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	98.42 259	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	98.94 225

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	98.47 246	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	98.78 181	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	91.57 160	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	98.79 164	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	98.41 260	98.68 146	97.04 86	98.52 116	98.80 175	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	97.60 418	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	95.83 198	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	98.45 248	98.76 125	97.82 33	98.45 121	98.93 153	96.65 21	99.83 90	97.38 148	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	95.12 200	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	91.82 152	99.95 9	99.82 6	99.82 14	98.75 246

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 70	97.73 295	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	93.67 294	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	98.72 183	98.66 154	97.51 49	98.15 132	98.83 172	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	98.50 241	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	91.96 149	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	98.80 156	99.26 16	98.82 7	99.87 4	99.60 10	90.95 193	99.93 34	99.76 11	99.73 63	99.12 195

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	97.02 406	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	97.42 143	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	99.10 117	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	99.15 250	98.83 40	99.56 108	99.20 179

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	96.80 181	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	98.30 273	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	97.91 331	99.58 3	97.20 75	98.33 129	99.00 141	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	98.77 184	95.06 82	99.55 179	98.95 35	99.87 1	99.12 195

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	98.57 271

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	99.10 117	95.73 51	99.13 255	98.71 44	99.49 119	99.09 203

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	99.06 132	95.27 70	99.93 34	97.07 158	99.63 89	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 160	99.30 83	95.25 230	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	99.25 172

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 101	99.45 61	96.32 162	98.28 276	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	97.75 352	98.89 75	97.71 36	98.33 129	98.97 143	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	95.41 220	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	98.11 306	98.29 264	97.19 76	98.99 75	99.02 135	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	94.46 248	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	96.82 268	99.53 42	90.68 382	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	94.63 235	98.61 111	98.97 143	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	97.90 183	99.16 115	95.08 239	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	98.71 252

APD-MVS		98.35 68	98.00 84	99.42 21	99.51 46	98.72 25	98.80 156	98.82 101	94.52 243	99.23 58	99.25 85	95.54 57	99.80 109	96.52 190	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	97.95 324	99.58 3	97.14 81	98.44 123	99.01 139	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	93.32 310	98.83 90	99.10 117	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	96.74 184	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	94.81 223	98.99 75	98.90 159	95.22 75	99.59 166	99.15 30	99.84 11	99.07 211

MP-MVS-pluss		98.31 73	97.92 86	99.49 16	99.72 17	98.88 19	98.43 256	98.78 121	94.10 259	97.69 180	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	89.38 233	99.92 43	99.80 8	99.38 134	98.69 254

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 185	98.86 151	94.99 245	98.58 217	99.00 53	98.29 20	99.73 22	99.60 10	91.70 155	99.92 43	99.63 21	99.73 63	98.76 245

MGCNet		98.23 76	97.91 87	99.21 49	98.06 258	97.96 72	98.58 217	95.51 445	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	97.60 192	99.36 58	94.45 95	99.93 34	97.14 155	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	98.43 221	96.78 99	98.87 85	98.84 168	93.72 108	99.01 279	98.91 37	99.50 117	99.19 183

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 164	98.54 185	95.24 231	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	99.21 178

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	88.83 253	99.91 56	99.71 15	99.07 150	98.61 264

fmvsm_s_conf0.1_n_a		98.08 82	98.04 81	98.21 145	97.66 301	95.39 221	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	99.35 143

dcpmvs_2		98.08 82	98.59 25	96.56 297	99.57 39	90.34 395	99.15 56	98.38 238	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	99.08 127	91.22 179	99.80 109	97.40 145	99.57 100	99.37 138

CANet		98.05 85	97.76 91	98.90 81	98.73 161	97.27 105	98.35 263	98.78 121	97.37 62	97.72 177	98.96 148	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	97.92 329	98.73 132	92.98 326	97.74 174	98.68 197	96.20 35	99.80 109	96.59 185	99.57 100	99.68 75

ETV-MVS		97.96 87	97.81 89	98.40 131	98.42 198	97.27 105	98.73 179	98.55 184	96.84 96	98.38 125	97.44 319	95.39 61	99.35 211	97.62 120	98.89 161	98.58 270

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	94.87 248	98.83 168	99.65 83

CDPH-MVS		97.94 89	97.49 106	99.28 41	99.47 56	98.44 36	97.91 331	98.67 151	92.57 342	98.77 94	98.85 167	95.93 45	99.72 136	95.56 226	99.69 73	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 323	99.00 134	89.54 411	97.43 374	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	96.89 224	99.50 29	90.46 203	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	95.48 424	96.83 132	98.95 102	98.60 165	98.58 14	98.93 81	99.55 18	88.57 258	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	98.33 265	98.89 75	92.62 339	98.05 142	98.94 151	95.34 66	99.65 153	96.04 206	99.42 128	99.19 183

CSCG		97.85 94	97.74 92	98.20 147	99.67 30	95.16 234	99.22 41	99.32 12	93.04 324	97.02 217	98.92 157	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	99.08 127	91.22 179	99.80 109	97.40 145	97.53 245	99.47 115

BP-MVS1		97.82 96	97.51 105	98.76 88	98.25 228	97.39 95	99.15 56	97.68 338	96.69 107	98.47 117	99.10 117	90.29 207	99.51 186	98.60 50	99.35 137	99.37 138

MG-MVS		97.81 97	97.60 96	98.44 125	99.12 120	95.97 179	97.75 352	98.78 121	96.89 95	98.46 118	99.22 88	93.90 107	99.68 148	94.81 252	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	98.84 168	90.33 206	99.83 90	98.53 55	96.66 268	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	97.26 203	97.53 313	94.97 84	99.33 214	97.38 148	99.20 146	99.05 212

PS-MVSNAJ		97.73 100	97.77 90	97.62 215	98.68 171	95.58 210	97.34 383	98.51 194	97.29 65	98.66 108	97.88 277	94.51 91	99.90 64	97.87 101	99.17 148	97.39 314

casdiffmvs_mvg		97.72 101	97.48 108	98.44 125	98.42 198	96.59 147	98.92 113	98.44 211	96.20 132	97.76 171	99.20 92	91.66 158	99.23 237	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	92.34 350	98.09 137	99.08 127	93.01 117	99.92 43	96.06 205	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	94.48 247	97.67 181	98.88 163	92.80 120	99.91 56	97.11 156	99.12 149	99.50 106

mvsany_test1		97.69 104	97.70 93	97.66 211	98.24 229	94.18 288	97.53 368	97.53 359	95.52 171	99.66 28	99.51 26	94.30 98	99.56 172	98.38 70	98.62 178	99.23 174

sasdasda		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 234	96.76 101	97.67 181	97.40 323	92.26 133	99.49 190	98.28 79	96.28 286	99.08 207

canonicalmvs		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 234	96.76 101	97.67 181	97.40 323	92.26 133	99.49 190	98.28 79	96.28 286	99.08 207

xiu_mvs_v2_base		97.66 107	97.70 93	97.56 219	98.61 180	95.46 218	97.44 372	98.46 207	97.15 80	98.65 109	98.15 252	94.33 97	99.80 109	97.84 104	98.66 177	97.41 312

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	99.05 133	89.74 220	99.51 186	96.86 177	98.86 165	99.28 162

baseline		97.64 108	97.44 111	98.25 142	98.35 210	96.20 166	99.00 88	98.32 251	96.33 129	98.03 145	99.17 99	91.35 171	99.16 246	98.10 87	98.29 212	99.39 134

casdiffmvs		97.63 110	97.41 113	98.28 137	98.33 218	96.14 170	98.82 147	98.32 251	96.38 125	97.95 156	99.21 90	91.23 178	99.23 237	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	98.38 238	96.69 107	97.58 194	97.42 322	92.10 142	99.50 189	98.28 79	96.25 289	99.08 207

xiu_mvs_v1_base_debu		97.60 112	97.56 100	97.72 200	98.35 210	95.98 174	97.86 341	98.51 194	97.13 82	99.01 72	98.40 224	91.56 161	99.80 109	98.53 55	98.68 173	97.37 316

xiu_mvs_v1_base		97.60 112	97.56 100	97.72 200	98.35 210	95.98 174	97.86 341	98.51 194	97.13 82	99.01 72	98.40 224	91.56 161	99.80 109	98.53 55	98.68 173	97.37 316

xiu_mvs_v1_base_debi		97.60 112	97.56 100	97.72 200	98.35 210	95.98 174	97.86 341	98.51 194	97.13 82	99.01 72	98.40 224	91.56 161	99.80 109	98.53 55	98.68 173	97.37 316

diffmvs_AUTHOR		97.59 115	97.44 111	98.01 176	98.26 227	95.47 217	98.12 303	98.36 244	96.38 125	98.84 87	99.10 117	91.13 184	99.26 227	98.24 83	98.56 183	99.30 157

diffmvs		97.58 116	97.40 114	98.13 160	98.32 221	95.81 201	98.06 312	98.37 240	96.20 132	98.74 96	98.89 162	91.31 174	99.25 230	98.16 85	98.52 187	99.34 145
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	97.03 403	97.29 65	98.73 98	98.90 159	89.41 232	99.32 215	98.68 45	98.86 165	99.42 130

MVSFormer		97.57 117	97.49 106	97.84 187	98.07 255	95.76 205	99.47 7	98.40 229	94.98 212	98.79 92	98.83 172	92.34 129	98.41 353	96.91 165	99.59 96	99.34 145

alignmvs		97.56 119	97.07 139	99.01 69	98.66 173	98.37 47	98.83 145	98.06 316	96.74 103	98.00 151	97.65 300	90.80 195	99.48 195	98.37 71	96.56 272	99.19 183

E3new		97.55 120	97.35 118	98.16 151	98.48 192	95.85 196	98.55 230	98.41 226	95.42 178	98.06 140	99.12 112	92.23 136	99.24 233	97.43 141	98.45 193	99.39 134

DPM-MVS		97.55 120	96.99 146	99.23 48	99.04 128	98.55 32	97.17 400	98.35 245	94.85 222	97.93 160	98.58 207	95.07 81	99.71 141	92.60 326	99.34 138	99.43 127

OMC-MVS		97.55 120	97.34 119	98.20 147	99.33 74	95.92 186	98.28 276	98.59 172	95.52 171	97.97 154	99.10 117	93.28 115	99.49 190	95.09 243	98.88 162	99.19 183

viewcassd2359sk11		97.53 123	97.32 120	98.16 151	98.45 195	95.83 198	98.57 226	98.42 225	95.52 171	98.07 138	99.12 112	91.81 153	99.25 230	97.46 140	98.48 192	99.41 133

LuminaMVS		97.49 124	97.18 133	98.42 129	97.50 316	97.15 118	98.45 248	97.68 338	96.56 115	98.68 103	98.78 181	89.84 217	99.32 215	98.60 50	98.57 182	98.79 237

E2		97.48 125	97.25 124	98.16 151	98.40 202	95.79 202	98.58 217	98.44 211	95.58 165	98.00 151	99.14 106	91.21 183	99.24 233	97.50 135	98.43 197	99.45 122

E3		97.48 125	97.25 124	98.16 151	98.38 205	95.79 202	98.58 217	98.44 211	95.58 165	98.00 151	99.14 106	91.25 177	99.24 233	97.50 135	98.44 194	99.45 122

KinetiMVS		97.48 125	97.05 141	98.78 86	98.37 208	97.30 101	98.99 91	98.70 141	97.18 77	99.02 70	99.01 139	87.50 287	99.67 149	95.33 233	99.33 140	99.37 138

viewmanbaseed2359cas		97.47 128	97.25 124	98.14 155	98.41 200	95.84 197	98.57 226	98.43 221	95.55 169	97.97 154	99.12 112	91.26 176	99.15 250	97.42 143	98.53 186	99.43 127

PAPM_NR		97.46 129	97.11 136	98.50 117	99.50 48	96.41 157	98.63 208	98.60 165	95.18 193	97.06 215	98.06 258	94.26 100	99.57 169	93.80 294	98.87 164	99.52 101

EPP-MVSNet		97.46 129	97.28 122	97.99 178	98.64 177	95.38 222	99.33 21	98.31 255	93.61 298	97.19 207	99.07 131	94.05 103	99.23 237	96.89 169	98.43 197	99.37 138

3Dnovator	94.51 5	97.46 129	96.93 150	99.07 64	97.78 289	97.64 81	99.35 16	99.06 48	97.02 87	93.75 341	99.16 102	89.25 237	99.92 43	97.22 154	99.75 55	99.64 86

CNLPA		97.45 132	97.03 143	98.73 90	99.05 127	97.44 94	98.07 311	98.53 188	95.32 186	96.80 229	98.53 212	93.32 113	99.72 136	94.31 275	99.31 141	99.02 216

lupinMVS		97.44 133	97.22 131	98.12 163	98.07 255	95.76 205	97.68 357	97.76 335	94.50 246	98.79 92	98.61 202	92.34 129	99.30 220	97.58 123	99.59 96	99.31 153

3Dnovator+	94.38 6	97.43 134	96.78 161	99.38 23	97.83 286	98.52 33	99.37 13	98.71 137	97.09 85	92.99 371	99.13 109	89.36 234	99.89 68	96.97 161	99.57 100	99.71 63

Vis-MVSNet		97.42 135	97.11 136	98.34 134	98.66 173	96.23 165	99.22 41	99.00 53	96.63 111	98.04 144	99.21 90	88.05 274	99.35 211	96.01 208	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	97.91 182	98.70 166	96.80 133	98.82 147	98.69 143	94.53 241	98.11 135	98.28 239	94.50 94	99.57 169	94.12 283	99.49 119	97.37 316

sss		97.39 137	96.98 148	98.61 101	98.60 181	96.61 142	98.22 283	98.93 65	93.97 269	98.01 150	98.48 217	91.98 146	99.85 84	96.45 192	98.15 216	99.39 134

test_cas_vis1_n_1920		97.38 138	97.36 117	97.45 223	98.95 142	93.25 326	99.00 88	98.53 188	97.70 37	99.77 18	99.35 60	84.71 343	99.85 84	98.57 52	99.66 79	99.26 170

PVSNet_Blended		97.38 138	97.12 135	98.14 155	99.25 96	95.35 225	97.28 388	99.26 16	93.13 320	97.94 158	98.21 247	92.74 121	99.81 102	96.88 171	99.40 132	99.27 163

WTY-MVS		97.37 140	96.92 151	98.72 91	98.86 151	96.89 131	98.31 270	98.71 137	95.26 189	97.67 181	98.56 211	92.21 138	99.78 124	95.89 210	96.85 262	99.48 113

AstraMVS		97.34 141	97.24 128	97.65 212	98.13 249	94.15 289	98.94 105	96.25 435	97.47 54	98.60 112	99.28 76	89.67 222	99.41 205	98.73 43	98.07 220	99.38 137

viewmacassd2359aftdt		97.32 142	97.07 139	98.08 167	98.30 223	95.69 207	98.62 211	98.44 211	95.56 167	97.86 166	99.22 88	89.91 215	99.14 253	97.29 151	98.43 197	99.42 130

jason		97.32 142	97.08 138	98.06 171	97.45 322	95.59 209	97.87 339	97.91 327	94.79 225	98.55 115	98.83 172	91.12 186	99.23 237	97.58 123	99.60 94	99.34 145
jason: jason.
MVS_Test		97.28 144	97.00 144	98.13 160	98.33 218	95.97 179	98.74 173	98.07 311	94.27 254	98.44 123	98.07 257	92.48 125	99.26 227	96.43 193	98.19 215	99.16 189

EPNet		97.28 144	96.87 153	98.51 114	94.98 433	96.14 170	98.90 116	97.02 406	98.28 21	95.99 264	99.11 115	91.36 170	99.89 68	96.98 160	99.19 147	99.50 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 146	97.00 144	98.03 173	98.46 193	95.99 173	98.62 211	98.44 211	94.77 226	97.24 204	98.93 153	91.22 179	99.28 224	96.54 187	98.74 172	98.84 233

mvsmamba		97.25 147	96.99 146	98.02 175	98.34 215	95.54 214	99.18 53	97.47 365	95.04 206	98.15 132	98.57 210	89.46 229	99.31 219	97.68 117	99.01 155	99.22 176

viewdifsd2359ckpt13		97.24 148	96.97 149	98.06 171	98.43 196	95.77 204	98.59 214	98.34 248	94.81 223	97.60 192	98.94 151	90.78 199	99.09 265	96.93 164	98.33 208	99.32 152

test_yl		97.22 149	96.78 161	98.54 109	98.73 161	96.60 143	98.45 248	98.31 255	94.70 229	98.02 147	98.42 222	90.80 195	99.70 142	96.81 178	96.79 264	99.34 145

DCV-MVSNet		97.22 149	96.78 161	98.54 109	98.73 161	96.60 143	98.45 248	98.31 255	94.70 229	98.02 147	98.42 222	90.80 195	99.70 142	96.81 178	96.79 264	99.34 145

IS-MVSNet		97.22 149	96.88 152	98.25 142	98.85 154	96.36 160	99.19 49	97.97 321	95.39 180	97.23 205	98.99 142	91.11 187	98.93 291	94.60 263	98.59 180	99.47 115

viewdifsd2359ckpt07		97.20 152	97.05 141	97.65 212	98.40 202	94.33 281	98.39 261	98.43 221	95.67 160	97.66 185	99.08 127	90.04 212	99.32 215	97.47 139	98.29 212	99.31 153

PLC	95.07 4	97.20 152	96.78 161	98.44 125	99.29 88	96.31 164	98.14 300	98.76 125	92.41 348	96.39 252	98.31 237	94.92 86	99.78 124	94.06 286	98.77 171	99.23 174
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 154	97.18 133	97.20 236	98.81 157	93.27 323	95.78 445	99.15 42	95.25 190	96.79 230	98.11 255	92.29 132	99.07 268	98.56 54	99.85 6	99.25 172

SSM_0407		97.17 155	96.87 153	98.08 167	98.19 237	95.90 188	98.52 233	98.44 211	94.77 226	96.75 231	98.93 153	91.22 179	99.22 241	96.54 187	98.43 197	99.10 200

LS3D		97.16 156	96.66 170	98.68 94	98.53 186	97.19 115	98.93 111	98.90 73	92.83 333	95.99 264	99.37 54	92.12 141	99.87 79	93.67 298	99.57 100	98.97 221

AdaColmap		97.15 157	96.70 166	98.48 120	99.16 115	96.69 139	98.01 318	98.89 75	94.44 249	96.83 225	98.68 197	90.69 200	99.76 130	94.36 271	99.29 142	98.98 220

viewdifsd2359ckpt09		97.13 158	96.79 159	98.14 155	98.43 196	95.90 188	98.52 233	98.37 240	94.32 252	97.33 199	98.86 166	90.23 210	99.16 246	96.81 178	98.25 214	99.36 142

mamv4		97.13 158	98.11 76	94.17 408	98.97 140	83.70 454	98.66 201	98.71 137	94.63 235	97.83 167	98.90 159	96.25 32	99.55 179	99.27 28	99.76 48	99.27 163

Effi-MVS+		97.12 160	96.69 167	98.39 132	98.19 237	96.72 138	97.37 379	98.43 221	93.71 287	97.65 186	98.02 261	92.20 139	99.25 230	96.87 174	97.79 229	99.19 183

CHOSEN 1792x2688		97.12 160	96.80 157	98.08 167	99.30 83	94.56 270	98.05 313	99.71 1	93.57 300	97.09 211	98.91 158	88.17 268	99.89 68	96.87 174	99.56 108	99.81 24

F-COLMAP		97.09 162	96.80 157	97.97 179	99.45 61	94.95 249	98.55 230	98.62 164	93.02 325	96.17 259	98.58 207	94.01 104	99.81 102	93.95 288	98.90 160	99.14 193

RRT-MVS		97.03 163	96.78 161	97.77 196	97.90 282	94.34 279	99.12 63	98.35 245	95.87 149	98.06 140	98.70 195	86.45 306	99.63 159	98.04 92	98.54 185	99.35 143

TAMVS		97.02 164	96.79 159	97.70 203	98.06 258	95.31 228	98.52 233	98.31 255	93.95 270	97.05 216	98.61 202	93.49 111	98.52 335	95.33 233	97.81 228	99.29 160

viewmambaseed2359dif		97.01 165	96.84 155	97.51 221	98.19 237	94.21 287	98.16 296	98.23 276	93.61 298	97.78 169	99.13 109	90.79 198	99.18 245	97.24 152	98.40 204	99.15 190

CDS-MVSNet		96.99 166	96.69 167	97.90 183	98.05 260	95.98 174	98.20 286	98.33 250	93.67 294	96.95 218	98.49 216	93.54 110	98.42 346	95.24 240	97.74 232	99.31 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 167	96.55 175	98.21 145	98.17 246	96.07 172	97.98 322	98.21 278	97.24 72	97.13 209	98.93 153	86.88 298	99.91 56	95.00 246	99.37 136	98.66 260

114514_t		96.93 168	96.27 188	98.92 78	99.50 48	97.63 82	98.85 139	98.90 73	84.80 450	97.77 170	99.11 115	92.84 119	99.66 152	94.85 249	99.77 42	99.47 115

MAR-MVS		96.91 169	96.40 182	98.45 123	98.69 169	96.90 129	98.66 201	98.68 146	92.40 349	97.07 214	97.96 268	91.54 164	99.75 132	93.68 296	98.92 159	98.69 254
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		96.90 170	96.49 179	98.14 155	99.33 74	95.56 211	97.38 377	99.65 2	92.34 350	97.61 189	98.20 248	89.29 236	99.10 264	96.97 161	97.60 237	99.77 40

Vis-MVSNet (Re-imp)		96.87 171	96.55 175	97.83 188	98.73 161	95.46 218	99.20 47	98.30 262	94.96 214	96.60 240	98.87 164	90.05 211	98.59 330	93.67 298	98.60 179	99.46 120

SDMVSNet		96.85 172	96.42 180	98.14 155	99.30 83	96.38 158	99.21 44	99.23 28	95.92 145	95.96 266	98.76 189	85.88 318	99.44 202	97.93 96	95.59 301	98.60 265

PAPR		96.84 173	96.24 190	98.65 97	98.72 165	96.92 128	97.36 381	98.57 179	93.33 309	96.67 235	97.57 309	94.30 98	99.56 172	91.05 369	98.59 180	99.47 115

HY-MVS	93.96 8	96.82 174	96.23 191	98.57 104	98.46 193	97.00 124	98.14 300	98.21 278	93.95 270	96.72 234	97.99 265	91.58 159	99.76 130	94.51 267	96.54 273	98.95 224

mamba_0408		96.81 175	96.38 183	98.09 166	98.19 237	95.90 188	95.69 446	98.32 251	94.51 244	96.75 231	98.73 191	90.99 191	99.27 226	95.83 213	98.43 197	99.10 200

UGNet		96.78 176	96.30 187	98.19 150	98.24 229	95.89 193	98.88 127	98.93 65	97.39 59	96.81 228	97.84 281	82.60 372	99.90 64	96.53 189	99.49 119	98.79 237
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		96.74 177	96.64 171	97.05 251	97.99 269	92.82 338	98.45 248	98.27 265	95.16 194	97.30 200	98.79 177	91.53 165	99.06 269	94.74 254	97.54 241	99.27 163

IMVS_0403		96.74 177	96.61 172	97.12 245	97.99 269	92.82 338	98.47 246	98.27 265	95.16 194	97.13 209	98.79 177	91.44 168	99.26 227	94.74 254	97.54 241	99.27 163

PVSNet_BlendedMVS		96.73 179	96.60 173	97.12 245	99.25 96	95.35 225	98.26 280	99.26 16	94.28 253	97.94 158	97.46 316	92.74 121	99.81 102	96.88 171	93.32 339	96.20 409

SSM_04072		96.71 180	96.38 183	97.68 206	98.19 237	95.90 188	95.69 446	98.32 251	94.51 244	96.75 231	98.73 191	90.99 191	98.02 392	95.83 213	98.43 197	99.10 200

test_vis1_n_1920		96.71 180	96.84 155	96.31 322	99.11 122	89.74 404	99.05 74	98.58 177	98.08 24	99.87 4	99.37 54	78.48 404	99.93 34	99.29 27	99.69 73	99.27 163

mvs_anonymous		96.70 182	96.53 177	97.18 239	98.19 237	93.78 299	98.31 270	98.19 282	94.01 266	94.47 298	98.27 242	92.08 144	98.46 341	97.39 147	97.91 224	99.31 153

Elysia		96.64 183	96.02 200	98.51 114	98.04 262	97.30 101	98.74 173	98.60 165	95.04 206	97.91 162	98.84 168	83.59 367	99.48 195	94.20 279	99.25 143	98.75 246

StellarMVS		96.64 183	96.02 200	98.51 114	98.04 262	97.30 101	98.74 173	98.60 165	95.04 206	97.91 162	98.84 168	83.59 367	99.48 195	94.20 279	99.25 143	98.75 246

1112_ss		96.63 185	96.00 202	98.50 117	98.56 182	96.37 159	98.18 294	98.10 304	92.92 329	94.84 286	98.43 220	92.14 140	99.58 168	94.35 272	96.51 274	99.56 100

PMMVS		96.60 186	96.33 186	97.41 227	97.90 282	93.93 295	97.35 382	98.41 226	92.84 332	97.76 171	97.45 318	91.10 188	99.20 242	96.26 198	97.91 224	99.11 198

DP-MVS		96.59 187	95.93 205	98.57 104	99.34 71	96.19 168	98.70 188	98.39 234	89.45 420	94.52 296	99.35 60	91.85 150	99.85 84	92.89 322	98.88 162	99.68 75

PatchMatch-RL		96.59 187	96.03 199	98.27 138	99.31 79	96.51 151	97.91 331	99.06 48	93.72 286	96.92 222	98.06 258	88.50 263	99.65 153	91.77 351	99.00 157	98.66 260

GeoE		96.58 189	96.07 196	98.10 165	98.35 210	95.89 193	99.34 17	98.12 298	93.12 321	96.09 260	98.87 164	89.71 221	98.97 281	92.95 318	98.08 219	99.43 127

icg_test_0407_2		96.56 190	96.50 178	96.73 274	97.99 269	92.82 338	97.18 397	98.27 265	95.16 194	97.30 200	98.79 177	91.53 165	98.10 383	94.74 254	97.54 241	99.27 163

XVG-OURS		96.55 191	96.41 181	96.99 254	98.75 160	93.76 300	97.50 371	98.52 191	95.67 160	96.83 225	99.30 74	88.95 251	99.53 182	95.88 211	96.26 288	97.69 305

FIs		96.51 192	96.12 195	97.67 208	97.13 346	97.54 87	99.36 14	99.22 33	95.89 147	94.03 327	98.35 230	91.98 146	98.44 344	96.40 194	92.76 347	97.01 324

XVG-OURS-SEG-HR		96.51 192	96.34 185	97.02 253	98.77 159	93.76 300	97.79 350	98.50 199	95.45 175	96.94 219	99.09 125	87.87 279	99.55 179	96.76 183	95.83 300	97.74 302

PS-MVSNAJss		96.43 194	96.26 189	96.92 263	95.84 413	95.08 239	99.16 55	98.50 199	95.87 149	93.84 336	98.34 234	94.51 91	98.61 326	96.88 171	93.45 334	97.06 322

test_fmvs1		96.42 195	96.67 169	95.66 352	98.82 156	88.53 431	98.80 156	98.20 280	96.39 124	99.64 30	99.20 92	80.35 392	99.67 149	99.04 33	99.57 100	98.78 241

FC-MVSNet-test		96.42 195	96.05 197	97.53 220	96.95 355	97.27 105	99.36 14	99.23 28	95.83 151	93.93 330	98.37 228	92.00 145	98.32 365	96.02 207	92.72 348	97.00 325

ab-mvs		96.42 195	95.71 216	98.55 107	98.63 178	96.75 136	97.88 338	98.74 129	93.84 276	96.54 245	98.18 250	85.34 329	99.75 132	95.93 209	96.35 278	99.15 190

FA-MVS(test-final)		96.41 198	95.94 204	97.82 190	98.21 233	95.20 233	97.80 348	97.58 349	93.21 315	97.36 198	97.70 293	89.47 227	99.56 172	94.12 283	97.99 221	98.71 252

PVSNet	91.96 18	96.35 199	96.15 192	96.96 258	99.17 111	92.05 355	96.08 438	98.68 146	93.69 290	97.75 173	97.80 287	88.86 252	99.69 147	94.26 277	99.01 155	99.15 190

Test_1112_low_res		96.34 200	95.66 221	98.36 133	98.56 182	95.94 182	97.71 355	98.07 311	92.10 359	94.79 290	97.29 331	91.75 154	99.56 172	94.17 281	96.50 275	99.58 98

viewdifsd2359ckpt11		96.30 201	96.13 193	96.81 269	98.10 252	92.10 351	98.49 244	98.40 229	96.02 140	97.61 189	99.31 71	86.37 308	99.29 222	97.52 131	93.36 338	99.04 213

viewmsd2359difaftdt		96.30 201	96.13 193	96.81 269	98.10 252	92.10 351	98.49 244	98.40 229	96.02 140	97.61 189	99.31 71	86.37 308	99.30 220	97.52 131	93.37 337	99.04 213

Effi-MVS+-dtu		96.29 203	96.56 174	95.51 357	97.89 284	90.22 396	98.80 156	98.10 304	96.57 114	96.45 250	96.66 388	90.81 194	98.91 294	95.72 220	97.99 221	97.40 313

QAPM		96.29 203	95.40 227	98.96 75	97.85 285	97.60 84	99.23 37	98.93 65	89.76 414	93.11 368	99.02 135	89.11 242	99.93 34	91.99 345	99.62 91	99.34 145

Fast-Effi-MVS+		96.28 205	95.70 218	98.03 173	98.29 225	95.97 179	98.58 217	98.25 274	91.74 367	95.29 279	97.23 336	91.03 190	99.15 250	92.90 320	97.96 223	98.97 221

nrg030		96.28 205	95.72 213	97.96 181	96.90 360	98.15 63	99.39 11	98.31 255	95.47 174	94.42 304	98.35 230	92.09 143	98.69 318	97.50 135	89.05 398	97.04 323

1314		96.25 207	95.73 212	97.79 192	97.13 346	95.55 213	98.19 289	98.59 172	93.47 304	92.03 397	97.82 285	91.33 172	99.49 190	94.62 262	98.44 194	98.32 285

sd_testset		96.17 208	95.76 211	97.42 226	99.30 83	94.34 279	98.82 147	99.08 46	95.92 145	95.96 266	98.76 189	82.83 371	99.32 215	95.56 226	95.59 301	98.60 265

h-mvs33		96.17 208	95.62 222	97.81 191	99.03 129	94.45 272	98.64 205	98.75 127	97.48 52	98.67 104	98.72 194	89.76 218	99.86 83	97.95 94	81.59 449	99.11 198

HQP_MVS		96.14 210	95.90 206	96.85 266	97.42 324	94.60 268	98.80 156	98.56 182	97.28 67	95.34 275	98.28 239	87.09 293	99.03 274	96.07 202	94.27 309	96.92 332

tttt0517		96.07 211	95.51 225	97.78 193	98.41 200	94.84 253	99.28 29	94.33 458	94.26 255	97.64 187	98.64 201	84.05 358	99.47 199	95.34 232	97.60 237	99.03 215

MVSTER		96.06 212	95.72 213	97.08 249	98.23 231	95.93 185	98.73 179	98.27 265	94.86 220	95.07 281	98.09 256	88.21 267	98.54 333	96.59 185	93.46 332	96.79 351

thisisatest0530		96.01 213	95.36 232	97.97 179	98.38 205	95.52 215	98.88 127	94.19 460	94.04 261	97.64 187	98.31 237	83.82 365	99.46 200	95.29 237	97.70 234	98.93 226

test_djsdf		96.00 214	95.69 219	96.93 260	95.72 415	95.49 216	99.47 7	98.40 229	94.98 212	94.58 294	97.86 278	89.16 240	98.41 353	96.91 165	94.12 317	96.88 341

EI-MVSNet		95.96 215	95.83 208	96.36 318	97.93 280	93.70 306	98.12 303	98.27 265	93.70 289	95.07 281	99.02 135	92.23 136	98.54 333	94.68 258	93.46 332	96.84 347

VortexMVS		95.95 216	95.79 209	96.42 314	98.29 225	93.96 294	98.68 194	98.31 255	96.02 140	94.29 312	97.57 309	89.47 227	98.37 360	97.51 134	91.93 355	96.94 330

ECVR-MVS		95.95 216	95.71 216	96.65 282	99.02 130	90.86 377	99.03 81	91.80 471	96.96 92	98.10 136	99.26 80	81.31 378	99.51 186	96.90 168	99.04 152	99.59 94

BH-untuned		95.95 216	95.72 213	96.65 282	98.55 184	92.26 347	98.23 282	97.79 333	93.73 284	94.62 293	98.01 263	88.97 250	99.00 280	93.04 315	98.51 188	98.68 256

test1111		95.94 219	95.78 210	96.41 315	98.99 137	90.12 397	99.04 78	92.45 470	96.99 91	98.03 145	99.27 79	81.40 377	99.48 195	96.87 174	99.04 152	99.63 88

MSDG		95.93 220	95.30 239	97.83 188	98.90 145	95.36 223	96.83 425	98.37 240	91.32 383	94.43 303	98.73 191	90.27 208	99.60 165	90.05 383	98.82 169	98.52 273

BH-RMVSNet		95.92 221	95.32 237	97.69 204	98.32 221	94.64 262	98.19 289	97.45 370	94.56 239	96.03 262	98.61 202	85.02 334	99.12 258	90.68 374	99.06 151	99.30 157

test_fmvs1_n		95.90 222	95.99 203	95.63 353	98.67 172	88.32 435	99.26 32	98.22 277	96.40 123	99.67 27	99.26 80	73.91 443	99.70 142	99.02 34	99.50 117	98.87 230

Fast-Effi-MVS+-dtu		95.87 223	95.85 207	95.91 339	97.74 294	91.74 361	98.69 191	98.15 294	95.56 167	94.92 284	97.68 298	88.98 249	98.79 312	93.19 310	97.78 230	97.20 320

LFMVS		95.86 224	94.98 254	98.47 121	98.87 150	96.32 162	98.84 143	96.02 436	93.40 307	98.62 110	99.20 92	74.99 435	99.63 159	97.72 110	97.20 250	99.46 120

baseline1		95.84 225	95.12 247	98.01 176	98.49 191	95.98 174	98.73 179	97.03 403	95.37 183	96.22 255	98.19 249	89.96 214	99.16 246	94.60 263	87.48 414	98.90 229

OpenMVS	93.04 13	95.83 226	95.00 252	98.32 135	97.18 343	97.32 98	99.21 44	98.97 57	89.96 410	91.14 406	99.05 133	86.64 301	99.92 43	93.38 304	99.47 122	97.73 303

IMVS_0404		95.82 227	95.52 223	96.73 274	97.99 269	92.82 338	97.23 390	98.27 265	95.16 194	94.31 310	98.79 177	85.63 322	98.10 383	94.74 254	97.54 241	99.27 163

VDD-MVS		95.82 227	95.23 241	97.61 216	98.84 155	93.98 293	98.68 194	97.40 374	95.02 210	97.95 156	99.34 68	74.37 441	99.78 124	98.64 48	96.80 263	99.08 207

UniMVSNet (Re)		95.78 229	95.19 243	97.58 217	96.99 353	97.47 91	98.79 164	99.18 37	95.60 163	93.92 331	97.04 358	91.68 156	98.48 337	95.80 217	87.66 413	96.79 351

VPA-MVSNet		95.75 230	95.11 248	97.69 204	97.24 335	97.27 105	98.94 105	99.23 28	95.13 199	95.51 273	97.32 329	85.73 320	98.91 294	97.33 150	89.55 389	96.89 340

HQP-MVS		95.72 231	95.40 227	96.69 280	97.20 339	94.25 285	98.05 313	98.46 207	96.43 118	94.45 299	97.73 290	86.75 299	98.96 285	95.30 235	94.18 313	96.86 346

hse-mvs2		95.71 232	95.30 239	96.93 260	98.50 187	93.53 311	98.36 262	98.10 304	97.48 52	98.67 104	97.99 265	89.76 218	99.02 277	97.95 94	80.91 455	98.22 288

UniMVSNet_NR-MVSNet		95.71 232	95.15 244	97.40 229	96.84 363	96.97 125	98.74 173	99.24 20	95.16 194	93.88 333	97.72 292	91.68 156	98.31 367	95.81 215	87.25 419	96.92 332

PatchmatchNet		95.71 232	95.52 223	96.29 324	97.58 307	90.72 381	96.84 424	97.52 360	94.06 260	97.08 212	96.96 368	89.24 238	98.90 297	92.03 344	98.37 205	99.26 170
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 235	95.33 236	96.76 273	96.16 399	94.63 263	98.43 256	98.39 234	96.64 110	95.02 283	98.78 181	85.15 333	99.05 270	95.21 242	94.20 312	96.60 374
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 235	95.38 231	96.61 290	97.61 304	93.84 298	98.91 115	98.44 211	95.25 190	94.28 313	98.47 218	86.04 317	99.12 258	95.50 229	93.95 322	96.87 344
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 237	95.69 219	95.44 361	97.54 312	88.54 430	96.97 410	97.56 352	93.50 302	97.52 196	96.93 372	89.49 225	99.16 246	95.25 239	96.42 277	98.64 262

FE-MVS		95.62 238	94.90 258	97.78 193	98.37 208	94.92 250	97.17 400	97.38 376	90.95 394	97.73 176	97.70 293	85.32 331	99.63 159	91.18 361	98.33 208	98.79 237

LPG-MVS_test		95.62 238	95.34 233	96.47 308	97.46 319	93.54 309	98.99 91	98.54 186	94.67 233	94.36 307	98.77 184	85.39 326	99.11 260	95.71 221	94.15 315	96.76 354

CLD-MVS		95.62 238	95.34 233	96.46 311	97.52 315	93.75 302	97.27 389	98.46 207	95.53 170	94.42 304	98.00 264	86.21 312	98.97 281	96.25 200	94.37 307	96.66 369
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 241	94.89 259	97.76 197	98.15 248	95.15 236	96.77 426	94.41 456	92.95 328	97.18 208	97.43 320	84.78 340	99.45 201	94.63 260	97.73 233	98.68 256

MonoMVSNet		95.51 242	95.45 226	95.68 350	95.54 420	90.87 376	98.92 113	97.37 377	95.79 153	95.53 272	97.38 325	89.58 224	97.68 414	96.40 194	92.59 349	98.49 275

thres600view7		95.49 243	94.77 262	97.67 208	98.98 138	95.02 241	98.85 139	96.90 413	95.38 181	96.63 237	96.90 374	84.29 350	99.59 166	88.65 407	96.33 279	98.40 279

test_vis1_n		95.47 244	95.13 245	96.49 305	97.77 290	90.41 391	99.27 31	98.11 301	96.58 112	99.66 28	99.18 98	67.00 457	99.62 163	99.21 29	99.40 132	99.44 125

SCA		95.46 245	95.13 245	96.46 311	97.67 299	91.29 369	97.33 384	97.60 348	94.68 232	96.92 222	97.10 343	83.97 360	98.89 298	92.59 328	98.32 211	99.20 179

IterMVS-LS		95.46 245	95.21 242	96.22 326	98.12 250	93.72 305	98.32 269	98.13 297	93.71 287	94.26 314	97.31 330	92.24 135	98.10 383	94.63 260	90.12 380	96.84 347
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 247	95.34 233	95.77 348	98.69 169	88.75 426	98.87 130	97.21 390	96.13 135	97.22 206	97.68 298	77.95 412	99.65 153	97.58 123	96.77 266	98.91 228

jajsoiax		95.45 247	95.03 251	96.73 274	95.42 428	94.63 263	99.14 59	98.52 191	95.74 155	93.22 361	98.36 229	83.87 363	98.65 323	96.95 163	94.04 318	96.91 337

CVMVSNet		95.43 249	96.04 198	93.57 416	97.93 280	83.62 455	98.12 303	98.59 172	95.68 159	96.56 241	99.02 135	87.51 285	97.51 423	93.56 302	97.44 246	99.60 92

anonymousdsp		95.42 250	94.91 257	96.94 259	95.10 432	95.90 188	99.14 59	98.41 226	93.75 281	93.16 364	97.46 316	87.50 287	98.41 353	95.63 225	94.03 319	96.50 393

DU-MVS		95.42 250	94.76 263	97.40 229	96.53 380	96.97 125	98.66 201	98.99 56	95.43 176	93.88 333	97.69 295	88.57 258	98.31 367	95.81 215	87.25 419	96.92 332

mvs_tets		95.41 252	95.00 252	96.65 282	95.58 419	94.42 274	99.00 88	98.55 184	95.73 157	93.21 362	98.38 227	83.45 369	98.63 324	97.09 157	94.00 320	96.91 337

thres100view900		95.38 253	94.70 267	97.41 227	98.98 138	94.92 250	98.87 130	96.90 413	95.38 181	96.61 239	96.88 375	84.29 350	99.56 172	88.11 410	96.29 283	97.76 300

thres400		95.38 253	94.62 271	97.65 212	98.94 143	94.98 246	98.68 194	96.93 411	95.33 184	96.55 243	96.53 394	84.23 354	99.56 172	88.11 410	96.29 283	98.40 279

BH-w/o		95.38 253	95.08 249	96.26 325	98.34 215	91.79 358	97.70 356	97.43 372	92.87 331	94.24 316	97.22 337	88.66 256	98.84 304	91.55 357	97.70 234	98.16 291

VDDNet		95.36 256	94.53 276	97.86 186	98.10 252	95.13 237	98.85 139	97.75 336	90.46 401	98.36 126	99.39 48	73.27 445	99.64 156	97.98 93	96.58 271	98.81 236

TAPA-MVS	93.98 7	95.35 257	94.56 275	97.74 199	99.13 119	94.83 255	98.33 265	98.64 159	86.62 438	96.29 254	98.61 202	94.00 105	99.29 222	80.00 455	99.41 129	99.09 203
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 258	94.98 254	96.43 313	97.67 299	93.48 313	98.73 179	98.44 211	94.94 218	92.53 384	98.53 212	84.50 349	99.14 253	95.48 230	94.00 320	96.66 369
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 259	94.87 260	96.71 277	99.29 88	93.24 327	98.58 217	98.11 301	89.92 411	93.57 346	99.10 117	86.37 308	99.79 121	90.78 372	98.10 218	97.09 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 260	94.72 266	97.13 243	98.05 260	93.26 324	97.87 339	97.20 391	94.96 214	96.18 258	95.66 427	80.97 384	99.35 211	94.47 269	97.08 253	98.78 241

tfpn200view9		95.32 260	94.62 271	97.43 225	98.94 143	94.98 246	98.68 194	96.93 411	95.33 184	96.55 243	96.53 394	84.23 354	99.56 172	88.11 410	96.29 283	97.76 300

Anonymous202405211		95.28 262	94.49 278	97.67 208	99.00 134	93.75 302	98.70 188	97.04 402	90.66 397	96.49 247	98.80 175	78.13 408	99.83 90	96.21 201	95.36 305	99.44 125

thres200		95.25 263	94.57 274	97.28 233	98.81 157	94.92 250	98.20 286	97.11 395	95.24 192	96.54 245	96.22 405	84.58 347	99.53 182	87.93 415	96.50 275	97.39 314

AllTest		95.24 264	94.65 270	96.99 254	99.25 96	93.21 328	98.59 214	98.18 285	91.36 379	93.52 348	98.77 184	84.67 344	99.72 136	89.70 390	97.87 226	98.02 295

LCM-MVSNet-Re		95.22 265	95.32 237	94.91 378	98.18 243	87.85 441	98.75 169	95.66 443	95.11 201	88.96 426	96.85 378	90.26 209	97.65 415	95.65 224	98.44 194	99.22 176

EPNet_dtu		95.21 266	94.95 256	95.99 334	96.17 397	90.45 389	98.16 296	97.27 385	96.77 100	93.14 367	98.33 235	90.34 205	98.42 346	85.57 430	98.81 170	99.09 203
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 267	94.45 284	97.46 222	96.75 370	96.56 149	98.86 134	98.65 158	93.30 312	93.27 360	98.27 242	84.85 338	98.87 301	94.82 251	91.26 366	96.96 327

D2MVS		95.18 268	95.08 249	95.48 358	97.10 348	92.07 354	98.30 273	99.13 44	94.02 263	92.90 372	96.73 384	89.48 226	98.73 316	94.48 268	93.60 331	95.65 423

WR-MVS		95.15 269	94.46 281	97.22 235	96.67 375	96.45 153	98.21 284	98.81 107	94.15 257	93.16 364	97.69 295	87.51 285	98.30 369	95.29 237	88.62 404	96.90 339

TranMVSNet+NR-MVSNet		95.14 270	94.48 279	97.11 247	96.45 386	96.36 160	99.03 81	99.03 51	95.04 206	93.58 345	97.93 271	88.27 266	98.03 391	94.13 282	86.90 424	96.95 329

myMVS_eth3d28		95.12 271	94.62 271	96.64 286	98.17 246	92.17 348	98.02 317	97.32 379	95.41 179	96.22 255	96.05 411	78.01 410	99.13 255	95.22 241	97.16 251	98.60 265

baseline2		95.11 272	94.52 277	96.87 265	96.65 376	93.56 308	98.27 279	94.10 462	93.45 305	92.02 398	97.43 320	87.45 290	99.19 243	93.88 291	97.41 248	97.87 298

miper_enhance_ethall		95.10 273	94.75 264	96.12 330	97.53 314	93.73 304	96.61 432	98.08 309	92.20 358	93.89 332	96.65 390	92.44 126	98.30 369	94.21 278	91.16 367	96.34 402

Anonymous20240529		95.10 273	94.22 294	97.75 198	99.01 132	94.26 284	98.87 130	98.83 98	85.79 446	96.64 236	98.97 143	78.73 401	99.85 84	96.27 197	94.89 306	99.12 195

test-LLR		95.10 273	94.87 260	95.80 345	96.77 367	89.70 406	96.91 415	95.21 448	95.11 201	94.83 288	95.72 424	87.71 281	98.97 281	93.06 313	98.50 189	98.72 249

WR-MVS_H		95.05 276	94.46 281	96.81 269	96.86 362	95.82 200	99.24 35	99.24 20	93.87 275	92.53 384	96.84 379	90.37 204	98.24 375	93.24 308	87.93 410	96.38 401

miper_ehance_all_eth		95.01 277	94.69 268	95.97 336	97.70 297	93.31 322	97.02 408	98.07 311	92.23 355	93.51 350	96.96 368	91.85 150	98.15 379	93.68 296	91.16 367	96.44 399

testing11		95.00 278	94.28 291	97.16 241	97.96 277	93.36 321	98.09 309	97.06 401	94.94 218	95.33 278	96.15 407	76.89 425	99.40 206	95.77 219	96.30 282	98.72 249

ADS-MVSNet		95.00 278	94.45 284	96.63 287	98.00 267	91.91 357	96.04 439	97.74 337	90.15 407	96.47 248	96.64 391	87.89 277	98.96 285	90.08 381	97.06 254	99.02 216

VPNet		94.99 280	94.19 296	97.40 229	97.16 344	96.57 148	98.71 184	98.97 57	95.67 160	94.84 286	98.24 246	80.36 391	98.67 322	96.46 191	87.32 418	96.96 327

EPMVS		94.99 280	94.48 279	96.52 303	97.22 337	91.75 360	97.23 390	91.66 472	94.11 258	97.28 202	96.81 381	85.70 321	98.84 304	93.04 315	97.28 249	98.97 221

testing91		94.98 282	94.25 293	97.20 236	97.94 278	93.41 316	98.00 320	97.58 349	94.99 211	95.45 274	96.04 412	77.20 420	99.42 204	94.97 247	96.02 296	98.78 241

NR-MVSNet		94.98 282	94.16 299	97.44 224	96.53 380	97.22 113	98.74 173	98.95 61	94.96 214	89.25 425	97.69 295	89.32 235	98.18 377	94.59 265	87.40 416	96.92 332

FMVSNet3		94.97 284	94.26 292	97.11 247	98.18 243	96.62 140	98.56 229	98.26 273	93.67 294	94.09 323	97.10 343	84.25 352	98.01 393	92.08 340	92.14 352	96.70 363

CostFormer		94.95 285	94.73 265	95.60 355	97.28 333	89.06 419	97.53 368	96.89 415	89.66 416	96.82 227	96.72 385	86.05 315	98.95 290	95.53 228	96.13 294	98.79 237

PAPM		94.95 285	94.00 312	97.78 193	97.04 350	95.65 208	96.03 441	98.25 274	91.23 388	94.19 319	97.80 287	91.27 175	98.86 303	82.61 447	97.61 236	98.84 233

CP-MVSNet		94.94 287	94.30 290	96.83 267	96.72 372	95.56 211	99.11 65	98.95 61	93.89 273	92.42 389	97.90 274	87.19 292	98.12 382	94.32 274	88.21 407	96.82 350

TR-MVS		94.94 287	94.20 295	97.17 240	97.75 291	94.14 290	97.59 365	97.02 406	92.28 354	95.75 270	97.64 303	83.88 362	98.96 285	89.77 387	96.15 293	98.40 279

RPSCF		94.87 289	95.40 227	93.26 422	98.89 146	82.06 461	98.33 265	98.06 316	90.30 406	96.56 241	99.26 80	87.09 293	99.49 190	93.82 293	96.32 280	98.24 286

testing99		94.83 290	94.08 304	97.07 250	97.94 278	93.13 330	98.10 308	97.17 393	94.86 220	95.34 275	96.00 416	76.31 428	99.40 206	95.08 244	95.90 297	98.68 256

GA-MVS		94.81 291	94.03 308	97.14 242	97.15 345	93.86 297	96.76 427	97.58 349	94.00 267	94.76 292	97.04 358	80.91 385	98.48 337	91.79 350	96.25 289	99.09 203

c3_l		94.79 292	94.43 286	95.89 341	97.75 291	93.12 332	97.16 402	98.03 318	92.23 355	93.46 354	97.05 357	91.39 169	98.01 393	93.58 301	89.21 396	96.53 385

V42		94.78 293	94.14 301	96.70 279	96.33 391	95.22 232	98.97 95	98.09 308	92.32 352	94.31 310	97.06 354	88.39 264	98.55 332	92.90 320	88.87 402	96.34 402

reproduce_monomvs		94.77 294	94.67 269	95.08 373	98.40 202	89.48 412	98.80 156	98.64 159	97.57 46	93.21 362	97.65 300	80.57 390	98.83 307	97.72 110	89.47 392	96.93 331

CR-MVSNet		94.76 295	94.15 300	96.59 293	97.00 351	93.43 314	94.96 454	97.56 352	92.46 343	96.93 220	96.24 401	88.15 269	97.88 406	87.38 418	96.65 269	98.46 277

v2v482		94.69 296	94.03 308	96.65 282	96.17 397	94.79 258	98.67 199	98.08 309	92.72 335	94.00 328	97.16 340	87.69 284	98.45 342	92.91 319	88.87 402	96.72 359

pmmvs4		94.69 296	93.99 314	96.81 269	95.74 414	95.94 182	97.40 375	97.67 341	90.42 403	93.37 357	97.59 307	89.08 243	98.20 376	92.97 317	91.67 360	96.30 405

cl22		94.68 298	94.19 296	96.13 329	98.11 251	93.60 307	96.94 412	98.31 255	92.43 347	93.32 359	96.87 377	86.51 302	98.28 373	94.10 285	91.16 367	96.51 391

eth_miper_zixun_eth		94.68 298	94.41 287	95.47 359	97.64 302	91.71 362	96.73 429	98.07 311	92.71 336	93.64 342	97.21 338	90.54 202	98.17 378	93.38 304	89.76 384	96.54 383

PCF-MVS	93.45 11	94.68 298	93.43 350	98.42 129	98.62 179	96.77 135	95.48 451	98.20 280	84.63 451	93.34 358	98.32 236	88.55 261	99.81 102	84.80 439	98.96 158	98.68 256
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 301	93.54 345	98.08 167	96.88 361	96.56 149	98.19 289	98.50 199	78.05 463	92.69 379	98.02 261	91.07 189	99.63 159	90.09 380	98.36 207	98.04 294

PS-CasMVS		94.67 301	93.99 314	96.71 277	96.68 374	95.26 229	99.13 62	99.03 51	93.68 292	92.33 390	97.95 269	85.35 328	98.10 383	93.59 300	88.16 409	96.79 351

cascas		94.63 303	93.86 324	96.93 260	96.91 359	94.27 283	96.00 442	98.51 194	85.55 447	94.54 295	96.23 403	84.20 356	98.87 301	95.80 217	96.98 259	97.66 306

tpmvs		94.60 304	94.36 289	95.33 365	97.46 319	88.60 429	96.88 421	97.68 338	91.29 385	93.80 338	96.42 398	88.58 257	99.24 233	91.06 367	96.04 295	98.17 290

LTVRE_ROB	92.95 15	94.60 304	93.90 320	96.68 281	97.41 327	94.42 274	98.52 233	98.59 172	91.69 370	91.21 405	98.35 230	84.87 337	99.04 273	91.06 367	93.44 335	96.60 374
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		94.59 306	93.92 317	96.60 292	96.21 393	94.78 259	98.59 214	98.14 296	91.86 366	94.21 318	97.02 361	87.97 275	98.41 353	91.72 352	89.57 387	96.61 373

ADS-MVSNet2		94.58 307	94.40 288	95.11 371	98.00 267	88.74 427	96.04 439	97.30 381	90.15 407	96.47 248	96.64 391	87.89 277	97.56 421	90.08 381	97.06 254	99.02 216

WBMVS		94.56 308	94.04 306	96.10 331	98.03 264	93.08 334	97.82 347	98.18 285	94.02 263	93.77 340	96.82 380	81.28 379	98.34 362	95.47 231	91.00 370	96.88 341

ACMH	92.88 16	94.55 309	93.95 316	96.34 320	97.63 303	93.26 324	98.81 155	98.49 204	93.43 306	89.74 419	98.53 212	81.91 374	99.08 267	93.69 295	93.30 340	96.70 363
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 310	93.85 325	96.63 287	97.98 275	93.06 335	98.77 168	97.84 330	93.67 294	93.80 338	98.04 260	76.88 426	98.96 285	94.79 253	92.86 345	97.86 299

XVG-ACMP-BASELINE		94.54 310	94.14 301	95.75 349	96.55 379	91.65 363	98.11 306	98.44 211	94.96 214	94.22 317	97.90 274	79.18 400	99.11 260	94.05 287	93.85 324	96.48 396

AUN-MVS		94.53 312	93.73 335	96.92 263	98.50 187	93.52 312	98.34 264	98.10 304	93.83 278	95.94 268	97.98 267	85.59 324	99.03 274	94.35 272	80.94 454	98.22 288

DIV-MVS_self_test		94.52 313	94.03 308	95.99 334	97.57 311	93.38 319	97.05 406	97.94 324	91.74 367	92.81 374	97.10 343	89.12 241	98.07 389	92.60 326	90.30 377	96.53 385

cl____		94.51 314	94.01 311	96.02 333	97.58 307	93.40 318	97.05 406	97.96 323	91.73 369	92.76 376	97.08 349	89.06 244	98.13 381	92.61 325	90.29 378	96.52 388

ETVMVS		94.50 315	93.44 349	97.68 206	98.18 243	95.35 225	98.19 289	97.11 395	93.73 284	96.40 251	95.39 430	74.53 438	98.84 304	91.10 363	96.31 281	98.84 233

GBi-Net		94.49 316	93.80 328	96.56 297	98.21 233	95.00 242	98.82 147	98.18 285	92.46 343	94.09 323	97.07 350	81.16 380	97.95 398	92.08 340	92.14 352	96.72 359

test1		94.49 316	93.80 328	96.56 297	98.21 233	95.00 242	98.82 147	98.18 285	92.46 343	94.09 323	97.07 350	81.16 380	97.95 398	92.08 340	92.14 352	96.72 359

dmvs_re		94.48 318	94.18 298	95.37 363	97.68 298	90.11 398	98.54 232	97.08 397	94.56 239	94.42 304	97.24 335	84.25 352	97.76 412	91.02 370	92.83 346	98.24 286

v8		94.47 319	93.77 331	96.57 296	96.36 389	94.83 255	99.05 74	98.19 282	91.92 363	93.16 364	96.97 366	88.82 255	98.48 337	91.69 353	87.79 411	96.39 400

FMVSNet2		94.47 319	93.61 341	97.04 252	98.21 233	96.43 155	98.79 164	98.27 265	92.46 343	93.50 351	97.09 347	81.16 380	98.00 395	91.09 364	91.93 355	96.70 363

test2506		94.44 321	93.91 319	96.04 332	99.02 130	88.99 422	99.06 72	79.47 484	96.96 92	98.36 126	99.26 80	77.21 419	99.52 185	96.78 182	99.04 152	99.59 94

Patchmatch-test		94.42 322	93.68 339	96.63 287	97.60 305	91.76 359	94.83 458	97.49 364	89.45 420	94.14 321	97.10 343	88.99 246	98.83 307	85.37 433	98.13 217	99.29 160

PEN-MVS		94.42 322	93.73 335	96.49 305	96.28 392	94.84 253	99.17 54	99.00 53	93.51 301	92.23 392	97.83 284	86.10 314	97.90 402	92.55 331	86.92 423	96.74 356

v144192		94.39 324	93.70 337	96.48 307	96.06 403	94.35 278	98.58 217	98.16 293	91.45 376	94.33 309	97.02 361	87.50 287	98.45 342	91.08 366	89.11 397	96.63 371

Baseline_NR-MVSNet		94.35 325	93.81 327	95.96 337	96.20 394	94.05 292	98.61 213	96.67 425	91.44 377	93.85 335	97.60 306	88.57 258	98.14 380	94.39 270	86.93 422	95.68 422

miper_lstm_enhance		94.33 326	94.07 305	95.11 371	97.75 291	90.97 373	97.22 392	98.03 318	91.67 371	92.76 376	96.97 366	90.03 213	97.78 411	92.51 333	89.64 386	96.56 380

v1192		94.32 327	93.58 342	96.53 302	96.10 401	94.45 272	98.50 241	98.17 291	91.54 374	94.19 319	97.06 354	86.95 297	98.43 345	90.14 379	89.57 387	96.70 363

UWE-MVS		94.30 328	93.89 322	95.53 356	97.83 286	88.95 423	97.52 370	93.25 464	94.44 249	96.63 237	97.07 350	78.70 402	99.28 224	91.99 345	97.56 240	98.36 282

ACMH+	92.99 14	94.30 328	93.77 331	95.88 342	97.81 288	92.04 356	98.71 184	98.37 240	93.99 268	90.60 412	98.47 218	80.86 387	99.05 270	92.75 324	92.40 351	96.55 382

v148		94.29 330	93.76 333	95.91 339	96.10 401	92.93 336	98.58 217	97.97 321	92.59 341	93.47 353	96.95 370	88.53 262	98.32 365	92.56 330	87.06 421	96.49 394

v10		94.29 330	93.55 344	96.51 304	96.39 388	94.80 257	98.99 91	98.19 282	91.35 381	93.02 370	96.99 364	88.09 271	98.41 353	90.50 376	88.41 406	96.33 404

SD_0403		94.28 332	94.46 281	93.73 412	98.02 265	85.32 450	98.31 270	98.40 229	94.75 228	93.59 343	98.16 251	89.01 245	96.54 442	82.32 448	97.58 239	99.34 145

MVP-Stereo		94.28 332	93.92 317	95.35 364	94.95 434	92.60 343	97.97 323	97.65 342	91.61 372	90.68 411	97.09 347	86.32 311	98.42 346	89.70 390	99.34 138	95.02 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 334	93.33 352	96.97 257	97.19 342	93.38 319	98.74 173	98.57 179	91.21 390	93.81 337	98.58 207	72.85 446	98.77 314	95.05 245	93.93 323	98.77 244

OurMVSNet-221017-0		94.21 335	94.00 312	94.85 383	95.60 418	89.22 417	98.89 120	97.43 372	95.29 187	92.18 394	98.52 215	82.86 370	98.59 330	93.46 303	91.76 358	96.74 356

v1921920		94.20 336	93.47 348	96.40 317	95.98 407	94.08 291	98.52 233	98.15 294	91.33 382	94.25 315	97.20 339	86.41 307	98.42 346	90.04 384	89.39 394	96.69 368

WB-MVSnew		94.19 337	94.04 306	94.66 391	96.82 365	92.14 349	97.86 341	95.96 439	93.50 302	95.64 271	96.77 383	88.06 273	97.99 396	84.87 436	96.86 260	93.85 456

v7n		94.19 337	93.43 350	96.47 308	95.90 410	94.38 277	99.26 32	98.34 248	91.99 361	92.76 376	97.13 342	88.31 265	98.52 335	89.48 395	87.70 412	96.52 388

tpm2		94.19 337	93.76 333	95.46 360	97.23 336	89.04 420	97.31 386	96.85 419	87.08 437	96.21 257	96.79 382	83.75 366	98.74 315	92.43 336	96.23 291	98.59 268

TESTMET0.1,1		94.18 340	93.69 338	95.63 353	96.92 357	89.12 418	96.91 415	94.78 453	93.17 317	94.88 285	96.45 397	78.52 403	98.92 292	93.09 312	98.50 189	98.85 231

dp		94.15 341	93.90 320	94.90 379	97.31 332	86.82 446	96.97 410	97.19 392	91.22 389	96.02 263	96.61 393	85.51 325	99.02 277	90.00 385	94.30 308	98.85 231

ET-MVSNet_ETH3D		94.13 342	92.98 360	97.58 217	98.22 232	96.20 166	97.31 386	95.37 447	94.53 241	79.56 465	97.63 305	86.51 302	97.53 422	96.91 165	90.74 372	99.02 216

tpm		94.13 342	93.80 328	95.12 370	96.50 382	87.91 440	97.44 372	95.89 442	92.62 339	96.37 253	96.30 400	84.13 357	98.30 369	93.24 308	91.66 361	99.14 193

testing222		94.12 344	93.03 359	97.37 232	98.02 265	94.66 260	97.94 327	96.65 427	94.63 235	95.78 269	95.76 419	71.49 447	98.92 292	91.17 362	95.88 298	98.52 273

IterMVS-SCA-FT		94.11 345	93.87 323	94.85 383	97.98 275	90.56 388	97.18 397	98.11 301	93.75 281	92.58 382	97.48 315	83.97 360	97.41 425	92.48 335	91.30 364	96.58 376

Anonymous20231211		94.10 346	93.26 355	96.61 290	99.11 122	94.28 282	99.01 86	98.88 78	86.43 440	92.81 374	97.57 309	81.66 376	98.68 321	94.83 250	89.02 400	96.88 341

IterMVS		94.09 347	93.85 325	94.80 387	97.99 269	90.35 394	97.18 397	98.12 298	93.68 292	92.46 388	97.34 326	84.05 358	97.41 425	92.51 333	91.33 363	96.62 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 348	93.51 346	95.80 345	96.77 367	89.70 406	96.91 415	95.21 448	92.89 330	94.83 288	95.72 424	77.69 414	98.97 281	93.06 313	98.50 189	98.72 249

test0.0.03 1		94.08 348	93.51 346	95.80 345	95.53 422	92.89 337	97.38 377	95.97 438	95.11 201	92.51 386	96.66 388	87.71 281	96.94 432	87.03 420	93.67 327	97.57 310

v1240		94.06 350	93.29 354	96.34 320	96.03 405	93.90 296	98.44 254	98.17 291	91.18 391	94.13 322	97.01 363	86.05 315	98.42 346	89.13 401	89.50 391	96.70 363

X-MVStestdata		94.06 350	92.30 376	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	43.50 479	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

DTE-MVSNet		93.98 352	93.26 355	96.14 328	96.06 403	94.39 276	99.20 47	98.86 91	93.06 323	91.78 399	97.81 286	85.87 319	97.58 420	90.53 375	86.17 428	96.46 398

pm-mvs1		93.94 353	93.06 358	96.59 293	96.49 383	95.16 234	98.95 102	98.03 318	92.32 352	91.08 407	97.84 281	84.54 348	98.41 353	92.16 338	86.13 431	96.19 410

MS-PatchMatch		93.84 354	93.63 340	94.46 401	96.18 396	89.45 413	97.76 351	98.27 265	92.23 355	92.13 395	97.49 314	79.50 397	98.69 318	89.75 388	99.38 134	95.25 428

tfpnnormal		93.66 355	92.70 366	96.55 301	96.94 356	95.94 182	98.97 95	99.19 36	91.04 392	91.38 404	97.34 326	84.94 336	98.61 326	85.45 432	89.02 400	95.11 432

EU-MVSNet		93.66 355	94.14 301	92.25 433	95.96 409	83.38 457	98.52 233	98.12 298	94.69 231	92.61 381	98.13 254	87.36 291	96.39 447	91.82 349	90.00 382	96.98 326

our_test_3		93.65 357	93.30 353	94.69 389	95.45 426	89.68 408	96.91 415	97.65 342	91.97 362	91.66 402	96.88 375	89.67 222	97.93 401	88.02 413	91.49 362	96.48 396

pmmvs5		93.65 357	92.97 361	95.68 350	95.49 423	92.37 344	98.20 286	97.28 384	89.66 416	92.58 382	97.26 332	82.14 373	98.09 387	93.18 311	90.95 371	96.58 376

SSC-MVS3.2		93.59 359	93.13 357	94.97 376	96.81 366	89.71 405	97.95 324	98.49 204	94.59 238	93.50 351	96.91 373	77.74 413	98.37 360	91.69 353	90.47 375	96.83 349

test_fmvs2		93.43 360	93.58 342	92.95 427	96.97 354	83.91 453	99.19 49	97.24 387	95.74 155	95.20 280	98.27 242	69.65 449	98.72 317	96.26 198	93.73 326	96.24 407

tpm cat1		93.36 361	92.80 363	95.07 374	97.58 307	87.97 439	96.76 427	97.86 329	82.17 458	93.53 347	96.04 412	86.13 313	99.13 255	89.24 399	95.87 299	98.10 293

JIA-IIPM		93.35 362	92.49 372	95.92 338	96.48 384	90.65 383	95.01 453	96.96 409	85.93 444	96.08 261	87.33 469	87.70 283	98.78 313	91.35 359	95.58 303	98.34 283

SixPastTwentyTwo		93.34 363	92.86 362	94.75 388	95.67 416	89.41 415	98.75 169	96.67 425	93.89 273	90.15 417	98.25 245	80.87 386	98.27 374	90.90 371	90.64 373	96.57 378

USDC		93.33 364	92.71 365	95.21 367	96.83 364	90.83 379	96.91 415	97.50 362	93.84 276	90.72 410	98.14 253	77.69 414	98.82 309	89.51 394	93.21 342	95.97 416

IB-MVS	91.98 17	93.27 365	91.97 380	97.19 238	97.47 318	93.41 316	97.09 405	95.99 437	93.32 310	92.47 387	95.73 422	78.06 409	99.53 182	94.59 265	82.98 442	98.62 263
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		93.26 366	92.21 377	96.41 315	97.73 295	93.13 330	95.65 448	97.03 403	91.27 387	94.04 326	96.06 410	75.33 433	97.19 428	86.56 422	96.23 291	98.92 227

ppachtmachnet_test		93.22 367	92.63 367	94.97 376	95.45 426	90.84 378	96.88 421	97.88 328	90.60 398	92.08 396	97.26 332	88.08 272	97.86 407	85.12 435	90.33 376	96.22 408

Patchmtry		93.22 367	92.35 375	95.84 344	96.77 367	93.09 333	94.66 461	97.56 352	87.37 436	92.90 372	96.24 401	88.15 269	97.90 402	87.37 419	90.10 381	96.53 385

testing3		93.19 369	92.48 373	95.30 366	98.07 255	92.27 345	98.64 205	97.17 393	93.94 272	93.98 329	97.04 358	67.97 454	96.01 452	88.40 408	97.14 252	97.63 307

FMVSNet1		93.19 369	92.07 378	96.56 297	97.54 312	95.00 242	98.82 147	98.18 285	90.38 404	92.27 391	97.07 350	73.68 444	97.95 398	89.36 397	91.30 364	96.72 359

LF4IMVS		93.14 371	92.79 364	94.20 406	95.88 411	88.67 428	97.66 359	97.07 399	93.81 279	91.71 400	97.65 300	77.96 411	98.81 310	91.47 358	91.92 357	95.12 431

mmtdpeth		93.12 372	92.61 368	94.63 393	97.60 305	89.68 408	99.21 44	97.32 379	94.02 263	97.72 177	94.42 441	77.01 424	99.44 202	99.05 32	77.18 466	94.78 441

testgi		93.06 373	92.45 374	94.88 381	96.43 387	89.90 400	98.75 169	97.54 358	95.60 163	91.63 403	97.91 273	74.46 440	97.02 430	86.10 426	93.67 327	97.72 304

PatchT		93.06 373	91.97 380	96.35 319	96.69 373	92.67 342	94.48 464	97.08 397	86.62 438	97.08 212	92.23 463	87.94 276	97.90 402	78.89 459	96.69 267	98.49 275

RPMNet		92.81 375	91.34 386	97.24 234	97.00 351	93.43 314	94.96 454	98.80 114	82.27 457	96.93 220	92.12 464	86.98 296	99.82 97	76.32 465	96.65 269	98.46 277

UWE-MVS-28		92.79 376	92.51 371	93.62 415	96.46 385	86.28 447	97.93 328	92.71 469	94.17 256	94.78 291	97.16 340	81.05 383	96.43 445	81.45 451	96.86 260	98.14 292

myMVS_eth3d		92.73 377	92.01 379	94.89 380	97.39 328	90.94 374	97.91 331	97.46 366	93.16 318	93.42 355	95.37 431	68.09 453	96.12 450	88.34 409	96.99 256	97.60 308

TransMVSNet (Re)		92.67 378	91.51 385	96.15 327	96.58 378	94.65 261	98.90 116	96.73 421	90.86 395	89.46 424	97.86 278	85.62 323	98.09 387	86.45 424	81.12 452	95.71 421

ttmdpeth		92.61 379	91.96 382	94.55 395	94.10 444	90.60 387	98.52 233	97.29 382	92.67 337	90.18 415	97.92 272	79.75 396	97.79 409	91.09 364	86.15 430	95.26 427

Syy-MVS		92.55 380	92.61 368	92.38 430	97.39 328	83.41 456	97.91 331	97.46 366	93.16 318	93.42 355	95.37 431	84.75 341	96.12 450	77.00 464	96.99 256	97.60 308

K. test v3		92.55 380	91.91 383	94.48 399	95.64 417	89.24 416	99.07 71	94.88 452	94.04 261	86.78 441	97.59 307	77.64 417	97.64 416	92.08 340	89.43 393	96.57 378

DSMNet-mixed		92.52 382	92.58 370	92.33 431	94.15 443	82.65 459	98.30 273	94.26 459	89.08 425	92.65 380	95.73 422	85.01 335	95.76 454	86.24 425	97.76 231	98.59 268

TinyColmap		92.31 383	91.53 384	94.65 392	96.92 357	89.75 403	96.92 413	96.68 424	90.45 402	89.62 421	97.85 280	76.06 431	98.81 310	86.74 421	92.51 350	95.41 425

gg-mvs-nofinetune		92.21 384	90.58 392	97.13 243	96.75 370	95.09 238	95.85 443	89.40 477	85.43 448	94.50 297	81.98 472	80.80 388	98.40 359	92.16 338	98.33 208	97.88 297

FMVSNet5		91.81 385	90.92 388	94.49 398	97.21 338	92.09 353	98.00 320	97.55 357	89.31 423	90.86 409	95.61 428	74.48 439	95.32 458	85.57 430	89.70 385	96.07 414

pmmvs6		91.77 386	90.63 391	95.17 369	94.69 440	91.24 370	98.67 199	97.92 326	86.14 442	89.62 421	97.56 312	75.79 432	98.34 362	90.75 373	84.56 435	95.94 417

Anonymous20231206		91.66 387	91.10 387	93.33 420	94.02 448	87.35 443	98.58 217	97.26 386	90.48 400	90.16 416	96.31 399	83.83 364	96.53 443	79.36 457	89.90 383	96.12 412

Patchmatch-RL test		91.49 388	90.85 389	93.41 418	91.37 459	84.40 451	92.81 468	95.93 441	91.87 365	87.25 437	94.87 437	88.99 246	96.53 443	92.54 332	82.00 445	99.30 157

test_0402		91.32 389	90.27 395	94.48 399	96.60 377	91.12 371	98.50 241	97.22 388	86.10 443	88.30 433	96.98 365	77.65 416	97.99 396	78.13 461	92.94 344	94.34 444

test_vis1_rt		91.29 390	90.65 390	93.19 424	97.45 322	86.25 448	98.57 226	90.90 475	93.30 312	86.94 440	93.59 450	62.07 466	99.11 260	97.48 138	95.58 303	94.22 447

PVSNet_0	88.72 19	91.28 391	90.03 398	95.00 375	97.99 269	87.29 444	94.84 457	98.50 199	92.06 360	89.86 418	95.19 433	79.81 395	99.39 209	92.27 337	69.79 472	98.33 284

mvs5depth		91.23 392	90.17 396	94.41 403	92.09 456	89.79 402	95.26 452	96.50 429	90.73 396	91.69 401	97.06 354	76.12 430	98.62 325	88.02 413	84.11 438	94.82 438

Anonymous20240521		91.18 393	90.44 393	93.42 417	93.70 449	88.47 432	98.94 105	97.56 352	88.46 429	89.56 423	95.08 436	77.15 422	96.97 431	83.92 442	89.55 389	94.82 438

EG-PatchMatch MVS		91.13 394	90.12 397	94.17 408	94.73 439	89.00 421	98.13 302	97.81 332	89.22 424	85.32 451	96.46 396	67.71 455	98.42 346	87.89 417	93.82 325	95.08 433

TDRefinement		91.06 395	89.68 400	95.21 367	85.35 477	91.49 366	98.51 240	97.07 399	91.47 375	88.83 430	97.84 281	77.31 418	99.09 265	92.79 323	77.98 464	95.04 435

sc_t1		91.01 396	89.39 402	95.85 343	95.99 406	90.39 392	98.43 256	97.64 344	78.79 461	92.20 393	97.94 270	66.00 459	98.60 329	91.59 356	85.94 432	98.57 271

UnsupCasMVSNet_eth		90.99 397	89.92 399	94.19 407	94.08 445	89.83 401	97.13 404	98.67 151	93.69 290	85.83 447	96.19 406	75.15 434	96.74 436	89.14 400	79.41 459	96.00 415

test20.03		90.89 398	90.38 394	92.43 429	93.48 450	88.14 438	98.33 265	97.56 352	93.40 307	87.96 434	96.71 386	80.69 389	94.13 464	79.15 458	86.17 428	95.01 437

MDA-MVSNet_test_wron		90.71 399	89.38 404	94.68 390	94.83 436	90.78 380	97.19 396	97.46 366	87.60 434	72.41 472	95.72 424	86.51 302	96.71 439	85.92 428	86.80 425	96.56 380

YYNet1		90.70 400	89.39 402	94.62 394	94.79 438	90.65 383	97.20 394	97.46 366	87.54 435	72.54 471	95.74 420	86.51 302	96.66 440	86.00 427	86.76 426	96.54 383

KD-MVS_self_test		90.38 401	89.38 404	93.40 419	92.85 453	88.94 424	97.95 324	97.94 324	90.35 405	90.25 414	93.96 447	79.82 394	95.94 453	84.62 441	76.69 467	95.33 426

pmmvs-eth3d		90.36 402	89.05 407	94.32 405	91.10 461	92.12 350	97.63 364	96.95 410	88.86 427	84.91 452	93.13 455	78.32 405	96.74 436	88.70 405	81.81 447	94.09 450

FE-MVSNET2		90.29 403	88.94 409	94.36 404	90.48 463	92.27 345	98.45 248	97.82 331	91.59 373	84.90 453	93.10 456	73.92 442	96.42 446	87.92 416	82.26 443	94.39 442

tt0320		90.26 404	88.73 411	94.86 382	96.12 400	90.62 385	98.17 295	97.63 345	77.46 464	89.68 420	96.04 412	69.19 451	97.79 409	88.98 402	85.29 434	96.16 411

CL-MVSNet_self_test		90.11 405	89.14 406	93.02 425	91.86 458	88.23 437	96.51 435	98.07 311	90.49 399	90.49 413	94.41 442	84.75 341	95.34 457	80.79 453	74.95 469	95.50 424

new_pmnet		90.06 406	89.00 408	93.22 423	94.18 442	88.32 435	96.42 437	96.89 415	86.19 441	85.67 448	93.62 449	77.18 421	97.10 429	81.61 450	89.29 395	94.23 446

MDA-MVSNet-bldmvs		89.97 407	88.35 413	94.83 386	95.21 430	91.34 367	97.64 361	97.51 361	88.36 431	71.17 473	96.13 408	79.22 399	96.63 441	83.65 443	86.27 427	96.52 388

tt0320-xc		89.79 408	88.11 415	94.84 385	96.19 395	90.61 386	98.16 296	97.22 388	77.35 465	88.75 431	96.70 387	65.94 460	97.63 417	89.31 398	83.39 440	96.28 406

CMPMVS	66.06 21	89.70 409	89.67 401	89.78 438	93.19 451	76.56 464	97.00 409	98.35 245	80.97 459	81.57 460	97.75 289	74.75 437	98.61 326	89.85 386	93.63 329	94.17 448
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 410	88.28 414	93.82 411	92.81 454	91.08 372	98.01 318	97.45 370	87.95 433	87.90 435	95.87 418	67.63 456	94.56 463	78.73 460	88.18 408	95.83 419

KD-MVS_2432*1600		89.61 411	87.96 419	94.54 396	94.06 446	91.59 364	95.59 449	97.63 345	89.87 412	88.95 427	94.38 444	78.28 406	96.82 434	84.83 437	68.05 473	95.21 429

miper_refine_blended		89.61 411	87.96 419	94.54 396	94.06 446	91.59 364	95.59 449	97.63 345	89.87 412	88.95 427	94.38 444	78.28 406	96.82 434	84.83 437	68.05 473	95.21 429

MVStest1		89.53 413	87.99 418	94.14 410	94.39 441	90.42 390	98.25 281	96.84 420	82.81 454	81.18 462	97.33 328	77.09 423	96.94 432	85.27 434	78.79 460	95.06 434

MVS-HIRNet		89.46 414	88.40 412	92.64 428	97.58 307	82.15 460	94.16 467	93.05 468	75.73 468	90.90 408	82.52 471	79.42 398	98.33 364	83.53 444	98.68 173	97.43 311

OpenMVS_ROB	86.42 20	89.00 415	87.43 423	93.69 414	93.08 452	89.42 414	97.91 331	96.89 415	78.58 462	85.86 446	94.69 438	69.48 450	98.29 372	77.13 463	93.29 341	93.36 458

mvsany_test3		88.80 416	88.04 416	91.09 437	89.78 466	81.57 462	97.83 346	95.49 446	93.81 279	87.53 436	93.95 448	56.14 469	97.43 424	94.68 258	83.13 441	94.26 445

FE-MVSNET		88.56 417	87.09 424	92.99 426	89.93 465	89.99 399	98.15 299	95.59 444	88.42 430	84.87 454	92.90 458	74.82 436	94.99 461	77.88 462	81.21 451	93.99 453

new-patchmatchnet		88.50 418	87.45 422	91.67 435	90.31 464	85.89 449	97.16 402	97.33 378	89.47 419	83.63 456	92.77 460	76.38 427	95.06 460	82.70 446	77.29 465	94.06 452

FE-MVSNET1		88.45 419	86.70 426	93.70 413	89.21 469	90.38 393	98.28 276	97.79 333	87.96 432	83.51 457	92.97 457	62.37 465	96.33 448	86.47 423	81.71 448	94.38 443

APD_test1		88.22 420	88.01 417	88.86 440	95.98 407	74.66 472	97.21 393	96.44 431	83.96 453	86.66 443	97.90 274	60.95 467	97.84 408	82.73 445	90.23 379	94.09 450

PM-MVS		87.77 421	86.55 427	91.40 436	91.03 462	83.36 458	96.92 413	95.18 450	91.28 386	86.48 445	93.42 451	53.27 470	96.74 436	89.43 396	81.97 446	94.11 449

dmvs_testset		87.64 422	88.93 410	83.79 449	95.25 429	63.36 481	97.20 394	91.17 473	93.07 322	85.64 449	95.98 417	85.30 332	91.52 471	69.42 470	87.33 417	96.49 394

test_fmvs3		87.17 423	87.06 425	87.50 442	91.21 460	75.66 467	99.05 74	96.61 428	92.79 334	88.85 429	92.78 459	43.72 473	93.49 465	93.95 288	84.56 435	93.34 459

UnsupCasMVSNet_bld		87.17 423	85.12 430	93.31 421	91.94 457	88.77 425	94.92 456	98.30 262	84.30 452	82.30 458	90.04 466	63.96 463	97.25 427	85.85 429	74.47 471	93.93 455

N_pmnet		87.12 425	87.77 421	85.17 446	95.46 425	61.92 482	97.37 379	70.66 487	85.83 445	88.73 432	96.04 412	85.33 330	97.76 412	80.02 454	90.48 374	95.84 418

pmmvs3		86.67 426	84.86 431	92.11 434	88.16 471	87.19 445	96.63 431	94.75 454	79.88 460	87.22 438	92.75 461	66.56 458	95.20 459	81.24 452	76.56 468	93.96 454

test_f		86.07 427	85.39 428	88.10 441	89.28 468	75.57 468	97.73 354	96.33 433	89.41 422	85.35 450	91.56 465	43.31 475	95.53 455	91.32 360	84.23 437	93.21 460

WB-MVS		84.86 428	85.33 429	83.46 450	89.48 467	69.56 476	98.19 289	96.42 432	89.55 418	81.79 459	94.67 439	84.80 339	90.12 472	52.44 476	80.64 456	90.69 463

SSC-MVS		84.27 429	84.71 432	82.96 454	89.19 470	68.83 477	98.08 310	96.30 434	89.04 426	81.37 461	94.47 440	84.60 346	89.89 473	49.80 478	79.52 458	90.15 464

dongtai		82.47 430	81.88 433	84.22 448	95.19 431	76.03 465	94.59 463	74.14 486	82.63 455	87.19 439	96.09 409	64.10 462	87.85 476	58.91 474	84.11 438	88.78 468

test_vis3_rt		79.22 431	77.40 438	84.67 447	86.44 475	74.85 471	97.66 359	81.43 482	84.98 449	67.12 475	81.91 473	28.09 483	97.60 418	88.96 403	80.04 457	81.55 473

test_method		79.03 432	78.17 434	81.63 455	86.06 476	54.40 487	82.75 476	96.89 415	39.54 479	80.98 463	95.57 429	58.37 468	94.73 462	84.74 440	78.61 461	95.75 420

testf1		79.02 433	77.70 435	82.99 452	88.10 472	66.90 478	94.67 459	93.11 465	71.08 470	74.02 468	93.41 452	34.15 479	93.25 466	72.25 468	78.50 462	88.82 466

APD_test2		79.02 433	77.70 435	82.99 452	88.10 472	66.90 478	94.67 459	93.11 465	71.08 470	74.02 468	93.41 452	34.15 479	93.25 466	72.25 468	78.50 462	88.82 466

LCM-MVSNet		78.70 435	76.24 441	86.08 444	77.26 483	71.99 474	94.34 465	96.72 422	61.62 474	76.53 466	89.33 467	33.91 481	92.78 469	81.85 449	74.60 470	93.46 457

kuosan		78.45 436	77.69 437	80.72 456	92.73 455	75.32 469	94.63 462	74.51 485	75.96 466	80.87 464	93.19 454	63.23 464	79.99 480	42.56 480	81.56 450	86.85 472

Gipuma		78.40 437	76.75 440	83.38 451	95.54 420	80.43 463	79.42 477	97.40 374	64.67 473	73.46 470	80.82 474	45.65 472	93.14 468	66.32 472	87.43 415	76.56 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 438	75.44 442	85.46 445	82.54 478	74.95 470	94.23 466	93.08 467	72.80 469	74.68 467	87.38 468	36.36 478	91.56 470	73.95 466	63.94 475	89.87 465

FPMVS		77.62 439	77.14 439	79.05 458	79.25 481	60.97 483	95.79 444	95.94 440	65.96 472	67.93 474	94.40 443	37.73 477	88.88 475	68.83 471	88.46 405	87.29 469

EGC-MVSNET		75.22 440	69.54 443	92.28 432	94.81 437	89.58 410	97.64 361	96.50 429	1.82 484	5.57 485	95.74 420	68.21 452	96.26 449	73.80 467	91.71 359	90.99 462

ANet_high		69.08 441	65.37 445	80.22 457	65.99 485	71.96 475	90.91 472	90.09 476	82.62 456	49.93 480	78.39 475	29.36 482	81.75 477	62.49 473	38.52 479	86.95 471

tmp_tt		68.90 442	66.97 444	74.68 460	50.78 487	59.95 484	87.13 473	83.47 481	38.80 480	62.21 476	96.23 403	64.70 461	76.91 482	88.91 404	30.49 480	87.19 470

PMVS	61.03 23	65.95 443	63.57 447	73.09 461	57.90 486	51.22 488	85.05 475	93.93 463	54.45 475	44.32 481	83.57 470	13.22 484	89.15 474	58.68 475	81.00 453	78.91 475
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 444	64.25 446	67.02 462	82.28 479	59.36 485	91.83 471	85.63 479	52.69 476	60.22 477	77.28 476	41.06 476	80.12 479	46.15 479	41.14 477	61.57 478

EMVS		64.07 445	63.26 448	66.53 463	81.73 480	58.81 486	91.85 470	84.75 480	51.93 478	59.09 478	75.13 477	43.32 474	79.09 481	42.03 481	39.47 478	61.69 477

MVE	62.14 22	63.28 446	59.38 449	74.99 459	74.33 484	65.47 480	85.55 474	80.50 483	52.02 477	51.10 479	75.00 478	10.91 487	80.50 478	51.60 477	53.40 476	78.99 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 447	30.18 451	30.16 464	78.61 482	43.29 489	66.79 478	14.21 488	17.31 481	14.82 484	11.93 484	11.55 486	41.43 483	37.08 482	19.30 481	5.76 481

cdsmvs_eth3d_5k		23.98 448	31.98 450	0.00 467	0.00 490	0.00 492	0.00 479	98.59 172	0.00 485	0.00 486	98.61 202	90.60 201	0.00 486	0.00 485	0.00 484	0.00 482

testmvs		21.48 449	24.95 452	11.09 466	14.89 488	6.47 491	96.56 433	9.87 489	7.55 482	17.93 482	39.02 480	9.43 488	5.90 485	16.56 484	12.72 482	20.91 480

test123		20.95 450	23.72 453	12.64 465	13.54 489	8.19 490	96.55 434	6.13 490	7.48 483	16.74 483	37.98 481	12.97 485	6.05 484	16.69 483	5.43 483	23.68 479

ab-mvs-re		8.20 451	10.94 454	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	98.43 220	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		7.88 452	10.50 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	94.51 91	0.00 486	0.00 485	0.00 484	0.00 482

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

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						90.94 374								88.66 406

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									95.08 205	99.60 32	99.16 102	97.86 2	98.47 340	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					0.00 490

eth-test					0.00 490

ZD-MVS					99.46 58	98.70 27		98.79 119	93.21 315	98.67 104	98.97 143	95.70 52	99.83 90	96.07 202	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	95.28 188	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	97.24 152	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	94.36 251	99.16 66	99.29 75	96.05 40	99.81 102	97.00 159	99.71 70

save fliter					99.46 58	98.38 40	98.21 284	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																99.20 179

test_part2					99.63 34	99.18 11				99.27 56

sam_mvs1												89.45 230				99.20 179

sam_mvs												88.99 246

ambc				89.49 439	86.66 474	75.78 466	92.66 469	96.72 422		86.55 444	92.50 462	46.01 471	97.90 402	90.32 377	82.09 444	94.80 440

MTGPA								98.74 129

test_post1							96.68 430				30.43 483	87.85 280	98.69 318	92.59 328

test_post											31.83 482	88.83 253	98.91 294

patchmatchnet-post											95.10 435	89.42 231	98.89 298

GG-mvs-BLEND				96.59 293	96.34 390	94.98 246	96.51 435	88.58 478		93.10 369	94.34 446	80.34 393	98.05 390	89.53 393	96.99 256	96.74 356

MTMP							98.89 120	94.14 461

gm-plane-assit					95.88 411	87.47 442			89.74 415		96.94 371		99.19 243	93.32 307

test9_res														96.39 196	99.57 100	99.69 70

TEST9					99.31 79	98.50 34	97.92 329	98.73 132	92.63 338	97.74 174	98.68 197	96.20 35	99.80 109

test_8					99.29 88	98.44 36	97.89 337	98.72 134	92.98 326	97.70 179	98.66 200	96.20 35	99.80 109

agg_prior2														95.87 212	99.57 100	99.68 75

agg_prior					99.30 83	98.38 40		98.72 134		97.57 195			99.81 102

TestCases				96.99 254	99.25 96	93.21 328		98.18 285	91.36 379	93.52 348	98.77 184	84.67 344	99.72 136	89.70 390	97.87 226	98.02 295

test_prior4						98.01 70	97.86 341

test_prior2							97.80 348		96.12 137	97.89 165	98.69 196	95.96 44		96.89 169	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							97.57 367		91.30 384	98.67 104			99.80 109	95.70 223

新几何2							97.64 361

新几何1				99.16 55	99.34 71	98.01 70		98.69 143	90.06 409	98.13 134	98.95 150	94.60 89	99.89 68	91.97 347	99.47 122	99.59 94

旧先验1					99.29 88	97.48 89		98.70 141			99.09 125	95.56 55			99.47 122	99.61 90

无先验							97.58 366	98.72 134	91.38 378				99.87 79	93.36 306		99.60 92

原ACMM2							97.67 358

原ACMM1				98.65 97	99.32 77	96.62 140		98.67 151	93.27 314	97.81 168	98.97 143	95.18 76	99.83 90	93.84 292	99.46 125	99.50 106

test222					99.23 104	97.17 116	97.40 375	98.66 154	88.68 428	98.05 142	98.96 148	94.14 102			99.53 113	99.61 90

testdata2													99.89 68	91.65 355

segment_acmp												96.85 16

testdata				98.26 141	99.20 109	95.36 223		98.68 146	91.89 364	98.60 112	99.10 117	94.44 96	99.82 97	94.27 276	99.44 126	99.58 98

testdata1							97.32 385		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					97.42 324	94.63 263

plane_prior6					97.35 331	94.61 266						87.09 293

plane_prior5								98.56 182					99.03 274	96.07 202	94.27 309	96.92 332

plane_prior4											98.28 239

plane_prior3						94.61 266			97.02 87	95.34 275

plane_prior2							98.80 156		97.28 67

plane_prior1					97.37 330

plane_prior						94.60 268	98.44 254		96.74 103						94.22 311

n2								0.00 491

nn								0.00 491

door-mid								94.37 457

lessismore_v0				94.45 402	94.93 435	88.44 433		91.03 474		86.77 442	97.64 303	76.23 429	98.42 346	90.31 378	85.64 433	96.51 391

LGP-MVS_train				96.47 308	97.46 319	93.54 309		98.54 186	94.67 233	94.36 307	98.77 184	85.39 326	99.11 260	95.71 221	94.15 315	96.76 354

test11								98.66 154

door								94.64 455

HQP5-MVS						94.25 285

HQP-NCC					97.20 339		98.05 313		96.43 118	94.45 299

ACMP_Plane					97.20 339		98.05 313		96.43 118	94.45 299

BP-MVS														95.30 235

HQP4-MVS										94.45 299			98.96 285			96.87 344

HQP3-MVS								98.46 207							94.18 313

HQP2-MVS												86.75 299

NP-MVS					97.28 333	94.51 271					97.73 290

MDTV_nov1_ep13_2view						84.26 452	96.89 420		90.97 393	97.90 164		89.89 216		93.91 290		99.18 188

MDTV_nov1_ep13			95.40 227		97.48 317	88.34 434	96.85 423	97.29 382	93.74 283	97.48 197	97.26 332	89.18 239	99.05 270	91.92 348	97.43 247

ACMMP++_ref															92.97 343

ACMMP++															93.61 330

Test By Simon												94.64 88

ITE_SJBPF				95.44 361	97.42 324	91.32 368		97.50 362	95.09 204	93.59 343	98.35 230	81.70 375	98.88 300	89.71 389	93.39 336	96.12 412

DeepMVS_CX				86.78 443	97.09 349	72.30 473		95.17 451	75.92 467	84.34 455	95.19 433	70.58 448	95.35 456	79.98 456	89.04 399	92.68 461

High-resolution multi-view benchmark