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.

MED-MVS		99.12 1	98.97 4	99.56 9	99.77 2	98.86 23	99.32 22	99.24 20	97.87 31	99.30 52	99.54 20	97.61 6	99.92 43	98.30 75	99.80 25	99.90 5

fmvsm_l_conf0.5_n_a		99.09 2	99.08 1	99.11 62	99.43 63	97.48 90	98.88 131	99.30 14	98.47 18	99.85 11	99.43 45	96.71 18	99.96 4	99.86 1	99.80 25	99.89 8

SED-MVS		99.09 2	98.91 5	99.63 5	99.71 24	99.24 5	99.02 86	98.87 85	97.65 41	99.73 23	99.48 35	97.53 8	99.94 14	98.43 67	99.81 16	99.70 67

DVP-MVS++		99.08 4	98.89 6	99.64 4	99.17 111	99.23 7	99.69 1	98.88 78	97.32 65	99.53 38	99.47 37	97.81 3	99.94 14	98.47 63	99.72 66	99.74 50

fmvsm_l_conf0.5_n		99.07 5	99.05 2	99.14 58	99.41 66	97.54 88	98.89 124	99.31 13	98.49 17	99.86 8	99.42 46	96.45 28	99.96 4	99.86 1	99.74 57	99.90 5

TestfortrainingZip a		99.05 6	98.85 9	99.65 2	99.77 2	99.13 11	99.32 22	99.01 52	97.87 31	99.74 21	99.54 20	96.71 18	99.92 43	98.35 72	99.33 139	99.90 5

DVP-MVS		99.03 7	98.83 11	99.63 5	99.72 17	99.25 2	98.97 98	98.58 176	97.62 43	99.45 40	99.46 42	97.42 10	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
APDe-MVS		99.02 8	98.84 10	99.55 11	99.57 39	98.96 18	99.39 11	98.93 65	97.38 62	99.41 44	99.54 20	96.66 20	99.84 88	98.86 39	99.85 6	99.87 12
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 31	99.43 10	99.38 8	97.60 46	99.58 34	99.47 37	95.36 64	99.93 34	98.87 38	99.57 98	99.78 33

reproduce_model		98.94 10	98.81 12	99.34 32	99.52 45	98.26 55	98.94 108	98.84 96	98.06 25	99.35 48	99.61 5	96.39 31	99.94 14	98.77 42	99.82 14	99.83 19

reproduce-ours		98.93 11	98.78 14	99.38 24	99.49 52	98.38 41	98.86 142	98.83 98	98.06 25	99.29 54	99.58 16	96.40 29	99.94 14	98.68 45	99.81 16	99.81 25

our_new_method		98.93 11	98.78 14	99.38 24	99.49 52	98.38 41	98.86 142	98.83 98	98.06 25	99.29 54	99.58 16	96.40 29	99.94 14	98.68 45	99.81 16	99.81 25

test_fmvsmconf_n		98.92 13	98.87 7	99.04 68	98.88 147	97.25 112	98.82 155	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 2	99.58 37	99.20 9	98.42 267	98.91 72	97.58 47	99.54 37	99.46 42	97.10 13	99.94 14	97.64 124	99.84 11	99.83 19
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 13	99.24 46	99.34 71	97.83 79	98.70 196	99.26 16	98.85 6	99.92 1	99.51 28	93.91 106	99.95 9	99.86 1	99.79 34	99.92 2

fmvsm_l_conf0.5_n_3		98.90 15	98.74 18	99.37 28	99.36 68	98.25 56	98.89 124	99.24 20	98.77 10	99.89 3	99.59 13	93.39 112	99.96 4	99.78 10	99.76 47	99.89 8

SteuartSystems-ACMMP		98.90 15	98.75 17	99.36 30	99.22 106	98.43 39	99.10 69	98.87 85	97.38 62	99.35 48	99.40 49	97.78 5	99.87 79	97.77 112	99.85 6	99.78 33
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 18	99.01 3	98.45 124	99.42 64	96.43 156	98.96 104	99.36 10	98.63 13	99.86 8	99.51 28	95.91 46	99.97 1	99.72 14	99.75 53	98.94 238

ME-MVS		98.83 19	98.60 24	99.52 14	99.58 37	98.86 23	98.69 199	98.93 65	97.00 91	99.17 63	99.35 62	96.62 23	99.90 64	98.30 75	99.80 25	99.79 29

TSAR-MVS + MP.		98.78 20	98.62 22	99.24 46	99.69 29	98.28 54	99.14 60	98.66 153	96.84 98	99.56 35	99.31 71	96.34 32	99.70 143	98.32 74	99.73 61	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 21	98.47 254	98.81 107	97.72 36	98.76 96	99.16 110	97.05 14	99.78 124	98.06 90	99.66 77	99.69 70

MSP-MVS		98.74 22	98.55 29	99.29 39	99.75 6	98.23 57	99.26 33	98.88 78	97.52 50	99.41 44	98.78 194	96.00 42	99.79 121	97.79 111	99.59 94	99.85 16
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 67	99.35 70	97.27 106	98.80 164	99.23 27	98.93 3	99.79 15	99.59 13	92.34 129	99.95 9	99.82 6	99.71 68	99.92 2

XVS		98.70 24	98.49 36	99.34 32	99.70 27	98.35 50	99.29 28	98.88 78	97.40 59	98.46 120	99.20 95	95.90 48	99.89 68	97.85 106	99.74 57	99.78 33

fmvsm_s_conf0.5_n_10		98.66 25	98.54 31	99.02 69	99.36 68	97.21 115	98.86 142	99.23 27	98.90 5	99.83 12	99.59 13	91.57 161	99.94 14	99.79 9	99.74 57	99.89 8

fmvsm_s_conf0.5_n_6		98.65 26	98.55 29	98.95 78	98.50 187	97.30 102	98.79 172	99.16 39	98.14 23	99.86 8	99.41 48	93.71 109	99.91 56	99.71 15	99.64 85	99.65 83

MCST-MVS		98.65 26	98.37 45	99.48 17	99.60 36	98.87 21	98.41 268	98.68 145	97.04 88	98.52 118	98.80 188	96.78 17	99.83 90	97.93 98	99.61 90	99.74 50

SD-MVS		98.64 28	98.68 19	98.53 113	99.33 74	98.36 49	98.90 120	98.85 95	97.28 69	99.72 26	99.39 50	96.63 22	97.60 448	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	98.54 110	99.40 67	95.83 203	98.79 172	99.17 37	98.94 2	99.92 1	99.61 5	92.49 124	99.93 34	99.86 1	99.76 47	99.86 13

HFP-MVS		98.63 29	98.40 42	99.32 38	99.72 17	98.29 53	99.23 38	98.96 60	96.10 143	98.94 78	99.17 107	96.06 39	99.92 43	97.62 125	99.78 39	99.75 48

ACMMP_NAP		98.61 31	98.30 60	99.55 11	99.62 35	98.95 19	98.82 155	98.81 107	95.80 159	99.16 67	99.47 37	95.37 63	99.92 43	97.89 103	99.75 53	99.79 29

region2R		98.61 31	98.38 44	99.29 39	99.74 12	98.16 63	99.23 38	98.93 65	96.15 137	98.94 78	99.17 107	95.91 46	99.94 14	97.55 137	99.79 34	99.78 33

NCCC		98.61 31	98.35 48	99.38 24	99.28 92	98.61 32	98.45 256	98.76 125	97.82 35	98.45 123	98.93 166	96.65 21	99.83 90	97.38 159	99.41 128	99.71 63

SF-MVS		98.59 34	98.32 59	99.41 23	99.54 41	98.71 27	99.04 80	98.81 107	95.12 213	99.32 51	99.39 50	96.22 33	99.84 88	97.72 115	99.73 61	99.67 79

ACMMPR		98.59 34	98.36 46	99.29 39	99.74 12	98.15 64	99.23 38	98.95 61	96.10 143	98.93 82	99.19 102	95.70 52	99.94 14	97.62 125	99.79 34	99.78 33

fmvsm_s_conf0.5_n_11		98.58 36	98.57 26	98.62 100	99.42 64	97.16 118	98.97 98	98.86 91	98.91 4	99.87 4	99.66 3	91.82 152	99.95 9	99.82 6	99.82 14	98.75 261

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 71	97.73 309	97.15 119	98.84 151	98.97 57	98.75 11	99.43 42	99.54 20	93.29 114	99.93 34	99.64 20	99.79 34	99.89 8

SMA-MVS		98.58 36	98.25 63	99.56 9	99.51 46	99.04 17	98.95 105	98.80 114	93.67 308	99.37 47	99.52 25	96.52 26	99.89 68	98.06 90	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 30	98.90 120	98.74 129	97.27 73	98.02 154	99.39 50	94.81 87	99.96 4	97.91 101	99.79 34	99.77 40

HPM-MVS++		98.58 36	98.25 63	99.55 11	99.50 48	99.08 12	98.72 191	98.66 153	97.51 51	98.15 138	98.83 185	95.70 52	99.92 43	97.53 140	99.67 74	99.66 82

SR-MVS		98.57 41	98.35 48	99.24 46	99.53 42	98.18 61	99.09 70	98.82 101	96.58 114	99.10 69	99.32 69	95.39 61	99.82 97	97.70 120	99.63 87	99.72 59

CP-MVS		98.57 41	98.36 46	99.19 51	99.66 31	97.86 75	99.34 17	98.87 85	95.96 150	98.60 114	99.13 118	96.05 40	99.94 14	97.77 112	99.86 2	99.77 40

MSLP-MVS++		98.56 43	98.57 26	98.55 108	99.26 95	96.80 134	98.71 192	99.05 49	97.28 69	98.84 88	99.28 76	96.47 27	99.40 207	98.52 61	99.70 70	99.47 116

DeepC-MVS_fast	96.70 1	98.55 44	98.34 54	99.18 53	99.25 96	98.04 69	98.50 249	98.78 121	97.72 36	98.92 84	99.28 76	95.27 70	99.82 97	97.55 137	99.77 41	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 59	99.49 52	97.86 75	99.11 66	98.80 114	96.49 119	99.17 63	99.35 62	95.34 66	99.82 97	97.72 115	99.65 80	99.71 63

fmvsm_s_conf0.5_n_5		98.53 46	98.35 48	99.08 64	99.07 127	97.46 94	98.68 202	99.20 33	97.50 52	99.87 4	99.50 31	91.96 149	99.96 4	99.76 11	99.65 80	99.82 23

fmvsm_s_conf0.5_n_3		98.53 46	98.45 39	98.79 86	99.23 104	97.32 99	98.80 164	99.26 16	98.82 7	99.87 4	99.60 10	90.95 196	99.93 34	99.76 11	99.73 61	99.12 208

APD-MVS_3200maxsize		98.53 46	98.33 58	99.15 57	99.50 48	97.92 74	99.15 57	98.81 107	96.24 133	99.20 60	99.37 56	95.30 68	99.80 109	97.73 114	99.67 74	99.72 59

MM		98.51 49	98.24 65	99.33 36	99.12 121	98.14 66	98.93 114	97.02 430	98.96 1	99.17 63	99.47 37	91.97 148	99.94 14	99.85 5	99.69 71	99.91 4

mPP-MVS		98.51 49	98.26 62	99.25 45	99.75 6	98.04 69	99.28 30	98.81 107	96.24 133	98.35 133	99.23 87	95.46 58	99.94 14	97.42 154	99.81 16	99.77 40

ZNCC-MVS		98.49 51	98.20 71	99.35 31	99.73 16	98.39 40	99.19 50	98.86 91	95.77 161	98.31 137	99.10 127	95.46 58	99.93 34	97.57 136	99.81 16	99.74 50

SPE-MVS-test		98.49 51	98.50 34	98.46 123	99.20 109	97.05 124	99.64 4	98.50 199	97.45 58	98.88 85	99.14 115	95.25 72	99.15 263	98.83 40	99.56 106	99.20 191

PGM-MVS		98.49 51	98.23 67	99.27 44	99.72 17	98.08 68	98.99 94	99.49 5	95.43 188	99.03 70	99.32 69	95.56 55	99.94 14	96.80 193	99.77 41	99.78 33

EI-MVSNet-Vis-set		98.47 54	98.39 43	98.69 94	99.46 58	96.49 153	98.30 282	98.69 142	97.21 76	98.84 88	99.36 60	95.41 60	99.78 124	98.62 49	99.65 80	99.80 28

MVS_111021_HR		98.47 54	98.34 54	98.88 83	99.22 106	97.32 99	97.91 344	99.58 3	97.20 77	98.33 135	99.00 154	95.99 43	99.64 157	98.05 92	99.76 47	99.69 70

BridgeMVS		98.45 56	98.35 48	98.74 90	98.65 176	97.55 86	99.19 50	98.60 164	96.72 108	99.35 48	98.77 197	95.06 82	99.55 181	98.95 34	99.87 1	99.12 208

test_fmvsmvis_n_1920		98.44 57	98.51 32	98.23 146	98.33 221	96.15 171	98.97 98	99.15 41	98.55 16	98.45 123	99.55 18	94.26 100	99.97 1	99.65 18	99.66 77	98.57 286

CS-MVS		98.44 57	98.49 36	98.31 137	99.08 126	96.73 138	99.67 3	98.47 206	97.17 80	98.94 78	99.10 127	95.73 51	99.13 268	98.71 44	99.49 117	99.09 216

GST-MVS		98.43 59	98.12 75	99.34 32	99.72 17	98.38 41	99.09 70	98.82 101	95.71 165	98.73 99	99.06 143	95.27 70	99.93 34	97.07 169	99.63 87	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 164	99.30 83	95.25 244	98.85 147	99.39 7	97.94 29	99.74 21	99.62 4	92.59 123	99.91 56	99.65 18	99.52 112	99.25 184

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 102	99.45 61	96.32 163	98.28 285	98.68 145	97.17 80	98.74 97	99.37 56	95.25 72	99.79 121	98.57 52	99.54 109	99.73 55

DELS-MVS		98.40 62	98.20 71	98.99 71	99.00 135	97.66 81	97.75 366	98.89 75	97.71 38	98.33 135	98.97 156	94.97 84	99.88 77	98.42 69	99.76 47	99.42 133
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	98.27 139	99.09 125	95.41 230	98.86 142	99.37 9	97.69 40	99.78 17	99.61 5	92.38 127	99.91 56	99.58 23	99.43 126	99.49 112

TSAR-MVS + GP.		98.38 63	98.24 65	98.81 85	99.22 106	97.25 112	98.11 319	98.29 279	97.19 78	98.99 76	99.02 148	96.22 33	99.67 150	98.52 61	98.56 184	99.51 104

HPM-MVS_fast		98.38 63	98.13 74	99.12 61	99.75 6	97.86 75	99.44 9	98.82 101	94.46 261	98.94 78	99.20 95	95.16 77	99.74 134	97.58 132	99.85 6	99.77 40

patch_mono-2		98.36 66	98.87 7	96.82 285	99.53 42	90.68 409	98.64 212	99.29 15	97.88 30	99.19 62	99.52 25	96.80 16	99.97 1	99.11 30	99.86 2	99.82 23

HPM-MVS		98.36 66	98.10 77	99.13 59	99.74 12	97.82 80	99.53 6	98.80 114	94.63 249	98.61 113	98.97 156	95.13 79	99.77 129	97.65 123	99.83 13	99.79 29
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	97.90 195	99.16 115	95.08 255	98.75 177	99.24 20	98.39 19	99.81 13	99.52 25	92.35 128	99.90 64	99.74 13	99.51 114	98.71 267

APD-MVS		98.35 68	98.00 83	99.42 22	99.51 46	98.72 26	98.80 164	98.82 101	94.52 256	99.23 59	99.25 86	95.54 57	99.80 109	96.52 202	99.77 41	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 96	99.27 93	96.90 130	97.95 337	99.58 3	97.14 83	98.44 126	99.01 152	95.03 83	99.62 164	97.91 101	99.75 53	99.50 107

PHI-MVS		98.34 70	98.06 78	99.18 53	99.15 118	98.12 67	99.04 80	99.09 44	93.32 327	98.83 91	99.10 127	96.54 24	99.83 90	97.70 120	99.76 47	99.59 94

MP-MVS		98.33 72	98.01 82	99.28 42	99.75 6	98.18 61	99.22 42	98.79 119	96.13 138	97.92 168	99.23 87	94.54 90	99.94 14	96.74 196	99.78 39	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 96	98.96 141	97.36 97	99.24 36	98.57 178	94.81 237	98.99 76	98.90 173	95.22 75	99.59 167	99.15 29	99.84 11	99.07 224

MP-MVS-pluss		98.31 73	97.92 85	99.49 16	99.72 17	98.88 20	98.43 264	98.78 121	94.10 273	97.69 191	99.42 46	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	98.57 105	99.25 96	97.11 121	98.66 209	99.20 33	98.82 7	99.79 15	99.60 10	89.38 245	99.92 43	99.80 8	99.38 133	98.69 269

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 197	98.86 151	94.99 261	98.58 225	99.00 53	98.29 20	99.73 23	99.60 10	91.70 155	99.92 43	99.63 21	99.73 61	98.76 260

MGCNet		98.23 76	97.91 86	99.21 50	98.06 272	97.96 73	98.58 225	95.51 472	98.58 14	98.87 86	99.26 80	92.99 118	99.95 9	99.62 22	99.67 74	99.73 55

ACMMP		98.23 76	97.95 84	99.09 63	99.74 12	97.62 84	99.03 83	99.41 6	95.98 148	97.60 205	99.36 60	94.45 95	99.93 34	97.14 166	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	98.49 120	98.34 217	97.26 111	99.61 5	98.43 226	96.78 101	98.87 86	98.84 181	93.72 108	99.01 296	98.91 37	99.50 115	99.19 195

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 169	98.54 185	95.24 245	98.87 134	99.24 20	97.50 52	99.70 27	99.67 1	91.33 173	99.89 68	99.47 25	99.54 109	99.21 190

fmvsm_s_conf0.1_n_2		98.14 81	98.02 81	98.53 113	98.88 147	97.07 123	98.69 199	98.82 101	98.78 9	99.77 18	99.61 5	88.83 267	99.91 56	99.71 15	99.07 150	98.61 279

fmvsm_s_conf0.1_n_a		98.08 82	98.04 80	98.21 147	97.66 315	95.39 235	98.89 124	99.17 37	97.24 74	99.76 20	99.67 1	91.13 185	99.88 77	99.39 26	99.41 128	99.35 148

dcpmvs_2		98.08 82	98.59 25	96.56 315	99.57 39	90.34 421	99.15 57	98.38 248	96.82 100	99.29 54	99.49 34	95.78 50	99.57 171	98.94 35	99.86 2	99.77 40

NormalMVS		98.07 84	97.90 87	98.59 104	99.75 6	96.60 144	98.94 108	98.60 164	97.86 33	98.71 102	99.08 138	91.22 180	99.80 109	97.40 156	99.57 98	99.37 143

CANet		98.05 85	97.76 90	98.90 82	98.73 161	97.27 106	98.35 271	98.78 121	97.37 64	97.72 188	98.96 161	91.53 166	99.92 43	98.79 41	99.65 80	99.51 104

train_agg		97.97 86	97.52 103	99.33 36	99.31 79	98.50 35	97.92 342	98.73 132	92.98 343	97.74 185	98.68 210	96.20 35	99.80 109	96.59 197	99.57 98	99.68 75

ETV-MVS		97.96 87	97.81 88	98.40 132	98.42 200	97.27 106	98.73 187	98.55 184	96.84 98	98.38 129	97.44 334	95.39 61	99.35 212	97.62 125	98.89 161	98.58 285

UA-Net		97.96 87	97.62 94	98.98 73	98.86 151	97.47 92	98.89 124	99.08 45	96.67 111	98.72 101	99.54 20	93.15 116	99.81 102	94.87 261	98.83 168	99.65 83

CDPH-MVS		97.94 89	97.49 105	99.28 42	99.47 56	98.44 37	97.91 344	98.67 150	92.57 361	98.77 95	98.85 180	95.93 45	99.72 137	95.56 239	99.69 71	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 343	99.00 135	89.54 437	97.43 391	98.87 85	98.16 22	99.26 58	99.38 55	96.12 38	99.64 157	98.30 75	99.77 41	99.72 59

DeepC-MVS	95.98 3	97.88 91	97.58 96	98.77 88	99.25 96	96.93 128	98.83 153	98.75 127	96.96 93	96.89 237	99.50 31	90.46 210	99.87 79	97.84 108	99.76 47	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	97.54 102	98.83 84	95.48 444	96.83 133	98.95 105	98.60 164	98.58 14	98.93 82	99.55 18	88.57 272	99.91 56	99.54 24	99.61 90	99.77 40

DP-MVS Recon		97.86 92	97.46 108	99.06 66	99.53 42	98.35 50	98.33 274	98.89 75	92.62 358	98.05 149	98.94 164	95.34 66	99.65 154	96.04 218	99.42 127	99.19 195

CSCG		97.85 94	97.74 91	98.20 149	99.67 30	95.16 249	99.22 42	99.32 12	93.04 341	97.02 230	98.92 171	95.36 64	99.91 56	97.43 152	99.64 85	99.52 101

SymmetryMVS		97.84 95	97.58 96	98.62 100	99.01 133	96.60 144	98.94 108	98.44 215	97.86 33	98.71 102	99.08 138	91.22 180	99.80 109	97.40 156	97.53 260	99.47 116

BP-MVS1		97.82 96	97.51 104	98.76 89	98.25 237	97.39 96	99.15 57	97.68 359	96.69 109	98.47 119	99.10 127	90.29 218	99.51 187	98.60 50	99.35 136	99.37 143

MG-MVS		97.81 97	97.60 95	98.44 126	99.12 121	95.97 184	97.75 366	98.78 121	96.89 96	98.46 120	99.22 90	93.90 107	99.68 149	94.81 265	99.52 112	99.67 79

VNet		97.79 98	97.40 115	98.96 76	98.88 147	97.55 86	98.63 215	98.93 65	96.74 105	99.02 71	98.84 181	90.33 217	99.83 90	98.53 55	96.66 283	99.50 107

EIA-MVS		97.75 99	97.58 96	98.27 139	98.38 207	96.44 155	99.01 89	98.60 164	95.88 154	97.26 216	97.53 328	94.97 84	99.33 215	97.38 159	99.20 146	99.05 225

PS-MVSNAJ		97.73 100	97.77 89	97.62 229	98.68 171	95.58 218	97.34 400	98.51 194	97.29 67	98.66 109	97.88 292	94.51 91	99.90 64	97.87 105	99.17 148	97.39 332

casdiffmvs_mvg		97.72 101	97.48 107	98.44 126	98.42 200	96.59 148	98.92 117	98.44 215	96.20 135	97.76 182	99.20 95	91.66 158	99.23 245	98.27 82	98.41 208	99.49 112
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	97.32 123	98.92 79	99.64 33	97.10 122	99.12 64	98.81 107	92.34 369	98.09 143	99.08 138	93.01 117	99.92 43	96.06 217	99.77 41	99.75 48

PVSNet_Blended_VisFu		97.70 103	97.46 108	98.44 126	99.27 93	95.91 192	98.63 215	99.16 39	94.48 260	97.67 193	98.88 176	92.80 120	99.91 56	97.11 167	99.12 149	99.50 107

mvsany_test1		97.69 104	97.70 92	97.66 225	98.24 240	94.18 305	97.53 382	97.53 380	95.52 183	99.66 29	99.51 28	94.30 98	99.56 174	98.38 70	98.62 178	99.23 186

sasdasda		97.67 105	97.23 133	98.98 73	98.70 166	98.38 41	99.34 17	98.39 241	96.76 103	97.67 193	97.40 338	92.26 133	99.49 191	98.28 79	96.28 301	99.08 220

canonicalmvs		97.67 105	97.23 133	98.98 73	98.70 166	98.38 41	99.34 17	98.39 241	96.76 103	97.67 193	97.40 338	92.26 133	99.49 191	98.28 79	96.28 301	99.08 220

xiu_mvs_v2_base		97.66 107	97.70 92	97.56 233	98.61 180	95.46 227	97.44 388	98.46 207	97.15 82	98.65 110	98.15 267	94.33 97	99.80 109	97.84 108	98.66 177	97.41 330

GDP-MVS		97.64 108	97.28 126	98.71 93	98.30 226	97.33 98	99.05 76	98.52 191	96.34 129	98.80 92	99.05 145	89.74 232	99.51 187	96.86 189	98.86 165	99.28 174

baseline		97.64 108	97.44 111	98.25 143	98.35 212	96.20 168	99.00 91	98.32 265	96.33 131	98.03 152	99.17 107	91.35 172	99.16 259	98.10 87	98.29 220	99.39 138

casdiffmvs		97.63 110	97.41 114	98.28 138	98.33 221	96.14 172	98.82 155	98.32 265	96.38 127	97.95 163	99.21 93	91.23 179	99.23 245	98.12 86	98.37 211	99.48 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
cashybrid2		97.62 111	97.43 113	98.19 153	98.48 192	95.83 203	99.07 72	98.42 230	96.27 132	98.09 143	99.26 80	91.00 193	99.30 221	97.81 110	98.48 193	99.44 126

MGCFI-Net		97.62 111	97.19 137	98.92 79	98.66 173	98.20 59	99.32 22	98.38 248	96.69 109	97.58 207	97.42 337	92.10 142	99.50 190	98.28 79	96.25 304	99.08 220

xiu_mvs_v1_base_debu		97.60 113	97.56 99	97.72 214	98.35 212	95.98 179	97.86 354	98.51 194	97.13 84	99.01 73	98.40 239	91.56 162	99.80 109	98.53 55	98.68 173	97.37 334

xiu_mvs_v1_base		97.60 113	97.56 99	97.72 214	98.35 212	95.98 179	97.86 354	98.51 194	97.13 84	99.01 73	98.40 239	91.56 162	99.80 109	98.53 55	98.68 173	97.37 334

xiu_mvs_v1_base_debi		97.60 113	97.56 99	97.72 214	98.35 212	95.98 179	97.86 354	98.51 194	97.13 84	99.01 73	98.40 239	91.56 162	99.80 109	98.53 55	98.68 173	97.37 334

diffmvs_AUTHOR		97.59 116	97.44 111	98.01 183	98.26 235	95.47 226	98.12 315	98.36 254	96.38 127	98.84 88	99.10 127	91.13 185	99.26 229	98.24 83	98.56 184	99.30 164

diffmvs		97.58 117	97.40 115	98.13 164	98.32 224	95.81 207	98.06 325	98.37 250	96.20 135	98.74 97	98.89 175	91.31 175	99.25 233	98.16 85	98.52 188	99.34 150
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		97.57 118	97.37 118	98.20 149	98.50 187	95.86 200	98.89 124	97.03 427	97.29 67	98.73 99	98.90 173	89.41 244	99.32 216	98.68 45	98.86 165	99.42 133

MVSFormer		97.57 118	97.49 105	97.84 201	98.07 269	95.76 211	99.47 7	98.40 235	94.98 226	98.79 93	98.83 185	92.34 129	98.41 371	96.91 177	99.59 94	99.34 150

alignmvs		97.56 120	97.07 150	99.01 70	98.66 173	98.37 48	98.83 153	98.06 332	96.74 105	98.00 158	97.65 315	90.80 198	99.48 196	98.37 71	96.56 287	99.19 195

nocashy02		97.55 121	97.45 110	97.87 199	98.22 244	95.13 252	98.35 271	98.35 255	96.57 116	98.45 123	99.15 114	91.60 159	99.18 254	97.99 94	98.36 213	99.29 167

E3new		97.55 121	97.35 121	98.16 155	98.48 192	95.85 201	98.55 238	98.41 232	95.42 190	98.06 147	99.12 122	92.23 136	99.24 241	97.43 152	98.45 196	99.39 138

DPM-MVS		97.55 121	96.99 157	99.23 49	99.04 129	98.55 33	97.17 420	98.35 255	94.85 236	97.93 167	98.58 221	95.07 81	99.71 142	92.60 351	99.34 137	99.43 130

OMC-MVS		97.55 121	97.34 122	98.20 149	99.33 74	95.92 191	98.28 285	98.59 171	95.52 183	97.97 161	99.10 127	93.28 115	99.49 191	95.09 256	98.88 162	99.19 195

onestephybrid01		97.54 125	97.36 119	98.06 176	98.25 237	95.63 216	98.26 288	98.33 261	96.13 138	98.65 110	99.13 118	91.02 192	99.25 233	98.07 89	98.42 206	99.31 159

balanced_ft_v1		97.54 125	97.38 117	98.02 181	98.34 217	95.58 218	99.32 22	98.40 235	95.88 154	98.43 128	98.65 214	88.95 264	99.59 167	98.94 35	99.48 120	98.90 242

viewcassd2359sk11		97.53 127	97.32 123	98.16 155	98.45 196	95.83 203	98.57 234	98.42 230	95.52 183	98.07 145	99.12 122	91.81 153	99.25 233	97.46 150	98.48 193	99.41 136

hybridcas		97.52 128	97.29 125	98.20 149	98.44 197	96.00 177	99.02 86	98.39 241	96.12 141	97.69 191	99.23 87	90.77 203	99.17 257	97.55 137	98.42 206	99.44 126

LuminaMVS		97.49 129	97.18 138	98.42 130	97.50 330	97.15 119	98.45 256	97.68 359	96.56 118	98.68 104	98.78 194	89.84 229	99.32 216	98.60 50	98.57 183	98.79 252

E2		97.48 130	97.25 128	98.16 155	98.40 204	95.79 208	98.58 225	98.44 215	95.58 172	98.00 158	99.14 115	91.21 184	99.24 241	97.50 145	98.43 200	99.45 123

E3		97.48 130	97.25 128	98.16 155	98.38 207	95.79 208	98.58 225	98.44 215	95.58 172	98.00 158	99.14 115	91.25 178	99.24 241	97.50 145	98.44 197	99.45 123

KinetiMVS		97.48 130	97.05 152	98.78 87	98.37 210	97.30 102	98.99 94	98.70 140	97.18 79	99.02 71	99.01 152	87.50 304	99.67 150	95.33 246	99.33 139	99.37 143

viewmanbaseed2359cas		97.47 133	97.25 128	98.14 159	98.41 202	95.84 202	98.57 234	98.43 226	95.55 179	97.97 161	99.12 122	91.26 177	99.15 263	97.42 154	98.53 187	99.43 130

PAPM_NR		97.46 134	97.11 147	98.50 118	99.50 48	96.41 158	98.63 215	98.60 164	95.18 206	97.06 228	98.06 273	94.26 100	99.57 171	93.80 310	98.87 164	99.52 101

EPP-MVSNet		97.46 134	97.28 126	97.99 185	98.64 177	95.38 236	99.33 21	98.31 270	93.61 314	97.19 220	99.07 142	94.05 103	99.23 245	96.89 181	98.43 200	99.37 143

3Dnovator	94.51 5	97.46 134	96.93 161	99.07 65	97.78 303	97.64 82	99.35 16	99.06 47	97.02 89	93.75 358	99.16 110	89.25 249	99.92 43	97.22 165	99.75 53	99.64 86

CNLPA		97.45 137	97.03 154	98.73 91	99.05 128	97.44 95	98.07 324	98.53 188	95.32 199	96.80 243	98.53 226	93.32 113	99.72 137	94.31 291	99.31 141	99.02 229

lupinMVS		97.44 138	97.22 135	98.12 167	98.07 269	95.76 211	97.68 371	97.76 356	94.50 259	98.79 93	98.61 216	92.34 129	99.30 221	97.58 132	99.59 94	99.31 159

3Dnovator+	94.38 6	97.43 139	96.78 173	99.38 24	97.83 300	98.52 34	99.37 13	98.71 137	97.09 87	92.99 388	99.13 118	89.36 246	99.89 68	96.97 173	99.57 98	99.71 63

Vis-MVSNet		97.42 140	97.11 147	98.34 135	98.66 173	96.23 167	99.22 42	99.00 53	96.63 113	98.04 151	99.21 93	88.05 290	99.35 212	96.01 220	99.21 145	99.45 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
hybridnocas07		97.41 141	97.21 136	97.99 185	98.24 240	95.42 229	98.21 293	98.32 265	95.97 149	98.38 129	98.93 166	90.48 209	99.21 250	97.92 100	98.46 195	99.34 150

API-MVS		97.41 141	97.25 128	97.91 194	98.70 166	96.80 134	98.82 155	98.69 142	94.53 254	98.11 141	98.28 254	94.50 94	99.57 171	94.12 299	99.49 117	97.37 334

sss		97.39 143	96.98 159	98.61 102	98.60 181	96.61 143	98.22 292	98.93 65	93.97 283	98.01 157	98.48 232	91.98 146	99.85 84	96.45 204	98.15 229	99.39 138

test_cas_vis1_n_1920		97.38 144	97.36 119	97.45 238	98.95 142	93.25 348	99.00 91	98.53 188	97.70 39	99.77 18	99.35 62	84.71 360	99.85 84	98.57 52	99.66 77	99.26 182

PVSNet_Blended		97.38 144	97.12 146	98.14 159	99.25 96	95.35 239	97.28 406	99.26 16	93.13 337	97.94 165	98.21 262	92.74 121	99.81 102	96.88 183	99.40 131	99.27 175

E5new		97.37 146	97.16 140	97.98 187	98.30 226	95.41 230	98.87 134	98.45 211	95.56 174	97.84 174	99.19 102	90.39 213	99.25 233	97.61 128	98.22 224	99.29 167

E6new		97.37 146	97.16 140	97.98 187	98.28 232	95.40 233	98.87 134	98.45 211	95.55 179	97.84 174	99.20 95	90.44 211	99.25 233	97.61 128	98.22 224	99.29 167

E6		97.37 146	97.16 140	97.98 187	98.28 232	95.40 233	98.87 134	98.45 211	95.55 179	97.84 174	99.20 95	90.44 211	99.25 233	97.61 128	98.22 224	99.29 167

E5		97.37 146	97.16 140	97.98 187	98.30 226	95.41 230	98.87 134	98.45 211	95.56 174	97.84 174	99.19 102	90.39 213	99.25 233	97.61 128	98.22 224	99.29 167

E4		97.37 146	97.13 145	98.12 167	98.27 234	95.70 213	98.59 221	98.44 215	95.56 174	97.80 179	99.18 105	90.57 207	99.26 229	97.45 151	98.28 222	99.40 137

WTY-MVS		97.37 146	96.92 162	98.72 92	98.86 151	96.89 132	98.31 279	98.71 137	95.26 202	97.67 193	98.56 225	92.21 138	99.78 124	95.89 222	96.85 277	99.48 114

hybrid		97.34 152	97.16 140	97.88 198	98.25 237	95.18 248	98.18 305	98.33 261	95.36 196	98.35 133	99.06 143	90.61 205	99.18 254	97.88 104	98.40 209	99.27 175

AstraMVS		97.34 152	97.24 132	97.65 226	98.13 263	94.15 306	98.94 108	96.25 462	97.47 56	98.60 114	99.28 76	89.67 234	99.41 206	98.73 43	98.07 233	99.38 142

viewmacassd2359aftdt		97.32 154	97.07 150	98.08 172	98.30 226	95.69 214	98.62 218	98.44 215	95.56 174	97.86 173	99.22 90	89.91 227	99.14 266	97.29 162	98.43 200	99.42 133

jason		97.32 154	97.08 149	98.06 176	97.45 336	95.59 217	97.87 352	97.91 343	94.79 239	98.55 117	98.83 185	91.12 187	99.23 245	97.58 132	99.60 92	99.34 150
jason: jason.
MVS_Test		97.28 156	97.00 155	98.13 164	98.33 221	95.97 184	98.74 181	98.07 327	94.27 268	98.44 126	98.07 272	92.48 125	99.26 229	96.43 205	98.19 228	99.16 201

EPNet		97.28 156	96.87 164	98.51 115	94.98 453	96.14 172	98.90 120	97.02 430	98.28 21	95.99 279	99.11 125	91.36 171	99.89 68	96.98 172	99.19 147	99.50 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 158	97.00 155	98.03 179	98.46 194	95.99 178	98.62 218	98.44 215	94.77 240	97.24 217	98.93 166	91.22 180	99.28 226	96.54 199	98.74 172	98.84 247

mvsmamba		97.25 159	96.99 157	98.02 181	98.34 217	95.54 223	99.18 54	97.47 386	95.04 219	98.15 138	98.57 224	89.46 241	99.31 220	97.68 122	99.01 155	99.22 188

viewdifsd2359ckpt13		97.24 160	96.97 160	98.06 176	98.43 198	95.77 210	98.59 221	98.34 259	94.81 237	97.60 205	98.94 164	90.78 202	99.09 278	96.93 176	98.33 216	99.32 158

test_yl		97.22 161	96.78 173	98.54 110	98.73 161	96.60 144	98.45 256	98.31 270	94.70 243	98.02 154	98.42 237	90.80 198	99.70 143	96.81 190	96.79 279	99.34 150

DCV-MVSNet		97.22 161	96.78 173	98.54 110	98.73 161	96.60 144	98.45 256	98.31 270	94.70 243	98.02 154	98.42 237	90.80 198	99.70 143	96.81 190	96.79 279	99.34 150

IS-MVSNet		97.22 161	96.88 163	98.25 143	98.85 154	96.36 161	99.19 50	97.97 337	95.39 192	97.23 218	98.99 155	91.11 188	98.93 309	94.60 279	98.59 180	99.47 116

viewdifsd2359ckpt07		97.20 164	97.05 152	97.65 226	98.40 204	94.33 297	98.39 269	98.43 226	95.67 167	97.66 197	99.08 138	90.04 224	99.32 216	97.47 149	98.29 220	99.31 159

PLC	95.07 4	97.20 164	96.78 173	98.44 126	99.29 88	96.31 165	98.14 312	98.76 125	92.41 367	96.39 266	98.31 252	94.92 86	99.78 124	94.06 302	98.77 171	99.23 186
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 166	97.18 138	97.20 251	98.81 157	93.27 345	95.78 471	99.15 41	95.25 203	96.79 244	98.11 270	92.29 132	99.07 281	98.56 54	99.85 6	99.25 184

SSM_0407		97.17 167	96.87 164	98.08 172	98.19 250	95.90 193	98.52 241	98.44 215	94.77 240	96.75 245	98.93 166	91.22 180	99.22 249	96.54 199	98.43 200	99.10 213

LS3D		97.16 168	96.66 182	98.68 95	98.53 186	97.19 116	98.93 114	98.90 73	92.83 351	95.99 279	99.37 56	92.12 141	99.87 79	93.67 314	99.57 98	98.97 234

AdaColmap		97.15 169	96.70 178	98.48 121	99.16 115	96.69 140	98.01 331	98.89 75	94.44 262	96.83 239	98.68 210	90.69 204	99.76 130	94.36 287	99.29 142	98.98 233

viewdifsd2359ckpt09		97.13 170	96.79 171	98.14 159	98.43 198	95.90 193	98.52 241	98.37 250	94.32 266	97.33 212	98.86 179	90.23 221	99.16 259	96.81 190	98.25 223	99.36 147

Effi-MVS+		97.12 171	96.69 179	98.39 133	98.19 250	96.72 139	97.37 396	98.43 226	93.71 301	97.65 199	98.02 276	92.20 139	99.25 233	96.87 186	97.79 243	99.19 195

CHOSEN 1792x2688		97.12 171	96.80 169	98.08 172	99.30 83	94.56 286	98.05 326	99.71 1	93.57 316	97.09 224	98.91 172	88.17 284	99.89 68	96.87 186	99.56 106	99.81 25

F-COLMAP		97.09 173	96.80 169	97.97 191	99.45 61	94.95 265	98.55 238	98.62 163	93.02 342	96.17 274	98.58 221	94.01 104	99.81 102	93.95 304	98.90 160	99.14 205

RRT-MVS		97.03 174	96.78 173	97.77 210	97.90 296	94.34 295	99.12 64	98.35 255	95.87 156	98.06 147	98.70 208	86.45 323	99.63 160	98.04 93	98.54 186	99.35 148

TAMVS		97.02 175	96.79 171	97.70 217	98.06 272	95.31 242	98.52 241	98.31 270	93.95 284	97.05 229	98.61 216	93.49 111	98.52 353	95.33 246	97.81 242	99.29 167

viewmambaseed2359dif		97.01 176	96.84 166	97.51 235	98.19 250	94.21 303	98.16 308	98.23 291	93.61 314	97.78 180	99.13 118	90.79 201	99.18 254	97.24 163	98.40 209	99.15 202

dtuplus		97.00 177	96.83 168	97.51 235	98.18 256	94.21 303	98.21 293	98.20 295	94.42 264	97.66 197	99.22 90	90.18 222	99.17 257	97.01 170	98.36 213	99.13 207

CDS-MVSNet		96.99 178	96.69 179	97.90 195	98.05 274	95.98 179	98.20 297	98.33 261	93.67 308	96.95 231	98.49 231	93.54 110	98.42 364	95.24 253	97.74 247	99.31 159
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
casdiffseed414692147		96.97 179	96.55 187	98.25 143	98.26 235	96.28 166	98.93 114	98.33 261	94.99 224	96.87 238	99.09 135	88.97 262	99.07 281	95.70 235	97.77 245	99.39 138

CANet_DTU		96.96 180	96.55 187	98.21 147	98.17 260	96.07 176	97.98 335	98.21 293	97.24 74	97.13 222	98.93 166	86.88 315	99.91 56	95.00 259	99.37 135	98.66 275

114514_t		96.93 181	96.27 201	98.92 79	99.50 48	97.63 83	98.85 147	98.90 73	84.80 479	97.77 181	99.11 125	92.84 119	99.66 153	94.85 262	99.77 41	99.47 116

MAR-MVS		96.91 182	96.40 195	98.45 124	98.69 169	96.90 130	98.66 209	98.68 145	92.40 368	97.07 227	97.96 283	91.54 165	99.75 132	93.68 312	98.92 159	98.69 269
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		96.90 183	96.49 192	98.14 159	99.33 74	95.56 220	97.38 394	99.65 2	92.34 369	97.61 202	98.20 263	89.29 248	99.10 277	96.97 173	97.60 252	99.77 40

Vis-MVSNet (Re-imp)		96.87 184	96.55 187	97.83 202	98.73 161	95.46 227	99.20 48	98.30 277	94.96 228	96.60 254	98.87 177	90.05 223	98.59 348	93.67 314	98.60 179	99.46 121

SDMVSNet		96.85 185	96.42 193	98.14 159	99.30 83	96.38 159	99.21 45	99.23 27	95.92 151	95.96 281	98.76 202	85.88 335	99.44 203	97.93 98	95.59 316	98.60 280

PAPR		96.84 186	96.24 203	98.65 98	98.72 165	96.92 129	97.36 398	98.57 178	93.33 326	96.67 249	97.57 324	94.30 98	99.56 174	91.05 395	98.59 180	99.47 116

HY-MVS	93.96 8	96.82 187	96.23 204	98.57 105	98.46 194	97.00 125	98.14 312	98.21 293	93.95 284	96.72 248	97.99 280	91.58 160	99.76 130	94.51 283	96.54 288	98.95 237

mamba_0408		96.81 188	96.38 196	98.09 171	98.19 250	95.90 193	95.69 472	98.32 265	94.51 257	96.75 245	98.73 204	90.99 194	99.27 228	95.83 225	98.43 200	99.10 213

UGNet		96.78 189	96.30 200	98.19 153	98.24 240	95.89 198	98.88 131	98.93 65	97.39 61	96.81 242	97.84 296	82.60 389	99.90 64	96.53 201	99.49 117	98.79 252
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		96.74 190	96.64 183	97.05 266	97.99 283	92.82 362	98.45 256	98.27 280	95.16 207	97.30 213	98.79 190	91.53 166	99.06 283	94.74 267	97.54 256	99.27 175

IMVS_0403		96.74 190	96.61 184	97.12 260	97.99 283	92.82 362	98.47 254	98.27 280	95.16 207	97.13 222	98.79 190	91.44 169	99.26 229	94.74 267	97.54 256	99.27 175

PVSNet_BlendedMVS		96.73 192	96.60 185	97.12 260	99.25 96	95.35 239	98.26 288	99.26 16	94.28 267	97.94 165	97.46 331	92.74 121	99.81 102	96.88 183	93.32 354	96.20 436

SSM_04072		96.71 193	96.38 196	97.68 220	98.19 250	95.90 193	95.69 472	98.32 265	94.51 257	96.75 245	98.73 204	90.99 194	98.02 420	95.83 225	98.43 200	99.10 213

test_vis1_n_1920		96.71 193	96.84 166	96.31 342	99.11 123	89.74 430	99.05 76	98.58 176	98.08 24	99.87 4	99.37 56	78.48 428	99.93 34	99.29 27	99.69 71	99.27 175

mvs_anonymous		96.70 195	96.53 190	97.18 254	98.19 250	93.78 316	98.31 279	98.19 298	94.01 280	94.47 313	98.27 257	92.08 144	98.46 359	97.39 158	97.91 238	99.31 159

Elysia		96.64 196	96.02 213	98.51 115	98.04 276	97.30 102	98.74 181	98.60 164	95.04 219	97.91 169	98.84 181	83.59 384	99.48 196	94.20 295	99.25 143	98.75 261

StellarMVS		96.64 196	96.02 213	98.51 115	98.04 276	97.30 102	98.74 181	98.60 164	95.04 219	97.91 169	98.84 181	83.59 384	99.48 196	94.20 295	99.25 143	98.75 261

1112_ss		96.63 198	96.00 215	98.50 118	98.56 182	96.37 160	98.18 305	98.10 320	92.92 346	94.84 301	98.43 235	92.14 140	99.58 170	94.35 288	96.51 289	99.56 100

PMMVS		96.60 199	96.33 199	97.41 242	97.90 296	93.93 312	97.35 399	98.41 232	92.84 350	97.76 182	97.45 333	91.10 189	99.20 251	96.26 210	97.91 238	99.11 211

DP-MVS		96.59 200	95.93 218	98.57 105	99.34 71	96.19 170	98.70 196	98.39 241	89.45 440	94.52 311	99.35 62	91.85 150	99.85 84	92.89 339	98.88 162	99.68 75

PatchMatch-RL		96.59 200	96.03 212	98.27 139	99.31 79	96.51 152	97.91 344	99.06 47	93.72 300	96.92 235	98.06 273	88.50 277	99.65 154	91.77 377	99.00 157	98.66 275

GeoE		96.58 202	96.07 209	98.10 170	98.35 212	95.89 198	99.34 17	98.12 314	93.12 338	96.09 275	98.87 177	89.71 233	98.97 299	92.95 335	98.08 232	99.43 130

icg_test_0407_2		96.56 203	96.50 191	96.73 291	97.99 283	92.82 362	97.18 417	98.27 280	95.16 207	97.30 213	98.79 190	91.53 166	98.10 405	94.74 267	97.54 256	99.27 175

XVG-OURS		96.55 204	96.41 194	96.99 269	98.75 160	93.76 317	97.50 385	98.52 191	95.67 167	96.83 239	99.30 74	88.95 264	99.53 183	95.88 223	96.26 303	97.69 323

FIs		96.51 205	96.12 208	97.67 222	97.13 360	97.54 88	99.36 14	99.22 32	95.89 153	94.03 342	98.35 245	91.98 146	98.44 362	96.40 206	92.76 362	97.01 342

XVG-OURS-SEG-HR		96.51 205	96.34 198	97.02 268	98.77 159	93.76 317	97.79 363	98.50 199	95.45 187	96.94 232	99.09 135	87.87 295	99.55 181	96.76 195	95.83 315	97.74 320

PS-MVSNAJss		96.43 207	96.26 202	96.92 280	95.84 431	95.08 255	99.16 56	98.50 199	95.87 156	93.84 353	98.34 249	94.51 91	98.61 344	96.88 183	93.45 349	97.06 340

test_fmvs1		96.42 208	96.67 181	95.66 379	98.82 156	88.53 457	98.80 164	98.20 295	96.39 126	99.64 31	99.20 95	80.35 414	99.67 150	99.04 32	99.57 98	98.78 256

FC-MVSNet-test		96.42 208	96.05 210	97.53 234	96.95 369	97.27 106	99.36 14	99.23 27	95.83 158	93.93 345	98.37 243	92.00 145	98.32 383	96.02 219	92.72 363	97.00 343

ab-mvs		96.42 208	95.71 229	98.55 108	98.63 178	96.75 137	97.88 351	98.74 129	93.84 290	96.54 259	98.18 265	85.34 346	99.75 132	95.93 221	96.35 293	99.15 202

FA-MVS(test-final)		96.41 211	95.94 217	97.82 204	98.21 246	95.20 247	97.80 361	97.58 370	93.21 332	97.36 211	97.70 308	89.47 239	99.56 174	94.12 299	97.99 235	98.71 267

PVSNet	91.96 18	96.35 212	96.15 205	96.96 275	99.17 111	92.05 382	96.08 464	98.68 145	93.69 304	97.75 184	97.80 302	88.86 266	99.69 148	94.26 293	99.01 155	99.15 202

Test_1112_low_res		96.34 213	95.66 234	98.36 134	98.56 182	95.94 187	97.71 369	98.07 327	92.10 379	94.79 305	97.29 347	91.75 154	99.56 174	94.17 297	96.50 290	99.58 98

viewdifsd2359ckpt11		96.30 214	96.13 206	96.81 286	98.10 266	92.10 378	98.49 252	98.40 235	96.02 145	97.61 202	99.31 71	86.37 325	99.29 224	97.52 141	93.36 353	99.04 226

viewmsd2359difaftdt		96.30 214	96.13 206	96.81 286	98.10 266	92.10 378	98.49 252	98.40 235	96.02 145	97.61 202	99.31 71	86.37 325	99.30 221	97.52 141	93.37 352	99.04 226

Effi-MVS+-dtu		96.29 216	96.56 186	95.51 384	97.89 298	90.22 422	98.80 164	98.10 320	96.57 116	96.45 264	96.66 405	90.81 197	98.91 312	95.72 232	97.99 235	97.40 331

QAPM		96.29 216	95.40 240	98.96 76	97.85 299	97.60 85	99.23 38	98.93 65	89.76 434	93.11 385	99.02 148	89.11 254	99.93 34	91.99 370	99.62 89	99.34 150

Fast-Effi-MVS+		96.28 218	95.70 231	98.03 179	98.29 230	95.97 184	98.58 225	98.25 289	91.74 387	95.29 294	97.23 352	91.03 191	99.15 263	92.90 337	97.96 237	98.97 234

nrg030		96.28 218	95.72 226	97.96 193	96.90 374	98.15 64	99.39 11	98.31 270	95.47 186	94.42 319	98.35 245	92.09 143	98.69 336	97.50 145	89.05 415	97.04 341

1314		96.25 220	95.73 225	97.79 206	97.13 360	95.55 222	98.19 300	98.59 171	93.47 320	92.03 421	97.82 300	91.33 173	99.49 191	94.62 277	98.44 197	98.32 300

sd_testset		96.17 221	95.76 224	97.42 241	99.30 83	94.34 295	98.82 155	99.08 45	95.92 151	95.96 281	98.76 202	82.83 388	99.32 216	95.56 239	95.59 316	98.60 280

h-mvs33		96.17 221	95.62 235	97.81 205	99.03 130	94.45 288	98.64 212	98.75 127	97.48 54	98.67 105	98.72 207	89.76 230	99.86 83	97.95 96	81.59 469	99.11 211

HQP_MVS		96.14 223	95.90 219	96.85 283	97.42 338	94.60 284	98.80 164	98.56 182	97.28 69	95.34 290	98.28 254	87.09 310	99.03 290	96.07 214	94.27 324	96.92 350

tttt0517		96.07 224	95.51 238	97.78 207	98.41 202	94.84 269	99.28 30	94.33 489	94.26 269	97.64 200	98.64 215	84.05 375	99.47 200	95.34 245	97.60 252	99.03 228

MVSTER		96.06 225	95.72 226	97.08 264	98.23 243	95.93 190	98.73 187	98.27 280	94.86 234	95.07 296	98.09 271	88.21 283	98.54 351	96.59 197	93.46 347	96.79 369

thisisatest0530		96.01 226	95.36 245	97.97 191	98.38 207	95.52 224	98.88 131	94.19 493	94.04 275	97.64 200	98.31 252	83.82 382	99.46 201	95.29 250	97.70 249	98.93 239

test_djsdf		96.00 227	95.69 232	96.93 277	95.72 434	95.49 225	99.47 7	98.40 235	94.98 226	94.58 309	97.86 293	89.16 252	98.41 371	96.91 177	94.12 332	96.88 359

EI-MVSNet		95.96 228	95.83 221	96.36 338	97.93 294	93.70 323	98.12 315	98.27 280	93.70 303	95.07 296	99.02 148	92.23 136	98.54 351	94.68 272	93.46 347	96.84 365

VortexMVS		95.95 229	95.79 222	96.42 333	98.29 230	93.96 311	98.68 202	98.31 270	96.02 145	94.29 327	97.57 324	89.47 239	98.37 378	97.51 144	91.93 372	96.94 348

ECVR-MVS		95.95 229	95.71 229	96.65 300	99.02 131	90.86 404	99.03 83	91.80 506	96.96 93	98.10 142	99.26 80	81.31 400	99.51 187	96.90 180	99.04 152	99.59 94

BH-untuned		95.95 229	95.72 226	96.65 300	98.55 184	92.26 373	98.23 291	97.79 355	93.73 298	94.62 308	98.01 278	88.97 262	99.00 297	93.04 332	98.51 189	98.68 271

test1111		95.94 232	95.78 223	96.41 334	98.99 138	90.12 423	99.04 80	92.45 505	96.99 92	98.03 152	99.27 79	81.40 399	99.48 196	96.87 186	99.04 152	99.63 88

MSDG		95.93 233	95.30 252	97.83 202	98.90 145	95.36 237	96.83 450	98.37 250	91.32 403	94.43 318	98.73 204	90.27 219	99.60 166	90.05 409	98.82 169	98.52 288

BH-RMVSNet		95.92 234	95.32 250	97.69 218	98.32 224	94.64 278	98.19 300	97.45 391	94.56 252	96.03 277	98.61 216	85.02 351	99.12 271	90.68 400	99.06 151	99.30 164

test_fmvs1_n		95.90 235	95.99 216	95.63 380	98.67 172	88.32 461	99.26 33	98.22 292	96.40 125	99.67 28	99.26 80	73.91 470	99.70 143	99.02 33	99.50 115	98.87 244

Fast-Effi-MVS+-dtu		95.87 236	95.85 220	95.91 364	97.74 308	91.74 388	98.69 199	98.15 310	95.56 174	94.92 299	97.68 313	88.98 261	98.79 330	93.19 326	97.78 244	97.20 338

LFMVS		95.86 237	94.98 268	98.47 122	98.87 150	96.32 163	98.84 151	96.02 463	93.40 324	98.62 112	99.20 95	74.99 462	99.63 160	97.72 115	97.20 265	99.46 121

baseline1		95.84 238	95.12 260	98.01 183	98.49 191	95.98 179	98.73 187	97.03 427	95.37 195	96.22 270	98.19 264	89.96 226	99.16 259	94.60 279	87.48 432	98.90 242

OpenMVS	93.04 13	95.83 239	95.00 266	98.32 136	97.18 357	97.32 99	99.21 45	98.97 57	89.96 430	91.14 431	99.05 145	86.64 318	99.92 43	93.38 320	99.47 121	97.73 321

IMVS_0404		95.82 240	95.52 236	96.73 291	97.99 283	92.82 362	97.23 408	98.27 280	95.16 207	94.31 325	98.79 190	85.63 339	98.10 405	94.74 267	97.54 256	99.27 175

VDD-MVS		95.82 240	95.23 254	97.61 230	98.84 155	93.98 310	98.68 202	97.40 395	95.02 223	97.95 163	99.34 68	74.37 468	99.78 124	98.64 48	96.80 278	99.08 220

UniMVSNet (Re)		95.78 242	95.19 256	97.58 231	96.99 367	97.47 92	98.79 172	99.18 36	95.60 170	93.92 346	97.04 374	91.68 156	98.48 355	95.80 229	87.66 431	96.79 369

VPA-MVSNet		95.75 243	95.11 261	97.69 218	97.24 349	97.27 106	98.94 108	99.23 27	95.13 212	95.51 288	97.32 345	85.73 337	98.91 312	97.33 161	89.55 406	96.89 358

HQP-MVS		95.72 244	95.40 240	96.69 297	97.20 353	94.25 301	98.05 326	98.46 207	96.43 121	94.45 314	97.73 305	86.75 316	98.96 303	95.30 248	94.18 328	96.86 364

hse-mvs2		95.71 245	95.30 252	96.93 277	98.50 187	93.53 328	98.36 270	98.10 320	97.48 54	98.67 105	97.99 280	89.76 230	99.02 294	97.95 96	80.91 475	98.22 303

UniMVSNet_NR-MVSNet		95.71 245	95.15 257	97.40 244	96.84 377	96.97 126	98.74 181	99.24 20	95.16 207	93.88 348	97.72 307	91.68 156	98.31 385	95.81 227	87.25 437	96.92 350

PatchmatchNet		95.71 245	95.52 236	96.29 344	97.58 321	90.72 408	96.84 449	97.52 381	94.06 274	97.08 225	96.96 384	89.24 250	98.90 315	92.03 369	98.37 211	99.26 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 248	95.33 249	96.76 290	96.16 414	94.63 279	98.43 264	98.39 241	96.64 112	95.02 298	98.78 194	85.15 350	99.05 284	95.21 255	94.20 327	96.60 395
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 248	95.38 244	96.61 308	97.61 318	93.84 315	98.91 119	98.44 215	95.25 203	94.28 328	98.47 233	86.04 334	99.12 271	95.50 242	93.95 337	96.87 362
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 250	95.69 232	95.44 388	97.54 326	88.54 456	96.97 432	97.56 373	93.50 318	97.52 209	96.93 389	89.49 237	99.16 259	95.25 252	96.42 292	98.64 277

FE-MVS		95.62 251	94.90 272	97.78 207	98.37 210	94.92 266	97.17 420	97.38 397	90.95 414	97.73 187	97.70 308	85.32 348	99.63 160	91.18 387	98.33 216	98.79 252

LPG-MVS_test		95.62 251	95.34 246	96.47 327	97.46 333	93.54 326	98.99 94	98.54 186	94.67 247	94.36 322	98.77 197	85.39 343	99.11 273	95.71 233	94.15 330	96.76 372

CLD-MVS		95.62 251	95.34 246	96.46 330	97.52 329	93.75 319	97.27 407	98.46 207	95.53 182	94.42 319	98.00 279	86.21 329	98.97 299	96.25 212	94.37 322	96.66 387
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 254	94.89 273	97.76 211	98.15 262	95.15 251	96.77 451	94.41 487	92.95 345	97.18 221	97.43 335	84.78 357	99.45 202	94.63 275	97.73 248	98.68 271

MonoMVSNet		95.51 255	95.45 239	95.68 377	95.54 440	90.87 403	98.92 117	97.37 398	95.79 160	95.53 287	97.38 340	89.58 236	97.68 444	96.40 206	92.59 364	98.49 290

thres600view7		95.49 256	94.77 276	97.67 222	98.98 139	95.02 257	98.85 147	96.90 438	95.38 193	96.63 251	96.90 391	84.29 367	99.59 167	88.65 433	96.33 294	98.40 294

test_vis1_n		95.47 257	95.13 258	96.49 324	97.77 304	90.41 418	99.27 32	98.11 317	96.58 114	99.66 29	99.18 105	67.00 485	99.62 164	99.21 28	99.40 131	99.44 126

SCA		95.46 258	95.13 258	96.46 330	97.67 313	91.29 396	97.33 401	97.60 369	94.68 246	96.92 235	97.10 359	83.97 377	98.89 316	92.59 353	98.32 219	99.20 191

IterMVS-LS		95.46 258	95.21 255	96.22 346	98.12 264	93.72 322	98.32 278	98.13 313	93.71 301	94.26 329	97.31 346	92.24 135	98.10 405	94.63 275	90.12 397	96.84 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 260	95.34 246	95.77 375	98.69 169	88.75 452	98.87 134	97.21 412	96.13 138	97.22 219	97.68 313	77.95 436	99.65 154	97.58 132	96.77 281	98.91 241

jajsoiax		95.45 260	95.03 265	96.73 291	95.42 448	94.63 279	99.14 60	98.52 191	95.74 162	93.22 378	98.36 244	83.87 380	98.65 341	96.95 175	94.04 333	96.91 355

CVMVSNet		95.43 262	96.04 211	93.57 442	97.93 294	83.62 485	98.12 315	98.59 171	95.68 166	96.56 255	99.02 148	87.51 302	97.51 453	93.56 318	97.44 261	99.60 92

anonymousdsp		95.42 263	94.91 271	96.94 276	95.10 452	95.90 193	99.14 60	98.41 232	93.75 295	93.16 381	97.46 331	87.50 304	98.41 371	95.63 238	94.03 334	96.50 420

DU-MVS		95.42 263	94.76 277	97.40 244	96.53 394	96.97 126	98.66 209	98.99 56	95.43 188	93.88 348	97.69 310	88.57 272	98.31 385	95.81 227	87.25 437	96.92 350

mvs_tets		95.41 265	95.00 266	96.65 300	95.58 439	94.42 290	99.00 91	98.55 184	95.73 164	93.21 379	98.38 242	83.45 386	98.63 342	97.09 168	94.00 335	96.91 355

thres100view900		95.38 266	94.70 281	97.41 242	98.98 139	94.92 266	98.87 134	96.90 438	95.38 193	96.61 253	96.88 392	84.29 367	99.56 174	88.11 437	96.29 298	97.76 318

thres400		95.38 266	94.62 285	97.65 226	98.94 143	94.98 262	98.68 202	96.93 436	95.33 197	96.55 257	96.53 411	84.23 371	99.56 174	88.11 437	96.29 298	98.40 294

BH-w/o		95.38 266	95.08 263	96.26 345	98.34 217	91.79 385	97.70 370	97.43 393	92.87 349	94.24 331	97.22 353	88.66 270	98.84 322	91.55 383	97.70 249	98.16 307

VDDNet		95.36 269	94.53 290	97.86 200	98.10 266	95.13 252	98.85 147	97.75 357	90.46 421	98.36 131	99.39 50	73.27 472	99.64 157	97.98 95	96.58 286	98.81 250

TAPA-MVS	93.98 7	95.35 270	94.56 289	97.74 213	99.13 119	94.83 271	98.33 274	98.64 158	86.62 466	96.29 268	98.61 216	94.00 105	99.29 224	80.00 485	99.41 128	99.09 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 271	94.98 268	96.43 332	97.67 313	93.48 330	98.73 187	98.44 215	94.94 232	92.53 402	98.53 226	84.50 366	99.14 266	95.48 243	94.00 335	96.66 387
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 272	94.87 274	96.71 294	99.29 88	93.24 349	98.58 225	98.11 317	89.92 431	93.57 363	99.10 127	86.37 325	99.79 121	90.78 398	98.10 231	97.09 339
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 273	94.72 280	97.13 258	98.05 274	93.26 346	97.87 352	97.20 415	94.96 228	96.18 273	95.66 449	80.97 406	99.35 212	94.47 285	97.08 268	98.78 256

tfpn200view9		95.32 273	94.62 285	97.43 240	98.94 143	94.98 262	98.68 202	96.93 436	95.33 197	96.55 257	96.53 411	84.23 371	99.56 174	88.11 437	96.29 298	97.76 318

Anonymous202405211		95.28 275	94.49 292	97.67 222	99.00 135	93.75 319	98.70 196	97.04 426	90.66 417	96.49 261	98.80 188	78.13 432	99.83 90	96.21 213	95.36 320	99.44 126

thres200		95.25 276	94.57 288	97.28 248	98.81 157	94.92 266	98.20 297	97.11 419	95.24 205	96.54 259	96.22 426	84.58 364	99.53 183	87.93 443	96.50 290	97.39 332

AllTest		95.24 277	94.65 284	96.99 269	99.25 96	93.21 350	98.59 221	98.18 301	91.36 399	93.52 365	98.77 197	84.67 361	99.72 137	89.70 416	97.87 240	98.02 312

LCM-MVSNet-Re		95.22 278	95.32 250	94.91 406	98.18 256	87.85 467	98.75 177	95.66 470	95.11 214	88.96 455	96.85 395	90.26 220	97.65 445	95.65 237	98.44 197	99.22 188

EPNet_dtu		95.21 279	94.95 270	95.99 357	96.17 412	90.45 416	98.16 308	97.27 407	96.77 102	93.14 384	98.33 250	90.34 216	98.42 364	85.57 459	98.81 170	99.09 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 280	94.45 298	97.46 237	96.75 384	96.56 150	98.86 142	98.65 157	93.30 329	93.27 377	98.27 257	84.85 355	98.87 319	94.82 264	91.26 383	96.96 345

D2MVS		95.18 281	95.08 263	95.48 385	97.10 362	92.07 381	98.30 282	99.13 43	94.02 277	92.90 389	96.73 401	89.48 238	98.73 334	94.48 284	93.60 346	95.65 451

WR-MVS		95.15 282	94.46 295	97.22 250	96.67 389	96.45 154	98.21 293	98.81 107	94.15 271	93.16 381	97.69 310	87.51 302	98.30 387	95.29 250	88.62 421	96.90 357

TranMVSNet+NR-MVSNet		95.14 283	94.48 293	97.11 262	96.45 401	96.36 161	99.03 83	99.03 50	95.04 219	93.58 362	97.93 286	88.27 282	98.03 419	94.13 298	86.90 442	96.95 347

myMVS_eth3d28		95.12 284	94.62 285	96.64 304	98.17 260	92.17 374	98.02 330	97.32 401	95.41 191	96.22 270	96.05 432	78.01 434	99.13 268	95.22 254	97.16 266	98.60 280

baseline2		95.11 285	94.52 291	96.87 282	96.65 390	93.56 325	98.27 287	94.10 495	93.45 321	92.02 422	97.43 335	87.45 307	99.19 252	93.88 307	97.41 263	97.87 316

miper_enhance_ethall		95.10 286	94.75 278	96.12 350	97.53 328	93.73 321	96.61 457	98.08 325	92.20 377	93.89 347	96.65 407	92.44 126	98.30 387	94.21 294	91.16 384	96.34 429

Anonymous20240529		95.10 286	94.22 310	97.75 212	99.01 133	94.26 300	98.87 134	98.83 98	85.79 474	96.64 250	98.97 156	78.73 425	99.85 84	96.27 209	94.89 321	99.12 208

test-LLR		95.10 286	94.87 274	95.80 372	96.77 381	89.70 432	96.91 438	95.21 476	95.11 214	94.83 303	95.72 445	87.71 297	98.97 299	93.06 330	98.50 190	98.72 264

dtuonly		95.08 289	95.10 262	95.02 402	96.53 394	87.27 471	96.33 463	97.21 412	93.41 323	96.28 269	98.51 230	87.71 297	98.99 298	91.88 374	98.01 234	98.80 251

WR-MVS_H		95.05 290	94.46 295	96.81 286	96.86 376	95.82 206	99.24 36	99.24 20	93.87 289	92.53 402	96.84 396	90.37 215	98.24 393	93.24 324	87.93 427	96.38 428

miper_ehance_all_eth		95.01 291	94.69 282	95.97 361	97.70 311	93.31 342	97.02 430	98.07 327	92.23 374	93.51 367	96.96 384	91.85 150	98.15 400	93.68 312	91.16 384	96.44 426

testing11		95.00 292	94.28 305	97.16 256	97.96 291	93.36 339	98.09 322	97.06 425	94.94 232	95.33 293	96.15 428	76.89 449	99.40 207	95.77 231	96.30 297	98.72 264

ADS-MVSNet		95.00 292	94.45 298	96.63 305	98.00 281	91.91 384	96.04 465	97.74 358	90.15 427	96.47 262	96.64 408	87.89 293	98.96 303	90.08 407	97.06 269	99.02 229

VPNet		94.99 294	94.19 312	97.40 244	97.16 358	96.57 149	98.71 192	98.97 57	95.67 167	94.84 301	98.24 261	80.36 413	98.67 340	96.46 203	87.32 436	96.96 345

EPMVS		94.99 294	94.48 293	96.52 321	97.22 351	91.75 387	97.23 408	91.66 507	94.11 272	97.28 215	96.81 398	85.70 338	98.84 322	93.04 332	97.28 264	98.97 234

testing91		94.98 296	94.25 309	97.20 251	97.94 292	93.41 333	98.00 333	97.58 370	94.99 224	95.45 289	96.04 433	77.20 444	99.42 205	94.97 260	96.02 311	98.78 256

NR-MVSNet		94.98 296	94.16 315	97.44 239	96.53 394	97.22 114	98.74 181	98.95 61	94.96 228	89.25 453	97.69 310	89.32 247	98.18 397	94.59 281	87.40 434	96.92 350

FMVSNet3		94.97 298	94.26 308	97.11 262	98.18 256	96.62 141	98.56 237	98.26 288	93.67 308	94.09 338	97.10 359	84.25 369	98.01 421	92.08 365	92.14 369	96.70 381

usedtu_dtu_shiyan1		94.96 299	94.28 305	96.98 272	95.93 425	96.11 174	97.08 426	98.39 241	93.62 312	93.86 350	96.40 417	88.28 280	98.21 394	92.61 348	92.36 367	96.63 389

FE-MVSNET3		94.96 299	94.28 305	96.98 272	95.93 425	96.11 174	97.08 426	98.39 241	93.62 312	93.86 350	96.40 417	88.28 280	98.21 394	92.61 348	92.36 367	96.63 389

CostFormer		94.95 301	94.73 279	95.60 382	97.28 347	89.06 445	97.53 382	96.89 440	89.66 436	96.82 241	96.72 402	86.05 332	98.95 308	95.53 241	96.13 309	98.79 252

PAPM		94.95 301	94.00 328	97.78 207	97.04 364	95.65 215	96.03 467	98.25 289	91.23 408	94.19 334	97.80 302	91.27 176	98.86 321	82.61 476	97.61 251	98.84 247

CP-MVSNet		94.94 303	94.30 304	96.83 284	96.72 386	95.56 220	99.11 66	98.95 61	93.89 287	92.42 408	97.90 289	87.19 309	98.12 404	94.32 290	88.21 424	96.82 368

TR-MVS		94.94 303	94.20 311	97.17 255	97.75 305	94.14 307	97.59 379	97.02 430	92.28 373	95.75 285	97.64 318	83.88 379	98.96 303	89.77 413	96.15 308	98.40 294

RPSCF		94.87 305	95.40 240	93.26 448	98.89 146	82.06 492	98.33 274	98.06 332	90.30 426	96.56 255	99.26 80	87.09 310	99.49 191	93.82 309	96.32 295	98.24 301

testing99		94.83 306	94.08 320	97.07 265	97.94 292	93.13 352	98.10 321	97.17 417	94.86 234	95.34 290	96.00 437	76.31 452	99.40 207	95.08 257	95.90 312	98.68 271

GA-MVS		94.81 307	94.03 324	97.14 257	97.15 359	93.86 314	96.76 452	97.58 370	94.00 281	94.76 307	97.04 374	80.91 407	98.48 355	91.79 376	96.25 304	99.09 216

c3_l		94.79 308	94.43 300	95.89 366	97.75 305	93.12 354	97.16 422	98.03 334	92.23 374	93.46 371	97.05 373	91.39 170	98.01 421	93.58 317	89.21 413	96.53 411

V42		94.78 309	94.14 317	96.70 296	96.33 406	95.22 246	98.97 98	98.09 324	92.32 371	94.31 325	97.06 370	88.39 278	98.55 350	92.90 337	88.87 419	96.34 429

reproduce_monomvs		94.77 310	94.67 283	95.08 400	98.40 204	89.48 438	98.80 164	98.64 158	97.57 48	93.21 379	97.65 315	80.57 412	98.83 325	97.72 115	89.47 409	96.93 349

CR-MVSNet		94.76 311	94.15 316	96.59 311	97.00 365	93.43 331	94.96 485	97.56 373	92.46 362	96.93 233	96.24 422	88.15 285	97.88 435	87.38 446	96.65 284	98.46 292

v2v482		94.69 312	94.03 324	96.65 300	96.17 412	94.79 274	98.67 207	98.08 325	92.72 353	94.00 343	97.16 356	87.69 301	98.45 360	92.91 336	88.87 419	96.72 377

pmmvs4		94.69 312	93.99 330	96.81 286	95.74 433	95.94 187	97.40 392	97.67 362	90.42 423	93.37 374	97.59 322	89.08 255	98.20 396	92.97 334	91.67 377	96.30 432

cl22		94.68 314	94.19 312	96.13 349	98.11 265	93.60 324	96.94 434	98.31 270	92.43 366	93.32 376	96.87 394	86.51 319	98.28 391	94.10 301	91.16 384	96.51 418

eth_miper_zixun_eth		94.68 314	94.41 301	95.47 386	97.64 316	91.71 389	96.73 454	98.07 327	92.71 354	93.64 359	97.21 354	90.54 208	98.17 398	93.38 320	89.76 401	96.54 409

PCF-MVS	93.45 11	94.68 314	93.43 366	98.42 130	98.62 179	96.77 136	95.48 478	98.20 295	84.63 480	93.34 375	98.32 251	88.55 275	99.81 102	84.80 468	98.96 158	98.68 271
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 317	93.54 361	98.08 172	96.88 375	96.56 150	98.19 300	98.50 199	78.05 498	92.69 396	98.02 276	91.07 190	99.63 160	90.09 406	98.36 213	98.04 311

PS-CasMVS		94.67 317	93.99 330	96.71 294	96.68 388	95.26 243	99.13 63	99.03 50	93.68 306	92.33 412	97.95 284	85.35 345	98.10 405	93.59 316	88.16 426	96.79 369

cascas		94.63 319	93.86 340	96.93 277	96.91 373	94.27 299	96.00 468	98.51 194	85.55 476	94.54 310	96.23 424	84.20 373	98.87 319	95.80 229	96.98 274	97.66 324

tpmvs		94.60 320	94.36 303	95.33 392	97.46 333	88.60 455	96.88 446	97.68 359	91.29 405	93.80 355	96.42 416	88.58 271	99.24 241	91.06 393	96.04 310	98.17 306

LTVRE_ROB	92.95 15	94.60 320	93.90 336	96.68 298	97.41 341	94.42 290	98.52 241	98.59 171	91.69 390	91.21 430	98.35 245	84.87 354	99.04 287	91.06 393	93.44 350	96.60 395
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		94.59 322	93.92 333	96.60 310	96.21 408	94.78 275	98.59 221	98.14 312	91.86 386	94.21 333	97.02 377	87.97 291	98.41 371	91.72 378	89.57 404	96.61 393

ADS-MVSNet2		94.58 323	94.40 302	95.11 398	98.00 281	88.74 453	96.04 465	97.30 403	90.15 427	96.47 262	96.64 408	87.89 293	97.56 451	90.08 407	97.06 269	99.02 229

WBMVS		94.56 324	94.04 322	96.10 351	98.03 278	93.08 356	97.82 360	98.18 301	94.02 277	93.77 357	96.82 397	81.28 401	98.34 380	95.47 244	91.00 387	96.88 359

ACMH	92.88 16	94.55 325	93.95 332	96.34 340	97.63 317	93.26 346	98.81 163	98.49 204	93.43 322	89.74 447	98.53 226	81.91 393	99.08 280	93.69 311	93.30 355	96.70 381
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 326	93.85 341	96.63 305	97.98 289	93.06 357	98.77 176	97.84 346	93.67 308	93.80 355	98.04 275	76.88 450	98.96 303	94.79 266	92.86 360	97.86 317

XVG-ACMP-BASELINE		94.54 326	94.14 317	95.75 376	96.55 393	91.65 390	98.11 319	98.44 215	94.96 228	94.22 332	97.90 289	79.18 423	99.11 273	94.05 303	93.85 339	96.48 423

AUN-MVS		94.53 328	93.73 351	96.92 280	98.50 187	93.52 329	98.34 273	98.10 320	93.83 292	95.94 283	97.98 282	85.59 341	99.03 290	94.35 288	80.94 474	98.22 303

DIV-MVS_self_test		94.52 329	94.03 324	95.99 357	97.57 325	93.38 337	97.05 428	97.94 340	91.74 387	92.81 391	97.10 359	89.12 253	98.07 413	92.60 351	90.30 394	96.53 411

cl____		94.51 330	94.01 327	96.02 353	97.58 321	93.40 336	97.05 428	97.96 339	91.73 389	92.76 393	97.08 365	89.06 256	98.13 402	92.61 348	90.29 395	96.52 414

ETVMVS		94.50 331	93.44 365	97.68 220	98.18 256	95.35 239	98.19 300	97.11 419	93.73 298	96.40 265	95.39 452	74.53 465	98.84 322	91.10 389	96.31 296	98.84 247

GBi-Net		94.49 332	93.80 344	96.56 315	98.21 246	95.00 258	98.82 155	98.18 301	92.46 362	94.09 338	97.07 366	81.16 402	97.95 426	92.08 365	92.14 369	96.72 377

test1		94.49 332	93.80 344	96.56 315	98.21 246	95.00 258	98.82 155	98.18 301	92.46 362	94.09 338	97.07 366	81.16 402	97.95 426	92.08 365	92.14 369	96.72 377

dmvs_re		94.48 334	94.18 314	95.37 390	97.68 312	90.11 424	98.54 240	97.08 421	94.56 252	94.42 319	97.24 351	84.25 369	97.76 441	91.02 396	92.83 361	98.24 301

v8		94.47 335	93.77 347	96.57 314	96.36 404	94.83 271	99.05 76	98.19 298	91.92 383	93.16 381	96.97 382	88.82 269	98.48 355	91.69 379	87.79 428	96.39 427

FMVSNet2		94.47 335	93.61 357	97.04 267	98.21 246	96.43 156	98.79 172	98.27 280	92.46 362	93.50 368	97.09 363	81.16 402	98.00 423	91.09 390	91.93 372	96.70 381

test2506		94.44 337	93.91 335	96.04 352	99.02 131	88.99 448	99.06 74	79.47 523	96.96 93	98.36 131	99.26 80	77.21 443	99.52 186	96.78 194	99.04 152	99.59 94

Patchmatch-test		94.42 338	93.68 355	96.63 305	97.60 319	91.76 386	94.83 489	97.49 385	89.45 440	94.14 336	97.10 359	88.99 258	98.83 325	85.37 462	98.13 230	99.29 167

PEN-MVS		94.42 338	93.73 351	96.49 324	96.28 407	94.84 269	99.17 55	99.00 53	93.51 317	92.23 414	97.83 299	86.10 331	97.90 430	92.55 356	86.92 441	96.74 374

v144192		94.39 340	93.70 353	96.48 326	96.06 418	94.35 294	98.58 225	98.16 309	91.45 396	94.33 324	97.02 377	87.50 304	98.45 360	91.08 392	89.11 414	96.63 389

Baseline_NR-MVSNet		94.35 341	93.81 343	95.96 362	96.20 409	94.05 309	98.61 220	96.67 451	91.44 397	93.85 352	97.60 321	88.57 272	98.14 401	94.39 286	86.93 440	95.68 450

miper_lstm_enhance		94.33 342	94.07 321	95.11 398	97.75 305	90.97 400	97.22 410	98.03 334	91.67 391	92.76 393	96.97 382	90.03 225	97.78 440	92.51 358	89.64 403	96.56 406

v1192		94.32 343	93.58 358	96.53 320	96.10 416	94.45 288	98.50 249	98.17 307	91.54 394	94.19 334	97.06 370	86.95 314	98.43 363	90.14 405	89.57 404	96.70 381

UWE-MVS		94.30 344	93.89 338	95.53 383	97.83 300	88.95 449	97.52 384	93.25 498	94.44 262	96.63 251	97.07 366	78.70 426	99.28 226	91.99 370	97.56 255	98.36 297

ACMH+	92.99 14	94.30 344	93.77 347	95.88 367	97.81 302	92.04 383	98.71 192	98.37 250	93.99 282	90.60 438	98.47 233	80.86 409	99.05 284	92.75 344	92.40 366	96.55 408

v148		94.29 346	93.76 349	95.91 364	96.10 416	92.93 360	98.58 225	97.97 337	92.59 360	93.47 370	96.95 386	88.53 276	98.32 383	92.56 355	87.06 439	96.49 421

v10		94.29 346	93.55 360	96.51 322	96.39 403	94.80 273	98.99 94	98.19 298	91.35 401	93.02 387	96.99 380	88.09 287	98.41 371	90.50 402	88.41 423	96.33 431

SD_0403		94.28 348	94.46 295	93.73 439	98.02 279	85.32 480	98.31 279	98.40 235	94.75 242	93.59 360	98.16 266	89.01 257	96.54 473	82.32 477	97.58 254	99.34 150

MVP-Stereo		94.28 348	93.92 333	95.35 391	94.95 454	92.60 368	97.97 336	97.65 363	91.61 392	90.68 437	97.09 363	86.32 328	98.42 364	89.70 416	99.34 137	95.02 466
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 350	93.33 368	96.97 274	97.19 356	93.38 337	98.74 181	98.57 178	91.21 410	93.81 354	98.58 221	72.85 474	98.77 332	95.05 258	93.93 338	98.77 259

OurMVSNet-221017-0		94.21 351	94.00 328	94.85 411	95.60 438	89.22 443	98.89 124	97.43 393	95.29 200	92.18 417	98.52 229	82.86 387	98.59 348	93.46 319	91.76 375	96.74 374

v1921920		94.20 352	93.47 364	96.40 336	95.98 422	94.08 308	98.52 241	98.15 310	91.33 402	94.25 330	97.20 355	86.41 324	98.42 364	90.04 410	89.39 411	96.69 386

WB-MVSnew		94.19 353	94.04 322	94.66 419	96.82 379	92.14 375	97.86 354	95.96 466	93.50 318	95.64 286	96.77 400	88.06 289	97.99 424	84.87 465	96.86 275	93.85 488

v7n		94.19 353	93.43 366	96.47 327	95.90 428	94.38 293	99.26 33	98.34 259	91.99 381	92.76 393	97.13 358	88.31 279	98.52 353	89.48 421	87.70 429	96.52 414

tpm2		94.19 353	93.76 349	95.46 387	97.23 350	89.04 446	97.31 404	96.85 444	87.08 459	96.21 272	96.79 399	83.75 383	98.74 333	92.43 361	96.23 306	98.59 283

TESTMET0.1,1		94.18 356	93.69 354	95.63 380	96.92 371	89.12 444	96.91 438	94.78 484	93.17 334	94.88 300	96.45 415	78.52 427	98.92 310	93.09 329	98.50 190	98.85 245

dp		94.15 357	93.90 336	94.90 407	97.31 346	86.82 473	96.97 432	97.19 416	91.22 409	96.02 278	96.61 410	85.51 342	99.02 294	90.00 411	94.30 323	98.85 245

ET-MVSNet_ETH3D		94.13 358	92.98 376	97.58 231	98.22 244	96.20 168	97.31 404	95.37 474	94.53 254	79.56 498	97.63 320	86.51 319	97.53 452	96.91 177	90.74 389	99.02 229

tpm		94.13 358	93.80 344	95.12 397	96.50 397	87.91 466	97.44 388	95.89 469	92.62 358	96.37 267	96.30 421	84.13 374	98.30 387	93.24 324	91.66 378	99.14 205

testing222		94.12 360	93.03 375	97.37 247	98.02 279	94.66 276	97.94 340	96.65 453	94.63 249	95.78 284	95.76 440	71.49 475	98.92 310	91.17 388	95.88 313	98.52 288

IterMVS-SCA-FT		94.11 361	93.87 339	94.85 411	97.98 289	90.56 415	97.18 417	98.11 317	93.75 295	92.58 399	97.48 330	83.97 377	97.41 455	92.48 360	91.30 381	96.58 402

Anonymous20231211		94.10 362	93.26 371	96.61 308	99.11 123	94.28 298	99.01 89	98.88 78	86.43 468	92.81 391	97.57 324	81.66 398	98.68 339	94.83 263	89.02 417	96.88 359

IterMVS		94.09 363	93.85 341	94.80 415	97.99 283	90.35 420	97.18 417	98.12 314	93.68 306	92.46 406	97.34 342	84.05 375	97.41 455	92.51 358	91.33 380	96.62 392
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 364	93.51 362	95.80 372	96.77 381	89.70 432	96.91 438	95.21 476	92.89 348	94.83 303	95.72 445	77.69 438	98.97 299	93.06 330	98.50 190	98.72 264

test0.0.03 1		94.08 364	93.51 362	95.80 372	95.53 442	92.89 361	97.38 394	95.97 465	95.11 214	92.51 404	96.66 405	87.71 297	96.94 463	87.03 449	93.67 342	97.57 328

v1240		94.06 366	93.29 370	96.34 340	96.03 420	93.90 313	98.44 262	98.17 307	91.18 411	94.13 337	97.01 379	86.05 332	98.42 364	89.13 427	89.50 408	96.70 381

X-MVStestdata		94.06 366	92.30 392	99.34 32	99.70 27	98.35 50	99.29 28	98.88 78	97.40 59	98.46 120	43.50 546	95.90 48	99.89 68	97.85 106	99.74 57	99.78 33

DTE-MVSNet		93.98 368	93.26 371	96.14 348	96.06 418	94.39 292	99.20 48	98.86 91	93.06 340	91.78 423	97.81 301	85.87 336	97.58 450	90.53 401	86.17 446	96.46 425

pm-mvs1		93.94 369	93.06 374	96.59 311	96.49 398	95.16 249	98.95 105	98.03 334	92.32 371	91.08 432	97.84 296	84.54 365	98.41 371	92.16 363	86.13 449	96.19 437

MS-PatchMatch		93.84 370	93.63 356	94.46 429	96.18 411	89.45 439	97.76 365	98.27 280	92.23 374	92.13 419	97.49 329	79.50 420	98.69 336	89.75 414	99.38 133	95.25 458

tfpnnormal		93.66 371	92.70 382	96.55 319	96.94 370	95.94 187	98.97 98	99.19 35	91.04 412	91.38 429	97.34 342	84.94 353	98.61 344	85.45 461	89.02 417	95.11 462

EU-MVSNet		93.66 371	94.14 317	92.25 462	95.96 424	83.38 487	98.52 241	98.12 314	94.69 245	92.61 398	98.13 269	87.36 308	96.39 478	91.82 375	90.00 399	96.98 344

our_test_3		93.65 373	93.30 369	94.69 417	95.45 446	89.68 434	96.91 438	97.65 363	91.97 382	91.66 426	96.88 392	89.67 234	97.93 429	88.02 441	91.49 379	96.48 423

pmmvs5		93.65 373	92.97 377	95.68 377	95.49 443	92.37 370	98.20 297	97.28 406	89.66 436	92.58 399	97.26 348	82.14 392	98.09 409	93.18 327	90.95 388	96.58 402

SSC-MVS3.2		93.59 375	93.13 373	94.97 404	96.81 380	89.71 431	97.95 337	98.49 204	94.59 251	93.50 368	96.91 390	77.74 437	98.37 378	91.69 379	90.47 392	96.83 367

test_fmvs2		93.43 376	93.58 358	92.95 455	96.97 368	83.91 484	99.19 50	97.24 409	95.74 162	95.20 295	98.27 257	69.65 477	98.72 335	96.26 210	93.73 341	96.24 434

tpm cat1		93.36 377	92.80 379	95.07 401	97.58 321	87.97 465	96.76 452	97.86 345	82.17 487	93.53 364	96.04 433	86.13 330	99.13 268	89.24 425	95.87 314	98.10 309

JIA-IIPM		93.35 378	92.49 388	95.92 363	96.48 399	90.65 410	95.01 483	96.96 434	85.93 472	96.08 276	87.33 511	87.70 300	98.78 331	91.35 385	95.58 318	98.34 298

SixPastTwentyTwo		93.34 379	92.86 378	94.75 416	95.67 435	89.41 441	98.75 177	96.67 451	93.89 287	90.15 444	98.25 260	80.87 408	98.27 392	90.90 397	90.64 390	96.57 404

USDC		93.33 380	92.71 381	95.21 394	96.83 378	90.83 406	96.91 438	97.50 383	93.84 290	90.72 436	98.14 268	77.69 438	98.82 327	89.51 420	93.21 357	95.97 443

IB-MVS	91.98 17	93.27 381	91.97 396	97.19 253	97.47 332	93.41 333	97.09 425	95.99 464	93.32 327	92.47 405	95.73 443	78.06 433	99.53 183	94.59 281	82.98 462	98.62 278
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		93.26 382	92.21 393	96.41 334	97.73 309	93.13 352	95.65 474	97.03 427	91.27 407	94.04 341	96.06 431	75.33 458	97.19 458	86.56 452	96.23 306	98.92 240

ppachtmachnet_test		93.22 383	92.63 383	94.97 404	95.45 446	90.84 405	96.88 446	97.88 344	90.60 418	92.08 420	97.26 348	88.08 288	97.86 436	85.12 464	90.33 393	96.22 435

Patchmtry		93.22 383	92.35 391	95.84 371	96.77 381	93.09 355	94.66 492	97.56 373	87.37 458	92.90 389	96.24 422	88.15 285	97.90 430	87.37 447	90.10 398	96.53 411

testing3		93.19 385	92.48 389	95.30 393	98.07 269	92.27 371	98.64 212	97.17 417	93.94 286	93.98 344	97.04 374	67.97 482	96.01 482	88.40 435	97.14 267	97.63 325

FMVSNet1		93.19 385	92.07 394	96.56 315	97.54 326	95.00 258	98.82 155	98.18 301	90.38 424	92.27 413	97.07 366	73.68 471	97.95 426	89.36 423	91.30 381	96.72 377

LF4IMVS		93.14 387	92.79 380	94.20 434	95.88 429	88.67 454	97.66 373	97.07 423	93.81 293	91.71 424	97.65 315	77.96 435	98.81 328	91.47 384	91.92 374	95.12 461

mmtdpeth		93.12 388	92.61 384	94.63 421	97.60 319	89.68 434	99.21 45	97.32 401	94.02 277	97.72 188	94.42 463	77.01 448	99.44 203	99.05 31	77.18 487	94.78 471

testgi		93.06 389	92.45 390	94.88 409	96.43 402	89.90 426	98.75 177	97.54 379	95.60 170	91.63 427	97.91 288	74.46 467	97.02 461	86.10 455	93.67 342	97.72 322

PatchT		93.06 389	91.97 396	96.35 339	96.69 387	92.67 367	94.48 496	97.08 421	86.62 466	97.08 225	92.23 492	87.94 292	97.90 430	78.89 491	96.69 282	98.49 290

RPMNet		92.81 391	91.34 402	97.24 249	97.00 365	93.43 331	94.96 485	98.80 114	82.27 486	96.93 233	92.12 493	86.98 313	99.82 97	76.32 499	96.65 284	98.46 292

UWE-MVS-28		92.79 392	92.51 387	93.62 441	96.46 400	86.28 475	97.93 341	92.71 503	94.17 270	94.78 306	97.16 356	81.05 405	96.43 476	81.45 480	96.86 275	98.14 308

myMVS_eth3d		92.73 393	92.01 395	94.89 408	97.39 342	90.94 401	97.91 344	97.46 387	93.16 335	93.42 372	95.37 453	68.09 481	96.12 480	88.34 436	96.99 271	97.60 326

TransMVSNet (Re)		92.67 394	91.51 401	96.15 347	96.58 392	94.65 277	98.90 120	96.73 447	90.86 415	89.46 452	97.86 293	85.62 340	98.09 409	86.45 453	81.12 472	95.71 449

ttmdpeth		92.61 395	91.96 398	94.55 423	94.10 466	90.60 414	98.52 241	97.29 404	92.67 355	90.18 442	97.92 287	79.75 418	97.79 438	91.09 390	86.15 448	95.26 457

Syy-MVS		92.55 396	92.61 384	92.38 458	97.39 342	83.41 486	97.91 344	97.46 387	93.16 335	93.42 372	95.37 453	84.75 358	96.12 480	77.00 497	96.99 271	97.60 326

K. test v3		92.55 396	91.91 399	94.48 427	95.64 436	89.24 442	99.07 72	94.88 483	94.04 275	86.78 472	97.59 322	77.64 441	97.64 446	92.08 365	89.43 410	96.57 404

DSMNet-mixed		92.52 398	92.58 386	92.33 459	94.15 464	82.65 490	98.30 282	94.26 491	89.08 446	92.65 397	95.73 443	85.01 352	95.76 484	86.24 454	97.76 246	98.59 283

TinyColmap		92.31 399	91.53 400	94.65 420	96.92 371	89.75 429	96.92 436	96.68 450	90.45 422	89.62 449	97.85 295	76.06 455	98.81 328	86.74 450	92.51 365	95.41 454

gg-mvs-nofinetune		92.21 400	90.58 409	97.13 258	96.75 384	95.09 254	95.85 469	89.40 513	85.43 477	94.50 312	81.98 517	80.80 410	98.40 377	92.16 363	98.33 216	97.88 315

FMVSNet5		91.81 401	90.92 405	94.49 426	97.21 352	92.09 380	98.00 333	97.55 378	89.31 443	90.86 435	95.61 450	74.48 466	95.32 488	85.57 459	89.70 402	96.07 441

pmmvs6		91.77 402	90.63 408	95.17 396	94.69 460	91.24 397	98.67 207	97.92 342	86.14 470	89.62 449	97.56 327	75.79 456	98.34 380	90.75 399	84.56 455	95.94 444

Anonymous20231206		91.66 403	91.10 404	93.33 446	94.02 470	87.35 469	98.58 225	97.26 408	90.48 420	90.16 443	96.31 420	83.83 381	96.53 474	79.36 488	89.90 400	96.12 439

Patchmatch-RL test		91.49 404	90.85 406	93.41 444	91.37 494	84.40 481	92.81 505	95.93 468	91.87 385	87.25 468	94.87 459	88.99 258	96.53 474	92.54 357	82.00 466	99.30 164

blended_shiyan8		91.42 405	89.89 418	96.01 354	91.50 491	93.30 343	97.48 386	97.83 347	86.93 461	92.57 401	92.37 490	82.46 390	98.13 402	92.86 342	74.99 495	96.61 393

blended_shiyan6		91.37 406	89.84 419	95.98 360	91.49 492	93.28 344	97.48 386	97.83 347	86.93 461	92.43 407	92.36 491	82.44 391	98.06 414	92.74 347	74.82 498	96.59 398

test_0402		91.32 407	90.27 412	94.48 427	96.60 391	91.12 398	98.50 249	97.22 410	86.10 471	88.30 464	96.98 381	77.65 440	97.99 424	78.13 493	92.94 359	94.34 474

dtuonlycased		91.29 408	91.26 403	91.36 466	95.63 437	84.25 483	96.93 435	97.21 412	92.16 378	88.34 463	96.47 413	79.56 419	95.18 491	87.37 447	87.70 429	94.64 472

test_vis1_rt		91.29 408	90.65 407	93.19 450	97.45 336	86.25 476	98.57 234	90.90 511	93.30 329	86.94 471	93.59 475	62.07 495	99.11 273	97.48 148	95.58 318	94.22 478

PVSNet_0	88.72 19	91.28 410	90.03 416	95.00 403	97.99 283	87.29 470	94.84 488	98.50 199	92.06 380	89.86 446	95.19 455	79.81 417	99.39 210	92.27 362	69.79 513	98.33 299

mvs5depth		91.23 411	90.17 414	94.41 431	92.09 486	89.79 428	95.26 481	96.50 456	90.73 416	91.69 425	97.06 370	76.12 454	98.62 343	88.02 441	84.11 458	94.82 468

Anonymous20240521		91.18 412	90.44 410	93.42 443	93.70 471	88.47 458	98.94 108	97.56 373	88.46 452	89.56 451	95.08 458	77.15 446	96.97 462	83.92 471	89.55 406	94.82 468

wanda-best-256-512		91.17 413	89.60 423	95.88 367	91.33 495	92.99 358	96.89 443	97.82 350	86.89 464	92.36 409	91.75 497	81.83 394	98.06 414	92.75 344	74.82 498	96.59 398

FE-blended-shiyan7		91.17 413	89.60 423	95.88 367	91.33 495	92.99 358	96.89 443	97.82 350	86.89 464	92.36 409	91.75 497	81.83 394	98.06 414	92.75 344	74.82 498	96.59 398

EG-PatchMatch MVS		91.13 415	90.12 415	94.17 436	94.73 459	89.00 447	98.13 314	97.81 354	89.22 444	85.32 482	96.46 414	67.71 483	98.42 364	87.89 445	93.82 340	95.08 463

TDRefinement		91.06 416	89.68 421	95.21 394	85.35 523	91.49 393	98.51 248	97.07 423	91.47 395	88.83 459	97.84 296	77.31 442	99.09 278	92.79 343	77.98 485	95.04 465

gbinet_0.2-2-1-0.02		91.03 417	89.37 429	96.01 354	91.39 493	93.41 333	97.19 415	97.82 350	87.00 460	92.18 417	91.87 496	78.97 424	98.04 418	93.13 328	74.75 502	96.60 395

sc_t1		91.01 418	89.39 425	95.85 370	95.99 421	90.39 419	98.43 264	97.64 365	78.79 495	92.20 416	97.94 285	66.00 488	98.60 347	91.59 382	85.94 450	98.57 286

UnsupCasMVSNet_eth		90.99 419	89.92 417	94.19 435	94.08 467	89.83 427	97.13 424	98.67 150	93.69 304	85.83 478	96.19 427	75.15 461	96.74 467	89.14 426	79.41 479	96.00 442

ArgMatch-Sym		90.92 420	90.22 413	93.02 452	95.81 432	86.50 474	97.32 402	97.01 433	92.67 355	91.02 433	97.35 341	66.90 486	97.17 459	88.53 434	85.40 452	95.39 455

0.4-1-1-0.1		90.89 421	88.97 435	96.67 299	94.15 464	92.76 366	95.28 480	95.03 481	89.11 445	90.43 440	89.57 506	75.41 457	99.04 287	94.70 271	77.06 488	98.20 305

test20.03		90.89 421	90.38 411	92.43 457	93.48 474	88.14 464	98.33 274	97.56 373	93.40 324	87.96 465	96.71 403	80.69 411	94.13 498	79.15 489	86.17 446	95.01 467

usedtu_blend_shiyan5		90.87 423	89.15 430	96.01 354	91.33 495	93.35 340	98.12 315	97.36 399	81.93 489	92.36 409	91.75 497	81.83 394	98.09 409	92.88 340	74.82 498	96.59 398

blend_shiyan4		90.76 424	89.01 433	95.99 357	91.69 490	93.35 340	97.44 388	97.83 347	86.93 461	92.23 414	91.98 494	75.19 460	98.09 409	92.88 340	74.96 496	96.52 414

MDA-MVSNet_test_wron		90.71 425	89.38 427	94.68 418	94.83 456	90.78 407	97.19 415	97.46 387	87.60 456	72.41 508	95.72 445	86.51 319	96.71 470	85.92 457	86.80 443	96.56 406

YYNet1		90.70 426	89.39 425	94.62 422	94.79 458	90.65 410	97.20 412	97.46 387	87.54 457	72.54 507	95.74 441	86.51 319	96.66 471	86.00 456	86.76 444	96.54 409

ArgMatch-SfM		90.55 427	89.69 420	93.14 451	95.91 427	86.12 477	97.20 412	96.81 446	92.91 347	91.39 428	96.95 386	65.65 490	97.72 443	88.03 440	82.36 463	95.57 452

0.4-1-1-0.2		90.43 428	88.45 439	96.38 337	93.34 476	92.12 376	93.88 502	95.04 480	88.62 451	90.00 445	88.31 509	75.31 459	99.03 290	94.61 278	76.91 490	98.01 314

KD-MVS_self_test		90.38 429	89.38 427	93.40 445	92.85 481	88.94 450	97.95 337	97.94 340	90.35 425	90.25 441	93.96 472	79.82 416	95.94 483	84.62 470	76.69 492	95.33 456

pmmvs-eth3d		90.36 430	89.05 432	94.32 433	91.10 500	92.12 376	97.63 378	96.95 435	88.86 448	84.91 483	93.13 481	78.32 429	96.74 467	88.70 431	81.81 468	94.09 481

0.3-1-1-0.015		90.29 431	88.21 443	96.51 322	93.56 473	92.44 369	94.41 497	95.03 481	88.71 449	89.20 454	88.50 508	73.12 473	99.04 287	94.67 274	76.70 491	98.05 310

FE-MVSNET2		90.29 431	88.94 436	94.36 432	90.48 506	92.27 371	98.45 256	97.82 350	91.59 393	84.90 484	93.10 482	73.92 469	96.42 477	87.92 444	82.26 464	94.39 473

tt0320		90.26 433	88.73 438	94.86 410	96.12 415	90.62 412	98.17 307	97.63 366	77.46 499	89.68 448	96.04 433	69.19 479	97.79 438	88.98 428	85.29 453	96.16 438

CL-MVSNet_self_test		90.11 434	89.14 431	93.02 452	91.86 488	88.23 463	96.51 460	98.07 327	90.49 419	90.49 439	94.41 464	84.75 358	95.34 487	80.79 482	74.95 497	95.50 453

new_pmnet		90.06 435	89.00 434	93.22 449	94.18 462	88.32 461	96.42 462	96.89 440	86.19 469	85.67 479	93.62 474	77.18 445	97.10 460	81.61 479	89.29 412	94.23 477

MDA-MVSNet-bldmvs		89.97 436	88.35 441	94.83 414	95.21 450	91.34 394	97.64 375	97.51 382	88.36 454	71.17 509	96.13 429	79.22 422	96.63 472	83.65 472	86.27 445	96.52 414

tt0320-xc		89.79 437	88.11 444	94.84 413	96.19 410	90.61 413	98.16 308	97.22 410	77.35 500	88.75 461	96.70 404	65.94 489	97.63 447	89.31 424	83.39 460	96.28 433

CMPMVS	66.06 21	89.70 438	89.67 422	89.78 470	93.19 479	76.56 499	97.00 431	98.35 255	80.97 490	81.57 491	97.75 304	74.75 464	98.61 344	89.85 412	93.63 344	94.17 479
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 439	88.28 442	93.82 438	92.81 482	91.08 399	98.01 331	97.45 391	87.95 455	87.90 466	95.87 439	67.63 484	94.56 496	78.73 492	88.18 425	95.83 447

KD-MVS_2432*1600		89.61 440	87.96 448	94.54 424	94.06 468	91.59 391	95.59 475	97.63 366	89.87 432	88.95 456	94.38 466	78.28 430	96.82 465	84.83 466	68.05 514	95.21 459

miper_refine_blended		89.61 440	87.96 448	94.54 424	94.06 468	91.59 391	95.59 475	97.63 366	89.87 432	88.95 456	94.38 466	78.28 430	96.82 465	84.83 466	68.05 514	95.21 459

MVStest1		89.53 442	87.99 447	94.14 437	94.39 461	90.42 417	98.25 290	96.84 445	82.81 483	81.18 493	97.33 344	77.09 447	96.94 463	85.27 463	78.79 480	95.06 464

MVS-HIRNet		89.46 443	88.40 440	92.64 456	97.58 321	82.15 491	94.16 501	93.05 502	75.73 505	90.90 434	82.52 515	79.42 421	98.33 382	83.53 473	98.68 173	97.43 329

OpenMVS_ROB	86.42 20	89.00 444	87.43 452	93.69 440	93.08 480	89.42 440	97.91 344	96.89 440	78.58 496	85.86 477	94.69 460	69.48 478	98.29 390	77.13 496	93.29 356	93.36 491

mvsany_test3		88.80 445	88.04 445	91.09 467	89.78 511	81.57 493	97.83 359	95.49 473	93.81 293	87.53 467	93.95 473	56.14 498	97.43 454	94.68 272	83.13 461	94.26 475

FE-MVSNET		88.56 446	87.09 453	92.99 454	89.93 510	89.99 425	98.15 311	95.59 471	88.42 453	84.87 485	92.90 484	74.82 463	94.99 493	77.88 494	81.21 471	93.99 484

new-patchmatchnet		88.50 447	87.45 451	91.67 464	90.31 508	85.89 478	97.16 422	97.33 400	89.47 439	83.63 488	92.77 487	76.38 451	95.06 492	82.70 475	77.29 486	94.06 483

APD_test1		88.22 448	88.01 446	88.86 474	95.98 422	74.66 509	97.21 411	96.44 458	83.96 482	86.66 474	97.90 289	60.95 496	97.84 437	82.73 474	90.23 396	94.09 481

PM-MVS		87.77 449	86.55 455	91.40 465	91.03 502	83.36 488	96.92 436	95.18 478	91.28 406	86.48 476	93.42 477	53.27 500	96.74 467	89.43 422	81.97 467	94.11 480

dmvs_testset		87.64 450	88.93 437	83.79 488	95.25 449	63.36 522	97.20 412	91.17 508	93.07 339	85.64 480	95.98 438	85.30 349	91.52 508	69.42 510	87.33 435	96.49 421

test_fmvs3		87.17 451	87.06 454	87.50 477	91.21 498	75.66 502	99.05 76	96.61 454	92.79 352	88.85 458	92.78 486	43.72 507	93.49 500	93.95 304	84.56 455	93.34 492

UnsupCasMVSNet_bld		87.17 451	85.12 459	93.31 447	91.94 487	88.77 451	94.92 487	98.30 277	84.30 481	82.30 489	90.04 504	63.96 493	97.25 457	85.85 458	74.47 505	93.93 486

N_pmnet		87.12 453	87.77 450	85.17 483	95.46 445	61.92 526	97.37 396	70.66 537	85.83 473	88.73 462	96.04 433	85.33 347	97.76 441	80.02 483	90.48 391	95.84 446

pmmvs3		86.67 454	84.86 460	92.11 463	88.16 515	87.19 472	96.63 456	94.75 485	79.88 492	87.22 469	92.75 488	66.56 487	95.20 490	81.24 481	76.56 493	93.96 485

test_f		86.07 455	85.39 457	88.10 475	89.28 513	75.57 503	97.73 368	96.33 460	89.41 442	85.35 481	91.56 500	43.31 509	95.53 485	91.32 386	84.23 457	93.21 493

MASt3R-SfM		85.54 456	85.89 456	84.50 486	90.13 509	66.13 520	92.89 504	95.33 475	85.73 475	88.77 460	96.36 419	52.50 501	94.89 494	86.66 451	84.65 454	92.50 498

WB-MVS		84.86 457	85.33 458	83.46 489	89.48 512	69.56 514	98.19 300	96.42 459	89.55 438	81.79 490	94.67 461	84.80 356	90.12 511	52.44 518	80.64 476	90.69 504

usedtu_dtu_shiyan2		84.80 458	82.31 463	92.27 461	86.38 520	85.55 479	97.77 364	96.56 455	78.34 497	83.90 487	93.50 476	54.16 499	95.32 488	77.55 495	72.62 506	95.92 445

DenseAffine		84.37 459	82.38 462	90.31 469	94.17 463	82.89 489	94.98 484	94.23 492	82.16 488	79.68 497	94.33 470	46.28 503	94.25 497	80.01 484	75.62 494	93.78 489

SSC-MVS		84.27 460	84.71 461	82.96 494	89.19 514	68.83 515	98.08 323	96.30 461	89.04 447	81.37 492	94.47 462	84.60 363	89.89 512	49.80 521	79.52 478	90.15 505

RoMa-SfM		83.81 461	82.08 464	89.00 473	93.33 477	79.94 496	95.51 477	92.48 504	79.75 493	79.89 496	95.69 448	46.23 504	93.20 503	78.90 490	76.93 489	93.87 487

LoFTR		83.16 462	80.62 466	90.80 468	92.28 485	80.01 495	95.35 479	94.33 489	80.44 491	70.79 510	92.93 483	46.38 502	98.17 398	75.01 501	78.03 484	94.24 476

dongtai		82.47 463	81.88 465	84.22 487	95.19 451	76.03 500	94.59 495	74.14 528	82.63 484	87.19 470	96.09 430	64.10 492	87.85 516	58.91 516	84.11 458	88.78 511

DKM		81.60 464	79.57 467	87.68 476	92.65 484	78.36 497	94.65 493	91.17 508	79.69 494	76.11 501	93.98 471	37.88 519	91.54 507	79.64 487	70.38 510	93.15 494

MatchFormer		80.21 465	77.20 474	89.24 472	91.79 489	77.21 498	95.16 482	93.59 497	72.46 509	67.08 513	89.93 505	43.14 510	97.90 430	67.07 512	74.55 504	92.61 497

RoMa-HiRes		79.77 466	77.89 469	85.41 482	90.81 503	74.77 508	94.26 499	86.78 517	75.97 501	77.00 499	94.37 468	39.39 514	90.60 509	74.98 502	67.46 516	90.84 503

DKM-HiRes		79.25 467	77.01 476	85.98 480	91.20 499	75.07 505	93.65 503	87.84 516	75.94 503	73.36 506	92.80 485	34.20 524	90.26 510	76.66 498	67.44 517	92.62 496

test_vis3_rt		79.22 468	77.40 473	84.67 484	86.44 519	74.85 507	97.66 373	81.43 521	84.98 478	67.12 512	81.91 518	28.09 532	97.60 448	88.96 429	80.04 477	81.55 521

test_method		79.03 469	78.17 468	81.63 495	86.06 521	54.40 537	82.75 525	96.89 440	39.54 530	80.98 494	95.57 451	58.37 497	94.73 495	84.74 469	78.61 481	95.75 448

testf1		79.02 470	77.70 470	82.99 492	88.10 516	66.90 518	94.67 490	93.11 499	71.08 511	74.02 503	93.41 478	34.15 525	93.25 501	72.25 506	78.50 482	88.82 509

APD_test2		79.02 470	77.70 470	82.99 492	88.10 516	66.90 518	94.67 490	93.11 499	71.08 511	74.02 503	93.41 478	34.15 525	93.25 501	72.25 506	78.50 482	88.82 509

LCM-MVSNet		78.70 472	76.24 478	86.08 479	77.26 539	71.99 511	94.34 498	96.72 448	61.62 516	76.53 500	89.33 507	33.91 528	92.78 505	81.85 478	74.60 503	93.46 490

kuosan		78.45 473	77.69 472	80.72 496	92.73 483	75.32 504	94.63 494	74.51 527	75.96 502	80.87 495	93.19 480	63.23 494	79.99 526	42.56 528	81.56 470	86.85 518

Gipuma		78.40 474	76.75 477	83.38 490	95.54 440	80.43 494	79.42 526	97.40 395	64.67 515	73.46 505	80.82 519	45.65 506	93.14 504	66.32 513	87.43 433	76.56 524
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 475	75.44 479	85.46 481	82.54 527	74.95 506	94.23 500	93.08 501	72.80 507	74.68 502	87.38 510	36.36 522	91.56 506	73.95 504	63.94 518	89.87 506

FPMVS		77.62 476	77.14 475	79.05 500	79.25 534	60.97 528	95.79 470	95.94 467	65.96 514	67.93 511	94.40 465	37.73 520	88.88 515	68.83 511	88.46 422	87.29 515

ELoFTR		75.37 477	72.33 480	84.51 485	84.48 525	68.41 517	91.57 509	88.78 514	73.84 506	62.84 517	90.14 502	27.38 533	94.11 499	71.45 509	60.46 521	91.00 501

EGC-MVSNET		75.22 478	69.54 482	92.28 460	94.81 457	89.58 436	97.64 375	96.50 456	1.82 551	5.57 552	95.74 441	68.21 480	96.26 479	73.80 505	91.71 376	90.99 502

PMatch-SfM		73.49 479	70.32 481	83.00 491	85.01 524	68.63 516	90.17 516	79.05 524	71.64 510	63.27 516	91.93 495	17.27 541	89.10 514	74.59 503	59.95 522	91.26 499

PDCNetPlus		71.79 480	69.26 483	79.39 499	85.67 522	69.92 513	90.34 514	62.32 539	72.62 508	65.36 515	90.26 501	39.20 516	86.38 518	75.32 500	42.24 532	81.88 520

SP-DiffGlue		70.13 481	69.16 484	73.04 509	77.73 537	57.48 532	88.44 519	74.91 526	50.96 522	66.64 514	85.99 512	41.44 511	73.46 532	64.21 514	72.15 507	88.19 514

PMatch-Up-SfM		70.03 482	66.48 488	80.70 497	82.00 529	63.20 523	88.10 520	71.07 533	67.59 513	60.07 522	90.10 503	14.49 546	87.80 517	71.95 508	52.95 526	91.09 500

ANet_high		69.08 483	65.37 490	80.22 498	65.99 552	71.96 512	90.91 513	90.09 512	82.62 485	49.93 532	78.39 526	29.36 531	81.75 523	62.49 515	38.52 536	86.95 517

tmp_tt		68.90 484	66.97 485	74.68 502	50.78 554	59.95 529	87.13 522	83.47 520	38.80 531	62.21 518	96.23 424	64.70 491	76.91 528	88.91 430	30.49 540	87.19 516

SP-LightGlue		68.17 485	66.54 487	73.06 508	91.08 501	55.79 533	91.09 511	72.78 530	48.55 526	60.77 520	79.95 523	38.55 517	74.10 530	45.47 523	70.64 509	89.28 507

SP-SuperGlue		68.14 486	66.58 486	72.81 510	90.65 505	55.53 534	91.37 510	73.04 529	49.07 525	61.03 519	80.24 522	38.13 518	74.06 531	45.46 524	70.26 511	88.84 508

ALIKED-LG		67.40 487	65.16 491	74.11 504	93.21 478	62.30 524	88.98 517	71.99 531	55.04 517	59.47 524	82.33 516	39.27 515	85.49 520	32.61 534	63.58 520	74.55 525

SP-NN		67.39 488	65.69 489	72.49 512	90.68 504	55.34 535	90.33 515	71.01 535	46.77 528	59.09 525	79.83 524	37.26 521	73.38 533	44.68 525	71.51 508	88.74 512

ALIKED-NN		66.93 489	64.81 492	73.32 506	93.41 475	62.03 525	87.55 521	71.25 532	50.21 523	59.98 523	82.57 514	39.72 513	84.03 522	34.94 532	63.64 519	73.90 526

SP-MNN		66.66 490	64.70 493	72.53 511	90.32 507	55.08 536	91.01 512	71.05 534	44.81 529	56.48 528	79.62 525	35.87 523	74.11 529	43.13 527	69.98 512	88.39 513

PMVS	61.03 23	65.95 491	63.57 495	73.09 507	57.90 553	51.22 539	85.05 524	93.93 496	54.45 518	44.32 534	83.57 513	13.22 548	89.15 513	58.68 517	81.00 473	78.91 523
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ALIKED-MNN		65.35 492	62.68 497	73.35 505	93.70 471	61.07 527	88.63 518	70.76 536	47.76 527	57.06 527	80.59 520	34.03 527	85.39 521	32.73 533	58.87 523	73.59 527

E-PMN		64.94 493	64.25 494	67.02 513	82.28 528	59.36 530	91.83 508	85.63 518	52.69 519	60.22 521	77.28 527	41.06 512	80.12 525	46.15 522	41.14 533	61.57 530

EMVS		64.07 494	63.26 496	66.53 514	81.73 530	58.81 531	91.85 507	84.75 519	51.93 521	59.09 525	75.13 530	43.32 508	79.09 527	42.03 529	39.47 534	61.69 529

MVE	62.14 22	63.28 495	59.38 498	74.99 501	74.33 544	65.47 521	85.55 523	80.50 522	52.02 520	51.10 530	75.00 531	10.91 553	80.50 524	51.60 520	53.40 525	78.99 522
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM		61.12 496	56.63 499	74.58 503	69.78 549	53.99 538	78.71 527	76.81 525	49.09 524	49.42 533	80.47 521	24.43 534	85.82 519	51.80 519	29.17 541	83.92 519

XFeat-NN		56.16 497	56.10 500	56.36 516	72.10 546	42.54 549	76.45 529	61.18 540	38.16 532	53.08 529	76.48 528	32.95 529	65.67 535	44.15 526	50.31 529	60.87 531

XFeat-MNN		55.84 498	55.19 501	57.82 515	69.33 550	43.25 544	78.25 528	62.64 538	37.53 533	50.90 531	76.32 529	32.43 530	68.13 534	42.00 530	47.26 531	62.07 528

SIFT-NN		49.27 499	49.25 502	49.32 517	83.88 526	45.20 540	74.57 530	53.44 541	32.44 534	42.88 535	64.93 532	20.60 535	61.35 536	16.59 536	53.96 524	41.40 532

SIFT-MNN		47.78 500	47.47 503	48.69 518	81.04 531	44.17 541	73.46 531	53.36 542	31.82 535	38.54 536	63.76 533	18.11 539	61.27 537	15.96 538	51.17 527	40.64 535

SIFT-NN-NCMNet		47.55 501	47.18 504	48.67 519	79.60 533	44.09 542	73.43 532	52.90 543	31.82 535	38.38 537	63.56 536	18.47 536	61.19 538	15.91 539	50.50 528	40.74 534

SIFT-NN-CMatch		45.31 502	44.49 505	47.75 520	76.46 540	42.98 547	70.17 536	49.20 546	31.63 538	37.94 538	63.68 535	18.19 538	59.32 541	15.91 539	37.27 537	40.95 533

SIFT-NCM-Cal		44.98 503	44.20 506	47.33 521	79.81 532	43.05 545	72.12 533	49.31 545	30.81 540	25.90 544	61.87 541	15.80 542	60.28 539	14.09 547	48.07 530	38.66 538

SIFT-NN-UMatch		44.69 504	43.84 507	47.24 522	74.56 543	42.59 548	71.89 534	49.78 544	31.80 537	29.27 541	63.70 534	18.26 537	59.43 540	15.86 541	39.43 535	39.71 536

SIFT-ConvMatch		43.26 505	42.18 509	46.50 523	78.34 536	43.05 545	68.67 538	47.17 547	31.06 539	30.28 540	62.56 538	15.43 543	58.95 543	14.92 543	31.22 539	37.51 540

SIFT-NN-PointCN		43.09 506	42.61 508	44.51 526	72.48 545	37.95 553	70.10 537	46.55 548	30.16 544	34.48 539	61.93 540	18.02 540	55.90 546	15.40 542	34.41 538	39.69 537

SIFT-UMatch		42.35 507	41.04 510	46.29 524	76.09 541	41.80 550	70.21 535	45.21 549	30.75 541	27.33 543	62.62 537	15.13 544	59.11 542	14.72 544	27.30 542	37.95 539

SIFT-CM-Cal		41.25 508	40.03 511	44.88 525	77.37 538	41.08 551	65.71 542	41.18 551	30.42 543	28.83 542	61.42 542	14.88 545	56.40 544	14.13 546	26.37 544	37.16 541

SIFT-UM-Cal		39.93 509	38.61 512	43.88 527	76.08 542	39.30 552	68.10 539	37.89 552	30.49 542	22.74 546	62.27 539	13.89 547	56.16 545	14.17 545	21.90 545	36.17 542

SIFT-PointCN		37.89 510	37.50 513	39.07 528	71.45 547	31.31 554	66.27 541	41.69 550	27.82 545	22.63 547	56.73 544	12.00 551	50.56 548	12.18 549	26.71 543	35.34 543

SIFT-PCN-Cal		36.85 511	36.40 514	38.19 529	71.43 548	30.42 555	64.34 543	37.72 553	27.48 546	22.98 545	57.03 543	12.99 549	51.22 547	12.51 548	21.13 546	32.92 544

SIFT-NCMNet		32.45 512	31.84 516	34.30 530	68.74 551	28.10 556	57.85 544	24.54 554	27.25 547	19.31 548	52.59 545	9.75 554	45.69 549	10.92 550	15.56 548	29.13 545

wuyk23d		30.17 513	30.18 517	30.16 531	78.61 535	43.29 543	66.79 540	14.21 555	17.31 548	14.82 551	11.93 551	11.55 552	41.43 550	37.08 531	19.30 547	5.76 548

cdsmvs_eth3d_5k		23.98 514	31.98 515	0.00 534	0.00 557	0.00 559	0.00 545	98.59 171	0.00 552	0.00 553	98.61 216	90.60 206	0.00 553	0.00 551	0.00 551	0.00 549

testmvs		21.48 515	24.95 518	11.09 533	14.89 555	6.47 558	96.56 458	9.87 556	7.55 549	17.93 549	39.02 547	9.43 555	5.90 552	16.56 537	12.72 549	20.91 547

test123		20.95 516	23.72 519	12.64 532	13.54 556	8.19 557	96.55 459	6.13 557	7.48 550	16.74 550	37.98 548	12.97 550	6.05 551	16.69 535	5.43 550	23.68 546

ab-mvs-re		8.20 517	10.94 520	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	98.43 235	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

pcd_1.5k_mvsjas		7.88 518	10.50 521	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	94.51 91	0.00 553	0.00 551	0.00 551	0.00 549

mmdepth		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 558	0.00 552	0.00 553	0.00 552	0.00 556	0.00 553	0.00 551	0.00 551	0.00 549

MED-MVS test				99.52 14	99.77 2	98.86 23	99.32 22	99.24 20	96.41 124	99.30 52	99.35 62		99.92 43	98.30 75	99.80 25	99.79 29

TestfortrainingZip				99.43 21	99.13 119	99.06 15	99.32 22	98.57 178	96.88 97	99.42 43	99.05 145	96.54 24	99.73 136		98.59 180	99.51 104

WAC-MVS						90.94 401								88.66 432

FOURS1					99.82 1	98.66 29	99.69 1	98.95 61	97.46 57	99.39 46

MSC_two_6792asdad				99.62 7	99.17 111	99.08 12		98.63 161					99.94 14	98.53 55	99.80 25	99.86 13

PC_three_1452									95.08 218	99.60 33	99.16 110	97.86 2	98.47 358	97.52 141	99.72 66	99.74 50

No_MVS				99.62 7	99.17 111	99.08 12		98.63 161					99.94 14	98.53 55	99.80 25	99.86 13

test_one_0601					99.66 31	99.25 2		98.86 91	97.55 49	99.20 60	99.47 37	97.57 7

eth-test2					0.00 557

eth-test					0.00 557

ZD-MVS					99.46 58	98.70 28		98.79 119	93.21 332	98.67 105	98.97 156	95.70 52	99.83 90	96.07 214	99.58 97

RE-MVS-def			98.34 54		99.49 52	97.86 75	99.11 66	98.80 114	96.49 119	99.17 63	99.35 62	95.29 69		97.72 115	99.65 80	99.71 63

IU-MVS					99.71 24	99.23 7		98.64 158	95.28 201	99.63 32				98.35 72	99.81 16	99.83 19

OPU-MVS				99.37 28	99.24 103	99.05 16	99.02 86				99.16 110	97.81 3	99.37 211	97.24 163	99.73 61	99.70 67

test_241102_TWO								98.87 85	97.65 41	99.53 38	99.48 35	97.34 12	99.94 14	98.43 67	99.80 25	99.83 19

test_241102_ONE					99.71 24	99.24 5		98.87 85	97.62 43	99.73 23	99.39 50	97.53 8	99.74 134

9.14			98.06 78		99.47 56		98.71 192	98.82 101	94.36 265	99.16 67	99.29 75	96.05 40	99.81 102	97.00 171	99.71 68

save fliter					99.46 58	98.38 41	98.21 293	98.71 137	97.95 28

test_0728_THIRD									97.32 65	99.45 40	99.46 42	97.88 1	99.94 14	98.47 63	99.86 2	99.85 16

test_0728_SECOND				99.71 1	99.72 17	99.35 1	98.97 98	98.88 78					99.94 14	98.47 63	99.81 16	99.84 18

test0726					99.72 17	99.25 2	99.06 74	98.88 78	97.62 43	99.56 35	99.50 31	97.42 10

GSMVS																99.20 191

test_part2					99.63 34	99.18 10				99.27 57

sam_mvs1												89.45 242				99.20 191

sam_mvs												88.99 258

ambc				89.49 471	86.66 518	75.78 501	92.66 506	96.72 448		86.55 475	92.50 489	46.01 505	97.90 430	90.32 403	82.09 465	94.80 470

MTGPA								98.74 129

test_post1							96.68 455				30.43 550	87.85 296	98.69 336	92.59 353

test_post											31.83 549	88.83 267	98.91 312

patchmatchnet-post											95.10 457	89.42 243	98.89 316

GG-mvs-BLEND				96.59 311	96.34 405	94.98 262	96.51 460	88.58 515		93.10 386	94.34 469	80.34 415	98.05 417	89.53 419	96.99 271	96.74 374

MTMP							98.89 124	94.14 494

gm-plane-assit					95.88 429	87.47 468			89.74 435		96.94 388		99.19 252	93.32 323

test9_res														96.39 208	99.57 98	99.69 70

TEST9					99.31 79	98.50 35	97.92 342	98.73 132	92.63 357	97.74 185	98.68 210	96.20 35	99.80 109

test_8					99.29 88	98.44 37	97.89 350	98.72 134	92.98 343	97.70 190	98.66 213	96.20 35	99.80 109

agg_prior2														95.87 224	99.57 98	99.68 75

agg_prior					99.30 83	98.38 41		98.72 134		97.57 208			99.81 102

TestCases				96.99 269	99.25 96	93.21 350		98.18 301	91.36 399	93.52 365	98.77 197	84.67 361	99.72 137	89.70 416	97.87 240	98.02 312

test_prior4						98.01 71	97.86 354

test_prior2							97.80 361		96.12 141	97.89 172	98.69 209	95.96 44		96.89 181	99.60 92

test_prior				99.19 51	99.31 79	98.22 58		98.84 96					99.70 143			99.65 83

旧先验2							97.57 381		91.30 404	98.67 105			99.80 109	95.70 235

新几何2							97.64 375

新几何1				99.16 56	99.34 71	98.01 71		98.69 142	90.06 429	98.13 140	98.95 163	94.60 89	99.89 68	91.97 372	99.47 121	99.59 94

旧先验1					99.29 88	97.48 90		98.70 140			99.09 135	95.56 55			99.47 121	99.61 90

无先验							97.58 380	98.72 134	91.38 398				99.87 79	93.36 322		99.60 92

原ACMM2							97.67 372

原ACMM1				98.65 98	99.32 77	96.62 141		98.67 150	93.27 331	97.81 178	98.97 156	95.18 76	99.83 90	93.84 308	99.46 124	99.50 107

test222					99.23 104	97.17 117	97.40 392	98.66 153	88.68 450	98.05 149	98.96 161	94.14 102			99.53 111	99.61 90

testdata2													99.89 68	91.65 381

segment_acmp												96.85 15

testdata				98.26 142	99.20 109	95.36 237		98.68 145	91.89 384	98.60 114	99.10 127	94.44 96	99.82 97	94.27 292	99.44 125	99.58 98

testdata1							97.32 402		96.34 129

test12				99.18 53	99.16 115	98.19 60		98.53 188		98.07 145		95.13 79	99.72 137		99.56 106	99.63 88

plane_prior7					97.42 338	94.63 279

plane_prior6					97.35 345	94.61 282						87.09 310

plane_prior5								98.56 182					99.03 290	96.07 214	94.27 324	96.92 350

plane_prior4											98.28 254

plane_prior3						94.61 282			97.02 89	95.34 290

plane_prior2							98.80 164		97.28 69

plane_prior1					97.37 344

plane_prior						94.60 284	98.44 262		96.74 105						94.22 326

n2								0.00 558

nn								0.00 558

door-mid								94.37 488

lessismore_v0				94.45 430	94.93 455	88.44 459		91.03 510		86.77 473	97.64 318	76.23 453	98.42 364	90.31 404	85.64 451	96.51 418

LGP-MVS_train				96.47 327	97.46 333	93.54 326		98.54 186	94.67 247	94.36 322	98.77 197	85.39 343	99.11 273	95.71 233	94.15 330	96.76 372

test11								98.66 153

door								94.64 486

HQP5-MVS						94.25 301

HQP-NCC					97.20 353		98.05 326		96.43 121	94.45 314

ACMP_Plane					97.20 353		98.05 326		96.43 121	94.45 314

BP-MVS														95.30 248

HQP4-MVS										94.45 314			98.96 303			96.87 362

HQP3-MVS								98.46 207							94.18 328

HQP2-MVS												86.75 316

NP-MVS					97.28 347	94.51 287					97.73 305

MDTV_nov1_ep13_2view						84.26 482	96.89 443		90.97 413	97.90 171		89.89 228		93.91 306		99.18 200

MDTV_nov1_ep13			95.40 240		97.48 331	88.34 460	96.85 448	97.29 404	93.74 297	97.48 210	97.26 348	89.18 251	99.05 284	91.92 373	97.43 262

ACMMP++_ref															92.97 358

ACMMP++															93.61 345

Test By Simon												94.64 88

ITE_SJBPF				95.44 388	97.42 338	91.32 395		97.50 383	95.09 217	93.59 360	98.35 245	81.70 397	98.88 318	89.71 415	93.39 351	96.12 439

DeepMVS_CX				86.78 478	97.09 363	72.30 510		95.17 479	75.92 504	84.34 486	95.19 455	70.58 476	95.35 486	79.98 486	89.04 416	92.68 495

High-resolution multi-view benchmark