High-res multi-view results

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

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

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

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

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

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

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

DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 63	95.39 11	99.29 1	98.28 47	94.78 59	98.93 18	98.87 29	96.04 2	99.86 9	97.45 44	99.58 23	99.59 28

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 36	98.27 50	95.13 38	99.19 11	98.89 26	95.54 5	99.85 18	97.52 40	99.66 10	99.56 36

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 131	94.92 48	98.73 28	98.87 29	95.08 8	99.84 23	97.52 40	99.67 6	99.48 52
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
DPE-MVS		97.86 4	97.65 9	98.47 5	99.17 34	95.78 7	97.21 186	98.35 38	95.16 36	98.71 30	98.80 36	95.05 10	99.89 3	96.70 63	99.73 1	99.73 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 5	97.73 8	98.08 18	99.15 35	94.82 28	98.81 8	98.30 43	94.76 62	98.30 38	98.90 23	93.77 17	99.68 69	97.93 27	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 6	97.44 21	98.37 7	98.90 55	95.86 6	97.27 177	98.08 88	95.81 18	97.87 52	98.31 75	94.26 13	99.68 69	97.02 52	99.49 39	99.57 32

fmvsm_l_conf0.5_n		97.65 7	97.75 7	97.34 57	98.21 100	92.75 88	97.83 92	98.73 10	95.04 43	99.30 5	98.84 34	93.34 22	99.78 43	99.32 6	99.13 92	99.50 48

fmvsm_l_conf0.5_n_3		97.64 8	97.60 11	97.79 30	98.14 107	93.94 52	97.93 78	98.65 20	96.70 6	99.38 3	99.07 10	89.92 88	99.81 30	99.16 12	99.43 49	99.61 26

fmvsm_l_conf0.5_n_a		97.63 9	97.76 6	97.26 64	98.25 94	92.59 96	97.81 97	98.68 15	94.93 46	99.24 8	98.87 29	93.52 20	99.79 40	99.32 6	99.21 77	99.40 62

SteuartSystems-ACMMP		97.62 10	97.53 15	97.87 24	98.39 83	94.25 40	98.43 23	98.27 50	95.34 30	98.11 41	98.56 45	94.53 12	99.71 61	96.57 67	99.62 17	99.65 19
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		97.59 11	97.79 5	96.97 82	98.28 89	91.49 139	97.61 131	98.71 12	97.10 4	99.70 1	98.93 20	90.95 73	99.77 46	99.35 5	99.53 29	99.65 19

MSP-MVS		97.59 11	97.54 14	97.73 38	99.40 11	93.77 57	98.53 15	98.29 45	95.55 25	98.56 33	97.81 123	93.90 15	99.65 73	96.62 64	99.21 77	99.77 2
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
lecture		97.58 13	97.63 10	97.43 54	99.37 16	92.93 82	98.86 7	98.85 5	95.27 32	98.65 31	98.90 23	91.97 49	99.80 35	97.63 36	99.21 77	99.57 32

test_fmvsm_n_1920		97.55 14	97.89 3	96.53 100	98.41 80	91.73 125	98.01 61	99.02 1	96.37 11	99.30 5	98.92 21	92.39 41	99.79 40	99.16 12	99.46 42	98.08 210

reproduce-ours		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 113	98.20 64	95.80 19	97.88 49	98.98 16	92.91 27	99.81 30	97.68 31	99.43 49	99.67 14

our_new_method		97.53 15	97.51 17	97.60 47	98.97 49	93.31 69	97.71 113	98.20 64	95.80 19	97.88 49	98.98 16	92.91 27	99.81 30	97.68 31	99.43 49	99.67 14

reproduce_model		97.51 17	97.51 17	97.50 50	98.99 48	93.01 78	97.79 100	98.21 62	95.73 22	97.99 45	99.03 13	92.63 36	99.82 28	97.80 29	99.42 52	99.67 14

test_fmvsmconf_n		97.49 18	97.56 13	97.29 60	97.44 159	92.37 103	97.91 80	98.88 4	95.83 17	98.92 21	99.05 12	91.45 58	99.80 35	99.12 14	99.46 42	99.69 13

TSAR-MVS + MP.		97.42 19	97.33 24	97.69 42	99.25 29	94.24 41	98.07 56	97.85 131	93.72 99	98.57 32	98.35 66	93.69 18	99.40 127	97.06 51	99.46 42	99.44 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		97.41 20	97.53 15	97.06 78	98.57 74	94.46 34	97.92 79	98.14 78	94.82 55	99.01 15	98.55 47	94.18 14	97.41 373	96.94 53	99.64 14	99.32 70
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
SF-MVS		97.39 21	97.13 26	98.17 15	99.02 44	95.28 19	98.23 40	98.27 50	92.37 159	98.27 39	98.65 43	93.33 23	99.72 59	96.49 69	99.52 31	99.51 45

SMA-MVS		97.35 22	97.03 35	98.30 8	99.06 40	95.42 10	97.94 76	98.18 71	90.57 239	98.85 25	98.94 19	93.33 23	99.83 26	96.72 61	99.68 4	99.63 22
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
HPM-MVS++		97.34 23	96.97 38	98.47 5	99.08 38	96.16 4	97.55 142	97.97 115	95.59 23	96.61 91	97.89 110	92.57 38	99.84 23	95.95 93	99.51 34	99.40 62

fmvsm_s_conf0.5_n_9		97.33 24	97.57 12	96.62 96	98.43 78	90.32 194	97.80 98	98.53 26	97.24 3	99.62 2	99.14 1	88.65 105	99.80 35	99.54 1	99.15 89	99.74 8

fmvsm_s_conf0.5_n_8		97.32 25	97.48 20	96.85 83	98.28 89	91.07 163	97.76 102	98.62 22	97.53 2	99.20 10	99.12 4	88.24 113	99.81 30	99.41 3	99.17 85	99.67 14

NCCC		97.30 26	97.03 35	98.11 17	98.77 58	95.06 25	97.34 170	98.04 103	95.96 13	97.09 73	97.88 113	93.18 25	99.71 61	95.84 98	99.17 85	99.56 36

MM		97.29 27	96.98 37	98.23 11	98.01 117	95.03 26	98.07 56	95.76 329	97.78 1	97.52 56	98.80 36	88.09 115	99.86 9	99.44 2	99.37 63	99.80 1

ACMMP_NAP		97.20 28	96.86 44	98.23 11	99.09 36	95.16 22	97.60 132	98.19 69	92.82 148	97.93 48	98.74 40	91.60 56	99.86 9	96.26 74	99.52 31	99.67 14

XVS		97.18 29	96.96 40	97.81 28	99.38 14	94.03 50	98.59 13	98.20 64	94.85 51	96.59 93	98.29 78	91.70 53	99.80 35	95.66 102	99.40 57	99.62 23

MCST-MVS		97.18 29	96.84 46	98.20 14	99.30 26	95.35 15	97.12 193	98.07 93	93.54 108	96.08 119	97.69 133	93.86 16	99.71 61	96.50 68	99.39 59	99.55 39

fmvsm_s_conf0.5_n_3		97.15 31	97.36 23	96.52 102	97.98 120	91.19 155	97.84 89	98.65 20	97.08 5	99.25 7	99.10 5	87.88 121	99.79 40	99.32 6	99.18 84	98.59 154

HFP-MVS		97.14 32	96.92 42	97.83 26	99.42 7	94.12 46	98.52 16	98.32 41	93.21 121	97.18 67	98.29 78	92.08 46	99.83 26	95.63 107	99.59 19	99.54 41

test_fmvsmconf0.1_n		97.09 33	97.06 30	97.19 69	95.67 289	92.21 110	97.95 75	98.27 50	95.78 21	98.40 37	99.00 14	89.99 86	99.78 43	99.06 16	99.41 55	99.59 28

fmvsm_s_conf0.5_n_6		97.08 34	97.17 25	96.81 84	97.28 164	91.73 125	97.75 104	98.50 27	94.86 50	99.22 9	98.78 38	89.75 91	99.76 48	99.10 15	99.29 68	98.94 113

MTAPA		97.08 34	96.78 54	97.97 23	99.37 16	94.42 36	97.24 179	98.08 88	95.07 42	96.11 117	98.59 44	90.88 76	99.90 2	96.18 86	99.50 36	99.58 31

region2R		97.07 36	96.84 46	97.77 34	99.46 2	93.79 55	98.52 16	98.24 58	93.19 124	97.14 70	98.34 69	91.59 57	99.87 7	95.46 113	99.59 19	99.64 21

ACMMPR		97.07 36	96.84 46	97.79 30	99.44 6	93.88 53	98.52 16	98.31 42	93.21 121	97.15 69	98.33 72	91.35 62	99.86 9	95.63 107	99.59 19	99.62 23

CP-MVS		97.02 38	96.81 51	97.64 45	99.33 23	93.54 60	98.80 9	98.28 47	92.99 134	96.45 105	98.30 77	91.90 50	99.85 18	95.61 109	99.68 4	99.54 41

SR-MVS		97.01 39	96.86 44	97.47 52	99.09 36	93.27 71	97.98 66	98.07 93	93.75 98	97.45 58	98.48 55	91.43 60	99.59 89	96.22 77	99.27 70	99.54 41

fmvsm_s_conf0.5_n_5		97.00 40	96.97 38	97.09 75	97.58 155	92.56 97	97.68 117	98.47 31	94.02 89	98.90 23	98.89 26	88.94 99	99.78 43	99.18 10	99.03 101	98.93 117

ZNCC-MVS		96.96 41	96.67 59	97.85 25	99.37 16	94.12 46	98.49 20	98.18 71	92.64 154	96.39 107	98.18 85	91.61 55	99.88 4	95.59 112	99.55 26	99.57 32

APD-MVS		96.95 42	96.60 61	98.01 20	99.03 43	94.93 27	97.72 111	98.10 86	91.50 190	98.01 44	98.32 74	92.33 42	99.58 92	94.85 127	99.51 34	99.53 44
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 43	97.06 30	96.59 97	98.72 60	91.86 123	97.67 118	98.49 28	94.66 67	97.24 66	98.41 61	92.31 44	98.94 189	96.61 65	99.46 42	98.96 109

DeepC-MVS_fast	93.89 2	96.93 44	96.64 60	97.78 32	98.64 69	94.30 37	97.41 160	98.04 103	94.81 57	96.59 93	98.37 64	91.24 65	99.64 81	95.16 118	99.52 31	99.42 61
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		96.89 45	97.04 34	96.45 113	98.29 88	91.66 132	99.03 4	97.85 131	95.84 16	96.90 77	97.97 103	91.24 65	98.75 215	96.92 54	99.33 65	98.94 113

SR-MVS-dyc-post		96.88 46	96.80 52	97.11 74	99.02 44	92.34 104	97.98 66	98.03 105	93.52 111	97.43 61	98.51 50	91.40 61	99.56 100	96.05 88	99.26 72	99.43 59

CS-MVS		96.86 47	97.06 30	96.26 129	98.16 106	91.16 160	99.09 3	97.87 126	95.30 31	97.06 74	98.03 95	91.72 51	98.71 225	97.10 50	99.17 85	98.90 122

mPP-MVS		96.86 47	96.60 61	97.64 45	99.40 11	93.44 62	98.50 19	98.09 87	93.27 120	95.95 125	98.33 72	91.04 70	99.88 4	95.20 116	99.57 25	99.60 27

fmvsm_s_conf0.5_n		96.85 49	97.13 26	96.04 142	98.07 114	90.28 195	97.97 72	98.76 9	94.93 46	98.84 26	99.06 11	88.80 102	99.65 73	99.06 16	98.63 117	98.18 196

GST-MVS		96.85 49	96.52 65	97.82 27	99.36 20	94.14 45	98.29 30	98.13 79	92.72 151	96.70 85	98.06 92	91.35 62	99.86 9	94.83 129	99.28 69	99.47 54

balanced_conf03		96.84 51	96.89 43	96.68 88	97.63 147	92.22 109	98.17 49	97.82 137	94.44 77	98.23 40	97.36 161	90.97 72	99.22 145	97.74 30	99.66 10	98.61 151

patch_mono-2		96.83 52	97.44 21	95.01 208	99.05 41	85.39 346	96.98 206	98.77 8	94.70 64	97.99 45	98.66 41	93.61 19	99.91 1	97.67 35	99.50 36	99.72 12

APD-MVS_3200maxsize		96.81 53	96.71 58	97.12 72	99.01 47	92.31 106	97.98 66	98.06 96	93.11 130	97.44 59	98.55 47	90.93 74	99.55 102	96.06 87	99.25 74	99.51 45

PGM-MVS		96.81 53	96.53 64	97.65 43	99.35 22	93.53 61	97.65 122	98.98 2	92.22 163	97.14 70	98.44 58	91.17 68	99.85 18	94.35 147	99.46 42	99.57 32

MP-MVS		96.77 55	96.45 72	97.72 39	99.39 13	93.80 54	98.41 24	98.06 96	93.37 116	95.54 143	98.34 69	90.59 80	99.88 4	94.83 129	99.54 28	99.49 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 55	96.46 71	97.71 41	98.40 81	94.07 48	98.21 43	98.45 33	89.86 256	97.11 72	98.01 98	92.52 39	99.69 67	96.03 91	99.53 29	99.36 68

fmvsm_s_conf0.5_n_4		96.75 57	97.07 29	95.79 163	97.76 136	89.57 219	97.66 121	98.66 18	95.36 28	99.03 14	98.90 23	88.39 110	99.73 55	99.17 11	98.66 115	98.08 210

fmvsm_s_conf0.5_n_a		96.75 57	96.93 41	96.20 134	97.64 145	90.72 177	98.00 62	98.73 10	94.55 71	98.91 22	99.08 7	88.22 114	99.63 82	98.91 19	98.37 130	98.25 191

MVS_0304		96.74 59	96.31 76	98.02 19	96.87 193	94.65 30	97.58 133	94.39 395	96.47 10	97.16 68	98.39 62	87.53 131	99.87 7	98.97 18	99.41 55	99.55 39

test_fmvsmvis_n_1920		96.70 60	96.84 46	96.31 123	96.62 217	91.73 125	97.98 66	98.30 43	96.19 12	96.10 118	98.95 18	89.42 92	99.76 48	98.90 20	99.08 96	97.43 250

MP-MVS-pluss		96.70 60	96.27 78	97.98 22	99.23 32	94.71 29	96.96 208	98.06 96	90.67 229	95.55 141	98.78 38	91.07 69	99.86 9	96.58 66	99.55 26	99.38 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 62	96.49 66	97.27 63	98.31 87	93.39 63	96.79 225	96.72 278	94.17 85	97.44 59	97.66 137	92.76 31	99.33 133	96.86 57	97.76 156	99.08 93

HPM-MVS		96.69 62	96.45 72	97.40 55	99.36 20	93.11 76	98.87 6	98.06 96	91.17 208	96.40 106	97.99 101	90.99 71	99.58 92	95.61 109	99.61 18	99.49 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 64	96.58 63	96.99 80	98.46 75	92.31 106	96.20 285	98.90 3	94.30 84	95.86 128	97.74 128	92.33 42	99.38 130	96.04 90	99.42 52	99.28 73

fmvsm_s_conf0.5_n_2		96.62 65	96.82 50	96.02 144	97.98 120	90.43 187	97.50 146	98.59 23	96.59 8	99.31 4	99.08 7	84.47 187	99.75 52	99.37 4	98.45 127	97.88 223

DELS-MVS		96.61 66	96.38 75	97.30 59	97.79 134	93.19 74	95.96 299	98.18 71	95.23 33	95.87 127	97.65 138	91.45 58	99.70 66	95.87 94	99.44 48	99.00 104
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
DeepPCF-MVS	93.97 1	96.61 66	97.09 28	95.15 199	98.09 110	86.63 313	96.00 297	98.15 76	95.43 26	97.95 47	98.56 45	93.40 21	99.36 131	96.77 58	99.48 40	99.45 55

fmvsm_s_conf0.1_n		96.58 68	96.77 55	96.01 147	96.67 215	90.25 196	97.91 80	98.38 34	94.48 75	98.84 26	99.14 1	88.06 116	99.62 83	98.82 21	98.60 119	98.15 200

MVSMamba_PlusPlus		96.51 69	96.48 67	96.59 97	98.07 114	91.97 120	98.14 50	97.79 139	90.43 243	97.34 64	97.52 153	91.29 64	99.19 148	98.12 26	99.64 14	98.60 152

EI-MVSNet-Vis-set		96.51 69	96.47 68	96.63 93	98.24 95	91.20 154	96.89 214	97.73 146	94.74 63	96.49 100	98.49 52	90.88 76	99.58 92	96.44 70	98.32 132	99.13 85

HPM-MVS_fast		96.51 69	96.27 78	97.22 66	99.32 24	92.74 89	98.74 10	98.06 96	90.57 239	96.77 82	98.35 66	90.21 83	99.53 106	94.80 132	99.63 16	99.38 66

fmvsm_s_conf0.5_n_7		96.45 72	96.80 52	95.37 191	97.29 163	88.38 263	97.23 183	98.47 31	95.14 37	98.43 36	99.09 6	87.58 128	99.72 59	98.80 23	99.21 77	98.02 214

EC-MVSNet		96.42 73	96.47 68	96.26 129	97.01 185	91.52 138	98.89 5	97.75 143	94.42 78	96.64 90	97.68 134	89.32 93	98.60 239	97.45 44	99.11 95	98.67 149

fmvsm_s_conf0.1_n_a		96.40 74	96.47 68	96.16 136	95.48 298	90.69 178	97.91 80	98.33 40	94.07 87	98.93 18	99.14 1	87.44 135	99.61 84	98.63 24	98.32 132	98.18 196

CANet		96.39 75	96.02 83	97.50 50	97.62 148	93.38 64	97.02 199	97.96 116	95.42 27	94.86 157	97.81 123	87.38 137	99.82 28	96.88 55	99.20 82	99.29 71

dcpmvs_2		96.37 76	97.05 33	94.31 253	98.96 51	84.11 367	97.56 137	97.51 178	93.92 93	97.43 61	98.52 49	92.75 32	99.32 135	97.32 49	99.50 36	99.51 45

NormalMVS		96.36 77	96.11 81	97.12 72	99.37 16	92.90 83	97.99 63	97.63 160	95.92 14	96.57 96	97.93 105	85.34 169	99.50 114	94.99 123	99.21 77	98.97 106

EI-MVSNet-UG-set		96.34 78	96.30 77	96.47 110	98.20 101	90.93 168	96.86 217	97.72 148	94.67 66	96.16 116	98.46 56	90.43 81	99.58 92	96.23 76	97.96 149	98.90 122

fmvsm_s_conf0.1_n_2		96.33 79	96.44 74	96.00 148	97.30 162	90.37 193	97.53 143	97.92 121	96.52 9	99.14 13	99.08 7	83.21 209	99.74 53	99.22 9	98.06 144	97.88 223

train_agg		96.30 80	95.83 88	97.72 39	98.70 61	94.19 42	96.41 262	98.02 108	88.58 302	96.03 120	97.56 150	92.73 34	99.59 89	95.04 120	99.37 63	99.39 64

ACMMP		96.27 81	95.93 84	97.28 62	99.24 30	92.62 94	98.25 36	98.81 6	92.99 134	94.56 166	98.39 62	88.96 98	99.85 18	94.57 142	97.63 157	99.36 68
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
MVS_111021_LR		96.24 82	96.19 80	96.39 118	98.23 99	91.35 147	96.24 283	98.79 7	93.99 91	95.80 130	97.65 138	89.92 88	99.24 143	95.87 94	99.20 82	98.58 155

test_fmvsmconf0.01_n		96.15 83	95.85 87	97.03 79	92.66 412	91.83 124	97.97 72	97.84 135	95.57 24	97.53 55	99.00 14	84.20 193	99.76 48	98.82 21	99.08 96	99.48 52

DeepC-MVS	93.07 3	96.06 84	95.66 89	97.29 60	97.96 122	93.17 75	97.30 175	98.06 96	93.92 93	93.38 204	98.66 41	86.83 143	99.73 55	95.60 111	99.22 76	98.96 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		96.05 85	95.91 85	96.46 112	99.24 30	90.47 184	98.30 29	98.57 25	89.01 284	93.97 185	97.57 148	92.62 37	99.76 48	94.66 136	99.27 70	99.15 83

sasdasda		96.02 86	95.45 96	97.75 36	97.59 151	95.15 23	98.28 31	97.60 165	94.52 73	96.27 111	96.12 241	87.65 125	99.18 151	96.20 82	94.82 238	98.91 119

ETV-MVS		96.02 86	95.89 86	96.40 116	97.16 170	92.44 101	97.47 155	97.77 142	94.55 71	96.48 101	94.51 323	91.23 67	98.92 192	95.65 105	98.19 138	97.82 231

canonicalmvs		96.02 86	95.45 96	97.75 36	97.59 151	95.15 23	98.28 31	97.60 165	94.52 73	96.27 111	96.12 241	87.65 125	99.18 151	96.20 82	94.82 238	98.91 119

CDPH-MVS		95.97 89	95.38 101	97.77 34	98.93 52	94.44 35	96.35 270	97.88 124	86.98 348	96.65 89	97.89 110	91.99 48	99.47 119	92.26 185	99.46 42	99.39 64

UA-Net		95.95 90	95.53 92	97.20 68	97.67 141	92.98 80	97.65 122	98.13 79	94.81 57	96.61 91	98.35 66	88.87 100	99.51 111	90.36 237	97.35 168	99.11 89

SymmetryMVS		95.94 91	95.54 91	97.15 70	97.85 130	92.90 83	97.99 63	96.91 265	95.92 14	96.57 96	97.93 105	85.34 169	99.50 114	94.99 123	96.39 203	99.05 97

MGCFI-Net		95.94 91	95.40 100	97.56 49	97.59 151	94.62 31	98.21 43	97.57 170	94.41 79	96.17 115	96.16 239	87.54 130	99.17 153	96.19 84	94.73 243	98.91 119

BP-MVS1		95.89 93	95.49 93	97.08 77	96.67 215	93.20 73	98.08 54	96.32 303	94.56 70	96.32 108	97.84 119	84.07 196	99.15 157	96.75 59	98.78 110	98.90 122

VNet		95.89 93	95.45 96	97.21 67	98.07 114	92.94 81	97.50 146	98.15 76	93.87 95	97.52 56	97.61 144	85.29 171	99.53 106	95.81 99	95.27 229	99.16 81

alignmvs		95.87 95	95.23 106	97.78 32	97.56 157	95.19 21	97.86 85	97.17 230	94.39 81	96.47 102	96.40 226	85.89 159	99.20 147	96.21 81	95.11 234	98.95 112

casdiffmvs_mvg		95.81 96	95.57 90	96.51 106	96.87 193	91.49 139	97.50 146	97.56 174	93.99 91	95.13 152	97.92 108	87.89 120	98.78 208	95.97 92	97.33 169	99.26 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		95.69 97	94.92 115	98.01 20	98.08 113	95.71 9	95.27 340	97.62 164	90.43 243	95.55 141	97.07 181	91.72 51	99.50 114	89.62 253	98.94 105	98.82 134

DP-MVS Recon		95.68 98	95.12 111	97.37 56	99.19 33	94.19 42	97.03 197	98.08 88	88.35 311	95.09 153	97.65 138	89.97 87	99.48 118	92.08 196	98.59 120	98.44 173

casdiffmvs		95.64 99	95.49 93	96.08 138	96.76 213	90.45 185	97.29 176	97.44 198	94.00 90	95.46 146	97.98 102	87.52 133	98.73 219	95.64 106	97.33 169	99.08 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS		95.62 100	95.13 109	97.09 75	96.79 204	93.26 72	97.89 83	97.83 136	93.58 103	96.80 79	97.82 121	83.06 216	99.16 155	94.40 145	97.95 150	98.87 128

MG-MVS		95.61 101	95.38 101	96.31 123	98.42 79	90.53 182	96.04 294	97.48 183	93.47 113	95.67 138	98.10 88	89.17 95	99.25 142	91.27 214	98.77 111	99.13 85

baseline		95.58 102	95.42 99	96.08 138	96.78 208	90.41 188	97.16 190	97.45 194	93.69 102	95.65 139	97.85 117	87.29 138	98.68 228	95.66 102	97.25 175	99.13 85

CPTT-MVS		95.57 103	95.19 107	96.70 87	99.27 28	91.48 141	98.33 27	98.11 84	87.79 329	95.17 151	98.03 95	87.09 141	99.61 84	93.51 163	99.42 52	99.02 98

EIA-MVS		95.53 104	95.47 95	95.71 171	97.06 178	89.63 215	97.82 94	97.87 126	93.57 104	93.92 186	95.04 295	90.61 79	98.95 187	94.62 138	98.68 114	98.54 158

3Dnovator+	91.43 4	95.40 105	94.48 134	98.16 16	96.90 192	95.34 16	98.48 21	97.87 126	94.65 68	88.53 334	98.02 97	83.69 200	99.71 61	93.18 171	98.96 104	99.44 57

PS-MVSNAJ		95.37 106	95.33 103	95.49 185	97.35 161	90.66 180	95.31 337	97.48 183	93.85 96	96.51 99	95.70 266	88.65 105	99.65 73	94.80 132	98.27 135	96.17 289

MVSFormer		95.37 106	95.16 108	95.99 149	96.34 249	91.21 152	98.22 41	97.57 170	91.42 194	96.22 113	97.32 162	86.20 155	97.92 323	94.07 150	99.05 98	98.85 130

diffmvs_AUTHOR		95.33 108	95.27 105	95.50 184	96.37 247	89.08 245	96.08 292	97.38 208	93.09 132	96.53 98	97.74 128	86.45 149	98.68 228	96.32 72	97.48 160	98.75 140

xiu_mvs_v2_base		95.32 109	95.29 104	95.40 190	97.22 166	90.50 183	95.44 330	97.44 198	93.70 101	96.46 103	96.18 236	88.59 109	99.53 106	94.79 135	97.81 153	96.17 289

PVSNet_Blended_VisFu		95.27 110	94.91 116	96.38 119	98.20 101	90.86 171	97.27 177	98.25 56	90.21 247	94.18 178	97.27 168	87.48 134	99.73 55	93.53 162	97.77 155	98.55 157

KinetiMVS		95.26 111	94.75 122	96.79 85	96.99 187	92.05 116	97.82 94	97.78 140	94.77 61	96.46 103	97.70 131	80.62 270	99.34 132	92.37 184	98.28 134	98.97 106

diffmvs		95.25 112	95.13 109	95.63 174	96.43 242	89.34 232	95.99 298	97.35 212	92.83 147	96.31 109	97.37 160	86.44 150	98.67 231	96.26 74	97.19 178	98.87 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		95.24 113	95.02 113	95.91 152	96.87 193	89.98 204	96.82 222	97.49 181	92.26 161	95.47 145	97.82 121	86.47 148	98.69 226	94.80 132	97.20 177	99.06 96

Vis-MVSNet		95.23 114	94.81 117	96.51 106	97.18 169	91.58 136	98.26 35	98.12 81	94.38 82	94.90 156	98.15 87	82.28 237	98.92 192	91.45 211	98.58 121	99.01 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 115	95.04 112	95.76 164	97.49 158	89.56 220	98.67 11	97.00 255	90.69 227	94.24 174	97.62 143	89.79 90	98.81 205	93.39 168	96.49 200	98.92 118

EPNet		95.20 116	94.56 128	97.14 71	92.80 409	92.68 93	97.85 88	94.87 379	96.64 7	92.46 221	97.80 125	86.23 152	99.65 73	93.72 160	98.62 118	99.10 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 117	94.44 136	97.44 53	96.56 225	93.36 66	98.65 12	98.36 35	94.12 86	89.25 317	98.06 92	82.20 239	99.77 46	93.41 167	99.32 66	99.18 80

guyue		95.17 118	94.96 114	95.82 160	96.97 189	89.65 214	97.56 137	95.58 341	94.82 55	95.72 133	97.42 158	82.90 221	98.84 201	96.71 62	96.93 184	98.96 109

OMC-MVS		95.09 119	94.70 123	96.25 132	98.46 75	91.28 148	96.43 258	97.57 170	92.04 172	94.77 162	97.96 104	87.01 142	99.09 168	91.31 213	96.77 188	98.36 180

viewmacassd2359aftdt		95.07 120	94.80 118	95.87 154	96.53 230	89.84 210	96.90 213	97.48 183	92.44 156	95.36 147	97.89 110	85.23 172	98.68 228	94.40 145	97.00 183	99.09 91

xiu_mvs_v1_base_debu		95.01 121	94.76 119	95.75 166	96.58 221	91.71 128	96.25 280	97.35 212	92.99 134	96.70 85	96.63 213	82.67 227	99.44 123	96.22 77	97.46 161	96.11 295

xiu_mvs_v1_base		95.01 121	94.76 119	95.75 166	96.58 221	91.71 128	96.25 280	97.35 212	92.99 134	96.70 85	96.63 213	82.67 227	99.44 123	96.22 77	97.46 161	96.11 295

xiu_mvs_v1_base_debi		95.01 121	94.76 119	95.75 166	96.58 221	91.71 128	96.25 280	97.35 212	92.99 134	96.70 85	96.63 213	82.67 227	99.44 123	96.22 77	97.46 161	96.11 295

PAPM_NR		95.01 121	94.59 126	96.26 129	98.89 56	90.68 179	97.24 179	97.73 146	91.80 177	92.93 218	96.62 216	89.13 96	99.14 160	89.21 266	97.78 154	98.97 106

lupinMVS		94.99 125	94.56 128	96.29 127	96.34 249	91.21 152	95.83 307	96.27 307	88.93 290	96.22 113	96.88 195	86.20 155	98.85 199	95.27 115	99.05 98	98.82 134

Effi-MVS+		94.93 126	94.45 135	96.36 121	96.61 218	91.47 142	96.41 262	97.41 203	91.02 216	94.50 168	95.92 250	87.53 131	98.78 208	93.89 156	96.81 187	98.84 133

IS-MVSNet		94.90 127	94.52 132	96.05 141	97.67 141	90.56 181	98.44 22	96.22 310	93.21 121	93.99 183	97.74 128	85.55 167	98.45 253	89.98 242	97.86 151	99.14 84

LuminaMVS		94.89 128	94.35 138	96.53 100	95.48 298	92.80 87	96.88 216	96.18 314	92.85 146	95.92 126	96.87 197	81.44 254	98.83 202	96.43 71	97.10 181	97.94 219

MVS_Test		94.89 128	94.62 125	95.68 172	96.83 199	89.55 221	96.70 236	97.17 230	91.17 208	95.60 140	96.11 245	87.87 122	98.76 212	93.01 179	97.17 179	98.72 144

PVSNet_Blended		94.87 130	94.56 128	95.81 161	98.27 91	89.46 227	95.47 329	98.36 35	88.84 293	94.36 171	96.09 246	88.02 117	99.58 92	93.44 165	98.18 139	98.40 176

jason		94.84 131	94.39 137	96.18 135	95.52 296	90.93 168	96.09 291	96.52 293	89.28 275	96.01 123	97.32 162	84.70 183	98.77 211	95.15 119	98.91 107	98.85 130
jason: jason.
API-MVS		94.84 131	94.49 133	95.90 153	97.90 128	92.00 119	97.80 98	97.48 183	89.19 278	94.81 160	96.71 202	88.84 101	99.17 153	88.91 273	98.76 112	96.53 278

AstraMVS		94.82 133	94.64 124	95.34 193	96.36 248	88.09 275	97.58 133	94.56 388	94.98 44	95.70 136	97.92 108	81.93 247	98.93 190	96.87 56	95.88 210	98.99 105

test_yl		94.78 134	94.23 141	96.43 114	97.74 137	91.22 150	96.85 218	97.10 236	91.23 205	95.71 134	96.93 190	84.30 190	99.31 137	93.10 172	95.12 232	98.75 140

DCV-MVSNet		94.78 134	94.23 141	96.43 114	97.74 137	91.22 150	96.85 218	97.10 236	91.23 205	95.71 134	96.93 190	84.30 190	99.31 137	93.10 172	95.12 232	98.75 140

SSM_0404		94.73 136	94.31 140	95.98 150	97.05 180	90.90 170	97.01 202	97.29 217	91.24 202	94.17 179	97.60 145	85.03 176	98.76 212	92.14 190	97.30 172	98.29 189

WTY-MVS		94.71 137	94.02 146	96.79 85	97.71 139	92.05 116	96.59 251	97.35 212	90.61 235	94.64 164	96.93 190	86.41 151	99.39 128	91.20 216	94.71 244	98.94 113

mamv4		94.66 138	96.10 82	90.37 393	98.01 117	73.41 443	96.82 222	97.78 140	89.95 254	94.52 167	97.43 157	92.91 27	99.09 168	98.28 25	99.16 88	98.60 152

mvsmamba		94.57 139	94.14 143	95.87 154	97.03 183	89.93 208	97.84 89	95.85 325	91.34 197	94.79 161	96.80 198	80.67 268	98.81 205	94.85 127	98.12 142	98.85 130

SSM_0407		94.54 140	94.12 145	95.80 162	96.79 204	90.38 190	96.79 225	97.29 217	91.24 202	93.68 190	97.60 145	85.03 176	98.67 231	92.14 190	96.51 196	98.35 182

RRT-MVS		94.51 141	94.35 138	94.98 211	96.40 243	86.55 316	97.56 137	97.41 203	93.19 124	94.93 155	97.04 183	79.12 298	99.30 139	96.19 84	97.32 171	99.09 91

sss		94.51 141	93.80 150	96.64 89	97.07 175	91.97 120	96.32 275	98.06 96	88.94 289	94.50 168	96.78 199	84.60 184	99.27 141	91.90 197	96.02 206	98.68 148

test_cas_vis1_n_1920		94.48 143	94.55 131	94.28 255	96.78 208	86.45 318	97.63 128	97.64 158	93.32 119	97.68 54	98.36 65	73.75 361	99.08 171	96.73 60	99.05 98	97.31 257

CANet_DTU		94.37 144	93.65 156	96.55 99	96.46 240	92.13 114	96.21 284	96.67 285	94.38 82	93.53 198	97.03 188	79.34 294	99.71 61	90.76 226	98.45 127	97.82 231

AdaColmap		94.34 145	93.68 155	96.31 123	98.59 71	91.68 131	96.59 251	97.81 138	89.87 255	92.15 232	97.06 182	83.62 203	99.54 104	89.34 260	98.07 143	97.70 236

viewmambaseed2359dif		94.28 146	94.14 143	94.71 229	96.21 253	86.97 303	95.93 301	97.11 235	89.00 285	95.00 154	97.70 131	86.02 158	98.59 243	93.71 161	96.59 195	98.57 156

CNLPA		94.28 146	93.53 161	96.52 102	98.38 84	92.55 98	96.59 251	96.88 269	90.13 251	91.91 240	97.24 170	85.21 173	99.09 168	87.64 299	97.83 152	97.92 220

MAR-MVS		94.22 148	93.46 166	96.51 106	98.00 119	92.19 113	97.67 118	97.47 187	88.13 319	93.00 213	95.84 254	84.86 182	99.51 111	87.99 286	98.17 140	97.83 230
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
PAPR		94.18 149	93.42 171	96.48 109	97.64 145	91.42 145	95.55 324	97.71 152	88.99 286	92.34 228	95.82 256	89.19 94	99.11 163	86.14 325	97.38 166	98.90 122

SDMVSNet		94.17 150	93.61 157	95.86 157	98.09 110	91.37 146	97.35 169	98.20 64	93.18 126	91.79 244	97.28 166	79.13 297	98.93 190	94.61 139	92.84 276	97.28 258

test_vis1_n_1920		94.17 150	94.58 127	92.91 324	97.42 160	82.02 393	97.83 92	97.85 131	94.68 65	98.10 42	98.49 52	70.15 385	99.32 135	97.91 28	98.82 108	97.40 252

h-mvs33		94.15 152	93.52 163	96.04 142	97.81 133	90.22 197	97.62 130	97.58 169	95.19 34	96.74 83	97.45 154	83.67 201	99.61 84	95.85 96	79.73 416	98.29 189

CHOSEN 1792x2688		94.15 152	93.51 164	96.06 140	98.27 91	89.38 230	95.18 346	98.48 30	85.60 371	93.76 189	97.11 179	83.15 212	99.61 84	91.33 212	98.72 113	99.19 79

Vis-MVSNet (Re-imp)		94.15 152	93.88 149	94.95 215	97.61 149	87.92 279	98.10 52	95.80 328	92.22 163	93.02 212	97.45 154	84.53 186	97.91 326	88.24 282	97.97 148	99.02 98

CDS-MVSNet		94.14 155	93.54 160	95.93 151	96.18 261	91.46 143	96.33 274	97.04 250	88.97 288	93.56 195	96.51 220	87.55 129	97.89 327	89.80 247	95.95 208	98.44 173
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 156	93.43 169	96.13 137	98.58 73	91.15 161	96.69 238	97.39 205	87.29 343	91.37 254	96.71 202	88.39 110	99.52 110	87.33 306	97.13 180	97.73 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 157	93.70 154	95.27 195	95.70 287	92.03 118	98.10 52	98.68 15	93.36 118	90.39 275	96.70 204	87.63 127	97.94 320	92.25 187	90.50 317	95.84 303

PVSNet_BlendedMVS		94.06 158	93.92 148	94.47 242	98.27 91	89.46 227	96.73 232	98.36 35	90.17 248	94.36 171	95.24 289	88.02 117	99.58 92	93.44 165	90.72 313	94.36 388

nrg030		94.05 159	93.31 173	96.27 128	95.22 321	94.59 32	98.34 26	97.46 189	92.93 141	91.21 264	96.64 209	87.23 140	98.22 273	94.99 123	85.80 364	95.98 299

UGNet		94.04 160	93.28 174	96.31 123	96.85 196	91.19 155	97.88 84	97.68 153	94.40 80	93.00 213	96.18 236	73.39 363	99.61 84	91.72 203	98.46 126	98.13 201
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
TAMVS		94.01 161	93.46 166	95.64 173	96.16 263	90.45 185	96.71 235	96.89 268	89.27 276	93.46 202	96.92 193	87.29 138	97.94 320	88.70 278	95.74 214	98.53 159

Elysia		94.00 162	93.12 179	96.64 89	96.08 273	92.72 91	97.50 146	97.63 160	91.15 210	94.82 158	97.12 177	74.98 348	99.06 177	90.78 224	98.02 145	98.12 203

StellarMVS		94.00 162	93.12 179	96.64 89	96.08 273	92.72 91	97.50 146	97.63 160	91.15 210	94.82 158	97.12 177	74.98 348	99.06 177	90.78 224	98.02 145	98.12 203

IMVS_0403		93.98 164	93.79 151	94.55 238	96.19 257	86.16 327	96.35 270	97.24 224	91.54 185	93.59 194	97.04 183	85.86 160	98.73 219	90.68 229	95.59 220	98.76 136

114514_t		93.95 165	93.06 182	96.63 93	99.07 39	91.61 133	97.46 157	97.96 116	77.99 434	93.00 213	97.57 148	86.14 157	99.33 133	89.22 265	99.15 89	98.94 113

IMVS_0407		93.94 166	93.75 152	94.49 241	96.19 257	86.16 327	96.35 270	97.24 224	91.54 185	93.50 199	97.04 183	85.64 165	98.54 246	90.68 229	95.59 220	98.76 136

FC-MVSNet-test		93.94 166	93.57 158	95.04 206	95.48 298	91.45 144	98.12 51	98.71 12	93.37 116	90.23 278	96.70 204	87.66 124	97.85 329	91.49 209	90.39 318	95.83 304

mvsany_test1		93.93 168	93.98 147	93.78 287	94.94 338	86.80 306	94.62 358	92.55 427	88.77 299	96.85 78	98.49 52	88.98 97	98.08 291	95.03 121	95.62 219	96.46 283

GeoE		93.89 169	93.28 174	95.72 170	96.96 190	89.75 213	98.24 39	96.92 264	89.47 269	92.12 234	97.21 172	84.42 188	98.39 261	87.71 293	96.50 199	99.01 101

HY-MVS	89.66 9	93.87 170	92.95 187	96.63 93	97.10 174	92.49 100	95.64 321	96.64 286	89.05 283	93.00 213	95.79 260	85.77 163	99.45 122	89.16 269	94.35 246	97.96 217

XVG-OURS-SEG-HR		93.86 171	93.55 159	94.81 221	97.06 178	88.53 259	95.28 338	97.45 194	91.68 182	94.08 182	97.68 134	82.41 235	98.90 195	93.84 158	92.47 282	96.98 266

VDD-MVS		93.82 172	93.08 181	96.02 144	97.88 129	89.96 207	97.72 111	95.85 325	92.43 157	95.86 128	98.44 58	68.42 402	99.39 128	96.31 73	94.85 236	98.71 146

mvs_anonymous		93.82 172	93.74 153	94.06 265	96.44 241	85.41 344	95.81 308	97.05 248	89.85 258	90.09 288	96.36 228	87.44 135	97.75 343	93.97 152	96.69 192	99.02 98

HQP_MVS		93.78 174	93.43 169	94.82 219	96.21 253	89.99 202	97.74 106	97.51 178	94.85 51	91.34 255	96.64 209	81.32 256	98.60 239	93.02 177	92.23 285	95.86 300

PS-MVSNAJss		93.74 175	93.51 164	94.44 244	93.91 376	89.28 237	97.75 104	97.56 174	92.50 155	89.94 291	96.54 219	88.65 105	98.18 278	93.83 159	90.90 311	95.86 300

XVG-OURS		93.72 176	93.35 172	94.80 224	97.07 175	88.61 254	94.79 355	97.46 189	91.97 175	93.99 183	97.86 116	81.74 250	98.88 196	92.64 183	92.67 281	96.92 270

mamba_0408		93.70 177	92.99 183	95.83 159	96.79 204	90.38 190	88.69 445	97.07 242	90.96 218	93.68 190	97.31 164	84.97 179	98.76 212	90.95 220	96.51 196	98.35 182

HyFIR lowres test		93.66 178	92.92 188	95.87 154	98.24 95	89.88 209	94.58 360	98.49 28	85.06 381	93.78 188	95.78 261	82.86 222	98.67 231	91.77 202	95.71 216	99.07 95

LFMVS		93.60 179	92.63 202	96.52 102	98.13 109	91.27 149	97.94 76	93.39 416	90.57 239	96.29 110	98.31 75	69.00 395	99.16 155	94.18 149	95.87 211	99.12 88

icg_test_0407_2		93.58 180	93.46 166	93.94 277	96.19 257	86.16 327	93.73 395	97.24 224	91.54 185	93.50 199	97.04 183	85.64 165	96.91 393	90.68 229	95.59 220	98.76 136

F-COLMAP		93.58 180	92.98 186	95.37 191	98.40 81	88.98 247	97.18 188	97.29 217	87.75 332	90.49 273	97.10 180	85.21 173	99.50 114	86.70 316	96.72 191	97.63 238

ab-mvs		93.57 182	92.55 206	96.64 89	97.28 164	91.96 122	95.40 331	97.45 194	89.81 260	93.22 210	96.28 232	79.62 291	99.46 120	90.74 227	93.11 273	98.50 163

LS3D		93.57 182	92.61 204	96.47 110	97.59 151	91.61 133	97.67 118	97.72 148	85.17 379	90.29 277	98.34 69	84.60 184	99.73 55	83.85 361	98.27 135	98.06 212

FA-MVS(test-final)		93.52 184	92.92 188	95.31 194	96.77 210	88.54 258	94.82 354	96.21 312	89.61 264	94.20 176	95.25 288	83.24 208	99.14 160	90.01 241	96.16 205	98.25 191

SSM_04072		93.51 185	92.99 183	95.05 204	96.79 204	90.38 190	88.69 445	97.07 242	90.96 218	93.68 190	97.31 164	84.97 179	96.42 404	90.95 220	96.51 196	98.35 182

viewdifsd2359ckpt11		93.46 186	93.22 177	94.17 258	96.11 270	85.42 342	96.43 258	97.07 242	92.91 142	94.20 176	98.00 99	80.82 266	98.73 219	94.42 143	89.04 331	98.34 186

viewmsd2359difaftdt		93.46 186	93.23 176	94.17 258	96.12 268	85.42 342	96.43 258	97.08 239	92.91 142	94.21 175	98.00 99	80.82 266	98.74 217	94.41 144	89.05 329	98.34 186

Fast-Effi-MVS+		93.46 186	92.75 196	95.59 177	96.77 210	90.03 199	96.81 224	97.13 232	88.19 314	91.30 258	94.27 341	86.21 154	98.63 236	87.66 298	96.46 202	98.12 203

hse-mvs2		93.45 189	92.99 183	94.81 221	97.02 184	88.59 255	96.69 238	96.47 296	95.19 34	96.74 83	96.16 239	83.67 201	98.48 252	95.85 96	79.13 420	97.35 255

QAPM		93.45 189	92.27 216	96.98 81	96.77 210	92.62 94	98.39 25	98.12 81	84.50 389	88.27 342	97.77 126	82.39 236	99.81 30	85.40 338	98.81 109	98.51 162

UniMVSNet_NR-MVSNet		93.37 191	92.67 200	95.47 188	95.34 310	92.83 85	97.17 189	98.58 24	92.98 139	90.13 283	95.80 257	88.37 112	97.85 329	91.71 204	83.93 393	95.73 314

1112_ss		93.37 191	92.42 213	96.21 133	97.05 180	90.99 164	96.31 276	96.72 278	86.87 351	89.83 295	96.69 206	86.51 147	99.14 160	88.12 283	93.67 267	98.50 163

UniMVSNet (Re)		93.31 193	92.55 206	95.61 176	95.39 304	93.34 67	97.39 165	98.71 12	93.14 129	90.10 287	94.83 306	87.71 123	98.03 302	91.67 207	83.99 392	95.46 323

OPM-MVS		93.28 194	92.76 194	94.82 219	94.63 354	90.77 175	96.65 242	97.18 228	93.72 99	91.68 248	97.26 169	79.33 295	98.63 236	92.13 193	92.28 284	95.07 351
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 195	92.48 211	95.51 182	95.70 287	92.39 102	97.86 85	98.66 18	92.30 160	92.09 236	95.37 281	80.49 273	98.40 256	93.95 153	85.86 363	95.75 312

test_fmvs1		93.21 196	93.53 161	92.25 347	96.55 227	81.20 400	97.40 164	96.96 257	90.68 228	96.80 79	98.04 94	69.25 393	98.40 256	97.58 39	98.50 122	97.16 263

MVSTER		93.20 197	92.81 193	94.37 247	96.56 225	89.59 218	97.06 196	97.12 233	91.24 202	91.30 258	95.96 248	82.02 243	98.05 298	93.48 164	90.55 315	95.47 322

test1111		93.19 198	92.82 192	94.30 254	97.58 155	84.56 361	98.21 43	89.02 446	93.53 109	94.58 165	98.21 82	72.69 364	99.05 180	93.06 175	98.48 125	99.28 73

ECVR-MVS		93.19 198	92.73 198	94.57 237	97.66 143	85.41 344	98.21 43	88.23 448	93.43 114	94.70 163	98.21 82	72.57 365	99.07 175	93.05 176	98.49 123	99.25 76

HQP-MVS		93.19 198	92.74 197	94.54 239	95.86 279	89.33 233	96.65 242	97.39 205	93.55 105	90.14 279	95.87 252	80.95 260	98.50 249	92.13 193	92.10 290	95.78 308

CHOSEN 280x420		93.12 201	92.72 199	94.34 250	96.71 214	87.27 293	90.29 435	97.72 148	86.61 355	91.34 255	95.29 283	84.29 192	98.41 255	93.25 169	98.94 105	97.35 255

sd_testset		93.10 202	92.45 212	95.05 204	98.09 110	89.21 239	96.89 214	97.64 158	93.18 126	91.79 244	97.28 166	75.35 345	98.65 234	88.99 271	92.84 276	97.28 258

Effi-MVS+-dtu		93.08 203	93.21 178	92.68 335	96.02 276	83.25 377	97.14 192	96.72 278	93.85 96	91.20 265	93.44 379	83.08 214	98.30 268	91.69 206	95.73 215	96.50 280

test_djsdf		93.07 204	92.76 194	94.00 269	93.49 391	88.70 253	98.22 41	97.57 170	91.42 194	90.08 289	95.55 274	82.85 223	97.92 323	94.07 150	91.58 297	95.40 330

VDDNet		93.05 205	92.07 220	96.02 144	96.84 197	90.39 189	98.08 54	95.85 325	86.22 363	95.79 131	98.46 56	67.59 405	99.19 148	94.92 126	94.85 236	98.47 168

thisisatest0530		93.03 206	92.21 218	95.49 185	97.07 175	89.11 244	97.49 154	92.19 429	90.16 249	94.09 181	96.41 225	76.43 336	99.05 180	90.38 236	95.68 217	98.31 188

EI-MVSNet		93.03 206	92.88 190	93.48 303	95.77 285	86.98 302	96.44 256	97.12 233	90.66 231	91.30 258	97.64 141	86.56 145	98.05 298	89.91 244	90.55 315	95.41 327

CLD-MVS		92.98 208	92.53 208	94.32 251	96.12 268	89.20 240	95.28 338	97.47 187	92.66 152	89.90 292	95.62 270	80.58 271	98.40 256	92.73 182	92.40 283	95.38 332
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 209	92.33 215	94.87 218	97.11 173	87.16 299	97.97 72	92.09 430	90.63 233	93.88 187	97.01 189	76.50 333	99.06 177	90.29 239	95.45 226	98.38 178

ACMM	89.79 8	92.96 209	92.50 210	94.35 248	96.30 251	88.71 252	97.58 133	97.36 211	91.40 196	90.53 272	96.65 208	79.77 287	98.75 215	91.24 215	91.64 295	95.59 318
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 211	92.56 205	94.10 263	96.16 263	88.26 267	97.65 122	97.46 189	91.29 198	90.12 285	97.16 174	79.05 300	98.73 219	92.25 187	91.89 293	95.31 337

BH-untuned		92.94 211	92.62 203	93.92 281	97.22 166	86.16 327	96.40 266	96.25 309	90.06 252	89.79 296	96.17 238	83.19 210	98.35 264	87.19 309	97.27 174	97.24 260

DU-MVS		92.90 213	92.04 222	95.49 185	94.95 336	92.83 85	97.16 190	98.24 58	93.02 133	90.13 283	95.71 264	83.47 204	97.85 329	91.71 204	83.93 393	95.78 308

PatchMatch-RL		92.90 213	92.02 224	95.56 178	98.19 103	90.80 173	95.27 340	97.18 228	87.96 321	91.86 243	95.68 267	80.44 274	98.99 185	84.01 356	97.54 159	96.89 271

VortexMVS		92.88 215	92.64 201	93.58 298	96.58 221	87.53 289	96.93 210	97.28 220	92.78 150	89.75 297	94.99 296	82.73 226	97.76 341	94.60 140	88.16 340	95.46 323

PMMVS		92.86 216	92.34 214	94.42 246	94.92 339	86.73 309	94.53 362	96.38 301	84.78 386	94.27 173	95.12 294	83.13 213	98.40 256	91.47 210	96.49 200	98.12 203

OpenMVS	89.19 12	92.86 216	91.68 237	96.40 116	95.34 310	92.73 90	98.27 33	98.12 81	84.86 384	85.78 386	97.75 127	78.89 307	99.74 53	87.50 303	98.65 116	96.73 275

Test_1112_low_res		92.84 218	91.84 231	95.85 158	97.04 182	89.97 206	95.53 326	96.64 286	85.38 374	89.65 302	95.18 290	85.86 160	99.10 165	87.70 294	93.58 272	98.49 165

baseline1		92.82 219	91.90 229	95.55 180	97.20 168	90.77 175	97.19 187	94.58 387	92.20 165	92.36 225	96.34 229	84.16 194	98.21 274	89.20 267	83.90 396	97.68 237

1314		92.81 220	92.03 223	95.14 200	95.33 313	89.52 224	96.04 294	97.44 198	87.72 333	86.25 383	95.33 282	83.84 198	98.79 207	89.26 263	97.05 182	97.11 264

DP-MVS		92.76 221	91.51 245	96.52 102	98.77 58	90.99 164	97.38 167	96.08 317	82.38 410	89.29 314	97.87 114	83.77 199	99.69 67	81.37 384	96.69 192	98.89 126

test_fmvs1_n		92.73 222	92.88 190	92.29 344	96.08 273	81.05 401	97.98 66	97.08 239	90.72 226	96.79 81	98.18 85	63.07 427	98.45 253	97.62 38	98.42 129	97.36 253

BH-RMVSNet		92.72 223	91.97 226	94.97 213	97.16 170	87.99 277	96.15 289	95.60 339	90.62 234	91.87 242	97.15 176	78.41 313	98.57 244	83.16 363	97.60 158	98.36 180

ACMP	89.59 10	92.62 224	92.14 219	94.05 266	96.40 243	88.20 270	97.36 168	97.25 223	91.52 189	88.30 340	96.64 209	78.46 312	98.72 224	91.86 200	91.48 299	95.23 344
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 225	92.52 209	92.44 337	96.82 201	81.89 394	96.92 211	93.71 413	92.41 158	84.30 399	94.60 318	85.08 175	97.03 387	91.51 208	97.36 167	98.40 176

TranMVSNet+NR-MVSNet		92.50 225	91.63 238	95.14 200	94.76 347	92.07 115	97.53 143	98.11 84	92.90 145	89.56 305	96.12 241	83.16 211	97.60 356	89.30 261	83.20 402	95.75 312

thres600view7		92.49 227	91.60 239	95.18 198	97.91 127	89.47 225	97.65 122	94.66 384	92.18 169	93.33 205	94.91 301	78.06 320	99.10 165	81.61 377	94.06 261	96.98 266

IMVS_0404		92.44 228	91.92 228	94.00 269	96.19 257	86.16 327	93.84 392	97.24 224	91.54 185	88.17 346	97.04 183	76.96 330	97.09 384	90.68 229	95.59 220	98.76 136

thres100view900		92.43 229	91.58 240	94.98 211	97.92 126	89.37 231	97.71 113	94.66 384	92.20 165	93.31 206	94.90 302	78.06 320	99.08 171	81.40 381	94.08 257	96.48 281

jajsoiax		92.42 230	91.89 230	94.03 268	93.33 399	88.50 260	97.73 108	97.53 176	92.00 174	88.85 326	96.50 221	75.62 343	98.11 285	93.88 157	91.56 298	95.48 320

thres400		92.42 230	91.52 243	95.12 202	97.85 130	89.29 235	97.41 160	94.88 376	92.19 167	93.27 208	94.46 328	78.17 316	99.08 171	81.40 381	94.08 257	96.98 266

tfpn200view9		92.38 232	91.52 243	94.95 215	97.85 130	89.29 235	97.41 160	94.88 376	92.19 167	93.27 208	94.46 328	78.17 316	99.08 171	81.40 381	94.08 257	96.48 281

test_vis1_n		92.37 233	92.26 217	92.72 332	94.75 348	82.64 383	98.02 60	96.80 275	91.18 207	97.77 53	97.93 105	58.02 437	98.29 269	97.63 36	98.21 137	97.23 261

WR-MVS		92.34 234	91.53 242	94.77 226	95.13 329	90.83 172	96.40 266	97.98 114	91.88 176	89.29 314	95.54 275	82.50 232	97.80 336	89.79 248	85.27 372	95.69 315

NR-MVSNet		92.34 234	91.27 253	95.53 181	94.95 336	93.05 77	97.39 165	98.07 93	92.65 153	84.46 397	95.71 264	85.00 178	97.77 340	89.71 249	83.52 399	95.78 308

mvs_tets		92.31 236	91.76 233	93.94 277	93.41 396	88.29 265	97.63 128	97.53 176	92.04 172	88.76 329	96.45 223	74.62 353	98.09 290	93.91 155	91.48 299	95.45 325

TAPA-MVS	90.10 7	92.30 237	91.22 256	95.56 178	98.33 86	89.60 217	96.79 225	97.65 156	81.83 414	91.52 250	97.23 171	87.94 119	98.91 194	71.31 438	98.37 130	98.17 199
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 238	91.30 251	95.25 196	96.60 219	88.90 249	94.36 371	92.32 428	87.92 322	93.43 203	94.57 319	77.28 327	99.00 184	89.42 258	95.86 212	97.86 227

Fast-Effi-MVS+-dtu		92.29 238	91.99 225	93.21 314	95.27 317	85.52 340	97.03 197	96.63 289	92.09 170	89.11 320	95.14 292	80.33 277	98.08 291	87.54 302	94.74 242	96.03 298

IterMVS-LS		92.29 238	91.94 227	93.34 308	96.25 252	86.97 303	96.57 254	97.05 248	90.67 229	89.50 308	94.80 308	86.59 144	97.64 351	89.91 244	86.11 362	95.40 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 241	91.74 236	93.73 288	97.77 135	83.69 374	92.88 415	96.72 278	87.91 323	93.00 213	94.86 304	78.51 311	99.05 180	86.53 317	97.45 165	98.47 168

VPNet		92.23 242	91.31 250	94.99 209	95.56 294	90.96 166	97.22 185	97.86 130	92.96 140	90.96 266	96.62 216	75.06 346	98.20 275	91.90 197	83.65 398	95.80 306

thres200		92.23 242	91.39 246	94.75 228	97.61 149	89.03 246	96.60 250	95.09 365	92.08 171	93.28 207	94.00 356	78.39 314	99.04 183	81.26 387	94.18 253	96.19 288

anonymousdsp		92.16 244	91.55 241	93.97 273	92.58 414	89.55 221	97.51 145	97.42 202	89.42 272	88.40 336	94.84 305	80.66 269	97.88 328	91.87 199	91.28 303	94.48 383

XXY-MVS		92.16 244	91.23 255	94.95 215	94.75 348	90.94 167	97.47 155	97.43 201	89.14 279	88.90 322	96.43 224	79.71 288	98.24 271	89.56 254	87.68 345	95.67 316

BH-w/o		92.14 246	91.75 234	93.31 309	96.99 187	85.73 337	95.67 316	95.69 334	88.73 300	89.26 316	94.82 307	82.97 219	98.07 295	85.26 341	96.32 204	96.13 294

testing3-2		92.10 247	92.05 221	92.27 345	97.71 139	79.56 420	97.42 159	94.41 394	93.53 109	93.22 210	95.49 277	69.16 394	99.11 163	93.25 169	94.22 251	98.13 201

Anonymous202405211		92.07 248	90.83 272	95.76 164	98.19 103	88.75 251	97.58 133	95.00 368	86.00 366	93.64 193	97.45 154	66.24 417	99.53 106	90.68 229	92.71 279	99.01 101

FE-MVS		92.05 249	91.05 261	95.08 203	96.83 199	87.93 278	93.91 389	95.70 332	86.30 360	94.15 180	94.97 297	76.59 332	99.21 146	84.10 354	96.86 185	98.09 209

WR-MVS_H		92.00 250	91.35 247	93.95 275	95.09 331	89.47 225	98.04 59	98.68 15	91.46 192	88.34 338	94.68 313	85.86 160	97.56 358	85.77 333	84.24 390	94.82 368

Anonymous20240529		91.98 251	90.73 278	95.73 169	98.14 107	89.40 229	97.99 63	97.72 148	79.63 428	93.54 197	97.41 159	69.94 387	99.56 100	91.04 219	91.11 306	98.22 193

MonoMVSNet		91.92 252	91.77 232	92.37 339	92.94 405	83.11 379	97.09 195	95.55 343	92.91 142	90.85 268	94.55 320	81.27 258	96.52 402	93.01 179	87.76 344	97.47 249

PatchmatchNet		91.91 253	91.35 247	93.59 297	95.38 305	84.11 367	93.15 410	95.39 348	89.54 266	92.10 235	93.68 369	82.82 224	98.13 281	84.81 345	95.32 228	98.52 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 254	91.02 262	94.53 240	96.54 228	86.55 316	95.86 305	95.64 338	91.77 179	91.89 241	93.47 378	69.94 387	98.86 197	90.23 240	93.86 264	98.18 196

CP-MVSNet		91.89 255	91.24 254	93.82 284	95.05 332	88.57 256	97.82 94	98.19 69	91.70 181	88.21 344	95.76 262	81.96 244	97.52 364	87.86 288	84.65 381	95.37 333

SCA		91.84 256	91.18 258	93.83 283	95.59 292	84.95 357	94.72 356	95.58 341	90.82 221	92.25 230	93.69 367	75.80 340	98.10 286	86.20 323	95.98 207	98.45 170

FMVSNet3		91.78 257	90.69 281	95.03 207	96.53 230	92.27 108	97.02 199	96.93 260	89.79 261	89.35 311	94.65 316	77.01 328	97.47 367	86.12 326	88.82 332	95.35 334

AUN-MVS		91.76 258	90.75 276	94.81 221	97.00 186	88.57 256	96.65 242	96.49 295	89.63 263	92.15 232	96.12 241	78.66 309	98.50 249	90.83 222	79.18 419	97.36 253

X-MVStestdata		91.71 259	89.67 325	97.81 28	99.38 14	94.03 50	98.59 13	98.20 64	94.85 51	96.59 93	32.69 463	91.70 53	99.80 35	95.66 102	99.40 57	99.62 23

MVS		91.71 259	90.44 288	95.51 182	95.20 323	91.59 135	96.04 294	97.45 194	73.44 444	87.36 362	95.60 271	85.42 168	99.10 165	85.97 330	97.46 161	95.83 304

EPNet_dtu		91.71 259	91.28 252	92.99 321	93.76 381	83.71 373	96.69 238	95.28 355	93.15 128	87.02 371	95.95 249	83.37 207	97.38 375	79.46 400	96.84 186	97.88 223
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 262	90.75 276	94.47 242	96.53 230	86.56 315	95.76 312	94.51 391	91.10 214	91.24 263	93.59 373	68.59 399	98.86 197	91.10 217	94.29 249	98.00 216

baseline2		91.63 263	90.86 268	93.94 277	94.33 365	86.32 320	95.92 302	91.64 434	89.37 273	86.94 374	94.69 312	81.62 252	98.69 226	88.64 279	94.57 245	96.81 273

testing99		91.62 264	90.72 279	94.32 251	96.48 237	86.11 332	95.81 308	94.76 381	91.55 184	91.75 246	93.44 379	68.55 400	98.82 203	90.43 234	93.69 266	98.04 213

test2506		91.60 265	90.78 273	94.04 267	97.66 143	83.81 370	98.27 33	75.53 464	93.43 114	95.23 149	98.21 82	67.21 408	99.07 175	93.01 179	98.49 123	99.25 76

miper_ehance_all_eth		91.59 266	91.13 259	92.97 322	95.55 295	86.57 314	94.47 365	96.88 269	87.77 330	88.88 324	94.01 355	86.22 153	97.54 360	89.49 255	86.93 353	94.79 373

v2v482		91.59 266	90.85 270	93.80 285	93.87 378	88.17 272	96.94 209	96.88 269	89.54 266	89.53 306	94.90 302	81.70 251	98.02 303	89.25 264	85.04 378	95.20 345

V42		91.58 268	90.87 267	93.73 288	94.05 373	88.50 260	97.32 173	96.97 256	88.80 298	89.71 298	94.33 336	82.54 231	98.05 298	89.01 270	85.07 376	94.64 381

PCF-MVS	89.48 11	91.56 269	89.95 313	96.36 121	96.60 219	92.52 99	92.51 420	97.26 221	79.41 429	88.90 322	96.56 218	84.04 197	99.55 102	77.01 414	97.30 172	97.01 265
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 270	90.76 274	93.94 277	96.52 233	85.06 353	95.22 343	94.54 389	90.47 242	91.98 238	92.71 390	72.02 368	98.74 217	88.10 284	95.26 230	98.01 215

PS-CasMVS		91.55 270	90.84 271	93.69 292	94.96 335	88.28 266	97.84 89	98.24 58	91.46 192	88.04 349	95.80 257	79.67 289	97.48 366	87.02 313	84.54 387	95.31 337

miper_enhance_ethall		91.54 272	91.01 263	93.15 316	95.35 309	87.07 301	93.97 384	96.90 266	86.79 352	89.17 318	93.43 382	86.55 146	97.64 351	89.97 243	86.93 353	94.74 377

myMVS_eth3d28		91.52 273	90.97 264	93.17 315	96.91 191	83.24 378	95.61 322	94.96 372	92.24 162	91.98 238	93.28 383	69.31 392	98.40 256	88.71 277	95.68 217	97.88 223

PAPM		91.52 273	90.30 294	95.20 197	95.30 316	89.83 211	93.38 406	96.85 272	86.26 362	88.59 332	95.80 257	84.88 181	98.15 280	75.67 419	95.93 209	97.63 238

ET-MVSNet_ETH3D		91.49 275	90.11 304	95.63 174	96.40 243	91.57 137	95.34 334	93.48 415	90.60 237	75.58 439	95.49 277	80.08 281	96.79 398	94.25 148	89.76 323	98.52 160

TR-MVS		91.48 276	90.59 284	94.16 261	96.40 243	87.33 290	95.67 316	95.34 354	87.68 334	91.46 252	95.52 276	76.77 331	98.35 264	82.85 368	93.61 270	96.79 274

tpmrst		91.44 277	91.32 249	91.79 362	95.15 327	79.20 426	93.42 405	95.37 350	88.55 305	93.49 201	93.67 370	82.49 233	98.27 270	90.41 235	89.34 327	97.90 221

test-LLR		91.42 278	91.19 257	92.12 350	94.59 355	80.66 404	94.29 376	92.98 420	91.11 212	90.76 270	92.37 398	79.02 302	98.07 295	88.81 274	96.74 189	97.63 238

MSDG		91.42 278	90.24 298	94.96 214	97.15 172	88.91 248	93.69 398	96.32 303	85.72 370	86.93 375	96.47 222	80.24 278	98.98 186	80.57 391	95.05 235	96.98 266

c3_l		91.38 280	90.89 266	92.88 326	95.58 293	86.30 321	94.68 357	96.84 273	88.17 315	88.83 328	94.23 344	85.65 164	97.47 367	89.36 259	84.63 382	94.89 363

GA-MVS		91.38 280	90.31 293	94.59 232	94.65 353	87.62 287	94.34 372	96.19 313	90.73 225	90.35 276	93.83 360	71.84 370	97.96 314	87.22 308	93.61 270	98.21 194

v1144		91.37 282	90.60 283	93.68 293	93.89 377	88.23 269	96.84 220	97.03 252	88.37 310	89.69 300	94.39 330	82.04 242	97.98 307	87.80 290	85.37 369	94.84 365

GBi-Net		91.35 283	90.27 296	94.59 232	96.51 234	91.18 157	97.50 146	96.93 260	88.82 295	89.35 311	94.51 323	73.87 357	97.29 379	86.12 326	88.82 332	95.31 337

test1		91.35 283	90.27 296	94.59 232	96.51 234	91.18 157	97.50 146	96.93 260	88.82 295	89.35 311	94.51 323	73.87 357	97.29 379	86.12 326	88.82 332	95.31 337

UniMVSNet_ETH3D		91.34 285	90.22 301	94.68 230	94.86 343	87.86 282	97.23 183	97.46 189	87.99 320	89.90 292	96.92 193	66.35 415	98.23 272	90.30 238	90.99 309	97.96 217

FMVSNet2		91.31 286	90.08 305	94.99 209	96.51 234	92.21 110	97.41 160	96.95 258	88.82 295	88.62 331	94.75 310	73.87 357	97.42 372	85.20 342	88.55 337	95.35 334

reproduce_monomvs		91.30 287	91.10 260	91.92 354	96.82 201	82.48 387	97.01 202	97.49 181	94.64 69	88.35 337	95.27 286	70.53 380	98.10 286	95.20 116	84.60 384	95.19 348

D2MVS		91.30 287	90.95 265	92.35 340	94.71 351	85.52 340	96.18 287	98.21 62	88.89 291	86.60 378	93.82 362	79.92 285	97.95 318	89.29 262	90.95 310	93.56 403

v8		91.29 289	90.53 287	93.57 300	94.15 369	88.12 274	97.34 170	97.06 247	88.99 286	88.32 339	94.26 343	83.08 214	98.01 304	87.62 300	83.92 395	94.57 382

CVMVSNet		91.23 290	91.75 234	89.67 402	95.77 285	74.69 438	96.44 256	94.88 376	85.81 368	92.18 231	97.64 141	79.07 299	95.58 420	88.06 285	95.86 212	98.74 143

cl22		91.21 291	90.56 286	93.14 317	96.09 272	86.80 306	94.41 369	96.58 292	87.80 328	88.58 333	93.99 357	80.85 265	97.62 354	89.87 246	86.93 353	94.99 354

PEN-MVS		91.20 292	90.44 288	93.48 303	94.49 359	87.91 281	97.76 102	98.18 71	91.29 198	87.78 353	95.74 263	80.35 276	97.33 377	85.46 337	82.96 403	95.19 348

Baseline_NR-MVSNet		91.20 292	90.62 282	92.95 323	93.83 379	88.03 276	97.01 202	95.12 364	88.42 309	89.70 299	95.13 293	83.47 204	97.44 370	89.66 252	83.24 401	93.37 407

cascas		91.20 292	90.08 305	94.58 236	94.97 334	89.16 243	93.65 400	97.59 168	79.90 427	89.40 309	92.92 388	75.36 344	98.36 263	92.14 190	94.75 241	96.23 285

CostFormer		91.18 295	90.70 280	92.62 336	94.84 344	81.76 395	94.09 382	94.43 392	84.15 392	92.72 220	93.77 364	79.43 293	98.20 275	90.70 228	92.18 288	97.90 221

tt0805		91.09 296	90.07 308	94.16 261	95.61 291	88.31 264	97.56 137	96.51 294	89.56 265	89.17 318	95.64 269	67.08 412	98.38 262	91.07 218	88.44 338	95.80 306

v1192		91.07 297	90.23 299	93.58 298	93.70 382	87.82 284	96.73 232	97.07 242	87.77 330	89.58 303	94.32 338	80.90 264	97.97 310	86.52 318	85.48 367	94.95 355

v144192		91.06 298	90.28 295	93.39 306	93.66 385	87.23 296	96.83 221	97.07 242	87.43 339	89.69 300	94.28 340	81.48 253	98.00 305	87.18 310	84.92 380	94.93 359

v10		91.04 299	90.23 299	93.49 302	94.12 370	88.16 273	97.32 173	97.08 239	88.26 313	88.29 341	94.22 346	82.17 240	97.97 310	86.45 320	84.12 391	94.33 389

eth_miper_zixun_eth		91.02 300	90.59 284	92.34 342	95.33 313	84.35 363	94.10 381	96.90 266	88.56 304	88.84 327	94.33 336	84.08 195	97.60 356	88.77 276	84.37 389	95.06 352

v148		90.99 301	90.38 290	92.81 329	93.83 379	85.80 334	96.78 229	96.68 283	89.45 271	88.75 330	93.93 359	82.96 220	97.82 333	87.83 289	83.25 400	94.80 371

LTVRE_ROB	88.41 13	90.99 301	89.92 315	94.19 257	96.18 261	89.55 221	96.31 276	97.09 238	87.88 324	85.67 387	95.91 251	78.79 308	98.57 244	81.50 378	89.98 320	94.44 386
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
DIV-MVS_self_test		90.97 303	90.33 291	92.88 326	95.36 308	86.19 326	94.46 367	96.63 289	87.82 326	88.18 345	94.23 344	82.99 217	97.53 362	87.72 291	85.57 366	94.93 359

cl____		90.96 304	90.32 292	92.89 325	95.37 307	86.21 324	94.46 367	96.64 286	87.82 326	88.15 347	94.18 347	82.98 218	97.54 360	87.70 294	85.59 365	94.92 361

pmmvs4		90.93 305	89.85 317	94.17 258	93.34 398	90.79 174	94.60 359	96.02 318	84.62 387	87.45 358	95.15 291	81.88 248	97.45 369	87.70 294	87.87 343	94.27 393

XVG-ACMP-BASELINE		90.93 305	90.21 302	93.09 318	94.31 367	85.89 333	95.33 335	97.26 221	91.06 215	89.38 310	95.44 280	68.61 398	98.60 239	89.46 256	91.05 307	94.79 373

v1921920		90.85 307	90.03 310	93.29 310	93.55 387	86.96 305	96.74 231	97.04 250	87.36 341	89.52 307	94.34 335	80.23 279	97.97 310	86.27 321	85.21 373	94.94 357

CR-MVSNet		90.82 308	89.77 321	93.95 275	94.45 361	87.19 297	90.23 436	95.68 336	86.89 350	92.40 222	92.36 401	80.91 262	97.05 386	81.09 388	93.95 262	97.60 243

v7n		90.76 309	89.86 316	93.45 305	93.54 388	87.60 288	97.70 116	97.37 209	88.85 292	87.65 355	94.08 353	81.08 259	98.10 286	84.68 347	83.79 397	94.66 380

RPSCF		90.75 310	90.86 268	90.42 392	96.84 197	76.29 436	95.61 322	96.34 302	83.89 395	91.38 253	97.87 114	76.45 334	98.78 208	87.16 311	92.23 285	96.20 287

MVP-Stereo		90.74 311	90.08 305	92.71 333	93.19 401	88.20 270	95.86 305	96.27 307	86.07 365	84.86 395	94.76 309	77.84 323	97.75 343	83.88 360	98.01 147	92.17 428
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 312	89.65 327	93.96 274	94.29 368	89.63 215	97.79 100	96.82 274	89.07 281	86.12 385	95.48 279	78.61 310	97.78 338	86.97 314	81.67 408	94.46 384

v1240		90.70 313	89.85 317	93.23 312	93.51 390	86.80 306	96.61 248	97.02 254	87.16 346	89.58 303	94.31 339	79.55 292	97.98 307	85.52 336	85.44 368	94.90 362

EPMVS		90.70 313	89.81 319	93.37 307	94.73 350	84.21 365	93.67 399	88.02 449	89.50 268	92.38 224	93.49 376	77.82 324	97.78 338	86.03 329	92.68 280	98.11 208

WBMVS		90.69 315	89.99 312	92.81 329	96.48 237	85.00 354	95.21 345	96.30 305	89.46 270	89.04 321	94.05 354	72.45 367	97.82 333	89.46 256	87.41 350	95.61 317

Anonymous20231211		90.63 316	89.42 332	94.27 256	98.24 95	89.19 242	98.05 58	97.89 122	79.95 426	88.25 343	94.96 298	72.56 366	98.13 281	89.70 250	85.14 374	95.49 319

DTE-MVSNet		90.56 317	89.75 323	93.01 320	93.95 374	87.25 294	97.64 126	97.65 156	90.74 224	87.12 366	95.68 267	79.97 284	97.00 390	83.33 362	81.66 409	94.78 375

ACMH	87.59 16	90.53 318	89.42 332	93.87 282	96.21 253	87.92 279	97.24 179	96.94 259	88.45 308	83.91 407	96.27 233	71.92 369	98.62 238	84.43 350	89.43 326	95.05 353
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 319	89.14 340	94.67 231	96.81 203	87.85 283	95.91 303	93.97 407	89.71 262	92.34 228	92.48 396	65.41 422	97.96 314	81.37 384	94.27 250	98.21 194

OurMVSNet-221017-0		90.51 320	90.19 303	91.44 371	93.41 396	81.25 398	96.98 206	96.28 306	91.68 182	86.55 380	96.30 230	74.20 356	97.98 307	88.96 272	87.40 351	95.09 350

miper_lstm_enhance		90.50 321	90.06 309	91.83 359	95.33 313	83.74 371	93.86 390	96.70 282	87.56 337	87.79 352	93.81 363	83.45 206	96.92 392	87.39 304	84.62 383	94.82 368

COLMAP_ROB	87.81 15	90.40 322	89.28 335	93.79 286	97.95 123	87.13 300	96.92 211	95.89 324	82.83 407	86.88 377	97.18 173	73.77 360	99.29 140	78.44 405	93.62 269	94.95 355
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 323	88.96 342	94.35 248	96.54 228	87.29 291	95.50 327	93.84 411	90.97 217	91.75 246	92.96 387	62.18 432	98.00 305	82.86 366	94.08 257	97.76 233

IterMVS-SCA-FT		90.31 323	89.81 319	91.82 360	95.52 296	84.20 366	94.30 375	96.15 315	90.61 235	87.39 361	94.27 341	75.80 340	96.44 403	87.34 305	86.88 357	94.82 368

MS-PatchMatch		90.27 325	89.77 321	91.78 363	94.33 365	84.72 360	95.55 324	96.73 277	86.17 364	86.36 382	95.28 285	71.28 374	97.80 336	84.09 355	98.14 141	92.81 413

tpm		90.25 326	89.74 324	91.76 365	93.92 375	79.73 419	93.98 383	93.54 414	88.28 312	91.99 237	93.25 384	77.51 326	97.44 370	87.30 307	87.94 342	98.12 203

AllTest		90.23 327	88.98 341	93.98 271	97.94 124	86.64 310	96.51 255	95.54 344	85.38 374	85.49 389	96.77 200	70.28 382	99.15 157	80.02 395	92.87 274	96.15 292

dmvs_re		90.21 328	89.50 330	92.35 340	95.47 302	85.15 350	95.70 315	94.37 397	90.94 220	88.42 335	93.57 374	74.63 352	95.67 417	82.80 369	89.57 325	96.22 286

ACMH+	87.92 14	90.20 329	89.18 338	93.25 311	96.48 237	86.45 318	96.99 205	96.68 283	88.83 294	84.79 396	96.22 235	70.16 384	98.53 247	84.42 351	88.04 341	94.77 376

test-mter		90.19 330	89.54 329	92.12 350	94.59 355	80.66 404	94.29 376	92.98 420	87.68 334	90.76 270	92.37 398	67.67 404	98.07 295	88.81 274	96.74 189	97.63 238

IterMVS		90.15 331	89.67 325	91.61 367	95.48 298	83.72 372	94.33 373	96.12 316	89.99 253	87.31 364	94.15 349	75.78 342	96.27 407	86.97 314	86.89 356	94.83 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 332	89.42 332	91.97 353	94.41 363	80.62 406	94.29 376	91.97 432	87.28 344	90.44 274	92.47 397	68.79 396	97.67 348	88.50 281	96.60 194	97.61 242

SD_0403		90.01 333	90.02 311	89.96 399	95.65 290	76.76 433	95.76 312	96.46 297	90.58 238	86.59 379	96.29 231	82.12 241	94.78 428	73.00 433	93.76 265	98.35 182

tpm2		89.96 334	89.21 337	92.23 348	94.91 341	81.25 398	93.78 393	94.42 393	80.62 424	91.56 249	93.44 379	76.44 335	97.94 320	85.60 335	92.08 292	97.49 247

UWE-MVS		89.91 335	89.48 331	91.21 375	95.88 278	78.23 431	94.91 353	90.26 442	89.11 280	92.35 227	94.52 322	68.76 397	97.96 314	83.95 358	95.59 220	97.42 251

IB-MVS	87.33 17	89.91 335	88.28 352	94.79 225	95.26 320	87.70 286	95.12 348	93.95 408	89.35 274	87.03 370	92.49 395	70.74 379	99.19 148	89.18 268	81.37 410	97.49 247
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
ADS-MVSNet		89.89 337	88.68 347	93.53 301	95.86 279	84.89 358	90.93 431	95.07 366	83.23 405	91.28 261	91.81 411	79.01 304	97.85 329	79.52 397	91.39 301	97.84 228

WB-MVSnew		89.88 338	89.56 328	90.82 384	94.57 358	83.06 380	95.65 320	92.85 422	87.86 325	90.83 269	94.10 350	79.66 290	96.88 394	76.34 415	94.19 252	92.54 419

FMVSNet1		89.88 338	88.31 351	94.59 232	95.41 303	91.18 157	97.50 146	96.93 260	86.62 354	87.41 360	94.51 323	65.94 420	97.29 379	83.04 365	87.43 348	95.31 337

pmmvs5		89.86 340	88.87 345	92.82 328	92.86 407	86.23 323	96.26 279	95.39 348	84.24 391	87.12 366	94.51 323	74.27 355	97.36 376	87.61 301	87.57 346	94.86 364

tpmvs		89.83 341	89.15 339	91.89 357	94.92 339	80.30 411	93.11 411	95.46 347	86.28 361	88.08 348	92.65 391	80.44 274	98.52 248	81.47 380	89.92 321	96.84 272

test_fmvs2		89.77 342	89.93 314	89.31 408	93.68 384	76.37 435	97.64 126	95.90 322	89.84 259	91.49 251	96.26 234	58.77 435	97.10 383	94.65 137	91.13 305	94.46 384

SSC-MVS3.2		89.74 343	89.26 336	91.19 378	95.16 324	80.29 412	94.53 362	97.03 252	91.79 178	88.86 325	94.10 350	69.94 387	97.82 333	85.29 339	86.66 358	95.45 325

mmtdpeth		89.70 344	88.96 342	91.90 356	95.84 284	84.42 362	97.46 157	95.53 346	90.27 246	94.46 170	90.50 420	69.74 391	98.95 187	97.39 48	69.48 445	92.34 422

tfpnnormal		89.70 344	88.40 350	93.60 296	95.15 327	90.10 198	97.56 137	98.16 75	87.28 344	86.16 384	94.63 317	77.57 325	98.05 298	74.48 423	84.59 385	92.65 416

ADS-MVSNet2		89.45 346	88.59 348	92.03 352	95.86 279	82.26 391	90.93 431	94.32 400	83.23 405	91.28 261	91.81 411	79.01 304	95.99 409	79.52 397	91.39 301	97.84 228

Patchmatch-test		89.42 347	87.99 354	93.70 291	95.27 317	85.11 351	88.98 443	94.37 397	81.11 418	87.10 369	93.69 367	82.28 237	97.50 365	74.37 425	94.76 240	98.48 167

test0.0.03 1		89.37 348	88.70 346	91.41 372	92.47 416	85.63 338	95.22 343	92.70 425	91.11 212	86.91 376	93.65 371	79.02 302	93.19 444	78.00 407	89.18 328	95.41 327

SixPastTwentyTwo		89.15 349	88.54 349	90.98 380	93.49 391	80.28 413	96.70 236	94.70 383	90.78 222	84.15 402	95.57 272	71.78 371	97.71 346	84.63 348	85.07 376	94.94 357

RPMNet		88.98 350	87.05 364	94.77 226	94.45 361	87.19 297	90.23 436	98.03 105	77.87 436	92.40 222	87.55 443	80.17 280	99.51 111	68.84 443	93.95 262	97.60 243

TransMVSNet (Re)		88.94 351	87.56 357	93.08 319	94.35 364	88.45 262	97.73 108	95.23 359	87.47 338	84.26 400	95.29 283	79.86 286	97.33 377	79.44 401	74.44 436	93.45 406

USDC		88.94 351	87.83 356	92.27 345	94.66 352	84.96 356	93.86 390	95.90 322	87.34 342	83.40 409	95.56 273	67.43 406	98.19 277	82.64 373	89.67 324	93.66 402

dp		88.90 353	88.26 353	90.81 385	94.58 357	76.62 434	92.85 416	94.93 373	85.12 380	90.07 290	93.07 385	75.81 339	98.12 284	80.53 392	87.42 349	97.71 235

PatchT		88.87 354	87.42 358	93.22 313	94.08 372	85.10 352	89.51 441	94.64 386	81.92 413	92.36 225	88.15 439	80.05 282	97.01 389	72.43 434	93.65 268	97.54 246

our_test_3		88.78 355	87.98 355	91.20 377	92.45 417	82.53 385	93.61 402	95.69 334	85.77 369	84.88 394	93.71 365	79.99 283	96.78 399	79.47 399	86.24 359	94.28 392

EU-MVSNet		88.72 356	88.90 344	88.20 412	93.15 402	74.21 440	96.63 247	94.22 402	85.18 378	87.32 363	95.97 247	76.16 337	94.98 426	85.27 340	86.17 360	95.41 327

Patchmtry		88.64 357	87.25 360	92.78 331	94.09 371	86.64 310	89.82 440	95.68 336	80.81 422	87.63 356	92.36 401	80.91 262	97.03 387	78.86 403	85.12 375	94.67 379

MIMVSNet		88.50 358	86.76 368	93.72 290	94.84 344	87.77 285	91.39 426	94.05 404	86.41 358	87.99 350	92.59 394	63.27 426	95.82 414	77.44 408	92.84 276	97.57 245

tpm cat1		88.36 359	87.21 362	91.81 361	95.13 329	80.55 407	92.58 419	95.70 332	74.97 440	87.45 358	91.96 409	78.01 322	98.17 279	80.39 393	88.74 335	96.72 276

ppachtmachnet_test		88.35 360	87.29 359	91.53 368	92.45 417	83.57 375	93.75 394	95.97 319	84.28 390	85.32 392	94.18 347	79.00 306	96.93 391	75.71 418	84.99 379	94.10 394

JIA-IIPM		88.26 361	87.04 365	91.91 355	93.52 389	81.42 397	89.38 442	94.38 396	80.84 421	90.93 267	80.74 451	79.22 296	97.92 323	82.76 370	91.62 296	96.38 284

testgi		87.97 362	87.21 362	90.24 395	92.86 407	80.76 402	96.67 241	94.97 370	91.74 180	85.52 388	95.83 255	62.66 430	94.47 431	76.25 416	88.36 339	95.48 320

LF4IMVS		87.94 363	87.25 360	89.98 398	92.38 419	80.05 417	94.38 370	95.25 358	87.59 336	84.34 398	94.74 311	64.31 424	97.66 350	84.83 344	87.45 347	92.23 425

gg-mvs-nofinetune		87.82 364	85.61 377	94.44 244	94.46 360	89.27 238	91.21 430	84.61 458	80.88 420	89.89 294	74.98 454	71.50 372	97.53 362	85.75 334	97.21 176	96.51 279

pmmvs6		87.81 365	86.19 373	92.69 334	91.32 424	86.30 321	97.34 170	96.41 300	80.59 425	84.05 406	94.37 332	67.37 407	97.67 348	84.75 346	79.51 418	94.09 396

testing3		87.67 366	86.88 367	90.05 397	96.14 266	80.71 403	97.10 194	92.85 422	90.15 250	87.54 357	94.55 320	55.70 442	94.10 434	73.77 429	94.10 256	95.35 334

K. test v3		87.64 367	86.75 369	90.32 394	93.02 404	79.48 424	96.61 248	92.08 431	90.66 231	80.25 428	94.09 352	67.21 408	96.65 401	85.96 331	80.83 412	94.83 366

Patchmatch-RL test		87.38 368	86.24 372	90.81 385	88.74 442	78.40 430	88.12 450	93.17 418	87.11 347	82.17 418	89.29 431	81.95 245	95.60 419	88.64 279	77.02 426	98.41 175

FMVSNet5		87.29 369	85.79 376	91.78 363	94.80 346	87.28 292	95.49 328	95.28 355	84.09 393	83.85 408	91.82 410	62.95 428	94.17 433	78.48 404	85.34 371	93.91 400

myMVS_eth3d		87.18 370	86.38 371	89.58 403	95.16 324	79.53 421	95.00 350	93.93 409	88.55 305	86.96 372	91.99 407	56.23 441	94.00 435	75.47 421	94.11 254	95.20 345

Syy-MVS		87.13 371	87.02 366	87.47 416	95.16 324	73.21 444	95.00 350	93.93 409	88.55 305	86.96 372	91.99 407	75.90 338	94.00 435	61.59 450	94.11 254	95.20 345

Anonymous20231206		87.09 372	86.14 374	89.93 400	91.22 425	80.35 409	96.11 290	95.35 351	83.57 402	84.16 401	93.02 386	73.54 362	95.61 418	72.16 435	86.14 361	93.84 401

EG-PatchMatch MVS		87.02 373	85.44 378	91.76 365	92.67 411	85.00 354	96.08 292	96.45 298	83.41 404	79.52 430	93.49 376	57.10 439	97.72 345	79.34 402	90.87 312	92.56 418

TinyColmap		86.82 374	85.35 381	91.21 375	94.91 341	82.99 381	93.94 386	94.02 406	83.58 401	81.56 420	94.68 313	62.34 431	98.13 281	75.78 417	87.35 352	92.52 420

UWE-MVS-28		86.81 375	86.41 370	88.02 414	92.87 406	74.60 439	95.38 333	86.70 454	88.17 315	87.28 365	94.67 315	70.83 378	93.30 442	67.45 444	94.31 248	96.17 289

mvs5depth		86.53 376	85.08 383	90.87 382	88.74 442	82.52 386	91.91 424	94.23 401	86.35 359	87.11 368	93.70 366	66.52 413	97.76 341	81.37 384	75.80 431	92.31 424

TDRefinement		86.53 376	84.76 388	91.85 358	82.23 457	84.25 364	96.38 268	95.35 351	84.97 383	84.09 404	94.94 299	65.76 421	98.34 267	84.60 349	74.52 435	92.97 410

sc_t1		86.48 378	84.10 394	93.63 294	93.45 394	85.76 336	96.79 225	94.71 382	73.06 445	86.45 381	94.35 333	55.13 443	97.95 318	84.38 352	78.55 423	97.18 262

test_0402		86.46 379	84.79 387	91.45 370	95.02 333	85.55 339	96.29 278	94.89 375	80.90 419	82.21 417	93.97 358	68.21 403	97.29 379	62.98 448	88.68 336	91.51 433

Anonymous20240521		86.42 380	85.44 378	89.34 407	90.33 429	79.79 418	96.73 232	95.92 320	83.71 400	83.25 411	91.36 416	63.92 425	96.01 408	78.39 406	85.36 370	92.22 426

DSMNet-mixed		86.34 381	86.12 375	87.00 420	89.88 433	70.43 446	94.93 352	90.08 443	77.97 435	85.42 391	92.78 389	74.44 354	93.96 437	74.43 424	95.14 231	96.62 277

CL-MVSNet_self_test		86.31 382	85.15 382	89.80 401	88.83 440	81.74 396	93.93 387	96.22 310	86.67 353	85.03 393	90.80 419	78.09 319	94.50 429	74.92 422	71.86 441	93.15 409

pmmvs-eth3d		86.22 383	84.45 390	91.53 368	88.34 444	87.25 294	94.47 365	95.01 367	83.47 403	79.51 431	89.61 429	69.75 390	95.71 415	83.13 364	76.73 429	91.64 430

test_vis1_rt		86.16 384	85.06 384	89.46 404	93.47 393	80.46 408	96.41 262	86.61 455	85.22 377	79.15 432	88.64 434	52.41 447	97.06 385	93.08 174	90.57 314	90.87 438

test20.03		86.14 385	85.40 380	88.35 410	90.12 430	80.06 416	95.90 304	95.20 360	88.59 301	81.29 421	93.62 372	71.43 373	92.65 445	71.26 439	81.17 411	92.34 422

UnsupCasMVSNet_eth		85.99 386	84.45 390	90.62 389	89.97 432	82.40 390	93.62 401	97.37 209	89.86 256	78.59 434	92.37 398	65.25 423	95.35 424	82.27 375	70.75 442	94.10 394

KD-MVS_self_test		85.95 387	84.95 385	88.96 409	89.55 436	79.11 427	95.13 347	96.42 299	85.91 367	84.07 405	90.48 421	70.03 386	94.82 427	80.04 394	72.94 439	92.94 411

ttmdpeth		85.91 388	84.76 388	89.36 406	89.14 437	80.25 414	95.66 319	93.16 419	83.77 398	83.39 410	95.26 287	66.24 417	95.26 425	80.65 390	75.57 432	92.57 417

YYNet1		85.87 389	84.23 392	90.78 388	92.38 419	82.46 389	93.17 408	95.14 363	82.12 412	67.69 447	92.36 401	78.16 318	95.50 422	77.31 410	79.73 416	94.39 387

MDA-MVSNet_test_wron		85.87 389	84.23 392	90.80 387	92.38 419	82.57 384	93.17 408	95.15 362	82.15 411	67.65 449	92.33 404	78.20 315	95.51 421	77.33 409	79.74 415	94.31 391

CMPMVS	62.92 21	85.62 391	84.92 386	87.74 415	89.14 437	73.12 445	94.17 379	96.80 275	73.98 441	73.65 443	94.93 300	66.36 414	97.61 355	83.95 358	91.28 303	92.48 421
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 392	83.64 395	90.92 381	95.27 317	79.49 423	90.55 434	95.60 339	83.76 399	83.00 414	89.95 426	71.09 375	97.97 310	82.75 371	60.79 456	95.31 337

tt0320		85.39 393	83.12 396	92.19 349	93.44 395	85.79 335	96.19 286	94.87 379	71.19 447	82.92 415	91.76 413	58.43 436	96.81 397	81.03 389	78.26 424	93.98 398

MDA-MVSNet-bldmvs		85.00 394	82.95 399	91.17 379	93.13 403	83.33 376	94.56 361	95.00 368	84.57 388	65.13 453	92.65 391	70.45 381	95.85 412	73.57 430	77.49 425	94.33 389

MIMVSNet1		84.93 395	83.05 397	90.56 390	89.56 435	84.84 359	95.40 331	95.35 351	83.91 394	80.38 426	92.21 406	57.23 438	93.34 441	70.69 441	82.75 406	93.50 404

tt0320-xc		84.83 396	82.33 404	92.31 343	93.66 385	86.20 325	96.17 288	94.06 403	71.26 446	82.04 419	92.22 405	55.07 444	96.72 400	81.49 379	75.04 434	94.02 397

KD-MVS_2432*1600		84.81 397	82.64 400	91.31 373	91.07 426	85.34 348	91.22 428	95.75 330	85.56 372	83.09 412	90.21 424	67.21 408	95.89 410	77.18 412	62.48 454	92.69 414

miper_refine_blended		84.81 397	82.64 400	91.31 373	91.07 426	85.34 348	91.22 428	95.75 330	85.56 372	83.09 412	90.21 424	67.21 408	95.89 410	77.18 412	62.48 454	92.69 414

OpenMVS_ROB	81.14 20	84.42 399	82.28 405	90.83 383	90.06 431	84.05 369	95.73 314	94.04 405	73.89 443	80.17 429	91.53 415	59.15 434	97.64 351	66.92 446	89.05 329	90.80 439

mvsany_test3		83.59 400	82.44 403	87.03 419	83.80 452	73.82 441	93.70 396	90.92 440	86.42 357	82.51 416	90.26 423	46.76 452	95.71 415	90.82 223	76.76 428	91.57 432

PM-MVS		83.48 401	81.86 407	88.31 411	87.83 446	77.59 432	93.43 404	91.75 433	86.91 349	80.63 424	89.91 427	44.42 453	95.84 413	85.17 343	76.73 429	91.50 434

test_fmvs3		83.21 402	83.02 398	83.78 425	86.77 449	68.34 451	96.76 230	94.91 374	86.49 356	84.14 403	89.48 430	36.04 457	91.73 447	91.86 200	80.77 413	91.26 437

new-patchmatchnet		83.18 403	81.87 406	87.11 418	86.88 448	75.99 437	93.70 396	95.18 361	85.02 382	77.30 437	88.40 436	65.99 419	93.88 438	74.19 427	70.18 443	91.47 435

new_pmnet		82.89 404	81.12 409	88.18 413	89.63 434	80.18 415	91.77 425	92.57 426	76.79 438	75.56 440	88.23 438	61.22 433	94.48 430	71.43 437	82.92 404	89.87 442

MVS-HIRNet		82.47 405	81.21 408	86.26 422	95.38 305	69.21 449	88.96 444	89.49 444	66.28 451	80.79 423	74.08 456	68.48 401	97.39 374	71.93 436	95.47 225	92.18 427

MVStest1		82.38 406	80.04 410	89.37 405	87.63 447	82.83 382	95.03 349	93.37 417	73.90 442	73.50 444	94.35 333	62.89 429	93.25 443	73.80 428	65.92 451	92.04 429

UnsupCasMVSNet_bld		82.13 407	79.46 412	90.14 396	88.00 445	82.47 388	90.89 433	96.62 291	78.94 431	75.61 438	84.40 449	56.63 440	96.31 406	77.30 411	66.77 450	91.63 431

dmvs_testset		81.38 408	82.60 402	77.73 431	91.74 423	51.49 466	93.03 413	84.21 459	89.07 281	78.28 435	91.25 417	76.97 329	88.53 454	56.57 454	82.24 407	93.16 408

test_f		80.57 409	79.62 411	83.41 426	83.38 455	67.80 453	93.57 403	93.72 412	80.80 423	77.91 436	87.63 442	33.40 458	92.08 446	87.14 312	79.04 421	90.34 441

pmmvs3		79.97 410	77.50 415	87.39 417	82.80 456	79.38 425	92.70 418	90.75 441	70.69 448	78.66 433	87.47 444	51.34 448	93.40 440	73.39 431	69.65 444	89.38 443

APD_test1		79.31 411	77.70 414	84.14 424	89.11 439	69.07 450	92.36 423	91.50 435	69.07 449	73.87 442	92.63 393	39.93 455	94.32 432	70.54 442	80.25 414	89.02 444

N_pmnet		78.73 412	78.71 413	78.79 430	92.80 409	46.50 469	94.14 380	43.71 471	78.61 432	80.83 422	91.66 414	74.94 350	96.36 405	67.24 445	84.45 388	93.50 404

WB-MVS		76.77 413	76.63 416	77.18 432	85.32 450	56.82 464	94.53 362	89.39 445	82.66 409	71.35 445	89.18 432	75.03 347	88.88 452	35.42 461	66.79 449	85.84 446

SSC-MVS		76.05 414	75.83 417	76.72 436	84.77 451	56.22 465	94.32 374	88.96 447	81.82 415	70.52 446	88.91 433	74.79 351	88.71 453	33.69 462	64.71 452	85.23 447

test_vis3_rt		72.73 415	70.55 418	79.27 429	80.02 458	68.13 452	93.92 388	74.30 466	76.90 437	58.99 457	73.58 457	20.29 466	95.37 423	84.16 353	72.80 440	74.31 454

LCM-MVSNet		72.55 416	69.39 420	82.03 427	70.81 467	65.42 456	90.12 438	94.36 399	55.02 457	65.88 451	81.72 450	24.16 465	89.96 448	74.32 426	68.10 448	90.71 440

FPMVS		71.27 417	69.85 419	75.50 437	74.64 462	59.03 462	91.30 427	91.50 435	58.80 454	57.92 458	88.28 437	29.98 461	85.53 457	53.43 455	82.84 405	81.95 450

PMMVS2		70.19 418	66.92 422	80.01 428	76.35 461	65.67 455	86.22 451	87.58 451	64.83 453	62.38 454	80.29 453	26.78 463	88.49 455	63.79 447	54.07 458	85.88 445

dongtai		69.99 419	69.33 421	71.98 440	88.78 441	61.64 460	89.86 439	59.93 470	75.67 439	74.96 441	85.45 446	50.19 449	81.66 459	43.86 458	55.27 457	72.63 455

testf1		69.31 420	66.76 423	76.94 434	78.61 459	61.93 458	88.27 448	86.11 456	55.62 455	59.69 455	85.31 447	20.19 467	89.32 449	57.62 451	69.44 446	79.58 451

APD_test2		69.31 420	66.76 423	76.94 434	78.61 459	61.93 458	88.27 448	86.11 456	55.62 455	59.69 455	85.31 447	20.19 467	89.32 449	57.62 451	69.44 446	79.58 451

EGC-MVSNET		68.77 422	63.01 428	86.07 423	92.49 415	82.24 392	93.96 385	90.96 439	0.71 468	2.62 469	90.89 418	53.66 445	93.46 439	57.25 453	84.55 386	82.51 449

Gipuma		67.86 423	65.41 425	75.18 438	92.66 412	73.45 442	66.50 459	94.52 390	53.33 458	57.80 459	66.07 459	30.81 459	89.20 451	48.15 457	78.88 422	62.90 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 424	64.89 426	69.79 441	72.62 465	35.23 473	65.19 460	92.83 424	20.35 463	65.20 452	88.08 440	43.14 454	82.70 458	73.12 432	63.46 453	91.45 436

kuosan		65.27 425	64.66 427	67.11 443	83.80 452	61.32 461	88.53 447	60.77 469	68.22 450	67.67 448	80.52 452	49.12 450	70.76 465	29.67 464	53.64 459	69.26 457

ANet_high		63.94 426	59.58 429	77.02 433	61.24 469	66.06 454	85.66 453	87.93 450	78.53 433	42.94 461	71.04 458	25.42 464	80.71 460	52.60 456	30.83 462	84.28 448

PMVS	53.92 22	58.58 427	55.40 430	68.12 442	51.00 470	48.64 467	78.86 456	87.10 453	46.77 459	35.84 465	74.28 455	8.76 469	86.34 456	42.07 459	73.91 437	69.38 456
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 428	52.56 432	55.43 445	74.43 463	47.13 468	83.63 455	76.30 463	42.23 460	42.59 462	62.22 461	28.57 462	74.40 462	31.53 463	31.51 461	44.78 460

MVE	50.73 23	53.25 429	48.81 434	66.58 444	65.34 468	57.50 463	72.49 458	70.94 467	40.15 462	39.28 464	63.51 460	6.89 471	73.48 464	38.29 460	42.38 460	68.76 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 430	51.31 433	54.39 446	72.62 465	45.39 470	83.84 454	75.51 465	41.13 461	40.77 463	59.65 462	30.08 460	73.60 463	28.31 465	29.90 463	44.18 461

tmp_tt		51.94 431	53.82 431	46.29 447	33.73 471	45.30 471	78.32 457	67.24 468	18.02 464	50.93 460	87.05 445	52.99 446	53.11 466	70.76 440	25.29 464	40.46 462

wuyk23d		25.11 432	24.57 436	26.74 448	73.98 464	39.89 472	57.88 461	9.80 472	12.27 465	10.39 466	6.97 468	7.03 470	36.44 467	25.43 466	17.39 465	3.89 465

cdsmvs_eth3d_5k		23.24 433	30.99 435	0.00 451	0.00 474	0.00 476	0.00 462	97.63 160	0.00 469	0.00 470	96.88 195	84.38 189	0.00 470	0.00 469	0.00 468	0.00 466

testmvs		13.36 434	16.33 437	4.48 450	5.04 472	2.26 475	93.18 407	3.28 473	2.70 466	8.24 467	21.66 464	2.29 473	2.19 468	7.58 467	2.96 466	9.00 464

test123		13.04 435	15.66 438	5.18 449	4.51 473	3.45 474	92.50 421	1.81 474	2.50 467	7.58 468	20.15 465	3.67 472	2.18 469	7.13 468	1.07 467	9.90 463

ab-mvs-re		8.06 436	10.74 439	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	96.69 206	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

pcd_1.5k_mvsjas		7.39 437	9.85 440	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	88.65 105	0.00 470	0.00 469	0.00 468	0.00 466

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

WAC-MVS						79.53 421								75.56 420

FOURS1					99.55 1	93.34 67	99.29 1	98.35 38	94.98 44	98.49 34

MSC_two_6792asdad				98.86 1	98.67 63	96.94 1		97.93 119					99.86 9	97.68 31	99.67 6	99.77 2

PC_three_1452									90.77 223	98.89 24	98.28 80	96.24 1	98.35 264	95.76 100	99.58 23	99.59 28

No_MVS				98.86 1	98.67 63	96.94 1		97.93 119					99.86 9	97.68 31	99.67 6	99.77 2

test_one_0601					99.32 24	95.20 20		98.25 56	95.13 38	98.48 35	98.87 29	95.16 7

eth-test2					0.00 474

eth-test					0.00 474

ZD-MVS					99.05 41	94.59 32		98.08 88	89.22 277	97.03 75	98.10 88	92.52 39	99.65 73	94.58 141	99.31 67

RE-MVS-def			96.72 57		99.02 44	92.34 104	97.98 66	98.03 105	93.52 111	97.43 61	98.51 50	90.71 78		96.05 88	99.26 72	99.43 59

IU-MVS					99.42 7	95.39 11		97.94 118	90.40 245	98.94 17				97.41 47	99.66 10	99.74 8

OPU-MVS				98.55 3	98.82 57	96.86 3	98.25 36				98.26 81	96.04 2	99.24 143	95.36 114	99.59 19	99.56 36

test_241102_TWO								98.27 50	95.13 38	98.93 18	98.89 26	94.99 11	99.85 18	97.52 40	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 50	95.09 41	99.19 11	98.81 35	95.54 5	99.65 73

9.14			96.75 56		98.93 52		97.73 108	98.23 61	91.28 201	97.88 49	98.44 58	93.00 26	99.65 73	95.76 100	99.47 41

save fliter					98.91 54	94.28 38	97.02 199	98.02 108	95.35 29

test_0728_THIRD									94.78 59	98.73 28	98.87 29	95.87 4	99.84 23	97.45 44	99.72 2	99.77 2

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 47					99.86 9	97.52 40	99.67 6	99.75 6

test0726					99.45 3	95.36 13	98.31 28	98.29 45	94.92 48	98.99 16	98.92 21	95.08 8

GSMVS																98.45 170

test_part2					99.28 27	95.74 8				98.10 42

sam_mvs1												82.76 225				98.45 170

sam_mvs												81.94 246

ambc				86.56 421	83.60 454	70.00 448	85.69 452	94.97 370		80.60 425	88.45 435	37.42 456	96.84 396	82.69 372	75.44 433	92.86 412

MTGPA								98.08 88

test_post1							92.81 417				16.58 467	80.53 272	97.68 347	86.20 323

test_post											17.58 466	81.76 249	98.08 291

patchmatchnet-post											90.45 422	82.65 230	98.10 286

GG-mvs-BLEND				93.62 295	93.69 383	89.20 240	92.39 422	83.33 460		87.98 351	89.84 428	71.00 376	96.87 395	82.08 376	95.40 227	94.80 371

MTMP							97.86 85	82.03 461

gm-plane-assit					93.22 400	78.89 429			84.82 385		93.52 375		98.64 235	87.72 291

test9_res														94.81 131	99.38 60	99.45 55

TEST9					98.70 61	94.19 42	96.41 262	98.02 108	88.17 315	96.03 120	97.56 150	92.74 33	99.59 89

test_8					98.67 63	94.06 49	96.37 269	98.01 111	88.58 302	95.98 124	97.55 152	92.73 34	99.58 92

agg_prior2														93.94 154	99.38 60	99.50 48

agg_prior					98.67 63	93.79 55		98.00 112		95.68 137			99.57 99

TestCases				93.98 271	97.94 124	86.64 310		95.54 344	85.38 374	85.49 389	96.77 200	70.28 382	99.15 157	80.02 395	92.87 274	96.15 292

test_prior4						93.66 58	96.42 261

test_prior2							96.35 270		92.80 149	96.03 120	97.59 147	92.01 47		95.01 122	99.38 60

test_prior				97.23 65	98.67 63	92.99 79		98.00 112					99.41 126			99.29 71

旧先验2							95.94 300		81.66 416	97.34 64			98.82 203	92.26 185

新几何2							95.79 310

新几何1				97.32 58	98.60 70	93.59 59		97.75 143	81.58 417	95.75 132	97.85 117	90.04 85	99.67 71	86.50 319	99.13 92	98.69 147

旧先验1					98.38 84	93.38 64		97.75 143			98.09 90	92.30 45			99.01 102	99.16 81

无先验							95.79 310	97.87 126	83.87 397				99.65 73	87.68 297		98.89 126

原ACMM2							95.67 316

原ACMM1				96.38 119	98.59 71	91.09 162		97.89 122	87.41 340	95.22 150	97.68 134	90.25 82	99.54 104	87.95 287	99.12 94	98.49 165

test222					98.24 95	92.21 110	95.33 335	97.60 165	79.22 430	95.25 148	97.84 119	88.80 102			99.15 89	98.72 144

testdata2													99.67 71	85.96 331

segment_acmp												92.89 30

testdata				95.46 189	98.18 105	88.90 249		97.66 154	82.73 408	97.03 75	98.07 91	90.06 84	98.85 199	89.67 251	98.98 103	98.64 150

testdata1							95.26 342		93.10 131

test12				97.65 43	98.46 75	94.26 39		97.66 154		95.52 144		90.89 75	99.46 120		99.25 74	99.22 78

plane_prior7					96.21 253	89.98 204

plane_prior6					96.10 271	90.00 200						81.32 256

plane_prior5								97.51 178					98.60 239	93.02 177	92.23 285	95.86 300

plane_prior4											96.64 209

plane_prior3						90.00 200			94.46 76	91.34 255

plane_prior2							97.74 106		94.85 51

plane_prior1					96.14 266

plane_prior						89.99 202	97.24 179		94.06 88						92.16 289

n2								0.00 475

nn								0.00 475

door-mid								91.06 438

lessismore_v0				90.45 391	91.96 422	79.09 428		87.19 452		80.32 427	94.39 330	66.31 416	97.55 359	84.00 357	76.84 427	94.70 378

LGP-MVS_train				94.10 263	96.16 263	88.26 267		97.46 189	91.29 198	90.12 285	97.16 174	79.05 300	98.73 219	92.25 187	91.89 293	95.31 337

test11								97.88 124

door								91.13 437

HQP5-MVS						89.33 233

HQP-NCC					95.86 279		96.65 242		93.55 105	90.14 279

ACMP_Plane					95.86 279		96.65 242		93.55 105	90.14 279

BP-MVS														92.13 193

HQP4-MVS										90.14 279			98.50 249			95.78 308

HQP3-MVS								97.39 205							92.10 290

HQP2-MVS												80.95 260

NP-MVS					95.99 277	89.81 212					95.87 252

MDTV_nov1_ep13_2view						70.35 447	93.10 412		83.88 396	93.55 196		82.47 234		86.25 322		98.38 178

MDTV_nov1_ep13			90.76 274		95.22 321	80.33 410	93.03 413	95.28 355	88.14 318	92.84 219	93.83 360	81.34 255	98.08 291	82.86 366	94.34 247

ACMMP++_ref															90.30 319

ACMMP++															91.02 308

Test By Simon												88.73 104

ITE_SJBPF				92.43 338	95.34 310	85.37 347		95.92 320	91.47 191	87.75 354	96.39 227	71.00 376	97.96 314	82.36 374	89.86 322	93.97 399

DeepMVS_CX				74.68 439	90.84 428	64.34 457		81.61 462	65.34 452	67.47 450	88.01 441	48.60 451	80.13 461	62.33 449	73.68 438	79.58 451

High-resolution multi-view benchmark