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		99.09 1	99.08 1	99.11 61	99.43 63	97.48 89	98.88 127	99.30 14	98.47 18	99.85 11	99.43 43	96.71 19	99.96 4	99.86 1	99.80 25	99.89 6

SED-MVS		99.09 1	98.91 4	99.63 5	99.71 24	99.24 6	99.02 84	98.87 85	97.65 39	99.73 22	99.48 33	97.53 9	99.94 14	98.43 67	99.81 16	99.70 67

DVP-MVS++		99.08 3	98.89 5	99.64 4	99.17 111	99.23 8	99.69 1	98.88 78	97.32 63	99.53 37	99.47 35	97.81 3	99.94 14	98.47 63	99.72 68	99.74 50

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 57	99.41 66	97.54 87	98.89 120	99.31 13	98.49 17	99.86 8	99.42 44	96.45 27	99.96 4	99.86 1	99.74 59	99.90 5

DVP-MVS		99.03 5	98.83 10	99.63 5	99.72 17	99.25 3	98.97 95	98.58 177	97.62 41	99.45 39	99.46 40	97.42 11	99.94 14	98.47 63	99.81 16	99.69 70
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
MED-MVS		99.02 6	98.85 8	99.52 13	99.77 2	98.86 22	99.32 22	99.24 20	97.00 89	99.30 50	99.35 60	97.61 6	99.92 43	98.30 75	99.80 25	99.79 28

TestfortrainingZip a		99.02 6	98.79 12	99.70 2	99.77 2	99.30 2	99.32 22	99.24 20	96.41 121	99.30 50	99.35 60	97.61 6	99.92 43	98.35 72	99.80 25	99.88 10

APDe-MVS		99.02 6	98.84 9	99.55 10	99.57 39	98.96 17	99.39 11	98.93 65	97.38 60	99.41 42	99.54 20	96.66 20	99.84 88	98.86 39	99.85 6	99.87 11
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lecture		98.95 9	98.78 14	99.45 19	99.75 6	98.63 30	99.43 10	99.38 8	97.60 44	99.58 33	99.47 35	95.36 64	99.93 34	98.87 38	99.57 100	99.78 33

reproduce_model		98.94 10	98.81 11	99.34 31	99.52 45	98.26 54	98.94 105	98.84 96	98.06 25	99.35 46	99.61 5	96.39 30	99.94 14	98.77 42	99.82 14	99.83 18

reproduce-ours		98.93 11	98.78 14	99.38 23	99.49 52	98.38 40	98.86 134	98.83 98	98.06 25	99.29 53	99.58 16	96.40 28	99.94 14	98.68 45	99.81 16	99.81 24

our_new_method		98.93 11	98.78 14	99.38 23	99.49 52	98.38 40	98.86 134	98.83 98	98.06 25	99.29 53	99.58 16	96.40 28	99.94 14	98.68 45	99.81 16	99.81 24

test_fmvsmconf_n		98.92 13	98.87 6	99.04 67	98.88 147	97.25 111	98.82 147	99.34 11	98.75 11	99.80 14	99.61 5	95.16 77	99.95 9	99.70 17	99.80 25	99.93 1

DPE-MVS		98.92 13	98.67 20	99.65 3	99.58 37	99.20 10	98.42 258	98.91 72	97.58 45	99.54 36	99.46 40	97.10 14	99.94 14	97.64 119	99.84 11	99.83 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_9		98.90 15	98.79 12	99.24 45	99.34 71	97.83 78	98.70 188	99.26 16	98.85 6	99.92 1	99.51 26	93.91 106	99.95 9	99.86 1	99.79 35	99.92 2

fmvsm_l_conf0.5_n_3		98.90 15	98.74 18	99.37 27	99.36 68	98.25 55	98.89 120	99.24 20	98.77 10	99.89 3	99.59 13	93.39 112	99.96 4	99.78 10	99.76 48	99.89 6

SteuartSystems-ACMMP		98.90 15	98.75 17	99.36 29	99.22 106	98.43 38	99.10 68	98.87 85	97.38 60	99.35 46	99.40 47	97.78 5	99.87 79	97.77 107	99.85 6	99.78 33
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 18	99.01 3	98.45 123	99.42 64	96.43 155	98.96 101	99.36 10	98.63 13	99.86 8	99.51 26	95.91 46	99.97 1	99.72 14	99.75 55	98.94 224

ME-MVS		98.83 19	98.60 24	99.52 13	99.58 37	98.86 22	98.69 191	98.93 65	97.00 89	99.17 62	99.35 60	96.62 23	99.90 64	98.30 75	99.80 25	99.79 28

TSAR-MVS + MP.		98.78 20	98.62 22	99.24 45	99.69 29	98.28 53	99.14 59	98.66 154	96.84 96	99.56 34	99.31 71	96.34 31	99.70 142	98.32 74	99.73 63	99.73 55
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		98.78 20	98.56 28	99.45 19	99.32 77	98.87 20	98.47 245	98.81 107	97.72 34	98.76 95	99.16 102	97.05 15	99.78 124	98.06 89	99.66 79	99.69 70

MSP-MVS		98.74 22	98.55 29	99.29 38	99.75 6	98.23 56	99.26 32	98.88 78	97.52 48	99.41 42	98.78 180	96.00 42	99.79 121	97.79 106	99.59 96	99.85 15
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
fmvsm_s_conf0.5_n_8		98.73 23	98.62 22	99.05 66	99.35 70	97.27 105	98.80 156	99.23 28	98.93 3	99.79 15	99.59 13	92.34 129	99.95 9	99.82 6	99.71 70	99.92 2

XVS		98.70 24	98.49 36	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	99.20 92	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

fmvsm_s_conf0.5_n_10		98.66 25	98.54 31	99.02 68	99.36 68	97.21 114	98.86 134	99.23 28	98.90 5	99.83 12	99.59 13	91.57 159	99.94 14	99.79 9	99.74 59	99.89 6

fmvsm_s_conf0.5_n_6		98.65 26	98.55 29	98.95 77	98.50 187	97.30 101	98.79 164	99.16 40	98.14 23	99.86 8	99.41 46	93.71 109	99.91 56	99.71 15	99.64 87	99.65 83

MCST-MVS		98.65 26	98.37 45	99.48 17	99.60 36	98.87 20	98.41 259	98.68 146	97.04 86	98.52 116	98.80 174	96.78 18	99.83 90	97.93 96	99.61 92	99.74 50

SD-MVS		98.64 28	98.68 19	98.53 112	99.33 74	98.36 48	98.90 116	98.85 95	97.28 67	99.72 25	99.39 48	96.63 22	97.60 417	98.17 84	99.85 6	99.64 86
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
fmvsm_s_conf0.5_n_9		98.63 29	98.66 21	98.54 109	99.40 67	95.83 197	98.79 164	99.17 38	98.94 2	99.92 1	99.61 5	92.49 124	99.93 34	99.86 1	99.76 48	99.86 12

HFP-MVS		98.63 29	98.40 42	99.32 37	99.72 17	98.29 52	99.23 37	98.96 60	96.10 138	98.94 77	99.17 99	96.06 39	99.92 43	97.62 120	99.78 40	99.75 48

ACMMP_NAP		98.61 31	98.30 60	99.55 10	99.62 35	98.95 18	98.82 147	98.81 107	95.80 152	99.16 66	99.47 35	95.37 63	99.92 43	97.89 100	99.75 55	99.79 28

region2R		98.61 31	98.38 44	99.29 38	99.74 12	98.16 62	99.23 37	98.93 65	96.15 134	98.94 77	99.17 99	95.91 46	99.94 14	97.55 128	99.79 35	99.78 33

NCCC		98.61 31	98.35 48	99.38 23	99.28 92	98.61 31	98.45 247	98.76 125	97.82 33	98.45 121	98.93 152	96.65 21	99.83 90	97.38 147	99.41 129	99.71 63

SF-MVS		98.59 34	98.32 59	99.41 22	99.54 41	98.71 26	99.04 78	98.81 107	95.12 199	99.32 49	99.39 48	96.22 33	99.84 88	97.72 110	99.73 63	99.67 79

ACMMPR		98.59 34	98.36 46	99.29 38	99.74 12	98.15 63	99.23 37	98.95 61	96.10 138	98.93 81	99.19 97	95.70 52	99.94 14	97.62 120	99.79 35	99.78 33

fmvsm_s_conf0.5_n_11		98.58 36	98.57 26	98.62 99	99.42 64	97.16 117	98.97 95	98.86 91	98.91 4	99.87 4	99.66 3	91.82 151	99.95 9	99.82 6	99.82 14	98.75 245

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 70	97.73 294	97.15 118	98.84 143	98.97 57	98.75 11	99.43 41	99.54 20	93.29 114	99.93 34	99.64 20	99.79 35	99.89 6

SMA-MVS		98.58 36	98.25 63	99.56 9	99.51 46	99.04 16	98.95 102	98.80 114	93.67 293	99.37 45	99.52 23	96.52 25	99.89 68	98.06 89	99.81 16	99.76 47
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
MTAPA		98.58 36	98.29 61	99.46 18	99.76 5	98.64 29	98.90 116	98.74 129	97.27 71	98.02 146	99.39 48	94.81 87	99.96 4	97.91 98	99.79 35	99.77 40

HPM-MVS++		98.58 36	98.25 63	99.55 10	99.50 48	99.08 12	98.72 183	98.66 154	97.51 49	98.15 132	98.83 171	95.70 52	99.92 43	97.53 130	99.67 76	99.66 82

SR-MVS		98.57 41	98.35 48	99.24 45	99.53 42	98.18 60	99.09 69	98.82 101	96.58 112	99.10 68	99.32 69	95.39 61	99.82 97	97.70 115	99.63 89	99.72 59

CP-MVS		98.57 41	98.36 46	99.19 50	99.66 31	97.86 74	99.34 17	98.87 85	95.96 144	98.60 112	99.13 109	96.05 40	99.94 14	97.77 107	99.86 2	99.77 40

MSLP-MVS++		98.56 43	98.57 26	98.55 107	99.26 95	96.80 133	98.71 184	99.05 50	97.28 67	98.84 87	99.28 76	96.47 26	99.40 206	98.52 61	99.70 72	99.47 115

DeepC-MVS_fast	96.70 1	98.55 44	98.34 54	99.18 52	99.25 96	98.04 68	98.50 240	98.78 121	97.72 34	98.92 83	99.28 76	95.27 70	99.82 97	97.55 128	99.77 42	99.69 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		98.54 45	98.35 48	99.13 58	99.49 52	97.86 74	99.11 65	98.80 114	96.49 116	99.17 62	99.35 60	95.34 66	99.82 97	97.72 110	99.65 82	99.71 63

fmvsm_s_conf0.5_n_5		98.53 46	98.35 48	99.08 63	99.07 126	97.46 93	98.68 194	99.20 34	97.50 50	99.87 4	99.50 29	91.96 148	99.96 4	99.76 11	99.65 82	99.82 22

fmvsm_s_conf0.5_n_3		98.53 46	98.45 39	98.79 85	99.23 104	97.32 98	98.80 156	99.26 16	98.82 7	99.87 4	99.60 10	90.95 192	99.93 34	99.76 11	99.73 63	99.12 194

APD-MVS_3200maxsize		98.53 46	98.33 58	99.15 56	99.50 48	97.92 73	99.15 56	98.81 107	96.24 130	99.20 59	99.37 54	95.30 68	99.80 109	97.73 109	99.67 76	99.72 59

MM		98.51 49	98.24 65	99.33 35	99.12 120	98.14 65	98.93 111	97.02 405	98.96 1	99.17 62	99.47 35	91.97 147	99.94 14	99.85 5	99.69 73	99.91 4

mPP-MVS		98.51 49	98.26 62	99.25 44	99.75 6	98.04 68	99.28 29	98.81 107	96.24 130	98.35 128	99.23 86	95.46 58	99.94 14	97.42 142	99.81 16	99.77 40

ZNCC-MVS		98.49 51	98.20 71	99.35 30	99.73 16	98.39 39	99.19 49	98.86 91	95.77 154	98.31 131	99.10 116	95.46 58	99.93 34	97.57 127	99.81 16	99.74 50

SPE-MVS-test		98.49 51	98.50 34	98.46 122	99.20 109	97.05 123	99.64 4	98.50 199	97.45 56	98.88 84	99.14 106	95.25 72	99.15 249	98.83 40	99.56 108	99.20 178

PGM-MVS		98.49 51	98.23 67	99.27 43	99.72 17	98.08 67	98.99 91	99.49 5	95.43 176	99.03 69	99.32 69	95.56 55	99.94 14	96.80 180	99.77 42	99.78 33

EI-MVSNet-Vis-set		98.47 54	98.39 43	98.69 93	99.46 58	96.49 152	98.30 272	98.69 143	97.21 74	98.84 87	99.36 58	95.41 60	99.78 124	98.62 49	99.65 82	99.80 27

MVS_111021_HR		98.47 54	98.34 54	98.88 82	99.22 106	97.32 98	97.91 330	99.58 3	97.20 75	98.33 129	99.00 140	95.99 43	99.64 156	98.05 91	99.76 48	99.69 70

balanced_conf03		98.45 56	98.35 48	98.74 89	98.65 176	97.55 85	99.19 49	98.60 165	96.72 106	99.35 46	98.77 183	95.06 82	99.55 179	98.95 35	99.87 1	99.12 194

test_fmvsmvis_n_1920		98.44 57	98.51 32	98.23 144	98.33 217	96.15 169	98.97 95	99.15 42	98.55 16	98.45 121	99.55 18	94.26 100	99.97 1	99.65 18	99.66 79	98.57 270

CS-MVS		98.44 57	98.49 36	98.31 136	99.08 125	96.73 137	99.67 3	98.47 206	97.17 78	98.94 77	99.10 116	95.73 51	99.13 254	98.71 44	99.49 119	99.09 202

GST-MVS		98.43 59	98.12 75	99.34 31	99.72 17	98.38 40	99.09 69	98.82 101	95.71 158	98.73 98	99.06 131	95.27 70	99.93 34	97.07 157	99.63 89	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 159	99.30 83	95.25 229	98.85 139	99.39 7	97.94 29	99.74 21	99.62 4	92.59 123	99.91 56	99.65 18	99.52 114	99.25 171

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 101	99.45 61	96.32 162	98.28 275	98.68 146	97.17 78	98.74 96	99.37 54	95.25 72	99.79 121	98.57 52	99.54 111	99.73 55

DELS-MVS		98.40 62	98.20 71	98.99 70	99.00 134	97.66 80	97.75 351	98.89 75	97.71 36	98.33 129	98.97 142	94.97 84	99.88 77	98.42 69	99.76 48	99.42 130
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
fmvsm_s_conf0.5_n_a		98.38 63	98.42 41	98.27 138	99.09 124	95.41 219	98.86 134	99.37 9	97.69 38	99.78 17	99.61 5	92.38 127	99.91 56	99.58 23	99.43 127	99.49 111

TSAR-MVS + GP.		98.38 63	98.24 65	98.81 84	99.22 106	97.25 111	98.11 305	98.29 263	97.19 76	98.99 75	99.02 134	96.22 33	99.67 149	98.52 61	98.56 183	99.51 104

HPM-MVS_fast		98.38 63	98.13 74	99.12 60	99.75 6	97.86 74	99.44 9	98.82 101	94.46 247	98.94 77	99.20 92	95.16 77	99.74 134	97.58 123	99.85 6	99.77 40

patch_mono-2		98.36 66	98.87 6	96.82 267	99.53 42	90.68 381	98.64 205	99.29 15	97.88 30	99.19 61	99.52 23	96.80 17	99.97 1	99.11 31	99.86 2	99.82 22

HPM-MVS		98.36 66	98.10 78	99.13 58	99.74 12	97.82 79	99.53 6	98.80 114	94.63 234	98.61 111	98.97 142	95.13 79	99.77 129	97.65 118	99.83 13	99.79 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_4		98.35 68	98.50 34	97.90 182	99.16 115	95.08 238	98.75 169	99.24 20	98.39 19	99.81 13	99.52 23	92.35 128	99.90 64	99.74 13	99.51 116	98.71 251

APD-MVS		98.35 68	98.00 84	99.42 21	99.51 46	98.72 25	98.80 156	98.82 101	94.52 242	99.23 58	99.25 85	95.54 57	99.80 109	96.52 189	99.77 42	99.74 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 70	98.23 67	98.67 95	99.27 93	96.90 129	97.95 323	99.58 3	97.14 81	98.44 123	99.01 138	95.03 83	99.62 163	97.91 98	99.75 55	99.50 106

PHI-MVS		98.34 70	98.06 79	99.18 52	99.15 118	98.12 66	99.04 78	99.09 45	93.32 309	98.83 90	99.10 116	96.54 24	99.83 90	97.70 115	99.76 48	99.59 94

MP-MVS		98.33 72	98.01 83	99.28 41	99.75 6	98.18 60	99.22 41	98.79 119	96.13 135	97.92 160	99.23 86	94.54 90	99.94 14	96.74 183	99.78 40	99.73 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus		98.31 73	98.19 73	98.67 95	98.96 141	97.36 96	99.24 35	98.57 179	94.81 222	98.99 75	98.90 158	95.22 75	99.59 166	99.15 30	99.84 11	99.07 210

MP-MVS-pluss		98.31 73	97.92 86	99.49 16	99.72 17	98.88 19	98.43 255	98.78 121	94.10 258	97.69 179	99.42 44	95.25 72	99.92 43	98.09 88	99.80 25	99.67 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_2		98.30 75	98.21 69	98.57 104	99.25 96	97.11 120	98.66 201	99.20 34	98.82 7	99.79 15	99.60 10	89.38 232	99.92 43	99.80 8	99.38 134	98.69 253

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 184	98.86 151	94.99 244	98.58 217	99.00 53	98.29 20	99.73 22	99.60 10	91.70 154	99.92 43	99.63 21	99.73 63	98.76 244

MGCNet		98.23 76	97.91 87	99.21 49	98.06 257	97.96 72	98.58 217	95.51 444	98.58 14	98.87 85	99.26 80	92.99 118	99.95 9	99.62 22	99.67 76	99.73 55

ACMMP		98.23 76	97.95 85	99.09 62	99.74 12	97.62 83	99.03 81	99.41 6	95.98 143	97.60 191	99.36 58	94.45 95	99.93 34	97.14 154	98.85 167	99.70 67
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
EC-MVSNet		98.21 79	98.11 76	98.49 119	98.34 214	97.26 110	99.61 5	98.43 221	96.78 99	98.87 85	98.84 167	93.72 108	99.01 278	98.91 37	99.50 117	99.19 182

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 163	98.54 185	95.24 230	98.87 130	99.24 20	97.50 50	99.70 26	99.67 1	91.33 171	99.89 68	99.47 25	99.54 111	99.21 177

fmvsm_s_conf0.1_n_2		98.14 81	98.02 82	98.53 112	98.88 147	97.07 122	98.69 191	98.82 101	98.78 9	99.77 18	99.61 5	88.83 252	99.91 56	99.71 15	99.07 150	98.61 263

fmvsm_s_conf0.1_n_a		98.08 82	98.04 81	98.21 145	97.66 300	95.39 220	98.89 120	99.17 38	97.24 72	99.76 20	99.67 1	91.13 183	99.88 77	99.39 26	99.41 129	99.35 142

dcpmvs_2		98.08 82	98.59 25	96.56 296	99.57 39	90.34 394	99.15 56	98.38 237	96.82 98	99.29 53	99.49 32	95.78 50	99.57 169	98.94 36	99.86 2	99.77 40

NormalMVS		98.07 84	97.90 88	98.59 103	99.75 6	96.60 143	98.94 105	98.60 165	97.86 31	98.71 101	99.08 126	91.22 178	99.80 109	97.40 144	99.57 100	99.37 137

CANet		98.05 85	97.76 91	98.90 81	98.73 161	97.27 105	98.35 262	98.78 121	97.37 62	97.72 176	98.96 147	91.53 164	99.92 43	98.79 41	99.65 82	99.51 104

train_agg		97.97 86	97.52 104	99.33 35	99.31 79	98.50 34	97.92 328	98.73 132	92.98 325	97.74 173	98.68 196	96.20 35	99.80 109	96.59 184	99.57 100	99.68 75

ETV-MVS		97.96 87	97.81 89	98.40 131	98.42 197	97.27 105	98.73 179	98.55 184	96.84 96	98.38 125	97.44 318	95.39 61	99.35 211	97.62 120	98.89 161	98.58 269

UA-Net		97.96 87	97.62 95	98.98 72	98.86 151	97.47 91	98.89 120	99.08 46	96.67 109	98.72 100	99.54 20	93.15 116	99.81 102	94.87 247	98.83 168	99.65 83

CDPH-MVS		97.94 89	97.49 106	99.28 41	99.47 56	98.44 36	97.91 330	98.67 151	92.57 341	98.77 94	98.85 166	95.93 45	99.72 136	95.56 225	99.69 73	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 322	99.00 134	89.54 410	97.43 373	98.87 85	98.16 22	99.26 57	99.38 53	96.12 38	99.64 156	98.30 75	99.77 42	99.72 59

DeepC-MVS	95.98 3	97.88 91	97.58 97	98.77 87	99.25 96	96.93 127	98.83 145	98.75 127	96.96 92	96.89 223	99.50 29	90.46 202	99.87 79	97.84 104	99.76 48	99.52 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n		97.86 92	97.54 103	98.83 83	95.48 423	96.83 132	98.95 102	98.60 165	98.58 14	98.93 81	99.55 18	88.57 257	99.91 56	99.54 24	99.61 92	99.77 40

DP-MVS Recon		97.86 92	97.46 109	99.06 65	99.53 42	98.35 49	98.33 264	98.89 75	92.62 338	98.05 141	98.94 150	95.34 66	99.65 153	96.04 205	99.42 128	99.19 182

CSCG		97.85 94	97.74 92	98.20 147	99.67 30	95.16 233	99.22 41	99.32 12	93.04 323	97.02 216	98.92 156	95.36 64	99.91 56	97.43 141	99.64 87	99.52 101

SymmetryMVS		97.84 95	97.58 97	98.62 99	99.01 132	96.60 143	98.94 105	98.44 211	97.86 31	98.71 101	99.08 126	91.22 178	99.80 109	97.40 144	97.53 244	99.47 115

BP-MVS1		97.82 96	97.51 105	98.76 88	98.25 227	97.39 95	99.15 56	97.68 337	96.69 107	98.47 117	99.10 116	90.29 206	99.51 186	98.60 50	99.35 137	99.37 137

MG-MVS		97.81 97	97.60 96	98.44 125	99.12 120	95.97 179	97.75 351	98.78 121	96.89 95	98.46 118	99.22 88	93.90 107	99.68 148	94.81 251	99.52 114	99.67 79

VNet		97.79 98	97.40 114	98.96 75	98.88 147	97.55 85	98.63 208	98.93 65	96.74 103	99.02 70	98.84 167	90.33 205	99.83 90	98.53 55	96.66 267	99.50 106

EIA-MVS		97.75 99	97.58 97	98.27 138	98.38 204	96.44 154	99.01 86	98.60 165	95.88 148	97.26 202	97.53 312	94.97 84	99.33 214	97.38 147	99.20 146	99.05 211

PS-MVSNAJ		97.73 100	97.77 90	97.62 214	98.68 171	95.58 209	97.34 382	98.51 194	97.29 65	98.66 108	97.88 276	94.51 91	99.90 64	97.87 101	99.17 148	97.39 313

casdiffmvs_mvg		97.72 101	97.48 108	98.44 125	98.42 197	96.59 147	98.92 113	98.44 211	96.20 132	97.76 170	99.20 92	91.66 157	99.23 236	98.27 82	98.41 202	99.49 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS		97.72 101	97.32 119	98.92 78	99.64 33	97.10 121	99.12 63	98.81 107	92.34 349	98.09 137	99.08 126	93.01 117	99.92 43	96.06 204	99.77 42	99.75 48

PVSNet_Blended_VisFu		97.70 103	97.46 109	98.44 125	99.27 93	95.91 187	98.63 208	99.16 40	94.48 246	97.67 180	98.88 162	92.80 120	99.91 56	97.11 155	99.12 149	99.50 106

mvsany_test1		97.69 104	97.70 93	97.66 210	98.24 228	94.18 287	97.53 367	97.53 358	95.52 171	99.66 28	99.51 26	94.30 98	99.56 172	98.38 70	98.62 178	99.23 173

sasdasda		97.67 105	97.23 128	98.98 72	98.70 166	98.38 40	99.34 17	98.39 233	96.76 101	97.67 180	97.40 322	92.26 133	99.49 190	98.28 79	96.28 285	99.08 206

canonicalmvs		97.67 105	97.23 128	98.98 72	98.70 166	98.38 40	99.34 17	98.39 233	96.76 101	97.67 180	97.40 322	92.26 133	99.49 190	98.28 79	96.28 285	99.08 206

xiu_mvs_v2_base		97.66 107	97.70 93	97.56 218	98.61 180	95.46 217	97.44 371	98.46 207	97.15 80	98.65 109	98.15 251	94.33 97	99.80 109	97.84 104	98.66 177	97.41 311

GDP-MVS		97.64 108	97.28 121	98.71 92	98.30 222	97.33 97	99.05 74	98.52 191	96.34 127	98.80 91	99.05 132	89.74 219	99.51 186	96.86 176	98.86 165	99.28 161

baseline		97.64 108	97.44 111	98.25 142	98.35 209	96.20 166	99.00 88	98.32 250	96.33 129	98.03 144	99.17 99	91.35 170	99.16 245	98.10 87	98.29 211	99.39 134

casdiffmvs		97.63 110	97.41 113	98.28 137	98.33 217	96.14 170	98.82 147	98.32 250	96.38 125	97.95 155	99.21 90	91.23 177	99.23 236	98.12 86	98.37 204	99.48 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net		97.62 111	97.19 131	98.92 78	98.66 173	98.20 58	99.32 22	98.38 237	96.69 107	97.58 193	97.42 321	92.10 141	99.50 189	98.28 79	96.25 288	99.08 206

xiu_mvs_v1_base_debu		97.60 112	97.56 100	97.72 199	98.35 209	95.98 174	97.86 340	98.51 194	97.13 82	99.01 72	98.40 223	91.56 160	99.80 109	98.53 55	98.68 173	97.37 315

xiu_mvs_v1_base		97.60 112	97.56 100	97.72 199	98.35 209	95.98 174	97.86 340	98.51 194	97.13 82	99.01 72	98.40 223	91.56 160	99.80 109	98.53 55	98.68 173	97.37 315

xiu_mvs_v1_base_debi		97.60 112	97.56 100	97.72 199	98.35 209	95.98 174	97.86 340	98.51 194	97.13 82	99.01 72	98.40 223	91.56 160	99.80 109	98.53 55	98.68 173	97.37 315

diffmvs_AUTHOR		97.59 115	97.44 111	98.01 175	98.26 226	95.47 216	98.12 302	98.36 243	96.38 125	98.84 87	99.10 116	91.13 183	99.26 227	98.24 83	98.56 183	99.30 156

diffmvs		97.58 116	97.40 114	98.13 159	98.32 220	95.81 200	98.06 311	98.37 239	96.20 132	98.74 96	98.89 161	91.31 173	99.25 230	98.16 85	98.52 187	99.34 144
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
guyue		97.57 117	97.37 116	98.20 147	98.50 187	95.86 195	98.89 120	97.03 402	97.29 65	98.73 98	98.90 158	89.41 231	99.32 215	98.68 45	98.86 165	99.42 130

MVSFormer		97.57 117	97.49 106	97.84 186	98.07 254	95.76 204	99.47 7	98.40 228	94.98 211	98.79 92	98.83 171	92.34 129	98.41 352	96.91 164	99.59 96	99.34 144

alignmvs		97.56 119	97.07 138	99.01 69	98.66 173	98.37 47	98.83 145	98.06 315	96.74 103	98.00 150	97.65 299	90.80 194	99.48 195	98.37 71	96.56 271	99.19 182

DPM-MVS		97.55 120	96.99 145	99.23 48	99.04 128	98.55 32	97.17 399	98.35 244	94.85 221	97.93 159	98.58 206	95.07 81	99.71 141	92.60 325	99.34 138	99.43 127

OMC-MVS		97.55 120	97.34 118	98.20 147	99.33 74	95.92 186	98.28 275	98.59 172	95.52 171	97.97 153	99.10 116	93.28 115	99.49 190	95.09 242	98.88 162	99.19 182

viewcassd2359sk11		97.53 122	97.32 119	98.16 151	98.45 194	95.83 197	98.57 226	98.42 225	95.52 171	98.07 138	99.12 112	91.81 152	99.25 230	97.46 140	98.48 192	99.41 133

LuminaMVS		97.49 123	97.18 132	98.42 129	97.50 315	97.15 118	98.45 247	97.68 337	96.56 115	98.68 103	98.78 180	89.84 216	99.32 215	98.60 50	98.57 182	98.79 236

E2		97.48 124	97.25 123	98.16 151	98.40 201	95.79 201	98.58 217	98.44 211	95.58 165	98.00 150	99.14 106	91.21 182	99.24 233	97.50 135	98.43 196	99.45 122

E3		97.48 124	97.25 123	98.16 151	98.38 204	95.79 201	98.58 217	98.44 211	95.58 165	98.00 150	99.14 106	91.25 176	99.24 233	97.50 135	98.44 193	99.45 122

KinetiMVS		97.48 124	97.05 140	98.78 86	98.37 207	97.30 101	98.99 91	98.70 141	97.18 77	99.02 70	99.01 138	87.50 286	99.67 149	95.33 232	99.33 140	99.37 137

viewmanbaseed2359cas		97.47 127	97.25 123	98.14 154	98.41 199	95.84 196	98.57 226	98.43 221	95.55 169	97.97 153	99.12 112	91.26 175	99.15 249	97.42 142	98.53 186	99.43 127

PAPM_NR		97.46 128	97.11 135	98.50 117	99.50 48	96.41 157	98.63 208	98.60 165	95.18 192	97.06 214	98.06 257	94.26 100	99.57 169	93.80 293	98.87 164	99.52 101

EPP-MVSNet		97.46 128	97.28 121	97.99 177	98.64 177	95.38 221	99.33 21	98.31 254	93.61 297	97.19 206	99.07 130	94.05 103	99.23 236	96.89 168	98.43 196	99.37 137

3Dnovator	94.51 5	97.46 128	96.93 149	99.07 64	97.78 288	97.64 81	99.35 16	99.06 48	97.02 87	93.75 340	99.16 102	89.25 236	99.92 43	97.22 153	99.75 55	99.64 86

CNLPA		97.45 131	97.03 142	98.73 90	99.05 127	97.44 94	98.07 310	98.53 188	95.32 185	96.80 228	98.53 211	93.32 113	99.72 136	94.31 274	99.31 141	99.02 215

lupinMVS		97.44 132	97.22 130	98.12 162	98.07 254	95.76 204	97.68 356	97.76 334	94.50 245	98.79 92	98.61 201	92.34 129	99.30 220	97.58 123	99.59 96	99.31 152

3Dnovator+	94.38 6	97.43 133	96.78 160	99.38 23	97.83 285	98.52 33	99.37 13	98.71 137	97.09 85	92.99 370	99.13 109	89.36 233	99.89 68	96.97 160	99.57 100	99.71 63

Vis-MVSNet		97.42 134	97.11 135	98.34 134	98.66 173	96.23 165	99.22 41	99.00 53	96.63 111	98.04 143	99.21 90	88.05 273	99.35 211	96.01 207	99.21 145	99.45 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 135	97.25 123	97.91 181	98.70 166	96.80 133	98.82 147	98.69 143	94.53 240	98.11 135	98.28 238	94.50 94	99.57 169	94.12 282	99.49 119	97.37 315

sss		97.39 136	96.98 147	98.61 101	98.60 181	96.61 142	98.22 282	98.93 65	93.97 268	98.01 149	98.48 216	91.98 145	99.85 84	96.45 191	98.15 215	99.39 134

test_cas_vis1_n_1920		97.38 137	97.36 117	97.45 222	98.95 142	93.25 325	99.00 88	98.53 188	97.70 37	99.77 18	99.35 60	84.71 342	99.85 84	98.57 52	99.66 79	99.26 169

PVSNet_Blended		97.38 137	97.12 134	98.14 154	99.25 96	95.35 224	97.28 387	99.26 16	93.13 319	97.94 157	98.21 246	92.74 121	99.81 102	96.88 170	99.40 132	99.27 162

WTY-MVS		97.37 139	96.92 150	98.72 91	98.86 151	96.89 131	98.31 269	98.71 137	95.26 188	97.67 180	98.56 210	92.21 137	99.78 124	95.89 209	96.85 261	99.48 113

AstraMVS		97.34 140	97.24 127	97.65 211	98.13 248	94.15 288	98.94 105	96.25 434	97.47 54	98.60 112	99.28 76	89.67 221	99.41 205	98.73 43	98.07 219	99.38 136

viewmacassd2359aftdt		97.32 141	97.07 138	98.08 166	98.30 222	95.69 206	98.62 211	98.44 211	95.56 167	97.86 165	99.22 88	89.91 214	99.14 252	97.29 150	98.43 196	99.42 130

jason		97.32 141	97.08 137	98.06 170	97.45 321	95.59 208	97.87 338	97.91 326	94.79 224	98.55 115	98.83 171	91.12 185	99.23 236	97.58 123	99.60 94	99.34 144
jason: jason.
MVS_Test		97.28 143	97.00 143	98.13 159	98.33 217	95.97 179	98.74 173	98.07 310	94.27 253	98.44 123	98.07 256	92.48 125	99.26 227	96.43 192	98.19 214	99.16 188

EPNet		97.28 143	96.87 152	98.51 114	94.98 432	96.14 170	98.90 116	97.02 405	98.28 21	95.99 263	99.11 114	91.36 169	99.89 68	96.98 159	99.19 147	99.50 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 145	97.00 143	98.03 172	98.46 192	95.99 173	98.62 211	98.44 211	94.77 225	97.24 203	98.93 152	91.22 178	99.28 224	96.54 186	98.74 172	98.84 232

mvsmamba		97.25 146	96.99 145	98.02 174	98.34 214	95.54 213	99.18 53	97.47 364	95.04 205	98.15 132	98.57 209	89.46 228	99.31 219	97.68 117	99.01 155	99.22 175

viewdifsd2359ckpt13		97.24 147	96.97 148	98.06 170	98.43 195	95.77 203	98.59 214	98.34 247	94.81 222	97.60 191	98.94 150	90.78 198	99.09 264	96.93 163	98.33 207	99.32 151

test_yl		97.22 148	96.78 160	98.54 109	98.73 161	96.60 143	98.45 247	98.31 254	94.70 228	98.02 146	98.42 221	90.80 194	99.70 142	96.81 177	96.79 263	99.34 144

DCV-MVSNet		97.22 148	96.78 160	98.54 109	98.73 161	96.60 143	98.45 247	98.31 254	94.70 228	98.02 146	98.42 221	90.80 194	99.70 142	96.81 177	96.79 263	99.34 144

IS-MVSNet		97.22 148	96.88 151	98.25 142	98.85 154	96.36 160	99.19 49	97.97 320	95.39 179	97.23 204	98.99 141	91.11 186	98.93 290	94.60 262	98.59 180	99.47 115

viewdifsd2359ckpt07		97.20 151	97.05 140	97.65 211	98.40 201	94.33 280	98.39 260	98.43 221	95.67 160	97.66 184	99.08 126	90.04 211	99.32 215	97.47 139	98.29 211	99.31 152

PLC	95.07 4	97.20 151	96.78 160	98.44 125	99.29 88	96.31 164	98.14 299	98.76 125	92.41 347	96.39 251	98.31 236	94.92 86	99.78 124	94.06 285	98.77 171	99.23 173
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 153	97.18 132	97.20 235	98.81 157	93.27 322	95.78 444	99.15 42	95.25 189	96.79 229	98.11 254	92.29 132	99.07 267	98.56 54	99.85 6	99.25 171

SSM_0407		97.17 154	96.87 152	98.08 166	98.19 236	95.90 188	98.52 232	98.44 211	94.77 225	96.75 230	98.93 152	91.22 178	99.22 240	96.54 186	98.43 196	99.10 199

LS3D		97.16 155	96.66 169	98.68 94	98.53 186	97.19 115	98.93 111	98.90 73	92.83 332	95.99 263	99.37 54	92.12 140	99.87 79	93.67 297	99.57 100	98.97 220

AdaColmap		97.15 156	96.70 165	98.48 120	99.16 115	96.69 139	98.01 317	98.89 75	94.44 248	96.83 224	98.68 196	90.69 199	99.76 130	94.36 270	99.29 142	98.98 219

viewdifsd2359ckpt09		97.13 157	96.79 158	98.14 154	98.43 195	95.90 188	98.52 232	98.37 239	94.32 251	97.33 198	98.86 165	90.23 209	99.16 245	96.81 177	98.25 213	99.36 141

mamv4		97.13 157	98.11 76	94.17 407	98.97 140	83.70 453	98.66 201	98.71 137	94.63 234	97.83 166	98.90 158	96.25 32	99.55 179	99.27 28	99.76 48	99.27 162

Effi-MVS+		97.12 159	96.69 166	98.39 132	98.19 236	96.72 138	97.37 378	98.43 221	93.71 286	97.65 185	98.02 260	92.20 138	99.25 230	96.87 173	97.79 228	99.19 182

CHOSEN 1792x2688		97.12 159	96.80 156	98.08 166	99.30 83	94.56 269	98.05 312	99.71 1	93.57 299	97.09 210	98.91 157	88.17 267	99.89 68	96.87 173	99.56 108	99.81 24

F-COLMAP		97.09 161	96.80 156	97.97 178	99.45 61	94.95 248	98.55 230	98.62 164	93.02 324	96.17 258	98.58 206	94.01 104	99.81 102	93.95 287	98.90 160	99.14 192

RRT-MVS		97.03 162	96.78 160	97.77 195	97.90 281	94.34 278	99.12 63	98.35 244	95.87 149	98.06 140	98.70 194	86.45 305	99.63 159	98.04 92	98.54 185	99.35 142

TAMVS		97.02 163	96.79 158	97.70 202	98.06 257	95.31 227	98.52 232	98.31 254	93.95 269	97.05 215	98.61 201	93.49 111	98.52 334	95.33 232	97.81 227	99.29 159

viewmambaseed2359dif		97.01 164	96.84 154	97.51 220	98.19 236	94.21 286	98.16 295	98.23 275	93.61 297	97.78 168	99.13 109	90.79 197	99.18 244	97.24 151	98.40 203	99.15 189

CDS-MVSNet		96.99 165	96.69 166	97.90 182	98.05 259	95.98 174	98.20 285	98.33 249	93.67 293	96.95 217	98.49 215	93.54 110	98.42 345	95.24 239	97.74 231	99.31 152
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 166	96.55 174	98.21 145	98.17 245	96.07 172	97.98 321	98.21 277	97.24 72	97.13 208	98.93 152	86.88 297	99.91 56	95.00 245	99.37 136	98.66 259

114514_t		96.93 167	96.27 187	98.92 78	99.50 48	97.63 82	98.85 139	98.90 73	84.80 449	97.77 169	99.11 114	92.84 119	99.66 152	94.85 248	99.77 42	99.47 115

MAR-MVS		96.91 168	96.40 181	98.45 123	98.69 169	96.90 129	98.66 201	98.68 146	92.40 348	97.07 213	97.96 267	91.54 163	99.75 132	93.68 295	98.92 159	98.69 253
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
HyFIR lowres test		96.90 169	96.49 178	98.14 154	99.33 74	95.56 210	97.38 376	99.65 2	92.34 349	97.61 188	98.20 247	89.29 235	99.10 263	96.97 160	97.60 236	99.77 40

Vis-MVSNet (Re-imp)		96.87 170	96.55 174	97.83 187	98.73 161	95.46 217	99.20 47	98.30 261	94.96 213	96.60 239	98.87 163	90.05 210	98.59 329	93.67 297	98.60 179	99.46 120

SDMVSNet		96.85 171	96.42 179	98.14 154	99.30 83	96.38 158	99.21 44	99.23 28	95.92 145	95.96 265	98.76 188	85.88 317	99.44 202	97.93 96	95.59 300	98.60 264

PAPR		96.84 172	96.24 189	98.65 97	98.72 165	96.92 128	97.36 380	98.57 179	93.33 308	96.67 234	97.57 308	94.30 98	99.56 172	91.05 368	98.59 180	99.47 115

HY-MVS	93.96 8	96.82 173	96.23 190	98.57 104	98.46 192	97.00 124	98.14 299	98.21 277	93.95 269	96.72 233	97.99 264	91.58 158	99.76 130	94.51 266	96.54 272	98.95 223

mamba_0408		96.81 174	96.38 182	98.09 165	98.19 236	95.90 188	95.69 445	98.32 250	94.51 243	96.75 230	98.73 190	90.99 190	99.27 226	95.83 212	98.43 196	99.10 199

UGNet		96.78 175	96.30 186	98.19 150	98.24 228	95.89 193	98.88 127	98.93 65	97.39 59	96.81 227	97.84 280	82.60 371	99.90 64	96.53 188	99.49 119	98.79 236
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
IMVS_0407		96.74 176	96.64 170	97.05 250	97.99 268	92.82 337	98.45 247	98.27 264	95.16 193	97.30 199	98.79 176	91.53 164	99.06 268	94.74 253	97.54 240	99.27 162

IMVS_0403		96.74 176	96.61 171	97.12 244	97.99 268	92.82 337	98.47 245	98.27 264	95.16 193	97.13 208	98.79 176	91.44 167	99.26 227	94.74 253	97.54 240	99.27 162

PVSNet_BlendedMVS		96.73 178	96.60 172	97.12 244	99.25 96	95.35 224	98.26 279	99.26 16	94.28 252	97.94 157	97.46 315	92.74 121	99.81 102	96.88 170	93.32 338	96.20 408

SSM_04072		96.71 179	96.38 182	97.68 205	98.19 236	95.90 188	95.69 445	98.32 250	94.51 243	96.75 230	98.73 190	90.99 190	98.02 391	95.83 212	98.43 196	99.10 199

test_vis1_n_1920		96.71 179	96.84 154	96.31 321	99.11 122	89.74 403	99.05 74	98.58 177	98.08 24	99.87 4	99.37 54	78.48 403	99.93 34	99.29 27	99.69 73	99.27 162

mvs_anonymous		96.70 181	96.53 176	97.18 238	98.19 236	93.78 298	98.31 269	98.19 281	94.01 265	94.47 297	98.27 241	92.08 143	98.46 340	97.39 146	97.91 223	99.31 152

Elysia		96.64 182	96.02 199	98.51 114	98.04 261	97.30 101	98.74 173	98.60 165	95.04 205	97.91 161	98.84 167	83.59 366	99.48 195	94.20 278	99.25 143	98.75 245

StellarMVS		96.64 182	96.02 199	98.51 114	98.04 261	97.30 101	98.74 173	98.60 165	95.04 205	97.91 161	98.84 167	83.59 366	99.48 195	94.20 278	99.25 143	98.75 245

1112_ss		96.63 184	96.00 201	98.50 117	98.56 182	96.37 159	98.18 293	98.10 303	92.92 328	94.84 285	98.43 219	92.14 139	99.58 168	94.35 271	96.51 273	99.56 100

PMMVS		96.60 185	96.33 185	97.41 226	97.90 281	93.93 294	97.35 381	98.41 226	92.84 331	97.76 170	97.45 317	91.10 187	99.20 241	96.26 197	97.91 223	99.11 197

DP-MVS		96.59 186	95.93 204	98.57 104	99.34 71	96.19 168	98.70 188	98.39 233	89.45 419	94.52 295	99.35 60	91.85 149	99.85 84	92.89 321	98.88 162	99.68 75

PatchMatch-RL		96.59 186	96.03 198	98.27 138	99.31 79	96.51 151	97.91 330	99.06 48	93.72 285	96.92 221	98.06 257	88.50 262	99.65 153	91.77 350	99.00 157	98.66 259

GeoE		96.58 188	96.07 195	98.10 164	98.35 209	95.89 193	99.34 17	98.12 297	93.12 320	96.09 259	98.87 163	89.71 220	98.97 280	92.95 317	98.08 218	99.43 127

icg_test_0407_2		96.56 189	96.50 177	96.73 273	97.99 268	92.82 337	97.18 396	98.27 264	95.16 193	97.30 199	98.79 176	91.53 164	98.10 382	94.74 253	97.54 240	99.27 162

XVG-OURS		96.55 190	96.41 180	96.99 253	98.75 160	93.76 299	97.50 370	98.52 191	95.67 160	96.83 224	99.30 74	88.95 250	99.53 182	95.88 210	96.26 287	97.69 304

FIs		96.51 191	96.12 194	97.67 207	97.13 345	97.54 87	99.36 14	99.22 33	95.89 147	94.03 326	98.35 229	91.98 145	98.44 343	96.40 193	92.76 346	97.01 323

XVG-OURS-SEG-HR		96.51 191	96.34 184	97.02 252	98.77 159	93.76 299	97.79 349	98.50 199	95.45 175	96.94 218	99.09 124	87.87 278	99.55 179	96.76 182	95.83 299	97.74 301

PS-MVSNAJss		96.43 193	96.26 188	96.92 262	95.84 412	95.08 238	99.16 55	98.50 199	95.87 149	93.84 335	98.34 233	94.51 91	98.61 325	96.88 170	93.45 333	97.06 321

test_fmvs1		96.42 194	96.67 168	95.66 351	98.82 156	88.53 430	98.80 156	98.20 279	96.39 124	99.64 30	99.20 92	80.35 391	99.67 149	99.04 33	99.57 100	98.78 240

FC-MVSNet-test		96.42 194	96.05 196	97.53 219	96.95 354	97.27 105	99.36 14	99.23 28	95.83 151	93.93 329	98.37 227	92.00 144	98.32 364	96.02 206	92.72 347	97.00 324

ab-mvs		96.42 194	95.71 215	98.55 107	98.63 178	96.75 136	97.88 337	98.74 129	93.84 275	96.54 244	98.18 249	85.34 328	99.75 132	95.93 208	96.35 277	99.15 189

FA-MVS(test-final)		96.41 197	95.94 203	97.82 189	98.21 232	95.20 232	97.80 347	97.58 348	93.21 314	97.36 197	97.70 292	89.47 226	99.56 172	94.12 282	97.99 220	98.71 251

PVSNet	91.96 18	96.35 198	96.15 191	96.96 257	99.17 111	92.05 354	96.08 437	98.68 146	93.69 289	97.75 172	97.80 286	88.86 251	99.69 147	94.26 276	99.01 155	99.15 189

Test_1112_low_res		96.34 199	95.66 220	98.36 133	98.56 182	95.94 182	97.71 354	98.07 310	92.10 358	94.79 289	97.29 330	91.75 153	99.56 172	94.17 280	96.50 274	99.58 98

viewdifsd2359ckpt11		96.30 200	96.13 192	96.81 268	98.10 251	92.10 350	98.49 243	98.40 228	96.02 140	97.61 188	99.31 71	86.37 307	99.29 222	97.52 131	93.36 337	99.04 212

viewmsd2359difaftdt		96.30 200	96.13 192	96.81 268	98.10 251	92.10 350	98.49 243	98.40 228	96.02 140	97.61 188	99.31 71	86.37 307	99.30 220	97.52 131	93.37 336	99.04 212

Effi-MVS+-dtu		96.29 202	96.56 173	95.51 356	97.89 283	90.22 395	98.80 156	98.10 303	96.57 114	96.45 249	96.66 387	90.81 193	98.91 293	95.72 219	97.99 220	97.40 312

QAPM		96.29 202	95.40 226	98.96 75	97.85 284	97.60 84	99.23 37	98.93 65	89.76 413	93.11 367	99.02 134	89.11 241	99.93 34	91.99 344	99.62 91	99.34 144

Fast-Effi-MVS+		96.28 204	95.70 217	98.03 172	98.29 224	95.97 179	98.58 217	98.25 273	91.74 366	95.29 278	97.23 335	91.03 189	99.15 249	92.90 319	97.96 222	98.97 220

nrg030		96.28 204	95.72 212	97.96 180	96.90 359	98.15 63	99.39 11	98.31 254	95.47 174	94.42 303	98.35 229	92.09 142	98.69 317	97.50 135	89.05 397	97.04 322

1314		96.25 206	95.73 211	97.79 191	97.13 345	95.55 212	98.19 288	98.59 172	93.47 303	92.03 396	97.82 284	91.33 171	99.49 190	94.62 261	98.44 193	98.32 284

sd_testset		96.17 207	95.76 210	97.42 225	99.30 83	94.34 278	98.82 147	99.08 46	95.92 145	95.96 265	98.76 188	82.83 370	99.32 215	95.56 225	95.59 300	98.60 264

h-mvs33		96.17 207	95.62 221	97.81 190	99.03 129	94.45 271	98.64 205	98.75 127	97.48 52	98.67 104	98.72 193	89.76 217	99.86 83	97.95 94	81.59 448	99.11 197

HQP_MVS		96.14 209	95.90 205	96.85 265	97.42 323	94.60 267	98.80 156	98.56 182	97.28 67	95.34 274	98.28 238	87.09 292	99.03 273	96.07 201	94.27 308	96.92 331

tttt0517		96.07 210	95.51 224	97.78 192	98.41 199	94.84 252	99.28 29	94.33 457	94.26 254	97.64 186	98.64 200	84.05 357	99.47 199	95.34 231	97.60 236	99.03 214

MVSTER		96.06 211	95.72 212	97.08 248	98.23 230	95.93 185	98.73 179	98.27 264	94.86 219	95.07 280	98.09 255	88.21 266	98.54 332	96.59 184	93.46 331	96.79 350

thisisatest0530		96.01 212	95.36 231	97.97 178	98.38 204	95.52 214	98.88 127	94.19 459	94.04 260	97.64 186	98.31 236	83.82 364	99.46 200	95.29 236	97.70 233	98.93 225

test_djsdf		96.00 213	95.69 218	96.93 259	95.72 414	95.49 215	99.47 7	98.40 228	94.98 211	94.58 293	97.86 277	89.16 239	98.41 352	96.91 164	94.12 316	96.88 340

EI-MVSNet		95.96 214	95.83 207	96.36 317	97.93 279	93.70 305	98.12 302	98.27 264	93.70 288	95.07 280	99.02 134	92.23 136	98.54 332	94.68 257	93.46 331	96.84 346

VortexMVS		95.95 215	95.79 208	96.42 313	98.29 224	93.96 293	98.68 194	98.31 254	96.02 140	94.29 311	97.57 308	89.47 226	98.37 359	97.51 134	91.93 354	96.94 329

ECVR-MVS		95.95 215	95.71 215	96.65 281	99.02 130	90.86 376	99.03 81	91.80 470	96.96 92	98.10 136	99.26 80	81.31 377	99.51 186	96.90 167	99.04 152	99.59 94

BH-untuned		95.95 215	95.72 212	96.65 281	98.55 184	92.26 346	98.23 281	97.79 332	93.73 283	94.62 292	98.01 262	88.97 249	99.00 279	93.04 314	98.51 188	98.68 255

test1111		95.94 218	95.78 209	96.41 314	98.99 137	90.12 396	99.04 78	92.45 469	96.99 91	98.03 144	99.27 79	81.40 376	99.48 195	96.87 173	99.04 152	99.63 88

MSDG		95.93 219	95.30 238	97.83 187	98.90 145	95.36 222	96.83 424	98.37 239	91.32 382	94.43 302	98.73 190	90.27 207	99.60 165	90.05 382	98.82 169	98.52 272

BH-RMVSNet		95.92 220	95.32 236	97.69 203	98.32 220	94.64 261	98.19 288	97.45 369	94.56 238	96.03 261	98.61 201	85.02 333	99.12 257	90.68 373	99.06 151	99.30 156

test_fmvs1_n		95.90 221	95.99 202	95.63 352	98.67 172	88.32 434	99.26 32	98.22 276	96.40 123	99.67 27	99.26 80	73.91 442	99.70 142	99.02 34	99.50 117	98.87 229

Fast-Effi-MVS+-dtu		95.87 222	95.85 206	95.91 338	97.74 293	91.74 360	98.69 191	98.15 293	95.56 167	94.92 283	97.68 297	88.98 248	98.79 311	93.19 309	97.78 229	97.20 319

LFMVS		95.86 223	94.98 253	98.47 121	98.87 150	96.32 162	98.84 143	96.02 435	93.40 306	98.62 110	99.20 92	74.99 434	99.63 159	97.72 110	97.20 249	99.46 120

baseline1		95.84 224	95.12 246	98.01 175	98.49 191	95.98 174	98.73 179	97.03 402	95.37 182	96.22 254	98.19 248	89.96 213	99.16 245	94.60 262	87.48 413	98.90 228

OpenMVS	93.04 13	95.83 225	95.00 251	98.32 135	97.18 342	97.32 98	99.21 44	98.97 57	89.96 409	91.14 405	99.05 132	86.64 300	99.92 43	93.38 303	99.47 122	97.73 302

IMVS_0404		95.82 226	95.52 222	96.73 273	97.99 268	92.82 337	97.23 389	98.27 264	95.16 193	94.31 309	98.79 176	85.63 321	98.10 382	94.74 253	97.54 240	99.27 162

VDD-MVS		95.82 226	95.23 240	97.61 215	98.84 155	93.98 292	98.68 194	97.40 373	95.02 209	97.95 155	99.34 68	74.37 440	99.78 124	98.64 48	96.80 262	99.08 206

UniMVSNet (Re)		95.78 228	95.19 242	97.58 216	96.99 352	97.47 91	98.79 164	99.18 37	95.60 163	93.92 330	97.04 357	91.68 155	98.48 336	95.80 216	87.66 412	96.79 350

VPA-MVSNet		95.75 229	95.11 247	97.69 203	97.24 334	97.27 105	98.94 105	99.23 28	95.13 198	95.51 272	97.32 328	85.73 319	98.91 293	97.33 149	89.55 388	96.89 339

HQP-MVS		95.72 230	95.40 226	96.69 279	97.20 338	94.25 284	98.05 312	98.46 207	96.43 118	94.45 298	97.73 289	86.75 298	98.96 284	95.30 234	94.18 312	96.86 345

hse-mvs2		95.71 231	95.30 238	96.93 259	98.50 187	93.53 310	98.36 261	98.10 303	97.48 52	98.67 104	97.99 264	89.76 217	99.02 276	97.95 94	80.91 454	98.22 287

UniMVSNet_NR-MVSNet		95.71 231	95.15 243	97.40 228	96.84 362	96.97 125	98.74 173	99.24 20	95.16 193	93.88 332	97.72 291	91.68 155	98.31 366	95.81 214	87.25 418	96.92 331

PatchmatchNet		95.71 231	95.52 222	96.29 323	97.58 306	90.72 380	96.84 423	97.52 359	94.06 259	97.08 211	96.96 367	89.24 237	98.90 296	92.03 343	98.37 204	99.26 169
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 234	95.33 235	96.76 272	96.16 398	94.63 262	98.43 255	98.39 233	96.64 110	95.02 282	98.78 180	85.15 332	99.05 269	95.21 241	94.20 311	96.60 373
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 234	95.38 230	96.61 289	97.61 303	93.84 297	98.91 115	98.44 211	95.25 189	94.28 312	98.47 217	86.04 316	99.12 257	95.50 228	93.95 321	96.87 343
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 236	95.69 218	95.44 360	97.54 311	88.54 429	96.97 409	97.56 351	93.50 301	97.52 195	96.93 371	89.49 224	99.16 245	95.25 238	96.42 276	98.64 261

FE-MVS		95.62 237	94.90 257	97.78 192	98.37 207	94.92 249	97.17 399	97.38 375	90.95 393	97.73 175	97.70 292	85.32 330	99.63 159	91.18 360	98.33 207	98.79 236

LPG-MVS_test		95.62 237	95.34 232	96.47 307	97.46 318	93.54 308	98.99 91	98.54 186	94.67 232	94.36 306	98.77 183	85.39 325	99.11 259	95.71 220	94.15 314	96.76 353

CLD-MVS		95.62 237	95.34 232	96.46 310	97.52 314	93.75 301	97.27 388	98.46 207	95.53 170	94.42 303	98.00 263	86.21 311	98.97 280	96.25 199	94.37 306	96.66 368
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 240	94.89 258	97.76 196	98.15 247	95.15 235	96.77 425	94.41 455	92.95 327	97.18 207	97.43 319	84.78 339	99.45 201	94.63 259	97.73 232	98.68 255

MonoMVSNet		95.51 241	95.45 225	95.68 349	95.54 419	90.87 375	98.92 113	97.37 376	95.79 153	95.53 271	97.38 324	89.58 223	97.68 413	96.40 193	92.59 348	98.49 274

thres600view7		95.49 242	94.77 261	97.67 207	98.98 138	95.02 240	98.85 139	96.90 412	95.38 180	96.63 236	96.90 373	84.29 349	99.59 166	88.65 406	96.33 278	98.40 278

test_vis1_n		95.47 243	95.13 244	96.49 304	97.77 289	90.41 390	99.27 31	98.11 300	96.58 112	99.66 28	99.18 98	67.00 456	99.62 163	99.21 29	99.40 132	99.44 125

SCA		95.46 244	95.13 244	96.46 310	97.67 298	91.29 368	97.33 383	97.60 347	94.68 231	96.92 221	97.10 342	83.97 359	98.89 297	92.59 327	98.32 210	99.20 178

IterMVS-LS		95.46 244	95.21 241	96.22 325	98.12 249	93.72 304	98.32 268	98.13 296	93.71 286	94.26 313	97.31 329	92.24 135	98.10 382	94.63 259	90.12 379	96.84 346
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 246	95.34 232	95.77 347	98.69 169	88.75 425	98.87 130	97.21 389	96.13 135	97.22 205	97.68 297	77.95 411	99.65 153	97.58 123	96.77 265	98.91 227

jajsoiax		95.45 246	95.03 250	96.73 273	95.42 427	94.63 262	99.14 59	98.52 191	95.74 155	93.22 360	98.36 228	83.87 362	98.65 322	96.95 162	94.04 317	96.91 336

CVMVSNet		95.43 248	96.04 197	93.57 415	97.93 279	83.62 454	98.12 302	98.59 172	95.68 159	96.56 240	99.02 134	87.51 284	97.51 422	93.56 301	97.44 245	99.60 92

anonymousdsp		95.42 249	94.91 256	96.94 258	95.10 431	95.90 188	99.14 59	98.41 226	93.75 280	93.16 363	97.46 315	87.50 286	98.41 352	95.63 224	94.03 318	96.50 392

DU-MVS		95.42 249	94.76 262	97.40 228	96.53 379	96.97 125	98.66 201	98.99 56	95.43 176	93.88 332	97.69 294	88.57 257	98.31 366	95.81 214	87.25 418	96.92 331

mvs_tets		95.41 251	95.00 251	96.65 281	95.58 418	94.42 273	99.00 88	98.55 184	95.73 157	93.21 361	98.38 226	83.45 368	98.63 323	97.09 156	94.00 319	96.91 336

thres100view900		95.38 252	94.70 266	97.41 226	98.98 138	94.92 249	98.87 130	96.90 412	95.38 180	96.61 238	96.88 374	84.29 349	99.56 172	88.11 409	96.29 282	97.76 299

thres400		95.38 252	94.62 270	97.65 211	98.94 143	94.98 245	98.68 194	96.93 410	95.33 183	96.55 242	96.53 393	84.23 353	99.56 172	88.11 409	96.29 282	98.40 278

BH-w/o		95.38 252	95.08 248	96.26 324	98.34 214	91.79 357	97.70 355	97.43 371	92.87 330	94.24 315	97.22 336	88.66 255	98.84 303	91.55 356	97.70 233	98.16 290

VDDNet		95.36 255	94.53 275	97.86 185	98.10 251	95.13 236	98.85 139	97.75 335	90.46 400	98.36 126	99.39 48	73.27 444	99.64 156	97.98 93	96.58 270	98.81 235

TAPA-MVS	93.98 7	95.35 256	94.56 274	97.74 198	99.13 119	94.83 254	98.33 264	98.64 159	86.62 437	96.29 253	98.61 201	94.00 105	99.29 222	80.00 454	99.41 129	99.09 202
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 257	94.98 253	96.43 312	97.67 298	93.48 312	98.73 179	98.44 211	94.94 217	92.53 383	98.53 211	84.50 348	99.14 252	95.48 229	94.00 319	96.66 368
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 258	94.87 259	96.71 276	99.29 88	93.24 326	98.58 217	98.11 300	89.92 410	93.57 345	99.10 116	86.37 307	99.79 121	90.78 371	98.10 217	97.09 320
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 259	94.72 265	97.13 242	98.05 259	93.26 323	97.87 338	97.20 390	94.96 213	96.18 257	95.66 426	80.97 383	99.35 211	94.47 268	97.08 252	98.78 240

tfpn200view9		95.32 259	94.62 270	97.43 224	98.94 143	94.98 245	98.68 194	96.93 410	95.33 183	96.55 242	96.53 393	84.23 353	99.56 172	88.11 409	96.29 282	97.76 299

Anonymous202405211		95.28 261	94.49 277	97.67 207	99.00 134	93.75 301	98.70 188	97.04 401	90.66 396	96.49 246	98.80 174	78.13 407	99.83 90	96.21 200	95.36 304	99.44 125

thres200		95.25 262	94.57 273	97.28 232	98.81 157	94.92 249	98.20 285	97.11 394	95.24 191	96.54 244	96.22 404	84.58 346	99.53 182	87.93 414	96.50 274	97.39 313

AllTest		95.24 263	94.65 269	96.99 253	99.25 96	93.21 327	98.59 214	98.18 284	91.36 378	93.52 347	98.77 183	84.67 343	99.72 136	89.70 389	97.87 225	98.02 294

LCM-MVSNet-Re		95.22 264	95.32 236	94.91 377	98.18 242	87.85 440	98.75 169	95.66 442	95.11 200	88.96 425	96.85 377	90.26 208	97.65 414	95.65 223	98.44 193	99.22 175

EPNet_dtu		95.21 265	94.95 255	95.99 333	96.17 396	90.45 388	98.16 295	97.27 384	96.77 100	93.14 366	98.33 234	90.34 204	98.42 345	85.57 429	98.81 170	99.09 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 266	94.45 283	97.46 221	96.75 369	96.56 149	98.86 134	98.65 158	93.30 311	93.27 359	98.27 241	84.85 337	98.87 300	94.82 250	91.26 365	96.96 326

D2MVS		95.18 267	95.08 248	95.48 357	97.10 347	92.07 353	98.30 272	99.13 44	94.02 262	92.90 371	96.73 383	89.48 225	98.73 315	94.48 267	93.60 330	95.65 422

WR-MVS		95.15 268	94.46 280	97.22 234	96.67 374	96.45 153	98.21 283	98.81 107	94.15 256	93.16 363	97.69 294	87.51 284	98.30 368	95.29 236	88.62 403	96.90 338

TranMVSNet+NR-MVSNet		95.14 269	94.48 278	97.11 246	96.45 385	96.36 160	99.03 81	99.03 51	95.04 205	93.58 344	97.93 270	88.27 265	98.03 390	94.13 281	86.90 423	96.95 328

myMVS_eth3d28		95.12 270	94.62 270	96.64 285	98.17 245	92.17 347	98.02 316	97.32 378	95.41 178	96.22 254	96.05 410	78.01 409	99.13 254	95.22 240	97.16 250	98.60 264

baseline2		95.11 271	94.52 276	96.87 264	96.65 375	93.56 307	98.27 278	94.10 461	93.45 304	92.02 397	97.43 319	87.45 289	99.19 242	93.88 290	97.41 247	97.87 297

miper_enhance_ethall		95.10 272	94.75 263	96.12 329	97.53 313	93.73 303	96.61 431	98.08 308	92.20 357	93.89 331	96.65 389	92.44 126	98.30 368	94.21 277	91.16 366	96.34 401

Anonymous20240529		95.10 272	94.22 293	97.75 197	99.01 132	94.26 283	98.87 130	98.83 98	85.79 445	96.64 235	98.97 142	78.73 400	99.85 84	96.27 196	94.89 305	99.12 194

test-LLR		95.10 272	94.87 259	95.80 344	96.77 366	89.70 405	96.91 414	95.21 447	95.11 200	94.83 287	95.72 423	87.71 280	98.97 280	93.06 312	98.50 189	98.72 248

WR-MVS_H		95.05 275	94.46 280	96.81 268	96.86 361	95.82 199	99.24 35	99.24 20	93.87 274	92.53 383	96.84 378	90.37 203	98.24 374	93.24 307	87.93 409	96.38 400

miper_ehance_all_eth		95.01 276	94.69 267	95.97 335	97.70 296	93.31 321	97.02 407	98.07 310	92.23 354	93.51 349	96.96 367	91.85 149	98.15 378	93.68 295	91.16 366	96.44 398

testing11		95.00 277	94.28 290	97.16 240	97.96 276	93.36 320	98.09 308	97.06 400	94.94 217	95.33 277	96.15 406	76.89 424	99.40 206	95.77 218	96.30 281	98.72 248

ADS-MVSNet		95.00 277	94.45 283	96.63 286	98.00 266	91.91 356	96.04 438	97.74 336	90.15 406	96.47 247	96.64 390	87.89 276	98.96 284	90.08 380	97.06 253	99.02 215

VPNet		94.99 279	94.19 295	97.40 228	97.16 343	96.57 148	98.71 184	98.97 57	95.67 160	94.84 285	98.24 245	80.36 390	98.67 321	96.46 190	87.32 417	96.96 326

EPMVS		94.99 279	94.48 278	96.52 302	97.22 336	91.75 359	97.23 389	91.66 471	94.11 257	97.28 201	96.81 380	85.70 320	98.84 303	93.04 314	97.28 248	98.97 220

testing91		94.98 281	94.25 292	97.20 235	97.94 277	93.41 315	98.00 319	97.58 348	94.99 210	95.45 273	96.04 411	77.20 419	99.42 204	94.97 246	96.02 295	98.78 240

NR-MVSNet		94.98 281	94.16 298	97.44 223	96.53 379	97.22 113	98.74 173	98.95 61	94.96 213	89.25 424	97.69 294	89.32 234	98.18 376	94.59 264	87.40 415	96.92 331

FMVSNet3		94.97 283	94.26 291	97.11 246	98.18 242	96.62 140	98.56 229	98.26 272	93.67 293	94.09 322	97.10 342	84.25 351	98.01 392	92.08 339	92.14 351	96.70 362

CostFormer		94.95 284	94.73 264	95.60 354	97.28 332	89.06 418	97.53 367	96.89 414	89.66 415	96.82 226	96.72 384	86.05 314	98.95 289	95.53 227	96.13 293	98.79 236

PAPM		94.95 284	94.00 311	97.78 192	97.04 349	95.65 207	96.03 440	98.25 273	91.23 387	94.19 318	97.80 286	91.27 174	98.86 302	82.61 446	97.61 235	98.84 232

CP-MVSNet		94.94 286	94.30 289	96.83 266	96.72 371	95.56 210	99.11 65	98.95 61	93.89 272	92.42 388	97.90 273	87.19 291	98.12 381	94.32 273	88.21 406	96.82 349

TR-MVS		94.94 286	94.20 294	97.17 239	97.75 290	94.14 289	97.59 364	97.02 405	92.28 353	95.75 269	97.64 302	83.88 361	98.96 284	89.77 386	96.15 292	98.40 278

RPSCF		94.87 288	95.40 226	93.26 421	98.89 146	82.06 460	98.33 264	98.06 315	90.30 405	96.56 240	99.26 80	87.09 292	99.49 190	93.82 292	96.32 279	98.24 285

testing99		94.83 289	94.08 303	97.07 249	97.94 277	93.13 329	98.10 307	97.17 392	94.86 219	95.34 274	96.00 415	76.31 427	99.40 206	95.08 243	95.90 296	98.68 255

GA-MVS		94.81 290	94.03 307	97.14 241	97.15 344	93.86 296	96.76 426	97.58 348	94.00 266	94.76 291	97.04 357	80.91 384	98.48 336	91.79 349	96.25 288	99.09 202

c3_l		94.79 291	94.43 285	95.89 340	97.75 290	93.12 331	97.16 401	98.03 317	92.23 354	93.46 353	97.05 356	91.39 168	98.01 392	93.58 300	89.21 395	96.53 384

V42		94.78 292	94.14 300	96.70 278	96.33 390	95.22 231	98.97 95	98.09 307	92.32 351	94.31 309	97.06 353	88.39 263	98.55 331	92.90 319	88.87 401	96.34 401

reproduce_monomvs		94.77 293	94.67 268	95.08 372	98.40 201	89.48 411	98.80 156	98.64 159	97.57 46	93.21 361	97.65 299	80.57 389	98.83 306	97.72 110	89.47 391	96.93 330

CR-MVSNet		94.76 294	94.15 299	96.59 292	97.00 350	93.43 313	94.96 453	97.56 351	92.46 342	96.93 219	96.24 400	88.15 268	97.88 405	87.38 417	96.65 268	98.46 276

v2v482		94.69 295	94.03 307	96.65 281	96.17 396	94.79 257	98.67 199	98.08 308	92.72 334	94.00 327	97.16 339	87.69 283	98.45 341	92.91 318	88.87 401	96.72 358

pmmvs4		94.69 295	93.99 313	96.81 268	95.74 413	95.94 182	97.40 374	97.67 340	90.42 402	93.37 356	97.59 306	89.08 242	98.20 375	92.97 316	91.67 359	96.30 404

cl22		94.68 297	94.19 295	96.13 328	98.11 250	93.60 306	96.94 411	98.31 254	92.43 346	93.32 358	96.87 376	86.51 301	98.28 372	94.10 284	91.16 366	96.51 390

eth_miper_zixun_eth		94.68 297	94.41 286	95.47 358	97.64 301	91.71 361	96.73 428	98.07 310	92.71 335	93.64 341	97.21 337	90.54 201	98.17 377	93.38 303	89.76 383	96.54 382

PCF-MVS	93.45 11	94.68 297	93.43 349	98.42 129	98.62 179	96.77 135	95.48 450	98.20 279	84.63 450	93.34 357	98.32 235	88.55 260	99.81 102	84.80 438	98.96 158	98.68 255
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 300	93.54 344	98.08 166	96.88 360	96.56 149	98.19 288	98.50 199	78.05 462	92.69 378	98.02 260	91.07 188	99.63 159	90.09 379	98.36 206	98.04 293

PS-CasMVS		94.67 300	93.99 313	96.71 276	96.68 373	95.26 228	99.13 62	99.03 51	93.68 291	92.33 389	97.95 268	85.35 327	98.10 382	93.59 299	88.16 408	96.79 350

cascas		94.63 302	93.86 323	96.93 259	96.91 358	94.27 282	96.00 441	98.51 194	85.55 446	94.54 294	96.23 402	84.20 355	98.87 300	95.80 216	96.98 258	97.66 305

tpmvs		94.60 303	94.36 288	95.33 364	97.46 318	88.60 428	96.88 420	97.68 337	91.29 384	93.80 337	96.42 397	88.58 256	99.24 233	91.06 366	96.04 294	98.17 289

LTVRE_ROB	92.95 15	94.60 303	93.90 319	96.68 280	97.41 326	94.42 273	98.52 232	98.59 172	91.69 369	91.21 404	98.35 229	84.87 336	99.04 272	91.06 366	93.44 334	96.60 373
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
v1144		94.59 305	93.92 316	96.60 291	96.21 392	94.78 258	98.59 214	98.14 295	91.86 365	94.21 317	97.02 360	87.97 274	98.41 352	91.72 351	89.57 386	96.61 372

ADS-MVSNet2		94.58 306	94.40 287	95.11 370	98.00 266	88.74 426	96.04 438	97.30 380	90.15 406	96.47 247	96.64 390	87.89 276	97.56 420	90.08 380	97.06 253	99.02 215

WBMVS		94.56 307	94.04 305	96.10 330	98.03 263	93.08 333	97.82 346	98.18 284	94.02 262	93.77 339	96.82 379	81.28 378	98.34 361	95.47 230	91.00 369	96.88 340

ACMH	92.88 16	94.55 308	93.95 315	96.34 319	97.63 302	93.26 323	98.81 155	98.49 204	93.43 305	89.74 418	98.53 211	81.91 373	99.08 266	93.69 294	93.30 339	96.70 362
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 309	93.85 324	96.63 286	97.98 274	93.06 334	98.77 168	97.84 329	93.67 293	93.80 337	98.04 259	76.88 425	98.96 284	94.79 252	92.86 344	97.86 298

XVG-ACMP-BASELINE		94.54 309	94.14 300	95.75 348	96.55 378	91.65 362	98.11 305	98.44 211	94.96 213	94.22 316	97.90 273	79.18 399	99.11 259	94.05 286	93.85 323	96.48 395

AUN-MVS		94.53 311	93.73 334	96.92 262	98.50 187	93.52 311	98.34 263	98.10 303	93.83 277	95.94 267	97.98 266	85.59 323	99.03 273	94.35 271	80.94 453	98.22 287

DIV-MVS_self_test		94.52 312	94.03 307	95.99 333	97.57 310	93.38 318	97.05 405	97.94 323	91.74 366	92.81 373	97.10 342	89.12 240	98.07 388	92.60 325	90.30 376	96.53 384

cl____		94.51 313	94.01 310	96.02 332	97.58 306	93.40 317	97.05 405	97.96 322	91.73 368	92.76 375	97.08 348	89.06 243	98.13 380	92.61 324	90.29 377	96.52 387

ETVMVS		94.50 314	93.44 348	97.68 205	98.18 242	95.35 224	98.19 288	97.11 394	93.73 283	96.40 250	95.39 429	74.53 437	98.84 303	91.10 362	96.31 280	98.84 232

GBi-Net		94.49 315	93.80 327	96.56 296	98.21 232	95.00 241	98.82 147	98.18 284	92.46 342	94.09 322	97.07 349	81.16 379	97.95 397	92.08 339	92.14 351	96.72 358

test1		94.49 315	93.80 327	96.56 296	98.21 232	95.00 241	98.82 147	98.18 284	92.46 342	94.09 322	97.07 349	81.16 379	97.95 397	92.08 339	92.14 351	96.72 358

dmvs_re		94.48 317	94.18 297	95.37 362	97.68 297	90.11 397	98.54 231	97.08 396	94.56 238	94.42 303	97.24 334	84.25 351	97.76 411	91.02 369	92.83 345	98.24 285

v8		94.47 318	93.77 330	96.57 295	96.36 388	94.83 254	99.05 74	98.19 281	91.92 362	93.16 363	96.97 365	88.82 254	98.48 336	91.69 352	87.79 410	96.39 399

FMVSNet2		94.47 318	93.61 340	97.04 251	98.21 232	96.43 155	98.79 164	98.27 264	92.46 342	93.50 350	97.09 346	81.16 379	98.00 394	91.09 363	91.93 354	96.70 362

test2506		94.44 320	93.91 318	96.04 331	99.02 130	88.99 421	99.06 72	79.47 483	96.96 92	98.36 126	99.26 80	77.21 418	99.52 185	96.78 181	99.04 152	99.59 94

Patchmatch-test		94.42 321	93.68 338	96.63 286	97.60 304	91.76 358	94.83 457	97.49 363	89.45 419	94.14 320	97.10 342	88.99 245	98.83 306	85.37 432	98.13 216	99.29 159

PEN-MVS		94.42 321	93.73 334	96.49 304	96.28 391	94.84 252	99.17 54	99.00 53	93.51 300	92.23 391	97.83 283	86.10 313	97.90 401	92.55 330	86.92 422	96.74 355

v144192		94.39 323	93.70 336	96.48 306	96.06 402	94.35 277	98.58 217	98.16 292	91.45 375	94.33 308	97.02 360	87.50 286	98.45 341	91.08 365	89.11 396	96.63 370

Baseline_NR-MVSNet		94.35 324	93.81 326	95.96 336	96.20 393	94.05 291	98.61 213	96.67 424	91.44 376	93.85 334	97.60 305	88.57 257	98.14 379	94.39 269	86.93 421	95.68 421

miper_lstm_enhance		94.33 325	94.07 304	95.11 370	97.75 290	90.97 372	97.22 391	98.03 317	91.67 370	92.76 375	96.97 365	90.03 212	97.78 410	92.51 332	89.64 385	96.56 379

v1192		94.32 326	93.58 341	96.53 301	96.10 400	94.45 271	98.50 240	98.17 290	91.54 373	94.19 318	97.06 353	86.95 296	98.43 344	90.14 378	89.57 386	96.70 362

UWE-MVS		94.30 327	93.89 321	95.53 355	97.83 285	88.95 422	97.52 369	93.25 463	94.44 248	96.63 236	97.07 349	78.70 401	99.28 224	91.99 344	97.56 239	98.36 281

ACMH+	92.99 14	94.30 327	93.77 330	95.88 341	97.81 287	92.04 355	98.71 184	98.37 239	93.99 267	90.60 411	98.47 217	80.86 386	99.05 269	92.75 323	92.40 350	96.55 381

v148		94.29 329	93.76 332	95.91 338	96.10 400	92.93 335	98.58 217	97.97 320	92.59 340	93.47 352	96.95 369	88.53 261	98.32 364	92.56 329	87.06 420	96.49 393

v10		94.29 329	93.55 343	96.51 303	96.39 387	94.80 256	98.99 91	98.19 281	91.35 380	93.02 369	96.99 363	88.09 270	98.41 352	90.50 375	88.41 405	96.33 403

SD_0403		94.28 331	94.46 280	93.73 411	98.02 264	85.32 449	98.31 269	98.40 228	94.75 227	93.59 342	98.16 250	89.01 244	96.54 441	82.32 447	97.58 238	99.34 144

MVP-Stereo		94.28 331	93.92 316	95.35 363	94.95 433	92.60 342	97.97 322	97.65 341	91.61 371	90.68 410	97.09 346	86.32 310	98.42 345	89.70 389	99.34 138	95.02 435
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 333	93.33 351	96.97 256	97.19 341	93.38 318	98.74 173	98.57 179	91.21 389	93.81 336	98.58 206	72.85 445	98.77 313	95.05 244	93.93 322	98.77 243

OurMVSNet-221017-0		94.21 334	94.00 311	94.85 382	95.60 417	89.22 416	98.89 120	97.43 371	95.29 186	92.18 393	98.52 214	82.86 369	98.59 329	93.46 302	91.76 357	96.74 355

v1921920		94.20 335	93.47 347	96.40 316	95.98 406	94.08 290	98.52 232	98.15 293	91.33 381	94.25 314	97.20 338	86.41 306	98.42 345	90.04 383	89.39 393	96.69 367

WB-MVSnew		94.19 336	94.04 305	94.66 390	96.82 364	92.14 348	97.86 340	95.96 438	93.50 301	95.64 270	96.77 382	88.06 272	97.99 395	84.87 435	96.86 259	93.85 455

v7n		94.19 336	93.43 349	96.47 307	95.90 409	94.38 276	99.26 32	98.34 247	91.99 360	92.76 375	97.13 341	88.31 264	98.52 334	89.48 394	87.70 411	96.52 387

tpm2		94.19 336	93.76 332	95.46 359	97.23 335	89.04 419	97.31 385	96.85 418	87.08 436	96.21 256	96.79 381	83.75 365	98.74 314	92.43 335	96.23 290	98.59 267

TESTMET0.1,1		94.18 339	93.69 337	95.63 352	96.92 356	89.12 417	96.91 414	94.78 452	93.17 316	94.88 284	96.45 396	78.52 402	98.92 291	93.09 311	98.50 189	98.85 230

dp		94.15 340	93.90 319	94.90 378	97.31 331	86.82 445	96.97 409	97.19 391	91.22 388	96.02 262	96.61 392	85.51 324	99.02 276	90.00 384	94.30 307	98.85 230

ET-MVSNet_ETH3D		94.13 341	92.98 359	97.58 216	98.22 231	96.20 166	97.31 385	95.37 446	94.53 240	79.56 464	97.63 304	86.51 301	97.53 421	96.91 164	90.74 371	99.02 215

tpm		94.13 341	93.80 327	95.12 369	96.50 381	87.91 439	97.44 371	95.89 441	92.62 338	96.37 252	96.30 399	84.13 356	98.30 368	93.24 307	91.66 360	99.14 192

testing222		94.12 343	93.03 358	97.37 231	98.02 264	94.66 259	97.94 326	96.65 426	94.63 234	95.78 268	95.76 418	71.49 446	98.92 291	91.17 361	95.88 297	98.52 272

IterMVS-SCA-FT		94.11 344	93.87 322	94.85 382	97.98 274	90.56 387	97.18 396	98.11 300	93.75 280	92.58 381	97.48 314	83.97 359	97.41 424	92.48 334	91.30 363	96.58 375

Anonymous20231211		94.10 345	93.26 354	96.61 289	99.11 122	94.28 281	99.01 86	98.88 78	86.43 439	92.81 373	97.57 308	81.66 375	98.68 320	94.83 249	89.02 399	96.88 340

IterMVS		94.09 346	93.85 324	94.80 386	97.99 268	90.35 393	97.18 396	98.12 297	93.68 291	92.46 387	97.34 325	84.05 357	97.41 424	92.51 332	91.33 362	96.62 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 347	93.51 345	95.80 344	96.77 366	89.70 405	96.91 414	95.21 447	92.89 329	94.83 287	95.72 423	77.69 413	98.97 280	93.06 312	98.50 189	98.72 248

test0.0.03 1		94.08 347	93.51 345	95.80 344	95.53 421	92.89 336	97.38 376	95.97 437	95.11 200	92.51 385	96.66 387	87.71 280	96.94 431	87.03 419	93.67 326	97.57 309

v1240		94.06 349	93.29 353	96.34 319	96.03 404	93.90 295	98.44 253	98.17 290	91.18 390	94.13 321	97.01 362	86.05 314	98.42 345	89.13 400	89.50 390	96.70 362

X-MVStestdata		94.06 349	92.30 375	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	43.50 478	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

DTE-MVSNet		93.98 351	93.26 354	96.14 327	96.06 402	94.39 275	99.20 47	98.86 91	93.06 322	91.78 398	97.81 285	85.87 318	97.58 419	90.53 374	86.17 427	96.46 397

pm-mvs1		93.94 352	93.06 357	96.59 292	96.49 382	95.16 233	98.95 102	98.03 317	92.32 351	91.08 406	97.84 280	84.54 347	98.41 352	92.16 337	86.13 430	96.19 409

MS-PatchMatch		93.84 353	93.63 339	94.46 400	96.18 395	89.45 412	97.76 350	98.27 264	92.23 354	92.13 394	97.49 313	79.50 396	98.69 317	89.75 387	99.38 134	95.25 427

tfpnnormal		93.66 354	92.70 365	96.55 300	96.94 355	95.94 182	98.97 95	99.19 36	91.04 391	91.38 403	97.34 325	84.94 335	98.61 325	85.45 431	89.02 399	95.11 431

EU-MVSNet		93.66 354	94.14 300	92.25 432	95.96 408	83.38 456	98.52 232	98.12 297	94.69 230	92.61 380	98.13 253	87.36 290	96.39 446	91.82 348	90.00 381	96.98 325

our_test_3		93.65 356	93.30 352	94.69 388	95.45 425	89.68 407	96.91 414	97.65 341	91.97 361	91.66 401	96.88 374	89.67 221	97.93 400	88.02 412	91.49 361	96.48 395

pmmvs5		93.65 356	92.97 360	95.68 349	95.49 422	92.37 343	98.20 285	97.28 383	89.66 415	92.58 381	97.26 331	82.14 372	98.09 386	93.18 310	90.95 370	96.58 375

SSC-MVS3.2		93.59 358	93.13 356	94.97 375	96.81 365	89.71 404	97.95 323	98.49 204	94.59 237	93.50 350	96.91 372	77.74 412	98.37 359	91.69 352	90.47 374	96.83 348

test_fmvs2		93.43 359	93.58 341	92.95 426	96.97 353	83.91 452	99.19 49	97.24 386	95.74 155	95.20 279	98.27 241	69.65 448	98.72 316	96.26 197	93.73 325	96.24 406

tpm cat1		93.36 360	92.80 362	95.07 373	97.58 306	87.97 438	96.76 426	97.86 328	82.17 457	93.53 346	96.04 411	86.13 312	99.13 254	89.24 398	95.87 298	98.10 292

JIA-IIPM		93.35 361	92.49 371	95.92 337	96.48 383	90.65 382	95.01 452	96.96 408	85.93 443	96.08 260	87.33 468	87.70 282	98.78 312	91.35 358	95.58 302	98.34 282

SixPastTwentyTwo		93.34 362	92.86 361	94.75 387	95.67 415	89.41 414	98.75 169	96.67 424	93.89 272	90.15 416	98.25 244	80.87 385	98.27 373	90.90 370	90.64 372	96.57 377

USDC		93.33 363	92.71 364	95.21 366	96.83 363	90.83 378	96.91 414	97.50 361	93.84 275	90.72 409	98.14 252	77.69 413	98.82 308	89.51 393	93.21 341	95.97 415

IB-MVS	91.98 17	93.27 364	91.97 379	97.19 237	97.47 317	93.41 315	97.09 404	95.99 436	93.32 309	92.47 386	95.73 421	78.06 408	99.53 182	94.59 264	82.98 441	98.62 262
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
MIMVSNet		93.26 365	92.21 376	96.41 314	97.73 294	93.13 329	95.65 447	97.03 402	91.27 386	94.04 325	96.06 409	75.33 432	97.19 427	86.56 421	96.23 290	98.92 226

ppachtmachnet_test		93.22 366	92.63 366	94.97 375	95.45 425	90.84 377	96.88 420	97.88 327	90.60 397	92.08 395	97.26 331	88.08 271	97.86 406	85.12 434	90.33 375	96.22 407

Patchmtry		93.22 366	92.35 374	95.84 343	96.77 366	93.09 332	94.66 460	97.56 351	87.37 435	92.90 371	96.24 400	88.15 268	97.90 401	87.37 418	90.10 380	96.53 384

testing3		93.19 368	92.48 372	95.30 365	98.07 254	92.27 344	98.64 205	97.17 392	93.94 271	93.98 328	97.04 357	67.97 453	96.01 451	88.40 407	97.14 251	97.63 306

FMVSNet1		93.19 368	92.07 377	96.56 296	97.54 311	95.00 241	98.82 147	98.18 284	90.38 403	92.27 390	97.07 349	73.68 443	97.95 397	89.36 396	91.30 363	96.72 358

LF4IMVS		93.14 370	92.79 363	94.20 405	95.88 410	88.67 427	97.66 358	97.07 398	93.81 278	91.71 399	97.65 299	77.96 410	98.81 309	91.47 357	91.92 356	95.12 430

mmtdpeth		93.12 371	92.61 367	94.63 392	97.60 304	89.68 407	99.21 44	97.32 378	94.02 262	97.72 176	94.42 440	77.01 423	99.44 202	99.05 32	77.18 465	94.78 440

testgi		93.06 372	92.45 373	94.88 380	96.43 386	89.90 399	98.75 169	97.54 357	95.60 163	91.63 402	97.91 272	74.46 439	97.02 429	86.10 425	93.67 326	97.72 303

PatchT		93.06 372	91.97 379	96.35 318	96.69 372	92.67 341	94.48 463	97.08 396	86.62 437	97.08 211	92.23 462	87.94 275	97.90 401	78.89 458	96.69 266	98.49 274

RPMNet		92.81 374	91.34 385	97.24 233	97.00 350	93.43 313	94.96 453	98.80 114	82.27 456	96.93 219	92.12 463	86.98 295	99.82 97	76.32 464	96.65 268	98.46 276

UWE-MVS-28		92.79 375	92.51 370	93.62 414	96.46 384	86.28 446	97.93 327	92.71 468	94.17 255	94.78 290	97.16 339	81.05 382	96.43 444	81.45 450	96.86 259	98.14 291

myMVS_eth3d		92.73 376	92.01 378	94.89 379	97.39 327	90.94 373	97.91 330	97.46 365	93.16 317	93.42 354	95.37 430	68.09 452	96.12 449	88.34 408	96.99 255	97.60 307

TransMVSNet (Re)		92.67 377	91.51 384	96.15 326	96.58 377	94.65 260	98.90 116	96.73 420	90.86 394	89.46 423	97.86 277	85.62 322	98.09 386	86.45 423	81.12 451	95.71 420

ttmdpeth		92.61 378	91.96 381	94.55 394	94.10 443	90.60 386	98.52 232	97.29 381	92.67 336	90.18 414	97.92 271	79.75 395	97.79 408	91.09 363	86.15 429	95.26 426

Syy-MVS		92.55 379	92.61 367	92.38 429	97.39 327	83.41 455	97.91 330	97.46 365	93.16 317	93.42 354	95.37 430	84.75 340	96.12 449	77.00 463	96.99 255	97.60 307

K. test v3		92.55 379	91.91 382	94.48 398	95.64 416	89.24 415	99.07 71	94.88 451	94.04 260	86.78 440	97.59 306	77.64 416	97.64 415	92.08 339	89.43 392	96.57 377

DSMNet-mixed		92.52 381	92.58 369	92.33 430	94.15 442	82.65 458	98.30 272	94.26 458	89.08 424	92.65 379	95.73 421	85.01 334	95.76 453	86.24 424	97.76 230	98.59 267

TinyColmap		92.31 382	91.53 383	94.65 391	96.92 356	89.75 402	96.92 412	96.68 423	90.45 401	89.62 420	97.85 279	76.06 430	98.81 309	86.74 420	92.51 349	95.41 424

gg-mvs-nofinetune		92.21 383	90.58 391	97.13 242	96.75 369	95.09 237	95.85 442	89.40 476	85.43 447	94.50 296	81.98 471	80.80 387	98.40 358	92.16 337	98.33 207	97.88 296

FMVSNet5		91.81 384	90.92 387	94.49 397	97.21 337	92.09 352	98.00 319	97.55 356	89.31 422	90.86 408	95.61 427	74.48 438	95.32 457	85.57 429	89.70 384	96.07 413

pmmvs6		91.77 385	90.63 390	95.17 368	94.69 439	91.24 369	98.67 199	97.92 325	86.14 441	89.62 420	97.56 311	75.79 431	98.34 361	90.75 372	84.56 434	95.94 416

Anonymous20231206		91.66 386	91.10 386	93.33 419	94.02 447	87.35 442	98.58 217	97.26 385	90.48 399	90.16 415	96.31 398	83.83 363	96.53 442	79.36 456	89.90 382	96.12 411

Patchmatch-RL test		91.49 387	90.85 388	93.41 417	91.37 458	84.40 450	92.81 467	95.93 440	91.87 364	87.25 436	94.87 436	88.99 245	96.53 442	92.54 331	82.00 444	99.30 156

test_0402		91.32 388	90.27 394	94.48 398	96.60 376	91.12 370	98.50 240	97.22 387	86.10 442	88.30 432	96.98 364	77.65 415	97.99 395	78.13 460	92.94 343	94.34 443

test_vis1_rt		91.29 389	90.65 389	93.19 423	97.45 321	86.25 447	98.57 226	90.90 474	93.30 311	86.94 439	93.59 449	62.07 465	99.11 259	97.48 138	95.58 302	94.22 446

PVSNet_0	88.72 19	91.28 390	90.03 397	95.00 374	97.99 268	87.29 443	94.84 456	98.50 199	92.06 359	89.86 417	95.19 432	79.81 394	99.39 209	92.27 336	69.79 471	98.33 283

mvs5depth		91.23 391	90.17 395	94.41 402	92.09 455	89.79 401	95.26 451	96.50 428	90.73 395	91.69 400	97.06 353	76.12 429	98.62 324	88.02 412	84.11 437	94.82 437

Anonymous20240521		91.18 392	90.44 392	93.42 416	93.70 448	88.47 431	98.94 105	97.56 351	88.46 428	89.56 422	95.08 435	77.15 421	96.97 430	83.92 441	89.55 388	94.82 437

EG-PatchMatch MVS		91.13 393	90.12 396	94.17 407	94.73 438	89.00 420	98.13 301	97.81 331	89.22 423	85.32 450	96.46 395	67.71 454	98.42 345	87.89 416	93.82 324	95.08 432

TDRefinement		91.06 394	89.68 399	95.21 366	85.35 476	91.49 365	98.51 239	97.07 398	91.47 374	88.83 429	97.84 280	77.31 417	99.09 264	92.79 322	77.98 463	95.04 434

sc_t1		91.01 395	89.39 401	95.85 342	95.99 405	90.39 391	98.43 255	97.64 343	78.79 460	92.20 392	97.94 269	66.00 458	98.60 328	91.59 355	85.94 431	98.57 270

UnsupCasMVSNet_eth		90.99 396	89.92 398	94.19 406	94.08 444	89.83 400	97.13 403	98.67 151	93.69 289	85.83 446	96.19 405	75.15 433	96.74 435	89.14 399	79.41 458	96.00 414

test20.03		90.89 397	90.38 393	92.43 428	93.48 449	88.14 437	98.33 264	97.56 351	93.40 306	87.96 433	96.71 385	80.69 388	94.13 463	79.15 457	86.17 427	95.01 436

MDA-MVSNet_test_wron		90.71 398	89.38 403	94.68 389	94.83 435	90.78 379	97.19 395	97.46 365	87.60 433	72.41 471	95.72 423	86.51 301	96.71 438	85.92 427	86.80 424	96.56 379

YYNet1		90.70 399	89.39 401	94.62 393	94.79 437	90.65 382	97.20 393	97.46 365	87.54 434	72.54 470	95.74 419	86.51 301	96.66 439	86.00 426	86.76 425	96.54 382

KD-MVS_self_test		90.38 400	89.38 403	93.40 418	92.85 452	88.94 423	97.95 323	97.94 323	90.35 404	90.25 413	93.96 446	79.82 393	95.94 452	84.62 440	76.69 466	95.33 425

pmmvs-eth3d		90.36 401	89.05 406	94.32 404	91.10 460	92.12 349	97.63 363	96.95 409	88.86 426	84.91 451	93.13 454	78.32 404	96.74 435	88.70 404	81.81 446	94.09 449

FE-MVSNET2		90.29 402	88.94 408	94.36 403	90.48 462	92.27 344	98.45 247	97.82 330	91.59 372	84.90 452	93.10 455	73.92 441	96.42 445	87.92 415	82.26 442	94.39 441

tt0320		90.26 403	88.73 410	94.86 381	96.12 399	90.62 384	98.17 294	97.63 344	77.46 463	89.68 419	96.04 411	69.19 450	97.79 408	88.98 401	85.29 433	96.16 410

CL-MVSNet_self_test		90.11 404	89.14 405	93.02 424	91.86 457	88.23 436	96.51 434	98.07 310	90.49 398	90.49 412	94.41 441	84.75 340	95.34 456	80.79 452	74.95 468	95.50 423

new_pmnet		90.06 405	89.00 407	93.22 422	94.18 441	88.32 434	96.42 436	96.89 414	86.19 440	85.67 447	93.62 448	77.18 420	97.10 428	81.61 449	89.29 394	94.23 445

MDA-MVSNet-bldmvs		89.97 406	88.35 412	94.83 385	95.21 429	91.34 366	97.64 360	97.51 360	88.36 430	71.17 472	96.13 407	79.22 398	96.63 440	83.65 442	86.27 426	96.52 387

tt0320-xc		89.79 407	88.11 414	94.84 384	96.19 394	90.61 385	98.16 295	97.22 387	77.35 464	88.75 430	96.70 386	65.94 459	97.63 416	89.31 397	83.39 439	96.28 405

CMPMVS	66.06 21	89.70 408	89.67 400	89.78 437	93.19 450	76.56 463	97.00 408	98.35 244	80.97 458	81.57 459	97.75 288	74.75 436	98.61 325	89.85 385	93.63 328	94.17 447
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 409	88.28 413	93.82 410	92.81 453	91.08 371	98.01 317	97.45 369	87.95 432	87.90 434	95.87 417	67.63 455	94.56 462	78.73 459	88.18 407	95.83 418

KD-MVS_2432*1600		89.61 410	87.96 418	94.54 395	94.06 445	91.59 363	95.59 448	97.63 344	89.87 411	88.95 426	94.38 443	78.28 405	96.82 433	84.83 436	68.05 472	95.21 428

miper_refine_blended		89.61 410	87.96 418	94.54 395	94.06 445	91.59 363	95.59 448	97.63 344	89.87 411	88.95 426	94.38 443	78.28 405	96.82 433	84.83 436	68.05 472	95.21 428

MVStest1		89.53 412	87.99 417	94.14 409	94.39 440	90.42 389	98.25 280	96.84 419	82.81 453	81.18 461	97.33 327	77.09 422	96.94 431	85.27 433	78.79 459	95.06 433

MVS-HIRNet		89.46 413	88.40 411	92.64 427	97.58 306	82.15 459	94.16 466	93.05 467	75.73 467	90.90 407	82.52 470	79.42 397	98.33 363	83.53 443	98.68 173	97.43 310

OpenMVS_ROB	86.42 20	89.00 414	87.43 422	93.69 413	93.08 451	89.42 413	97.91 330	96.89 414	78.58 461	85.86 445	94.69 437	69.48 449	98.29 371	77.13 462	93.29 340	93.36 457

mvsany_test3		88.80 415	88.04 415	91.09 436	89.78 465	81.57 461	97.83 345	95.49 445	93.81 278	87.53 435	93.95 447	56.14 468	97.43 423	94.68 257	83.13 440	94.26 444

FE-MVSNET		88.56 416	87.09 423	92.99 425	89.93 464	89.99 398	98.15 298	95.59 443	88.42 429	84.87 453	92.90 457	74.82 435	94.99 460	77.88 461	81.21 450	93.99 452

new-patchmatchnet		88.50 417	87.45 421	91.67 434	90.31 463	85.89 448	97.16 401	97.33 377	89.47 418	83.63 455	92.77 459	76.38 426	95.06 459	82.70 445	77.29 464	94.06 451

FE-MVSNET1		88.45 418	86.70 425	93.70 412	89.21 468	90.38 392	98.28 275	97.79 332	87.96 431	83.51 456	92.97 456	62.37 464	96.33 447	86.47 422	81.71 447	94.38 442

APD_test1		88.22 419	88.01 416	88.86 439	95.98 406	74.66 471	97.21 392	96.44 430	83.96 452	86.66 442	97.90 273	60.95 466	97.84 407	82.73 444	90.23 378	94.09 449

PM-MVS		87.77 420	86.55 426	91.40 435	91.03 461	83.36 457	96.92 412	95.18 449	91.28 385	86.48 444	93.42 450	53.27 469	96.74 435	89.43 395	81.97 445	94.11 448

dmvs_testset		87.64 421	88.93 409	83.79 448	95.25 428	63.36 480	97.20 393	91.17 472	93.07 321	85.64 448	95.98 416	85.30 331	91.52 470	69.42 469	87.33 416	96.49 393

test_fmvs3		87.17 422	87.06 424	87.50 441	91.21 459	75.66 466	99.05 74	96.61 427	92.79 333	88.85 428	92.78 458	43.72 472	93.49 464	93.95 287	84.56 434	93.34 458

UnsupCasMVSNet_bld		87.17 422	85.12 429	93.31 420	91.94 456	88.77 424	94.92 455	98.30 261	84.30 451	82.30 457	90.04 465	63.96 462	97.25 426	85.85 428	74.47 470	93.93 454

N_pmnet		87.12 424	87.77 420	85.17 445	95.46 424	61.92 481	97.37 378	70.66 486	85.83 444	88.73 431	96.04 411	85.33 329	97.76 411	80.02 453	90.48 373	95.84 417

pmmvs3		86.67 425	84.86 430	92.11 433	88.16 470	87.19 444	96.63 430	94.75 453	79.88 459	87.22 437	92.75 460	66.56 457	95.20 458	81.24 451	76.56 467	93.96 453

test_f		86.07 426	85.39 427	88.10 440	89.28 467	75.57 467	97.73 353	96.33 432	89.41 421	85.35 449	91.56 464	43.31 474	95.53 454	91.32 359	84.23 436	93.21 459

WB-MVS		84.86 427	85.33 428	83.46 449	89.48 466	69.56 475	98.19 288	96.42 431	89.55 417	81.79 458	94.67 438	84.80 338	90.12 471	52.44 475	80.64 455	90.69 462

SSC-MVS		84.27 428	84.71 431	82.96 453	89.19 469	68.83 476	98.08 309	96.30 433	89.04 425	81.37 460	94.47 439	84.60 345	89.89 472	49.80 477	79.52 457	90.15 463

dongtai		82.47 429	81.88 432	84.22 447	95.19 430	76.03 464	94.59 462	74.14 485	82.63 454	87.19 438	96.09 408	64.10 461	87.85 475	58.91 473	84.11 437	88.78 467

test_vis3_rt		79.22 430	77.40 437	84.67 446	86.44 474	74.85 470	97.66 358	81.43 481	84.98 448	67.12 474	81.91 472	28.09 482	97.60 417	88.96 402	80.04 456	81.55 472

test_method		79.03 431	78.17 433	81.63 454	86.06 475	54.40 486	82.75 475	96.89 414	39.54 478	80.98 462	95.57 428	58.37 467	94.73 461	84.74 439	78.61 460	95.75 419

testf1		79.02 432	77.70 434	82.99 451	88.10 471	66.90 477	94.67 458	93.11 464	71.08 469	74.02 467	93.41 451	34.15 478	93.25 465	72.25 467	78.50 461	88.82 465

APD_test2		79.02 432	77.70 434	82.99 451	88.10 471	66.90 477	94.67 458	93.11 464	71.08 469	74.02 467	93.41 451	34.15 478	93.25 465	72.25 467	78.50 461	88.82 465

LCM-MVSNet		78.70 434	76.24 440	86.08 443	77.26 482	71.99 473	94.34 464	96.72 421	61.62 473	76.53 465	89.33 466	33.91 480	92.78 468	81.85 448	74.60 469	93.46 456

kuosan		78.45 435	77.69 436	80.72 455	92.73 454	75.32 468	94.63 461	74.51 484	75.96 465	80.87 463	93.19 453	63.23 463	79.99 479	42.56 479	81.56 449	86.85 471

Gipuma		78.40 436	76.75 439	83.38 450	95.54 419	80.43 462	79.42 476	97.40 373	64.67 472	73.46 469	80.82 473	45.65 471	93.14 467	66.32 471	87.43 414	76.56 475
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 437	75.44 441	85.46 444	82.54 477	74.95 469	94.23 465	93.08 466	72.80 468	74.68 466	87.38 467	36.36 477	91.56 469	73.95 465	63.94 474	89.87 464

FPMVS		77.62 438	77.14 438	79.05 457	79.25 480	60.97 482	95.79 443	95.94 439	65.96 471	67.93 473	94.40 442	37.73 476	88.88 474	68.83 470	88.46 404	87.29 468

EGC-MVSNET		75.22 439	69.54 442	92.28 431	94.81 436	89.58 409	97.64 360	96.50 428	1.82 483	5.57 484	95.74 419	68.21 451	96.26 448	73.80 466	91.71 358	90.99 461

ANet_high		69.08 440	65.37 444	80.22 456	65.99 484	71.96 474	90.91 471	90.09 475	82.62 455	49.93 479	78.39 474	29.36 481	81.75 476	62.49 472	38.52 478	86.95 470

tmp_tt		68.90 441	66.97 443	74.68 459	50.78 486	59.95 483	87.13 472	83.47 480	38.80 479	62.21 475	96.23 402	64.70 460	76.91 481	88.91 403	30.49 479	87.19 469

PMVS	61.03 23	65.95 442	63.57 446	73.09 460	57.90 485	51.22 487	85.05 474	93.93 462	54.45 474	44.32 480	83.57 469	13.22 483	89.15 473	58.68 474	81.00 452	78.91 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 443	64.25 445	67.02 461	82.28 478	59.36 484	91.83 470	85.63 478	52.69 475	60.22 476	77.28 475	41.06 475	80.12 478	46.15 478	41.14 476	61.57 477

EMVS		64.07 444	63.26 447	66.53 462	81.73 479	58.81 485	91.85 469	84.75 479	51.93 477	59.09 477	75.13 476	43.32 473	79.09 480	42.03 480	39.47 477	61.69 476

MVE	62.14 22	63.28 445	59.38 448	74.99 458	74.33 483	65.47 479	85.55 473	80.50 482	52.02 476	51.10 478	75.00 477	10.91 486	80.50 477	51.60 476	53.40 475	78.99 473
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 446	30.18 450	30.16 463	78.61 481	43.29 488	66.79 477	14.21 487	17.31 480	14.82 483	11.93 483	11.55 485	41.43 482	37.08 481	19.30 480	5.76 480

cdsmvs_eth3d_5k		23.98 447	31.98 449	0.00 466	0.00 489	0.00 491	0.00 478	98.59 172	0.00 484	0.00 485	98.61 201	90.60 200	0.00 485	0.00 484	0.00 483	0.00 481

testmvs		21.48 448	24.95 451	11.09 465	14.89 487	6.47 490	96.56 432	9.87 488	7.55 481	17.93 481	39.02 479	9.43 487	5.90 484	16.56 483	12.72 481	20.91 479

test123		20.95 449	23.72 452	12.64 464	13.54 488	8.19 489	96.55 433	6.13 489	7.48 482	16.74 482	37.98 480	12.97 484	6.05 483	16.69 482	5.43 482	23.68 478

ab-mvs-re		8.20 450	10.94 453	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	98.43 219	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

pcd_1.5k_mvsjas		7.88 451	10.50 454	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	94.51 91	0.00 485	0.00 484	0.00 483	0.00 481

mmdepth		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

monomultidepth		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

test_blank		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uanet_test		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

DCPMVS		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

sosnet-low-res		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

sosnet		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uncertanet		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

Regformer		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uanet		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

MED-MVS test				99.52 13	99.77 2	98.86 22	99.32 22	99.24 20	96.41 121	99.30 50	99.35 60		99.92 43	98.30 75	99.80 25	99.79 28

TestfortrainingZip							99.32 22

WAC-MVS						90.94 373								88.66 405

FOURS1					99.82 1	98.66 28	99.69 1	98.95 61	97.46 55	99.39 44

MSC_two_6792asdad				99.62 7	99.17 111	99.08 12		98.63 162					99.94 14	98.53 55	99.80 25	99.86 12

PC_three_1452									95.08 204	99.60 32	99.16 102	97.86 2	98.47 339	97.52 131	99.72 68	99.74 50

No_MVS				99.62 7	99.17 111	99.08 12		98.63 162					99.94 14	98.53 55	99.80 25	99.86 12

test_one_0601					99.66 31	99.25 3		98.86 91	97.55 47	99.20 59	99.47 35	97.57 8

eth-test2					0.00 489

eth-test					0.00 489

ZD-MVS					99.46 58	98.70 27		98.79 119	93.21 314	98.67 104	98.97 142	95.70 52	99.83 90	96.07 201	99.58 99

RE-MVS-def			98.34 54		99.49 52	97.86 74	99.11 65	98.80 114	96.49 116	99.17 62	99.35 60	95.29 69		97.72 110	99.65 82	99.71 63

IU-MVS					99.71 24	99.23 8		98.64 159	95.28 187	99.63 31				98.35 72	99.81 16	99.83 18

OPU-MVS				99.37 27	99.24 103	99.05 15	99.02 84				99.16 102	97.81 3	99.37 210	97.24 151	99.73 63	99.70 67

test_241102_TWO								98.87 85	97.65 39	99.53 37	99.48 33	97.34 13	99.94 14	98.43 67	99.80 25	99.83 18

test_241102_ONE					99.71 24	99.24 6		98.87 85	97.62 41	99.73 22	99.39 48	97.53 9	99.74 134

9.14			98.06 79		99.47 56		98.71 184	98.82 101	94.36 250	99.16 66	99.29 75	96.05 40	99.81 102	97.00 158	99.71 70

save fliter					99.46 58	98.38 40	98.21 283	98.71 137	97.95 28

test_0728_THIRD									97.32 63	99.45 39	99.46 40	97.88 1	99.94 14	98.47 63	99.86 2	99.85 15

test_0728_SECOND				99.71 1	99.72 17	99.35 1	98.97 95	98.88 78					99.94 14	98.47 63	99.81 16	99.84 17

test0726					99.72 17	99.25 3	99.06 72	98.88 78	97.62 41	99.56 34	99.50 29	97.42 11

GSMVS																99.20 178

test_part2					99.63 34	99.18 11				99.27 56

sam_mvs1												89.45 229				99.20 178

sam_mvs												88.99 245

ambc				89.49 438	86.66 473	75.78 465	92.66 468	96.72 421		86.55 443	92.50 461	46.01 470	97.90 401	90.32 376	82.09 443	94.80 439

MTGPA								98.74 129

test_post1							96.68 429				30.43 482	87.85 279	98.69 317	92.59 327

test_post											31.83 481	88.83 252	98.91 293

patchmatchnet-post											95.10 434	89.42 230	98.89 297

GG-mvs-BLEND				96.59 292	96.34 389	94.98 245	96.51 434	88.58 477		93.10 368	94.34 445	80.34 392	98.05 389	89.53 392	96.99 255	96.74 355

MTMP							98.89 120	94.14 460

gm-plane-assit					95.88 410	87.47 441			89.74 414		96.94 370		99.19 242	93.32 306

test9_res														96.39 195	99.57 100	99.69 70

TEST9					99.31 79	98.50 34	97.92 328	98.73 132	92.63 337	97.74 173	98.68 196	96.20 35	99.80 109

test_8					99.29 88	98.44 36	97.89 336	98.72 134	92.98 325	97.70 178	98.66 199	96.20 35	99.80 109

agg_prior2														95.87 211	99.57 100	99.68 75

agg_prior					99.30 83	98.38 40		98.72 134		97.57 194			99.81 102

TestCases				96.99 253	99.25 96	93.21 327		98.18 284	91.36 378	93.52 347	98.77 183	84.67 343	99.72 136	89.70 389	97.87 225	98.02 294

test_prior4						98.01 70	97.86 340

test_prior2							97.80 347		96.12 137	97.89 164	98.69 195	95.96 44		96.89 168	99.60 94

test_prior				99.19 50	99.31 79	98.22 57		98.84 96					99.70 142			99.65 83

旧先验2							97.57 366		91.30 383	98.67 104			99.80 109	95.70 222

新几何2							97.64 360

新几何1				99.16 55	99.34 71	98.01 70		98.69 143	90.06 408	98.13 134	98.95 149	94.60 89	99.89 68	91.97 346	99.47 122	99.59 94

旧先验1					99.29 88	97.48 89		98.70 141			99.09 124	95.56 55			99.47 122	99.61 90

无先验							97.58 365	98.72 134	91.38 377				99.87 79	93.36 305		99.60 92

原ACMM2							97.67 357

原ACMM1				98.65 97	99.32 77	96.62 140		98.67 151	93.27 313	97.81 167	98.97 142	95.18 76	99.83 90	93.84 291	99.46 125	99.50 106

test222					99.23 104	97.17 116	97.40 374	98.66 154	88.68 427	98.05 141	98.96 147	94.14 102			99.53 113	99.61 90

testdata2													99.89 68	91.65 354

segment_acmp												96.85 16

testdata				98.26 141	99.20 109	95.36 222		98.68 146	91.89 363	98.60 112	99.10 116	94.44 96	99.82 97	94.27 275	99.44 126	99.58 98

testdata1							97.32 384		96.34 127

test12				99.18 52	99.16 115	98.19 59		98.53 188		98.07 138		95.13 79	99.72 136		99.56 108	99.63 88

plane_prior7					97.42 323	94.63 262

plane_prior6					97.35 330	94.61 265						87.09 292

plane_prior5								98.56 182					99.03 273	96.07 201	94.27 308	96.92 331

plane_prior4											98.28 238

plane_prior3						94.61 265			97.02 87	95.34 274

plane_prior2							98.80 156		97.28 67

plane_prior1					97.37 329

plane_prior						94.60 267	98.44 253		96.74 103						94.22 310

n2								0.00 490

nn								0.00 490

door-mid								94.37 456

lessismore_v0				94.45 401	94.93 434	88.44 432		91.03 473		86.77 441	97.64 302	76.23 428	98.42 345	90.31 377	85.64 432	96.51 390

LGP-MVS_train				96.47 307	97.46 318	93.54 308		98.54 186	94.67 232	94.36 306	98.77 183	85.39 325	99.11 259	95.71 220	94.15 314	96.76 353

test11								98.66 154

door								94.64 454

HQP5-MVS						94.25 284

HQP-NCC					97.20 338		98.05 312		96.43 118	94.45 298

ACMP_Plane					97.20 338		98.05 312		96.43 118	94.45 298

BP-MVS														95.30 234

HQP4-MVS										94.45 298			98.96 284			96.87 343

HQP3-MVS								98.46 207							94.18 312

HQP2-MVS												86.75 298

NP-MVS					97.28 332	94.51 270					97.73 289

MDTV_nov1_ep13_2view						84.26 451	96.89 419		90.97 392	97.90 163		89.89 215		93.91 289		99.18 187

MDTV_nov1_ep13			95.40 226		97.48 316	88.34 433	96.85 422	97.29 381	93.74 282	97.48 196	97.26 331	89.18 238	99.05 269	91.92 347	97.43 246

ACMMP++_ref															92.97 342

ACMMP++															93.61 329

Test By Simon												94.64 88

ITE_SJBPF				95.44 360	97.42 323	91.32 367		97.50 361	95.09 203	93.59 342	98.35 229	81.70 374	98.88 299	89.71 388	93.39 335	96.12 411

DeepMVS_CX				86.78 442	97.09 348	72.30 472		95.17 450	75.92 466	84.34 454	95.19 432	70.58 447	95.35 455	79.98 455	89.04 398	92.68 460

High-resolution multi-view benchmark