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	98.86 137	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	98.86 137	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	98.82 150	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	98.42 263	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	98.70 191	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	98.94 229

ME-MVS		98.83 19	98.60 24	99.52 13	99.58 37	98.86 22	98.69 194	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	98.47 250	98.81 107	97.72 34	98.76 95	99.16 106	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	98.78 185	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	98.80 159	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	98.86 137	99.23 28	98.90 5	99.83 12	99.59 13	91.57 160	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	98.79 167	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	98.41 264	98.68 146	97.04 86	98.52 116	98.80 179	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	97.60 426	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	95.83 199	98.79 167	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	99.17 103	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	98.82 150	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	99.17 103	95.91 46	99.94 14	97.55 131	99.79 35	99.78 33

NCCC		98.61 31	98.35 48	99.38 23	99.28 92	98.61 31	98.45 252	98.76 125	97.82 33	98.45 121	98.93 157	96.65 21	99.83 90	97.38 152	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	95.12 204	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 99	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	91.82 152	99.95 9	99.82 6	99.82 14	98.75 250

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 70	97.73 299	97.15 118	98.84 146	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	93.67 298	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	98.02 147	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	98.72 186	98.66 154	97.51 49	98.15 132	98.83 176	95.70 52	99.92 43	97.53 133	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	99.13 113	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	98.71 187	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	98.50 245	98.78 121	97.72 34	98.92 83	99.28 76	95.27 70	99.82 97	97.55 131	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	98.68 197	99.20 34	97.50 50	99.87 4	99.50 29	91.96 149	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	98.80 159	99.26 16	98.82 7	99.87 4	99.60 10	90.95 193	99.93 34	99.76 11	99.73 63	99.12 199

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	97.02 413	98.96 1	99.17 62	99.47 35	91.97 148	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	97.42 147	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	99.10 121	95.46 58	99.93 34	97.57 130	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	99.14 110	95.25 72	99.15 254	98.83 40	99.56 108	99.20 183

PGM-MVS		98.49 51	98.23 67	99.27 43	99.72 17	98.08 67	98.99 91	99.49 5	95.43 180	99.03 69	99.32 69	95.56 55	99.94 14	96.80 185	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	98.30 277	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	97.91 335	99.58 3	97.20 75	98.33 129	99.00 145	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	98.77 188	95.06 82	99.55 179	98.95 35	99.87 1	99.12 199

test_fmvsmvis_n_1920		98.44 57	98.51 32	98.23 144	98.33 218	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	98.57 275

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	99.10 121	95.73 51	99.13 259	98.71 44	99.49 119	99.09 207

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	99.06 136	95.27 70	99.93 34	97.07 162	99.63 89	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 160	99.30 83	95.25 235	98.85 142	99.39 7	97.94 29	99.74 21	99.62 4	92.59 123	99.91 56	99.65 18	99.52 114	99.25 176

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 101	99.45 61	96.32 162	98.28 280	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	97.75 356	98.89 75	97.71 36	98.33 129	98.97 147	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	95.41 222	98.86 137	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	98.11 310	98.29 269	97.19 76	98.99 75	99.02 139	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	94.46 252	98.94 77	99.20 92	95.16 77	99.74 134	97.58 126	99.85 6	99.77 40

patch_mono-2		98.36 66	98.87 6	96.82 273	99.53 42	90.68 390	98.64 208	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	94.63 239	98.61 111	98.97 147	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	97.90 187	99.16 115	95.08 244	98.75 172	99.24 20	98.39 19	99.81 13	99.52 23	92.35 128	99.90 64	99.74 13	99.51 116	98.71 256

APD-MVS		98.35 68	98.00 84	99.42 21	99.51 46	98.72 25	98.80 159	98.82 101	94.52 247	99.23 58	99.25 85	95.54 57	99.80 109	96.52 194	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	97.95 328	99.58 3	97.14 81	98.44 123	99.01 143	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	93.32 315	98.83 90	99.10 121	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	97.92 161	99.23 86	94.54 90	99.94 14	96.74 188	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	94.81 227	98.99 75	98.90 163	95.22 75	99.59 166	99.15 30	99.84 11	99.07 215

MP-MVS-pluss		98.31 73	97.92 86	99.49 16	99.72 17	98.88 19	98.43 260	98.78 121	94.10 263	97.69 184	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	98.66 204	99.20 34	98.82 7	99.79 15	99.60 10	89.38 237	99.92 43	99.80 8	99.38 134	98.69 258

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 189	98.86 151	94.99 250	98.58 221	99.00 53	98.29 20	99.73 22	99.60 10	91.70 155	99.92 43	99.63 21	99.73 63	98.76 249

MGCNet		98.23 76	97.91 87	99.21 49	98.06 262	97.96 72	98.58 221	95.51 452	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	97.60 196	99.36 58	94.45 95	99.93 34	97.14 159	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	98.34 215	97.26 110	99.61 5	98.43 225	96.78 99	98.87 85	98.84 172	93.72 108	99.01 283	98.91 37	99.50 117	99.19 187

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 165	98.54 185	95.24 236	98.87 130	99.24 20	97.50 50	99.70 26	99.67 1	91.33 172	99.89 68	99.47 25	99.54 111	99.21 182

fmvsm_s_conf0.1_n_2		98.14 81	98.02 82	98.53 112	98.88 147	97.07 122	98.69 194	98.82 101	98.78 9	99.77 18	99.61 5	88.83 257	99.91 56	99.71 15	99.07 150	98.61 268

fmvsm_s_conf0.1_n_a		98.08 82	98.04 81	98.21 145	97.66 305	95.39 226	98.89 120	99.17 38	97.24 72	99.76 20	99.67 1	91.13 184	99.88 77	99.39 26	99.41 129	99.35 144

dcpmvs_2		98.08 82	98.59 25	96.56 302	99.57 39	90.34 402	99.15 56	98.38 243	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	99.08 131	91.22 179	99.80 109	97.40 149	99.57 100	99.37 139

CANet		98.05 85	97.76 91	98.90 81	98.73 161	97.27 105	98.35 267	98.78 121	97.37 62	97.72 181	98.96 152	91.53 165	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	97.92 333	98.73 132	92.98 331	97.74 178	98.68 201	96.20 35	99.80 109	96.59 189	99.57 100	99.68 75

ETV-MVS		97.96 87	97.81 89	98.40 131	98.42 198	97.27 105	98.73 182	98.55 184	96.84 96	98.38 125	97.44 323	95.39 61	99.35 211	97.62 120	98.89 161	98.58 274

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	94.87 252	98.83 168	99.65 83

CDPH-MVS		97.94 89	97.49 106	99.28 41	99.47 56	98.44 36	97.91 335	98.67 151	92.57 347	98.77 94	98.85 171	95.93 45	99.72 136	95.56 230	99.69 73	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 328	99.00 134	89.54 418	97.43 380	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	98.83 148	98.75 127	96.96 92	96.89 228	99.50 29	90.46 204	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	95.48 429	96.83 132	98.95 102	98.60 165	98.58 14	98.93 81	99.55 18	88.57 262	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	98.33 269	98.89 75	92.62 344	98.05 142	98.94 155	95.34 66	99.65 153	96.04 210	99.42 128	99.19 187

CSCG		97.85 94	97.74 92	98.20 147	99.67 30	95.16 239	99.22 41	99.32 12	93.04 329	97.02 221	98.92 161	95.36 64	99.91 56	97.43 145	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	98.44 214	97.86 31	98.71 101	99.08 131	91.22 179	99.80 109	97.40 149	97.53 249	99.47 115

BP-MVS1		97.82 96	97.51 105	98.76 88	98.25 232	97.39 95	99.15 56	97.68 344	96.69 107	98.47 117	99.10 121	90.29 211	99.51 186	98.60 50	99.35 137	99.37 139

MG-MVS		97.81 97	97.60 96	98.44 125	99.12 120	95.97 180	97.75 356	98.78 121	96.89 95	98.46 118	99.22 88	93.90 107	99.68 148	94.81 256	99.52 114	99.67 79

VNet		97.79 98	97.40 114	98.96 75	98.88 147	97.55 85	98.63 211	98.93 65	96.74 103	99.02 70	98.84 172	90.33 210	99.83 90	98.53 55	96.66 272	99.50 106

EIA-MVS		97.75 99	97.58 97	98.27 138	98.38 205	96.44 154	99.01 86	98.60 165	95.88 148	97.26 207	97.53 317	94.97 84	99.33 214	97.38 152	99.20 146	99.05 216

PS-MVSNAJ		97.73 100	97.77 90	97.62 219	98.68 171	95.58 212	97.34 389	98.51 194	97.29 65	98.66 108	97.88 281	94.51 91	99.90 64	97.87 101	99.17 148	97.39 318

casdiffmvs_mvg		97.72 101	97.48 108	98.44 125	98.42 198	96.59 147	98.92 113	98.44 214	96.20 132	97.76 175	99.20 92	91.66 158	99.23 241	98.27 82	98.41 203	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	97.32 120	98.92 78	99.64 33	97.10 121	99.12 63	98.81 107	92.34 355	98.09 137	99.08 131	93.01 117	99.92 43	96.06 209	99.77 42	99.75 48

PVSNet_Blended_VisFu		97.70 103	97.46 109	98.44 125	99.27 93	95.91 188	98.63 211	99.16 40	94.48 251	97.67 185	98.88 167	92.80 120	99.91 56	97.11 160	99.12 149	99.50 106

mvsany_test1		97.69 104	97.70 93	97.66 215	98.24 233	94.18 293	97.53 372	97.53 365	95.52 175	99.66 28	99.51 26	94.30 98	99.56 172	98.38 70	98.62 178	99.23 178

sasdasda		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 238	96.76 101	97.67 185	97.40 327	92.26 133	99.49 190	98.28 79	96.28 290	99.08 211

canonicalmvs		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 238	96.76 101	97.67 185	97.40 327	92.26 133	99.49 190	98.28 79	96.28 290	99.08 211

xiu_mvs_v2_base		97.66 107	97.70 93	97.56 223	98.61 180	95.46 220	97.44 377	98.46 207	97.15 80	98.65 109	98.15 256	94.33 97	99.80 109	97.84 104	98.66 177	97.41 316

GDP-MVS		97.64 108	97.28 122	98.71 92	98.30 223	97.33 97	99.05 74	98.52 191	96.34 127	98.80 91	99.05 137	89.74 224	99.51 186	96.86 181	98.86 165	99.28 166

baseline		97.64 108	97.44 111	98.25 142	98.35 210	96.20 166	99.00 88	98.32 256	96.33 129	98.03 145	99.17 103	91.35 171	99.16 250	98.10 87	98.29 212	99.39 135

casdiffmvs		97.63 110	97.41 113	98.28 137	98.33 218	96.14 170	98.82 150	98.32 256	96.38 125	97.95 156	99.21 90	91.23 178	99.23 241	98.12 86	98.37 205	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	97.19 132	98.92 78	98.66 173	98.20 58	99.32 22	98.38 243	96.69 107	97.58 198	97.42 326	92.10 142	99.50 189	98.28 79	96.25 293	99.08 211

xiu_mvs_v1_base_debu		97.60 112	97.56 100	97.72 204	98.35 210	95.98 175	97.86 345	98.51 194	97.13 82	99.01 72	98.40 228	91.56 161	99.80 109	98.53 55	98.68 173	97.37 320

xiu_mvs_v1_base		97.60 112	97.56 100	97.72 204	98.35 210	95.98 175	97.86 345	98.51 194	97.13 82	99.01 72	98.40 228	91.56 161	99.80 109	98.53 55	98.68 173	97.37 320

xiu_mvs_v1_base_debi		97.60 112	97.56 100	97.72 204	98.35 210	95.98 175	97.86 345	98.51 194	97.13 82	99.01 72	98.40 228	91.56 161	99.80 109	98.53 55	98.68 173	97.37 320

diffmvs_AUTHOR		97.59 115	97.44 111	98.01 177	98.26 231	95.47 219	98.12 306	98.36 249	96.38 125	98.84 87	99.10 121	91.13 184	99.26 227	98.24 83	98.56 183	99.30 158

diffmvs		97.58 116	97.40 114	98.13 160	98.32 221	95.81 202	98.06 316	98.37 245	96.20 132	98.74 96	98.89 166	91.31 174	99.25 231	98.16 85	98.52 187	99.34 146
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	95.86 196	98.89 120	97.03 410	97.29 65	98.73 98	98.90 163	89.41 236	99.32 215	98.68 45	98.86 165	99.42 130

MVSFormer		97.57 117	97.49 106	97.84 191	98.07 259	95.76 206	99.47 7	98.40 233	94.98 216	98.79 92	98.83 176	92.34 129	98.41 357	96.91 169	99.59 96	99.34 146

alignmvs		97.56 119	97.07 143	99.01 69	98.66 173	98.37 47	98.83 148	98.06 321	96.74 103	98.00 151	97.65 304	90.80 195	99.48 195	98.37 71	96.56 276	99.19 187

E3new		97.55 120	97.35 118	98.16 151	98.48 192	95.85 197	98.55 234	98.41 230	95.42 182	98.06 140	99.12 116	92.23 136	99.24 237	97.43 145	98.45 193	99.39 135

DPM-MVS		97.55 120	96.99 150	99.23 48	99.04 128	98.55 32	97.17 406	98.35 250	94.85 226	97.93 160	98.58 211	95.07 81	99.71 141	92.60 334	99.34 138	99.43 127

OMC-MVS		97.55 120	97.34 119	98.20 147	99.33 74	95.92 187	98.28 280	98.59 172	95.52 175	97.97 154	99.10 121	93.28 115	99.49 190	95.09 247	98.88 162	99.19 187

viewcassd2359sk11		97.53 123	97.32 120	98.16 151	98.45 195	95.83 199	98.57 230	98.42 229	95.52 175	98.07 138	99.12 116	91.81 153	99.25 231	97.46 143	98.48 192	99.41 133

LuminaMVS		97.49 124	97.18 133	98.42 129	97.50 320	97.15 118	98.45 252	97.68 344	96.56 115	98.68 103	98.78 185	89.84 221	99.32 215	98.60 50	98.57 182	98.79 241

E2		97.48 125	97.25 124	98.16 151	98.40 202	95.79 203	98.58 221	98.44 214	95.58 165	98.00 151	99.14 110	91.21 183	99.24 237	97.50 138	98.43 197	99.45 122

E3		97.48 125	97.25 124	98.16 151	98.38 205	95.79 203	98.58 221	98.44 214	95.58 165	98.00 151	99.14 110	91.25 177	99.24 237	97.50 138	98.44 194	99.45 122

KinetiMVS		97.48 125	97.05 145	98.78 86	98.37 208	97.30 101	98.99 91	98.70 141	97.18 77	99.02 70	99.01 143	87.50 292	99.67 149	95.33 237	99.33 140	99.37 139

viewmanbaseed2359cas		97.47 128	97.25 124	98.14 155	98.41 200	95.84 198	98.57 230	98.43 225	95.55 171	97.97 154	99.12 116	91.26 176	99.15 254	97.42 147	98.53 186	99.43 127

PAPM_NR		97.46 129	97.11 140	98.50 117	99.50 48	96.41 157	98.63 211	98.60 165	95.18 197	97.06 219	98.06 262	94.26 100	99.57 169	93.80 298	98.87 164	99.52 101

EPP-MVSNet		97.46 129	97.28 122	97.99 179	98.64 177	95.38 227	99.33 21	98.31 260	93.61 303	97.19 211	99.07 135	94.05 103	99.23 241	96.89 173	98.43 197	99.37 139

3Dnovator	94.51 5	97.46 129	96.93 154	99.07 64	97.78 293	97.64 81	99.35 16	99.06 48	97.02 87	93.75 346	99.16 106	89.25 241	99.92 43	97.22 158	99.75 55	99.64 86

CNLPA		97.45 132	97.03 147	98.73 90	99.05 127	97.44 94	98.07 315	98.53 188	95.32 190	96.80 233	98.53 216	93.32 113	99.72 136	94.31 279	99.31 141	99.02 220

lupinMVS		97.44 133	97.22 131	98.12 163	98.07 259	95.76 206	97.68 361	97.76 341	94.50 250	98.79 92	98.61 206	92.34 129	99.30 220	97.58 126	99.59 96	99.31 154

3Dnovator+	94.38 6	97.43 134	96.78 165	99.38 23	97.83 290	98.52 33	99.37 13	98.71 137	97.09 85	92.99 376	99.13 113	89.36 238	99.89 68	96.97 165	99.57 100	99.71 63

Vis-MVSNet		97.42 135	97.11 140	98.34 134	98.66 173	96.23 165	99.22 41	99.00 53	96.63 111	98.04 144	99.21 90	88.05 279	99.35 211	96.01 212	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		97.41 136	97.25 124	97.91 186	98.70 166	96.80 133	98.82 150	98.69 143	94.53 245	98.11 135	98.28 243	94.50 94	99.57 169	94.12 287	99.49 119	97.37 320

sss		97.39 137	96.98 152	98.61 101	98.60 181	96.61 142	98.22 286	98.93 65	93.97 273	98.01 150	98.48 221	91.98 146	99.85 84	96.45 196	98.15 220	99.39 135

test_cas_vis1_n_1920		97.38 138	97.36 117	97.45 227	98.95 142	93.25 334	99.00 88	98.53 188	97.70 37	99.77 18	99.35 60	84.71 348	99.85 84	98.57 52	99.66 79	99.26 174

PVSNet_Blended		97.38 138	97.12 139	98.14 155	99.25 96	95.35 230	97.28 394	99.26 16	93.13 325	97.94 158	98.21 251	92.74 121	99.81 102	96.88 175	99.40 132	99.27 167

E6new		97.37 140	97.16 135	97.98 180	98.28 228	95.40 224	98.87 130	98.45 211	95.55 171	97.84 167	99.20 92	90.44 205	99.25 231	97.61 123	98.22 216	99.29 161

E6		97.37 140	97.16 135	97.98 180	98.28 228	95.40 224	98.87 130	98.45 211	95.55 171	97.84 167	99.20 92	90.44 205	99.25 231	97.61 123	98.22 216	99.29 161

E5		97.37 140	97.16 135	97.98 180	98.30 223	95.41 222	98.87 130	98.45 211	95.56 167	97.84 167	99.19 99	90.39 207	99.25 231	97.61 123	98.22 216	99.29 161

E4		97.37 140	97.13 138	98.12 163	98.27 230	95.70 208	98.59 217	98.44 214	95.56 167	97.80 172	99.18 101	90.57 202	99.26 227	97.45 144	98.28 214	99.40 134

WTY-MVS		97.37 140	96.92 155	98.72 91	98.86 151	96.89 131	98.31 274	98.71 137	95.26 193	97.67 185	98.56 215	92.21 138	99.78 124	95.89 214	96.85 266	99.48 113

AstraMVS		97.34 145	97.24 128	97.65 216	98.13 253	94.15 294	98.94 105	96.25 442	97.47 54	98.60 112	99.28 76	89.67 226	99.41 205	98.73 43	98.07 224	99.38 138

viewmacassd2359aftdt		97.32 146	97.07 143	98.08 168	98.30 223	95.69 209	98.62 214	98.44 214	95.56 167	97.86 166	99.22 88	89.91 219	99.14 257	97.29 155	98.43 197	99.42 130

jason		97.32 146	97.08 142	98.06 172	97.45 326	95.59 211	97.87 343	97.91 332	94.79 229	98.55 115	98.83 176	91.12 186	99.23 241	97.58 126	99.60 94	99.34 146
jason: jason.
MVS_Test		97.28 148	97.00 148	98.13 160	98.33 218	95.97 180	98.74 176	98.07 316	94.27 258	98.44 123	98.07 261	92.48 125	99.26 227	96.43 197	98.19 219	99.16 193

EPNet		97.28 148	96.87 157	98.51 114	94.98 438	96.14 170	98.90 116	97.02 413	98.28 21	95.99 268	99.11 119	91.36 170	99.89 68	96.98 164	99.19 147	99.50 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 150	97.00 148	98.03 174	98.46 193	95.99 174	98.62 214	98.44 214	94.77 230	97.24 208	98.93 157	91.22 179	99.28 224	96.54 191	98.74 172	98.84 237

mvsmamba		97.25 151	96.99 150	98.02 176	98.34 215	95.54 216	99.18 53	97.47 371	95.04 210	98.15 132	98.57 214	89.46 233	99.31 219	97.68 117	99.01 155	99.22 180

viewdifsd2359ckpt13		97.24 152	96.97 153	98.06 172	98.43 196	95.77 205	98.59 217	98.34 253	94.81 227	97.60 196	98.94 155	90.78 199	99.09 269	96.93 168	98.33 208	99.32 153

test_yl		97.22 153	96.78 165	98.54 109	98.73 161	96.60 143	98.45 252	98.31 260	94.70 233	98.02 147	98.42 226	90.80 195	99.70 142	96.81 182	96.79 268	99.34 146

DCV-MVSNet		97.22 153	96.78 165	98.54 109	98.73 161	96.60 143	98.45 252	98.31 260	94.70 233	98.02 147	98.42 226	90.80 195	99.70 142	96.81 182	96.79 268	99.34 146

IS-MVSNet		97.22 153	96.88 156	98.25 142	98.85 154	96.36 160	99.19 49	97.97 326	95.39 184	97.23 209	98.99 146	91.11 187	98.93 295	94.60 267	98.59 180	99.47 115

viewdifsd2359ckpt07		97.20 156	97.05 145	97.65 216	98.40 202	94.33 286	98.39 265	98.43 225	95.67 160	97.66 189	99.08 131	90.04 216	99.32 215	97.47 142	98.29 212	99.31 154

PLC	95.07 4	97.20 156	96.78 165	98.44 125	99.29 88	96.31 164	98.14 303	98.76 125	92.41 353	96.39 256	98.31 241	94.92 86	99.78 124	94.06 290	98.77 171	99.23 178
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 158	97.18 133	97.20 240	98.81 157	93.27 331	95.78 452	99.15 42	95.25 194	96.79 234	98.11 259	92.29 132	99.07 272	98.56 54	99.85 6	99.25 176

SSM_0407		97.17 159	96.87 157	98.08 168	98.19 241	95.90 189	98.52 237	98.44 214	94.77 230	96.75 235	98.93 157	91.22 179	99.22 245	96.54 191	98.43 197	99.10 204

LS3D		97.16 160	96.66 174	98.68 94	98.53 186	97.19 115	98.93 111	98.90 73	92.83 338	95.99 268	99.37 54	92.12 141	99.87 79	93.67 302	99.57 100	98.97 225

AdaColmap		97.15 161	96.70 170	98.48 120	99.16 115	96.69 139	98.01 322	98.89 75	94.44 253	96.83 229	98.68 201	90.69 200	99.76 130	94.36 275	99.29 142	98.98 224

viewdifsd2359ckpt09		97.13 162	96.79 163	98.14 155	98.43 196	95.90 189	98.52 237	98.37 245	94.32 256	97.33 203	98.86 170	90.23 214	99.16 250	96.81 182	98.25 215	99.36 143

mamv4		97.13 162	98.11 76	94.17 416	98.97 140	83.70 461	98.66 204	98.71 137	94.63 239	97.83 170	98.90 163	96.25 32	99.55 179	99.27 28	99.76 48	99.27 167

Effi-MVS+		97.12 164	96.69 171	98.39 132	98.19 241	96.72 138	97.37 385	98.43 225	93.71 291	97.65 190	98.02 265	92.20 139	99.25 231	96.87 178	97.79 233	99.19 187

CHOSEN 1792x2688		97.12 164	96.80 161	98.08 168	99.30 83	94.56 275	98.05 317	99.71 1	93.57 305	97.09 215	98.91 162	88.17 273	99.89 68	96.87 178	99.56 108	99.81 24

F-COLMAP		97.09 166	96.80 161	97.97 183	99.45 61	94.95 254	98.55 234	98.62 164	93.02 330	96.17 263	98.58 211	94.01 104	99.81 102	93.95 292	98.90 160	99.14 197

RRT-MVS		97.03 167	96.78 165	97.77 200	97.90 286	94.34 284	99.12 63	98.35 250	95.87 149	98.06 140	98.70 199	86.45 311	99.63 159	98.04 92	98.54 185	99.35 144

TAMVS		97.02 168	96.79 163	97.70 207	98.06 262	95.31 233	98.52 237	98.31 260	93.95 274	97.05 220	98.61 206	93.49 111	98.52 339	95.33 237	97.81 232	99.29 161

viewmambaseed2359dif		97.01 169	96.84 159	97.51 225	98.19 241	94.21 292	98.16 299	98.23 281	93.61 303	97.78 173	99.13 113	90.79 198	99.18 249	97.24 156	98.40 204	99.15 194

CDS-MVSNet		96.99 170	96.69 171	97.90 187	98.05 264	95.98 175	98.20 289	98.33 255	93.67 298	96.95 222	98.49 220	93.54 110	98.42 350	95.24 244	97.74 236	99.31 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 171	96.55 179	98.21 145	98.17 250	96.07 173	97.98 326	98.21 283	97.24 72	97.13 213	98.93 157	86.88 303	99.91 56	95.00 250	99.37 136	98.66 264

114514_t		96.93 172	96.27 192	98.92 78	99.50 48	97.63 82	98.85 142	98.90 73	84.80 456	97.77 174	99.11 119	92.84 119	99.66 152	94.85 253	99.77 42	99.47 115

MAR-MVS		96.91 173	96.40 186	98.45 123	98.69 169	96.90 129	98.66 204	98.68 146	92.40 354	97.07 218	97.96 272	91.54 164	99.75 132	93.68 300	98.92 159	98.69 258
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		96.90 174	96.49 183	98.14 155	99.33 74	95.56 213	97.38 383	99.65 2	92.34 355	97.61 193	98.20 252	89.29 240	99.10 268	96.97 165	97.60 241	99.77 40

Vis-MVSNet (Re-imp)		96.87 175	96.55 179	97.83 192	98.73 161	95.46 220	99.20 47	98.30 267	94.96 218	96.60 244	98.87 168	90.05 215	98.59 334	93.67 302	98.60 179	99.46 120

SDMVSNet		96.85 176	96.42 184	98.14 155	99.30 83	96.38 158	99.21 44	99.23 28	95.92 145	95.96 270	98.76 193	85.88 323	99.44 202	97.93 96	95.59 305	98.60 269

PAPR		96.84 177	96.24 194	98.65 97	98.72 165	96.92 128	97.36 387	98.57 179	93.33 314	96.67 239	97.57 313	94.30 98	99.56 172	91.05 377	98.59 180	99.47 115

HY-MVS	93.96 8	96.82 178	96.23 195	98.57 104	98.46 193	97.00 124	98.14 303	98.21 283	93.95 274	96.72 238	97.99 269	91.58 159	99.76 130	94.51 271	96.54 277	98.95 228

mamba_0408		96.81 179	96.38 187	98.09 167	98.19 241	95.90 189	95.69 453	98.32 256	94.51 248	96.75 235	98.73 195	90.99 191	99.27 226	95.83 217	98.43 197	99.10 204

UGNet		96.78 180	96.30 191	98.19 150	98.24 233	95.89 194	98.88 127	98.93 65	97.39 59	96.81 232	97.84 285	82.60 377	99.90 64	96.53 193	99.49 119	98.79 241
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		96.74 181	96.64 175	97.05 255	97.99 273	92.82 346	98.45 252	98.27 270	95.16 198	97.30 204	98.79 181	91.53 165	99.06 273	94.74 258	97.54 245	99.27 167

IMVS_0403		96.74 181	96.61 176	97.12 249	97.99 273	92.82 346	98.47 250	98.27 270	95.16 198	97.13 213	98.79 181	91.44 168	99.26 227	94.74 258	97.54 245	99.27 167

PVSNet_BlendedMVS		96.73 183	96.60 177	97.12 249	99.25 96	95.35 230	98.26 283	99.26 16	94.28 257	97.94 158	97.46 320	92.74 121	99.81 102	96.88 175	93.32 343	96.20 417

SSM_04072		96.71 184	96.38 187	97.68 210	98.19 241	95.90 189	95.69 453	98.32 256	94.51 248	96.75 235	98.73 195	90.99 191	98.02 400	95.83 217	98.43 197	99.10 204

test_vis1_n_1920		96.71 184	96.84 159	96.31 327	99.11 122	89.74 411	99.05 74	98.58 177	98.08 24	99.87 4	99.37 54	78.48 411	99.93 34	99.29 27	99.69 73	99.27 167

mvs_anonymous		96.70 186	96.53 181	97.18 243	98.19 241	93.78 304	98.31 274	98.19 287	94.01 270	94.47 302	98.27 246	92.08 144	98.46 345	97.39 151	97.91 228	99.31 154

Elysia		96.64 187	96.02 204	98.51 114	98.04 266	97.30 101	98.74 176	98.60 165	95.04 210	97.91 162	98.84 172	83.59 372	99.48 195	94.20 283	99.25 143	98.75 250

StellarMVS		96.64 187	96.02 204	98.51 114	98.04 266	97.30 101	98.74 176	98.60 165	95.04 210	97.91 162	98.84 172	83.59 372	99.48 195	94.20 283	99.25 143	98.75 250

1112_ss		96.63 189	96.00 206	98.50 117	98.56 182	96.37 159	98.18 297	98.10 309	92.92 334	94.84 290	98.43 224	92.14 140	99.58 168	94.35 276	96.51 278	99.56 100

PMMVS		96.60 190	96.33 190	97.41 231	97.90 286	93.93 300	97.35 388	98.41 230	92.84 337	97.76 175	97.45 322	91.10 188	99.20 246	96.26 202	97.91 228	99.11 202

DP-MVS		96.59 191	95.93 209	98.57 104	99.34 71	96.19 168	98.70 191	98.39 238	89.45 425	94.52 300	99.35 60	91.85 150	99.85 84	92.89 326	98.88 162	99.68 75

PatchMatch-RL		96.59 191	96.03 203	98.27 138	99.31 79	96.51 151	97.91 335	99.06 48	93.72 290	96.92 226	98.06 262	88.50 267	99.65 153	91.77 359	99.00 157	98.66 264

GeoE		96.58 193	96.07 200	98.10 166	98.35 210	95.89 194	99.34 17	98.12 303	93.12 326	96.09 264	98.87 168	89.71 225	98.97 285	92.95 322	98.08 223	99.43 127

icg_test_0407_2		96.56 194	96.50 182	96.73 279	97.99 273	92.82 346	97.18 403	98.27 270	95.16 198	97.30 204	98.79 181	91.53 165	98.10 388	94.74 258	97.54 245	99.27 167

XVG-OURS		96.55 195	96.41 185	96.99 258	98.75 160	93.76 305	97.50 375	98.52 191	95.67 160	96.83 229	99.30 74	88.95 255	99.53 182	95.88 215	96.26 292	97.69 309

FIs		96.51 196	96.12 199	97.67 212	97.13 350	97.54 87	99.36 14	99.22 33	95.89 147	94.03 331	98.35 234	91.98 146	98.44 348	96.40 198	92.76 351	97.01 328

XVG-OURS-SEG-HR		96.51 196	96.34 189	97.02 257	98.77 159	93.76 305	97.79 354	98.50 199	95.45 179	96.94 223	99.09 129	87.87 284	99.55 179	96.76 187	95.83 304	97.74 306

PS-MVSNAJss		96.43 198	96.26 193	96.92 268	95.84 418	95.08 244	99.16 55	98.50 199	95.87 149	93.84 341	98.34 238	94.51 91	98.61 330	96.88 175	93.45 338	97.06 326

test_fmvs1		96.42 199	96.67 173	95.66 360	98.82 156	88.53 438	98.80 159	98.20 285	96.39 124	99.64 30	99.20 92	80.35 399	99.67 149	99.04 33	99.57 100	98.78 245

FC-MVSNet-test		96.42 199	96.05 201	97.53 224	96.95 359	97.27 105	99.36 14	99.23 28	95.83 151	93.93 334	98.37 232	92.00 145	98.32 369	96.02 211	92.72 352	97.00 329

ab-mvs		96.42 199	95.71 220	98.55 107	98.63 178	96.75 136	97.88 342	98.74 129	93.84 280	96.54 249	98.18 254	85.34 334	99.75 132	95.93 213	96.35 282	99.15 194

FA-MVS(test-final)		96.41 202	95.94 208	97.82 194	98.21 237	95.20 238	97.80 352	97.58 355	93.21 320	97.36 202	97.70 297	89.47 231	99.56 172	94.12 287	97.99 225	98.71 256

PVSNet	91.96 18	96.35 203	96.15 196	96.96 263	99.17 111	92.05 363	96.08 445	98.68 146	93.69 294	97.75 177	97.80 291	88.86 256	99.69 147	94.26 281	99.01 155	99.15 194

Test_1112_low_res		96.34 204	95.66 225	98.36 133	98.56 182	95.94 183	97.71 359	98.07 316	92.10 364	94.79 294	97.29 335	91.75 154	99.56 172	94.17 285	96.50 279	99.58 98

viewdifsd2359ckpt11		96.30 205	96.13 197	96.81 274	98.10 256	92.10 359	98.49 248	98.40 233	96.02 140	97.61 193	99.31 71	86.37 313	99.29 222	97.52 134	93.36 342	99.04 217

viewmsd2359difaftdt		96.30 205	96.13 197	96.81 274	98.10 256	92.10 359	98.49 248	98.40 233	96.02 140	97.61 193	99.31 71	86.37 313	99.30 220	97.52 134	93.37 341	99.04 217

Effi-MVS+-dtu		96.29 207	96.56 178	95.51 365	97.89 288	90.22 403	98.80 159	98.10 309	96.57 114	96.45 254	96.66 392	90.81 194	98.91 298	95.72 224	97.99 225	97.40 317

QAPM		96.29 207	95.40 231	98.96 75	97.85 289	97.60 84	99.23 37	98.93 65	89.76 419	93.11 373	99.02 139	89.11 246	99.93 34	91.99 353	99.62 91	99.34 146

Fast-Effi-MVS+		96.28 209	95.70 222	98.03 174	98.29 226	95.97 180	98.58 221	98.25 279	91.74 372	95.29 283	97.23 340	91.03 190	99.15 254	92.90 324	97.96 227	98.97 225

nrg030		96.28 209	95.72 217	97.96 185	96.90 364	98.15 63	99.39 11	98.31 260	95.47 178	94.42 308	98.35 234	92.09 143	98.69 322	97.50 138	89.05 403	97.04 327

1314		96.25 211	95.73 216	97.79 196	97.13 350	95.55 215	98.19 292	98.59 172	93.47 309	92.03 405	97.82 289	91.33 172	99.49 190	94.62 266	98.44 194	98.32 289

sd_testset		96.17 212	95.76 215	97.42 230	99.30 83	94.34 284	98.82 150	99.08 46	95.92 145	95.96 270	98.76 193	82.83 376	99.32 215	95.56 230	95.59 305	98.60 269

h-mvs33		96.17 212	95.62 226	97.81 195	99.03 129	94.45 277	98.64 208	98.75 127	97.48 52	98.67 104	98.72 198	89.76 222	99.86 83	97.95 94	81.59 453	99.11 202

HQP_MVS		96.14 214	95.90 210	96.85 271	97.42 328	94.60 273	98.80 159	98.56 182	97.28 67	95.34 279	98.28 243	87.09 298	99.03 278	96.07 206	94.27 313	96.92 336

tttt0517		96.07 215	95.51 229	97.78 197	98.41 200	94.84 258	99.28 29	94.33 465	94.26 259	97.64 191	98.64 205	84.05 363	99.47 199	95.34 236	97.60 241	99.03 219

MVSTER		96.06 216	95.72 217	97.08 253	98.23 235	95.93 186	98.73 182	98.27 270	94.86 224	95.07 285	98.09 260	88.21 272	98.54 337	96.59 189	93.46 336	96.79 355

thisisatest0530		96.01 217	95.36 236	97.97 183	98.38 205	95.52 217	98.88 127	94.19 467	94.04 265	97.64 191	98.31 241	83.82 370	99.46 200	95.29 241	97.70 238	98.93 230

test_djsdf		96.00 218	95.69 223	96.93 265	95.72 420	95.49 218	99.47 7	98.40 233	94.98 216	94.58 298	97.86 282	89.16 244	98.41 357	96.91 169	94.12 321	96.88 345

EI-MVSNet		95.96 219	95.83 212	96.36 323	97.93 284	93.70 311	98.12 306	98.27 270	93.70 293	95.07 285	99.02 139	92.23 136	98.54 337	94.68 262	93.46 336	96.84 351

VortexMVS		95.95 220	95.79 213	96.42 319	98.29 226	93.96 299	98.68 197	98.31 260	96.02 140	94.29 316	97.57 313	89.47 231	98.37 364	97.51 137	91.93 360	96.94 334

ECVR-MVS		95.95 220	95.71 220	96.65 287	99.02 130	90.86 385	99.03 81	91.80 478	96.96 92	98.10 136	99.26 80	81.31 385	99.51 186	96.90 172	99.04 152	99.59 94

BH-untuned		95.95 220	95.72 217	96.65 287	98.55 184	92.26 355	98.23 285	97.79 340	93.73 288	94.62 297	98.01 267	88.97 254	99.00 284	93.04 319	98.51 188	98.68 260

test1111		95.94 223	95.78 214	96.41 320	98.99 137	90.12 404	99.04 78	92.45 477	96.99 91	98.03 145	99.27 79	81.40 384	99.48 195	96.87 178	99.04 152	99.63 88

MSDG		95.93 224	95.30 243	97.83 192	98.90 145	95.36 228	96.83 432	98.37 245	91.32 388	94.43 307	98.73 195	90.27 212	99.60 165	90.05 391	98.82 169	98.52 277

BH-RMVSNet		95.92 225	95.32 241	97.69 208	98.32 221	94.64 267	98.19 292	97.45 376	94.56 243	96.03 266	98.61 206	85.02 339	99.12 262	90.68 382	99.06 151	99.30 158

test_fmvs1_n		95.90 226	95.99 207	95.63 361	98.67 172	88.32 442	99.26 32	98.22 282	96.40 123	99.67 27	99.26 80	73.91 451	99.70 142	99.02 34	99.50 117	98.87 234

Fast-Effi-MVS+-dtu		95.87 227	95.85 211	95.91 347	97.74 298	91.74 369	98.69 194	98.15 299	95.56 167	94.92 288	97.68 302	88.98 253	98.79 316	93.19 314	97.78 234	97.20 324

LFMVS		95.86 228	94.98 258	98.47 121	98.87 150	96.32 162	98.84 146	96.02 443	93.40 312	98.62 110	99.20 92	74.99 443	99.63 159	97.72 110	97.20 254	99.46 120

baseline1		95.84 229	95.12 251	98.01 177	98.49 191	95.98 175	98.73 182	97.03 410	95.37 187	96.22 259	98.19 253	89.96 218	99.16 250	94.60 267	87.48 419	98.90 233

OpenMVS	93.04 13	95.83 230	95.00 256	98.32 135	97.18 347	97.32 98	99.21 44	98.97 57	89.96 415	91.14 414	99.05 137	86.64 306	99.92 43	93.38 308	99.47 122	97.73 307

IMVS_0404		95.82 231	95.52 227	96.73 279	97.99 273	92.82 346	97.23 396	98.27 270	95.16 198	94.31 314	98.79 181	85.63 327	98.10 388	94.74 258	97.54 245	99.27 167

VDD-MVS		95.82 231	95.23 245	97.61 220	98.84 155	93.98 298	98.68 197	97.40 380	95.02 214	97.95 156	99.34 68	74.37 449	99.78 124	98.64 48	96.80 267	99.08 211

UniMVSNet (Re)		95.78 233	95.19 247	97.58 221	96.99 357	97.47 91	98.79 167	99.18 37	95.60 163	93.92 335	97.04 362	91.68 156	98.48 341	95.80 221	87.66 418	96.79 355

VPA-MVSNet		95.75 234	95.11 252	97.69 208	97.24 339	97.27 105	98.94 105	99.23 28	95.13 203	95.51 277	97.32 333	85.73 325	98.91 298	97.33 154	89.55 394	96.89 344

HQP-MVS		95.72 235	95.40 231	96.69 285	97.20 343	94.25 290	98.05 317	98.46 207	96.43 118	94.45 303	97.73 294	86.75 304	98.96 289	95.30 239	94.18 317	96.86 350

hse-mvs2		95.71 236	95.30 243	96.93 265	98.50 187	93.53 316	98.36 266	98.10 309	97.48 52	98.67 104	97.99 269	89.76 222	99.02 281	97.95 94	80.91 459	98.22 292

UniMVSNet_NR-MVSNet		95.71 236	95.15 248	97.40 233	96.84 367	96.97 125	98.74 176	99.24 20	95.16 198	93.88 337	97.72 296	91.68 156	98.31 371	95.81 219	87.25 424	96.92 336

PatchmatchNet		95.71 236	95.52 227	96.29 329	97.58 311	90.72 389	96.84 431	97.52 366	94.06 264	97.08 216	96.96 372	89.24 242	98.90 301	92.03 352	98.37 205	99.26 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 239	95.33 240	96.76 278	96.16 403	94.63 268	98.43 260	98.39 238	96.64 110	95.02 287	98.78 185	85.15 338	99.05 274	95.21 246	94.20 316	96.60 379
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 239	95.38 235	96.61 295	97.61 308	93.84 303	98.91 115	98.44 214	95.25 194	94.28 317	98.47 222	86.04 322	99.12 262	95.50 233	93.95 326	96.87 348
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 241	95.69 223	95.44 369	97.54 316	88.54 437	96.97 417	97.56 358	93.50 307	97.52 200	96.93 376	89.49 229	99.16 250	95.25 243	96.42 281	98.64 266

FE-MVS		95.62 242	94.90 262	97.78 197	98.37 208	94.92 255	97.17 406	97.38 382	90.95 399	97.73 180	97.70 297	85.32 336	99.63 159	91.18 369	98.33 208	98.79 241

LPG-MVS_test		95.62 242	95.34 237	96.47 313	97.46 323	93.54 314	98.99 91	98.54 186	94.67 237	94.36 311	98.77 188	85.39 331	99.11 264	95.71 225	94.15 319	96.76 358

CLD-MVS		95.62 242	95.34 237	96.46 316	97.52 319	93.75 307	97.27 395	98.46 207	95.53 174	94.42 308	98.00 268	86.21 317	98.97 285	96.25 204	94.37 311	96.66 373
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 245	94.89 263	97.76 201	98.15 252	95.15 241	96.77 433	94.41 463	92.95 333	97.18 212	97.43 324	84.78 345	99.45 201	94.63 264	97.73 237	98.68 260

MonoMVSNet		95.51 246	95.45 230	95.68 358	95.54 425	90.87 384	98.92 113	97.37 383	95.79 153	95.53 276	97.38 329	89.58 228	97.68 422	96.40 198	92.59 353	98.49 279

thres600view7		95.49 247	94.77 266	97.67 212	98.98 138	95.02 246	98.85 142	96.90 420	95.38 185	96.63 241	96.90 378	84.29 355	99.59 166	88.65 415	96.33 283	98.40 283

test_vis1_n		95.47 248	95.13 249	96.49 310	97.77 294	90.41 399	99.27 31	98.11 306	96.58 112	99.66 28	99.18 101	67.00 465	99.62 163	99.21 29	99.40 132	99.44 125

SCA		95.46 249	95.13 249	96.46 316	97.67 303	91.29 377	97.33 390	97.60 354	94.68 236	96.92 226	97.10 347	83.97 365	98.89 302	92.59 336	98.32 211	99.20 183

IterMVS-LS		95.46 249	95.21 246	96.22 331	98.12 254	93.72 310	98.32 273	98.13 302	93.71 291	94.26 318	97.31 334	92.24 135	98.10 388	94.63 264	90.12 385	96.84 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 251	95.34 237	95.77 356	98.69 169	88.75 433	98.87 130	97.21 397	96.13 135	97.22 210	97.68 302	77.95 419	99.65 153	97.58 126	96.77 270	98.91 232

jajsoiax		95.45 251	95.03 255	96.73 279	95.42 433	94.63 268	99.14 59	98.52 191	95.74 155	93.22 366	98.36 233	83.87 368	98.65 327	96.95 167	94.04 322	96.91 341

CVMVSNet		95.43 253	96.04 202	93.57 423	97.93 284	83.62 462	98.12 306	98.59 172	95.68 159	96.56 245	99.02 139	87.51 290	97.51 431	93.56 306	97.44 250	99.60 92

anonymousdsp		95.42 254	94.91 261	96.94 264	95.10 437	95.90 189	99.14 59	98.41 230	93.75 285	93.16 369	97.46 320	87.50 292	98.41 357	95.63 229	94.03 323	96.50 401

DU-MVS		95.42 254	94.76 267	97.40 233	96.53 384	96.97 125	98.66 204	98.99 56	95.43 180	93.88 337	97.69 299	88.57 262	98.31 371	95.81 219	87.25 424	96.92 336

mvs_tets		95.41 256	95.00 256	96.65 287	95.58 424	94.42 279	99.00 88	98.55 184	95.73 157	93.21 367	98.38 231	83.45 374	98.63 328	97.09 161	94.00 324	96.91 341

thres100view900		95.38 257	94.70 271	97.41 231	98.98 138	94.92 255	98.87 130	96.90 420	95.38 185	96.61 243	96.88 379	84.29 355	99.56 172	88.11 418	96.29 287	97.76 304

thres400		95.38 257	94.62 275	97.65 216	98.94 143	94.98 251	98.68 197	96.93 418	95.33 188	96.55 247	96.53 398	84.23 359	99.56 172	88.11 418	96.29 287	98.40 283

BH-w/o		95.38 257	95.08 253	96.26 330	98.34 215	91.79 366	97.70 360	97.43 378	92.87 336	94.24 320	97.22 341	88.66 260	98.84 308	91.55 365	97.70 238	98.16 295

VDDNet		95.36 260	94.53 280	97.86 190	98.10 256	95.13 242	98.85 142	97.75 342	90.46 406	98.36 126	99.39 48	73.27 453	99.64 156	97.98 93	96.58 275	98.81 240

TAPA-MVS	93.98 7	95.35 261	94.56 279	97.74 203	99.13 119	94.83 260	98.33 269	98.64 159	86.62 444	96.29 258	98.61 206	94.00 105	99.29 222	80.00 462	99.41 129	99.09 207
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 262	94.98 258	96.43 318	97.67 303	93.48 318	98.73 182	98.44 214	94.94 222	92.53 389	98.53 216	84.50 354	99.14 257	95.48 234	94.00 324	96.66 373
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 263	94.87 264	96.71 282	99.29 88	93.24 335	98.58 221	98.11 306	89.92 416	93.57 351	99.10 121	86.37 313	99.79 121	90.78 380	98.10 222	97.09 325
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 264	94.72 270	97.13 247	98.05 264	93.26 332	97.87 343	97.20 398	94.96 218	96.18 262	95.66 432	80.97 391	99.35 211	94.47 273	97.08 257	98.78 245

tfpn200view9		95.32 264	94.62 275	97.43 229	98.94 143	94.98 251	98.68 197	96.93 418	95.33 188	96.55 247	96.53 398	84.23 359	99.56 172	88.11 418	96.29 287	97.76 304

Anonymous202405211		95.28 266	94.49 282	97.67 212	99.00 134	93.75 307	98.70 191	97.04 409	90.66 402	96.49 251	98.80 179	78.13 415	99.83 90	96.21 205	95.36 309	99.44 125

thres200		95.25 267	94.57 278	97.28 237	98.81 157	94.92 255	98.20 289	97.11 402	95.24 196	96.54 249	96.22 410	84.58 352	99.53 182	87.93 423	96.50 279	97.39 318

AllTest		95.24 268	94.65 274	96.99 258	99.25 96	93.21 336	98.59 217	98.18 290	91.36 384	93.52 353	98.77 188	84.67 349	99.72 136	89.70 398	97.87 230	98.02 299

LCM-MVSNet-Re		95.22 269	95.32 241	94.91 386	98.18 247	87.85 448	98.75 172	95.66 450	95.11 205	88.96 434	96.85 382	90.26 213	97.65 423	95.65 228	98.44 194	99.22 180

EPNet_dtu		95.21 270	94.95 260	95.99 340	96.17 401	90.45 397	98.16 299	97.27 392	96.77 100	93.14 372	98.33 239	90.34 209	98.42 350	85.57 437	98.81 170	99.09 207
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 271	94.45 288	97.46 226	96.75 374	96.56 149	98.86 137	98.65 158	93.30 317	93.27 365	98.27 246	84.85 343	98.87 305	94.82 255	91.26 371	96.96 331

D2MVS		95.18 272	95.08 253	95.48 366	97.10 352	92.07 362	98.30 277	99.13 44	94.02 267	92.90 377	96.73 388	89.48 230	98.73 320	94.48 272	93.60 335	95.65 431

WR-MVS		95.15 273	94.46 285	97.22 239	96.67 379	96.45 153	98.21 287	98.81 107	94.15 261	93.16 369	97.69 299	87.51 290	98.30 373	95.29 241	88.62 409	96.90 343

TranMVSNet+NR-MVSNet		95.14 274	94.48 283	97.11 251	96.45 390	96.36 160	99.03 81	99.03 51	95.04 210	93.58 350	97.93 275	88.27 271	98.03 399	94.13 286	86.90 429	96.95 333

myMVS_eth3d28		95.12 275	94.62 275	96.64 291	98.17 250	92.17 356	98.02 321	97.32 386	95.41 183	96.22 259	96.05 416	78.01 417	99.13 259	95.22 245	97.16 255	98.60 269

baseline2		95.11 276	94.52 281	96.87 270	96.65 380	93.56 313	98.27 282	94.10 469	93.45 310	92.02 406	97.43 324	87.45 295	99.19 247	93.88 295	97.41 252	97.87 302

miper_enhance_ethall		95.10 277	94.75 268	96.12 335	97.53 318	93.73 309	96.61 439	98.08 314	92.20 363	93.89 336	96.65 394	92.44 126	98.30 373	94.21 282	91.16 372	96.34 410

Anonymous20240529		95.10 277	94.22 299	97.75 202	99.01 132	94.26 289	98.87 130	98.83 98	85.79 452	96.64 240	98.97 147	78.73 408	99.85 84	96.27 201	94.89 310	99.12 199

test-LLR		95.10 277	94.87 264	95.80 353	96.77 371	89.70 413	96.91 422	95.21 455	95.11 205	94.83 292	95.72 429	87.71 286	98.97 285	93.06 317	98.50 189	98.72 253

WR-MVS_H		95.05 280	94.46 285	96.81 274	96.86 366	95.82 201	99.24 35	99.24 20	93.87 279	92.53 389	96.84 383	90.37 208	98.24 379	93.24 312	87.93 415	96.38 409

miper_ehance_all_eth		95.01 281	94.69 272	95.97 344	97.70 301	93.31 329	97.02 415	98.07 316	92.23 360	93.51 355	96.96 372	91.85 150	98.15 384	93.68 300	91.16 372	96.44 407

testing11		95.00 282	94.28 295	97.16 245	97.96 281	93.36 326	98.09 313	97.06 408	94.94 222	95.33 282	96.15 412	76.89 432	99.40 206	95.77 223	96.30 286	98.72 253

ADS-MVSNet		95.00 282	94.45 288	96.63 292	98.00 271	91.91 365	96.04 446	97.74 343	90.15 412	96.47 252	96.64 395	87.89 282	98.96 289	90.08 389	97.06 258	99.02 220

VPNet		94.99 284	94.19 301	97.40 233	97.16 348	96.57 148	98.71 187	98.97 57	95.67 160	94.84 290	98.24 250	80.36 398	98.67 326	96.46 195	87.32 423	96.96 331

EPMVS		94.99 284	94.48 283	96.52 308	97.22 341	91.75 368	97.23 396	91.66 479	94.11 262	97.28 206	96.81 385	85.70 326	98.84 308	93.04 319	97.28 253	98.97 225

testing91		94.98 286	94.25 298	97.20 240	97.94 282	93.41 321	98.00 324	97.58 355	94.99 215	95.45 278	96.04 417	77.20 427	99.42 204	94.97 251	96.02 300	98.78 245

NR-MVSNet		94.98 286	94.16 304	97.44 228	96.53 384	97.22 113	98.74 176	98.95 61	94.96 218	89.25 433	97.69 299	89.32 239	98.18 382	94.59 269	87.40 421	96.92 336

FMVSNet3		94.97 288	94.26 297	97.11 251	98.18 247	96.62 140	98.56 233	98.26 278	93.67 298	94.09 327	97.10 347	84.25 357	98.01 401	92.08 348	92.14 357	96.70 367

FE-MVSNET3		94.96 289	94.28 295	96.98 261	95.93 414	96.11 172	97.08 412	98.39 238	93.62 302	93.86 339	96.40 403	88.28 270	98.21 380	92.61 332	92.36 356	96.63 375

CostFormer		94.95 290	94.73 269	95.60 363	97.28 337	89.06 426	97.53 372	96.89 422	89.66 421	96.82 231	96.72 389	86.05 320	98.95 294	95.53 232	96.13 298	98.79 241

PAPM		94.95 290	94.00 317	97.78 197	97.04 354	95.65 210	96.03 448	98.25 279	91.23 393	94.19 323	97.80 291	91.27 175	98.86 307	82.61 454	97.61 240	98.84 237

CP-MVSNet		94.94 292	94.30 294	96.83 272	96.72 376	95.56 213	99.11 65	98.95 61	93.89 277	92.42 395	97.90 278	87.19 297	98.12 387	94.32 278	88.21 412	96.82 354

TR-MVS		94.94 292	94.20 300	97.17 244	97.75 295	94.14 295	97.59 369	97.02 413	92.28 359	95.75 274	97.64 307	83.88 367	98.96 289	89.77 395	96.15 297	98.40 283

RPSCF		94.87 294	95.40 231	93.26 429	98.89 146	82.06 468	98.33 269	98.06 321	90.30 411	96.56 245	99.26 80	87.09 298	99.49 190	93.82 297	96.32 284	98.24 290

testing99		94.83 295	94.08 309	97.07 254	97.94 282	93.13 338	98.10 312	97.17 400	94.86 224	95.34 279	96.00 421	76.31 435	99.40 206	95.08 248	95.90 301	98.68 260

GA-MVS		94.81 296	94.03 313	97.14 246	97.15 349	93.86 302	96.76 434	97.58 355	94.00 271	94.76 296	97.04 362	80.91 392	98.48 341	91.79 358	96.25 293	99.09 207

c3_l		94.79 297	94.43 290	95.89 349	97.75 295	93.12 340	97.16 408	98.03 323	92.23 360	93.46 359	97.05 361	91.39 169	98.01 401	93.58 305	89.21 401	96.53 392

V42		94.78 298	94.14 306	96.70 284	96.33 395	95.22 237	98.97 95	98.09 313	92.32 357	94.31 314	97.06 358	88.39 268	98.55 336	92.90 324	88.87 407	96.34 410

reproduce_monomvs		94.77 299	94.67 273	95.08 381	98.40 202	89.48 419	98.80 159	98.64 159	97.57 46	93.21 367	97.65 304	80.57 397	98.83 311	97.72 110	89.47 397	96.93 335

CR-MVSNet		94.76 300	94.15 305	96.59 298	97.00 355	93.43 319	94.96 461	97.56 358	92.46 348	96.93 224	96.24 406	88.15 274	97.88 414	87.38 426	96.65 273	98.46 281

v2v482		94.69 301	94.03 313	96.65 287	96.17 401	94.79 263	98.67 202	98.08 314	92.72 340	94.00 332	97.16 344	87.69 289	98.45 346	92.91 323	88.87 407	96.72 363

pmmvs4		94.69 301	93.99 319	96.81 274	95.74 419	95.94 183	97.40 381	97.67 347	90.42 408	93.37 362	97.59 311	89.08 247	98.20 381	92.97 321	91.67 365	96.30 413

cl22		94.68 303	94.19 301	96.13 334	98.11 255	93.60 312	96.94 419	98.31 260	92.43 352	93.32 364	96.87 381	86.51 307	98.28 377	94.10 289	91.16 372	96.51 399

eth_miper_zixun_eth		94.68 303	94.41 291	95.47 367	97.64 306	91.71 370	96.73 436	98.07 316	92.71 341	93.64 347	97.21 342	90.54 203	98.17 383	93.38 308	89.76 389	96.54 390

PCF-MVS	93.45 11	94.68 303	93.43 355	98.42 129	98.62 179	96.77 135	95.48 458	98.20 285	84.63 457	93.34 363	98.32 240	88.55 265	99.81 102	84.80 446	98.96 158	98.68 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 306	93.54 350	98.08 168	96.88 365	96.56 149	98.19 292	98.50 199	78.05 470	92.69 384	98.02 265	91.07 189	99.63 159	90.09 388	98.36 207	98.04 298

PS-CasMVS		94.67 306	93.99 319	96.71 282	96.68 378	95.26 234	99.13 62	99.03 51	93.68 296	92.33 397	97.95 273	85.35 333	98.10 388	93.59 304	88.16 414	96.79 355

cascas		94.63 308	93.86 329	96.93 265	96.91 363	94.27 288	96.00 449	98.51 194	85.55 453	94.54 299	96.23 408	84.20 361	98.87 305	95.80 221	96.98 263	97.66 310

tpmvs		94.60 309	94.36 293	95.33 373	97.46 323	88.60 436	96.88 428	97.68 344	91.29 390	93.80 343	96.42 402	88.58 261	99.24 237	91.06 375	96.04 299	98.17 294

LTVRE_ROB	92.95 15	94.60 309	93.90 325	96.68 286	97.41 331	94.42 279	98.52 237	98.59 172	91.69 375	91.21 413	98.35 234	84.87 342	99.04 277	91.06 375	93.44 339	96.60 379
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		94.59 311	93.92 322	96.60 297	96.21 397	94.78 264	98.59 217	98.14 301	91.86 371	94.21 322	97.02 365	87.97 280	98.41 357	91.72 360	89.57 392	96.61 378

ADS-MVSNet2		94.58 312	94.40 292	95.11 379	98.00 271	88.74 434	96.04 446	97.30 388	90.15 412	96.47 252	96.64 395	87.89 282	97.56 429	90.08 389	97.06 258	99.02 220

WBMVS		94.56 313	94.04 311	96.10 336	98.03 268	93.08 342	97.82 351	98.18 290	94.02 267	93.77 345	96.82 384	81.28 386	98.34 366	95.47 235	91.00 375	96.88 345

ACMH	92.88 16	94.55 314	93.95 321	96.34 325	97.63 307	93.26 332	98.81 158	98.49 204	93.43 311	89.74 427	98.53 216	81.91 380	99.08 271	93.69 299	93.30 344	96.70 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 315	93.85 330	96.63 292	97.98 279	93.06 343	98.77 171	97.84 335	93.67 298	93.80 343	98.04 264	76.88 433	98.96 289	94.79 257	92.86 349	97.86 303

XVG-ACMP-BASELINE		94.54 315	94.14 306	95.75 357	96.55 383	91.65 371	98.11 310	98.44 214	94.96 218	94.22 321	97.90 278	79.18 407	99.11 264	94.05 291	93.85 328	96.48 404

AUN-MVS		94.53 317	93.73 340	96.92 268	98.50 187	93.52 317	98.34 268	98.10 309	93.83 282	95.94 272	97.98 271	85.59 329	99.03 278	94.35 276	80.94 458	98.22 292

DIV-MVS_self_test		94.52 318	94.03 313	95.99 340	97.57 315	93.38 324	97.05 413	97.94 329	91.74 372	92.81 379	97.10 347	89.12 245	98.07 396	92.60 334	90.30 382	96.53 392

cl____		94.51 319	94.01 316	96.02 338	97.58 311	93.40 323	97.05 413	97.96 328	91.73 374	92.76 381	97.08 353	89.06 248	98.13 386	92.61 332	90.29 383	96.52 395

ETVMVS		94.50 320	93.44 354	97.68 210	98.18 247	95.35 230	98.19 292	97.11 402	93.73 288	96.40 255	95.39 435	74.53 446	98.84 308	91.10 371	96.31 285	98.84 237

GBi-Net		94.49 321	93.80 333	96.56 302	98.21 237	95.00 247	98.82 150	98.18 290	92.46 348	94.09 327	97.07 354	81.16 387	97.95 406	92.08 348	92.14 357	96.72 363

test1		94.49 321	93.80 333	96.56 302	98.21 237	95.00 247	98.82 150	98.18 290	92.46 348	94.09 327	97.07 354	81.16 387	97.95 406	92.08 348	92.14 357	96.72 363

dmvs_re		94.48 323	94.18 303	95.37 371	97.68 302	90.11 405	98.54 236	97.08 404	94.56 243	94.42 308	97.24 339	84.25 357	97.76 420	91.02 378	92.83 350	98.24 290

v8		94.47 324	93.77 336	96.57 301	96.36 393	94.83 260	99.05 74	98.19 287	91.92 368	93.16 369	96.97 370	88.82 259	98.48 341	91.69 361	87.79 416	96.39 408

FMVSNet2		94.47 324	93.61 346	97.04 256	98.21 237	96.43 155	98.79 167	98.27 270	92.46 348	93.50 356	97.09 351	81.16 387	98.00 403	91.09 372	91.93 360	96.70 367

test2506		94.44 326	93.91 324	96.04 337	99.02 130	88.99 429	99.06 72	79.47 491	96.96 92	98.36 126	99.26 80	77.21 426	99.52 185	96.78 186	99.04 152	99.59 94

Patchmatch-test		94.42 327	93.68 344	96.63 292	97.60 309	91.76 367	94.83 465	97.49 370	89.45 425	94.14 325	97.10 347	88.99 250	98.83 311	85.37 440	98.13 221	99.29 161

PEN-MVS		94.42 327	93.73 340	96.49 310	96.28 396	94.84 258	99.17 54	99.00 53	93.51 306	92.23 399	97.83 288	86.10 319	97.90 410	92.55 339	86.92 428	96.74 360

v144192		94.39 329	93.70 342	96.48 312	96.06 407	94.35 283	98.58 221	98.16 298	91.45 381	94.33 313	97.02 365	87.50 292	98.45 346	91.08 374	89.11 402	96.63 375

Baseline_NR-MVSNet		94.35 330	93.81 332	95.96 345	96.20 398	94.05 297	98.61 216	96.67 432	91.44 382	93.85 340	97.60 310	88.57 262	98.14 385	94.39 274	86.93 427	95.68 430

miper_lstm_enhance		94.33 331	94.07 310	95.11 379	97.75 295	90.97 381	97.22 398	98.03 323	91.67 376	92.76 381	96.97 370	90.03 217	97.78 419	92.51 341	89.64 391	96.56 387

v1192		94.32 332	93.58 347	96.53 307	96.10 405	94.45 277	98.50 245	98.17 296	91.54 379	94.19 323	97.06 358	86.95 302	98.43 349	90.14 387	89.57 392	96.70 367

UWE-MVS		94.30 333	93.89 327	95.53 364	97.83 290	88.95 430	97.52 374	93.25 471	94.44 253	96.63 241	97.07 354	78.70 409	99.28 224	91.99 353	97.56 244	98.36 286

ACMH+	92.99 14	94.30 333	93.77 336	95.88 350	97.81 292	92.04 364	98.71 187	98.37 245	93.99 272	90.60 420	98.47 222	80.86 394	99.05 274	92.75 330	92.40 355	96.55 389

v148		94.29 335	93.76 338	95.91 347	96.10 405	92.93 344	98.58 221	97.97 326	92.59 346	93.47 358	96.95 374	88.53 266	98.32 369	92.56 338	87.06 426	96.49 402

v10		94.29 335	93.55 349	96.51 309	96.39 392	94.80 262	98.99 91	98.19 287	91.35 386	93.02 375	96.99 368	88.09 276	98.41 357	90.50 384	88.41 411	96.33 412

SD_0403		94.28 337	94.46 285	93.73 420	98.02 269	85.32 457	98.31 274	98.40 233	94.75 232	93.59 348	98.16 255	89.01 249	96.54 450	82.32 455	97.58 243	99.34 146

MVP-Stereo		94.28 337	93.92 322	95.35 372	94.95 439	92.60 351	97.97 327	97.65 348	91.61 377	90.68 419	97.09 351	86.32 316	98.42 350	89.70 398	99.34 138	95.02 444
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 339	93.33 357	96.97 262	97.19 346	93.38 324	98.74 176	98.57 179	91.21 395	93.81 342	98.58 211	72.85 454	98.77 318	95.05 249	93.93 327	98.77 248

OurMVSNet-221017-0		94.21 340	94.00 317	94.85 391	95.60 423	89.22 424	98.89 120	97.43 378	95.29 191	92.18 402	98.52 219	82.86 375	98.59 334	93.46 307	91.76 363	96.74 360

v1921920		94.20 341	93.47 353	96.40 322	95.98 411	94.08 296	98.52 237	98.15 299	91.33 387	94.25 319	97.20 343	86.41 312	98.42 350	90.04 392	89.39 399	96.69 372

WB-MVSnew		94.19 342	94.04 311	94.66 399	96.82 369	92.14 357	97.86 345	95.96 446	93.50 307	95.64 275	96.77 387	88.06 278	97.99 404	84.87 443	96.86 264	93.85 463

v7n		94.19 342	93.43 355	96.47 313	95.90 415	94.38 282	99.26 32	98.34 253	91.99 366	92.76 381	97.13 346	88.31 269	98.52 339	89.48 403	87.70 417	96.52 395

tpm2		94.19 342	93.76 338	95.46 368	97.23 340	89.04 427	97.31 392	96.85 426	87.08 441	96.21 261	96.79 386	83.75 371	98.74 319	92.43 344	96.23 295	98.59 272

TESTMET0.1,1		94.18 345	93.69 343	95.63 361	96.92 361	89.12 425	96.91 422	94.78 460	93.17 322	94.88 289	96.45 401	78.52 410	98.92 296	93.09 316	98.50 189	98.85 235

dp		94.15 346	93.90 325	94.90 387	97.31 336	86.82 453	96.97 417	97.19 399	91.22 394	96.02 267	96.61 397	85.51 330	99.02 281	90.00 393	94.30 312	98.85 235

ET-MVSNet_ETH3D		94.13 347	92.98 365	97.58 221	98.22 236	96.20 166	97.31 392	95.37 454	94.53 245	79.56 472	97.63 309	86.51 307	97.53 430	96.91 169	90.74 377	99.02 220

tpm		94.13 347	93.80 333	95.12 378	96.50 386	87.91 447	97.44 377	95.89 449	92.62 344	96.37 257	96.30 405	84.13 362	98.30 373	93.24 312	91.66 366	99.14 197

testing222		94.12 349	93.03 364	97.37 236	98.02 269	94.66 265	97.94 331	96.65 434	94.63 239	95.78 273	95.76 424	71.49 455	98.92 296	91.17 370	95.88 302	98.52 277

IterMVS-SCA-FT		94.11 350	93.87 328	94.85 391	97.98 279	90.56 396	97.18 403	98.11 306	93.75 285	92.58 387	97.48 319	83.97 365	97.41 433	92.48 343	91.30 369	96.58 383

Anonymous20231211		94.10 351	93.26 360	96.61 295	99.11 122	94.28 287	99.01 86	98.88 78	86.43 446	92.81 379	97.57 313	81.66 383	98.68 325	94.83 254	89.02 405	96.88 345

IterMVS		94.09 352	93.85 330	94.80 395	97.99 273	90.35 401	97.18 403	98.12 303	93.68 296	92.46 393	97.34 330	84.05 363	97.41 433	92.51 341	91.33 368	96.62 377
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 353	93.51 351	95.80 353	96.77 371	89.70 413	96.91 422	95.21 455	92.89 335	94.83 292	95.72 429	77.69 421	98.97 285	93.06 317	98.50 189	98.72 253

test0.0.03 1		94.08 353	93.51 351	95.80 353	95.53 427	92.89 345	97.38 383	95.97 445	95.11 205	92.51 391	96.66 392	87.71 286	96.94 440	87.03 428	93.67 331	97.57 314

v1240		94.06 355	93.29 359	96.34 325	96.03 409	93.90 301	98.44 258	98.17 296	91.18 396	94.13 326	97.01 367	86.05 320	98.42 350	89.13 409	89.50 396	96.70 367

X-MVStestdata		94.06 355	92.30 381	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	43.50 486	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

DTE-MVSNet		93.98 357	93.26 360	96.14 333	96.06 407	94.39 281	99.20 47	98.86 91	93.06 328	91.78 407	97.81 290	85.87 324	97.58 428	90.53 383	86.17 433	96.46 406

pm-mvs1		93.94 358	93.06 363	96.59 298	96.49 387	95.16 239	98.95 102	98.03 323	92.32 357	91.08 415	97.84 285	84.54 353	98.41 357	92.16 346	86.13 436	96.19 418

MS-PatchMatch		93.84 359	93.63 345	94.46 409	96.18 400	89.45 420	97.76 355	98.27 270	92.23 360	92.13 403	97.49 318	79.50 404	98.69 322	89.75 396	99.38 134	95.25 436

tfpnnormal		93.66 360	92.70 371	96.55 306	96.94 360	95.94 183	98.97 95	99.19 36	91.04 397	91.38 412	97.34 330	84.94 341	98.61 330	85.45 439	89.02 405	95.11 440

EU-MVSNet		93.66 360	94.14 306	92.25 440	95.96 413	83.38 464	98.52 237	98.12 303	94.69 235	92.61 386	98.13 258	87.36 296	96.39 455	91.82 357	90.00 387	96.98 330

our_test_3		93.65 362	93.30 358	94.69 397	95.45 431	89.68 415	96.91 422	97.65 348	91.97 367	91.66 410	96.88 379	89.67 226	97.93 409	88.02 421	91.49 367	96.48 404

pmmvs5		93.65 362	92.97 366	95.68 358	95.49 428	92.37 352	98.20 289	97.28 391	89.66 421	92.58 387	97.26 336	82.14 379	98.09 392	93.18 315	90.95 376	96.58 383

SSC-MVS3.2		93.59 364	93.13 362	94.97 384	96.81 370	89.71 412	97.95 328	98.49 204	94.59 242	93.50 356	96.91 377	77.74 420	98.37 364	91.69 361	90.47 380	96.83 353

test_fmvs2		93.43 365	93.58 347	92.95 434	96.97 358	83.91 460	99.19 49	97.24 394	95.74 155	95.20 284	98.27 246	69.65 457	98.72 321	96.26 202	93.73 330	96.24 415

tpm cat1		93.36 366	92.80 368	95.07 382	97.58 311	87.97 446	96.76 434	97.86 334	82.17 464	93.53 352	96.04 417	86.13 318	99.13 259	89.24 407	95.87 303	98.10 297

JIA-IIPM		93.35 367	92.49 377	95.92 346	96.48 388	90.65 391	95.01 460	96.96 416	85.93 450	96.08 265	87.33 476	87.70 288	98.78 317	91.35 367	95.58 307	98.34 287

SixPastTwentyTwo		93.34 368	92.86 367	94.75 396	95.67 421	89.41 422	98.75 172	96.67 432	93.89 277	90.15 425	98.25 249	80.87 393	98.27 378	90.90 379	90.64 378	96.57 385

USDC		93.33 369	92.71 370	95.21 375	96.83 368	90.83 387	96.91 422	97.50 368	93.84 280	90.72 418	98.14 257	77.69 421	98.82 313	89.51 402	93.21 346	95.97 424

IB-MVS	91.98 17	93.27 370	91.97 385	97.19 242	97.47 322	93.41 321	97.09 411	95.99 444	93.32 315	92.47 392	95.73 427	78.06 416	99.53 182	94.59 269	82.98 447	98.62 267
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		93.26 371	92.21 382	96.41 320	97.73 299	93.13 338	95.65 455	97.03 410	91.27 392	94.04 330	96.06 415	75.33 440	97.19 436	86.56 430	96.23 295	98.92 231

ppachtmachnet_test		93.22 372	92.63 372	94.97 384	95.45 431	90.84 386	96.88 428	97.88 333	90.60 403	92.08 404	97.26 336	88.08 277	97.86 415	85.12 442	90.33 381	96.22 416

Patchmtry		93.22 372	92.35 380	95.84 352	96.77 371	93.09 341	94.66 468	97.56 358	87.37 440	92.90 377	96.24 406	88.15 274	97.90 410	87.37 427	90.10 386	96.53 392

testing3		93.19 374	92.48 378	95.30 374	98.07 259	92.27 353	98.64 208	97.17 400	93.94 276	93.98 333	97.04 362	67.97 462	96.01 459	88.40 416	97.14 256	97.63 311

FMVSNet1		93.19 374	92.07 383	96.56 302	97.54 316	95.00 247	98.82 150	98.18 290	90.38 409	92.27 398	97.07 354	73.68 452	97.95 406	89.36 405	91.30 369	96.72 363

LF4IMVS		93.14 376	92.79 369	94.20 414	95.88 416	88.67 435	97.66 363	97.07 406	93.81 283	91.71 408	97.65 304	77.96 418	98.81 314	91.47 366	91.92 362	95.12 439

mmtdpeth		93.12 377	92.61 373	94.63 401	97.60 309	89.68 415	99.21 44	97.32 386	94.02 267	97.72 181	94.42 446	77.01 431	99.44 202	99.05 32	77.18 470	94.78 449

testgi		93.06 378	92.45 379	94.88 389	96.43 391	89.90 407	98.75 172	97.54 364	95.60 163	91.63 411	97.91 277	74.46 448	97.02 438	86.10 433	93.67 331	97.72 308

PatchT		93.06 378	91.97 385	96.35 324	96.69 377	92.67 350	94.48 471	97.08 404	86.62 444	97.08 216	92.23 468	87.94 281	97.90 410	78.89 466	96.69 271	98.49 279

RPMNet		92.81 380	91.34 391	97.24 238	97.00 355	93.43 319	94.96 461	98.80 114	82.27 463	96.93 224	92.12 469	86.98 301	99.82 97	76.32 472	96.65 273	98.46 281

UWE-MVS-28		92.79 381	92.51 376	93.62 422	96.46 389	86.28 454	97.93 332	92.71 476	94.17 260	94.78 295	97.16 344	81.05 390	96.43 453	81.45 458	96.86 264	98.14 296

myMVS_eth3d		92.73 382	92.01 384	94.89 388	97.39 332	90.94 382	97.91 335	97.46 372	93.16 323	93.42 360	95.37 436	68.09 461	96.12 457	88.34 417	96.99 260	97.60 312

TransMVSNet (Re)		92.67 383	91.51 390	96.15 332	96.58 382	94.65 266	98.90 116	96.73 428	90.86 400	89.46 432	97.86 282	85.62 328	98.09 392	86.45 431	81.12 456	95.71 429

ttmdpeth		92.61 384	91.96 387	94.55 403	94.10 449	90.60 395	98.52 237	97.29 389	92.67 342	90.18 423	97.92 276	79.75 403	97.79 417	91.09 372	86.15 435	95.26 435

Syy-MVS		92.55 385	92.61 373	92.38 437	97.39 332	83.41 463	97.91 335	97.46 372	93.16 323	93.42 360	95.37 436	84.75 346	96.12 457	77.00 471	96.99 260	97.60 312

K. test v3		92.55 385	91.91 388	94.48 407	95.64 422	89.24 423	99.07 71	94.88 459	94.04 265	86.78 449	97.59 311	77.64 424	97.64 424	92.08 348	89.43 398	96.57 385

DSMNet-mixed		92.52 387	92.58 375	92.33 438	94.15 448	82.65 466	98.30 277	94.26 466	89.08 430	92.65 385	95.73 427	85.01 340	95.76 461	86.24 432	97.76 235	98.59 272

TinyColmap		92.31 388	91.53 389	94.65 400	96.92 361	89.75 410	96.92 420	96.68 431	90.45 407	89.62 429	97.85 284	76.06 438	98.81 314	86.74 429	92.51 354	95.41 433

gg-mvs-nofinetune		92.21 389	90.58 397	97.13 247	96.75 374	95.09 243	95.85 450	89.40 484	85.43 454	94.50 301	81.98 479	80.80 395	98.40 363	92.16 346	98.33 208	97.88 301

FMVSNet5		91.81 390	90.92 393	94.49 406	97.21 342	92.09 361	98.00 324	97.55 363	89.31 428	90.86 417	95.61 433	74.48 447	95.32 465	85.57 437	89.70 390	96.07 422

pmmvs6		91.77 391	90.63 396	95.17 377	94.69 445	91.24 378	98.67 202	97.92 331	86.14 448	89.62 429	97.56 316	75.79 439	98.34 366	90.75 381	84.56 440	95.94 425

Anonymous20231206		91.66 392	91.10 392	93.33 427	94.02 453	87.35 450	98.58 221	97.26 393	90.48 405	90.16 424	96.31 404	83.83 369	96.53 451	79.36 464	89.90 388	96.12 420

Patchmatch-RL test		91.49 393	90.85 394	93.41 425	91.37 466	84.40 458	92.81 475	95.93 448	91.87 370	87.25 445	94.87 442	88.99 250	96.53 451	92.54 340	82.00 450	99.30 158

blended_shiyan6		91.37 394	89.84 405	95.98 343	91.49 465	93.28 330	97.48 376	97.83 336	86.93 442	92.43 394	92.36 467	82.44 378	98.06 397	92.74 331	74.82 475	96.59 381

test_0402		91.32 395	90.27 400	94.48 407	96.60 381	91.12 379	98.50 245	97.22 395	86.10 449	88.30 441	96.98 369	77.65 423	97.99 404	78.13 468	92.94 348	94.34 451

test_vis1_rt		91.29 396	90.65 395	93.19 431	97.45 326	86.25 455	98.57 230	90.90 482	93.30 317	86.94 448	93.59 455	62.07 473	99.11 264	97.48 141	95.58 307	94.22 454

PVSNet_0	88.72 19	91.28 397	90.03 403	95.00 383	97.99 273	87.29 451	94.84 464	98.50 199	92.06 365	89.86 426	95.19 438	79.81 402	99.39 209	92.27 345	69.79 479	98.33 288

mvs5depth		91.23 398	90.17 401	94.41 411	92.09 461	89.79 409	95.26 459	96.50 436	90.73 401	91.69 409	97.06 358	76.12 437	98.62 329	88.02 421	84.11 443	94.82 446

Anonymous20240521		91.18 399	90.44 398	93.42 424	93.70 454	88.47 439	98.94 105	97.56 358	88.46 434	89.56 431	95.08 441	77.15 429	96.97 439	83.92 449	89.55 394	94.82 446

EG-PatchMatch MVS		91.13 400	90.12 402	94.17 416	94.73 444	89.00 428	98.13 305	97.81 339	89.22 429	85.32 459	96.46 400	67.71 463	98.42 350	87.89 425	93.82 329	95.08 441

TDRefinement		91.06 401	89.68 406	95.21 375	85.35 484	91.49 374	98.51 244	97.07 406	91.47 380	88.83 438	97.84 285	77.31 425	99.09 269	92.79 329	77.98 468	95.04 443

sc_t1		91.01 402	89.39 408	95.85 351	95.99 410	90.39 400	98.43 260	97.64 350	78.79 468	92.20 401	97.94 274	66.00 467	98.60 333	91.59 364	85.94 437	98.57 275

UnsupCasMVSNet_eth		90.99 403	89.92 404	94.19 415	94.08 450	89.83 408	97.13 410	98.67 151	93.69 294	85.83 455	96.19 411	75.15 442	96.74 444	89.14 408	79.41 463	96.00 423

test20.03		90.89 404	90.38 399	92.43 436	93.48 455	88.14 445	98.33 269	97.56 358	93.40 312	87.96 442	96.71 390	80.69 396	94.13 471	79.15 465	86.17 433	95.01 445

usedtu_blend_shiyan5		90.87 405	89.15 412	96.01 339	91.33 467	93.35 327	98.12 306	97.36 384	81.93 465	92.36 396	91.75 471	81.83 381	98.09 392	92.88 327	74.82 475	96.59 381

blend_shiyan4		90.76 406	89.01 415	95.99 340	91.69 464	93.35 327	97.44 377	97.83 336	86.93 442	92.23 399	91.98 470	75.19 441	98.09 392	92.88 327	74.96 473	96.52 395

MDA-MVSNet_test_wron		90.71 407	89.38 410	94.68 398	94.83 441	90.78 388	97.19 402	97.46 372	87.60 438	72.41 479	95.72 429	86.51 307	96.71 447	85.92 435	86.80 430	96.56 387

YYNet1		90.70 408	89.39 408	94.62 402	94.79 443	90.65 391	97.20 400	97.46 372	87.54 439	72.54 478	95.74 425	86.51 307	96.66 448	86.00 434	86.76 431	96.54 390

KD-MVS_self_test		90.38 409	89.38 410	93.40 426	92.85 458	88.94 431	97.95 328	97.94 329	90.35 410	90.25 422	93.96 452	79.82 401	95.94 460	84.62 448	76.69 471	95.33 434

pmmvs-eth3d		90.36 410	89.05 414	94.32 413	91.10 469	92.12 358	97.63 368	96.95 417	88.86 432	84.91 460	93.13 460	78.32 412	96.74 444	88.70 413	81.81 452	94.09 457

FE-MVSNET2		90.29 411	88.94 417	94.36 412	90.48 471	92.27 353	98.45 252	97.82 338	91.59 378	84.90 461	93.10 461	73.92 450	96.42 454	87.92 424	82.26 448	94.39 450

tt0320		90.26 412	88.73 419	94.86 390	96.12 404	90.62 393	98.17 298	97.63 351	77.46 471	89.68 428	96.04 417	69.19 459	97.79 417	88.98 410	85.29 439	96.16 419

CL-MVSNet_self_test		90.11 413	89.14 413	93.02 432	91.86 463	88.23 444	96.51 442	98.07 316	90.49 404	90.49 421	94.41 447	84.75 346	95.34 464	80.79 460	74.95 474	95.50 432

new_pmnet		90.06 414	89.00 416	93.22 430	94.18 447	88.32 442	96.42 444	96.89 422	86.19 447	85.67 456	93.62 454	77.18 428	97.10 437	81.61 457	89.29 400	94.23 453

MDA-MVSNet-bldmvs		89.97 415	88.35 421	94.83 394	95.21 435	91.34 375	97.64 365	97.51 367	88.36 436	71.17 480	96.13 413	79.22 406	96.63 449	83.65 450	86.27 432	96.52 395

tt0320-xc		89.79 416	88.11 423	94.84 393	96.19 399	90.61 394	98.16 299	97.22 395	77.35 472	88.75 439	96.70 391	65.94 468	97.63 425	89.31 406	83.39 445	96.28 414

CMPMVS	66.06 21	89.70 417	89.67 407	89.78 445	93.19 456	76.56 471	97.00 416	98.35 250	80.97 466	81.57 467	97.75 293	74.75 445	98.61 330	89.85 394	93.63 333	94.17 455
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 418	88.28 422	93.82 419	92.81 459	91.08 380	98.01 322	97.45 376	87.95 437	87.90 443	95.87 423	67.63 464	94.56 470	78.73 467	88.18 413	95.83 427

KD-MVS_2432*1600		89.61 419	87.96 427	94.54 404	94.06 451	91.59 372	95.59 456	97.63 351	89.87 417	88.95 435	94.38 449	78.28 413	96.82 442	84.83 444	68.05 480	95.21 437

miper_refine_blended		89.61 419	87.96 427	94.54 404	94.06 451	91.59 372	95.59 456	97.63 351	89.87 417	88.95 435	94.38 449	78.28 413	96.82 442	84.83 444	68.05 480	95.21 437

MVStest1		89.53 421	87.99 426	94.14 418	94.39 446	90.42 398	98.25 284	96.84 427	82.81 460	81.18 469	97.33 332	77.09 430	96.94 440	85.27 441	78.79 464	95.06 442

MVS-HIRNet		89.46 422	88.40 420	92.64 435	97.58 311	82.15 467	94.16 474	93.05 475	75.73 475	90.90 416	82.52 478	79.42 405	98.33 368	83.53 451	98.68 173	97.43 315

OpenMVS_ROB	86.42 20	89.00 423	87.43 431	93.69 421	93.08 457	89.42 421	97.91 335	96.89 422	78.58 469	85.86 454	94.69 443	69.48 458	98.29 376	77.13 470	93.29 345	93.36 465

mvsany_test3		88.80 424	88.04 424	91.09 444	89.78 474	81.57 469	97.83 350	95.49 453	93.81 283	87.53 444	93.95 453	56.14 476	97.43 432	94.68 262	83.13 446	94.26 452

FE-MVSNET		88.56 425	87.09 432	92.99 433	89.93 473	89.99 406	98.15 302	95.59 451	88.42 435	84.87 462	92.90 462	74.82 444	94.99 468	77.88 469	81.21 455	93.99 460

new-patchmatchnet		88.50 426	87.45 430	91.67 442	90.31 472	85.89 456	97.16 408	97.33 385	89.47 424	83.63 464	92.77 464	76.38 434	95.06 467	82.70 453	77.29 469	94.06 459

APD_test1		88.22 427	88.01 425	88.86 447	95.98 411	74.66 479	97.21 399	96.44 438	83.96 459	86.66 451	97.90 278	60.95 474	97.84 416	82.73 452	90.23 384	94.09 457

PM-MVS		87.77 428	86.55 434	91.40 443	91.03 470	83.36 465	96.92 420	95.18 457	91.28 391	86.48 453	93.42 456	53.27 477	96.74 444	89.43 404	81.97 451	94.11 456

dmvs_testset		87.64 429	88.93 418	83.79 456	95.25 434	63.36 488	97.20 400	91.17 480	93.07 327	85.64 457	95.98 422	85.30 337	91.52 478	69.42 477	87.33 422	96.49 402

test_fmvs3		87.17 430	87.06 433	87.50 449	91.21 468	75.66 474	99.05 74	96.61 435	92.79 339	88.85 437	92.78 463	43.72 480	93.49 472	93.95 292	84.56 440	93.34 466

UnsupCasMVSNet_bld		87.17 430	85.12 437	93.31 428	91.94 462	88.77 432	94.92 463	98.30 267	84.30 458	82.30 465	90.04 473	63.96 471	97.25 435	85.85 436	74.47 478	93.93 462

N_pmnet		87.12 432	87.77 429	85.17 453	95.46 430	61.92 489	97.37 385	70.66 494	85.83 451	88.73 440	96.04 417	85.33 335	97.76 420	80.02 461	90.48 379	95.84 426

pmmvs3		86.67 433	84.86 438	92.11 441	88.16 478	87.19 452	96.63 438	94.75 461	79.88 467	87.22 446	92.75 465	66.56 466	95.20 466	81.24 459	76.56 472	93.96 461

test_f		86.07 434	85.39 435	88.10 448	89.28 476	75.57 475	97.73 358	96.33 440	89.41 427	85.35 458	91.56 472	43.31 482	95.53 462	91.32 368	84.23 442	93.21 467

WB-MVS		84.86 435	85.33 436	83.46 457	89.48 475	69.56 483	98.19 292	96.42 439	89.55 423	81.79 466	94.67 444	84.80 344	90.12 479	52.44 483	80.64 460	90.69 470

SSC-MVS		84.27 436	84.71 439	82.96 461	89.19 477	68.83 484	98.08 314	96.30 441	89.04 431	81.37 468	94.47 445	84.60 351	89.89 480	49.80 485	79.52 462	90.15 471

dongtai		82.47 437	81.88 440	84.22 455	95.19 436	76.03 472	94.59 470	74.14 493	82.63 461	87.19 447	96.09 414	64.10 470	87.85 483	58.91 481	84.11 443	88.78 475

test_vis3_rt		79.22 438	77.40 445	84.67 454	86.44 482	74.85 478	97.66 363	81.43 489	84.98 455	67.12 482	81.91 480	28.09 490	97.60 426	88.96 411	80.04 461	81.55 480

test_method		79.03 439	78.17 441	81.63 462	86.06 483	54.40 494	82.75 483	96.89 422	39.54 486	80.98 470	95.57 434	58.37 475	94.73 469	84.74 447	78.61 465	95.75 428

testf1		79.02 440	77.70 442	82.99 459	88.10 479	66.90 485	94.67 466	93.11 472	71.08 477	74.02 475	93.41 457	34.15 486	93.25 473	72.25 475	78.50 466	88.82 473

APD_test2		79.02 440	77.70 442	82.99 459	88.10 479	66.90 485	94.67 466	93.11 472	71.08 477	74.02 475	93.41 457	34.15 486	93.25 473	72.25 475	78.50 466	88.82 473

LCM-MVSNet		78.70 442	76.24 448	86.08 451	77.26 490	71.99 481	94.34 472	96.72 429	61.62 481	76.53 473	89.33 474	33.91 488	92.78 476	81.85 456	74.60 477	93.46 464

kuosan		78.45 443	77.69 444	80.72 463	92.73 460	75.32 476	94.63 469	74.51 492	75.96 473	80.87 471	93.19 459	63.23 472	79.99 487	42.56 487	81.56 454	86.85 479

Gipuma		78.40 444	76.75 447	83.38 458	95.54 425	80.43 470	79.42 484	97.40 380	64.67 480	73.46 477	80.82 481	45.65 479	93.14 475	66.32 479	87.43 420	76.56 483
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 445	75.44 449	85.46 452	82.54 485	74.95 477	94.23 473	93.08 474	72.80 476	74.68 474	87.38 475	36.36 485	91.56 477	73.95 473	63.94 482	89.87 472

FPMVS		77.62 446	77.14 446	79.05 465	79.25 488	60.97 490	95.79 451	95.94 447	65.96 479	67.93 481	94.40 448	37.73 484	88.88 482	68.83 478	88.46 410	87.29 476

EGC-MVSNET		75.22 447	69.54 450	92.28 439	94.81 442	89.58 417	97.64 365	96.50 436	1.82 491	5.57 492	95.74 425	68.21 460	96.26 456	73.80 474	91.71 364	90.99 469

ANet_high		69.08 448	65.37 452	80.22 464	65.99 492	71.96 482	90.91 479	90.09 483	82.62 462	49.93 487	78.39 482	29.36 489	81.75 484	62.49 480	38.52 486	86.95 478

tmp_tt		68.90 449	66.97 451	74.68 467	50.78 494	59.95 491	87.13 480	83.47 488	38.80 487	62.21 483	96.23 408	64.70 469	76.91 489	88.91 412	30.49 487	87.19 477

PMVS	61.03 23	65.95 450	63.57 454	73.09 468	57.90 493	51.22 495	85.05 482	93.93 470	54.45 482	44.32 488	83.57 477	13.22 491	89.15 481	58.68 482	81.00 457	78.91 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 451	64.25 453	67.02 469	82.28 486	59.36 492	91.83 478	85.63 486	52.69 483	60.22 484	77.28 483	41.06 483	80.12 486	46.15 486	41.14 484	61.57 485

EMVS		64.07 452	63.26 455	66.53 470	81.73 487	58.81 493	91.85 477	84.75 487	51.93 485	59.09 485	75.13 484	43.32 481	79.09 488	42.03 488	39.47 485	61.69 484

MVE	62.14 22	63.28 453	59.38 456	74.99 466	74.33 491	65.47 487	85.55 481	80.50 490	52.02 484	51.10 486	75.00 485	10.91 494	80.50 485	51.60 484	53.40 483	78.99 481
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 454	30.18 458	30.16 471	78.61 489	43.29 496	66.79 485	14.21 495	17.31 488	14.82 491	11.93 491	11.55 493	41.43 490	37.08 489	19.30 488	5.76 488

cdsmvs_eth3d_5k		23.98 455	31.98 457	0.00 474	0.00 497	0.00 499	0.00 486	98.59 172	0.00 492	0.00 493	98.61 206	90.60 201	0.00 493	0.00 492	0.00 491	0.00 489

testmvs		21.48 456	24.95 459	11.09 473	14.89 495	6.47 498	96.56 440	9.87 496	7.55 489	17.93 489	39.02 487	9.43 495	5.90 492	16.56 491	12.72 489	20.91 487

test123		20.95 457	23.72 460	12.64 472	13.54 496	8.19 497	96.55 441	6.13 497	7.48 490	16.74 490	37.98 488	12.97 492	6.05 491	16.69 490	5.43 490	23.68 486

ab-mvs-re		8.20 458	10.94 461	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	98.43 224	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

pcd_1.5k_mvsjas		7.88 459	10.50 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	94.51 91	0.00 493	0.00 492	0.00 491	0.00 489

mmdepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

monomultidepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

test_blank		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet_test		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

DCPMVS		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

sosnet-low-res		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

sosnet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uncertanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

Regformer		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

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						90.94 382								88.66 414

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									95.08 209	99.60 32	99.16 106	97.86 2	98.47 344	97.52 134	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					0.00 497

eth-test					0.00 497

ZD-MVS					99.46 58	98.70 27		98.79 119	93.21 320	98.67 104	98.97 147	95.70 52	99.83 90	96.07 206	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	95.28 192	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				99.16 106	97.81 3	99.37 210	97.24 156	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		98.71 187	98.82 101	94.36 255	99.16 66	99.29 75	96.05 40	99.81 102	97.00 163	99.71 70

save fliter					99.46 58	98.38 40	98.21 287	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																99.20 183

test_part2					99.63 34	99.18 11				99.27 56

sam_mvs1												89.45 234				99.20 183

sam_mvs												88.99 250

ambc				89.49 446	86.66 481	75.78 473	92.66 476	96.72 429		86.55 452	92.50 466	46.01 478	97.90 410	90.32 385	82.09 449	94.80 448

MTGPA								98.74 129

test_post1							96.68 437				30.43 490	87.85 285	98.69 322	92.59 336

test_post											31.83 489	88.83 257	98.91 298

patchmatchnet-post											95.10 440	89.42 235	98.89 302

GG-mvs-BLEND				96.59 298	96.34 394	94.98 251	96.51 442	88.58 485		93.10 374	94.34 451	80.34 400	98.05 398	89.53 401	96.99 260	96.74 360

MTMP							98.89 120	94.14 468

gm-plane-assit					95.88 416	87.47 449			89.74 420		96.94 375		99.19 247	93.32 311

test9_res														96.39 200	99.57 100	99.69 70

TEST9					99.31 79	98.50 34	97.92 333	98.73 132	92.63 343	97.74 178	98.68 201	96.20 35	99.80 109

test_8					99.29 88	98.44 36	97.89 341	98.72 134	92.98 331	97.70 183	98.66 204	96.20 35	99.80 109

agg_prior2														95.87 216	99.57 100	99.68 75

agg_prior					99.30 83	98.38 40		98.72 134		97.57 199			99.81 102

TestCases				96.99 258	99.25 96	93.21 336		98.18 290	91.36 384	93.52 353	98.77 188	84.67 349	99.72 136	89.70 398	97.87 230	98.02 299

test_prior4						98.01 70	97.86 345

test_prior2							97.80 352		96.12 137	97.89 165	98.69 200	95.96 44		96.89 173	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							97.57 371		91.30 389	98.67 104			99.80 109	95.70 227

新几何2							97.64 365

新几何1				99.16 55	99.34 71	98.01 70		98.69 143	90.06 414	98.13 134	98.95 154	94.60 89	99.89 68	91.97 355	99.47 122	99.59 94

旧先验1					99.29 88	97.48 89		98.70 141			99.09 129	95.56 55			99.47 122	99.61 90

无先验							97.58 370	98.72 134	91.38 383				99.87 79	93.36 310		99.60 92

原ACMM2							97.67 362

原ACMM1				98.65 97	99.32 77	96.62 140		98.67 151	93.27 319	97.81 171	98.97 147	95.18 76	99.83 90	93.84 296	99.46 125	99.50 106

test222					99.23 104	97.17 116	97.40 381	98.66 154	88.68 433	98.05 142	98.96 152	94.14 102			99.53 113	99.61 90

testdata2													99.89 68	91.65 363

segment_acmp												96.85 16

testdata				98.26 141	99.20 109	95.36 228		98.68 146	91.89 369	98.60 112	99.10 121	94.44 96	99.82 97	94.27 280	99.44 126	99.58 98

testdata1							97.32 391		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					97.42 328	94.63 268

plane_prior6					97.35 335	94.61 271						87.09 298

plane_prior5								98.56 182					99.03 278	96.07 206	94.27 313	96.92 336

plane_prior4											98.28 243

plane_prior3						94.61 271			97.02 87	95.34 279

plane_prior2							98.80 159		97.28 67

plane_prior1					97.37 334

plane_prior						94.60 273	98.44 258		96.74 103						94.22 315

n2								0.00 498

nn								0.00 498

door-mid								94.37 464

lessismore_v0				94.45 410	94.93 440	88.44 440		91.03 481		86.77 450	97.64 307	76.23 436	98.42 350	90.31 386	85.64 438	96.51 399

LGP-MVS_train				96.47 313	97.46 323	93.54 314		98.54 186	94.67 237	94.36 311	98.77 188	85.39 331	99.11 264	95.71 225	94.15 319	96.76 358

test11								98.66 154

door								94.64 462

HQP5-MVS						94.25 290

HQP-NCC					97.20 343		98.05 317		96.43 118	94.45 303

ACMP_Plane					97.20 343		98.05 317		96.43 118	94.45 303

BP-MVS														95.30 239

HQP4-MVS										94.45 303			98.96 289			96.87 348

HQP3-MVS								98.46 207							94.18 317

HQP2-MVS												86.75 304

NP-MVS					97.28 337	94.51 276					97.73 294

MDTV_nov1_ep13_2view						84.26 459	96.89 427		90.97 398	97.90 164		89.89 220		93.91 294		99.18 192

MDTV_nov1_ep13			95.40 231		97.48 321	88.34 441	96.85 430	97.29 389	93.74 287	97.48 201	97.26 336	89.18 243	99.05 274	91.92 356	97.43 251

ACMMP++_ref															92.97 347

ACMMP++															93.61 334

Test By Simon												94.64 88

ITE_SJBPF				95.44 369	97.42 328	91.32 376		97.50 368	95.09 208	93.59 348	98.35 234	81.70 382	98.88 304	89.71 397	93.39 340	96.12 420

DeepMVS_CX				86.78 450	97.09 353	72.30 480		95.17 458	75.92 474	84.34 463	95.19 438	70.58 456	95.35 463	79.98 463	89.04 404	92.68 468

High-resolution multi-view benchmark