 414	27.64 406	25.46 409	28.45 409	21.98 412	48.89 408	55.80 407	23.56 408	12.51 406

testmvs		39.17 374	43.78 376	25.37 391	36.04 414	16.84 416	98.36 380	26.56 413	20.06 407	38.51 408	67.32 404	29.64 411	15.30 410	37.59 408	39.90 406	43.98 405

test123		39.01 375	42.50 377	28.53 390	39.17 413	20.91 415	98.75 358	19.17 415	19.83 408	38.57 407	66.67 405	33.16 410	15.42 409	37.50 409	29.66 407	49.26 404

cdsmvs_eth3d_5k		24.64 376	32.85 379	0.00 392	0.00 415	0.00 417	0.00 403	99.51 115	0.00 410	0.00 411	99.56 221	96.58 148	0.00 411	0.00 410	0.00 409	0.00 407

ab-mvs-re		8.30 377	11.06 380	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	99.58 214	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		8.27 378	11.03 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	99.01 18	0.00 411	0.00 410	0.00 409	0.00 407

test_blank		0.13 379	0.17 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	1.57 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet_test		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		0.02 380	0.03 383	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.27 411	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						97.16 278								95.47 334

FOURS1					99.91 1	99.93 1	99.87 9	99.56 69	99.10 27	99.81 37

MSC_two_6792asdad				99.87 11	99.51 170	99.76 37		99.33 256					99.96 30	98.87 106	99.84 78	99.89 20

PC_three_1452									98.18 133	99.84 29	99.70 156	99.31 3	98.52 369	98.30 185	99.80 98	99.81 61

No_MVS				99.87 11	99.51 170	99.76 37		99.33 256					99.96 30	98.87 106	99.84 78	99.89 20

test_one_0601					99.81 46	99.88 8		99.49 143	98.97 51	99.65 89	99.81 89	99.09 14

eth-test2					0.00 415

eth-test					0.00 415

ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 277	99.56 115	99.54 229	98.58 72	99.96 30	96.93 295	99.75 113

RE-MVS-def			99.34 36		99.76 65	99.82 22	99.63 82	99.52 101	98.38 105	99.76 56	99.82 75	98.75 55		98.61 147	99.81 94	99.77 82

IU-MVS					99.84 32	99.88 8		99.32 266	98.30 115	99.84 29				98.86 111	99.85 70	99.89 20

OPU-MVS				99.64 78	99.56 156	99.72 42	99.60 95				99.70 156	99.27 5	99.42 274	98.24 187	99.80 98	99.79 74

test_241102_TWO								99.48 155	99.08 33	99.88 20	99.81 89	98.94 29	99.96 30	98.91 100	99.84 78	99.88 26

test_241102_ONE					99.84 32	99.90 2		99.48 155	99.07 35	99.91 16	99.74 141	99.20 7	99.76 200

9.14			99.10 76		99.72 91		99.40 215	99.51 115	97.53 212	99.64 93	99.78 120	98.84 41	99.91 105	97.63 242	99.82 91

save fliter					99.76 65	99.59 70	99.14 291	99.40 219	99.00 43

test_0728_THIRD									98.99 45	99.81 37	99.80 102	99.09 14	99.96 30	98.85 113	99.90 40	99.88 26

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 116	99.51 115					99.96 30	98.93 97	99.86 63	99.88 26

test0726					99.85 26	99.89 4	99.62 87	99.50 135	99.10 27	99.86 27	99.82 75	98.94 29

GSMVS																99.52 167

test_part2					99.81 46	99.83 16				99.77 51

sam_mvs1												94.86 209				99.52 167

sam_mvs												94.72 221

ambc				93.06 370	92.68 401	82.36 395	98.47 377	98.73 360		95.09 378	97.41 384	55.55 402	99.10 332	96.42 314	91.32 374	97.71 376

MTGPA								99.47 174

test_post1							99.23 275				65.14 407	94.18 247	99.71 220	97.58 246

test_post											65.99 406	94.65 227	99.73 210

patchmatchnet-post											98.70 358	94.79 213	99.74 204

GG-mvs-BLEND				98.45 270	98.55 359	98.16 232	99.43 198	93.68 406		97.23 355	98.46 364	89.30 343	99.22 311	95.43 336	98.22 229	97.98 370

MTMP							99.54 139	98.88 339

gm-plane-assit					98.54 360	92.96 378			94.65 360		99.15 321		99.64 245	97.56 251

test9_res														97.49 257	99.72 119	99.75 88

TEST9					99.67 111	99.65 57	99.05 310	99.41 213	96.22 321	98.95 247	99.49 246	98.77 51	99.91 105

test_8					99.67 111	99.61 67	99.03 315	99.41 213	96.28 315	98.93 251	99.48 251	98.76 52	99.91 105

agg_prior2														97.21 275	99.73 118	99.75 88

agg_prior					99.67 111	99.62 65		99.40 219		98.87 261			99.91 105

TestCases				99.31 146	99.86 20	98.48 216		99.61 48	97.85 172	99.36 165	99.85 53	95.95 169	99.85 148	96.66 308	99.83 87	99.59 150

test_prior4						99.56 75	98.99 326

test_prior2							98.96 333		98.34 111	99.01 237	99.52 236	98.68 64		97.96 210	99.74 116

test_prior				99.68 68	99.67 111	99.48 89		99.56 69					99.83 168			99.74 92

旧先验2							98.96 333		96.70 284	99.47 132			99.94 69	98.19 190

新几何2							99.01 323

新几何1				99.75 58	99.75 73	99.59 70		99.54 85	96.76 280	99.29 180	99.64 190	98.43 83	99.94 69	96.92 297	99.66 129	99.72 103

旧先验1					99.74 80	99.59 70		99.54 85			99.69 166	98.47 80			99.68 127	99.73 97

无先验							98.99 326	99.51 115	96.89 274				99.93 84	97.53 254		99.72 103

原ACMM2							98.95 337

原ACMM1				99.65 73	99.73 87	99.33 103		99.47 174	97.46 218	99.12 216	99.66 183	98.67 66	99.91 105	97.70 239	99.69 124	99.71 112

test222					99.75 73	99.49 87	98.91 343	99.49 143	96.42 309	99.34 171	99.65 184	98.28 92			99.69 124	99.72 103

testdata2													99.95 59	96.67 307

segment_acmp												98.96 24

testdata				99.54 97	99.75 73	98.95 163		99.51 115	97.07 258	99.43 141	99.70 156	98.87 37	99.94 69	97.76 230	99.64 132	99.72 103

testdata1							98.85 348		98.32 114

test12				99.75 58	99.64 128	99.61 67		99.29 278		99.21 199		98.38 87	99.89 127		99.74 116	99.74 92

plane_prior7					99.29 239	97.03 291

plane_prior6					99.27 244	96.98 295						92.71 285

plane_prior5								99.47 174					99.69 231	97.78 226	97.63 256	98.67 292

plane_prior4											99.61 205

plane_prior3						97.00 293			98.69 79	99.11 218

plane_prior2							99.39 219		98.97 51

plane_prior1					99.26 246

plane_prior						96.97 296	99.21 281		98.45 99						97.60 259

n2								0.00 416

nn								0.00 416

door-mid								98.05 380

lessismore_v0				97.79 324	98.69 347	95.44 345		94.75 403		95.71 373	99.87 44	88.69 351	99.32 294	95.89 323	94.93 339	98.62 314

LGP-MVS_train				98.49 261	99.33 227	97.05 287		99.55 77	97.46 218	99.24 191	99.83 67	92.58 290	99.72 214	98.09 197	97.51 267	98.68 285

test11								99.35 245

door								97.92 381

HQP5-MVS						96.83 303

HQP-NCC					99.19 262		98.98 329		98.24 121	98.66 288

ACMP_Plane					99.19 262		98.98 329		98.24 121	98.66 288

BP-MVS														97.19 279

HQP4-MVS										98.66 288			99.64 245			98.64 304

HQP3-MVS								99.39 222							97.58 261

HQP2-MVS												92.47 294

NP-MVS					99.23 252	96.92 299					99.40 272

MDTV_nov1_ep13_2view						95.18 351	99.35 235		96.84 277	99.58 111		95.19 200		97.82 223		99.46 187

MDTV_nov1_ep13			98.32 171		99.11 283	94.44 362	99.27 261	98.74 355	97.51 215	99.40 153	99.62 201	94.78 214	99.76 200	97.59 245	98.81 199

ACMMP++_ref															97.19 289

ACMMP++															97.43 279

Test By Simon												98.75 55

ITE_SJBPF				98.08 302	99.29 239	96.37 321		98.92 330	98.34 111	98.83 266	99.75 136	91.09 324	99.62 251	95.82 324	97.40 281	98.25 355

DeepMVS_CX				93.34 368	99.29 239	82.27 396		99.22 291	85.15 393	96.33 367	99.05 331	90.97 326	99.73 210	93.57 361	97.77 251	98.01 366

High-resolution multi-view benchmark