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 24	99.32 22	99.24 20	97.87 31	99.30 52	99.54 20	97.61 6	99.92 43	98.30 77	99.80 25	99.90 5

fmvsm_l_conf0.5_n_a		99.09 2	99.08 1	99.11 62	99.43 64	97.48 91	98.88 132	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 87	98.87 85	97.65 41	99.73 23	99.48 35	97.53 8	99.94 14	98.43 68	99.81 16	99.70 67

DVP-MVS++		99.08 4	98.89 6	99.64 4	99.17 112	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 64	99.72 67	99.74 50

fmvsm_l_conf0.5_n		99.07 5	99.05 2	99.14 58	99.41 67	97.54 89	98.89 125	99.31 13	98.49 17	99.86 8	99.42 46	96.45 29	99.96 4	99.86 1	99.74 58	99.90 5

TestfortrainingZip a		99.05 6	98.85 9	99.65 2	99.77 2	99.13 12	99.32 22	99.01 52	97.87 31	99.74 21	99.54 20	96.71 18	99.92 43	98.35 74	99.33 141	99.90 5

DVP-MVS		99.03 7	98.83 11	99.63 5	99.72 17	99.25 2	98.97 99	98.58 177	97.62 43	99.45 40	99.46 42	97.42 10	99.94 14	98.47 64	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 40	98.96 19	99.39 11	98.93 65	97.38 62	99.41 44	99.54 20	96.66 20	99.84 89	98.86 40	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 32	99.43 10	99.38 8	97.60 46	99.58 34	99.47 37	95.36 65	99.93 34	98.87 39	99.57 99	99.78 33

reproduce_model		98.94 10	98.81 12	99.34 32	99.52 46	98.26 56	98.94 109	98.84 96	98.06 25	99.35 48	99.61 5	96.39 32	99.94 14	98.77 43	99.82 14	99.83 19

reproduce-ours		98.93 11	98.78 14	99.38 24	99.49 53	98.38 42	98.86 143	98.83 98	98.06 25	99.29 54	99.58 16	96.40 30	99.94 14	98.68 46	99.81 16	99.81 25

our_new_method		98.93 11	98.78 14	99.38 24	99.49 53	98.38 42	98.86 143	98.83 98	98.06 25	99.29 54	99.58 16	96.40 30	99.94 14	98.68 46	99.81 16	99.81 25

test_fmvsmconf_n		98.92 13	98.87 7	99.04 68	98.88 148	97.25 113	98.82 156	99.34 11	98.75 11	99.80 14	99.61 5	95.16 78	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 38	99.20 9	98.42 268	98.91 72	97.58 47	99.54 37	99.46 42	97.10 13	99.94 14	97.64 126	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 72	97.83 80	98.70 197	99.26 16	98.85 6	99.92 1	99.51 28	93.91 107	99.95 9	99.86 1	99.79 35	99.92 2

fmvsm_l_conf0.5_n_3		98.90 15	98.74 18	99.37 28	99.36 69	98.25 57	98.89 125	99.24 20	98.77 10	99.89 3	99.59 13	93.39 113	99.96 4	99.78 10	99.76 48	99.89 8

SteuartSystems-ACMMP		98.90 15	98.75 17	99.36 30	99.22 107	98.43 40	99.10 69	98.87 85	97.38 62	99.35 48	99.40 49	97.78 5	99.87 80	97.77 114	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 65	96.43 157	98.96 105	99.36 10	98.63 13	99.86 8	99.51 28	95.91 47	99.97 1	99.72 14	99.75 54	98.94 238

aaEdge-Enhanced		98.83 19	98.60 24	99.52 14	99.58 38	98.86 24	98.69 200	98.93 65	97.00 91	99.17 63	99.35 62	96.62 23	99.90 65	98.30 77	99.80 25	99.79 29

TSAR-MVS + MP.		98.78 20	98.62 22	99.24 46	99.69 29	98.28 55	99.14 60	98.66 154	96.84 98	99.56 35	99.31 71	96.34 33	99.70 144	98.32 76	99.73 62	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 78	98.87 22	98.47 255	98.81 108	97.72 36	98.76 97	99.16 110	97.05 14	99.78 125	98.06 92	99.66 78	99.69 70

MSP-MVS		98.74 22	98.55 29	99.29 39	99.75 6	98.23 58	99.26 33	98.88 78	97.52 50	99.41 44	98.78 194	96.00 43	99.79 122	97.79 113	99.59 95	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 71	97.27 107	98.80 165	99.23 27	98.93 3	99.79 15	99.59 13	92.34 130	99.95 9	99.82 6	99.71 69	99.92 2

XVS		98.70 24	98.49 36	99.34 32	99.70 27	98.35 51	99.29 28	98.88 78	97.40 59	98.46 121	99.20 95	95.90 49	99.89 69	97.85 108	99.74 58	99.78 33

fmvsm_s_conf0.5_n_10		98.66 25	98.54 31	99.02 69	99.36 69	97.21 116	98.86 143	99.23 27	98.90 5	99.83 12	99.59 13	91.57 162	99.94 14	99.79 9	99.74 58	99.89 8

fmvsm_s_conf0.5_n_6		98.65 26	98.55 29	98.95 78	98.50 188	97.30 103	98.79 173	99.16 39	98.14 23	99.86 8	99.41 48	93.71 110	99.91 57	99.71 15	99.64 86	99.65 83

MCST-MVS		98.65 26	98.37 45	99.48 17	99.60 37	98.87 22	98.41 269	98.68 146	97.04 88	98.52 119	98.80 188	96.78 17	99.83 91	97.93 100	99.61 91	99.74 50

SD-MVS		98.64 28	98.68 19	98.53 113	99.33 75	98.36 50	98.90 121	98.85 95	97.28 69	99.72 26	99.39 50	96.63 22	97.60 452	98.17 86	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 68	95.83 204	98.79 173	99.17 37	98.94 2	99.92 1	99.61 5	92.49 125	99.93 34	99.86 1	99.76 48	99.86 13

HFP-MVS		98.63 29	98.40 42	99.32 38	99.72 17	98.29 54	99.23 38	98.96 60	96.10 144	98.94 79	99.17 107	96.06 40	99.92 43	97.62 127	99.78 40	99.75 48

ACMMP_NAP		98.61 31	98.30 60	99.55 11	99.62 36	98.95 20	98.82 156	98.81 108	95.80 160	99.16 67	99.47 37	95.37 64	99.92 43	97.89 105	99.75 54	99.79 29

region2R		98.61 31	98.38 44	99.29 39	99.74 12	98.16 64	99.23 38	98.93 65	96.15 138	98.94 79	99.17 107	95.91 47	99.94 14	97.55 139	99.79 35	99.78 33

NCCC		98.61 31	98.35 48	99.38 24	99.28 93	98.61 33	98.45 257	98.76 126	97.82 35	98.45 124	98.93 166	96.65 21	99.83 91	97.38 161	99.41 129	99.71 63

SF-MVS		98.59 34	98.32 59	99.41 23	99.54 42	98.71 28	99.04 81	98.81 108	95.12 214	99.32 51	99.39 50	96.22 34	99.84 89	97.72 117	99.73 62	99.67 79

ACMMPR		98.59 34	98.36 46	99.29 39	99.74 12	98.15 65	99.23 38	98.95 61	96.10 144	98.93 83	99.19 102	95.70 53	99.94 14	97.62 127	99.79 35	99.78 33

fmvsm_s_conf0.5_n_11		98.58 36	98.57 26	98.62 100	99.42 65	97.16 119	98.97 99	98.86 91	98.91 4	99.87 4	99.66 3	91.82 153	99.95 9	99.82 6	99.82 14	98.75 263

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 71	97.73 312	97.15 120	98.84 152	98.97 57	98.75 11	99.43 42	99.54 20	93.29 115	99.93 34	99.64 20	99.79 35	99.89 8

SMA-MVS		98.58 36	98.25 63	99.56 9	99.51 47	99.04 18	98.95 106	98.80 115	93.67 310	99.37 47	99.52 25	96.52 26	99.89 69	98.06 92	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 31	98.90 121	98.74 130	97.27 73	98.02 155	99.39 50	94.81 88	99.96 4	97.91 103	99.79 35	99.77 40

HPM-MVS++		98.58 36	98.25 63	99.55 11	99.50 49	99.08 13	98.72 192	98.66 154	97.51 51	98.15 139	98.83 185	95.70 53	99.92 43	97.53 142	99.67 75	99.66 82

SR-MVS		98.57 41	98.35 48	99.24 46	99.53 43	98.18 62	99.09 70	98.82 102	96.58 114	99.10 70	99.32 69	95.39 62	99.82 98	97.70 122	99.63 88	99.72 59

CP-MVS		98.57 41	98.36 46	99.19 51	99.66 31	97.86 76	99.34 17	98.87 85	95.96 151	98.60 115	99.13 118	96.05 41	99.94 14	97.77 114	99.86 2	99.77 40

MSLP-MVS++		98.56 43	98.57 26	98.55 108	99.26 96	96.80 135	98.71 193	99.05 49	97.28 69	98.84 89	99.28 76	96.47 28	99.40 209	98.52 62	99.70 71	99.47 116

DeepC-MVS_fast	96.70 1	98.55 44	98.34 54	99.18 53	99.25 97	98.04 70	98.50 250	98.78 122	97.72 36	98.92 85	99.28 76	95.27 71	99.82 98	97.55 139	99.77 42	99.69 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		98.54 45	98.35 48	99.13 59	99.49 53	97.86 76	99.11 66	98.80 115	96.49 119	99.17 63	99.35 62	95.34 67	99.82 98	97.72 117	99.65 81	99.71 63

fmvsm_s_conf0.5_n_5		98.53 46	98.35 48	99.08 64	99.07 128	97.46 95	98.68 203	99.20 33	97.50 52	99.87 4	99.50 31	91.96 150	99.96 4	99.76 11	99.65 81	99.82 23

fmvsm_s_conf0.5_n_3		98.53 46	98.45 39	98.79 86	99.23 105	97.32 100	98.80 165	99.26 16	98.82 7	99.87 4	99.60 10	90.95 197	99.93 34	99.76 11	99.73 62	99.12 208

APD-MVS_3200maxsize		98.53 46	98.33 58	99.15 57	99.50 49	97.92 75	99.15 57	98.81 108	96.24 134	99.20 60	99.37 56	95.30 69	99.80 110	97.73 116	99.67 75	99.72 59

MM		98.51 49	98.24 65	99.33 36	99.12 122	98.14 67	98.93 115	97.02 433	98.96 1	99.17 63	99.47 37	91.97 149	99.94 14	99.85 5	99.69 72	99.91 4

mPP-MVS		98.51 49	98.26 62	99.25 45	99.75 6	98.04 70	99.28 30	98.81 108	96.24 134	98.35 134	99.23 87	95.46 59	99.94 14	97.42 156	99.81 16	99.77 40

ZNCC-MVS		98.49 51	98.20 71	99.35 31	99.73 16	98.39 41	99.19 50	98.86 91	95.77 162	98.31 138	99.10 127	95.46 59	99.93 34	97.57 138	99.81 16	99.74 50

SPE-MVS-test		98.49 51	98.50 34	98.46 123	99.20 110	97.05 125	99.64 4	98.50 200	97.45 58	98.88 86	99.14 115	95.25 73	99.15 265	98.83 41	99.56 107	99.20 191

PGM-MVS		98.49 51	98.23 67	99.27 44	99.72 17	98.08 69	98.99 95	99.49 5	95.43 189	99.03 71	99.32 69	95.56 56	99.94 14	96.80 195	99.77 42	99.78 33

EI-MVSNet-Vis-set		98.47 54	98.39 43	98.69 94	99.46 59	96.49 154	98.30 283	98.69 143	97.21 76	98.84 89	99.36 60	95.41 61	99.78 125	98.62 50	99.65 81	99.80 28

MVS_111021_HR		98.47 54	98.34 54	98.88 83	99.22 107	97.32 100	97.91 346	99.58 3	97.20 77	98.33 136	99.00 154	95.99 44	99.64 158	98.05 94	99.76 48	99.69 70

BridgeMVS		98.45 56	98.35 48	98.74 90	98.65 177	97.55 87	99.19 50	98.60 165	96.72 108	99.35 48	98.77 197	95.06 83	99.55 182	98.95 35	99.87 1	99.12 208

test_fmvsmvis_n_1920		98.44 57	98.51 32	98.23 146	98.33 223	96.15 172	98.97 99	99.15 41	98.55 16	98.45 124	99.55 18	94.26 101	99.97 1	99.65 18	99.66 78	98.57 288

CS-MVS		98.44 57	98.49 36	98.31 137	99.08 127	96.73 139	99.67 3	98.47 207	97.17 80	98.94 79	99.10 127	95.73 52	99.13 270	98.71 45	99.49 118	99.09 216

GST-MVS		98.43 59	98.12 75	99.34 32	99.72 17	98.38 42	99.09 70	98.82 102	95.71 166	98.73 100	99.06 143	95.27 71	99.93 34	97.07 171	99.63 88	99.72 59

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 164	99.30 84	95.25 245	98.85 148	99.39 7	97.94 29	99.74 21	99.62 4	92.59 124	99.91 57	99.65 18	99.52 113	99.25 184

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 102	99.45 62	96.32 164	98.28 286	98.68 146	97.17 80	98.74 98	99.37 56	95.25 73	99.79 122	98.57 53	99.54 110	99.73 55

DELS-MVS		98.40 62	98.20 71	98.99 71	99.00 136	97.66 82	97.75 368	98.89 75	97.71 38	98.33 136	98.97 156	94.97 85	99.88 78	98.42 70	99.76 48	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	99.09 126	95.41 231	98.86 143	99.37 9	97.69 40	99.78 17	99.61 5	92.38 128	99.91 57	99.58 23	99.43 127	99.49 112

TSAR-MVS + GP.		98.38 63	98.24 65	98.81 85	99.22 107	97.25 113	98.11 321	98.29 280	97.19 78	98.99 77	99.02 148	96.22 34	99.67 151	98.52 62	98.56 186	99.51 104

HPM-MVS_fast		98.38 63	98.13 74	99.12 61	99.75 6	97.86 76	99.44 9	98.82 102	94.46 262	98.94 79	99.20 95	95.16 78	99.74 135	97.58 134	99.85 6	99.77 40

patch_mono-2		98.36 66	98.87 7	96.82 286	99.53 43	90.68 411	98.64 213	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 81	99.53 6	98.80 115	94.63 250	98.61 114	98.97 156	95.13 80	99.77 130	97.65 125	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	99.16 116	95.08 256	98.75 178	99.24 20	98.39 19	99.81 13	99.52 25	92.35 129	99.90 65	99.74 13	99.51 115	98.71 269

APD-MVS		98.35 68	98.00 83	99.42 22	99.51 47	98.72 27	98.80 165	98.82 102	94.52 257	99.23 59	99.25 86	95.54 58	99.80 110	96.52 204	99.77 42	99.74 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 70	98.23 67	98.67 96	99.27 94	96.90 131	97.95 339	99.58 3	97.14 83	98.44 127	99.01 152	95.03 84	99.62 165	97.91 103	99.75 54	99.50 107

PHI-MVS		98.34 70	98.06 78	99.18 53	99.15 119	98.12 68	99.04 81	99.09 44	93.32 329	98.83 92	99.10 127	96.54 24	99.83 91	97.70 122	99.76 48	99.59 94

MP-MVS		98.33 72	98.01 82	99.28 42	99.75 6	98.18 62	99.22 42	98.79 120	96.13 139	97.92 170	99.23 87	94.54 91	99.94 14	96.74 198	99.78 40	99.73 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus		98.31 73	98.19 73	98.67 96	98.96 142	97.36 98	99.24 36	98.57 179	94.81 238	98.99 77	98.90 173	95.22 76	99.59 168	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 21	98.43 265	98.78 122	94.10 274	97.69 193	99.42 46	95.25 73	99.92 43	98.09 90	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 97	97.11 122	98.66 210	99.20 33	98.82 7	99.79 15	99.60 10	89.38 246	99.92 43	99.80 8	99.38 135	98.69 271

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 197	98.86 152	94.99 262	98.58 226	99.00 53	98.29 20	99.73 23	99.60 10	91.70 156	99.92 43	99.63 21	99.73 62	98.76 262

MGCNet		98.23 76	97.91 86	99.21 50	98.06 274	97.96 74	98.58 226	95.51 476	98.58 14	98.87 87	99.26 80	92.99 119	99.95 9	99.62 22	99.67 75	99.73 55

ACMMP		98.23 76	97.95 84	99.09 63	99.74 12	97.62 85	99.03 84	99.41 6	95.98 149	97.60 207	99.36 60	94.45 96	99.93 34	97.14 168	98.85 169	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	98.34 219	97.26 112	99.61 5	98.43 227	96.78 101	98.87 87	98.84 181	93.72 109	99.01 299	98.91 38	99.50 116	99.19 195

fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 169	98.54 186	95.24 246	98.87 135	99.24 20	97.50 52	99.70 27	99.67 1	91.33 174	99.89 69	99.47 25	99.54 110	99.21 190

fmvsm_s_conf0.1_n_2		98.14 81	98.02 81	98.53 113	98.88 148	97.07 124	98.69 200	98.82 102	98.78 9	99.77 18	99.61 5	88.83 268	99.91 57	99.71 15	99.07 152	98.61 281

fmvsm_s_conf0.1_n_a		98.08 82	98.04 80	98.21 147	97.66 318	95.39 236	98.89 125	99.17 37	97.24 74	99.76 20	99.67 1	91.13 186	99.88 78	99.39 26	99.41 129	99.35 148

dcpmvs_2		98.08 82	98.59 25	96.56 316	99.57 40	90.34 423	99.15 57	98.38 249	96.82 100	99.29 54	99.49 34	95.78 51	99.57 172	98.94 36	99.86 2	99.77 40

NormalMVS		98.07 84	97.90 87	98.59 104	99.75 6	96.60 145	98.94 109	98.60 165	97.86 33	98.71 103	99.08 138	91.22 181	99.80 110	97.40 158	99.57 99	99.37 143

CANet		98.05 85	97.76 90	98.90 82	98.73 162	97.27 107	98.35 272	98.78 122	97.37 64	97.72 190	98.96 161	91.53 167	99.92 43	98.79 42	99.65 81	99.51 104

train_agg		97.97 86	97.52 103	99.33 36	99.31 80	98.50 36	97.92 344	98.73 133	92.98 345	97.74 187	98.68 211	96.20 36	99.80 110	96.59 199	99.57 99	99.68 75

ETV-MVS		97.96 87	97.81 88	98.40 132	98.42 201	97.27 107	98.73 188	98.55 185	96.84 98	98.38 130	97.44 335	95.39 62	99.35 214	97.62 127	98.89 163	98.58 287

UA-Net		97.96 87	97.62 94	98.98 73	98.86 152	97.47 93	98.89 125	99.08 45	96.67 111	98.72 102	99.54 20	93.15 117	99.81 103	94.87 264	98.83 170	99.65 83

CDPH-MVS		97.94 89	97.49 105	99.28 42	99.47 57	98.44 38	97.91 346	98.67 151	92.57 363	98.77 96	98.85 180	95.93 46	99.72 138	95.56 242	99.69 72	99.68 75

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 344	99.00 136	89.54 439	97.43 393	98.87 85	98.16 22	99.26 58	99.38 55	96.12 39	99.64 158	98.30 77	99.77 42	99.72 59

DeepC-MVS	95.98 3	97.88 91	97.58 96	98.77 88	99.25 97	96.93 129	98.83 154	98.75 128	96.96 93	96.89 239	99.50 31	90.46 211	99.87 80	97.84 110	99.76 48	99.52 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n		97.86 92	97.54 102	98.83 84	95.48 447	96.83 134	98.95 106	98.60 165	98.58 14	98.93 83	99.55 18	88.57 273	99.91 57	99.54 24	99.61 91	99.77 40

DP-MVS Recon		97.86 92	97.46 108	99.06 66	99.53 43	98.35 51	98.33 275	98.89 75	92.62 360	98.05 150	98.94 164	95.34 67	99.65 155	96.04 220	99.42 128	99.19 195

CSCG		97.85 94	97.74 91	98.20 149	99.67 30	95.16 250	99.22 42	99.32 12	93.04 343	97.02 232	98.92 171	95.36 65	99.91 57	97.43 154	99.64 86	99.52 101

SymmetryMVS		97.84 95	97.58 96	98.62 100	99.01 134	96.60 145	98.94 109	98.44 216	97.86 33	98.71 103	99.08 138	91.22 181	99.80 110	97.40 158	97.53 262	99.47 116

BP-MVS1		97.82 96	97.51 104	98.76 89	98.25 239	97.39 97	99.15 57	97.68 361	96.69 109	98.47 120	99.10 127	90.29 219	99.51 188	98.60 51	99.35 138	99.37 143

MG-MVS		97.81 97	97.60 95	98.44 126	99.12 122	95.97 185	97.75 368	98.78 122	96.89 96	98.46 121	99.22 90	93.90 108	99.68 150	94.81 268	99.52 113	99.67 79

VNet		97.79 98	97.40 115	98.96 76	98.88 148	97.55 87	98.63 216	98.93 65	96.74 105	99.02 72	98.84 181	90.33 218	99.83 91	98.53 56	96.66 286	99.50 107

EIA-MVS		97.75 99	97.58 96	98.27 139	98.38 208	96.44 156	99.01 90	98.60 165	95.88 155	97.26 218	97.53 329	94.97 85	99.33 217	97.38 161	99.20 148	99.05 225

PS-MVSNAJ		97.73 100	97.77 89	97.62 229	98.68 172	95.58 219	97.34 402	98.51 195	97.29 67	98.66 110	97.88 293	94.51 92	99.90 65	97.87 107	99.17 150	97.39 334

casdiffmvs_mvg		97.72 101	97.48 107	98.44 126	98.42 201	96.59 149	98.92 118	98.44 216	96.20 136	97.76 184	99.20 95	91.66 159	99.23 247	98.27 84	98.41 210	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	97.10 123	99.12 64	98.81 108	92.34 371	98.09 144	99.08 138	93.01 118	99.92 43	96.06 219	99.77 42	99.75 48

PVSNet_Blended_VisFu		97.70 103	97.46 108	98.44 126	99.27 94	95.91 193	98.63 216	99.16 39	94.48 261	97.67 195	98.88 176	92.80 121	99.91 57	97.11 169	99.12 151	99.50 107

mvsany_test1		97.69 104	97.70 92	97.66 225	98.24 242	94.18 306	97.53 384	97.53 382	95.52 184	99.66 29	99.51 28	94.30 99	99.56 175	98.38 72	98.62 180	99.23 186

sasdasda		97.67 105	97.23 133	98.98 73	98.70 167	98.38 42	99.34 17	98.39 242	96.76 103	97.67 195	97.40 339	92.26 134	99.49 192	98.28 81	96.28 304	99.08 220

canonicalmvs		97.67 105	97.23 133	98.98 73	98.70 167	98.38 42	99.34 17	98.39 242	96.76 103	97.67 195	97.40 339	92.26 134	99.49 192	98.28 81	96.28 304	99.08 220

xiu_mvs_v2_base		97.66 107	97.70 92	97.56 233	98.61 181	95.46 228	97.44 390	98.46 208	97.15 82	98.65 111	98.15 268	94.33 98	99.80 110	97.84 110	98.66 179	97.41 332

GDP-MVS		97.64 108	97.28 126	98.71 93	98.30 228	97.33 99	99.05 77	98.52 192	96.34 130	98.80 93	99.05 145	89.74 233	99.51 188	96.86 191	98.86 167	99.28 174

baseline		97.64 108	97.44 111	98.25 143	98.35 214	96.20 169	99.00 92	98.32 266	96.33 132	98.03 153	99.17 107	91.35 173	99.16 261	98.10 89	98.29 222	99.39 138

casdiffmvs		97.63 110	97.41 114	98.28 138	98.33 223	96.14 173	98.82 156	98.32 266	96.38 127	97.95 164	99.21 93	91.23 180	99.23 247	98.12 88	98.37 213	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
Casviewmamba		97.62 111	97.43 113	98.19 153	98.48 193	95.83 204	99.07 72	98.42 231	96.27 133	98.09 144	99.26 80	91.00 194	99.30 223	97.81 112	98.48 195	99.44 126

MGCFI-Net		97.62 111	97.19 137	98.92 79	98.66 174	98.20 60	99.32 22	98.38 249	96.69 109	97.58 209	97.42 338	92.10 143	99.50 191	98.28 81	96.25 307	99.08 220

xiu_mvs_v1_base_debu		97.60 113	97.56 99	97.72 214	98.35 214	95.98 180	97.86 356	98.51 195	97.13 84	99.01 74	98.40 240	91.56 163	99.80 110	98.53 56	98.68 175	97.37 336

xiu_mvs_v1_base		97.60 113	97.56 99	97.72 214	98.35 214	95.98 180	97.86 356	98.51 195	97.13 84	99.01 74	98.40 240	91.56 163	99.80 110	98.53 56	98.68 175	97.37 336

xiu_mvs_v1_base_debi		97.60 113	97.56 99	97.72 214	98.35 214	95.98 180	97.86 356	98.51 195	97.13 84	99.01 74	98.40 240	91.56 163	99.80 110	98.53 56	98.68 175	97.37 336

diffmvs_AUTHOR		97.59 116	97.44 111	98.01 183	98.26 237	95.47 227	98.12 317	98.36 255	96.38 127	98.84 89	99.10 127	91.13 186	99.26 231	98.24 85	98.56 186	99.30 164

diffmvs		97.58 117	97.40 115	98.13 164	98.32 226	95.81 208	98.06 327	98.37 251	96.20 136	98.74 98	98.89 175	91.31 176	99.25 235	98.16 87	98.52 190	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	98.50 188	95.86 201	98.89 125	97.03 430	97.29 67	98.73 100	98.90 173	89.41 245	99.32 218	98.68 46	98.86 167	99.42 133

MVSFormer		97.57 118	97.49 105	97.84 201	98.07 271	95.76 212	99.47 7	98.40 236	94.98 227	98.79 94	98.83 185	92.34 130	98.41 374	96.91 179	99.59 95	99.34 150

alignmvs		97.56 120	97.07 150	99.01 70	98.66 174	98.37 49	98.83 154	98.06 333	96.74 105	98.00 159	97.65 316	90.80 199	99.48 198	98.37 73	96.56 290	99.19 195

viewmamba		97.55 121	97.45 110	97.87 199	98.22 246	95.13 253	98.35 272	98.35 256	96.57 116	98.45 124	99.15 114	91.60 160	99.18 256	97.99 96	98.36 215	99.29 167

E3new		97.55 121	97.35 121	98.16 155	98.48 193	95.85 202	98.55 239	98.41 233	95.42 191	98.06 148	99.12 122	92.23 137	99.24 243	97.43 154	98.45 198	99.39 138

DPM-MVS		97.55 121	96.99 158	99.23 49	99.04 130	98.55 34	97.17 422	98.35 256	94.85 237	97.93 169	98.58 222	95.07 82	99.71 143	92.60 354	99.34 139	99.43 130

OMC-MVS		97.55 121	97.34 122	98.20 149	99.33 75	95.92 192	98.28 286	98.59 172	95.52 184	97.97 162	99.10 127	93.28 116	99.49 192	95.09 259	98.88 164	99.19 195

onestephybrid01		97.54 125	97.36 119	98.06 176	98.25 239	95.63 217	98.26 289	98.33 262	96.13 139	98.65 111	99.13 118	91.02 193	99.25 235	98.07 91	98.42 208	99.31 159

balanced_ft_v1		97.54 125	97.38 117	98.02 181	98.34 219	95.58 219	99.32 22	98.40 236	95.88 155	98.43 129	98.65 215	88.95 265	99.59 168	98.94 36	99.48 121	98.90 243

viewcassd2359sk11		97.53 127	97.32 123	98.16 155	98.45 197	95.83 204	98.57 235	98.42 231	95.52 184	98.07 146	99.12 122	91.81 154	99.25 235	97.46 152	98.48 195	99.41 136

hybridcas		97.52 128	97.29 125	98.20 149	98.44 198	96.00 178	99.02 87	98.39 242	96.12 142	97.69 193	99.23 87	90.77 204	99.17 259	97.55 139	98.42 208	99.44 126

LuminaMVS		97.49 129	97.18 138	98.42 130	97.50 333	97.15 120	98.45 257	97.68 361	96.56 118	98.68 105	98.78 194	89.84 230	99.32 218	98.60 51	98.57 185	98.79 254

E2		97.48 130	97.25 128	98.16 155	98.40 205	95.79 209	98.58 226	98.44 216	95.58 173	98.00 159	99.14 115	91.21 185	99.24 243	97.50 147	98.43 202	99.45 123

E3		97.48 130	97.25 128	98.16 155	98.38 208	95.79 209	98.58 226	98.44 216	95.58 173	98.00 159	99.14 115	91.25 179	99.24 243	97.50 147	98.44 199	99.45 123

KinetiMVS		97.48 130	97.05 153	98.78 87	98.37 211	97.30 103	98.99 95	98.70 141	97.18 79	99.02 72	99.01 152	87.50 305	99.67 151	95.33 249	99.33 141	99.37 143

viewmanbaseed2359cas		97.47 133	97.25 128	98.14 159	98.41 203	95.84 203	98.57 235	98.43 227	95.55 180	97.97 162	99.12 122	91.26 178	99.15 265	97.42 156	98.53 189	99.43 130

PAPM_NR		97.46 134	97.11 147	98.50 118	99.50 49	96.41 159	98.63 216	98.60 165	95.18 207	97.06 230	98.06 274	94.26 101	99.57 172	93.80 313	98.87 166	99.52 101

EPP-MVSNet		97.46 134	97.28 126	97.99 185	98.64 178	95.38 237	99.33 21	98.31 271	93.61 316	97.19 222	99.07 142	94.05 104	99.23 247	96.89 183	98.43 202	99.37 143

3Dnovator	94.51 5	97.46 134	96.93 162	99.07 65	97.78 306	97.64 83	99.35 16	99.06 47	97.02 89	93.75 361	99.16 110	89.25 250	99.92 43	97.22 167	99.75 54	99.64 86

CNLPA		97.45 137	97.03 155	98.73 91	99.05 129	97.44 96	98.07 326	98.53 189	95.32 200	96.80 245	98.53 227	93.32 114	99.72 138	94.31 294	99.31 143	99.02 229

lupinMVS		97.44 138	97.22 135	98.12 167	98.07 271	95.76 212	97.68 373	97.76 358	94.50 260	98.79 94	98.61 217	92.34 130	99.30 223	97.58 134	99.59 95	99.31 159

3Dnovator+	94.38 6	97.43 139	96.78 174	99.38 24	97.83 302	98.52 35	99.37 13	98.71 138	97.09 87	92.99 391	99.13 118	89.36 247	99.89 69	96.97 175	99.57 99	99.71 63

Vis-MVSNet		97.42 140	97.11 147	98.34 135	98.66 174	96.23 168	99.22 42	99.00 53	96.63 113	98.04 152	99.21 93	88.05 291	99.35 214	96.01 222	99.21 147	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	98.24 242	95.42 230	98.21 294	98.32 266	95.97 150	98.38 130	98.93 166	90.48 210	99.21 252	97.92 102	98.46 197	99.34 150

API-MVS		97.41 141	97.25 128	97.91 194	98.70 167	96.80 135	98.82 156	98.69 143	94.53 255	98.11 142	98.28 255	94.50 95	99.57 172	94.12 302	99.49 118	97.37 336

sss		97.39 143	96.98 160	98.61 102	98.60 182	96.61 144	98.22 293	98.93 65	93.97 284	98.01 158	98.48 233	91.98 147	99.85 85	96.45 206	98.15 231	99.39 138

test_cas_vis1_n_1920		97.38 144	97.36 119	97.45 238	98.95 143	93.25 349	99.00 92	98.53 189	97.70 39	99.77 18	99.35 62	84.71 362	99.85 85	98.57 53	99.66 78	99.26 182

PVSNet_Blended		97.38 144	97.12 146	98.14 159	99.25 97	95.35 240	97.28 408	99.26 16	93.13 339	97.94 166	98.21 263	92.74 122	99.81 103	96.88 185	99.40 132	99.27 175

E5new		97.37 146	97.16 140	97.98 187	98.30 228	95.41 231	98.87 135	98.45 212	95.56 175	97.84 176	99.19 102	90.39 214	99.25 235	97.61 130	98.22 226	99.29 167

E6new		97.37 146	97.16 140	97.98 187	98.28 234	95.40 234	98.87 135	98.45 212	95.55 180	97.84 176	99.20 95	90.44 212	99.25 235	97.61 130	98.22 226	99.29 167

E6		97.37 146	97.16 140	97.98 187	98.28 234	95.40 234	98.87 135	98.45 212	95.55 180	97.84 176	99.20 95	90.44 212	99.25 235	97.61 130	98.22 226	99.29 167

E5		97.37 146	97.16 140	97.98 187	98.30 228	95.41 231	98.87 135	98.45 212	95.56 175	97.84 176	99.19 102	90.39 214	99.25 235	97.61 130	98.22 226	99.29 167

E4		97.37 146	97.13 145	98.12 167	98.27 236	95.70 214	98.59 222	98.44 216	95.56 175	97.80 181	99.18 105	90.57 208	99.26 231	97.45 153	98.28 224	99.40 137

WTY-MVS		97.37 146	96.92 163	98.72 92	98.86 152	96.89 133	98.31 280	98.71 138	95.26 203	97.67 195	98.56 226	92.21 139	99.78 125	95.89 224	96.85 279	99.48 114

hybrid		97.34 152	97.16 140	97.88 198	98.25 239	95.18 249	98.18 307	98.33 262	95.36 197	98.35 134	99.06 143	90.61 206	99.18 256	97.88 106	98.40 211	99.27 175

AstraMVS		97.34 152	97.24 132	97.65 226	98.13 265	94.15 307	98.94 109	96.25 466	97.47 56	98.60 115	99.28 76	89.67 235	99.41 208	98.73 44	98.07 235	99.38 142

viewmacassd2359aftdt		97.32 154	97.07 150	98.08 172	98.30 228	95.69 215	98.62 219	98.44 216	95.56 175	97.86 175	99.22 90	89.91 228	99.14 268	97.29 164	98.43 202	99.42 133

jason		97.32 154	97.08 149	98.06 176	97.45 339	95.59 218	97.87 354	97.91 345	94.79 240	98.55 118	98.83 185	91.12 188	99.23 247	97.58 134	99.60 93	99.34 150
jason: jason.
MVS_Test		97.28 156	97.00 156	98.13 164	98.33 223	95.97 185	98.74 182	98.07 328	94.27 269	98.44 127	98.07 273	92.48 126	99.26 231	96.43 207	98.19 230	99.16 201

EPNet		97.28 156	96.87 165	98.51 115	94.98 456	96.14 173	98.90 121	97.02 433	98.28 21	95.99 281	99.11 125	91.36 172	99.89 69	96.98 174	99.19 149	99.50 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_0404		97.26 158	97.00 156	98.03 179	98.46 195	95.99 179	98.62 219	98.44 216	94.77 241	97.24 219	98.93 166	91.22 181	99.28 228	96.54 201	98.74 174	98.84 249

mvsmamba		97.25 159	96.99 158	98.02 181	98.34 219	95.54 224	99.18 54	97.47 388	95.04 220	98.15 139	98.57 225	89.46 242	99.31 222	97.68 124	99.01 157	99.22 188

viewdifsd2359ckpt13		97.24 160	96.97 161	98.06 176	98.43 199	95.77 211	98.59 222	98.34 260	94.81 238	97.60 207	98.94 164	90.78 203	99.09 280	96.93 178	98.33 218	99.32 158

test_yl		97.22 161	96.78 174	98.54 110	98.73 162	96.60 145	98.45 257	98.31 271	94.70 244	98.02 155	98.42 238	90.80 199	99.70 144	96.81 192	96.79 281	99.34 150

DCV-MVSNet		97.22 161	96.78 174	98.54 110	98.73 162	96.60 145	98.45 257	98.31 271	94.70 244	98.02 155	98.42 238	90.80 199	99.70 144	96.81 192	96.79 281	99.34 150

IS-MVSNet		97.22 161	96.88 164	98.25 143	98.85 155	96.36 162	99.19 50	97.97 339	95.39 193	97.23 220	98.99 155	91.11 189	98.93 312	94.60 282	98.59 182	99.47 116

viewdifsd2359ckpt07		97.20 164	97.05 153	97.65 226	98.40 205	94.33 298	98.39 270	98.43 227	95.67 168	97.66 199	99.08 138	90.04 225	99.32 218	97.47 151	98.29 222	99.31 159

PLC	95.07 4	97.20 164	96.78 174	98.44 126	99.29 89	96.31 166	98.14 314	98.76 126	92.41 369	96.39 268	98.31 253	94.92 87	99.78 125	94.06 305	98.77 173	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	98.81 158	93.27 346	95.78 474	99.15 41	95.25 204	96.79 246	98.11 271	92.29 133	99.07 284	98.56 55	99.85 6	99.25 184

SSM_0407		97.17 167	96.87 165	98.08 172	98.19 252	95.90 194	98.52 242	98.44 216	94.77 241	96.75 247	98.93 166	91.22 181	99.22 251	96.54 201	98.43 202	99.10 213

LS3D		97.16 168	96.66 183	98.68 95	98.53 187	97.19 117	98.93 115	98.90 73	92.83 353	95.99 281	99.37 56	92.12 142	99.87 80	93.67 317	99.57 99	98.97 234

AdaColmap		97.15 169	96.70 179	98.48 121	99.16 116	96.69 141	98.01 333	98.89 75	94.44 263	96.83 241	98.68 211	90.69 205	99.76 131	94.36 290	99.29 144	98.98 233

viewdifsd2359ckpt09		97.13 170	96.79 172	98.14 159	98.43 199	95.90 194	98.52 242	98.37 251	94.32 267	97.33 214	98.86 179	90.23 222	99.16 261	96.81 192	98.25 225	99.36 147

Effi-MVS+		97.12 171	96.69 180	98.39 133	98.19 252	96.72 140	97.37 398	98.43 227	93.71 303	97.65 201	98.02 277	92.20 140	99.25 235	96.87 188	97.79 245	99.19 195

CHOSEN 1792x2688		97.12 171	96.80 170	98.08 172	99.30 84	94.56 287	98.05 328	99.71 1	93.57 318	97.09 226	98.91 172	88.17 285	99.89 69	96.87 188	99.56 107	99.81 25

F-COLMAP		97.09 173	96.80 170	97.97 191	99.45 62	94.95 266	98.55 239	98.62 164	93.02 344	96.17 276	98.58 222	94.01 105	99.81 103	93.95 307	98.90 162	99.14 205

RRT-MVS		97.03 174	96.78 174	97.77 210	97.90 298	94.34 296	99.12 64	98.35 256	95.87 157	98.06 148	98.70 209	86.45 325	99.63 161	98.04 95	98.54 188	99.35 148

TAMVS		97.02 175	96.79 172	97.70 217	98.06 274	95.31 243	98.52 242	98.31 271	93.95 285	97.05 231	98.61 217	93.49 112	98.52 356	95.33 249	97.81 244	99.29 167

viewmambaseed2359dif		97.01 176	96.84 167	97.51 235	98.19 252	94.21 304	98.16 310	98.23 292	93.61 316	97.78 182	99.13 118	90.79 202	99.18 256	97.24 165	98.40 211	99.15 202

dtuplus		97.00 177	96.83 169	97.51 235	98.18 258	94.21 304	98.21 294	98.20 296	94.42 265	97.66 199	99.22 90	90.18 223	99.17 259	97.01 172	98.36 215	99.13 207

CDS-MVSNet		96.99 178	96.69 180	97.90 195	98.05 276	95.98 180	98.20 298	98.33 262	93.67 310	96.95 233	98.49 232	93.54 111	98.42 367	95.24 256	97.74 249	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	96.55 188	98.25 143	98.26 237	96.28 167	98.93 115	98.33 262	94.99 225	96.87 240	99.09 135	88.97 263	99.07 284	95.70 237	97.77 247	99.39 138

CANet_DTU		96.96 180	96.55 188	98.21 147	98.17 262	96.07 177	97.98 337	98.21 294	97.24 74	97.13 224	98.93 166	86.88 317	99.91 57	95.00 262	99.37 137	98.66 277

114514_t		96.93 181	96.27 202	98.92 79	99.50 49	97.63 84	98.85 148	98.90 73	84.80 482	97.77 183	99.11 125	92.84 120	99.66 154	94.85 265	99.77 42	99.47 116

MAR-MVS		96.91 182	96.40 196	98.45 124	98.69 170	96.90 131	98.66 210	98.68 146	92.40 370	97.07 229	97.96 284	91.54 166	99.75 133	93.68 315	98.92 161	98.69 271
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	96.49 193	98.14 159	99.33 75	95.56 221	97.38 396	99.65 2	92.34 371	97.61 204	98.20 264	89.29 249	99.10 279	96.97 175	97.60 254	99.77 40

Vis-MVSNet (Re-imp)		96.87 184	96.55 188	97.83 202	98.73 162	95.46 228	99.20 48	98.30 278	94.96 229	96.60 256	98.87 177	90.05 224	98.59 351	93.67 317	98.60 181	99.46 121

SDMVSNet		96.85 185	96.42 194	98.14 159	99.30 84	96.38 160	99.21 45	99.23 27	95.92 152	95.96 283	98.76 202	85.88 337	99.44 205	97.93 100	95.59 319	98.60 282

PAPR		96.84 186	96.24 204	98.65 98	98.72 166	96.92 130	97.36 400	98.57 179	93.33 328	96.67 251	97.57 325	94.30 99	99.56 175	91.05 398	98.59 182	99.47 116

HY-MVS	93.96 8	96.82 187	96.23 205	98.57 105	98.46 195	97.00 126	98.14 314	98.21 294	93.95 285	96.72 250	97.99 281	91.58 161	99.76 131	94.51 286	96.54 291	98.95 237

mamba_0408		96.81 188	96.38 197	98.09 171	98.19 252	95.90 194	95.69 475	98.32 266	94.51 258	96.75 247	98.73 205	90.99 195	99.27 230	95.83 227	98.43 202	99.10 213

UGNet		96.78 189	96.30 201	98.19 153	98.24 242	95.89 199	98.88 132	98.93 65	97.39 61	96.81 244	97.84 297	82.60 391	99.90 65	96.53 203	99.49 118	98.79 254
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
PRO-TEST		96.74 190	97.06 152	95.76 377	98.37 211	88.85 453	99.06 74	98.02 338	96.35 129	97.94 166	98.76 202	87.22 310	99.49 192	98.42 70	99.40 132	98.94 238

IMVS_0407		96.74 190	96.64 184	97.05 266	97.99 285	92.82 363	98.45 257	98.27 281	95.16 208	97.30 215	98.79 190	91.53 167	99.06 286	94.74 270	97.54 258	99.27 175

IMVS_0403		96.74 190	96.61 185	97.12 260	97.99 285	92.82 363	98.47 255	98.27 281	95.16 208	97.13 224	98.79 190	91.44 170	99.26 231	94.74 270	97.54 258	99.27 175

PVSNet_BlendedMVS		96.73 193	96.60 186	97.12 260	99.25 97	95.35 240	98.26 289	99.26 16	94.28 268	97.94 166	97.46 332	92.74 122	99.81 103	96.88 185	93.32 357	96.20 438

SSM_04072		96.71 194	96.38 197	97.68 220	98.19 252	95.90 194	95.69 475	98.32 266	94.51 258	96.75 247	98.73 205	90.99 195	98.02 423	95.83 227	98.43 202	99.10 213

test_vis1_n_1920		96.71 194	96.84 167	96.31 343	99.11 124	89.74 432	99.05 77	98.58 177	98.08 24	99.87 4	99.37 56	78.48 431	99.93 34	99.29 27	99.69 72	99.27 175

mvs_anonymous		96.70 196	96.53 191	97.18 254	98.19 252	93.78 317	98.31 280	98.19 299	94.01 281	94.47 316	98.27 258	92.08 145	98.46 362	97.39 160	97.91 240	99.31 159

Elysia		96.64 197	96.02 214	98.51 115	98.04 278	97.30 103	98.74 182	98.60 165	95.04 220	97.91 171	98.84 181	83.59 386	99.48 198	94.20 298	99.25 145	98.75 263

StellarMVS		96.64 197	96.02 214	98.51 115	98.04 278	97.30 103	98.74 182	98.60 165	95.04 220	97.91 171	98.84 181	83.59 386	99.48 198	94.20 298	99.25 145	98.75 263

1112_ss		96.63 199	96.00 216	98.50 118	98.56 183	96.37 161	98.18 307	98.10 321	92.92 348	94.84 303	98.43 236	92.14 141	99.58 171	94.35 291	96.51 292	99.56 100

PMMVS		96.60 200	96.33 200	97.41 242	97.90 298	93.93 313	97.35 401	98.41 233	92.84 352	97.76 184	97.45 334	91.10 190	99.20 253	96.26 212	97.91 240	99.11 211

DP-MVS		96.59 201	95.93 219	98.57 105	99.34 72	96.19 171	98.70 197	98.39 242	89.45 442	94.52 314	99.35 62	91.85 151	99.85 85	92.89 342	98.88 164	99.68 75

PatchMatch-RL		96.59 201	96.03 213	98.27 139	99.31 80	96.51 153	97.91 346	99.06 47	93.72 302	96.92 237	98.06 274	88.50 278	99.65 155	91.77 380	99.00 159	98.66 277

GeoE		96.58 203	96.07 210	98.10 170	98.35 214	95.89 199	99.34 17	98.12 315	93.12 340	96.09 277	98.87 177	89.71 234	98.97 302	92.95 338	98.08 234	99.43 130

icg_test_0407_2		96.56 204	96.50 192	96.73 292	97.99 285	92.82 363	97.18 419	98.27 281	95.16 208	97.30 215	98.79 190	91.53 167	98.10 408	94.74 270	97.54 258	99.27 175

XVG-OURS		96.55 205	96.41 195	96.99 269	98.75 161	93.76 318	97.50 387	98.52 192	95.67 168	96.83 241	99.30 74	88.95 265	99.53 184	95.88 225	96.26 306	97.69 325

FIs		96.51 206	96.12 209	97.67 222	97.13 363	97.54 89	99.36 14	99.22 32	95.89 154	94.03 345	98.35 246	91.98 147	98.44 365	96.40 208	92.76 365	97.01 344

XVG-OURS-SEG-HR		96.51 206	96.34 199	97.02 268	98.77 160	93.76 318	97.79 365	98.50 200	95.45 188	96.94 234	99.09 135	87.87 296	99.55 182	96.76 197	95.83 318	97.74 322

PS-MVSNAJss		96.43 208	96.26 203	96.92 280	95.84 434	95.08 256	99.16 56	98.50 200	95.87 157	93.84 356	98.34 250	94.51 92	98.61 347	96.88 185	93.45 352	97.06 342

test_fmvs1		96.42 209	96.67 182	95.66 381	98.82 157	88.53 460	98.80 165	98.20 296	96.39 126	99.64 31	99.20 95	80.35 416	99.67 151	99.04 32	99.57 99	98.78 258

FC-MVSNet-test		96.42 209	96.05 211	97.53 234	96.95 372	97.27 107	99.36 14	99.23 27	95.83 159	93.93 348	98.37 244	92.00 146	98.32 386	96.02 221	92.72 366	97.00 345

ab-mvs		96.42 209	95.71 230	98.55 108	98.63 179	96.75 138	97.88 353	98.74 130	93.84 292	96.54 261	98.18 266	85.34 348	99.75 133	95.93 223	96.35 296	99.15 202

FA-MVS(test-final)		96.41 212	95.94 218	97.82 204	98.21 248	95.20 248	97.80 363	97.58 372	93.21 334	97.36 213	97.70 309	89.47 240	99.56 175	94.12 302	97.99 237	98.71 269

PVSNet	91.96 18	96.35 213	96.15 206	96.96 275	99.17 112	92.05 384	96.08 467	98.68 146	93.69 306	97.75 186	97.80 303	88.86 267	99.69 149	94.26 296	99.01 157	99.15 202

Test_1112_low_res		96.34 214	95.66 235	98.36 134	98.56 183	95.94 188	97.71 371	98.07 328	92.10 381	94.79 307	97.29 348	91.75 155	99.56 175	94.17 300	96.50 293	99.58 98

viewdifsd2359ckpt11		96.30 215	96.13 207	96.81 287	98.10 268	92.10 380	98.49 253	98.40 236	96.02 146	97.61 204	99.31 71	86.37 327	99.29 226	97.52 143	93.36 356	99.04 226

viewmsd2359difaftdt		96.30 215	96.13 207	96.81 287	98.10 268	92.10 380	98.49 253	98.40 236	96.02 146	97.61 204	99.31 71	86.37 327	99.30 223	97.52 143	93.37 355	99.04 226

Effi-MVS+-dtu		96.29 217	96.56 187	95.51 386	97.89 300	90.22 424	98.80 165	98.10 321	96.57 116	96.45 266	96.66 406	90.81 198	98.91 315	95.72 234	97.99 237	97.40 333

QAPM		96.29 217	95.40 241	98.96 76	97.85 301	97.60 86	99.23 38	98.93 65	89.76 436	93.11 388	99.02 148	89.11 255	99.93 34	91.99 373	99.62 90	99.34 150

Fast-Effi-MVS+		96.28 219	95.70 232	98.03 179	98.29 232	95.97 185	98.58 226	98.25 290	91.74 389	95.29 296	97.23 353	91.03 192	99.15 265	92.90 340	97.96 239	98.97 234

nrg030		96.28 219	95.72 227	97.96 193	96.90 377	98.15 65	99.39 11	98.31 271	95.47 187	94.42 322	98.35 246	92.09 144	98.69 339	97.50 147	89.05 419	97.04 343

1314		96.25 221	95.73 226	97.79 206	97.13 363	95.55 223	98.19 301	98.59 172	93.47 322	92.03 424	97.82 301	91.33 174	99.49 192	94.62 280	98.44 199	98.32 302

sd_testset		96.17 222	95.76 225	97.42 241	99.30 84	94.34 296	98.82 156	99.08 45	95.92 152	95.96 283	98.76 202	82.83 390	99.32 218	95.56 242	95.59 319	98.60 282

h-mvs33		96.17 222	95.62 236	97.81 205	99.03 131	94.45 289	98.64 213	98.75 128	97.48 54	98.67 106	98.72 208	89.76 231	99.86 84	97.95 98	81.59 473	99.11 211

HQP_MVS		96.14 224	95.90 220	96.85 284	97.42 341	94.60 285	98.80 165	98.56 183	97.28 69	95.34 292	98.28 255	87.09 312	99.03 293	96.07 216	94.27 327	96.92 352

tttt0517		96.07 225	95.51 239	97.78 207	98.41 203	94.84 270	99.28 30	94.33 493	94.26 270	97.64 202	98.64 216	84.05 377	99.47 202	95.34 248	97.60 254	99.03 228

MVSTER		96.06 226	95.72 227	97.08 264	98.23 245	95.93 191	98.73 188	98.27 281	94.86 235	95.07 298	98.09 272	88.21 284	98.54 354	96.59 199	93.46 350	96.79 371

thisisatest0530		96.01 227	95.36 246	97.97 191	98.38 208	95.52 225	98.88 132	94.19 497	94.04 276	97.64 202	98.31 253	83.82 384	99.46 203	95.29 253	97.70 251	98.93 240

test_djsdf		96.00 228	95.69 233	96.93 277	95.72 437	95.49 226	99.47 7	98.40 236	94.98 227	94.58 312	97.86 294	89.16 253	98.41 374	96.91 179	94.12 335	96.88 361

EI-MVSNet		95.96 229	95.83 222	96.36 339	97.93 296	93.70 324	98.12 317	98.27 281	93.70 305	95.07 298	99.02 148	92.23 137	98.54 354	94.68 275	93.46 350	96.84 367

VortexMVS		95.95 230	95.79 223	96.42 334	98.29 232	93.96 312	98.68 203	98.31 271	96.02 146	94.29 330	97.57 325	89.47 240	98.37 381	97.51 146	91.93 375	96.94 350

ECVR-MVS		95.95 230	95.71 230	96.65 301	99.02 132	90.86 406	99.03 84	91.80 510	96.96 93	98.10 143	99.26 80	81.31 402	99.51 188	96.90 182	99.04 154	99.59 94

BH-untuned		95.95 230	95.72 227	96.65 301	98.55 185	92.26 375	98.23 292	97.79 357	93.73 300	94.62 311	98.01 279	88.97 263	99.00 300	93.04 335	98.51 191	98.68 273

test1111		95.94 233	95.78 224	96.41 335	98.99 139	90.12 425	99.04 81	92.45 509	96.99 92	98.03 153	99.27 79	81.40 401	99.48 198	96.87 188	99.04 154	99.63 88

MSDG		95.93 234	95.30 253	97.83 202	98.90 146	95.36 238	96.83 452	98.37 251	91.32 405	94.43 321	98.73 205	90.27 220	99.60 167	90.05 412	98.82 171	98.52 290

BH-RMVSNet		95.92 235	95.32 251	97.69 218	98.32 226	94.64 279	98.19 301	97.45 393	94.56 253	96.03 279	98.61 217	85.02 353	99.12 273	90.68 403	99.06 153	99.30 164

test_fmvs1_n		95.90 236	95.99 217	95.63 382	98.67 173	88.32 464	99.26 33	98.22 293	96.40 125	99.67 28	99.26 80	73.91 473	99.70 144	99.02 34	99.50 116	98.87 245

Fast-Effi-MVS+-dtu		95.87 237	95.85 221	95.91 365	97.74 311	91.74 390	98.69 200	98.15 311	95.56 175	94.92 301	97.68 314	88.98 262	98.79 333	93.19 329	97.78 246	97.20 340

LFMVS		95.86 238	94.98 269	98.47 122	98.87 151	96.32 164	98.84 152	96.02 467	93.40 326	98.62 113	99.20 95	74.99 465	99.63 161	97.72 117	97.20 267	99.46 121

baseline1		95.84 239	95.12 261	98.01 183	98.49 192	95.98 180	98.73 188	97.03 430	95.37 196	96.22 272	98.19 265	89.96 227	99.16 261	94.60 282	87.48 436	98.90 243

OpenMVS	93.04 13	95.83 240	95.00 267	98.32 136	97.18 360	97.32 100	99.21 45	98.97 57	89.96 432	91.14 434	99.05 145	86.64 320	99.92 43	93.38 323	99.47 122	97.73 323

IMVS_0404		95.82 241	95.52 237	96.73 292	97.99 285	92.82 363	97.23 410	98.27 281	95.16 208	94.31 328	98.79 190	85.63 341	98.10 408	94.74 270	97.54 258	99.27 175

VDD-MVS		95.82 241	95.23 255	97.61 230	98.84 156	93.98 311	98.68 203	97.40 397	95.02 224	97.95 164	99.34 68	74.37 471	99.78 125	98.64 49	96.80 280	99.08 220

UniMVSNet (Re)		95.78 243	95.19 257	97.58 231	96.99 370	97.47 93	98.79 173	99.18 36	95.60 171	93.92 349	97.04 375	91.68 157	98.48 358	95.80 231	87.66 435	96.79 371

VPA-MVSNet		95.75 244	95.11 262	97.69 218	97.24 352	97.27 107	98.94 109	99.23 27	95.13 213	95.51 290	97.32 346	85.73 339	98.91 315	97.33 163	89.55 410	96.89 360

HQP-MVS		95.72 245	95.40 241	96.69 298	97.20 356	94.25 302	98.05 328	98.46 208	96.43 121	94.45 317	97.73 306	86.75 318	98.96 306	95.30 251	94.18 331	96.86 366

hse-mvs2		95.71 246	95.30 253	96.93 277	98.50 188	93.53 329	98.36 271	98.10 321	97.48 54	98.67 106	97.99 281	89.76 231	99.02 297	97.95 98	80.91 479	98.22 305

UniMVSNet_NR-MVSNet		95.71 246	95.15 258	97.40 244	96.84 380	96.97 127	98.74 182	99.24 20	95.16 208	93.88 351	97.72 308	91.68 157	98.31 388	95.81 229	87.25 441	96.92 352

PatchmatchNet		95.71 246	95.52 237	96.29 345	97.58 324	90.72 410	96.84 451	97.52 383	94.06 275	97.08 227	96.96 385	89.24 251	98.90 318	92.03 372	98.37 213	99.26 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 249	95.33 250	96.76 291	96.16 417	94.63 280	98.43 265	98.39 242	96.64 112	95.02 300	98.78 194	85.15 352	99.05 287	95.21 258	94.20 330	96.60 397
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 249	95.38 245	96.61 309	97.61 321	93.84 316	98.91 120	98.44 216	95.25 204	94.28 331	98.47 234	86.04 336	99.12 273	95.50 245	93.95 340	96.87 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 251	95.69 233	95.44 390	97.54 329	88.54 459	96.97 434	97.56 375	93.50 320	97.52 211	96.93 390	89.49 238	99.16 261	95.25 255	96.42 295	98.64 279

FE-MVS		95.62 252	94.90 273	97.78 207	98.37 211	94.92 267	97.17 422	97.38 399	90.95 416	97.73 189	97.70 309	85.32 350	99.63 161	91.18 390	98.33 218	98.79 254

LPG-MVS_test		95.62 252	95.34 247	96.47 328	97.46 336	93.54 327	98.99 95	98.54 187	94.67 248	94.36 325	98.77 197	85.39 345	99.11 275	95.71 235	94.15 333	96.76 374

CLD-MVS		95.62 252	95.34 247	96.46 331	97.52 332	93.75 320	97.27 409	98.46 208	95.53 183	94.42 322	98.00 280	86.21 331	98.97 302	96.25 214	94.37 325	96.66 389
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 255	94.89 274	97.76 211	98.15 264	95.15 252	96.77 453	94.41 491	92.95 347	97.18 223	97.43 336	84.78 359	99.45 204	94.63 278	97.73 250	98.68 273

MonoMVSNet		95.51 256	95.45 240	95.68 379	95.54 443	90.87 405	98.92 118	97.37 400	95.79 161	95.53 289	97.38 341	89.58 237	97.68 448	96.40 208	92.59 367	98.49 292

thres600view7		95.49 257	94.77 277	97.67 222	98.98 140	95.02 258	98.85 148	96.90 441	95.38 194	96.63 253	96.90 392	84.29 369	99.59 168	88.65 436	96.33 297	98.40 296

test_vis1_n		95.47 258	95.13 259	96.49 325	97.77 307	90.41 420	99.27 32	98.11 318	96.58 114	99.66 29	99.18 105	67.00 488	99.62 165	99.21 28	99.40 132	99.44 126

SCA		95.46 259	95.13 259	96.46 331	97.67 316	91.29 398	97.33 403	97.60 371	94.68 247	96.92 237	97.10 360	83.97 379	98.89 319	92.59 356	98.32 221	99.20 191

IterMVS-LS		95.46 259	95.21 256	96.22 347	98.12 266	93.72 323	98.32 279	98.13 314	93.71 303	94.26 332	97.31 347	92.24 136	98.10 408	94.63 278	90.12 401	96.84 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 261	95.34 247	95.77 376	98.69 170	88.75 455	98.87 135	97.21 415	96.13 139	97.22 221	97.68 314	77.95 439	99.65 155	97.58 134	96.77 283	98.91 242

jajsoiax		95.45 261	95.03 266	96.73 292	95.42 451	94.63 280	99.14 60	98.52 192	95.74 163	93.22 381	98.36 245	83.87 382	98.65 344	96.95 177	94.04 336	96.91 357

CVMVSNet		95.43 263	96.04 212	93.57 444	97.93 296	83.62 489	98.12 317	98.59 172	95.68 167	96.56 257	99.02 148	87.51 303	97.51 457	93.56 321	97.44 263	99.60 92

anonymousdsp		95.42 264	94.91 272	96.94 276	95.10 455	95.90 194	99.14 60	98.41 233	93.75 297	93.16 384	97.46 332	87.50 305	98.41 374	95.63 240	94.03 337	96.50 422

DU-MVS		95.42 264	94.76 278	97.40 244	96.53 397	96.97 127	98.66 210	98.99 56	95.43 189	93.88 351	97.69 311	88.57 273	98.31 388	95.81 229	87.25 441	96.92 352

mvs_tets		95.41 266	95.00 267	96.65 301	95.58 442	94.42 291	99.00 92	98.55 185	95.73 165	93.21 382	98.38 243	83.45 388	98.63 345	97.09 170	94.00 338	96.91 357

thres100view900		95.38 267	94.70 282	97.41 242	98.98 140	94.92 267	98.87 135	96.90 441	95.38 194	96.61 255	96.88 393	84.29 369	99.56 175	88.11 440	96.29 301	97.76 320

thres400		95.38 267	94.62 286	97.65 226	98.94 144	94.98 263	98.68 203	96.93 439	95.33 198	96.55 259	96.53 412	84.23 373	99.56 175	88.11 440	96.29 301	98.40 296

BH-w/o		95.38 267	95.08 264	96.26 346	98.34 219	91.79 387	97.70 372	97.43 395	92.87 351	94.24 334	97.22 354	88.66 271	98.84 325	91.55 386	97.70 251	98.16 309

VDDNet		95.36 270	94.53 291	97.86 200	98.10 268	95.13 253	98.85 148	97.75 359	90.46 423	98.36 132	99.39 50	73.27 475	99.64 158	97.98 97	96.58 289	98.81 252

TAPA-MVS	93.98 7	95.35 271	94.56 290	97.74 213	99.13 120	94.83 272	98.33 275	98.64 159	86.62 468	96.29 270	98.61 217	94.00 106	99.29 226	80.00 489	99.41 129	99.09 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 272	94.98 269	96.43 333	97.67 316	93.48 331	98.73 188	98.44 216	94.94 233	92.53 405	98.53 227	84.50 368	99.14 268	95.48 246	94.00 338	96.66 389
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 273	94.87 275	96.71 295	99.29 89	93.24 350	98.58 226	98.11 318	89.92 433	93.57 366	99.10 127	86.37 327	99.79 122	90.78 401	98.10 233	97.09 341
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 274	94.72 281	97.13 258	98.05 276	93.26 347	97.87 354	97.20 418	94.96 229	96.18 275	95.66 451	80.97 408	99.35 214	94.47 288	97.08 270	98.78 258

tfpn200view9		95.32 274	94.62 286	97.43 240	98.94 144	94.98 263	98.68 203	96.93 439	95.33 198	96.55 259	96.53 412	84.23 373	99.56 175	88.11 440	96.29 301	97.76 320

Anonymous202405211		95.28 276	94.49 293	97.67 222	99.00 136	93.75 320	98.70 197	97.04 429	90.66 419	96.49 263	98.80 188	78.13 435	99.83 91	96.21 215	95.36 323	99.44 126

thres200		95.25 277	94.57 289	97.28 248	98.81 158	94.92 267	98.20 298	97.11 422	95.24 206	96.54 261	96.22 427	84.58 366	99.53 184	87.93 446	96.50 293	97.39 334

AllTest		95.24 278	94.65 285	96.99 269	99.25 97	93.21 351	98.59 222	98.18 302	91.36 401	93.52 368	98.77 197	84.67 363	99.72 138	89.70 419	97.87 242	98.02 314

LCM-MVSNet-Re		95.22 279	95.32 251	94.91 408	98.18 258	87.85 471	98.75 178	95.66 474	95.11 215	88.96 458	96.85 396	90.26 221	97.65 449	95.65 239	98.44 199	99.22 188

EPNet_dtu		95.21 280	94.95 271	95.99 358	96.17 415	90.45 418	98.16 310	97.27 410	96.77 102	93.14 387	98.33 251	90.34 217	98.42 367	85.57 462	98.81 172	99.09 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 281	94.45 299	97.46 237	96.75 387	96.56 151	98.86 143	98.65 158	93.30 331	93.27 380	98.27 258	84.85 357	98.87 322	94.82 267	91.26 386	96.96 347

D2MVS		95.18 282	95.08 264	95.48 387	97.10 365	92.07 383	98.30 283	99.13 43	94.02 278	92.90 392	96.73 402	89.48 239	98.73 337	94.48 287	93.60 349	95.65 454

WR-MVS		95.15 283	94.46 296	97.22 250	96.67 392	96.45 155	98.21 294	98.81 108	94.15 272	93.16 384	97.69 311	87.51 303	98.30 390	95.29 253	88.62 425	96.90 359

TranMVSNet+NR-MVSNet		95.14 284	94.48 294	97.11 262	96.45 404	96.36 162	99.03 84	99.03 50	95.04 220	93.58 365	97.93 287	88.27 283	98.03 422	94.13 301	86.90 446	96.95 349

myMVS_eth3d28		95.12 285	94.62 286	96.64 305	98.17 262	92.17 376	98.02 332	97.32 403	95.41 192	96.22 272	96.05 433	78.01 437	99.13 270	95.22 257	97.16 268	98.60 282

baseline2		95.11 286	94.52 292	96.87 282	96.65 393	93.56 326	98.27 288	94.10 499	93.45 323	92.02 425	97.43 336	87.45 308	99.19 254	93.88 310	97.41 265	97.87 318

miper_enhance_ethall		95.10 287	94.75 279	96.12 351	97.53 331	93.73 322	96.61 459	98.08 326	92.20 379	93.89 350	96.65 408	92.44 127	98.30 390	94.21 297	91.16 387	96.34 431

Anonymous20240529		95.10 287	94.22 312	97.75 212	99.01 134	94.26 301	98.87 135	98.83 98	85.79 476	96.64 252	98.97 156	78.73 428	99.85 85	96.27 211	94.89 324	99.12 208

test-LLR		95.10 287	94.87 275	95.80 373	96.77 384	89.70 434	96.91 440	95.21 480	95.11 215	94.83 305	95.72 447	87.71 298	98.97 302	93.06 333	98.50 192	98.72 266

dtuonly		95.08 290	95.10 263	95.02 404	96.53 397	87.27 475	96.33 466	97.21 415	93.41 325	96.28 271	98.51 231	87.71 298	98.99 301	91.88 377	98.01 236	98.80 253

WR-MVS_H		95.05 291	94.46 296	96.81 287	96.86 379	95.82 207	99.24 36	99.24 20	93.87 291	92.53 405	96.84 397	90.37 216	98.24 396	93.24 327	87.93 431	96.38 430

miper_ehance_all_eth		95.01 292	94.69 283	95.97 362	97.70 314	93.31 343	97.02 432	98.07 328	92.23 376	93.51 370	96.96 385	91.85 151	98.15 403	93.68 315	91.16 387	96.44 428

testing11		95.00 293	94.28 307	97.16 256	97.96 293	93.36 340	98.09 324	97.06 428	94.94 233	95.33 295	96.15 429	76.89 452	99.40 209	95.77 233	96.30 300	98.72 266

ADS-MVSNet		95.00 293	94.45 299	96.63 306	98.00 283	91.91 386	96.04 468	97.74 360	90.15 429	96.47 264	96.64 409	87.89 294	98.96 306	90.08 410	97.06 271	99.02 229

VPNet		94.99 295	94.19 314	97.40 244	97.16 361	96.57 150	98.71 193	98.97 57	95.67 168	94.84 303	98.24 262	80.36 415	98.67 343	96.46 205	87.32 440	96.96 347

EPMVS		94.99 295	94.48 294	96.52 322	97.22 354	91.75 389	97.23 410	91.66 511	94.11 273	97.28 217	96.81 399	85.70 340	98.84 325	93.04 335	97.28 266	98.97 234

testing91		94.98 297	94.25 311	97.20 251	97.94 294	93.41 334	98.00 335	97.58 372	94.99 225	95.45 291	96.04 435	77.20 447	99.42 207	94.97 263	96.02 314	98.78 258

NR-MVSNet		94.98 297	94.16 317	97.44 239	96.53 397	97.22 115	98.74 182	98.95 61	94.96 229	89.25 456	97.69 311	89.32 248	98.18 400	94.59 284	87.40 438	96.92 352

nomal-1		94.97 299	94.34 305	96.86 283	97.79 305	92.62 369	98.19 301	96.71 453	93.89 288	94.74 310	96.05 433	79.44 423	99.09 280	95.58 241	96.68 285	98.86 246

FMVSNet3		94.97 299	94.26 310	97.11 262	98.18 258	96.62 142	98.56 238	98.26 289	93.67 310	94.09 341	97.10 360	84.25 371	98.01 424	92.08 368	92.14 372	96.70 383

usedtu_dtu_shiyan1		94.96 301	94.28 307	96.98 272	95.93 428	96.11 175	97.08 428	98.39 242	93.62 314	93.86 353	96.40 418	88.28 281	98.21 397	92.61 351	92.36 370	96.63 391

FE-MVSNET3		94.96 301	94.28 307	96.98 272	95.93 428	96.11 175	97.08 428	98.39 242	93.62 314	93.86 353	96.40 418	88.28 281	98.21 397	92.61 351	92.36 370	96.63 391

CostFormer		94.95 303	94.73 280	95.60 384	97.28 350	89.06 447	97.53 384	96.89 443	89.66 438	96.82 243	96.72 403	86.05 334	98.95 311	95.53 244	96.13 312	98.79 254

PAPM		94.95 303	94.00 330	97.78 207	97.04 367	95.65 216	96.03 470	98.25 290	91.23 410	94.19 337	97.80 303	91.27 177	98.86 324	82.61 479	97.61 253	98.84 249

CP-MVSNet		94.94 305	94.30 306	96.83 285	96.72 389	95.56 221	99.11 66	98.95 61	93.89 288	92.42 411	97.90 290	87.19 311	98.12 407	94.32 293	88.21 428	96.82 370

TR-MVS		94.94 305	94.20 313	97.17 255	97.75 308	94.14 308	97.59 381	97.02 433	92.28 375	95.75 287	97.64 319	83.88 381	98.96 306	89.77 416	96.15 311	98.40 296

RPSCF		94.87 307	95.40 241	93.26 450	98.89 147	82.06 496	98.33 275	98.06 333	90.30 428	96.56 257	99.26 80	87.09 312	99.49 192	93.82 312	96.32 298	98.24 303

testing99		94.83 308	94.08 322	97.07 265	97.94 294	93.13 353	98.10 323	97.17 420	94.86 235	95.34 292	96.00 439	76.31 455	99.40 209	95.08 260	95.90 315	98.68 273

GA-MVS		94.81 309	94.03 326	97.14 257	97.15 362	93.86 315	96.76 454	97.58 372	94.00 282	94.76 309	97.04 375	80.91 409	98.48 358	91.79 379	96.25 307	99.09 216

c3_l		94.79 310	94.43 301	95.89 367	97.75 308	93.12 355	97.16 424	98.03 335	92.23 376	93.46 374	97.05 374	91.39 171	98.01 424	93.58 320	89.21 417	96.53 413

V42		94.78 311	94.14 319	96.70 297	96.33 409	95.22 247	98.97 99	98.09 325	92.32 373	94.31 328	97.06 371	88.39 279	98.55 353	92.90 340	88.87 423	96.34 431

reproduce_monomvs		94.77 312	94.67 284	95.08 402	98.40 205	89.48 440	98.80 165	98.64 159	97.57 48	93.21 382	97.65 316	80.57 414	98.83 328	97.72 117	89.47 413	96.93 351

CR-MVSNet		94.76 313	94.15 318	96.59 312	97.00 368	93.43 332	94.96 488	97.56 375	92.46 364	96.93 235	96.24 423	88.15 286	97.88 438	87.38 449	96.65 287	98.46 294

v2v482		94.69 314	94.03 326	96.65 301	96.17 415	94.79 275	98.67 208	98.08 326	92.72 355	94.00 346	97.16 357	87.69 302	98.45 363	92.91 339	88.87 423	96.72 379

pmmvs4		94.69 314	93.99 332	96.81 287	95.74 436	95.94 188	97.40 394	97.67 364	90.42 425	93.37 377	97.59 323	89.08 256	98.20 399	92.97 337	91.67 380	96.30 434

cl22		94.68 316	94.19 314	96.13 350	98.11 267	93.60 325	96.94 436	98.31 271	92.43 368	93.32 379	96.87 395	86.51 321	98.28 394	94.10 304	91.16 387	96.51 420

eth_miper_zixun_eth		94.68 316	94.41 302	95.47 388	97.64 319	91.71 391	96.73 456	98.07 328	92.71 356	93.64 362	97.21 355	90.54 209	98.17 401	93.38 323	89.76 405	96.54 411

PCF-MVS	93.45 11	94.68 316	93.43 368	98.42 130	98.62 180	96.77 137	95.48 481	98.20 296	84.63 483	93.34 378	98.32 252	88.55 276	99.81 103	84.80 471	98.96 160	98.68 273
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 319	93.54 363	98.08 172	96.88 378	96.56 151	98.19 301	98.50 200	78.05 501	92.69 399	98.02 277	91.07 191	99.63 161	90.09 409	98.36 215	98.04 313

PS-CasMVS		94.67 319	93.99 332	96.71 295	96.68 391	95.26 244	99.13 63	99.03 50	93.68 308	92.33 415	97.95 285	85.35 347	98.10 408	93.59 319	88.16 430	96.79 371

cascas		94.63 321	93.86 342	96.93 277	96.91 376	94.27 300	96.00 471	98.51 195	85.55 479	94.54 313	96.23 425	84.20 375	98.87 322	95.80 231	96.98 276	97.66 326

tpmvs		94.60 322	94.36 304	95.33 394	97.46 336	88.60 458	96.88 448	97.68 361	91.29 407	93.80 358	96.42 417	88.58 272	99.24 243	91.06 396	96.04 313	98.17 308

LTVRE_ROB	92.95 15	94.60 322	93.90 338	96.68 299	97.41 344	94.42 291	98.52 242	98.59 172	91.69 392	91.21 433	98.35 246	84.87 356	99.04 290	91.06 396	93.44 353	96.60 397
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		94.59 324	93.92 335	96.60 311	96.21 411	94.78 276	98.59 222	98.14 313	91.86 388	94.21 336	97.02 378	87.97 292	98.41 374	91.72 381	89.57 408	96.61 395

ADS-MVSNet2		94.58 325	94.40 303	95.11 400	98.00 283	88.74 456	96.04 468	97.30 406	90.15 429	96.47 264	96.64 409	87.89 294	97.56 455	90.08 410	97.06 271	99.02 229

WBMVS		94.56 326	94.04 324	96.10 352	98.03 280	93.08 357	97.82 362	98.18 302	94.02 278	93.77 360	96.82 398	81.28 403	98.34 383	95.47 247	91.00 390	96.88 361

ACMH	92.88 16	94.55 327	93.95 334	96.34 341	97.63 320	93.26 347	98.81 164	98.49 205	93.43 324	89.74 450	98.53 227	81.91 395	99.08 283	93.69 314	93.30 358	96.70 383
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 328	93.85 343	96.63 306	97.98 291	93.06 358	98.77 177	97.84 348	93.67 310	93.80 358	98.04 276	76.88 453	98.96 306	94.79 269	92.86 363	97.86 319

XVG-ACMP-BASELINE		94.54 328	94.14 319	95.75 378	96.55 396	91.65 392	98.11 321	98.44 216	94.96 229	94.22 335	97.90 290	79.18 426	99.11 275	94.05 306	93.85 342	96.48 425

AUN-MVS		94.53 330	93.73 353	96.92 280	98.50 188	93.52 330	98.34 274	98.10 321	93.83 294	95.94 285	97.98 283	85.59 343	99.03 293	94.35 291	80.94 478	98.22 305

DIV-MVS_self_test		94.52 331	94.03 326	95.99 358	97.57 328	93.38 338	97.05 430	97.94 342	91.74 389	92.81 394	97.10 360	89.12 254	98.07 416	92.60 354	90.30 398	96.53 413

cl____		94.51 332	94.01 329	96.02 354	97.58 324	93.40 337	97.05 430	97.96 341	91.73 391	92.76 396	97.08 366	89.06 257	98.13 405	92.61 351	90.29 399	96.52 416

ETVMVS		94.50 333	93.44 367	97.68 220	98.18 258	95.35 240	98.19 301	97.11 422	93.73 300	96.40 267	95.39 454	74.53 468	98.84 325	91.10 392	96.31 299	98.84 249

GBi-Net		94.49 334	93.80 346	96.56 316	98.21 248	95.00 259	98.82 156	98.18 302	92.46 364	94.09 341	97.07 367	81.16 404	97.95 429	92.08 368	92.14 372	96.72 379

test1		94.49 334	93.80 346	96.56 316	98.21 248	95.00 259	98.82 156	98.18 302	92.46 364	94.09 341	97.07 367	81.16 404	97.95 429	92.08 368	92.14 372	96.72 379

dmvs_re		94.48 336	94.18 316	95.37 392	97.68 315	90.11 426	98.54 241	97.08 424	94.56 253	94.42 322	97.24 352	84.25 371	97.76 445	91.02 399	92.83 364	98.24 303

v8		94.47 337	93.77 349	96.57 315	96.36 407	94.83 272	99.05 77	98.19 299	91.92 385	93.16 384	96.97 383	88.82 270	98.48 358	91.69 382	87.79 432	96.39 429

FMVSNet2		94.47 337	93.61 359	97.04 267	98.21 248	96.43 157	98.79 173	98.27 281	92.46 364	93.50 371	97.09 364	81.16 404	98.00 426	91.09 393	91.93 375	96.70 383

test2506		94.44 339	93.91 337	96.04 353	99.02 132	88.99 450	99.06 74	79.47 527	96.96 93	98.36 132	99.26 80	77.21 446	99.52 187	96.78 196	99.04 154	99.59 94

Patchmatch-test		94.42 340	93.68 357	96.63 306	97.60 322	91.76 388	94.83 492	97.49 387	89.45 442	94.14 339	97.10 360	88.99 259	98.83 328	85.37 465	98.13 232	99.29 167

PEN-MVS		94.42 340	93.73 353	96.49 325	96.28 410	94.84 270	99.17 55	99.00 53	93.51 319	92.23 417	97.83 300	86.10 333	97.90 433	92.55 359	86.92 445	96.74 376

v144192		94.39 342	93.70 355	96.48 327	96.06 421	94.35 295	98.58 226	98.16 310	91.45 398	94.33 327	97.02 378	87.50 305	98.45 363	91.08 395	89.11 418	96.63 391

Baseline_NR-MVSNet		94.35 343	93.81 345	95.96 363	96.20 412	94.05 310	98.61 221	96.67 455	91.44 399	93.85 355	97.60 322	88.57 273	98.14 404	94.39 289	86.93 444	95.68 453

miper_lstm_enhance		94.33 344	94.07 323	95.11 400	97.75 308	90.97 402	97.22 412	98.03 335	91.67 393	92.76 396	96.97 383	90.03 226	97.78 443	92.51 361	89.64 407	96.56 408

v1192		94.32 345	93.58 360	96.53 321	96.10 419	94.45 289	98.50 250	98.17 308	91.54 396	94.19 337	97.06 371	86.95 316	98.43 366	90.14 408	89.57 408	96.70 383

UWE-MVS		94.30 346	93.89 340	95.53 385	97.83 302	88.95 451	97.52 386	93.25 502	94.44 263	96.63 253	97.07 367	78.70 429	99.28 228	91.99 373	97.56 257	98.36 299

ACMH+	92.99 14	94.30 346	93.77 349	95.88 368	97.81 304	92.04 385	98.71 193	98.37 251	93.99 283	90.60 441	98.47 234	80.86 411	99.05 287	92.75 347	92.40 369	96.55 410

v148		94.29 348	93.76 351	95.91 365	96.10 419	92.93 361	98.58 226	97.97 339	92.59 362	93.47 373	96.95 387	88.53 277	98.32 386	92.56 358	87.06 443	96.49 423

v10		94.29 348	93.55 362	96.51 323	96.39 406	94.80 274	98.99 95	98.19 299	91.35 403	93.02 390	96.99 381	88.09 288	98.41 374	90.50 405	88.41 427	96.33 433

SD_0403		94.28 350	94.46 296	93.73 441	98.02 281	85.32 484	98.31 280	98.40 236	94.75 243	93.59 363	98.16 267	89.01 258	96.54 477	82.32 480	97.58 256	99.34 150

MVP-Stereo		94.28 350	93.92 335	95.35 393	94.95 457	92.60 370	97.97 338	97.65 365	91.61 394	90.68 440	97.09 364	86.32 330	98.42 367	89.70 419	99.34 139	95.02 469
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 352	93.33 370	96.97 274	97.19 359	93.38 338	98.74 182	98.57 179	91.21 412	93.81 357	98.58 222	72.85 477	98.77 335	95.05 261	93.93 341	98.77 261

OurMVSNet-221017-0		94.21 353	94.00 330	94.85 413	95.60 441	89.22 445	98.89 125	97.43 395	95.29 201	92.18 420	98.52 230	82.86 389	98.59 351	93.46 322	91.76 378	96.74 376

v1921920		94.20 354	93.47 366	96.40 337	95.98 425	94.08 309	98.52 242	98.15 311	91.33 404	94.25 333	97.20 356	86.41 326	98.42 367	90.04 413	89.39 415	96.69 388

WB-MVSnew		94.19 355	94.04 324	94.66 421	96.82 382	92.14 377	97.86 356	95.96 470	93.50 320	95.64 288	96.77 401	88.06 290	97.99 427	84.87 468	96.86 277	93.85 491

v7n		94.19 355	93.43 368	96.47 328	95.90 431	94.38 294	99.26 33	98.34 260	91.99 383	92.76 396	97.13 359	88.31 280	98.52 356	89.48 424	87.70 433	96.52 416

tpm2		94.19 355	93.76 351	95.46 389	97.23 353	89.04 448	97.31 406	96.85 447	87.08 461	96.21 274	96.79 400	83.75 385	98.74 336	92.43 364	96.23 309	98.59 285

TESTMET0.1,1		94.18 358	93.69 356	95.63 382	96.92 374	89.12 446	96.91 440	94.78 488	93.17 336	94.88 302	96.45 416	78.52 430	98.92 313	93.09 332	98.50 192	98.85 247

dp		94.15 359	93.90 338	94.90 409	97.31 349	86.82 477	96.97 434	97.19 419	91.22 411	96.02 280	96.61 411	85.51 344	99.02 297	90.00 414	94.30 326	98.85 247

ET-MVSNet_ETH3D		94.13 360	92.98 378	97.58 231	98.22 246	96.20 169	97.31 406	95.37 478	94.53 255	79.56 501	97.63 321	86.51 321	97.53 456	96.91 179	90.74 392	99.02 229

tpm		94.13 360	93.80 346	95.12 399	96.50 400	87.91 470	97.44 390	95.89 473	92.62 360	96.37 269	96.30 422	84.13 376	98.30 390	93.24 327	91.66 381	99.14 205

testing222		94.12 362	93.03 377	97.37 247	98.02 281	94.66 277	97.94 342	96.65 457	94.63 250	95.78 286	95.76 442	71.49 478	98.92 313	91.17 391	95.88 316	98.52 290

IterMVS-SCA-FT		94.11 363	93.87 341	94.85 413	97.98 291	90.56 417	97.18 419	98.11 318	93.75 297	92.58 402	97.48 331	83.97 379	97.41 459	92.48 363	91.30 384	96.58 404

Anonymous20231211		94.10 364	93.26 373	96.61 309	99.11 124	94.28 299	99.01 90	98.88 78	86.43 470	92.81 394	97.57 325	81.66 400	98.68 342	94.83 266	89.02 421	96.88 361

IterMVS		94.09 365	93.85 343	94.80 417	97.99 285	90.35 422	97.18 419	98.12 315	93.68 308	92.46 409	97.34 343	84.05 377	97.41 459	92.51 361	91.33 383	96.62 394
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 366	93.51 364	95.80 373	96.77 384	89.70 434	96.91 440	95.21 480	92.89 350	94.83 305	95.72 447	77.69 441	98.97 302	93.06 333	98.50 192	98.72 266

test0.0.03 1		94.08 366	93.51 364	95.80 373	95.53 445	92.89 362	97.38 396	95.97 469	95.11 215	92.51 407	96.66 406	87.71 298	96.94 467	87.03 452	93.67 345	97.57 330

v1240		94.06 368	93.29 372	96.34 341	96.03 423	93.90 314	98.44 263	98.17 308	91.18 413	94.13 340	97.01 380	86.05 334	98.42 367	89.13 430	89.50 412	96.70 383

X-MVStestdata		94.06 368	92.30 394	99.34 32	99.70 27	98.35 51	99.29 28	98.88 78	97.40 59	98.46 121	43.50 552	95.90 49	99.89 69	97.85 108	99.74 58	99.78 33

DTE-MVSNet		93.98 370	93.26 373	96.14 349	96.06 421	94.39 293	99.20 48	98.86 91	93.06 342	91.78 426	97.81 302	85.87 338	97.58 454	90.53 404	86.17 450	96.46 427

pm-mvs1		93.94 371	93.06 376	96.59 312	96.49 401	95.16 250	98.95 106	98.03 335	92.32 373	91.08 435	97.84 297	84.54 367	98.41 374	92.16 366	86.13 453	96.19 439

MS-PatchMatch		93.84 372	93.63 358	94.46 431	96.18 414	89.45 441	97.76 367	98.27 281	92.23 376	92.13 422	97.49 330	79.50 422	98.69 339	89.75 417	99.38 135	95.25 461

tfpnnormal		93.66 373	92.70 384	96.55 320	96.94 373	95.94 188	98.97 99	99.19 35	91.04 414	91.38 432	97.34 343	84.94 355	98.61 347	85.45 464	89.02 421	95.11 465

EU-MVSNet		93.66 373	94.14 319	92.25 464	95.96 427	83.38 491	98.52 242	98.12 315	94.69 246	92.61 401	98.13 270	87.36 309	96.39 482	91.82 378	90.00 403	96.98 346

our_test_3		93.65 375	93.30 371	94.69 419	95.45 449	89.68 436	96.91 440	97.65 365	91.97 384	91.66 429	96.88 393	89.67 235	97.93 432	88.02 444	91.49 382	96.48 425

pmmvs5		93.65 375	92.97 379	95.68 379	95.49 446	92.37 372	98.20 298	97.28 409	89.66 438	92.58 402	97.26 349	82.14 394	98.09 412	93.18 330	90.95 391	96.58 404

SSC-MVS3.2		93.59 377	93.13 375	94.97 406	96.81 383	89.71 433	97.95 339	98.49 205	94.59 252	93.50 371	96.91 391	77.74 440	98.37 381	91.69 382	90.47 396	96.83 369

test_fmvs2		93.43 378	93.58 360	92.95 457	96.97 371	83.91 488	99.19 50	97.24 412	95.74 163	95.20 297	98.27 258	69.65 480	98.72 338	96.26 212	93.73 344	96.24 436

tpm cat1		93.36 379	92.80 381	95.07 403	97.58 324	87.97 469	96.76 454	97.86 347	82.17 490	93.53 367	96.04 435	86.13 332	99.13 270	89.24 428	95.87 317	98.10 311

JIA-IIPM		93.35 380	92.49 390	95.92 364	96.48 402	90.65 412	95.01 486	96.96 437	85.93 474	96.08 278	87.33 514	87.70 301	98.78 334	91.35 388	95.58 321	98.34 300

SixPastTwentyTwo		93.34 381	92.86 380	94.75 418	95.67 438	89.41 443	98.75 178	96.67 455	93.89 288	90.15 447	98.25 261	80.87 410	98.27 395	90.90 400	90.64 393	96.57 406

USDC		93.33 382	92.71 383	95.21 396	96.83 381	90.83 408	96.91 440	97.50 385	93.84 292	90.72 439	98.14 269	77.69 441	98.82 330	89.51 423	93.21 360	95.97 445

IB-MVS	91.98 17	93.27 383	91.97 398	97.19 253	97.47 335	93.41 334	97.09 427	95.99 468	93.32 329	92.47 408	95.73 445	78.06 436	99.53 184	94.59 284	82.98 466	98.62 280
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		93.26 384	92.21 395	96.41 335	97.73 312	93.13 353	95.65 477	97.03 430	91.27 409	94.04 344	96.06 432	75.33 461	97.19 462	86.56 455	96.23 309	98.92 241

ppachtmachnet_test		93.22 385	92.63 385	94.97 406	95.45 449	90.84 407	96.88 448	97.88 346	90.60 420	92.08 423	97.26 349	88.08 289	97.86 439	85.12 467	90.33 397	96.22 437

Patchmtry		93.22 385	92.35 393	95.84 372	96.77 384	93.09 356	94.66 495	97.56 375	87.37 460	92.90 392	96.24 423	88.15 286	97.90 433	87.37 450	90.10 402	96.53 413

testing3		93.19 387	92.48 391	95.30 395	98.07 271	92.27 373	98.64 213	97.17 420	93.94 287	93.98 347	97.04 375	67.97 485	96.01 486	88.40 438	97.14 269	97.63 327

FMVSNet1		93.19 387	92.07 396	96.56 316	97.54 329	95.00 259	98.82 156	98.18 302	90.38 426	92.27 416	97.07 367	73.68 474	97.95 429	89.36 426	91.30 384	96.72 379

LF4IMVS		93.14 389	92.79 382	94.20 436	95.88 432	88.67 457	97.66 375	97.07 426	93.81 295	91.71 427	97.65 316	77.96 438	98.81 331	91.47 387	91.92 377	95.12 464

mmtdpeth		93.12 390	92.61 386	94.63 423	97.60 322	89.68 436	99.21 45	97.32 403	94.02 278	97.72 190	94.42 465	77.01 451	99.44 205	99.05 31	77.18 491	94.78 474

testgi		93.06 391	92.45 392	94.88 411	96.43 405	89.90 428	98.75 178	97.54 381	95.60 171	91.63 430	97.91 289	74.46 470	97.02 465	86.10 458	93.67 345	97.72 324

PatchT		93.06 391	91.97 398	96.35 340	96.69 390	92.67 368	94.48 499	97.08 424	86.62 468	97.08 227	92.23 494	87.94 293	97.90 433	78.89 495	96.69 284	98.49 292

RPMNet		92.81 393	91.34 404	97.24 249	97.00 368	93.43 332	94.96 488	98.80 115	82.27 489	96.93 235	92.12 495	86.98 315	99.82 98	76.32 503	96.65 287	98.46 294

UWE-MVS-28		92.79 394	92.51 389	93.62 443	96.46 403	86.28 479	97.93 343	92.71 507	94.17 271	94.78 308	97.16 357	81.05 407	96.43 480	81.45 483	96.86 277	98.14 310

myMVS_eth3d		92.73 395	92.01 397	94.89 410	97.39 345	90.94 403	97.91 346	97.46 389	93.16 337	93.42 375	95.37 455	68.09 484	96.12 484	88.34 439	96.99 273	97.60 328

TransMVSNet (Re)		92.67 396	91.51 403	96.15 348	96.58 395	94.65 278	98.90 121	96.73 450	90.86 417	89.46 455	97.86 294	85.62 342	98.09 412	86.45 456	81.12 476	95.71 452

ttmdpeth		92.61 397	91.96 400	94.55 425	94.10 469	90.60 416	98.52 242	97.29 407	92.67 357	90.18 445	97.92 288	79.75 420	97.79 441	91.09 393	86.15 452	95.26 460

Syy-MVS		92.55 398	92.61 386	92.38 460	97.39 345	83.41 490	97.91 346	97.46 389	93.16 337	93.42 375	95.37 455	84.75 360	96.12 484	77.00 501	96.99 273	97.60 328

K. test v3		92.55 398	91.91 401	94.48 429	95.64 439	89.24 444	99.07 72	94.88 487	94.04 276	86.78 475	97.59 323	77.64 444	97.64 450	92.08 368	89.43 414	96.57 406

DSMNet-mixed		92.52 400	92.58 388	92.33 461	94.15 467	82.65 494	98.30 283	94.26 495	89.08 448	92.65 400	95.73 445	85.01 354	95.76 488	86.24 457	97.76 248	98.59 285

TinyColmap		92.31 401	91.53 402	94.65 422	96.92 374	89.75 431	96.92 438	96.68 454	90.45 424	89.62 452	97.85 296	76.06 458	98.81 331	86.74 453	92.51 368	95.41 457

gg-mvs-nofinetune		92.21 402	90.58 411	97.13 258	96.75 387	95.09 255	95.85 472	89.40 517	85.43 480	94.50 315	81.98 521	80.80 412	98.40 380	92.16 366	98.33 218	97.88 317

FMVSNet5		91.81 403	90.92 407	94.49 428	97.21 355	92.09 382	98.00 335	97.55 380	89.31 445	90.86 438	95.61 452	74.48 469	95.32 492	85.57 462	89.70 406	96.07 443

pmmvs6		91.77 404	90.63 410	95.17 398	94.69 463	91.24 399	98.67 208	97.92 344	86.14 472	89.62 452	97.56 328	75.79 459	98.34 383	90.75 402	84.56 459	95.94 446

Anonymous20231206		91.66 405	91.10 406	93.33 448	94.02 473	87.35 473	98.58 226	97.26 411	90.48 422	90.16 446	96.31 421	83.83 383	96.53 478	79.36 492	89.90 404	96.12 441

Patchmatch-RL test		91.49 406	90.85 408	93.41 446	91.37 497	84.40 485	92.81 508	95.93 472	91.87 387	87.25 471	94.87 461	88.99 259	96.53 478	92.54 360	82.00 470	99.30 164

blended_shiyan8		91.42 407	89.89 420	96.01 355	91.50 494	93.30 344	97.48 388	97.83 349	86.93 463	92.57 404	92.37 492	82.46 392	98.13 405	92.86 345	74.99 499	96.61 395

blended_shiyan6		91.37 408	89.84 421	95.98 361	91.49 495	93.28 345	97.48 388	97.83 349	86.93 463	92.43 410	92.36 493	82.44 393	98.06 417	92.74 350	74.82 502	96.59 400

test_0402		91.32 409	90.27 414	94.48 429	96.60 394	91.12 400	98.50 250	97.22 413	86.10 473	88.30 467	96.98 382	77.65 443	97.99 427	78.13 497	92.94 362	94.34 477

dtuonlycased		91.29 410	91.26 405	91.36 468	95.63 440	84.25 487	96.93 437	97.21 415	92.16 380	88.34 466	96.47 414	79.56 421	95.18 495	87.37 450	87.70 433	94.64 475

test_vis1_rt		91.29 410	90.65 409	93.19 452	97.45 339	86.25 480	98.57 235	90.90 515	93.30 331	86.94 474	93.59 477	62.07 498	99.11 275	97.48 150	95.58 321	94.22 481

PVSNet_0	88.72 19	91.28 412	90.03 418	95.00 405	97.99 285	87.29 474	94.84 491	98.50 200	92.06 382	89.86 449	95.19 457	79.81 419	99.39 212	92.27 365	69.79 517	98.33 301

mvs5depth		91.23 413	90.17 416	94.41 433	92.09 489	89.79 430	95.26 484	96.50 460	90.73 418	91.69 428	97.06 371	76.12 457	98.62 346	88.02 444	84.11 462	94.82 471

Anonymous20240521		91.18 414	90.44 412	93.42 445	93.70 474	88.47 461	98.94 109	97.56 375	88.46 454	89.56 454	95.08 460	77.15 449	96.97 466	83.92 474	89.55 410	94.82 471

wanda-best-256-512		91.17 415	89.60 425	95.88 368	91.33 498	92.99 359	96.89 445	97.82 352	86.89 466	92.36 412	91.75 499	81.83 396	98.06 417	92.75 347	74.82 502	96.59 400

FE-blended-shiyan7		91.17 415	89.60 425	95.88 368	91.33 498	92.99 359	96.89 445	97.82 352	86.89 466	92.36 412	91.75 499	81.83 396	98.06 417	92.75 347	74.82 502	96.59 400

EG-PatchMatch MVS		91.13 417	90.12 417	94.17 438	94.73 462	89.00 449	98.13 316	97.81 356	89.22 446	85.32 485	96.46 415	67.71 486	98.42 367	87.89 448	93.82 343	95.08 466

TDRefinement		91.06 418	89.68 423	95.21 396	85.35 526	91.49 395	98.51 249	97.07 426	91.47 397	88.83 462	97.84 297	77.31 445	99.09 280	92.79 346	77.98 489	95.04 468

gbinet_0.2-2-1-0.02		91.03 419	89.37 431	96.01 355	91.39 496	93.41 334	97.19 417	97.82 352	87.00 462	92.18 420	91.87 498	78.97 427	98.04 421	93.13 331	74.75 506	96.60 397

sc_t1		91.01 420	89.39 427	95.85 371	95.99 424	90.39 421	98.43 265	97.64 367	78.79 498	92.20 419	97.94 286	66.00 491	98.60 350	91.59 385	85.94 454	98.57 288

UnsupCasMVSNet_eth		90.99 421	89.92 419	94.19 437	94.08 470	89.83 429	97.13 426	98.67 151	93.69 306	85.83 481	96.19 428	75.15 464	96.74 471	89.14 429	79.41 483	96.00 444

ArgMatch-Sym		90.92 422	90.22 415	93.02 454	95.81 435	86.50 478	97.32 404	97.01 436	92.67 357	91.02 436	97.35 342	66.90 489	97.17 463	88.53 437	85.40 456	95.39 458

0.4-1-1-0.1		90.89 423	88.97 437	96.67 300	94.15 467	92.76 367	95.28 483	95.03 485	89.11 447	90.43 443	89.57 509	75.41 460	99.04 290	94.70 274	77.06 492	98.20 307

test20.03		90.89 423	90.38 413	92.43 459	93.48 477	88.14 467	98.33 275	97.56 375	93.40 326	87.96 468	96.71 404	80.69 413	94.13 502	79.15 493	86.17 450	95.01 470

usedtu_blend_shiyan5		90.87 425	89.15 432	96.01 355	91.33 498	93.35 341	98.12 317	97.36 401	81.93 492	92.36 412	91.75 499	81.83 396	98.09 412	92.88 343	74.82 502	96.59 400

blend_shiyan4		90.76 426	89.01 435	95.99 358	91.69 493	93.35 341	97.44 390	97.83 349	86.93 463	92.23 417	91.98 496	75.19 463	98.09 412	92.88 343	74.96 500	96.52 416

MDA-MVSNet_test_wron		90.71 427	89.38 429	94.68 420	94.83 459	90.78 409	97.19 417	97.46 389	87.60 458	72.41 511	95.72 447	86.51 321	96.71 474	85.92 460	86.80 447	96.56 408

YYNet1		90.70 428	89.39 427	94.62 424	94.79 461	90.65 412	97.20 414	97.46 389	87.54 459	72.54 510	95.74 443	86.51 321	96.66 475	86.00 459	86.76 448	96.54 411

ArgMatch-SfM		90.55 429	89.69 422	93.14 453	95.91 430	86.12 481	97.20 414	96.81 449	92.91 349	91.39 431	96.95 387	65.65 493	97.72 447	88.03 443	82.36 467	95.57 455

0.4-1-1-0.2		90.43 430	88.45 441	96.38 338	93.34 479	92.12 378	93.88 505	95.04 484	88.62 453	90.00 448	88.31 512	75.31 462	99.03 293	94.61 281	76.91 494	98.01 316

KD-MVS_self_test		90.38 431	89.38 429	93.40 447	92.85 484	88.94 452	97.95 339	97.94 342	90.35 427	90.25 444	93.96 474	79.82 418	95.94 487	84.62 473	76.69 496	95.33 459

pmmvs-eth3d		90.36 432	89.05 434	94.32 435	91.10 503	92.12 378	97.63 380	96.95 438	88.86 450	84.91 486	93.13 483	78.32 432	96.74 471	88.70 434	81.81 472	94.09 484

0.3-1-1-0.015		90.29 433	88.21 445	96.51 323	93.56 476	92.44 371	94.41 500	95.03 485	88.71 451	89.20 457	88.50 511	73.12 476	99.04 290	94.67 277	76.70 495	98.05 312

FE-MVSNET2		90.29 433	88.94 438	94.36 434	90.48 509	92.27 373	98.45 257	97.82 352	91.59 395	84.90 487	93.10 484	73.92 472	96.42 481	87.92 447	82.26 468	94.39 476

tt0320		90.26 435	88.73 440	94.86 412	96.12 418	90.62 414	98.17 309	97.63 368	77.46 502	89.68 451	96.04 435	69.19 482	97.79 441	88.98 431	85.29 457	96.16 440

CL-MVSNet_self_test		90.11 436	89.14 433	93.02 454	91.86 491	88.23 466	96.51 463	98.07 328	90.49 421	90.49 442	94.41 466	84.75 360	95.34 491	80.79 485	74.95 501	95.50 456

new_pmnet		90.06 437	89.00 436	93.22 451	94.18 465	88.32 464	96.42 465	96.89 443	86.19 471	85.67 482	93.62 476	77.18 448	97.10 464	81.61 482	89.29 416	94.23 480

MDA-MVSNet-bldmvs		89.97 438	88.35 443	94.83 416	95.21 453	91.34 396	97.64 377	97.51 384	88.36 456	71.17 513	96.13 430	79.22 425	96.63 476	83.65 475	86.27 449	96.52 416

tt0320-xc		89.79 439	88.11 446	94.84 415	96.19 413	90.61 415	98.16 310	97.22 413	77.35 503	88.75 464	96.70 405	65.94 492	97.63 451	89.31 427	83.39 464	96.28 435

CMPMVS	66.06 21	89.70 440	89.67 424	89.78 472	93.19 482	76.56 503	97.00 433	98.35 256	80.97 493	81.57 494	97.75 305	74.75 467	98.61 347	89.85 415	93.63 347	94.17 482
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 441	88.28 444	93.82 440	92.81 485	91.08 401	98.01 333	97.45 393	87.95 457	87.90 469	95.87 441	67.63 487	94.56 500	78.73 496	88.18 429	95.83 450

KD-MVS_2432*1600		89.61 442	87.96 450	94.54 426	94.06 471	91.59 393	95.59 478	97.63 368	89.87 434	88.95 459	94.38 468	78.28 433	96.82 469	84.83 469	68.05 518	95.21 462

miper_refine_blended		89.61 442	87.96 450	94.54 426	94.06 471	91.59 393	95.59 478	97.63 368	89.87 434	88.95 459	94.38 468	78.28 433	96.82 469	84.83 469	68.05 518	95.21 462

MVStest1		89.53 444	87.99 449	94.14 439	94.39 464	90.42 419	98.25 291	96.84 448	82.81 486	81.18 496	97.33 345	77.09 450	96.94 467	85.27 466	78.79 484	95.06 467

MVS-HIRNet		89.46 445	88.40 442	92.64 458	97.58 324	82.15 495	94.16 504	93.05 506	75.73 508	90.90 437	82.52 519	79.42 424	98.33 385	83.53 476	98.68 175	97.43 331

OpenMVS_ROB	86.42 20	89.00 446	87.43 454	93.69 442	93.08 483	89.42 442	97.91 346	96.89 443	78.58 499	85.86 480	94.69 462	69.48 481	98.29 393	77.13 500	93.29 359	93.36 494

mvsany_test3		88.80 447	88.04 447	91.09 469	89.78 514	81.57 497	97.83 361	95.49 477	93.81 295	87.53 470	93.95 475	56.14 501	97.43 458	94.68 275	83.13 465	94.26 478

FE-MVSNET		88.56 448	87.09 455	92.99 456	89.93 513	89.99 427	98.15 313	95.59 475	88.42 455	84.87 488	92.90 486	74.82 466	94.99 497	77.88 498	81.21 475	93.99 487

new-patchmatchnet		88.50 449	87.45 453	91.67 466	90.31 511	85.89 482	97.16 424	97.33 402	89.47 441	83.63 491	92.77 489	76.38 454	95.06 496	82.70 478	77.29 490	94.06 486

APD_test1		88.22 450	88.01 448	88.86 476	95.98 425	74.66 513	97.21 413	96.44 462	83.96 485	86.66 477	97.90 290	60.95 499	97.84 440	82.73 477	90.23 400	94.09 484

PM-MVS		87.77 451	86.55 457	91.40 467	91.03 505	83.36 492	96.92 438	95.18 482	91.28 408	86.48 479	93.42 479	53.27 503	96.74 471	89.43 425	81.97 471	94.11 483

dmvs_testset		87.64 452	88.93 439	83.79 490	95.25 452	63.36 526	97.20 414	91.17 512	93.07 341	85.64 483	95.98 440	85.30 351	91.52 512	69.42 514	87.33 439	96.49 423

test_fmvs3		87.17 453	87.06 456	87.50 479	91.21 501	75.66 506	99.05 77	96.61 458	92.79 354	88.85 461	92.78 488	43.72 510	93.49 504	93.95 307	84.56 459	93.34 495

UnsupCasMVSNet_bld		87.17 453	85.12 461	93.31 449	91.94 490	88.77 454	94.92 490	98.30 278	84.30 484	82.30 492	90.04 507	63.96 496	97.25 461	85.85 461	74.47 509	93.93 489

N_pmnet		87.12 455	87.77 452	85.17 485	95.46 448	61.92 530	97.37 398	70.66 542	85.83 475	88.73 465	96.04 435	85.33 349	97.76 445	80.02 487	90.48 395	95.84 449

pmmvs3		86.67 456	84.86 462	92.11 465	88.16 518	87.19 476	96.63 458	94.75 489	79.88 495	87.22 472	92.75 490	66.56 490	95.20 494	81.24 484	76.56 497	93.96 488

test_f		86.07 457	85.39 459	88.10 477	89.28 516	75.57 507	97.73 370	96.33 464	89.41 444	85.35 484	91.56 502	43.31 512	95.53 489	91.32 389	84.23 461	93.21 496

MASt3R-SfM		85.54 458	85.89 458	84.50 488	90.13 512	66.13 524	92.89 507	95.33 479	85.73 477	88.77 463	96.36 420	52.50 504	94.89 498	86.66 454	84.65 458	92.50 501

WB-MVS		84.86 459	85.33 460	83.46 491	89.48 515	69.56 518	98.19 301	96.42 463	89.55 440	81.79 493	94.67 463	84.80 358	90.12 515	52.44 524	80.64 480	90.69 507

usedtu_dtu_shiyan2		84.80 460	82.31 465	92.27 463	86.38 523	85.55 483	97.77 366	96.56 459	78.34 500	83.90 490	93.50 478	54.16 502	95.32 492	77.55 499	72.62 510	95.92 447

DenseAffine		84.37 461	82.38 464	90.31 471	94.17 466	82.89 493	94.98 487	94.23 496	82.16 491	79.68 500	94.33 472	46.28 506	94.25 501	80.01 488	75.62 498	93.78 492

SSC-MVS		84.27 462	84.71 463	82.96 496	89.19 517	68.83 519	98.08 325	96.30 465	89.04 449	81.37 495	94.47 464	84.60 365	89.89 516	49.80 527	79.52 482	90.15 508

RoMa-SfM		83.81 463	82.08 466	89.00 475	93.33 480	79.94 500	95.51 480	92.48 508	79.75 496	79.89 499	95.69 450	46.23 507	93.20 507	78.90 494	76.93 493	93.87 490

LoFTR		83.16 464	80.62 468	90.80 470	92.28 488	80.01 499	95.35 482	94.33 493	80.44 494	70.79 514	92.93 485	46.38 505	98.17 401	75.01 505	78.03 488	94.24 479

dongtai		82.47 465	81.88 467	84.22 489	95.19 454	76.03 504	94.59 498	74.14 532	82.63 487	87.19 473	96.09 431	64.10 495	87.85 520	58.91 522	84.11 462	88.78 514

DKM		81.60 466	79.57 469	87.68 478	92.65 487	78.36 501	94.65 496	91.17 512	79.69 497	76.11 504	93.98 473	37.88 522	91.54 511	79.64 491	70.38 514	93.15 497

MatchFormer		80.21 467	77.20 476	89.24 474	91.79 492	77.21 502	95.16 485	93.59 501	72.46 512	67.08 517	89.93 508	43.14 513	97.90 433	67.07 516	74.55 508	92.61 500

RoMa-HiRes		79.77 468	77.89 471	85.41 484	90.81 506	74.77 512	94.26 502	86.78 521	75.97 504	77.00 502	94.37 470	39.39 517	90.60 513	74.98 506	67.46 520	90.84 506

DKM-HiRes		79.25 469	77.01 478	85.98 482	91.20 502	75.07 509	93.65 506	87.84 520	75.94 506	73.36 509	92.80 487	34.20 527	90.26 514	76.66 502	67.44 521	92.62 499

test_vis3_rt		79.22 470	77.40 475	84.67 486	86.44 522	74.85 511	97.66 375	81.43 525	84.98 481	67.12 516	81.91 522	28.09 536	97.60 452	88.96 432	80.04 481	81.55 524

test_method		79.03 471	78.17 470	81.63 497	86.06 524	54.40 541	82.75 528	96.89 443	39.54 533	80.98 497	95.57 453	58.37 500	94.73 499	84.74 472	78.61 485	95.75 451

testf1		79.02 472	77.70 472	82.99 494	88.10 519	66.90 522	94.67 493	93.11 503	71.08 514	74.02 506	93.41 480	34.15 528	93.25 505	72.25 510	78.50 486	88.82 512

APD_test2		79.02 472	77.70 472	82.99 494	88.10 519	66.90 522	94.67 493	93.11 503	71.08 514	74.02 506	93.41 480	34.15 528	93.25 505	72.25 510	78.50 486	88.82 512

LCM-MVSNet		78.70 474	76.24 480	86.08 481	77.26 542	71.99 515	94.34 501	96.72 451	61.62 519	76.53 503	89.33 510	33.91 531	92.78 509	81.85 481	74.60 507	93.46 493

kuosan		78.45 475	77.69 474	80.72 498	92.73 486	75.32 508	94.63 497	74.51 531	75.96 505	80.87 498	93.19 482	63.23 497	79.99 530	42.56 534	81.56 474	86.85 521

Gipuma		78.40 476	76.75 479	83.38 492	95.54 443	80.43 498	79.42 529	97.40 397	64.67 518	73.46 508	80.82 523	45.65 509	93.14 508	66.32 517	87.43 437	76.56 527
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 477	75.44 481	85.46 483	82.54 530	74.95 510	94.23 503	93.08 505	72.80 510	74.68 505	87.38 513	36.36 525	91.56 510	73.95 508	63.94 522	89.87 509

FPMVS		77.62 478	77.14 477	79.05 502	79.25 537	60.97 532	95.79 473	95.94 471	65.96 517	67.93 515	94.40 467	37.73 523	88.88 519	68.83 515	88.46 426	87.29 518

ELoFTR		75.37 479	72.33 482	84.51 487	84.48 528	68.41 521	91.57 512	88.78 518	73.84 509	62.84 521	90.14 505	27.38 537	94.11 503	71.45 513	60.46 526	91.00 504

EGC-MVSNET		75.22 480	69.54 484	92.28 462	94.81 460	89.58 438	97.64 377	96.50 460	1.82 557	5.57 559	95.74 443	68.21 483	96.26 483	73.80 509	91.71 379	90.99 505

PMatch-SfM		73.49 481	70.32 483	83.00 493	85.01 527	68.63 520	90.17 519	79.05 528	71.64 513	63.27 520	91.93 497	17.27 547	89.10 518	74.59 507	59.95 527	91.26 502

PDCNetPlus		71.79 482	69.26 485	79.39 501	85.67 525	69.92 517	90.34 517	62.32 544	72.62 511	65.36 519	90.26 504	39.20 519	86.38 522	75.32 504	42.24 539	81.88 523

SP-DiffGlue		70.13 483	69.16 486	73.04 511	77.73 540	57.48 536	88.44 522	74.91 530	50.96 525	66.64 518	85.99 515	41.44 514	73.46 536	64.21 518	72.15 511	88.19 517

PMatch-Up-SfM		70.03 484	66.48 490	80.70 499	82.00 532	63.20 527	88.10 523	71.07 538	67.59 516	60.07 527	90.10 506	14.49 552	87.80 521	71.95 512	52.95 532	91.09 503

ANet_high		69.08 485	65.37 492	80.22 500	65.99 556	71.96 516	90.91 516	90.09 516	82.62 488	49.93 538	78.39 530	29.36 535	81.75 527	62.49 519	38.52 543	86.95 520

tmp_tt		68.90 486	66.97 487	74.68 504	50.78 558	59.95 533	87.13 525	83.47 524	38.80 534	62.21 522	96.23 425	64.70 494	76.91 532	88.91 433	30.49 547	87.19 519

SP-LightGlue		68.17 487	66.54 489	73.06 510	91.08 504	55.79 537	91.09 514	72.78 535	48.55 529	60.77 525	79.95 527	38.55 520	74.10 534	45.47 529	70.64 513	89.28 510

SP-SuperGlue		68.14 488	66.58 488	72.81 512	90.65 508	55.53 538	91.37 513	73.04 534	49.07 528	61.03 523	80.24 526	38.13 521	74.06 535	45.46 530	70.26 515	88.84 511

ALIKED-LG		67.40 489	65.16 493	74.11 506	93.21 481	62.30 528	88.98 520	71.99 536	55.04 520	59.47 529	82.33 520	39.27 518	85.49 524	32.61 541	63.58 524	74.55 528

SP-NN		67.39 490	65.69 491	72.49 514	90.68 507	55.34 539	90.33 518	71.01 540	46.77 531	59.09 530	79.83 528	37.26 524	73.38 537	44.68 531	71.51 512	88.74 515

ALIKED-NN		66.93 491	64.81 494	73.32 508	93.41 478	62.03 529	87.55 524	71.25 537	50.21 526	59.98 528	82.57 518	39.72 516	84.03 526	34.94 538	63.64 523	73.90 529

SP-MNN		66.66 492	64.70 495	72.53 513	90.32 510	55.08 540	91.01 515	71.05 539	44.81 532	56.48 533	79.62 529	35.87 526	74.11 533	43.13 533	69.98 516	88.39 516

PMVS	61.03 23	65.95 493	63.57 497	73.09 509	57.90 557	51.22 543	85.05 527	93.93 500	54.45 521	44.32 540	83.57 516	13.22 554	89.15 517	58.68 523	81.00 477	78.91 526
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ALIKED-MNN		65.35 494	62.68 499	73.35 507	93.70 474	61.07 531	88.63 521	70.76 541	47.76 530	57.06 532	80.59 524	34.03 530	85.39 525	32.73 540	58.87 528	73.59 530

E-PMN		64.94 495	64.25 496	67.02 515	82.28 531	59.36 534	91.83 511	85.63 522	52.69 522	60.22 526	77.28 531	41.06 515	80.12 529	46.15 528	41.14 540	61.57 535

EMVS		64.07 496	63.26 498	66.53 516	81.73 533	58.81 535	91.85 510	84.75 523	51.93 524	59.09 530	75.13 534	43.32 511	79.09 531	42.03 535	39.47 541	61.69 534

MVE	62.14 22	63.28 497	59.38 500	74.99 503	74.33 547	65.47 525	85.55 526	80.50 526	52.02 523	51.10 536	75.00 535	10.91 559	80.50 528	51.60 526	53.40 531	78.99 525
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM		61.12 498	56.63 501	74.58 505	69.78 552	53.99 542	78.71 530	76.81 529	49.09 527	49.42 539	80.47 525	24.43 539	85.82 523	51.80 525	29.17 548	83.92 522

XFeat-NN		56.16 499	56.10 502	56.36 518	72.10 549	42.54 553	76.45 532	61.18 545	38.16 535	53.08 534	76.48 532	32.95 533	65.67 539	44.15 532	50.31 536	60.87 536

XFeat-MNN		55.84 500	55.19 504	57.82 517	69.33 553	43.25 548	78.25 531	62.64 543	37.53 536	50.90 537	76.32 533	32.43 534	68.13 538	42.00 536	47.26 538	62.07 533

VLMVS_CLIP		53.81 501	55.23 503	49.55 519	44.37 559	26.59 562	64.46 546	73.52 533	28.42 548	60.82 524	83.22 517	22.09 540	59.35 545	62.16 520	58.00 529	62.70 532

MVS_clip		51.49 502	54.55 505	42.29 531	67.55 555	32.35 558	60.25 548	21.09 561	22.72 552	71.30 512	91.13 503	33.91 531	28.07 556	61.97 521	61.05 525	66.44 531

SIFT-NN		49.27 503	49.25 506	49.32 520	83.88 529	45.20 544	74.57 533	53.44 546	32.44 537	42.88 541	64.93 538	20.60 541	61.35 540	16.59 544	53.96 530	41.40 538

SIFT-MNN		47.78 504	47.47 507	48.69 521	81.04 534	44.17 545	73.46 534	53.36 547	31.82 538	38.54 542	63.76 539	18.11 545	61.27 541	15.96 546	51.17 534	40.64 541

SIFT-NN-NCMNet		47.55 505	47.18 508	48.67 522	79.60 536	44.09 546	73.43 535	52.90 548	31.82 538	38.38 543	63.56 542	18.47 542	61.19 542	15.91 547	50.50 535	40.74 540

SIFT-NN-CMatch		45.31 506	44.49 509	47.75 523	76.46 543	42.98 551	70.17 539	49.20 551	31.63 541	37.94 544	63.68 541	18.19 544	59.32 546	15.91 547	37.27 544	40.95 539

SIFT-NCM-Cal		44.98 507	44.20 510	47.33 524	79.81 535	43.05 549	72.12 536	49.31 550	30.81 543	25.90 551	61.87 547	15.80 548	60.28 543	14.09 555	48.07 537	38.66 544

SIFT-NN-UMatch		44.69 508	43.84 511	47.24 525	74.56 546	42.59 552	71.89 537	49.78 549	31.80 540	29.27 548	63.70 540	18.26 543	59.43 544	15.86 549	39.43 542	39.71 542

SIFT-ConvMatch		43.26 509	42.18 513	46.50 526	78.34 539	43.05 549	68.67 541	47.17 552	31.06 542	30.28 547	62.56 544	15.43 549	58.95 548	14.92 551	31.22 546	37.51 546

SIFT-NN-PointCN		43.09 510	42.61 512	44.51 529	72.48 548	37.95 557	70.10 540	46.55 553	30.16 547	34.48 546	61.93 546	18.02 546	55.90 551	15.40 550	34.41 545	39.69 543

SIFT-UMatch		42.35 511	41.04 514	46.29 527	76.09 544	41.80 554	70.21 538	45.21 554	30.75 544	27.33 550	62.62 543	15.13 550	59.11 547	14.72 552	27.30 550	37.95 545

SIFT-CM-Cal		41.25 512	40.03 515	44.88 528	77.37 541	41.08 555	65.71 545	41.18 556	30.42 546	28.83 549	61.42 548	14.88 551	56.40 549	14.13 554	26.37 552	37.16 547

SIFT-UM-Cal		39.93 513	38.61 517	43.88 530	76.08 545	39.30 556	68.10 542	37.89 557	30.49 545	22.74 553	62.27 545	13.89 553	56.16 550	14.17 553	21.90 553	36.17 548

SIFT-PointCN		37.89 514	37.50 518	39.07 532	71.45 550	31.31 559	66.27 544	41.69 555	27.82 549	22.63 554	56.73 550	12.00 557	50.56 553	12.18 557	26.71 551	35.34 549

VLMVS		37.31 515	39.19 516	31.67 535	40.61 560	24.46 563	44.56 550	28.63 559	5.66 556	51.94 535	71.15 536	25.03 538	27.90 557	33.30 539	51.87 533	42.64 537

SIFT-PCN-Cal		36.85 516	36.40 519	38.19 533	71.43 551	30.42 560	64.34 547	37.72 558	27.48 550	22.98 552	57.03 549	12.99 555	51.22 552	12.51 556	21.13 554	32.92 550

SIFT-NCMNet		32.45 517	31.84 521	34.30 534	68.74 554	28.10 561	57.85 549	24.54 560	27.25 551	19.31 555	52.59 551	9.75 560	45.69 554	10.92 558	15.56 556	29.13 552

wuyk23d		30.17 518	30.18 522	30.16 536	78.61 538	43.29 547	66.79 543	14.21 562	17.31 553	14.82 558	11.93 557	11.55 558	41.43 555	37.08 537	19.30 555	5.76 555

cdsmvs_eth3d_5k		23.98 519	31.98 520	0.00 540	0.00 564	0.00 567	0.00 552	98.59 172	0.00 559	0.00 560	98.61 217	90.60 207	0.00 560	0.00 559	0.00 559	0.00 556

testmvs		21.48 520	24.95 523	11.09 538	14.89 562	6.47 565	96.56 460	9.87 563	7.55 554	17.93 556	39.02 553	9.43 561	5.90 559	16.56 545	12.72 557	20.91 554

test123		20.95 521	23.72 524	12.64 537	13.54 563	8.19 564	96.55 462	6.13 564	7.48 555	16.74 557	37.98 554	12.97 556	6.05 558	16.69 543	5.43 558	23.68 553

MVS_baseline		19.65 522	22.57 525	10.89 539	26.60 561	2.25 566	14.08 551	3.93 565	1.15 558	37.00 545	69.35 537	4.91 562	0.00 560	17.88 542	28.24 549	30.42 551

ab-mvs-re		8.20 523	10.94 526	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	98.43 236	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

pcd_1.5k_mvsjas		7.88 524	10.50 527	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	94.51 92	0.00 560	0.00 559	0.00 559	0.00 556

mmdepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

monomultidepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

test_blank		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uanet_test		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

DCPMVS		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

sosnet-low-res		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

sosnet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uncertanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

Regformer		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

PatchmatchNet2					0.00 564	88.11 468	96.56 460	97.31 405	85.66 478
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														80.13 486	90.51 394	95.88 448
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													97.78 443
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					99.64 33	99.18 10		98.83 98		99.13 69		96.51 27	99.92 43	99.03 33	99.80 25

aaatest				99.52 14	99.77 2	98.86 24	99.32 22	99.24 20	96.41 124	99.30 52	99.35 62		99.92 43	98.30 77	99.80 25	99.79 29

TestfortrainingZip				99.43 21	99.13 120	99.06 16	99.32 22	98.57 179	96.88 97	99.42 43	99.05 145	96.54 24	99.73 137		98.59 182	99.51 104

WAC-MVS						90.94 403								88.66 435

FOURS1					99.82 1	98.66 30	99.69 1	98.95 61	97.46 57	99.39 46

MSC_two_6792asdad				99.62 7	99.17 112	99.08 13		98.63 162					99.94 14	98.53 56	99.80 25	99.86 13

PC_three_1452									95.08 219	99.60 33	99.16 110	97.86 2	98.47 361	97.52 143	99.72 67	99.74 50

No_MVS				99.62 7	99.17 112	99.08 13		98.63 162					99.94 14	98.53 56	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					0.00 564

eth-test					0.00 564

ZD-MVS					99.46 59	98.70 29		98.79 120	93.21 334	98.67 106	98.97 156	95.70 53	99.83 91	96.07 216	99.58 98

RE-MVS-def			98.34 54		99.49 53	97.86 76	99.11 66	98.80 115	96.49 119	99.17 63	99.35 62	95.29 70		97.72 117	99.65 81	99.71 63

IU-MVS					99.71 24	99.23 7		98.64 159	95.28 202	99.63 32				98.35 74	99.81 16	99.83 19

OPU-MVS				99.37 28	99.24 104	99.05 17	99.02 87				99.16 110	97.81 3	99.37 213	97.24 165	99.73 62	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 68	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	99.74 135

9.14			98.06 78		99.47 57		98.71 193	98.82 102	94.36 266	99.16 67	99.29 75	96.05 41	99.81 103	97.00 173	99.71 69

save fliter					99.46 59	98.38 42	98.21 294	98.71 138	97.95 28

test_0728_THIRD									97.32 65	99.45 40	99.46 42	97.88 1	99.94 14	98.47 64	99.86 2	99.85 16

test_0728_SECOND				99.71 1	99.72 17	99.35 1	98.97 99	98.88 78					99.94 14	98.47 64	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 35	99.18 10				99.27 57

sam_mvs1												89.45 243				99.20 191

sam_mvs												88.99 259

ambc				89.49 473	86.66 521	75.78 505	92.66 509	96.72 451		86.55 478	92.50 491	46.01 508	97.90 433	90.32 406	82.09 469	94.80 473

MTGPA								98.74 130

test_post1							96.68 457				30.43 556	87.85 297	98.69 339	92.59 356

test_post											31.83 555	88.83 268	98.91 315

patchmatchnet-post											95.10 459	89.42 244	98.89 319

GG-mvs-BLEND				96.59 312	96.34 408	94.98 263	96.51 463	88.58 519		93.10 389	94.34 471	80.34 417	98.05 420	89.53 422	96.99 273	96.74 376

MTMP							98.89 125	94.14 498

gm-plane-assit					95.88 432	87.47 472			89.74 437		96.94 389		99.19 254	93.32 326

test9_res														96.39 210	99.57 99	99.69 70

TEST9					99.31 80	98.50 36	97.92 344	98.73 133	92.63 359	97.74 187	98.68 211	96.20 36	99.80 110

test_8					99.29 89	98.44 38	97.89 352	98.72 135	92.98 345	97.70 192	98.66 214	96.20 36	99.80 110

agg_prior2														95.87 226	99.57 99	99.68 75

agg_prior					99.30 84	98.38 42		98.72 135		97.57 210			99.81 103

TestCases				96.99 269	99.25 97	93.21 351		98.18 302	91.36 401	93.52 368	98.77 197	84.67 363	99.72 138	89.70 419	97.87 242	98.02 314

test_prior4						98.01 72	97.86 356

test_prior2							97.80 363		96.12 142	97.89 174	98.69 210	95.96 45		96.89 183	99.60 93

test_prior				99.19 51	99.31 80	98.22 59		98.84 96					99.70 144			99.65 83

旧先验2							97.57 383		91.30 406	98.67 106			99.80 110	95.70 237

新几何2							97.64 377

新几何1				99.16 56	99.34 72	98.01 72		98.69 143	90.06 431	98.13 141	98.95 163	94.60 90	99.89 69	91.97 375	99.47 122	99.59 94

旧先验1					99.29 89	97.48 91		98.70 141			99.09 135	95.56 56			99.47 122	99.61 90

无先验							97.58 382	98.72 135	91.38 400				99.87 80	93.36 325		99.60 92

原ACMM2							97.67 374

原ACMM1				98.65 98	99.32 78	96.62 142		98.67 151	93.27 333	97.81 180	98.97 156	95.18 77	99.83 91	93.84 311	99.46 125	99.50 107

test222					99.23 105	97.17 118	97.40 394	98.66 154	88.68 452	98.05 150	98.96 161	94.14 103			99.53 112	99.61 90

testdata2													99.89 69	91.65 384

segment_acmp												96.85 15

testdata				98.26 142	99.20 110	95.36 238		98.68 146	91.89 386	98.60 115	99.10 127	94.44 97	99.82 98	94.27 295	99.44 126	99.58 98

testdata1							97.32 404		96.34 130

test12				99.18 53	99.16 116	98.19 61		98.53 189		98.07 146		95.13 80	99.72 138		99.56 107	99.63 88

plane_prior7					97.42 341	94.63 280

plane_prior6					97.35 348	94.61 283						87.09 312

plane_prior5								98.56 183					99.03 293	96.07 216	94.27 327	96.92 352

plane_prior4											98.28 255

plane_prior3						94.61 283			97.02 89	95.34 292

plane_prior2							98.80 165		97.28 69

plane_prior1					97.37 347

plane_prior						94.60 285	98.44 263		96.74 105						94.22 329

n2								0.00 566

nn								0.00 566

door-mid								94.37 492

lessismore_v0				94.45 432	94.93 458	88.44 462		91.03 514		86.77 476	97.64 319	76.23 456	98.42 367	90.31 407	85.64 455	96.51 420

LGP-MVS_train				96.47 328	97.46 336	93.54 327		98.54 187	94.67 248	94.36 325	98.77 197	85.39 345	99.11 275	95.71 235	94.15 333	96.76 374

test11								98.66 154

door								94.64 490

HQP5-MVS						94.25 302

HQP-NCC					97.20 356		98.05 328		96.43 121	94.45 317

ACMP_Plane					97.20 356		98.05 328		96.43 121	94.45 317

BP-MVS														95.30 251

HQP4-MVS										94.45 317			98.96 306			96.87 364

HQP3-MVS								98.46 208							94.18 331

HQP2-MVS												86.75 318

NP-MVS					97.28 350	94.51 288					97.73 306

MDTV_nov1_ep13_2view						84.26 486	96.89 445		90.97 415	97.90 173		89.89 229		93.91 309		99.18 200

MDTV_nov1_ep13			95.40 241		97.48 334	88.34 463	96.85 450	97.29 407	93.74 299	97.48 212	97.26 349	89.18 252	99.05 287	91.92 376	97.43 264

ACMMP++_ref															92.97 361

ACMMP++															93.61 348

Test By Simon												94.64 89

ITE_SJBPF				95.44 390	97.42 341	91.32 397		97.50 385	95.09 218	93.59 363	98.35 246	81.70 399	98.88 321	89.71 418	93.39 354	96.12 441

DeepMVS_CX				86.78 480	97.09 366	72.30 514		95.17 483	75.92 507	84.34 489	95.19 457	70.58 479	95.35 490	79.98 490	89.04 420	92.68 498

High-resolution multi-view benchmark