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	98.42 260	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	98.94 226

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	98.47 247	98.81 107	97.72 34	98.76 95	99.16 103	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	98.78 182	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	98.41 261	98.68 146	97.04 86	98.52 116	98.80 176	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	97.60 420	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	98.79 164	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	99.17 100	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	99.17 100	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	98.45 249	98.76 125	97.82 33	98.45 121	98.93 154	96.65 21	99.83 90	97.38 149	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	95.12 201	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	98.75 247

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 70	97.73 296	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	93.67 295	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	98.83 173	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	99.13 110	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	98.50 242	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	99.12 196

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	97.02 407	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	97.42 144	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	99.10 118	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	99.14 107	95.25 72	99.15 251	98.83 40	99.56 108	99.20 180

PGM-MVS		98.49 51	98.23 67	99.27 43	99.72 17	98.08 67	98.99 91	99.49 5	95.43 177	99.03 69	99.32 69	95.56 55	99.94 14	96.80 182	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	98.30 274	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	99.00 142	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	98.77 185	95.06 82	99.55 179	98.95 35	99.87 1	99.12 196

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	98.57 272

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	99.10 118	95.73 51	99.13 256	98.71 44	99.49 119	99.09 204

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	99.06 133	95.27 70	99.93 34	97.07 159	99.63 89	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 160	99.30 83	95.25 232	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	99.25 173

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 101	99.45 61	96.32 162	98.28 277	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	98.97 144	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	98.86 134	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	98.29 266	97.19 76	98.99 75	99.02 136	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	94.46 249	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	96.82 270	99.53 42	90.68 384	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	94.63 236	98.61 111	98.97 144	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	97.90 184	99.16 115	95.08 241	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	98.71 253

APD-MVS		98.35 68	98.00 84	99.42 21	99.51 46	98.72 25	98.80 156	98.82 101	94.52 244	99.23 58	99.25 85	95.54 57	99.80 109	96.52 191	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	99.01 140	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	93.32 312	98.83 90	99.10 118	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	96.74 185	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	94.81 224	98.99 75	98.90 160	95.22 75	99.59 166	99.15 30	99.84 11	99.07 212

MP-MVS-pluss		98.31 73	97.92 86	99.49 16	99.72 17	98.88 19	98.43 257	98.78 121	94.10 260	97.69 181	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	89.38 234	99.92 43	99.80 8	99.38 134	98.69 255

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 186	98.86 151	94.99 247	98.58 218	99.00 53	98.29 20	99.73 22	99.60 10	91.70 155	99.92 43	99.63 21	99.73 63	98.76 246

MGCNet		98.23 76	97.91 87	99.21 49	98.06 259	97.96 72	98.58 218	95.51 446	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	97.60 193	99.36 58	94.45 95	99.93 34	97.14 156	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	98.43 222	96.78 99	98.87 85	98.84 169	93.72 108	99.01 280	98.91 37	99.50 117	99.19 184

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 165	98.54 185	95.24 233	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	99.21 179

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	88.83 254	99.91 56	99.71 15	99.07 150	98.61 265

fmvsm_s_conf0.1_n_a		98.08 82	98.04 81	98.21 145	97.66 302	95.39 223	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	96.56 299	99.57 39	90.34 396	99.15 56	98.38 240	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	99.08 128	91.22 179	99.80 109	97.40 146	99.57 100	99.37 139

CANet		98.05 85	97.76 91	98.90 81	98.73 161	97.27 105	98.35 264	98.78 121	97.37 62	97.72 178	98.96 149	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	92.98 328	97.74 175	98.68 198	96.20 35	99.80 109	96.59 186	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	97.44 320	95.39 61	99.35 211	97.62 120	98.89 161	98.58 271

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	94.87 249	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	92.57 344	98.77 94	98.85 168	95.93 45	99.72 136	95.56 227	99.69 73	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 325	99.00 134	89.54 412	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	96.89 225	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	95.48 426	96.83 132	98.95 102	98.60 165	98.58 14	98.93 81	99.55 18	88.57 259	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	98.33 266	98.89 75	92.62 341	98.05 142	98.94 152	95.34 66	99.65 153	96.04 207	99.42 128	99.19 184

CSCG		97.85 94	97.74 92	98.20 147	99.67 30	95.16 236	99.22 41	99.32 12	93.04 326	97.02 218	98.92 158	95.36 64	99.91 56	97.43 142	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	99.08 128	91.22 179	99.80 109	97.40 146	97.53 246	99.47 115

BP-MVS1		97.82 96	97.51 105	98.76 88	98.25 229	97.39 95	99.15 56	97.68 339	96.69 107	98.47 117	99.10 118	90.29 208	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	97.75 352	98.78 121	96.89 95	98.46 118	99.22 88	93.90 107	99.68 148	94.81 253	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	98.84 169	90.33 207	99.83 90	98.53 55	96.66 269	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	97.26 204	97.53 314	94.97 84	99.33 214	97.38 149	99.20 146	99.05 213

PS-MVSNAJ		97.73 100	97.77 90	97.62 216	98.68 171	95.58 212	97.34 383	98.51 194	97.29 65	98.66 108	97.88 278	94.51 91	99.90 64	97.87 101	99.17 148	97.39 315

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	97.76 172	99.20 92	91.66 158	99.23 238	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	92.34 352	98.09 137	99.08 128	93.01 117	99.92 43	96.06 206	99.77 42	99.75 48

PVSNet_Blended_VisFu		97.70 103	97.46 109	98.44 125	99.27 93	95.91 188	98.63 208	99.16 40	94.48 248	97.67 182	98.88 164	92.80 120	99.91 56	97.11 157	99.12 149	99.50 106

mvsany_test1		97.69 104	97.70 93	97.66 212	98.24 230	94.18 290	97.53 368	97.53 360	95.52 172	99.66 28	99.51 26	94.30 98	99.56 172	98.38 70	98.62 178	99.23 175

sasdasda		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 235	96.76 101	97.67 182	97.40 324	92.26 133	99.49 190	98.28 79	96.28 287	99.08 208

canonicalmvs		97.67 105	97.23 129	98.98 72	98.70 166	98.38 40	99.34 17	98.39 235	96.76 101	97.67 182	97.40 324	92.26 133	99.49 190	98.28 79	96.28 287	99.08 208

xiu_mvs_v2_base		97.66 107	97.70 93	97.56 220	98.61 180	95.46 220	97.44 372	98.46 207	97.15 80	98.65 109	98.15 253	94.33 97	99.80 109	97.84 104	98.66 177	97.41 313

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	99.05 134	89.74 221	99.51 186	96.86 178	98.86 165	99.28 163

baseline		97.64 108	97.44 111	98.25 142	98.35 210	96.20 166	99.00 88	98.32 253	96.33 129	98.03 145	99.17 100	91.35 171	99.16 247	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	98.82 147	98.32 253	96.38 125	97.95 156	99.21 90	91.23 178	99.23 238	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	98.38 240	96.69 107	97.58 195	97.42 323	92.10 142	99.50 189	98.28 79	96.25 290	99.08 208

xiu_mvs_v1_base_debu		97.60 112	97.56 100	97.72 201	98.35 210	95.98 175	97.86 341	98.51 194	97.13 82	99.01 72	98.40 225	91.56 161	99.80 109	98.53 55	98.68 173	97.37 317

xiu_mvs_v1_base		97.60 112	97.56 100	97.72 201	98.35 210	95.98 175	97.86 341	98.51 194	97.13 82	99.01 72	98.40 225	91.56 161	99.80 109	98.53 55	98.68 173	97.37 317

xiu_mvs_v1_base_debi		97.60 112	97.56 100	97.72 201	98.35 210	95.98 175	97.86 341	98.51 194	97.13 82	99.01 72	98.40 225	91.56 161	99.80 109	98.53 55	98.68 173	97.37 317

diffmvs_AUTHOR		97.59 115	97.44 111	98.01 177	98.26 228	95.47 219	98.12 303	98.36 246	96.38 125	98.84 87	99.10 118	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	98.06 312	98.37 242	96.20 132	98.74 96	98.89 163	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	97.03 404	97.29 65	98.73 98	98.90 160	89.41 233	99.32 215	98.68 45	98.86 165	99.42 130

MVSFormer		97.57 117	97.49 106	97.84 188	98.07 256	95.76 206	99.47 7	98.40 230	94.98 213	98.79 92	98.83 173	92.34 129	98.41 354	96.91 166	99.59 96	99.34 146

alignmvs		97.56 119	97.07 140	99.01 69	98.66 173	98.37 47	98.83 145	98.06 318	96.74 103	98.00 151	97.65 301	90.80 195	99.48 195	98.37 71	96.56 273	99.19 184

E3new		97.55 120	97.35 118	98.16 151	98.48 192	95.85 197	98.55 231	98.41 227	95.42 179	98.06 140	99.12 113	92.23 136	99.24 234	97.43 142	98.45 193	99.39 135

DPM-MVS		97.55 120	96.99 147	99.23 48	99.04 128	98.55 32	97.17 400	98.35 247	94.85 223	97.93 160	98.58 208	95.07 81	99.71 141	92.60 328	99.34 138	99.43 127

OMC-MVS		97.55 120	97.34 119	98.20 147	99.33 74	95.92 187	98.28 277	98.59 172	95.52 172	97.97 154	99.10 118	93.28 115	99.49 190	95.09 244	98.88 162	99.19 184

viewcassd2359sk11		97.53 123	97.32 120	98.16 151	98.45 195	95.83 199	98.57 227	98.42 226	95.52 172	98.07 138	99.12 113	91.81 153	99.25 231	97.46 140	98.48 192	99.41 133

LuminaMVS		97.49 124	97.18 133	98.42 129	97.50 317	97.15 118	98.45 249	97.68 339	96.56 115	98.68 103	98.78 182	89.84 218	99.32 215	98.60 50	98.57 182	98.79 238

E2		97.48 125	97.25 124	98.16 151	98.40 202	95.79 203	98.58 218	98.44 211	95.58 165	98.00 151	99.14 107	91.21 183	99.24 234	97.50 135	98.43 197	99.45 122

E3		97.48 125	97.25 124	98.16 151	98.38 205	95.79 203	98.58 218	98.44 211	95.58 165	98.00 151	99.14 107	91.25 177	99.24 234	97.50 135	98.44 194	99.45 122

KinetiMVS		97.48 125	97.05 142	98.78 86	98.37 208	97.30 101	98.99 91	98.70 141	97.18 77	99.02 70	99.01 140	87.50 289	99.67 149	95.33 234	99.33 140	99.37 139

viewmanbaseed2359cas		97.47 128	97.25 124	98.14 155	98.41 200	95.84 198	98.57 227	98.43 222	95.55 170	97.97 154	99.12 113	91.26 176	99.15 251	97.42 144	98.53 186	99.43 127

PAPM_NR		97.46 129	97.11 137	98.50 117	99.50 48	96.41 157	98.63 208	98.60 165	95.18 194	97.06 216	98.06 259	94.26 100	99.57 169	93.80 295	98.87 164	99.52 101

EPP-MVSNet		97.46 129	97.28 122	97.99 179	98.64 177	95.38 224	99.33 21	98.31 257	93.61 300	97.19 208	99.07 132	94.05 103	99.23 238	96.89 170	98.43 197	99.37 139

3Dnovator	94.51 5	97.46 129	96.93 151	99.07 64	97.78 290	97.64 81	99.35 16	99.06 48	97.02 87	93.75 343	99.16 103	89.25 238	99.92 43	97.22 155	99.75 55	99.64 86

CNLPA		97.45 132	97.03 144	98.73 90	99.05 127	97.44 94	98.07 311	98.53 188	95.32 187	96.80 230	98.53 213	93.32 113	99.72 136	94.31 276	99.31 141	99.02 217

lupinMVS		97.44 133	97.22 131	98.12 163	98.07 256	95.76 206	97.68 357	97.76 336	94.50 247	98.79 92	98.61 203	92.34 129	99.30 220	97.58 123	99.59 96	99.31 154

3Dnovator+	94.38 6	97.43 134	96.78 162	99.38 23	97.83 287	98.52 33	99.37 13	98.71 137	97.09 85	92.99 373	99.13 110	89.36 235	99.89 68	96.97 162	99.57 100	99.71 63

Vis-MVSNet		97.42 135	97.11 137	98.34 134	98.66 173	96.23 165	99.22 41	99.00 53	96.63 111	98.04 144	99.21 90	88.05 276	99.35 211	96.01 209	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	97.91 183	98.70 166	96.80 133	98.82 147	98.69 143	94.53 242	98.11 135	98.28 240	94.50 94	99.57 169	94.12 284	99.49 119	97.37 317

sss		97.39 137	96.98 149	98.61 101	98.60 181	96.61 142	98.22 283	98.93 65	93.97 270	98.01 150	98.48 218	91.98 146	99.85 84	96.45 193	98.15 217	99.39 135

test_cas_vis1_n_1920		97.38 138	97.36 117	97.45 224	98.95 142	93.25 328	99.00 88	98.53 188	97.70 37	99.77 18	99.35 60	84.71 345	99.85 84	98.57 52	99.66 79	99.26 171

PVSNet_Blended		97.38 138	97.12 136	98.14 155	99.25 96	95.35 227	97.28 388	99.26 16	93.13 322	97.94 158	98.21 248	92.74 121	99.81 102	96.88 172	99.40 132	99.27 164

E4		97.37 140	97.13 135	98.12 163	98.27 227	95.70 208	98.59 214	98.44 211	95.56 167	97.80 169	99.18 98	90.57 202	99.26 227	97.45 141	98.28 214	99.40 134

WTY-MVS		97.37 140	96.92 152	98.72 91	98.86 151	96.89 131	98.31 271	98.71 137	95.26 190	97.67 182	98.56 212	92.21 138	99.78 124	95.89 211	96.85 263	99.48 113

AstraMVS		97.34 142	97.24 128	97.65 213	98.13 250	94.15 291	98.94 105	96.25 436	97.47 54	98.60 112	99.28 76	89.67 223	99.41 205	98.73 43	98.07 221	99.38 138

viewmacassd2359aftdt		97.32 143	97.07 140	98.08 168	98.30 223	95.69 209	98.62 211	98.44 211	95.56 167	97.86 166	99.22 88	89.91 216	99.14 254	97.29 152	98.43 197	99.42 130

jason		97.32 143	97.08 139	98.06 172	97.45 323	95.59 211	97.87 339	97.91 329	94.79 226	98.55 115	98.83 173	91.12 186	99.23 238	97.58 123	99.60 94	99.34 146
jason: jason.
MVS_Test		97.28 145	97.00 145	98.13 160	98.33 218	95.97 180	98.74 173	98.07 313	94.27 255	98.44 123	98.07 258	92.48 125	99.26 227	96.43 194	98.19 216	99.16 190

EPNet		97.28 145	96.87 154	98.51 114	94.98 435	96.14 170	98.90 116	97.02 407	98.28 21	95.99 265	99.11 116	91.36 170	99.89 68	96.98 161	99.19 147	99.50 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 147	97.00 145	98.03 174	98.46 193	95.99 174	98.62 211	98.44 211	94.77 227	97.24 205	98.93 154	91.22 179	99.28 224	96.54 188	98.74 172	98.84 234

mvsmamba		97.25 148	96.99 147	98.02 176	98.34 215	95.54 216	99.18 53	97.47 366	95.04 207	98.15 132	98.57 211	89.46 230	99.31 219	97.68 117	99.01 155	99.22 177

viewdifsd2359ckpt13		97.24 149	96.97 150	98.06 172	98.43 196	95.77 205	98.59 214	98.34 250	94.81 224	97.60 193	98.94 152	90.78 199	99.09 266	96.93 165	98.33 208	99.32 153

test_yl		97.22 150	96.78 162	98.54 109	98.73 161	96.60 143	98.45 249	98.31 257	94.70 230	98.02 147	98.42 223	90.80 195	99.70 142	96.81 179	96.79 265	99.34 146

DCV-MVSNet		97.22 150	96.78 162	98.54 109	98.73 161	96.60 143	98.45 249	98.31 257	94.70 230	98.02 147	98.42 223	90.80 195	99.70 142	96.81 179	96.79 265	99.34 146

IS-MVSNet		97.22 150	96.88 153	98.25 142	98.85 154	96.36 160	99.19 49	97.97 323	95.39 181	97.23 206	98.99 143	91.11 187	98.93 292	94.60 264	98.59 180	99.47 115

viewdifsd2359ckpt07		97.20 153	97.05 142	97.65 213	98.40 202	94.33 283	98.39 262	98.43 222	95.67 160	97.66 186	99.08 128	90.04 213	99.32 215	97.47 139	98.29 212	99.31 154

PLC	95.07 4	97.20 153	96.78 162	98.44 125	99.29 88	96.31 164	98.14 300	98.76 125	92.41 350	96.39 253	98.31 238	94.92 86	99.78 124	94.06 287	98.77 171	99.23 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 155	97.18 133	97.20 237	98.81 157	93.27 325	95.78 446	99.15 42	95.25 191	96.79 231	98.11 256	92.29 132	99.07 269	98.56 54	99.85 6	99.25 173

SSM_0407		97.17 156	96.87 154	98.08 168	98.19 238	95.90 189	98.52 234	98.44 211	94.77 227	96.75 232	98.93 154	91.22 179	99.22 242	96.54 188	98.43 197	99.10 201

LS3D		97.16 157	96.66 171	98.68 94	98.53 186	97.19 115	98.93 111	98.90 73	92.83 335	95.99 265	99.37 54	92.12 141	99.87 79	93.67 299	99.57 100	98.97 222

AdaColmap		97.15 158	96.70 167	98.48 120	99.16 115	96.69 139	98.01 318	98.89 75	94.44 250	96.83 226	98.68 198	90.69 200	99.76 130	94.36 272	99.29 142	98.98 221

viewdifsd2359ckpt09		97.13 159	96.79 160	98.14 155	98.43 196	95.90 189	98.52 234	98.37 242	94.32 253	97.33 200	98.86 167	90.23 211	99.16 247	96.81 179	98.25 215	99.36 143

mamv4		97.13 159	98.11 76	94.17 410	98.97 140	83.70 455	98.66 201	98.71 137	94.63 236	97.83 167	98.90 160	96.25 32	99.55 179	99.27 28	99.76 48	99.27 164

Effi-MVS+		97.12 161	96.69 168	98.39 132	98.19 238	96.72 138	97.37 379	98.43 222	93.71 288	97.65 187	98.02 262	92.20 139	99.25 231	96.87 175	97.79 230	99.19 184

CHOSEN 1792x2688		97.12 161	96.80 158	98.08 168	99.30 83	94.56 272	98.05 313	99.71 1	93.57 302	97.09 212	98.91 159	88.17 270	99.89 68	96.87 175	99.56 108	99.81 24

F-COLMAP		97.09 163	96.80 158	97.97 180	99.45 61	94.95 251	98.55 231	98.62 164	93.02 327	96.17 260	98.58 208	94.01 104	99.81 102	93.95 289	98.90 160	99.14 194

RRT-MVS		97.03 164	96.78 162	97.77 197	97.90 283	94.34 281	99.12 63	98.35 247	95.87 149	98.06 140	98.70 196	86.45 308	99.63 159	98.04 92	98.54 185	99.35 144

TAMVS		97.02 165	96.79 160	97.70 204	98.06 259	95.31 230	98.52 234	98.31 257	93.95 271	97.05 217	98.61 203	93.49 111	98.52 336	95.33 234	97.81 229	99.29 161

viewmambaseed2359dif		97.01 166	96.84 156	97.51 222	98.19 238	94.21 289	98.16 296	98.23 278	93.61 300	97.78 170	99.13 110	90.79 198	99.18 246	97.24 153	98.40 204	99.15 191

CDS-MVSNet		96.99 167	96.69 168	97.90 184	98.05 261	95.98 175	98.20 286	98.33 252	93.67 295	96.95 219	98.49 217	93.54 110	98.42 347	95.24 241	97.74 233	99.31 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 168	96.55 176	98.21 145	98.17 247	96.07 173	97.98 322	98.21 280	97.24 72	97.13 210	98.93 154	86.88 300	99.91 56	95.00 247	99.37 136	98.66 261

114514_t		96.93 169	96.27 189	98.92 78	99.50 48	97.63 82	98.85 139	98.90 73	84.80 451	97.77 171	99.11 116	92.84 119	99.66 152	94.85 250	99.77 42	99.47 115

MAR-MVS		96.91 170	96.40 183	98.45 123	98.69 169	96.90 129	98.66 201	98.68 146	92.40 351	97.07 215	97.96 269	91.54 164	99.75 132	93.68 297	98.92 159	98.69 255
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		96.90 171	96.49 180	98.14 155	99.33 74	95.56 213	97.38 377	99.65 2	92.34 352	97.61 190	98.20 249	89.29 237	99.10 265	96.97 162	97.60 238	99.77 40

Vis-MVSNet (Re-imp)		96.87 172	96.55 176	97.83 189	98.73 161	95.46 220	99.20 47	98.30 264	94.96 215	96.60 241	98.87 165	90.05 212	98.59 331	93.67 299	98.60 179	99.46 120

SDMVSNet		96.85 173	96.42 181	98.14 155	99.30 83	96.38 158	99.21 44	99.23 28	95.92 145	95.96 267	98.76 190	85.88 320	99.44 202	97.93 96	95.59 302	98.60 266

PAPR		96.84 174	96.24 191	98.65 97	98.72 165	96.92 128	97.36 381	98.57 179	93.33 311	96.67 236	97.57 310	94.30 98	99.56 172	91.05 371	98.59 180	99.47 115

HY-MVS	93.96 8	96.82 175	96.23 192	98.57 104	98.46 193	97.00 124	98.14 300	98.21 280	93.95 271	96.72 235	97.99 266	91.58 159	99.76 130	94.51 268	96.54 274	98.95 225

mamba_0408		96.81 176	96.38 184	98.09 167	98.19 238	95.90 189	95.69 447	98.32 253	94.51 245	96.75 232	98.73 192	90.99 191	99.27 226	95.83 214	98.43 197	99.10 201

UGNet		96.78 177	96.30 188	98.19 150	98.24 230	95.89 194	98.88 127	98.93 65	97.39 59	96.81 229	97.84 282	82.60 374	99.90 64	96.53 190	99.49 119	98.79 238
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		96.74 178	96.64 172	97.05 252	97.99 270	92.82 340	98.45 249	98.27 267	95.16 195	97.30 201	98.79 178	91.53 165	99.06 270	94.74 255	97.54 242	99.27 164

IMVS_0403		96.74 178	96.61 173	97.12 246	97.99 270	92.82 340	98.47 247	98.27 267	95.16 195	97.13 210	98.79 178	91.44 168	99.26 227	94.74 255	97.54 242	99.27 164

PVSNet_BlendedMVS		96.73 180	96.60 174	97.12 246	99.25 96	95.35 227	98.26 280	99.26 16	94.28 254	97.94 158	97.46 317	92.74 121	99.81 102	96.88 172	93.32 340	96.20 411

SSM_04072		96.71 181	96.38 184	97.68 207	98.19 238	95.90 189	95.69 447	98.32 253	94.51 245	96.75 232	98.73 192	90.99 191	98.02 394	95.83 214	98.43 197	99.10 201

test_vis1_n_1920		96.71 181	96.84 156	96.31 324	99.11 122	89.74 405	99.05 74	98.58 177	98.08 24	99.87 4	99.37 54	78.48 406	99.93 34	99.29 27	99.69 73	99.27 164

mvs_anonymous		96.70 183	96.53 178	97.18 240	98.19 238	93.78 301	98.31 271	98.19 284	94.01 267	94.47 299	98.27 243	92.08 144	98.46 342	97.39 148	97.91 225	99.31 154

Elysia		96.64 184	96.02 201	98.51 114	98.04 263	97.30 101	98.74 173	98.60 165	95.04 207	97.91 162	98.84 169	83.59 369	99.48 195	94.20 280	99.25 143	98.75 247

StellarMVS		96.64 184	96.02 201	98.51 114	98.04 263	97.30 101	98.74 173	98.60 165	95.04 207	97.91 162	98.84 169	83.59 369	99.48 195	94.20 280	99.25 143	98.75 247

1112_ss		96.63 186	96.00 203	98.50 117	98.56 182	96.37 159	98.18 294	98.10 306	92.92 331	94.84 287	98.43 221	92.14 140	99.58 168	94.35 273	96.51 275	99.56 100

PMMVS		96.60 187	96.33 187	97.41 228	97.90 283	93.93 297	97.35 382	98.41 227	92.84 334	97.76 172	97.45 319	91.10 188	99.20 243	96.26 199	97.91 225	99.11 199

DP-MVS		96.59 188	95.93 206	98.57 104	99.34 71	96.19 168	98.70 188	98.39 235	89.45 422	94.52 297	99.35 60	91.85 150	99.85 84	92.89 323	98.88 162	99.68 75

PatchMatch-RL		96.59 188	96.03 200	98.27 138	99.31 79	96.51 151	97.91 331	99.06 48	93.72 287	96.92 223	98.06 259	88.50 264	99.65 153	91.77 353	99.00 157	98.66 261

GeoE		96.58 190	96.07 197	98.10 166	98.35 210	95.89 194	99.34 17	98.12 300	93.12 323	96.09 261	98.87 165	89.71 222	98.97 282	92.95 319	98.08 220	99.43 127

icg_test_0407_2		96.56 191	96.50 179	96.73 276	97.99 270	92.82 340	97.18 397	98.27 267	95.16 195	97.30 201	98.79 178	91.53 165	98.10 385	94.74 255	97.54 242	99.27 164

XVG-OURS		96.55 192	96.41 182	96.99 255	98.75 160	93.76 302	97.50 371	98.52 191	95.67 160	96.83 226	99.30 74	88.95 252	99.53 182	95.88 212	96.26 289	97.69 306

FIs		96.51 193	96.12 196	97.67 209	97.13 347	97.54 87	99.36 14	99.22 33	95.89 147	94.03 328	98.35 231	91.98 146	98.44 345	96.40 195	92.76 348	97.01 325

XVG-OURS-SEG-HR		96.51 193	96.34 186	97.02 254	98.77 159	93.76 302	97.79 350	98.50 199	95.45 176	96.94 220	99.09 126	87.87 281	99.55 179	96.76 184	95.83 301	97.74 303

PS-MVSNAJss		96.43 195	96.26 190	96.92 265	95.84 415	95.08 241	99.16 55	98.50 199	95.87 149	93.84 338	98.34 235	94.51 91	98.61 327	96.88 172	93.45 335	97.06 323

test_fmvs1		96.42 196	96.67 170	95.66 354	98.82 156	88.53 432	98.80 156	98.20 282	96.39 124	99.64 30	99.20 92	80.35 394	99.67 149	99.04 33	99.57 100	98.78 242

FC-MVSNet-test		96.42 196	96.05 198	97.53 221	96.95 356	97.27 105	99.36 14	99.23 28	95.83 151	93.93 331	98.37 229	92.00 145	98.32 366	96.02 208	92.72 349	97.00 326

ab-mvs		96.42 196	95.71 217	98.55 107	98.63 178	96.75 136	97.88 338	98.74 129	93.84 277	96.54 246	98.18 251	85.34 331	99.75 132	95.93 210	96.35 279	99.15 191

FA-MVS(test-final)		96.41 199	95.94 205	97.82 191	98.21 234	95.20 235	97.80 348	97.58 350	93.21 317	97.36 199	97.70 294	89.47 228	99.56 172	94.12 284	97.99 222	98.71 253

PVSNet	91.96 18	96.35 200	96.15 193	96.96 260	99.17 111	92.05 357	96.08 439	98.68 146	93.69 291	97.75 174	97.80 288	88.86 253	99.69 147	94.26 278	99.01 155	99.15 191

Test_1112_low_res		96.34 201	95.66 222	98.36 133	98.56 182	95.94 183	97.71 355	98.07 313	92.10 361	94.79 291	97.29 332	91.75 154	99.56 172	94.17 282	96.50 276	99.58 98

viewdifsd2359ckpt11		96.30 202	96.13 194	96.81 271	98.10 253	92.10 353	98.49 245	98.40 230	96.02 140	97.61 190	99.31 71	86.37 310	99.29 222	97.52 131	93.36 339	99.04 214

viewmsd2359difaftdt		96.30 202	96.13 194	96.81 271	98.10 253	92.10 353	98.49 245	98.40 230	96.02 140	97.61 190	99.31 71	86.37 310	99.30 220	97.52 131	93.37 338	99.04 214

Effi-MVS+-dtu		96.29 204	96.56 175	95.51 359	97.89 285	90.22 397	98.80 156	98.10 306	96.57 114	96.45 251	96.66 389	90.81 194	98.91 295	95.72 221	97.99 222	97.40 314

QAPM		96.29 204	95.40 228	98.96 75	97.85 286	97.60 84	99.23 37	98.93 65	89.76 416	93.11 370	99.02 136	89.11 243	99.93 34	91.99 347	99.62 91	99.34 146

Fast-Effi-MVS+		96.28 206	95.70 219	98.03 174	98.29 225	95.97 180	98.58 218	98.25 276	91.74 369	95.29 280	97.23 337	91.03 190	99.15 251	92.90 321	97.96 224	98.97 222

nrg030		96.28 206	95.72 214	97.96 182	96.90 361	98.15 63	99.39 11	98.31 257	95.47 175	94.42 305	98.35 231	92.09 143	98.69 319	97.50 135	89.05 400	97.04 324

1314		96.25 208	95.73 213	97.79 193	97.13 347	95.55 215	98.19 289	98.59 172	93.47 306	92.03 399	97.82 286	91.33 172	99.49 190	94.62 263	98.44 194	98.32 286

sd_testset		96.17 209	95.76 212	97.42 227	99.30 83	94.34 281	98.82 147	99.08 46	95.92 145	95.96 267	98.76 190	82.83 373	99.32 215	95.56 227	95.59 302	98.60 266

h-mvs33		96.17 209	95.62 223	97.81 192	99.03 129	94.45 274	98.64 205	98.75 127	97.48 52	98.67 104	98.72 195	89.76 219	99.86 83	97.95 94	81.59 450	99.11 199

HQP_MVS		96.14 211	95.90 207	96.85 268	97.42 325	94.60 270	98.80 156	98.56 182	97.28 67	95.34 276	98.28 240	87.09 295	99.03 275	96.07 203	94.27 310	96.92 333

tttt0517		96.07 212	95.51 226	97.78 194	98.41 200	94.84 255	99.28 29	94.33 459	94.26 256	97.64 188	98.64 202	84.05 360	99.47 199	95.34 233	97.60 238	99.03 216

MVSTER		96.06 213	95.72 214	97.08 250	98.23 232	95.93 186	98.73 179	98.27 267	94.86 221	95.07 282	98.09 257	88.21 269	98.54 334	96.59 186	93.46 333	96.79 352

thisisatest0530		96.01 214	95.36 233	97.97 180	98.38 205	95.52 217	98.88 127	94.19 461	94.04 262	97.64 188	98.31 238	83.82 367	99.46 200	95.29 238	97.70 235	98.93 227

test_djsdf		96.00 215	95.69 220	96.93 262	95.72 417	95.49 218	99.47 7	98.40 230	94.98 213	94.58 295	97.86 279	89.16 241	98.41 354	96.91 166	94.12 318	96.88 342

EI-MVSNet		95.96 216	95.83 209	96.36 320	97.93 281	93.70 308	98.12 303	98.27 267	93.70 290	95.07 282	99.02 136	92.23 136	98.54 334	94.68 259	93.46 333	96.84 348

VortexMVS		95.95 217	95.79 210	96.42 316	98.29 225	93.96 296	98.68 194	98.31 257	96.02 140	94.29 313	97.57 310	89.47 228	98.37 361	97.51 134	91.93 357	96.94 331

ECVR-MVS		95.95 217	95.71 217	96.65 284	99.02 130	90.86 379	99.03 81	91.80 472	96.96 92	98.10 136	99.26 80	81.31 380	99.51 186	96.90 169	99.04 152	99.59 94

BH-untuned		95.95 217	95.72 214	96.65 284	98.55 184	92.26 349	98.23 282	97.79 335	93.73 285	94.62 294	98.01 264	88.97 251	99.00 281	93.04 316	98.51 188	98.68 257

test1111		95.94 220	95.78 211	96.41 317	98.99 137	90.12 398	99.04 78	92.45 471	96.99 91	98.03 145	99.27 79	81.40 379	99.48 195	96.87 175	99.04 152	99.63 88

MSDG		95.93 221	95.30 240	97.83 189	98.90 145	95.36 225	96.83 426	98.37 242	91.32 385	94.43 304	98.73 192	90.27 209	99.60 165	90.05 385	98.82 169	98.52 274

BH-RMVSNet		95.92 222	95.32 238	97.69 205	98.32 221	94.64 264	98.19 289	97.45 371	94.56 240	96.03 263	98.61 203	85.02 336	99.12 259	90.68 376	99.06 151	99.30 158

test_fmvs1_n		95.90 223	95.99 204	95.63 355	98.67 172	88.32 436	99.26 32	98.22 279	96.40 123	99.67 27	99.26 80	73.91 445	99.70 142	99.02 34	99.50 117	98.87 231

Fast-Effi-MVS+-dtu		95.87 224	95.85 208	95.91 341	97.74 295	91.74 363	98.69 191	98.15 296	95.56 167	94.92 285	97.68 299	88.98 250	98.79 313	93.19 311	97.78 231	97.20 321

LFMVS		95.86 225	94.98 255	98.47 121	98.87 150	96.32 162	98.84 143	96.02 437	93.40 309	98.62 110	99.20 92	74.99 437	99.63 159	97.72 110	97.20 251	99.46 120

baseline1		95.84 226	95.12 248	98.01 177	98.49 191	95.98 175	98.73 179	97.03 404	95.37 184	96.22 256	98.19 250	89.96 215	99.16 247	94.60 264	87.48 416	98.90 230

OpenMVS	93.04 13	95.83 227	95.00 253	98.32 135	97.18 344	97.32 98	99.21 44	98.97 57	89.96 412	91.14 408	99.05 134	86.64 303	99.92 43	93.38 305	99.47 122	97.73 304

IMVS_0404		95.82 228	95.52 224	96.73 276	97.99 270	92.82 340	97.23 390	98.27 267	95.16 195	94.31 311	98.79 178	85.63 324	98.10 385	94.74 255	97.54 242	99.27 164

VDD-MVS		95.82 228	95.23 242	97.61 217	98.84 155	93.98 295	98.68 194	97.40 375	95.02 211	97.95 156	99.34 68	74.37 443	99.78 124	98.64 48	96.80 264	99.08 208

UniMVSNet (Re)		95.78 230	95.19 244	97.58 218	96.99 354	97.47 91	98.79 164	99.18 37	95.60 163	93.92 332	97.04 359	91.68 156	98.48 338	95.80 218	87.66 415	96.79 352

VPA-MVSNet		95.75 231	95.11 249	97.69 205	97.24 336	97.27 105	98.94 105	99.23 28	95.13 200	95.51 274	97.32 330	85.73 322	98.91 295	97.33 151	89.55 391	96.89 341

HQP-MVS		95.72 232	95.40 228	96.69 282	97.20 340	94.25 287	98.05 313	98.46 207	96.43 118	94.45 300	97.73 291	86.75 301	98.96 286	95.30 236	94.18 314	96.86 347

hse-mvs2		95.71 233	95.30 240	96.93 262	98.50 187	93.53 313	98.36 263	98.10 306	97.48 52	98.67 104	97.99 266	89.76 219	99.02 278	97.95 94	80.91 456	98.22 289

UniMVSNet_NR-MVSNet		95.71 233	95.15 245	97.40 230	96.84 364	96.97 125	98.74 173	99.24 20	95.16 195	93.88 334	97.72 293	91.68 156	98.31 368	95.81 216	87.25 421	96.92 333

PatchmatchNet		95.71 233	95.52 224	96.29 326	97.58 308	90.72 383	96.84 425	97.52 361	94.06 261	97.08 213	96.96 369	89.24 239	98.90 298	92.03 346	98.37 205	99.26 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 236	95.33 237	96.76 275	96.16 400	94.63 265	98.43 257	98.39 235	96.64 110	95.02 284	98.78 182	85.15 335	99.05 271	95.21 243	94.20 313	96.60 376
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 236	95.38 232	96.61 292	97.61 305	93.84 300	98.91 115	98.44 211	95.25 191	94.28 314	98.47 219	86.04 319	99.12 259	95.50 230	93.95 323	96.87 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 238	95.69 220	95.44 363	97.54 313	88.54 431	96.97 411	97.56 353	93.50 304	97.52 197	96.93 373	89.49 226	99.16 247	95.25 240	96.42 278	98.64 263

FE-MVS		95.62 239	94.90 259	97.78 194	98.37 208	94.92 252	97.17 400	97.38 377	90.95 396	97.73 177	97.70 294	85.32 333	99.63 159	91.18 363	98.33 208	98.79 238

LPG-MVS_test		95.62 239	95.34 234	96.47 310	97.46 320	93.54 311	98.99 91	98.54 186	94.67 234	94.36 308	98.77 185	85.39 328	99.11 261	95.71 222	94.15 316	96.76 355

CLD-MVS		95.62 239	95.34 234	96.46 313	97.52 316	93.75 304	97.27 389	98.46 207	95.53 171	94.42 305	98.00 265	86.21 314	98.97 282	96.25 201	94.37 308	96.66 370
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 242	94.89 260	97.76 198	98.15 249	95.15 238	96.77 427	94.41 457	92.95 330	97.18 209	97.43 321	84.78 342	99.45 201	94.63 261	97.73 234	98.68 257

MonoMVSNet		95.51 243	95.45 227	95.68 352	95.54 422	90.87 378	98.92 113	97.37 378	95.79 153	95.53 273	97.38 326	89.58 225	97.68 416	96.40 195	92.59 350	98.49 276

thres600view7		95.49 244	94.77 263	97.67 209	98.98 138	95.02 243	98.85 139	96.90 414	95.38 182	96.63 238	96.90 375	84.29 352	99.59 166	88.65 409	96.33 280	98.40 280

test_vis1_n		95.47 245	95.13 246	96.49 307	97.77 291	90.41 393	99.27 31	98.11 303	96.58 112	99.66 28	99.18 98	67.00 459	99.62 163	99.21 29	99.40 132	99.44 125

SCA		95.46 246	95.13 246	96.46 313	97.67 300	91.29 371	97.33 384	97.60 349	94.68 233	96.92 223	97.10 344	83.97 362	98.89 299	92.59 330	98.32 211	99.20 180

IterMVS-LS		95.46 246	95.21 243	96.22 328	98.12 251	93.72 307	98.32 270	98.13 299	93.71 288	94.26 315	97.31 331	92.24 135	98.10 385	94.63 261	90.12 382	96.84 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 248	95.34 234	95.77 350	98.69 169	88.75 427	98.87 130	97.21 391	96.13 135	97.22 207	97.68 299	77.95 414	99.65 153	97.58 123	96.77 267	98.91 229

jajsoiax		95.45 248	95.03 252	96.73 276	95.42 430	94.63 265	99.14 59	98.52 191	95.74 155	93.22 363	98.36 230	83.87 365	98.65 324	96.95 164	94.04 319	96.91 338

CVMVSNet		95.43 250	96.04 199	93.57 417	97.93 281	83.62 456	98.12 303	98.59 172	95.68 159	96.56 242	99.02 136	87.51 287	97.51 425	93.56 303	97.44 247	99.60 92

anonymousdsp		95.42 251	94.91 258	96.94 261	95.10 434	95.90 189	99.14 59	98.41 227	93.75 282	93.16 366	97.46 317	87.50 289	98.41 354	95.63 226	94.03 320	96.50 395

DU-MVS		95.42 251	94.76 264	97.40 230	96.53 381	96.97 125	98.66 201	98.99 56	95.43 177	93.88 334	97.69 296	88.57 259	98.31 368	95.81 216	87.25 421	96.92 333

mvs_tets		95.41 253	95.00 253	96.65 284	95.58 421	94.42 276	99.00 88	98.55 184	95.73 157	93.21 364	98.38 228	83.45 371	98.63 325	97.09 158	94.00 321	96.91 338

thres100view900		95.38 254	94.70 268	97.41 228	98.98 138	94.92 252	98.87 130	96.90 414	95.38 182	96.61 240	96.88 376	84.29 352	99.56 172	88.11 412	96.29 284	97.76 301

thres400		95.38 254	94.62 272	97.65 213	98.94 143	94.98 248	98.68 194	96.93 412	95.33 185	96.55 244	96.53 395	84.23 356	99.56 172	88.11 412	96.29 284	98.40 280

BH-w/o		95.38 254	95.08 250	96.26 327	98.34 215	91.79 360	97.70 356	97.43 373	92.87 333	94.24 317	97.22 338	88.66 257	98.84 305	91.55 359	97.70 235	98.16 292

VDDNet		95.36 257	94.53 277	97.86 187	98.10 253	95.13 239	98.85 139	97.75 337	90.46 403	98.36 126	99.39 48	73.27 447	99.64 156	97.98 93	96.58 272	98.81 237

TAPA-MVS	93.98 7	95.35 258	94.56 276	97.74 200	99.13 119	94.83 257	98.33 266	98.64 159	86.62 439	96.29 255	98.61 203	94.00 105	99.29 222	80.00 456	99.41 129	99.09 204
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 259	94.98 255	96.43 315	97.67 300	93.48 315	98.73 179	98.44 211	94.94 219	92.53 386	98.53 213	84.50 351	99.14 254	95.48 231	94.00 321	96.66 370
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 260	94.87 261	96.71 279	99.29 88	93.24 329	98.58 218	98.11 303	89.92 413	93.57 348	99.10 118	86.37 310	99.79 121	90.78 374	98.10 219	97.09 322
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 261	94.72 267	97.13 244	98.05 261	93.26 326	97.87 339	97.20 392	94.96 215	96.18 259	95.66 429	80.97 386	99.35 211	94.47 270	97.08 254	98.78 242

tfpn200view9		95.32 261	94.62 272	97.43 226	98.94 143	94.98 248	98.68 194	96.93 412	95.33 185	96.55 244	96.53 395	84.23 356	99.56 172	88.11 412	96.29 284	97.76 301

Anonymous202405211		95.28 263	94.49 279	97.67 209	99.00 134	93.75 304	98.70 188	97.04 403	90.66 399	96.49 248	98.80 176	78.13 410	99.83 90	96.21 202	95.36 306	99.44 125

thres200		95.25 264	94.57 275	97.28 234	98.81 157	94.92 252	98.20 286	97.11 396	95.24 193	96.54 246	96.22 407	84.58 349	99.53 182	87.93 417	96.50 276	97.39 315

AllTest		95.24 265	94.65 271	96.99 255	99.25 96	93.21 330	98.59 214	98.18 287	91.36 381	93.52 350	98.77 185	84.67 346	99.72 136	89.70 392	97.87 227	98.02 296

LCM-MVSNet-Re		95.22 266	95.32 238	94.91 380	98.18 244	87.85 442	98.75 169	95.66 444	95.11 202	88.96 428	96.85 379	90.26 210	97.65 417	95.65 225	98.44 194	99.22 177

EPNet_dtu		95.21 267	94.95 257	95.99 336	96.17 398	90.45 391	98.16 296	97.27 386	96.77 100	93.14 369	98.33 236	90.34 206	98.42 347	85.57 431	98.81 170	99.09 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 268	94.45 285	97.46 223	96.75 371	96.56 149	98.86 134	98.65 158	93.30 314	93.27 362	98.27 243	84.85 340	98.87 302	94.82 252	91.26 368	96.96 328

D2MVS		95.18 269	95.08 250	95.48 360	97.10 349	92.07 356	98.30 274	99.13 44	94.02 264	92.90 374	96.73 385	89.48 227	98.73 317	94.48 269	93.60 332	95.65 425

WR-MVS		95.15 270	94.46 282	97.22 236	96.67 376	96.45 153	98.21 284	98.81 107	94.15 258	93.16 366	97.69 296	87.51 287	98.30 370	95.29 238	88.62 406	96.90 340

TranMVSNet+NR-MVSNet		95.14 271	94.48 280	97.11 248	96.45 387	96.36 160	99.03 81	99.03 51	95.04 207	93.58 347	97.93 272	88.27 268	98.03 393	94.13 283	86.90 426	96.95 330

myMVS_eth3d28		95.12 272	94.62 272	96.64 288	98.17 247	92.17 350	98.02 317	97.32 380	95.41 180	96.22 256	96.05 413	78.01 412	99.13 256	95.22 242	97.16 252	98.60 266

baseline2		95.11 273	94.52 278	96.87 267	96.65 377	93.56 310	98.27 279	94.10 463	93.45 307	92.02 400	97.43 321	87.45 292	99.19 244	93.88 292	97.41 249	97.87 299

miper_enhance_ethall		95.10 274	94.75 265	96.12 332	97.53 315	93.73 306	96.61 433	98.08 311	92.20 360	93.89 333	96.65 391	92.44 126	98.30 370	94.21 279	91.16 369	96.34 404

Anonymous20240529		95.10 274	94.22 296	97.75 199	99.01 132	94.26 286	98.87 130	98.83 98	85.79 447	96.64 237	98.97 144	78.73 403	99.85 84	96.27 198	94.89 307	99.12 196

test-LLR		95.10 274	94.87 261	95.80 347	96.77 368	89.70 407	96.91 416	95.21 449	95.11 202	94.83 289	95.72 426	87.71 283	98.97 282	93.06 314	98.50 189	98.72 250

WR-MVS_H		95.05 277	94.46 282	96.81 271	96.86 363	95.82 201	99.24 35	99.24 20	93.87 276	92.53 386	96.84 380	90.37 205	98.24 376	93.24 309	87.93 412	96.38 403

miper_ehance_all_eth		95.01 278	94.69 269	95.97 338	97.70 298	93.31 324	97.02 409	98.07 313	92.23 357	93.51 352	96.96 369	91.85 150	98.15 381	93.68 297	91.16 369	96.44 401

testing11		95.00 279	94.28 292	97.16 242	97.96 278	93.36 323	98.09 309	97.06 402	94.94 219	95.33 279	96.15 409	76.89 427	99.40 206	95.77 220	96.30 283	98.72 250

ADS-MVSNet		95.00 279	94.45 285	96.63 289	98.00 268	91.91 359	96.04 440	97.74 338	90.15 409	96.47 249	96.64 392	87.89 279	98.96 286	90.08 383	97.06 255	99.02 217

VPNet		94.99 281	94.19 298	97.40 230	97.16 345	96.57 148	98.71 184	98.97 57	95.67 160	94.84 287	98.24 247	80.36 393	98.67 323	96.46 192	87.32 420	96.96 328

EPMVS		94.99 281	94.48 280	96.52 305	97.22 338	91.75 362	97.23 390	91.66 473	94.11 259	97.28 203	96.81 382	85.70 323	98.84 305	93.04 316	97.28 250	98.97 222

testing91		94.98 283	94.25 295	97.20 237	97.94 279	93.41 318	98.00 320	97.58 350	94.99 212	95.45 275	96.04 414	77.20 422	99.42 204	94.97 248	96.02 297	98.78 242

NR-MVSNet		94.98 283	94.16 301	97.44 225	96.53 381	97.22 113	98.74 173	98.95 61	94.96 215	89.25 427	97.69 296	89.32 236	98.18 379	94.59 266	87.40 418	96.92 333

FMVSNet3		94.97 285	94.26 294	97.11 248	98.18 244	96.62 140	98.56 230	98.26 275	93.67 295	94.09 324	97.10 344	84.25 354	98.01 395	92.08 342	92.14 354	96.70 364

FE-MVSNET3		94.96 286	94.28 292	96.98 258	95.93 411	96.11 172	97.08 406	98.39 235	93.62 299	93.86 336	96.40 400	88.28 267	98.21 377	92.61 326	92.36 353	96.63 372

CostFormer		94.95 287	94.73 266	95.60 357	97.28 334	89.06 420	97.53 368	96.89 416	89.66 418	96.82 228	96.72 386	86.05 317	98.95 291	95.53 229	96.13 295	98.79 238

PAPM		94.95 287	94.00 314	97.78 194	97.04 351	95.65 210	96.03 442	98.25 276	91.23 390	94.19 320	97.80 288	91.27 175	98.86 304	82.61 448	97.61 237	98.84 234

CP-MVSNet		94.94 289	94.30 291	96.83 269	96.72 373	95.56 213	99.11 65	98.95 61	93.89 274	92.42 391	97.90 275	87.19 294	98.12 384	94.32 275	88.21 409	96.82 351

TR-MVS		94.94 289	94.20 297	97.17 241	97.75 292	94.14 292	97.59 365	97.02 407	92.28 356	95.75 271	97.64 304	83.88 364	98.96 286	89.77 389	96.15 294	98.40 280

RPSCF		94.87 291	95.40 228	93.26 423	98.89 146	82.06 462	98.33 266	98.06 318	90.30 408	96.56 242	99.26 80	87.09 295	99.49 190	93.82 294	96.32 281	98.24 287

testing99		94.83 292	94.08 306	97.07 251	97.94 279	93.13 332	98.10 308	97.17 394	94.86 221	95.34 276	96.00 418	76.31 430	99.40 206	95.08 245	95.90 298	98.68 257

GA-MVS		94.81 293	94.03 310	97.14 243	97.15 346	93.86 299	96.76 428	97.58 350	94.00 268	94.76 293	97.04 359	80.91 387	98.48 338	91.79 352	96.25 290	99.09 204

c3_l		94.79 294	94.43 287	95.89 343	97.75 292	93.12 334	97.16 402	98.03 320	92.23 357	93.46 356	97.05 358	91.39 169	98.01 395	93.58 302	89.21 398	96.53 387

V42		94.78 295	94.14 303	96.70 281	96.33 392	95.22 234	98.97 95	98.09 310	92.32 354	94.31 311	97.06 355	88.39 265	98.55 333	92.90 321	88.87 404	96.34 404

reproduce_monomvs		94.77 296	94.67 270	95.08 375	98.40 202	89.48 413	98.80 156	98.64 159	97.57 46	93.21 364	97.65 301	80.57 392	98.83 308	97.72 110	89.47 394	96.93 332

CR-MVSNet		94.76 297	94.15 302	96.59 295	97.00 352	93.43 316	94.96 455	97.56 353	92.46 345	96.93 221	96.24 403	88.15 271	97.88 408	87.38 420	96.65 270	98.46 278

v2v482		94.69 298	94.03 310	96.65 284	96.17 398	94.79 260	98.67 199	98.08 311	92.72 337	94.00 329	97.16 341	87.69 286	98.45 343	92.91 320	88.87 404	96.72 360

pmmvs4		94.69 298	93.99 316	96.81 271	95.74 416	95.94 183	97.40 375	97.67 342	90.42 405	93.37 359	97.59 308	89.08 244	98.20 378	92.97 318	91.67 362	96.30 407

cl22		94.68 300	94.19 298	96.13 331	98.11 252	93.60 309	96.94 413	98.31 257	92.43 349	93.32 361	96.87 378	86.51 304	98.28 374	94.10 286	91.16 369	96.51 393

eth_miper_zixun_eth		94.68 300	94.41 288	95.47 361	97.64 303	91.71 364	96.73 430	98.07 313	92.71 338	93.64 344	97.21 339	90.54 203	98.17 380	93.38 305	89.76 386	96.54 385

PCF-MVS	93.45 11	94.68 300	93.43 352	98.42 129	98.62 179	96.77 135	95.48 452	98.20 282	84.63 452	93.34 360	98.32 237	88.55 262	99.81 102	84.80 440	98.96 158	98.68 257
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 303	93.54 347	98.08 168	96.88 362	96.56 149	98.19 289	98.50 199	78.05 464	92.69 381	98.02 262	91.07 189	99.63 159	90.09 382	98.36 207	98.04 295

PS-CasMVS		94.67 303	93.99 316	96.71 279	96.68 375	95.26 231	99.13 62	99.03 51	93.68 293	92.33 392	97.95 270	85.35 330	98.10 385	93.59 301	88.16 411	96.79 352

cascas		94.63 305	93.86 326	96.93 262	96.91 360	94.27 285	96.00 443	98.51 194	85.55 448	94.54 296	96.23 405	84.20 358	98.87 302	95.80 218	96.98 260	97.66 307

tpmvs		94.60 306	94.36 290	95.33 367	97.46 320	88.60 430	96.88 422	97.68 339	91.29 387	93.80 340	96.42 399	88.58 258	99.24 234	91.06 369	96.04 296	98.17 291

LTVRE_ROB	92.95 15	94.60 306	93.90 322	96.68 283	97.41 328	94.42 276	98.52 234	98.59 172	91.69 372	91.21 407	98.35 231	84.87 339	99.04 274	91.06 369	93.44 336	96.60 376
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		94.59 308	93.92 319	96.60 294	96.21 394	94.78 261	98.59 214	98.14 298	91.86 368	94.21 319	97.02 362	87.97 277	98.41 354	91.72 354	89.57 389	96.61 375

ADS-MVSNet2		94.58 309	94.40 289	95.11 373	98.00 268	88.74 428	96.04 440	97.30 382	90.15 409	96.47 249	96.64 392	87.89 279	97.56 423	90.08 383	97.06 255	99.02 217

WBMVS		94.56 310	94.04 308	96.10 333	98.03 265	93.08 336	97.82 347	98.18 287	94.02 264	93.77 342	96.82 381	81.28 381	98.34 363	95.47 232	91.00 372	96.88 342

ACMH	92.88 16	94.55 311	93.95 318	96.34 322	97.63 304	93.26 326	98.81 155	98.49 204	93.43 308	89.74 421	98.53 213	81.91 376	99.08 268	93.69 296	93.30 341	96.70 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 312	93.85 327	96.63 289	97.98 276	93.06 337	98.77 168	97.84 332	93.67 295	93.80 340	98.04 261	76.88 428	98.96 286	94.79 254	92.86 346	97.86 300

XVG-ACMP-BASELINE		94.54 312	94.14 303	95.75 351	96.55 380	91.65 365	98.11 306	98.44 211	94.96 215	94.22 318	97.90 275	79.18 402	99.11 261	94.05 288	93.85 325	96.48 398

AUN-MVS		94.53 314	93.73 337	96.92 265	98.50 187	93.52 314	98.34 265	98.10 306	93.83 279	95.94 269	97.98 268	85.59 326	99.03 275	94.35 273	80.94 455	98.22 289

DIV-MVS_self_test		94.52 315	94.03 310	95.99 336	97.57 312	93.38 321	97.05 407	97.94 326	91.74 369	92.81 376	97.10 344	89.12 242	98.07 391	92.60 328	90.30 379	96.53 387

cl____		94.51 316	94.01 313	96.02 335	97.58 308	93.40 320	97.05 407	97.96 325	91.73 371	92.76 378	97.08 350	89.06 245	98.13 383	92.61 326	90.29 380	96.52 390

ETVMVS		94.50 317	93.44 351	97.68 207	98.18 244	95.35 227	98.19 289	97.11 396	93.73 285	96.40 252	95.39 432	74.53 440	98.84 305	91.10 365	96.31 282	98.84 234

GBi-Net		94.49 318	93.80 330	96.56 299	98.21 234	95.00 244	98.82 147	98.18 287	92.46 345	94.09 324	97.07 351	81.16 382	97.95 400	92.08 342	92.14 354	96.72 360

test1		94.49 318	93.80 330	96.56 299	98.21 234	95.00 244	98.82 147	98.18 287	92.46 345	94.09 324	97.07 351	81.16 382	97.95 400	92.08 342	92.14 354	96.72 360

dmvs_re		94.48 320	94.18 300	95.37 365	97.68 299	90.11 399	98.54 233	97.08 398	94.56 240	94.42 305	97.24 336	84.25 354	97.76 414	91.02 372	92.83 347	98.24 287

v8		94.47 321	93.77 333	96.57 298	96.36 390	94.83 257	99.05 74	98.19 284	91.92 365	93.16 366	96.97 367	88.82 256	98.48 338	91.69 355	87.79 413	96.39 402

FMVSNet2		94.47 321	93.61 343	97.04 253	98.21 234	96.43 155	98.79 164	98.27 267	92.46 345	93.50 353	97.09 348	81.16 382	98.00 397	91.09 366	91.93 357	96.70 364

test2506		94.44 323	93.91 321	96.04 334	99.02 130	88.99 423	99.06 72	79.47 485	96.96 92	98.36 126	99.26 80	77.21 421	99.52 185	96.78 183	99.04 152	99.59 94

Patchmatch-test		94.42 324	93.68 341	96.63 289	97.60 306	91.76 361	94.83 459	97.49 365	89.45 422	94.14 322	97.10 344	88.99 247	98.83 308	85.37 434	98.13 218	99.29 161

PEN-MVS		94.42 324	93.73 337	96.49 307	96.28 393	94.84 255	99.17 54	99.00 53	93.51 303	92.23 394	97.83 285	86.10 316	97.90 404	92.55 333	86.92 425	96.74 357

v144192		94.39 326	93.70 339	96.48 309	96.06 404	94.35 280	98.58 218	98.16 295	91.45 378	94.33 310	97.02 362	87.50 289	98.45 343	91.08 368	89.11 399	96.63 372

Baseline_NR-MVSNet		94.35 327	93.81 329	95.96 339	96.20 395	94.05 294	98.61 213	96.67 426	91.44 379	93.85 337	97.60 307	88.57 259	98.14 382	94.39 271	86.93 424	95.68 424

miper_lstm_enhance		94.33 328	94.07 307	95.11 373	97.75 292	90.97 375	97.22 392	98.03 320	91.67 373	92.76 378	96.97 367	90.03 214	97.78 413	92.51 335	89.64 388	96.56 382

v1192		94.32 329	93.58 344	96.53 304	96.10 402	94.45 274	98.50 242	98.17 293	91.54 376	94.19 320	97.06 355	86.95 299	98.43 346	90.14 381	89.57 389	96.70 364

UWE-MVS		94.30 330	93.89 324	95.53 358	97.83 287	88.95 424	97.52 370	93.25 465	94.44 250	96.63 238	97.07 351	78.70 404	99.28 224	91.99 347	97.56 241	98.36 283

ACMH+	92.99 14	94.30 330	93.77 333	95.88 344	97.81 289	92.04 358	98.71 184	98.37 242	93.99 269	90.60 414	98.47 219	80.86 389	99.05 271	92.75 325	92.40 352	96.55 384

v148		94.29 332	93.76 335	95.91 341	96.10 402	92.93 338	98.58 218	97.97 323	92.59 343	93.47 355	96.95 371	88.53 263	98.32 366	92.56 332	87.06 423	96.49 396

v10		94.29 332	93.55 346	96.51 306	96.39 389	94.80 259	98.99 91	98.19 284	91.35 383	93.02 372	96.99 365	88.09 273	98.41 354	90.50 378	88.41 408	96.33 406

SD_0403		94.28 334	94.46 282	93.73 414	98.02 266	85.32 451	98.31 271	98.40 230	94.75 229	93.59 345	98.16 252	89.01 246	96.54 444	82.32 449	97.58 240	99.34 146

MVP-Stereo		94.28 334	93.92 319	95.35 366	94.95 436	92.60 345	97.97 323	97.65 343	91.61 374	90.68 413	97.09 348	86.32 313	98.42 347	89.70 392	99.34 138	95.02 438
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 336	93.33 354	96.97 259	97.19 343	93.38 321	98.74 173	98.57 179	91.21 392	93.81 339	98.58 208	72.85 448	98.77 315	95.05 246	93.93 324	98.77 245

OurMVSNet-221017-0		94.21 337	94.00 314	94.85 385	95.60 420	89.22 418	98.89 120	97.43 373	95.29 188	92.18 396	98.52 216	82.86 372	98.59 331	93.46 304	91.76 360	96.74 357

v1921920		94.20 338	93.47 350	96.40 319	95.98 408	94.08 293	98.52 234	98.15 296	91.33 384	94.25 316	97.20 340	86.41 309	98.42 347	90.04 386	89.39 396	96.69 369

WB-MVSnew		94.19 339	94.04 308	94.66 393	96.82 366	92.14 351	97.86 341	95.96 440	93.50 304	95.64 272	96.77 384	88.06 275	97.99 398	84.87 437	96.86 261	93.85 457

v7n		94.19 339	93.43 352	96.47 310	95.90 412	94.38 279	99.26 32	98.34 250	91.99 363	92.76 378	97.13 343	88.31 266	98.52 336	89.48 397	87.70 414	96.52 390

tpm2		94.19 339	93.76 335	95.46 362	97.23 337	89.04 421	97.31 386	96.85 420	87.08 438	96.21 258	96.79 383	83.75 368	98.74 316	92.43 338	96.23 292	98.59 269

TESTMET0.1,1		94.18 342	93.69 340	95.63 355	96.92 358	89.12 419	96.91 416	94.78 454	93.17 319	94.88 286	96.45 398	78.52 405	98.92 293	93.09 313	98.50 189	98.85 232

dp		94.15 343	93.90 322	94.90 381	97.31 333	86.82 447	96.97 411	97.19 393	91.22 391	96.02 264	96.61 394	85.51 327	99.02 278	90.00 387	94.30 309	98.85 232

ET-MVSNet_ETH3D		94.13 344	92.98 362	97.58 218	98.22 233	96.20 166	97.31 386	95.37 448	94.53 242	79.56 466	97.63 306	86.51 304	97.53 424	96.91 166	90.74 374	99.02 217

tpm		94.13 344	93.80 330	95.12 372	96.50 383	87.91 441	97.44 372	95.89 443	92.62 341	96.37 254	96.30 402	84.13 359	98.30 370	93.24 309	91.66 363	99.14 194

testing222		94.12 346	93.03 361	97.37 233	98.02 266	94.66 262	97.94 327	96.65 428	94.63 236	95.78 270	95.76 421	71.49 449	98.92 293	91.17 364	95.88 299	98.52 274

IterMVS-SCA-FT		94.11 347	93.87 325	94.85 385	97.98 276	90.56 390	97.18 397	98.11 303	93.75 282	92.58 384	97.48 316	83.97 362	97.41 427	92.48 337	91.30 366	96.58 378

Anonymous20231211		94.10 348	93.26 357	96.61 292	99.11 122	94.28 284	99.01 86	98.88 78	86.43 441	92.81 376	97.57 310	81.66 378	98.68 322	94.83 251	89.02 402	96.88 342

IterMVS		94.09 349	93.85 327	94.80 389	97.99 270	90.35 395	97.18 397	98.12 300	93.68 293	92.46 390	97.34 327	84.05 360	97.41 427	92.51 335	91.33 365	96.62 374
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 350	93.51 348	95.80 347	96.77 368	89.70 407	96.91 416	95.21 449	92.89 332	94.83 289	95.72 426	77.69 416	98.97 282	93.06 314	98.50 189	98.72 250

test0.0.03 1		94.08 350	93.51 348	95.80 347	95.53 424	92.89 339	97.38 377	95.97 439	95.11 202	92.51 388	96.66 389	87.71 283	96.94 434	87.03 422	93.67 328	97.57 311

v1240		94.06 352	93.29 356	96.34 322	96.03 406	93.90 298	98.44 255	98.17 293	91.18 393	94.13 323	97.01 364	86.05 317	98.42 347	89.13 403	89.50 393	96.70 364

X-MVStestdata		94.06 352	92.30 378	99.34 31	99.70 27	98.35 49	99.29 27	98.88 78	97.40 57	98.46 118	43.50 480	95.90 48	99.89 68	97.85 102	99.74 59	99.78 33

DTE-MVSNet		93.98 354	93.26 357	96.14 330	96.06 404	94.39 278	99.20 47	98.86 91	93.06 325	91.78 401	97.81 287	85.87 321	97.58 422	90.53 377	86.17 430	96.46 400

pm-mvs1		93.94 355	93.06 360	96.59 295	96.49 384	95.16 236	98.95 102	98.03 320	92.32 354	91.08 409	97.84 282	84.54 350	98.41 354	92.16 340	86.13 433	96.19 412

MS-PatchMatch		93.84 356	93.63 342	94.46 403	96.18 397	89.45 414	97.76 351	98.27 267	92.23 357	92.13 397	97.49 315	79.50 399	98.69 319	89.75 390	99.38 134	95.25 430

tfpnnormal		93.66 357	92.70 368	96.55 303	96.94 357	95.94 183	98.97 95	99.19 36	91.04 394	91.38 406	97.34 327	84.94 338	98.61 327	85.45 433	89.02 402	95.11 434

EU-MVSNet		93.66 357	94.14 303	92.25 434	95.96 410	83.38 458	98.52 234	98.12 300	94.69 232	92.61 383	98.13 255	87.36 293	96.39 449	91.82 351	90.00 384	96.98 327

our_test_3		93.65 359	93.30 355	94.69 391	95.45 428	89.68 409	96.91 416	97.65 343	91.97 364	91.66 404	96.88 376	89.67 223	97.93 403	88.02 415	91.49 364	96.48 398

pmmvs5		93.65 359	92.97 363	95.68 352	95.49 425	92.37 346	98.20 286	97.28 385	89.66 418	92.58 384	97.26 333	82.14 375	98.09 389	93.18 312	90.95 373	96.58 378

SSC-MVS3.2		93.59 361	93.13 359	94.97 378	96.81 367	89.71 406	97.95 324	98.49 204	94.59 239	93.50 353	96.91 374	77.74 415	98.37 361	91.69 355	90.47 377	96.83 350

test_fmvs2		93.43 362	93.58 344	92.95 428	96.97 355	83.91 454	99.19 49	97.24 388	95.74 155	95.20 281	98.27 243	69.65 451	98.72 318	96.26 199	93.73 327	96.24 409

tpm cat1		93.36 363	92.80 365	95.07 376	97.58 308	87.97 440	96.76 428	97.86 331	82.17 459	93.53 349	96.04 414	86.13 315	99.13 256	89.24 401	95.87 300	98.10 294

JIA-IIPM		93.35 364	92.49 374	95.92 340	96.48 385	90.65 385	95.01 454	96.96 410	85.93 445	96.08 262	87.33 470	87.70 285	98.78 314	91.35 361	95.58 304	98.34 284

SixPastTwentyTwo		93.34 365	92.86 364	94.75 390	95.67 418	89.41 416	98.75 169	96.67 426	93.89 274	90.15 419	98.25 246	80.87 388	98.27 375	90.90 373	90.64 375	96.57 380

USDC		93.33 366	92.71 367	95.21 369	96.83 365	90.83 381	96.91 416	97.50 363	93.84 277	90.72 412	98.14 254	77.69 416	98.82 310	89.51 396	93.21 343	95.97 418

IB-MVS	91.98 17	93.27 367	91.97 382	97.19 239	97.47 319	93.41 318	97.09 405	95.99 438	93.32 312	92.47 389	95.73 424	78.06 411	99.53 182	94.59 266	82.98 444	98.62 264
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		93.26 368	92.21 379	96.41 317	97.73 296	93.13 332	95.65 449	97.03 404	91.27 389	94.04 327	96.06 412	75.33 435	97.19 430	86.56 424	96.23 292	98.92 228

ppachtmachnet_test		93.22 369	92.63 369	94.97 378	95.45 428	90.84 380	96.88 422	97.88 330	90.60 400	92.08 398	97.26 333	88.08 274	97.86 409	85.12 436	90.33 378	96.22 410

Patchmtry		93.22 369	92.35 377	95.84 346	96.77 368	93.09 335	94.66 462	97.56 353	87.37 437	92.90 374	96.24 403	88.15 271	97.90 404	87.37 421	90.10 383	96.53 387

testing3		93.19 371	92.48 375	95.30 368	98.07 256	92.27 347	98.64 205	97.17 394	93.94 273	93.98 330	97.04 359	67.97 456	96.01 453	88.40 410	97.14 253	97.63 308

FMVSNet1		93.19 371	92.07 380	96.56 299	97.54 313	95.00 244	98.82 147	98.18 287	90.38 406	92.27 393	97.07 351	73.68 446	97.95 400	89.36 399	91.30 366	96.72 360

LF4IMVS		93.14 373	92.79 366	94.20 408	95.88 413	88.67 429	97.66 359	97.07 400	93.81 280	91.71 402	97.65 301	77.96 413	98.81 311	91.47 360	91.92 359	95.12 433

mmtdpeth		93.12 374	92.61 370	94.63 395	97.60 306	89.68 409	99.21 44	97.32 380	94.02 264	97.72 178	94.42 443	77.01 426	99.44 202	99.05 32	77.18 467	94.78 443

testgi		93.06 375	92.45 376	94.88 383	96.43 388	89.90 401	98.75 169	97.54 359	95.60 163	91.63 405	97.91 274	74.46 442	97.02 432	86.10 427	93.67 328	97.72 305

PatchT		93.06 375	91.97 382	96.35 321	96.69 374	92.67 344	94.48 465	97.08 398	86.62 439	97.08 213	92.23 464	87.94 278	97.90 404	78.89 460	96.69 268	98.49 276

RPMNet		92.81 377	91.34 388	97.24 235	97.00 352	93.43 316	94.96 455	98.80 114	82.27 458	96.93 221	92.12 465	86.98 298	99.82 97	76.32 466	96.65 270	98.46 278

UWE-MVS-28		92.79 378	92.51 373	93.62 416	96.46 386	86.28 448	97.93 328	92.71 470	94.17 257	94.78 292	97.16 341	81.05 385	96.43 447	81.45 452	96.86 261	98.14 293

myMVS_eth3d		92.73 379	92.01 381	94.89 382	97.39 329	90.94 376	97.91 331	97.46 367	93.16 320	93.42 357	95.37 433	68.09 455	96.12 451	88.34 411	96.99 257	97.60 309

TransMVSNet (Re)		92.67 380	91.51 387	96.15 329	96.58 379	94.65 263	98.90 116	96.73 422	90.86 397	89.46 426	97.86 279	85.62 325	98.09 389	86.45 425	81.12 453	95.71 423

ttmdpeth		92.61 381	91.96 384	94.55 397	94.10 446	90.60 389	98.52 234	97.29 383	92.67 339	90.18 417	97.92 273	79.75 398	97.79 411	91.09 366	86.15 432	95.26 429

Syy-MVS		92.55 382	92.61 370	92.38 431	97.39 329	83.41 457	97.91 331	97.46 367	93.16 320	93.42 357	95.37 433	84.75 343	96.12 451	77.00 465	96.99 257	97.60 309

K. test v3		92.55 382	91.91 385	94.48 401	95.64 419	89.24 417	99.07 71	94.88 453	94.04 262	86.78 443	97.59 308	77.64 419	97.64 418	92.08 342	89.43 395	96.57 380

DSMNet-mixed		92.52 384	92.58 372	92.33 432	94.15 445	82.65 460	98.30 274	94.26 460	89.08 427	92.65 382	95.73 424	85.01 337	95.76 455	86.24 426	97.76 232	98.59 269

TinyColmap		92.31 385	91.53 386	94.65 394	96.92 358	89.75 404	96.92 414	96.68 425	90.45 404	89.62 423	97.85 281	76.06 433	98.81 311	86.74 423	92.51 351	95.41 427

gg-mvs-nofinetune		92.21 386	90.58 394	97.13 244	96.75 371	95.09 240	95.85 444	89.40 478	85.43 449	94.50 298	81.98 473	80.80 390	98.40 360	92.16 340	98.33 208	97.88 298

FMVSNet5		91.81 387	90.92 390	94.49 400	97.21 339	92.09 355	98.00 320	97.55 358	89.31 425	90.86 411	95.61 430	74.48 441	95.32 459	85.57 431	89.70 387	96.07 416

pmmvs6		91.77 388	90.63 393	95.17 371	94.69 442	91.24 372	98.67 199	97.92 328	86.14 443	89.62 423	97.56 313	75.79 434	98.34 363	90.75 375	84.56 437	95.94 419

Anonymous20231206		91.66 389	91.10 389	93.33 421	94.02 450	87.35 444	98.58 218	97.26 387	90.48 402	90.16 418	96.31 401	83.83 366	96.53 445	79.36 458	89.90 385	96.12 414

Patchmatch-RL test		91.49 390	90.85 391	93.41 419	91.37 461	84.40 452	92.81 469	95.93 442	91.87 367	87.25 439	94.87 439	88.99 247	96.53 445	92.54 334	82.00 447	99.30 158

test_0402		91.32 391	90.27 397	94.48 401	96.60 378	91.12 373	98.50 242	97.22 389	86.10 444	88.30 435	96.98 366	77.65 418	97.99 398	78.13 462	92.94 345	94.34 445

test_vis1_rt		91.29 392	90.65 392	93.19 425	97.45 323	86.25 449	98.57 227	90.90 476	93.30 314	86.94 442	93.59 452	62.07 467	99.11 261	97.48 138	95.58 304	94.22 448

PVSNet_0	88.72 19	91.28 393	90.03 400	95.00 377	97.99 270	87.29 445	94.84 458	98.50 199	92.06 362	89.86 420	95.19 435	79.81 397	99.39 209	92.27 339	69.79 473	98.33 285

mvs5depth		91.23 394	90.17 398	94.41 405	92.09 458	89.79 403	95.26 453	96.50 430	90.73 398	91.69 403	97.06 355	76.12 432	98.62 326	88.02 415	84.11 440	94.82 440

Anonymous20240521		91.18 395	90.44 395	93.42 418	93.70 451	88.47 433	98.94 105	97.56 353	88.46 431	89.56 425	95.08 438	77.15 424	96.97 433	83.92 443	89.55 391	94.82 440

EG-PatchMatch MVS		91.13 396	90.12 399	94.17 410	94.73 441	89.00 422	98.13 302	97.81 334	89.22 426	85.32 453	96.46 397	67.71 457	98.42 347	87.89 419	93.82 326	95.08 435

TDRefinement		91.06 397	89.68 402	95.21 369	85.35 478	91.49 368	98.51 241	97.07 400	91.47 377	88.83 432	97.84 282	77.31 420	99.09 266	92.79 324	77.98 465	95.04 437

sc_t1		91.01 398	89.39 404	95.85 345	95.99 407	90.39 394	98.43 257	97.64 345	78.79 462	92.20 395	97.94 271	66.00 461	98.60 330	91.59 358	85.94 434	98.57 272

UnsupCasMVSNet_eth		90.99 399	89.92 401	94.19 409	94.08 447	89.83 402	97.13 404	98.67 151	93.69 291	85.83 449	96.19 408	75.15 436	96.74 438	89.14 402	79.41 460	96.00 417

test20.03		90.89 400	90.38 396	92.43 430	93.48 452	88.14 439	98.33 266	97.56 353	93.40 309	87.96 436	96.71 387	80.69 391	94.13 465	79.15 459	86.17 430	95.01 439

MDA-MVSNet_test_wron		90.71 401	89.38 406	94.68 392	94.83 438	90.78 382	97.19 396	97.46 367	87.60 435	72.41 473	95.72 426	86.51 304	96.71 441	85.92 429	86.80 427	96.56 382

YYNet1		90.70 402	89.39 404	94.62 396	94.79 440	90.65 385	97.20 394	97.46 367	87.54 436	72.54 472	95.74 422	86.51 304	96.66 442	86.00 428	86.76 428	96.54 385

KD-MVS_self_test		90.38 403	89.38 406	93.40 420	92.85 455	88.94 425	97.95 324	97.94 326	90.35 407	90.25 416	93.96 449	79.82 396	95.94 454	84.62 442	76.69 468	95.33 428

pmmvs-eth3d		90.36 404	89.05 409	94.32 407	91.10 463	92.12 352	97.63 364	96.95 411	88.86 429	84.91 454	93.13 457	78.32 407	96.74 438	88.70 407	81.81 449	94.09 451

FE-MVSNET2		90.29 405	88.94 411	94.36 406	90.48 465	92.27 347	98.45 249	97.82 333	91.59 375	84.90 455	93.10 458	73.92 444	96.42 448	87.92 418	82.26 445	94.39 444

tt0320		90.26 406	88.73 413	94.86 384	96.12 401	90.62 387	98.17 295	97.63 346	77.46 465	89.68 422	96.04 414	69.19 453	97.79 411	88.98 404	85.29 436	96.16 413

CL-MVSNet_self_test		90.11 407	89.14 408	93.02 426	91.86 460	88.23 438	96.51 436	98.07 313	90.49 401	90.49 415	94.41 444	84.75 343	95.34 458	80.79 454	74.95 470	95.50 426

new_pmnet		90.06 408	89.00 410	93.22 424	94.18 444	88.32 436	96.42 438	96.89 416	86.19 442	85.67 450	93.62 451	77.18 423	97.10 431	81.61 451	89.29 397	94.23 447

MDA-MVSNet-bldmvs		89.97 409	88.35 415	94.83 388	95.21 432	91.34 369	97.64 361	97.51 362	88.36 433	71.17 474	96.13 410	79.22 401	96.63 443	83.65 444	86.27 429	96.52 390

tt0320-xc		89.79 410	88.11 417	94.84 387	96.19 396	90.61 388	98.16 296	97.22 389	77.35 466	88.75 433	96.70 388	65.94 462	97.63 419	89.31 400	83.39 442	96.28 408

CMPMVS	66.06 21	89.70 411	89.67 403	89.78 439	93.19 453	76.56 465	97.00 410	98.35 247	80.97 460	81.57 461	97.75 290	74.75 439	98.61 327	89.85 388	93.63 330	94.17 449
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 412	88.28 416	93.82 413	92.81 456	91.08 374	98.01 318	97.45 371	87.95 434	87.90 437	95.87 420	67.63 458	94.56 464	78.73 461	88.18 410	95.83 421

KD-MVS_2432*1600		89.61 413	87.96 421	94.54 398	94.06 448	91.59 366	95.59 450	97.63 346	89.87 414	88.95 429	94.38 446	78.28 408	96.82 436	84.83 438	68.05 474	95.21 431

miper_refine_blended		89.61 413	87.96 421	94.54 398	94.06 448	91.59 366	95.59 450	97.63 346	89.87 414	88.95 429	94.38 446	78.28 408	96.82 436	84.83 438	68.05 474	95.21 431

MVStest1		89.53 415	87.99 420	94.14 412	94.39 443	90.42 392	98.25 281	96.84 421	82.81 455	81.18 463	97.33 329	77.09 425	96.94 434	85.27 435	78.79 461	95.06 436

MVS-HIRNet		89.46 416	88.40 414	92.64 429	97.58 308	82.15 461	94.16 468	93.05 469	75.73 469	90.90 410	82.52 472	79.42 400	98.33 365	83.53 445	98.68 173	97.43 312

OpenMVS_ROB	86.42 20	89.00 417	87.43 425	93.69 415	93.08 454	89.42 415	97.91 331	96.89 416	78.58 463	85.86 448	94.69 440	69.48 452	98.29 373	77.13 464	93.29 342	93.36 459

mvsany_test3		88.80 418	88.04 418	91.09 438	89.78 468	81.57 463	97.83 346	95.49 447	93.81 280	87.53 438	93.95 450	56.14 470	97.43 426	94.68 259	83.13 443	94.26 446

FE-MVSNET		88.56 419	87.09 426	92.99 427	89.93 467	89.99 400	98.15 299	95.59 445	88.42 432	84.87 456	92.90 459	74.82 438	94.99 462	77.88 463	81.21 452	93.99 454

new-patchmatchnet		88.50 420	87.45 424	91.67 436	90.31 466	85.89 450	97.16 402	97.33 379	89.47 421	83.63 458	92.77 461	76.38 429	95.06 461	82.70 447	77.29 466	94.06 453

APD_test1		88.22 421	88.01 419	88.86 441	95.98 408	74.66 473	97.21 393	96.44 432	83.96 454	86.66 445	97.90 275	60.95 468	97.84 410	82.73 446	90.23 381	94.09 451

PM-MVS		87.77 422	86.55 428	91.40 437	91.03 464	83.36 459	96.92 414	95.18 451	91.28 388	86.48 447	93.42 453	53.27 471	96.74 438	89.43 398	81.97 448	94.11 450

dmvs_testset		87.64 423	88.93 412	83.79 450	95.25 431	63.36 482	97.20 394	91.17 474	93.07 324	85.64 451	95.98 419	85.30 334	91.52 472	69.42 471	87.33 419	96.49 396

test_fmvs3		87.17 424	87.06 427	87.50 443	91.21 462	75.66 468	99.05 74	96.61 429	92.79 336	88.85 431	92.78 460	43.72 474	93.49 466	93.95 289	84.56 437	93.34 460

UnsupCasMVSNet_bld		87.17 424	85.12 431	93.31 422	91.94 459	88.77 426	94.92 457	98.30 264	84.30 453	82.30 459	90.04 467	63.96 465	97.25 429	85.85 430	74.47 472	93.93 456

N_pmnet		87.12 426	87.77 423	85.17 447	95.46 427	61.92 483	97.37 379	70.66 488	85.83 446	88.73 434	96.04 414	85.33 332	97.76 414	80.02 455	90.48 376	95.84 420

pmmvs3		86.67 427	84.86 432	92.11 435	88.16 472	87.19 446	96.63 432	94.75 455	79.88 461	87.22 440	92.75 462	66.56 460	95.20 460	81.24 453	76.56 469	93.96 455

test_f		86.07 428	85.39 429	88.10 442	89.28 470	75.57 469	97.73 354	96.33 434	89.41 424	85.35 452	91.56 466	43.31 476	95.53 456	91.32 362	84.23 439	93.21 461

WB-MVS		84.86 429	85.33 430	83.46 451	89.48 469	69.56 477	98.19 289	96.42 433	89.55 420	81.79 460	94.67 441	84.80 341	90.12 473	52.44 477	80.64 457	90.69 464

SSC-MVS		84.27 430	84.71 433	82.96 455	89.19 471	68.83 478	98.08 310	96.30 435	89.04 428	81.37 462	94.47 442	84.60 348	89.89 474	49.80 479	79.52 459	90.15 465

dongtai		82.47 431	81.88 434	84.22 449	95.19 433	76.03 466	94.59 464	74.14 487	82.63 456	87.19 441	96.09 411	64.10 464	87.85 477	58.91 475	84.11 440	88.78 469

test_vis3_rt		79.22 432	77.40 439	84.67 448	86.44 476	74.85 472	97.66 359	81.43 483	84.98 450	67.12 476	81.91 474	28.09 484	97.60 420	88.96 405	80.04 458	81.55 474

test_method		79.03 433	78.17 435	81.63 456	86.06 477	54.40 488	82.75 477	96.89 416	39.54 480	80.98 464	95.57 431	58.37 469	94.73 463	84.74 441	78.61 462	95.75 422

testf1		79.02 434	77.70 436	82.99 453	88.10 473	66.90 479	94.67 460	93.11 466	71.08 471	74.02 469	93.41 454	34.15 480	93.25 467	72.25 469	78.50 463	88.82 467

APD_test2		79.02 434	77.70 436	82.99 453	88.10 473	66.90 479	94.67 460	93.11 466	71.08 471	74.02 469	93.41 454	34.15 480	93.25 467	72.25 469	78.50 463	88.82 467

LCM-MVSNet		78.70 436	76.24 442	86.08 445	77.26 484	71.99 475	94.34 466	96.72 423	61.62 475	76.53 467	89.33 468	33.91 482	92.78 470	81.85 450	74.60 471	93.46 458

kuosan		78.45 437	77.69 438	80.72 457	92.73 457	75.32 470	94.63 463	74.51 486	75.96 467	80.87 465	93.19 456	63.23 466	79.99 481	42.56 481	81.56 451	86.85 473

Gipuma		78.40 438	76.75 441	83.38 452	95.54 422	80.43 464	79.42 478	97.40 375	64.67 474	73.46 471	80.82 475	45.65 473	93.14 469	66.32 473	87.43 417	76.56 477
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 439	75.44 443	85.46 446	82.54 479	74.95 471	94.23 467	93.08 468	72.80 470	74.68 468	87.38 469	36.36 479	91.56 471	73.95 467	63.94 476	89.87 466

FPMVS		77.62 440	77.14 440	79.05 459	79.25 482	60.97 484	95.79 445	95.94 441	65.96 473	67.93 475	94.40 445	37.73 478	88.88 476	68.83 472	88.46 407	87.29 470

EGC-MVSNET		75.22 441	69.54 444	92.28 433	94.81 439	89.58 411	97.64 361	96.50 430	1.82 485	5.57 486	95.74 422	68.21 454	96.26 450	73.80 468	91.71 361	90.99 463

ANet_high		69.08 442	65.37 446	80.22 458	65.99 486	71.96 476	90.91 473	90.09 477	82.62 457	49.93 481	78.39 476	29.36 483	81.75 478	62.49 474	38.52 480	86.95 472

tmp_tt		68.90 443	66.97 445	74.68 461	50.78 488	59.95 485	87.13 474	83.47 482	38.80 481	62.21 477	96.23 405	64.70 463	76.91 483	88.91 406	30.49 481	87.19 471

PMVS	61.03 23	65.95 444	63.57 448	73.09 462	57.90 487	51.22 489	85.05 476	93.93 464	54.45 476	44.32 482	83.57 471	13.22 485	89.15 475	58.68 476	81.00 454	78.91 476
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 445	64.25 447	67.02 463	82.28 480	59.36 486	91.83 472	85.63 480	52.69 477	60.22 478	77.28 477	41.06 477	80.12 480	46.15 480	41.14 478	61.57 479

EMVS		64.07 446	63.26 449	66.53 464	81.73 481	58.81 487	91.85 471	84.75 481	51.93 479	59.09 479	75.13 478	43.32 475	79.09 482	42.03 482	39.47 479	61.69 478

MVE	62.14 22	63.28 447	59.38 450	74.99 460	74.33 485	65.47 481	85.55 475	80.50 484	52.02 478	51.10 480	75.00 479	10.91 488	80.50 479	51.60 478	53.40 477	78.99 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 448	30.18 452	30.16 465	78.61 483	43.29 490	66.79 479	14.21 489	17.31 482	14.82 485	11.93 485	11.55 487	41.43 484	37.08 483	19.30 482	5.76 482

cdsmvs_eth3d_5k		23.98 449	31.98 451	0.00 468	0.00 491	0.00 493	0.00 480	98.59 172	0.00 486	0.00 487	98.61 203	90.60 201	0.00 487	0.00 486	0.00 485	0.00 483

testmvs		21.48 450	24.95 453	11.09 467	14.89 489	6.47 492	96.56 434	9.87 490	7.55 483	17.93 483	39.02 481	9.43 489	5.90 486	16.56 485	12.72 483	20.91 481

test123		20.95 451	23.72 454	12.64 466	13.54 490	8.19 491	96.55 435	6.13 491	7.48 484	16.74 484	37.98 482	12.97 486	6.05 485	16.69 484	5.43 484	23.68 480

ab-mvs-re		8.20 452	10.94 455	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	98.43 221	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		7.88 453	10.50 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	94.51 91	0.00 487	0.00 486	0.00 485	0.00 483

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

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						90.94 376								88.66 408

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									95.08 206	99.60 32	99.16 103	97.86 2	98.47 341	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					0.00 491

eth-test					0.00 491

ZD-MVS					99.46 58	98.70 27		98.79 119	93.21 317	98.67 104	98.97 144	95.70 52	99.83 90	96.07 203	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	95.28 189	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				99.16 103	97.81 3	99.37 210	97.24 153	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	94.36 252	99.16 66	99.29 75	96.05 40	99.81 102	97.00 160	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																99.20 180

test_part2					99.63 34	99.18 11				99.27 56

sam_mvs1												89.45 231				99.20 180

sam_mvs												88.99 247

ambc				89.49 440	86.66 475	75.78 467	92.66 470	96.72 423		86.55 446	92.50 463	46.01 472	97.90 404	90.32 379	82.09 446	94.80 442

MTGPA								98.74 129

test_post1							96.68 431				30.43 484	87.85 282	98.69 319	92.59 330

test_post											31.83 483	88.83 254	98.91 295

patchmatchnet-post											95.10 437	89.42 232	98.89 299

GG-mvs-BLEND				96.59 295	96.34 391	94.98 248	96.51 436	88.58 479		93.10 371	94.34 448	80.34 395	98.05 392	89.53 395	96.99 257	96.74 357

MTMP							98.89 120	94.14 462

gm-plane-assit					95.88 413	87.47 443			89.74 417		96.94 372		99.19 244	93.32 308

test9_res														96.39 197	99.57 100	99.69 70

TEST9					99.31 79	98.50 34	97.92 329	98.73 132	92.63 340	97.74 175	98.68 198	96.20 35	99.80 109

test_8					99.29 88	98.44 36	97.89 337	98.72 134	92.98 328	97.70 180	98.66 201	96.20 35	99.80 109

agg_prior2														95.87 213	99.57 100	99.68 75

agg_prior					99.30 83	98.38 40		98.72 134		97.57 196			99.81 102

TestCases				96.99 255	99.25 96	93.21 330		98.18 287	91.36 381	93.52 350	98.77 185	84.67 346	99.72 136	89.70 392	97.87 227	98.02 296

test_prior4						98.01 70	97.86 341

test_prior2							97.80 348		96.12 137	97.89 165	98.69 197	95.96 44		96.89 170	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		91.30 386	98.67 104			99.80 109	95.70 224

新几何2							97.64 361

新几何1				99.16 55	99.34 71	98.01 70		98.69 143	90.06 411	98.13 134	98.95 151	94.60 89	99.89 68	91.97 349	99.47 122	99.59 94

旧先验1					99.29 88	97.48 89		98.70 141			99.09 126	95.56 55			99.47 122	99.61 90

无先验							97.58 366	98.72 134	91.38 380				99.87 79	93.36 307		99.60 92

原ACMM2							97.67 358

原ACMM1				98.65 97	99.32 77	96.62 140		98.67 151	93.27 316	97.81 168	98.97 144	95.18 76	99.83 90	93.84 293	99.46 125	99.50 106

test222					99.23 104	97.17 116	97.40 375	98.66 154	88.68 430	98.05 142	98.96 149	94.14 102			99.53 113	99.61 90

testdata2													99.89 68	91.65 357

segment_acmp												96.85 16

testdata				98.26 141	99.20 109	95.36 225		98.68 146	91.89 366	98.60 112	99.10 118	94.44 96	99.82 97	94.27 277	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					97.42 325	94.63 265

plane_prior6					97.35 332	94.61 268						87.09 295

plane_prior5								98.56 182					99.03 275	96.07 203	94.27 310	96.92 333

plane_prior4											98.28 240

plane_prior3						94.61 268			97.02 87	95.34 276

plane_prior2							98.80 156		97.28 67

plane_prior1					97.37 331

plane_prior						94.60 270	98.44 255		96.74 103						94.22 312

n2								0.00 492

nn								0.00 492

door-mid								94.37 458

lessismore_v0				94.45 404	94.93 437	88.44 434		91.03 475		86.77 444	97.64 304	76.23 431	98.42 347	90.31 380	85.64 435	96.51 393

LGP-MVS_train				96.47 310	97.46 320	93.54 311		98.54 186	94.67 234	94.36 308	98.77 185	85.39 328	99.11 261	95.71 222	94.15 316	96.76 355

test11								98.66 154

door								94.64 456

HQP5-MVS						94.25 287

HQP-NCC					97.20 340		98.05 313		96.43 118	94.45 300

ACMP_Plane					97.20 340		98.05 313		96.43 118	94.45 300

BP-MVS														95.30 236

HQP4-MVS										94.45 300			98.96 286			96.87 345

HQP3-MVS								98.46 207							94.18 314

HQP2-MVS												86.75 301

NP-MVS					97.28 334	94.51 273					97.73 291

MDTV_nov1_ep13_2view						84.26 453	96.89 421		90.97 395	97.90 164		89.89 217		93.91 291		99.18 189

MDTV_nov1_ep13			95.40 228		97.48 318	88.34 435	96.85 424	97.29 383	93.74 284	97.48 198	97.26 333	89.18 240	99.05 271	91.92 350	97.43 248

ACMMP++_ref															92.97 344

ACMMP++															93.61 331

Test By Simon												94.64 88

ITE_SJBPF				95.44 363	97.42 325	91.32 370		97.50 363	95.09 205	93.59 345	98.35 231	81.70 377	98.88 301	89.71 391	93.39 337	96.12 414

DeepMVS_CX				86.78 444	97.09 350	72.30 474		95.17 452	75.92 468	84.34 457	95.19 435	70.58 450	95.35 457	79.98 457	89.04 401	92.68 462

High-resolution multi-view benchmark