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.

DVP-MVS++		98.06 1	97.99 1	98.28 10	98.67 67	95.39 12	99.29 1	98.28 52	94.78 61	98.93 20	98.87 31	96.04 2	99.86 9	97.45 46	99.58 23	99.59 32

SED-MVS		98.05 2	97.99 1	98.24 11	99.42 10	95.30 18	98.25 40	98.27 55	95.13 40	99.19 13	98.89 28	95.54 5	99.85 21	97.52 42	99.66 10	99.56 40

TestfortrainingZip a		97.92 3	97.70 10	98.58 3	99.56 1	96.08 5	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51	95.46 7	99.86 9	96.63 69	99.58 23	99.80 1

MED-MVS		97.91 4	97.88 4	98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	94.23 87	98.73 30	98.53 51	95.46 7	99.86 9	97.40 50	99.58 23	99.65 20

DVP-MVS		97.91 4	97.81 5	98.22 14	99.45 6	95.36 14	98.21 47	97.85 137	94.92 50	98.73 30	98.87 31	95.08 10	99.84 26	97.52 42	99.67 6	99.48 56
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
DPE-MVS		97.86 6	97.65 11	98.47 6	99.17 38	95.78 8	97.21 193	98.35 42	95.16 38	98.71 35	98.80 38	95.05 12	99.89 3	96.70 68	99.73 1	99.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 7	97.73 9	98.08 19	99.15 39	94.82 29	98.81 8	98.30 48	94.76 64	98.30 43	98.90 25	93.77 19	99.68 75	97.93 29	99.69 3	99.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 8	97.44 24	98.37 8	98.90 59	95.86 7	97.27 184	98.08 93	95.81 20	97.87 58	98.31 81	94.26 15	99.68 75	97.02 57	99.49 43	99.57 36

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 61	98.21 106	92.75 92	97.83 98	98.73 10	95.04 45	99.30 7	98.84 36	93.34 24	99.78 49	99.32 7	99.13 98	99.50 52

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 34	98.14 113	93.94 56	97.93 83	98.65 24	96.70 8	99.38 5	99.07 11	89.92 91	99.81 35	99.16 14	99.43 53	99.61 30

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 68	98.25 99	92.59 100	97.81 103	98.68 19	94.93 48	99.24 10	98.87 31	93.52 22	99.79 46	99.32 7	99.21 83	99.40 66

SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 28	98.39 88	94.25 44	98.43 27	98.27 55	95.34 32	98.11 47	98.56 47	94.53 14	99.71 67	96.57 73	99.62 17	99.65 20
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		97.59 13	97.79 6	96.97 86	98.28 94	91.49 144	97.61 138	98.71 13	97.10 5	99.70 1	98.93 22	90.95 76	99.77 52	99.35 6	99.53 33	99.65 20

MSP-MVS		97.59 13	97.54 17	97.73 42	99.40 14	93.77 61	98.53 19	98.29 50	95.55 27	98.56 38	97.81 131	93.90 17	99.65 79	96.62 70	99.21 83	99.77 3
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
lecture		97.58 15	97.63 12	97.43 58	99.37 19	92.93 86	98.86 7	98.85 5	95.27 34	98.65 36	98.90 25	91.97 52	99.80 40	97.63 38	99.21 83	99.57 36

test_fmvsm_n_1920		97.55 16	97.89 3	96.53 105	98.41 85	91.73 130	98.01 66	99.02 1	96.37 13	99.30 7	98.92 23	92.39 44	99.79 46	99.16 14	99.46 46	98.08 223

ME-MVS		97.54 17	97.39 27	98.00 23	99.21 36	94.50 35	97.75 110	98.34 44	94.23 87	98.15 46	98.53 51	93.32 27	99.84 26	97.40 50	99.58 23	99.65 20

reproduce-ours		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 120	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 33	99.43 53	99.67 15

our_new_method		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 120	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 33	99.43 53	99.67 15

reproduce_model		97.51 20	97.51 20	97.50 54	98.99 52	93.01 82	97.79 106	98.21 67	95.73 24	97.99 51	99.03 15	92.63 39	99.82 33	97.80 31	99.42 56	99.67 15

test_fmvsmconf_n		97.49 21	97.56 16	97.29 64	97.44 165	92.37 107	97.91 85	98.88 4	95.83 19	98.92 23	99.05 14	91.45 61	99.80 40	99.12 16	99.46 46	99.69 14

TSAR-MVS + MP.		97.42 22	97.33 29	97.69 46	99.25 32	94.24 45	98.07 60	97.85 137	93.72 103	98.57 37	98.35 72	93.69 20	99.40 133	97.06 56	99.46 46	99.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		97.41 23	97.53 18	97.06 82	98.57 78	94.46 38	97.92 84	98.14 83	94.82 57	99.01 17	98.55 49	94.18 16	97.41 386	96.94 58	99.64 14	99.32 74
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
SF-MVS		97.39 24	97.13 31	98.17 16	99.02 48	95.28 20	98.23 44	98.27 55	92.37 166	98.27 44	98.65 45	93.33 25	99.72 65	96.49 75	99.52 35	99.51 49

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 44	95.42 11	97.94 81	98.18 76	90.57 251	98.85 27	98.94 21	93.33 25	99.83 31	96.72 66	99.68 4	99.63 26
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
HPM-MVS++		97.34 26	96.97 43	98.47 6	99.08 42	96.16 4	97.55 149	97.97 121	95.59 25	96.61 97	97.89 116	92.57 41	99.84 26	95.95 99	99.51 38	99.40 66

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 201	97.80 104	98.53 30	97.24 4	99.62 2	99.14 2	88.65 109	99.80 40	99.54 1	99.15 95	99.74 9

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 88	98.28 94	91.07 169	97.76 108	98.62 26	97.53 2	99.20 12	99.12 5	88.24 117	99.81 35	99.41 3	99.17 91	99.67 15

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 119	98.42 83	91.37 151	98.04 63	98.00 117	97.30 3	99.45 4	99.21 1	89.28 97	99.80 40	99.27 10	99.35 69	98.12 215

NCCC		97.30 29	97.03 40	98.11 18	98.77 62	95.06 26	97.34 177	98.04 108	95.96 15	97.09 79	97.88 119	93.18 28	99.71 67	95.84 104	99.17 91	99.56 40

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 86	98.24 100	91.96 126	97.89 88	98.72 12	96.77 7	99.46 3	99.06 12	87.78 127	99.84 26	99.40 4	99.27 75	99.12 92

MM		97.29 31	96.98 42	98.23 12	98.01 123	95.03 27	98.07 60	95.76 344	97.78 1	97.52 62	98.80 38	88.09 119	99.86 9	99.44 2	99.37 67	99.80 1

ACMMP_NAP		97.20 33	96.86 49	98.23 12	99.09 40	95.16 23	97.60 139	98.19 74	92.82 154	97.93 54	98.74 42	91.60 59	99.86 9	96.26 80	99.52 35	99.67 15

XVS		97.18 34	96.96 45	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	98.29 84	91.70 56	99.80 40	95.66 108	99.40 61	99.62 27

MCST-MVS		97.18 34	96.84 51	98.20 15	99.30 29	95.35 16	97.12 200	98.07 98	93.54 112	96.08 125	97.69 144	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 107	97.98 126	91.19 161	97.84 95	98.65 24	97.08 6	99.25 9	99.10 6	87.88 125	99.79 46	99.32 7	99.18 90	98.59 166

HFP-MVS		97.14 37	96.92 47	97.83 30	99.42 10	94.12 50	98.52 20	98.32 46	93.21 127	97.18 73	98.29 84	92.08 49	99.83 31	95.63 113	99.59 19	99.54 45

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 73	95.67 302	92.21 114	97.95 80	98.27 55	95.78 23	98.40 42	99.00 16	89.99 89	99.78 49	99.06 18	99.41 59	99.59 32

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 89	97.28 170	91.73 130	97.75 110	98.50 31	94.86 52	99.22 11	98.78 40	89.75 94	99.76 54	99.10 17	99.29 73	98.94 120

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 186	98.08 93	95.07 44	96.11 123	98.59 46	90.88 79	99.90 2	96.18 92	99.50 40	99.58 35

region2R		97.07 41	96.84 51	97.77 38	99.46 5	93.79 59	98.52 20	98.24 63	93.19 130	97.14 76	98.34 75	91.59 60	99.87 7	95.46 119	99.59 19	99.64 25

ACMMPR		97.07 41	96.84 51	97.79 34	99.44 9	93.88 57	98.52 20	98.31 47	93.21 127	97.15 75	98.33 78	91.35 65	99.86 9	95.63 113	99.59 19	99.62 27

CP-MVS		97.02 43	96.81 56	97.64 49	99.33 26	93.54 64	98.80 9	98.28 52	92.99 140	96.45 111	98.30 83	91.90 53	99.85 21	95.61 115	99.68 4	99.54 45

SR-MVS		97.01 44	96.86 49	97.47 56	99.09 40	93.27 75	97.98 71	98.07 98	93.75 102	97.45 64	98.48 61	91.43 63	99.59 95	96.22 83	99.27 75	99.54 45

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 79	97.58 161	92.56 101	97.68 124	98.47 35	94.02 93	98.90 25	98.89 28	88.94 103	99.78 49	99.18 12	99.03 107	98.93 124

ZNCC-MVS		96.96 46	96.67 64	97.85 29	99.37 19	94.12 50	98.49 24	98.18 76	92.64 161	96.39 113	98.18 91	91.61 58	99.88 4	95.59 118	99.55 30	99.57 36

APD-MVS		96.95 47	96.60 66	98.01 21	99.03 47	94.93 28	97.72 118	98.10 91	91.50 201	98.01 50	98.32 80	92.33 45	99.58 98	94.85 133	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 48	97.06 35	96.59 102	98.72 64	91.86 128	97.67 125	98.49 32	94.66 69	97.24 72	98.41 67	92.31 47	98.94 195	96.61 71	99.46 46	98.96 114

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 36	98.64 73	94.30 41	97.41 167	98.04 108	94.81 59	96.59 99	98.37 70	91.24 68	99.64 87	95.16 124	99.52 35	99.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		96.89 50	97.04 39	96.45 118	98.29 93	91.66 137	99.03 4	97.85 137	95.84 18	96.90 83	97.97 109	91.24 68	98.75 226	96.92 59	99.33 70	98.94 120

SR-MVS-dyc-post		96.88 51	96.80 57	97.11 78	99.02 48	92.34 108	97.98 71	98.03 110	93.52 115	97.43 67	98.51 56	91.40 64	99.56 106	96.05 94	99.26 78	99.43 63

CS-MVS		96.86 52	97.06 35	96.26 135	98.16 112	91.16 166	99.09 3	97.87 132	95.30 33	97.06 80	98.03 101	91.72 54	98.71 236	97.10 55	99.17 91	98.90 129

mPP-MVS		96.86 52	96.60 66	97.64 49	99.40 14	93.44 66	98.50 23	98.09 92	93.27 126	95.95 131	98.33 78	91.04 73	99.88 4	95.20 122	99.57 29	99.60 31

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 149	98.07 120	90.28 202	97.97 77	98.76 9	94.93 48	98.84 28	99.06 12	88.80 106	99.65 79	99.06 18	98.63 123	98.18 208

GST-MVS		96.85 54	96.52 70	97.82 31	99.36 23	94.14 49	98.29 34	98.13 84	92.72 157	96.70 91	98.06 98	91.35 65	99.86 9	94.83 136	99.28 74	99.47 58

balanced_conf03		96.84 56	96.89 48	96.68 93	97.63 153	92.22 113	98.17 53	97.82 143	94.44 79	98.23 45	97.36 174	90.97 75	99.22 151	97.74 32	99.66 10	98.61 163

patch_mono-2		96.83 57	97.44 24	95.01 220	99.05 45	85.39 360	96.98 213	98.77 8	94.70 66	97.99 51	98.66 43	93.61 21	99.91 1	97.67 37	99.50 40	99.72 13

APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 76	99.01 51	92.31 110	97.98 71	98.06 101	93.11 136	97.44 65	98.55 49	90.93 77	99.55 108	96.06 93	99.25 80	99.51 49

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 129	98.98 2	92.22 172	97.14 76	98.44 64	91.17 71	99.85 21	94.35 158	99.46 46	99.57 36

MP-MVS		96.77 60	96.45 77	97.72 43	99.39 16	93.80 58	98.41 28	98.06 101	93.37 122	95.54 149	98.34 75	90.59 83	99.88 4	94.83 136	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 60	96.46 76	97.71 45	98.40 86	94.07 52	98.21 47	98.45 37	89.86 269	97.11 78	98.01 104	92.52 42	99.69 73	96.03 97	99.53 33	99.36 72

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 173	97.76 142	89.57 231	97.66 128	98.66 22	95.36 30	99.03 16	98.90 25	88.39 114	99.73 61	99.17 13	98.66 121	98.08 223

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 140	97.64 151	90.72 184	98.00 67	98.73 10	94.55 73	98.91 24	99.08 8	88.22 118	99.63 88	98.91 21	98.37 136	98.25 203

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 140	94.39 410	96.47 12	97.16 74	98.39 68	87.53 136	99.87 7	98.97 20	99.41 59	99.55 43

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 129	96.62 230	91.73 130	97.98 71	98.30 48	96.19 14	96.10 124	98.95 20	89.42 95	99.76 54	98.90 22	99.08 102	97.43 263

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 215	98.06 101	90.67 241	95.55 147	98.78 40	91.07 72	99.86 9	96.58 72	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 237	96.72 291	94.17 89	97.44 65	97.66 148	92.76 34	99.33 139	96.86 62	97.76 162	99.08 98

HPM-MVS		96.69 67	96.45 77	97.40 59	99.36 23	93.11 80	98.87 6	98.06 101	91.17 220	96.40 112	97.99 107	90.99 74	99.58 98	95.61 115	99.61 18	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 300	98.90 3	94.30 86	95.86 134	97.74 139	92.33 45	99.38 136	96.04 96	99.42 56	99.28 77

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 151	97.98 126	90.43 194	97.50 153	98.59 27	96.59 10	99.31 6	99.08 8	84.47 199	99.75 58	99.37 5	98.45 133	97.88 236

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 140	93.19 78	95.96 315	98.18 76	95.23 35	95.87 133	97.65 149	91.45 61	99.70 72	95.87 100	99.44 52	99.00 109
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
DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 211	98.09 116	86.63 326	96.00 313	98.15 81	95.43 28	97.95 53	98.56 47	93.40 23	99.36 137	96.77 63	99.48 44	99.45 59

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 154	96.67 228	90.25 203	97.91 85	98.38 38	94.48 77	98.84 28	99.14 2	88.06 120	99.62 89	98.82 23	98.60 125	98.15 212

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 54	97.79 145	90.43 256	97.34 70	97.52 164	91.29 67	99.19 154	98.12 28	99.64 14	98.60 164

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 223	97.73 152	94.74 65	96.49 106	98.49 58	90.88 79	99.58 98	96.44 76	98.32 138	99.13 89

HPM-MVS_fast		96.51 74	96.27 83	97.22 70	99.32 27	92.74 93	98.74 10	98.06 101	90.57 251	96.77 88	98.35 72	90.21 86	99.53 112	94.80 140	99.63 16	99.38 70

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 203	97.29 169	88.38 275	97.23 190	98.47 35	95.14 39	98.43 41	99.09 7	87.58 133	99.72 65	98.80 25	99.21 83	98.02 227

EC-MVSNet		96.42 78	96.47 73	96.26 135	97.01 192	91.52 143	98.89 5	97.75 149	94.42 80	96.64 96	97.68 145	89.32 96	98.60 252	97.45 46	99.11 101	98.67 161

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 142	95.48 311	90.69 185	97.91 85	98.33 45	94.07 91	98.93 20	99.14 2	87.44 141	99.61 90	98.63 26	98.32 138	98.18 208

CANet		96.39 80	96.02 88	97.50 54	97.62 154	93.38 68	97.02 206	97.96 122	95.42 29	94.86 167	97.81 131	87.38 143	99.82 33	96.88 60	99.20 88	99.29 75

dcpmvs_2		96.37 81	97.05 38	94.31 266	98.96 55	84.11 382	97.56 144	97.51 188	93.92 97	97.43 67	98.52 55	92.75 35	99.32 141	97.32 54	99.50 40	99.51 49

NormalMVS		96.36 82	96.11 86	97.12 76	99.37 19	92.90 87	97.99 68	97.63 166	95.92 16	96.57 102	97.93 111	85.34 181	99.50 120	94.99 129	99.21 83	98.97 111

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 174	96.86 226	97.72 154	94.67 68	96.16 122	98.46 62	90.43 84	99.58 98	96.23 82	97.96 155	98.90 129

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 155	97.30 168	90.37 200	97.53 150	97.92 127	96.52 11	99.14 15	99.08 8	83.21 221	99.74 59	99.22 11	98.06 150	97.88 236

train_agg		96.30 85	95.83 93	97.72 43	98.70 65	94.19 46	96.41 275	98.02 113	88.58 315	96.03 126	97.56 161	92.73 37	99.59 95	95.04 126	99.37 67	99.39 68

ACMMP		96.27 86	95.93 89	97.28 66	99.24 33	92.62 98	98.25 40	98.81 6	92.99 140	94.56 177	98.39 68	88.96 102	99.85 21	94.57 152	97.63 163	99.36 72
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
MVS_111021_LR		96.24 87	96.19 85	96.39 124	98.23 105	91.35 153	96.24 297	98.79 7	93.99 95	95.80 136	97.65 149	89.92 91	99.24 149	95.87 100	99.20 88	98.58 167

test_fmvsmconf0.01_n		96.15 88	95.85 92	97.03 83	92.66 425	91.83 129	97.97 77	97.84 141	95.57 26	97.53 61	99.00 16	84.20 205	99.76 54	98.82 23	99.08 102	99.48 56

DeepC-MVS	93.07 3	96.06 89	95.66 94	97.29 64	97.96 128	93.17 79	97.30 182	98.06 101	93.92 97	93.38 217	98.66 43	86.83 150	99.73 61	95.60 117	99.22 82	98.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		96.05 90	95.91 90	96.46 117	99.24 33	90.47 191	98.30 33	98.57 29	89.01 297	93.97 198	97.57 159	92.62 40	99.76 54	94.66 146	99.27 75	99.15 87

sasdasda		96.02 91	95.45 101	97.75 40	97.59 157	95.15 24	98.28 35	97.60 171	94.52 75	96.27 117	96.12 254	87.65 130	99.18 157	96.20 88	94.82 251	98.91 126

ETV-MVS		96.02 91	95.89 91	96.40 122	97.16 176	92.44 105	97.47 162	97.77 148	94.55 73	96.48 107	94.51 336	91.23 70	98.92 198	95.65 111	98.19 144	97.82 244

canonicalmvs		96.02 91	95.45 101	97.75 40	97.59 157	95.15 24	98.28 35	97.60 171	94.52 75	96.27 117	96.12 254	87.65 130	99.18 157	96.20 88	94.82 251	98.91 126

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 284	97.88 130	86.98 362	96.65 95	97.89 116	91.99 51	99.47 125	92.26 198	99.46 46	99.39 68

UA-Net		95.95 95	95.53 97	97.20 72	97.67 147	92.98 84	97.65 129	98.13 84	94.81 59	96.61 97	98.35 72	88.87 104	99.51 117	90.36 250	97.35 174	99.11 94

SymmetryMVS		95.94 96	95.54 96	97.15 74	97.85 136	92.90 87	97.99 68	96.91 278	95.92 16	96.57 102	97.93 111	85.34 181	99.50 120	94.99 129	96.39 216	99.05 102

MGCFI-Net		95.94 96	95.40 105	97.56 53	97.59 157	94.62 32	98.21 47	97.57 178	94.41 81	96.17 121	96.16 252	87.54 135	99.17 159	96.19 90	94.73 256	98.91 126

BP-MVS1		95.89 98	95.49 98	97.08 81	96.67 228	93.20 77	98.08 58	96.32 317	94.56 72	96.32 114	97.84 125	84.07 208	99.15 163	96.75 64	98.78 116	98.90 129

VNet		95.89 98	95.45 101	97.21 71	98.07 120	92.94 85	97.50 153	98.15 81	93.87 99	97.52 62	97.61 155	85.29 183	99.53 112	95.81 105	95.27 242	99.16 85

alignmvs		95.87 100	95.23 111	97.78 36	97.56 163	95.19 22	97.86 91	97.17 243	94.39 83	96.47 108	96.40 239	85.89 168	99.20 153	96.21 87	95.11 247	98.95 117

casdiffmvs_mvg		95.81 101	95.57 95	96.51 111	96.87 203	91.49 144	97.50 153	97.56 182	93.99 95	95.13 162	97.92 114	87.89 124	98.78 215	95.97 98	97.33 175	99.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 356	97.62 170	90.43 256	95.55 147	97.07 194	91.72 54	99.50 120	89.62 266	98.94 111	98.82 145

DP-MVS Recon		95.68 103	95.12 116	97.37 60	99.19 37	94.19 46	97.03 204	98.08 93	88.35 324	95.09 163	97.65 149	89.97 90	99.48 124	92.08 209	98.59 126	98.44 185

casdiffmvs		95.64 104	95.49 98	96.08 145	96.76 225	90.45 192	97.29 183	97.44 208	94.00 94	95.46 152	97.98 108	87.52 138	98.73 230	95.64 112	97.33 175	99.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS		95.62 105	95.13 114	97.09 79	96.79 214	93.26 76	97.89 88	97.83 142	93.58 107	96.80 85	97.82 129	83.06 228	99.16 161	94.40 155	97.95 156	98.87 139

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 189	96.04 310	97.48 193	93.47 117	95.67 144	98.10 94	89.17 99	99.25 148	91.27 227	98.77 117	99.13 89

baseline		95.58 107	95.42 104	96.08 145	96.78 219	90.41 195	97.16 197	97.45 204	93.69 106	95.65 145	97.85 123	87.29 144	98.68 240	95.66 108	97.25 181	99.13 89

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 343	95.17 161	98.03 101	87.09 148	99.61 90	93.51 176	99.42 56	99.02 103

EIA-MVS		95.53 109	95.47 100	95.71 183	97.06 184	89.63 227	97.82 100	97.87 132	93.57 108	93.92 199	95.04 308	90.61 82	98.95 193	94.62 148	98.68 120	98.54 170

3Dnovator+	91.43 4	95.40 110	94.48 145	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 347	98.02 103	83.69 212	99.71 67	93.18 184	98.96 110	99.44 61

PS-MVSNAJ		95.37 111	95.33 108	95.49 197	97.35 167	90.66 187	95.31 353	97.48 193	93.85 100	96.51 105	95.70 279	88.65 109	99.65 79	94.80 140	98.27 141	96.17 302

MVSFormer		95.37 111	95.16 113	95.99 156	96.34 262	91.21 158	98.22 45	97.57 178	91.42 205	96.22 119	97.32 175	86.20 164	97.92 336	94.07 161	99.05 104	98.85 141

diffmvs_AUTHOR		95.33 113	95.27 110	95.50 196	96.37 260	89.08 257	96.08 308	97.38 219	93.09 138	96.53 104	97.74 139	86.45 158	98.68 240	96.32 78	97.48 166	98.75 152

xiu_mvs_v2_base		95.32 114	95.29 109	95.40 202	97.22 172	90.50 190	95.44 346	97.44 208	93.70 105	96.46 109	96.18 249	88.59 113	99.53 112	94.79 143	97.81 159	96.17 302

E3new		95.28 115	95.11 117	95.80 170	97.03 189	89.76 221	96.78 241	97.54 185	92.06 182	95.40 153	97.75 136	87.49 139	98.76 220	94.85 133	97.10 187	98.88 137

PVSNet_Blended_VisFu		95.27 116	94.91 125	96.38 125	98.20 107	90.86 177	97.27 184	98.25 61	90.21 260	94.18 191	97.27 181	87.48 140	99.73 61	93.53 175	97.77 161	98.55 169

viewcassd2359sk11		95.26 117	95.09 118	95.80 170	96.95 198	89.72 223	96.80 236	97.56 182	92.21 174	95.37 154	97.80 133	87.17 147	98.77 218	94.82 138	97.10 187	98.90 129

KinetiMVS		95.26 117	94.75 131	96.79 90	96.99 194	92.05 120	97.82 100	97.78 146	94.77 63	96.46 109	97.70 142	80.62 282	99.34 138	92.37 197	98.28 140	98.97 111

diffmvs		95.25 119	95.13 114	95.63 186	96.43 255	89.34 244	95.99 314	97.35 224	92.83 153	96.31 115	97.37 173	86.44 159	98.67 243	96.26 80	97.19 184	98.87 139
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		95.24 120	95.02 120	95.91 159	96.87 203	89.98 212	96.82 231	97.49 191	92.26 170	95.47 151	97.82 129	86.47 157	98.69 238	94.80 140	97.20 183	99.06 101

Vis-MVSNet		95.23 121	94.81 126	96.51 111	97.18 175	91.58 141	98.26 39	98.12 86	94.38 84	94.90 166	98.15 93	82.28 249	98.92 198	91.45 224	98.58 127	99.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 122	95.04 119	95.76 176	97.49 164	89.56 232	98.67 15	97.00 268	90.69 239	94.24 187	97.62 154	89.79 93	98.81 211	93.39 181	96.49 213	98.92 125

E2		95.20 123	95.00 121	95.79 173	96.79 214	89.66 224	96.82 231	97.58 175	92.35 167	95.28 156	97.83 127	86.68 152	98.76 220	94.79 143	96.92 193	98.95 117

E3		95.20 123	95.00 121	95.79 173	96.77 221	89.66 224	96.82 231	97.58 175	92.35 167	95.28 156	97.83 127	86.69 151	98.76 220	94.79 143	96.92 193	98.95 117

EPNet		95.20 123	94.56 138	97.14 75	92.80 422	92.68 97	97.85 94	94.87 394	96.64 9	92.46 234	97.80 133	86.23 161	99.65 79	93.72 171	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 126	94.44 147	97.44 57	96.56 238	93.36 70	98.65 16	98.36 39	94.12 90	89.25 330	98.06 98	82.20 251	99.77 52	93.41 180	99.32 71	99.18 84

guyue		95.17 127	94.96 123	95.82 168	96.97 196	89.65 226	97.56 144	95.58 356	94.82 57	95.72 139	97.42 170	82.90 233	98.84 207	96.71 67	96.93 192	98.96 114

OMC-MVS		95.09 128	94.70 132	96.25 138	98.46 79	91.28 154	96.43 271	97.57 178	92.04 183	94.77 172	97.96 110	87.01 149	99.09 174	91.31 226	96.77 198	98.36 192

viewmacassd2359aftdt		95.07 129	94.80 127	95.87 162	96.53 243	89.84 218	96.90 222	97.48 193	92.44 163	95.36 155	97.89 116	85.23 184	98.68 240	94.40 155	97.00 191	99.09 96

xiu_mvs_v1_base_debu		95.01 130	94.76 128	95.75 178	96.58 234	91.71 133	96.25 294	97.35 224	92.99 140	96.70 91	96.63 226	82.67 239	99.44 129	96.22 83	97.46 167	96.11 308

xiu_mvs_v1_base		95.01 130	94.76 128	95.75 178	96.58 234	91.71 133	96.25 294	97.35 224	92.99 140	96.70 91	96.63 226	82.67 239	99.44 129	96.22 83	97.46 167	96.11 308

xiu_mvs_v1_base_debi		95.01 130	94.76 128	95.75 178	96.58 234	91.71 133	96.25 294	97.35 224	92.99 140	96.70 91	96.63 226	82.67 239	99.44 129	96.22 83	97.46 167	96.11 308

PAPM_NR		95.01 130	94.59 136	96.26 135	98.89 60	90.68 186	97.24 186	97.73 152	91.80 188	92.93 231	96.62 229	89.13 100	99.14 166	89.21 279	97.78 160	98.97 111

lupinMVS		94.99 134	94.56 138	96.29 133	96.34 262	91.21 158	95.83 323	96.27 322	88.93 303	96.22 119	96.88 208	86.20 164	98.85 205	95.27 121	99.05 104	98.82 145

Effi-MVS+		94.93 135	94.45 146	96.36 127	96.61 231	91.47 147	96.41 275	97.41 214	91.02 228	94.50 180	95.92 263	87.53 136	98.78 215	93.89 167	96.81 197	98.84 144

IS-MVSNet		94.90 136	94.52 142	96.05 148	97.67 147	90.56 188	98.44 26	96.22 325	93.21 127	93.99 196	97.74 139	85.55 178	98.45 266	89.98 255	97.86 157	99.14 88

LuminaMVS		94.89 137	94.35 150	96.53 105	95.48 311	92.80 91	96.88 225	96.18 329	92.85 152	95.92 132	96.87 210	81.44 266	98.83 208	96.43 77	97.10 187	97.94 232

MVS_Test		94.89 137	94.62 135	95.68 184	96.83 209	89.55 233	96.70 249	97.17 243	91.17 220	95.60 146	96.11 258	87.87 126	98.76 220	93.01 192	97.17 185	98.72 156

viewdifsd2359ckpt13		94.87 139	94.52 142	95.90 160	96.88 202	90.19 205	96.92 219	97.36 222	91.26 213	94.65 174	97.46 165	85.79 172	98.64 247	93.64 173	96.76 199	98.88 137

PVSNet_Blended		94.87 139	94.56 138	95.81 169	98.27 96	89.46 239	95.47 345	98.36 39	88.84 306	94.36 183	96.09 259	88.02 121	99.58 98	93.44 178	98.18 145	98.40 188

jason		94.84 141	94.39 148	96.18 141	95.52 309	90.93 174	96.09 307	96.52 306	89.28 288	96.01 129	97.32 175	84.70 195	98.77 218	95.15 125	98.91 113	98.85 141
jason: jason.
API-MVS		94.84 141	94.49 144	95.90 160	97.90 134	92.00 123	97.80 104	97.48 193	89.19 291	94.81 170	96.71 215	88.84 105	99.17 159	88.91 286	98.76 118	96.53 291

AstraMVS		94.82 143	94.64 134	95.34 205	96.36 261	88.09 287	97.58 140	94.56 403	94.98 46	95.70 142	97.92 114	81.93 259	98.93 196	96.87 61	95.88 223	98.99 110

viewdifsd2359ckpt09		94.81 144	94.37 149	96.12 144	96.91 199	90.75 183	96.94 216	97.31 229	90.51 254	94.31 185	97.38 172	85.70 174	98.71 236	93.54 174	96.75 200	98.90 129

test_yl		94.78 145	94.23 153	96.43 119	97.74 143	91.22 156	96.85 227	97.10 249	91.23 217	95.71 140	96.93 203	84.30 202	99.31 143	93.10 185	95.12 245	98.75 152

DCV-MVSNet		94.78 145	94.23 153	96.43 119	97.74 143	91.22 156	96.85 227	97.10 249	91.23 217	95.71 140	96.93 203	84.30 202	99.31 143	93.10 185	95.12 245	98.75 152

viewdifsd2359ckpt07		94.76 147	94.68 133	95.01 220	96.76 225	87.41 302	96.38 281	97.43 211	92.65 159	94.52 178	97.75 136	85.55 178	98.81 211	94.36 157	96.69 204	98.82 145

SSM_0404		94.73 148	94.31 152	95.98 157	97.05 186	90.90 176	97.01 209	97.29 230	91.24 214	94.17 192	97.60 156	85.03 188	98.76 220	92.14 203	97.30 178	98.29 201

WTY-MVS		94.71 149	94.02 158	96.79 90	97.71 145	92.05 120	96.59 264	97.35 224	90.61 247	94.64 175	96.93 203	86.41 160	99.39 134	91.20 229	94.71 257	98.94 120

mamv4		94.66 150	96.10 87	90.37 408	98.01 123	73.41 459	96.82 231	97.78 146	89.95 267	94.52 178	97.43 169	92.91 30	99.09 174	98.28 27	99.16 94	98.60 164

mvsmamba		94.57 151	94.14 155	95.87 162	97.03 189	89.93 216	97.84 95	95.85 340	91.34 208	94.79 171	96.80 211	80.67 280	98.81 211	94.85 133	98.12 148	98.85 141

SSM_0407		94.54 152	94.12 157	95.80 170	96.79 214	90.38 197	96.79 237	97.29 230	91.24 214	93.68 203	97.60 156	85.03 188	98.67 243	92.14 203	96.51 209	98.35 194

RRT-MVS		94.51 153	94.35 150	94.98 224	96.40 256	86.55 329	97.56 144	97.41 214	93.19 130	94.93 165	97.04 196	79.12 310	99.30 145	96.19 90	97.32 177	99.09 96

sss		94.51 153	93.80 162	96.64 94	97.07 181	91.97 124	96.32 289	98.06 101	88.94 302	94.50 180	96.78 212	84.60 196	99.27 147	91.90 210	96.02 219	98.68 160

test_cas_vis1_n_1920		94.48 155	94.55 141	94.28 268	96.78 219	86.45 332	97.63 135	97.64 164	93.32 125	97.68 60	98.36 71	73.75 373	99.08 177	96.73 65	99.05 104	97.31 270

CANet_DTU		94.37 156	93.65 168	96.55 104	96.46 253	92.13 118	96.21 298	96.67 298	94.38 84	93.53 211	97.03 201	79.34 306	99.71 67	90.76 239	98.45 133	97.82 244

AdaColmap		94.34 157	93.68 167	96.31 129	98.59 75	91.68 136	96.59 264	97.81 144	89.87 268	92.15 245	97.06 195	83.62 215	99.54 110	89.34 273	98.07 149	97.70 249

viewmambaseed2359dif		94.28 158	94.14 155	94.71 242	96.21 266	86.97 316	95.93 317	97.11 248	89.00 298	95.00 164	97.70 142	86.02 167	98.59 256	93.71 172	96.59 208	98.57 168

CNLPA		94.28 158	93.53 173	96.52 107	98.38 89	92.55 102	96.59 264	96.88 282	90.13 264	91.91 253	97.24 183	85.21 185	99.09 174	87.64 312	97.83 158	97.92 233

MAR-MVS		94.22 160	93.46 178	96.51 111	98.00 125	92.19 117	97.67 125	97.47 197	88.13 332	93.00 226	95.84 267	84.86 194	99.51 117	87.99 299	98.17 146	97.83 243
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
PAPR		94.18 161	93.42 183	96.48 114	97.64 151	91.42 150	95.55 340	97.71 158	88.99 299	92.34 241	95.82 269	89.19 98	99.11 169	86.14 338	97.38 172	98.90 129

SDMVSNet		94.17 162	93.61 169	95.86 165	98.09 116	91.37 151	97.35 176	98.20 69	93.18 132	91.79 257	97.28 179	79.13 309	98.93 196	94.61 149	92.84 289	97.28 271

test_vis1_n_1920		94.17 162	94.58 137	92.91 337	97.42 166	82.02 409	97.83 98	97.85 137	94.68 67	98.10 48	98.49 58	70.15 397	99.32 141	97.91 30	98.82 114	97.40 265

h-mvs33		94.15 164	93.52 175	96.04 149	97.81 139	90.22 204	97.62 137	97.58 175	95.19 36	96.74 89	97.45 166	83.67 213	99.61 90	95.85 102	79.73 429	98.29 201

CHOSEN 1792x2688		94.15 164	93.51 176	96.06 147	98.27 96	89.38 242	95.18 363	98.48 34	85.60 385	93.76 202	97.11 192	83.15 224	99.61 90	91.33 225	98.72 119	99.19 83

Vis-MVSNet (Re-imp)		94.15 164	93.88 161	94.95 228	97.61 155	87.92 291	98.10 56	95.80 343	92.22 172	93.02 225	97.45 166	84.53 198	97.91 339	88.24 295	97.97 154	99.02 103

CDS-MVSNet		94.14 167	93.54 172	95.93 158	96.18 274	91.46 148	96.33 288	97.04 263	88.97 301	93.56 208	96.51 233	87.55 134	97.89 340	89.80 260	95.95 221	98.44 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 168	93.43 181	96.13 143	98.58 77	91.15 167	96.69 251	97.39 216	87.29 357	91.37 267	96.71 215	88.39 114	99.52 116	87.33 319	97.13 186	97.73 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 169	93.70 166	95.27 207	95.70 300	92.03 122	98.10 56	98.68 19	93.36 124	90.39 288	96.70 217	87.63 132	97.94 333	92.25 200	90.50 330	95.84 316

PVSNet_BlendedMVS		94.06 170	93.92 160	94.47 255	98.27 96	89.46 239	96.73 245	98.36 39	90.17 261	94.36 183	95.24 302	88.02 121	99.58 98	93.44 178	90.72 326	94.36 401

nrg030		94.05 171	93.31 185	96.27 134	95.22 334	94.59 33	98.34 30	97.46 199	92.93 147	91.21 277	96.64 222	87.23 146	98.22 286	94.99 129	85.80 377	95.98 312

UGNet		94.04 172	93.28 186	96.31 129	96.85 206	91.19 161	97.88 90	97.68 159	94.40 82	93.00 226	96.18 249	73.39 375	99.61 90	91.72 216	98.46 132	98.13 213
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
TAMVS		94.01 173	93.46 178	95.64 185	96.16 276	90.45 192	96.71 248	96.89 281	89.27 289	93.46 215	96.92 206	87.29 144	97.94 333	88.70 291	95.74 227	98.53 171

Elysia		94.00 174	93.12 191	96.64 94	96.08 286	92.72 95	97.50 153	97.63 166	91.15 222	94.82 168	97.12 190	74.98 360	99.06 183	90.78 237	98.02 151	98.12 215

StellarMVS		94.00 174	93.12 191	96.64 94	96.08 286	92.72 95	97.50 153	97.63 166	91.15 222	94.82 168	97.12 190	74.98 360	99.06 183	90.78 237	98.02 151	98.12 215

IMVS_0403		93.98 176	93.79 163	94.55 251	96.19 270	86.16 341	96.35 284	97.24 237	91.54 196	93.59 207	97.04 196	85.86 169	98.73 230	90.68 242	95.59 233	98.76 148

114514_t		93.95 177	93.06 194	96.63 98	99.07 43	91.61 138	97.46 164	97.96 122	77.99 450	93.00 226	97.57 159	86.14 166	99.33 139	89.22 278	99.15 95	98.94 120

IMVS_0407		93.94 178	93.75 164	94.49 254	96.19 270	86.16 341	96.35 284	97.24 237	91.54 196	93.50 212	97.04 196	85.64 176	98.54 259	90.68 242	95.59 233	98.76 148

FC-MVSNet-test		93.94 178	93.57 170	95.04 218	95.48 311	91.45 149	98.12 55	98.71 13	93.37 122	90.23 291	96.70 217	87.66 129	97.85 342	91.49 222	90.39 331	95.83 317

mvsany_test1		93.93 180	93.98 159	93.78 300	94.94 351	86.80 319	94.62 375	92.55 443	88.77 312	96.85 84	98.49 58	88.98 101	98.08 304	95.03 127	95.62 232	96.46 296

GeoE		93.89 181	93.28 186	95.72 182	96.96 197	89.75 222	98.24 43	96.92 277	89.47 282	92.12 247	97.21 185	84.42 200	98.39 274	87.71 306	96.50 212	99.01 106

HY-MVS	89.66 9	93.87 182	92.95 199	96.63 98	97.10 180	92.49 104	95.64 337	96.64 299	89.05 296	93.00 226	95.79 273	85.77 173	99.45 128	89.16 282	94.35 259	97.96 230

XVG-OURS-SEG-HR		93.86 183	93.55 171	94.81 234	97.06 184	88.53 271	95.28 354	97.45 204	91.68 193	94.08 195	97.68 145	82.41 247	98.90 201	93.84 169	92.47 295	96.98 279

VDD-MVS		93.82 184	93.08 193	96.02 151	97.88 135	89.96 215	97.72 118	95.85 340	92.43 164	95.86 134	98.44 64	68.42 414	99.39 134	96.31 79	94.85 249	98.71 158

mvs_anonymous		93.82 184	93.74 165	94.06 278	96.44 254	85.41 358	95.81 324	97.05 261	89.85 271	90.09 301	96.36 241	87.44 141	97.75 356	93.97 163	96.69 204	99.02 103

HQP_MVS		93.78 186	93.43 181	94.82 232	96.21 266	89.99 210	97.74 113	97.51 188	94.85 53	91.34 268	96.64 222	81.32 268	98.60 252	93.02 190	92.23 298	95.86 313

PS-MVSNAJss		93.74 187	93.51 176	94.44 257	93.91 389	89.28 249	97.75 110	97.56 182	92.50 162	89.94 304	96.54 232	88.65 109	98.18 291	93.83 170	90.90 324	95.86 313

XVG-OURS		93.72 188	93.35 184	94.80 237	97.07 181	88.61 266	94.79 372	97.46 199	91.97 186	93.99 196	97.86 122	81.74 262	98.88 202	92.64 196	92.67 294	96.92 283

mamba_0408		93.70 189	92.99 195	95.83 167	96.79 214	90.38 197	88.69 462	97.07 255	90.96 230	93.68 203	97.31 177	84.97 191	98.76 220	90.95 233	96.51 209	98.35 194

HyFIR lowres test		93.66 190	92.92 200	95.87 162	98.24 100	89.88 217	94.58 377	98.49 32	85.06 395	93.78 201	95.78 274	82.86 234	98.67 243	91.77 215	95.71 229	99.07 100

LFMVS		93.60 191	92.63 214	96.52 107	98.13 115	91.27 155	97.94 81	93.39 431	90.57 251	96.29 116	98.31 81	69.00 407	99.16 161	94.18 160	95.87 224	99.12 92

icg_test_0407_2		93.58 192	93.46 178	93.94 290	96.19 270	86.16 341	93.73 412	97.24 237	91.54 196	93.50 212	97.04 196	85.64 176	96.91 406	90.68 242	95.59 233	98.76 148

F-COLMAP		93.58 192	92.98 198	95.37 203	98.40 86	88.98 259	97.18 195	97.29 230	87.75 346	90.49 286	97.10 193	85.21 185	99.50 120	86.70 329	96.72 203	97.63 251

ab-mvs		93.57 194	92.55 218	96.64 94	97.28 170	91.96 126	95.40 347	97.45 204	89.81 273	93.22 223	96.28 245	79.62 303	99.46 126	90.74 240	93.11 286	98.50 175

LS3D		93.57 194	92.61 216	96.47 115	97.59 157	91.61 138	97.67 125	97.72 154	85.17 393	90.29 290	98.34 75	84.60 196	99.73 61	83.85 374	98.27 141	98.06 225

FA-MVS(test-final)		93.52 196	92.92 200	95.31 206	96.77 221	88.54 270	94.82 371	96.21 327	89.61 277	94.20 189	95.25 301	83.24 220	99.14 166	90.01 254	96.16 218	98.25 203

SSM_04072		93.51 197	92.99 195	95.05 216	96.79 214	90.38 197	88.69 462	97.07 255	90.96 230	93.68 203	97.31 177	84.97 191	96.42 417	90.95 233	96.51 209	98.35 194

viewdifsd2359ckpt11		93.46 198	93.22 189	94.17 271	96.11 283	85.42 356	96.43 271	97.07 255	92.91 148	94.20 189	98.00 105	80.82 278	98.73 230	94.42 153	89.04 344	98.34 198

viewmsd2359difaftdt		93.46 198	93.23 188	94.17 271	96.12 281	85.42 356	96.43 271	97.08 252	92.91 148	94.21 188	98.00 105	80.82 278	98.74 228	94.41 154	89.05 342	98.34 198

Fast-Effi-MVS+		93.46 198	92.75 208	95.59 189	96.77 221	90.03 207	96.81 235	97.13 245	88.19 327	91.30 271	94.27 354	86.21 163	98.63 249	87.66 311	96.46 215	98.12 215

hse-mvs2		93.45 201	92.99 195	94.81 234	97.02 191	88.59 267	96.69 251	96.47 309	95.19 36	96.74 89	96.16 252	83.67 213	98.48 265	95.85 102	79.13 433	97.35 268

QAPM		93.45 201	92.27 228	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 403	88.27 355	97.77 135	82.39 248	99.81 35	85.40 351	98.81 115	98.51 174

UniMVSNet_NR-MVSNet		93.37 203	92.67 212	95.47 200	95.34 323	92.83 89	97.17 196	98.58 28	92.98 145	90.13 296	95.80 270	88.37 116	97.85 342	91.71 217	83.93 406	95.73 327

1112_ss		93.37 203	92.42 225	96.21 139	97.05 186	90.99 170	96.31 290	96.72 291	86.87 365	89.83 308	96.69 219	86.51 156	99.14 166	88.12 296	93.67 280	98.50 175

UniMVSNet (Re)		93.31 205	92.55 218	95.61 188	95.39 317	93.34 71	97.39 172	98.71 13	93.14 135	90.10 300	94.83 319	87.71 128	98.03 315	91.67 220	83.99 405	95.46 336

OPM-MVS		93.28 206	92.76 206	94.82 232	94.63 367	90.77 181	96.65 255	97.18 241	93.72 103	91.68 261	97.26 182	79.33 307	98.63 249	92.13 206	92.28 297	95.07 364
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 207	92.48 223	95.51 194	95.70 300	92.39 106	97.86 91	98.66 22	92.30 169	92.09 249	95.37 294	80.49 285	98.40 269	93.95 164	85.86 376	95.75 325

test_fmvs1		93.21 208	93.53 173	92.25 360	96.55 240	81.20 416	97.40 171	96.96 270	90.68 240	96.80 85	98.04 100	69.25 405	98.40 269	97.58 41	98.50 128	97.16 276

MVSTER		93.20 209	92.81 205	94.37 260	96.56 238	89.59 230	97.06 203	97.12 246	91.24 214	91.30 271	95.96 261	82.02 255	98.05 311	93.48 177	90.55 328	95.47 335

test1111		93.19 210	92.82 204	94.30 267	97.58 161	84.56 376	98.21 47	89.02 462	93.53 113	94.58 176	98.21 88	72.69 376	99.05 186	93.06 188	98.48 131	99.28 77

ECVR-MVS		93.19 210	92.73 210	94.57 250	97.66 149	85.41 358	98.21 47	88.23 464	93.43 120	94.70 173	98.21 88	72.57 377	99.07 181	93.05 189	98.49 129	99.25 80

HQP-MVS		93.19 210	92.74 209	94.54 252	95.86 292	89.33 245	96.65 255	97.39 216	93.55 109	90.14 292	95.87 265	80.95 272	98.50 262	92.13 206	92.10 303	95.78 321

CHOSEN 280x420		93.12 213	92.72 211	94.34 263	96.71 227	87.27 306	90.29 452	97.72 154	86.61 369	91.34 268	95.29 296	84.29 204	98.41 268	93.25 182	98.94 111	97.35 268

sd_testset		93.10 214	92.45 224	95.05 216	98.09 116	89.21 251	96.89 223	97.64 164	93.18 132	91.79 257	97.28 179	75.35 357	98.65 246	88.99 284	92.84 289	97.28 271

Effi-MVS+-dtu		93.08 215	93.21 190	92.68 348	96.02 289	83.25 392	97.14 199	96.72 291	93.85 100	91.20 278	93.44 392	83.08 226	98.30 281	91.69 219	95.73 228	96.50 293

test_djsdf		93.07 216	92.76 206	94.00 282	93.49 404	88.70 265	98.22 45	97.57 178	91.42 205	90.08 302	95.55 287	82.85 235	97.92 336	94.07 161	91.58 310	95.40 343

VDDNet		93.05 217	92.07 232	96.02 151	96.84 207	90.39 196	98.08 58	95.85 340	86.22 377	95.79 137	98.46 62	67.59 417	99.19 154	94.92 132	94.85 249	98.47 180

thisisatest0530		93.03 218	92.21 230	95.49 197	97.07 181	89.11 256	97.49 161	92.19 445	90.16 262	94.09 194	96.41 238	76.43 348	99.05 186	90.38 249	95.68 230	98.31 200

EI-MVSNet		93.03 218	92.88 202	93.48 316	95.77 298	86.98 315	96.44 269	97.12 246	90.66 243	91.30 271	97.64 152	86.56 154	98.05 311	89.91 257	90.55 328	95.41 340

CLD-MVS		92.98 220	92.53 220	94.32 264	96.12 281	89.20 252	95.28 354	97.47 197	92.66 158	89.90 305	95.62 283	80.58 283	98.40 269	92.73 195	92.40 296	95.38 345
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 221	92.33 227	94.87 231	97.11 179	87.16 312	97.97 77	92.09 446	90.63 245	93.88 200	97.01 202	76.50 345	99.06 183	90.29 252	95.45 239	98.38 190

ACMM	89.79 8	92.96 221	92.50 222	94.35 261	96.30 264	88.71 264	97.58 140	97.36 222	91.40 207	90.53 285	96.65 221	79.77 299	98.75 226	91.24 228	91.64 308	95.59 331
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 223	92.56 217	94.10 276	96.16 276	88.26 279	97.65 129	97.46 199	91.29 209	90.12 298	97.16 187	79.05 312	98.73 230	92.25 200	91.89 306	95.31 350

BH-untuned		92.94 223	92.62 215	93.92 294	97.22 172	86.16 341	96.40 279	96.25 324	90.06 265	89.79 309	96.17 251	83.19 222	98.35 277	87.19 322	97.27 180	97.24 273

DU-MVS		92.90 225	92.04 234	95.49 197	94.95 349	92.83 89	97.16 197	98.24 63	93.02 139	90.13 296	95.71 277	83.47 216	97.85 342	91.71 217	83.93 406	95.78 321

PatchMatch-RL		92.90 225	92.02 236	95.56 190	98.19 109	90.80 179	95.27 356	97.18 241	87.96 334	91.86 256	95.68 280	80.44 286	98.99 191	84.01 369	97.54 165	96.89 284

VortexMVS		92.88 227	92.64 213	93.58 311	96.58 234	87.53 301	96.93 218	97.28 233	92.78 156	89.75 310	94.99 309	82.73 238	97.76 354	94.60 150	88.16 353	95.46 336

PMMVS		92.86 228	92.34 226	94.42 259	94.92 352	86.73 322	94.53 379	96.38 315	84.78 400	94.27 186	95.12 307	83.13 225	98.40 269	91.47 223	96.49 213	98.12 215

OpenMVS	89.19 12	92.86 228	91.68 249	96.40 122	95.34 323	92.73 94	98.27 37	98.12 86	84.86 398	85.78 399	97.75 136	78.89 319	99.74 59	87.50 316	98.65 122	96.73 288

Test_1112_low_res		92.84 230	91.84 243	95.85 166	97.04 188	89.97 214	95.53 342	96.64 299	85.38 388	89.65 315	95.18 303	85.86 169	99.10 171	87.70 307	93.58 285	98.49 177

baseline1		92.82 231	91.90 241	95.55 192	97.20 174	90.77 181	97.19 194	94.58 402	92.20 175	92.36 238	96.34 242	84.16 206	98.21 287	89.20 280	83.90 409	97.68 250

1314		92.81 232	92.03 235	95.14 212	95.33 326	89.52 236	96.04 310	97.44 208	87.72 347	86.25 396	95.33 295	83.84 210	98.79 214	89.26 276	97.05 190	97.11 277

DP-MVS		92.76 233	91.51 257	96.52 107	98.77 62	90.99 170	97.38 174	96.08 332	82.38 426	89.29 327	97.87 120	83.77 211	99.69 73	81.37 398	96.69 204	98.89 135

test_fmvs1_n		92.73 234	92.88 202	92.29 357	96.08 286	81.05 417	97.98 71	97.08 252	90.72 238	96.79 87	98.18 91	63.07 441	98.45 266	97.62 40	98.42 135	97.36 266

BH-RMVSNet		92.72 235	91.97 238	94.97 226	97.16 176	87.99 289	96.15 305	95.60 354	90.62 246	91.87 255	97.15 189	78.41 325	98.57 257	83.16 376	97.60 164	98.36 192

ACMP	89.59 10	92.62 236	92.14 231	94.05 279	96.40 256	88.20 282	97.36 175	97.25 236	91.52 200	88.30 353	96.64 222	78.46 324	98.72 235	91.86 213	91.48 312	95.23 357
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 237	92.52 221	92.44 350	96.82 211	81.89 410	96.92 219	93.71 428	92.41 165	84.30 412	94.60 331	85.08 187	97.03 400	91.51 221	97.36 173	98.40 188

TranMVSNet+NR-MVSNet		92.50 237	91.63 250	95.14 212	94.76 360	92.07 119	97.53 150	98.11 89	92.90 151	89.56 318	96.12 254	83.16 223	97.60 369	89.30 274	83.20 415	95.75 325

thres600view7		92.49 239	91.60 251	95.18 210	97.91 133	89.47 237	97.65 129	94.66 399	92.18 179	93.33 218	94.91 314	78.06 332	99.10 171	81.61 391	94.06 274	96.98 279

IMVS_0404		92.44 240	91.92 240	94.00 282	96.19 270	86.16 341	93.84 409	97.24 237	91.54 196	88.17 359	97.04 196	76.96 342	97.09 397	90.68 242	95.59 233	98.76 148

thres100view900		92.43 241	91.58 252	94.98 224	97.92 132	89.37 243	97.71 120	94.66 399	92.20 175	93.31 219	94.90 315	78.06 332	99.08 177	81.40 395	94.08 270	96.48 294

jajsoiax		92.42 242	91.89 242	94.03 281	93.33 412	88.50 272	97.73 115	97.53 186	92.00 185	88.85 339	96.50 234	75.62 355	98.11 298	93.88 168	91.56 311	95.48 333

thres400		92.42 242	91.52 255	95.12 214	97.85 136	89.29 247	97.41 167	94.88 391	92.19 177	93.27 221	94.46 341	78.17 328	99.08 177	81.40 395	94.08 270	96.98 279

tfpn200view9		92.38 244	91.52 255	94.95 228	97.85 136	89.29 247	97.41 167	94.88 391	92.19 177	93.27 221	94.46 341	78.17 328	99.08 177	81.40 395	94.08 270	96.48 294

test_vis1_n		92.37 245	92.26 229	92.72 345	94.75 361	82.64 399	98.02 65	96.80 288	91.18 219	97.77 59	97.93 111	58.02 451	98.29 282	97.63 38	98.21 143	97.23 274

WR-MVS		92.34 246	91.53 254	94.77 239	95.13 342	90.83 178	96.40 279	97.98 120	91.88 187	89.29 327	95.54 288	82.50 244	97.80 349	89.79 261	85.27 385	95.69 328

NR-MVSNet		92.34 246	91.27 265	95.53 193	94.95 349	93.05 81	97.39 172	98.07 98	92.65 159	84.46 410	95.71 277	85.00 190	97.77 353	89.71 262	83.52 412	95.78 321

mvs_tets		92.31 248	91.76 245	93.94 290	93.41 409	88.29 277	97.63 135	97.53 186	92.04 183	88.76 342	96.45 236	74.62 365	98.09 303	93.91 166	91.48 312	95.45 338

TAPA-MVS	90.10 7	92.30 249	91.22 268	95.56 190	98.33 91	89.60 229	96.79 237	97.65 162	81.83 430	91.52 263	97.23 184	87.94 123	98.91 200	71.31 453	98.37 136	98.17 211
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 250	91.30 263	95.25 208	96.60 232	88.90 261	94.36 388	92.32 444	87.92 335	93.43 216	94.57 332	77.28 339	99.00 190	89.42 271	95.86 225	97.86 240

Fast-Effi-MVS+-dtu		92.29 250	91.99 237	93.21 327	95.27 330	85.52 354	97.03 204	96.63 302	92.09 180	89.11 333	95.14 305	80.33 289	98.08 304	87.54 315	94.74 255	96.03 311

IterMVS-LS		92.29 250	91.94 239	93.34 321	96.25 265	86.97 316	96.57 267	97.05 261	90.67 241	89.50 321	94.80 321	86.59 153	97.64 364	89.91 257	86.11 375	95.40 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 253	91.74 248	93.73 301	97.77 141	83.69 389	92.88 432	96.72 291	87.91 336	93.00 226	94.86 317	78.51 323	99.05 186	86.53 330	97.45 171	98.47 180

VPNet		92.23 254	91.31 262	94.99 222	95.56 307	90.96 172	97.22 192	97.86 136	92.96 146	90.96 279	96.62 229	75.06 358	98.20 288	91.90 210	83.65 411	95.80 319

thres200		92.23 254	91.39 258	94.75 241	97.61 155	89.03 258	96.60 263	95.09 380	92.08 181	93.28 220	94.00 369	78.39 326	99.04 189	81.26 401	94.18 266	96.19 301

anonymousdsp		92.16 256	91.55 253	93.97 286	92.58 427	89.55 233	97.51 152	97.42 213	89.42 285	88.40 349	94.84 318	80.66 281	97.88 341	91.87 212	91.28 316	94.48 396

XXY-MVS		92.16 256	91.23 267	94.95 228	94.75 361	90.94 173	97.47 162	97.43 211	89.14 292	88.90 335	96.43 237	79.71 300	98.24 284	89.56 267	87.68 358	95.67 329

BH-w/o		92.14 258	91.75 246	93.31 322	96.99 194	85.73 351	95.67 332	95.69 349	88.73 313	89.26 329	94.82 320	82.97 231	98.07 308	85.26 354	96.32 217	96.13 307

testing3-2		92.10 259	92.05 233	92.27 358	97.71 145	79.56 436	97.42 166	94.41 409	93.53 113	93.22 223	95.49 290	69.16 406	99.11 169	93.25 182	94.22 264	98.13 213

Anonymous202405211		92.07 260	90.83 284	95.76 176	98.19 109	88.75 263	97.58 140	95.00 383	86.00 380	93.64 206	97.45 166	66.24 429	99.53 112	90.68 242	92.71 292	99.01 106

FE-MVS		92.05 261	91.05 273	95.08 215	96.83 209	87.93 290	93.91 406	95.70 347	86.30 374	94.15 193	94.97 310	76.59 344	99.21 152	84.10 367	96.86 195	98.09 222

WR-MVS_H		92.00 262	91.35 259	93.95 288	95.09 344	89.47 237	98.04 63	98.68 19	91.46 203	88.34 351	94.68 326	85.86 169	97.56 371	85.77 346	84.24 403	94.82 381

Anonymous20240529		91.98 263	90.73 290	95.73 181	98.14 113	89.40 241	97.99 68	97.72 154	79.63 444	93.54 210	97.41 171	69.94 399	99.56 106	91.04 232	91.11 319	98.22 205

MonoMVSNet		91.92 264	91.77 244	92.37 352	92.94 418	83.11 395	97.09 202	95.55 358	92.91 148	90.85 281	94.55 333	81.27 270	96.52 415	93.01 192	87.76 357	97.47 262

PatchmatchNet		91.91 265	91.35 259	93.59 310	95.38 318	84.11 382	93.15 427	95.39 363	89.54 279	92.10 248	93.68 382	82.82 236	98.13 294	84.81 358	95.32 241	98.52 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 266	91.02 274	94.53 253	96.54 241	86.55 329	95.86 321	95.64 353	91.77 190	91.89 254	93.47 391	69.94 399	98.86 203	90.23 253	93.86 277	98.18 208

CP-MVSNet		91.89 267	91.24 266	93.82 297	95.05 345	88.57 268	97.82 100	98.19 74	91.70 192	88.21 357	95.76 275	81.96 256	97.52 377	87.86 301	84.65 394	95.37 346

SCA		91.84 268	91.18 270	93.83 296	95.59 305	84.95 372	94.72 373	95.58 356	90.82 233	92.25 243	93.69 380	75.80 352	98.10 299	86.20 336	95.98 220	98.45 182

FMVSNet3		91.78 269	90.69 293	95.03 219	96.53 243	92.27 112	97.02 206	96.93 273	89.79 274	89.35 324	94.65 329	77.01 340	97.47 380	86.12 339	88.82 345	95.35 347

AUN-MVS		91.76 270	90.75 288	94.81 234	97.00 193	88.57 268	96.65 255	96.49 308	89.63 276	92.15 245	96.12 254	78.66 321	98.50 262	90.83 235	79.18 432	97.36 266

X-MVStestdata		91.71 271	89.67 337	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 479	91.70 56	99.80 40	95.66 108	99.40 61	99.62 27

MVS		91.71 271	90.44 300	95.51 194	95.20 336	91.59 140	96.04 310	97.45 204	73.44 460	87.36 375	95.60 284	85.42 180	99.10 171	85.97 343	97.46 167	95.83 317

EPNet_dtu		91.71 271	91.28 264	92.99 334	93.76 394	83.71 388	96.69 251	95.28 370	93.15 134	87.02 384	95.95 262	83.37 219	97.38 388	79.46 415	96.84 196	97.88 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 274	90.75 288	94.47 255	96.53 243	86.56 328	95.76 328	94.51 406	91.10 226	91.24 276	93.59 386	68.59 411	98.86 203	91.10 230	94.29 262	98.00 229

baseline2		91.63 275	90.86 280	93.94 290	94.33 378	86.32 334	95.92 318	91.64 450	89.37 286	86.94 387	94.69 325	81.62 264	98.69 238	88.64 292	94.57 258	96.81 286

testing99		91.62 276	90.72 291	94.32 264	96.48 250	86.11 346	95.81 324	94.76 396	91.55 195	91.75 259	93.44 392	68.55 412	98.82 209	90.43 247	93.69 279	98.04 226

test2506		91.60 277	90.78 285	94.04 280	97.66 149	83.81 385	98.27 37	75.53 480	93.43 120	95.23 159	98.21 88	67.21 420	99.07 181	93.01 192	98.49 129	99.25 80

miper_ehance_all_eth		91.59 278	91.13 271	92.97 335	95.55 308	86.57 327	94.47 382	96.88 282	87.77 344	88.88 337	94.01 368	86.22 162	97.54 373	89.49 268	86.93 366	94.79 386

v2v482		91.59 278	90.85 282	93.80 298	93.87 391	88.17 284	96.94 216	96.88 282	89.54 279	89.53 319	94.90 315	81.70 263	98.02 316	89.25 277	85.04 391	95.20 358

V42		91.58 280	90.87 279	93.73 301	94.05 386	88.50 272	97.32 180	96.97 269	88.80 311	89.71 311	94.33 349	82.54 243	98.05 311	89.01 283	85.07 389	94.64 394

PCF-MVS	89.48 11	91.56 281	89.95 325	96.36 127	96.60 232	92.52 103	92.51 437	97.26 234	79.41 445	88.90 335	96.56 231	84.04 209	99.55 108	77.01 429	97.30 178	97.01 278
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 282	90.76 286	93.94 290	96.52 246	85.06 367	95.22 359	94.54 404	90.47 255	91.98 251	92.71 403	72.02 380	98.74 228	88.10 297	95.26 243	98.01 228

PS-CasMVS		91.55 282	90.84 283	93.69 305	94.96 348	88.28 278	97.84 95	98.24 63	91.46 203	88.04 362	95.80 270	79.67 301	97.48 379	87.02 326	84.54 400	95.31 350

miper_enhance_ethall		91.54 284	91.01 275	93.15 329	95.35 322	87.07 314	93.97 401	96.90 279	86.79 366	89.17 331	93.43 395	86.55 155	97.64 364	89.97 256	86.93 366	94.74 390

myMVS_eth3d28		91.52 285	90.97 276	93.17 328	96.91 199	83.24 393	95.61 338	94.96 387	92.24 171	91.98 251	93.28 396	69.31 404	98.40 269	88.71 290	95.68 230	97.88 236

PAPM		91.52 285	90.30 306	95.20 209	95.30 329	89.83 219	93.38 423	96.85 285	86.26 376	88.59 345	95.80 270	84.88 193	98.15 293	75.67 434	95.93 222	97.63 251

ET-MVSNet_ETH3D		91.49 287	90.11 316	95.63 186	96.40 256	91.57 142	95.34 350	93.48 430	90.60 249	75.58 455	95.49 290	80.08 293	96.79 411	94.25 159	89.76 336	98.52 172

TR-MVS		91.48 288	90.59 296	94.16 274	96.40 256	87.33 303	95.67 332	95.34 369	87.68 348	91.46 265	95.52 289	76.77 343	98.35 277	82.85 381	93.61 283	96.79 287

tpmrst		91.44 289	91.32 261	91.79 375	95.15 340	79.20 442	93.42 422	95.37 365	88.55 318	93.49 214	93.67 383	82.49 245	98.27 283	90.41 248	89.34 340	97.90 234

test-LLR		91.42 290	91.19 269	92.12 363	94.59 368	80.66 420	94.29 393	92.98 436	91.11 224	90.76 283	92.37 411	79.02 314	98.07 308	88.81 287	96.74 201	97.63 251

MSDG		91.42 290	90.24 310	94.96 227	97.15 178	88.91 260	93.69 415	96.32 317	85.72 384	86.93 388	96.47 235	80.24 290	98.98 192	80.57 405	95.05 248	96.98 279

c3_l		91.38 292	90.89 278	92.88 339	95.58 306	86.30 335	94.68 374	96.84 286	88.17 328	88.83 341	94.23 357	85.65 175	97.47 380	89.36 272	84.63 395	94.89 376

GA-MVS		91.38 292	90.31 305	94.59 245	94.65 366	87.62 299	94.34 389	96.19 328	90.73 237	90.35 289	93.83 373	71.84 382	97.96 327	87.22 321	93.61 283	98.21 206

v1144		91.37 294	90.60 295	93.68 306	93.89 390	88.23 281	96.84 229	97.03 265	88.37 323	89.69 313	94.39 343	82.04 254	97.98 320	87.80 303	85.37 382	94.84 378

GBi-Net		91.35 295	90.27 308	94.59 245	96.51 247	91.18 163	97.50 153	96.93 273	88.82 308	89.35 324	94.51 336	73.87 369	97.29 392	86.12 339	88.82 345	95.31 350

test1		91.35 295	90.27 308	94.59 245	96.51 247	91.18 163	97.50 153	96.93 273	88.82 308	89.35 324	94.51 336	73.87 369	97.29 392	86.12 339	88.82 345	95.31 350

UniMVSNet_ETH3D		91.34 297	90.22 313	94.68 243	94.86 356	87.86 294	97.23 190	97.46 199	87.99 333	89.90 305	96.92 206	66.35 427	98.23 285	90.30 251	90.99 322	97.96 230

FMVSNet2		91.31 298	90.08 317	94.99 222	96.51 247	92.21 114	97.41 167	96.95 271	88.82 308	88.62 344	94.75 323	73.87 369	97.42 385	85.20 355	88.55 350	95.35 347

reproduce_monomvs		91.30 299	91.10 272	91.92 367	96.82 211	82.48 403	97.01 209	97.49 191	94.64 71	88.35 350	95.27 299	70.53 392	98.10 299	95.20 122	84.60 397	95.19 361

D2MVS		91.30 299	90.95 277	92.35 353	94.71 364	85.52 354	96.18 302	98.21 67	88.89 304	86.60 391	93.82 375	79.92 297	97.95 331	89.29 275	90.95 323	93.56 416

v8		91.29 301	90.53 299	93.57 313	94.15 382	88.12 286	97.34 177	97.06 260	88.99 299	88.32 352	94.26 356	83.08 226	98.01 317	87.62 313	83.92 408	94.57 395

CVMVSNet		91.23 302	91.75 246	89.67 417	95.77 298	74.69 454	96.44 269	94.88 391	85.81 382	92.18 244	97.64 152	79.07 311	95.58 433	88.06 298	95.86 225	98.74 155

cl22		91.21 303	90.56 298	93.14 330	96.09 285	86.80 319	94.41 386	96.58 305	87.80 342	88.58 346	93.99 370	80.85 277	97.62 367	89.87 259	86.93 366	94.99 367

PEN-MVS		91.20 304	90.44 300	93.48 316	94.49 372	87.91 293	97.76 108	98.18 76	91.29 209	87.78 366	95.74 276	80.35 288	97.33 390	85.46 350	82.96 416	95.19 361

Baseline_NR-MVSNet		91.20 304	90.62 294	92.95 336	93.83 392	88.03 288	97.01 209	95.12 379	88.42 322	89.70 312	95.13 306	83.47 216	97.44 383	89.66 265	83.24 414	93.37 420

cascas		91.20 304	90.08 317	94.58 249	94.97 347	89.16 255	93.65 417	97.59 174	79.90 443	89.40 322	92.92 401	75.36 356	98.36 276	92.14 203	94.75 254	96.23 298

CostFormer		91.18 307	90.70 292	92.62 349	94.84 357	81.76 411	94.09 399	94.43 407	84.15 407	92.72 233	93.77 377	79.43 305	98.20 288	90.70 241	92.18 301	97.90 234

tt0805		91.09 308	90.07 320	94.16 274	95.61 304	88.31 276	97.56 144	96.51 307	89.56 278	89.17 331	95.64 282	67.08 424	98.38 275	91.07 231	88.44 351	95.80 319

v1192		91.07 309	90.23 311	93.58 311	93.70 395	87.82 296	96.73 245	97.07 255	87.77 344	89.58 316	94.32 351	80.90 276	97.97 323	86.52 331	85.48 380	94.95 368

v144192		91.06 310	90.28 307	93.39 319	93.66 398	87.23 309	96.83 230	97.07 255	87.43 353	89.69 313	94.28 353	81.48 265	98.00 318	87.18 323	84.92 393	94.93 372

v10		91.04 311	90.23 311	93.49 315	94.12 383	88.16 285	97.32 180	97.08 252	88.26 326	88.29 354	94.22 359	82.17 252	97.97 323	86.45 333	84.12 404	94.33 402

eth_miper_zixun_eth		91.02 312	90.59 296	92.34 355	95.33 326	84.35 378	94.10 398	96.90 279	88.56 317	88.84 340	94.33 349	84.08 207	97.60 369	88.77 289	84.37 402	95.06 365

v148		90.99 313	90.38 302	92.81 342	93.83 392	85.80 348	96.78 241	96.68 296	89.45 284	88.75 343	93.93 372	82.96 232	97.82 346	87.83 302	83.25 413	94.80 384

LTVRE_ROB	88.41 13	90.99 313	89.92 327	94.19 270	96.18 274	89.55 233	96.31 290	97.09 251	87.88 337	85.67 400	95.91 264	78.79 320	98.57 257	81.50 392	89.98 333	94.44 399
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
DIV-MVS_self_test		90.97 315	90.33 303	92.88 339	95.36 321	86.19 340	94.46 384	96.63 302	87.82 340	88.18 358	94.23 357	82.99 229	97.53 375	87.72 304	85.57 379	94.93 372

cl____		90.96 316	90.32 304	92.89 338	95.37 320	86.21 338	94.46 384	96.64 299	87.82 340	88.15 360	94.18 360	82.98 230	97.54 373	87.70 307	85.59 378	94.92 374

pmmvs4		90.93 317	89.85 329	94.17 271	93.34 411	90.79 180	94.60 376	96.02 333	84.62 401	87.45 371	95.15 304	81.88 260	97.45 382	87.70 307	87.87 356	94.27 406

XVG-ACMP-BASELINE		90.93 317	90.21 314	93.09 331	94.31 380	85.89 347	95.33 351	97.26 234	91.06 227	89.38 323	95.44 293	68.61 410	98.60 252	89.46 269	91.05 320	94.79 386

v1921920		90.85 319	90.03 322	93.29 323	93.55 400	86.96 318	96.74 244	97.04 263	87.36 355	89.52 320	94.34 348	80.23 291	97.97 323	86.27 334	85.21 386	94.94 370

CR-MVSNet		90.82 320	89.77 333	93.95 288	94.45 374	87.19 310	90.23 453	95.68 351	86.89 364	92.40 235	92.36 414	80.91 274	97.05 399	81.09 402	93.95 275	97.60 256

v7n		90.76 321	89.86 328	93.45 318	93.54 401	87.60 300	97.70 123	97.37 220	88.85 305	87.65 368	94.08 366	81.08 271	98.10 299	84.68 360	83.79 410	94.66 393

RPSCF		90.75 322	90.86 280	90.42 407	96.84 207	76.29 452	95.61 338	96.34 316	83.89 410	91.38 266	97.87 120	76.45 346	98.78 215	87.16 324	92.23 298	96.20 300

MVP-Stereo		90.74 323	90.08 317	92.71 346	93.19 414	88.20 282	95.86 321	96.27 322	86.07 379	84.86 408	94.76 322	77.84 335	97.75 356	83.88 373	98.01 153	92.17 441
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 324	89.65 339	93.96 287	94.29 381	89.63 227	97.79 106	96.82 287	89.07 294	86.12 398	95.48 292	78.61 322	97.78 351	86.97 327	81.67 421	94.46 397

v1240		90.70 325	89.85 329	93.23 325	93.51 403	86.80 319	96.61 261	97.02 267	87.16 360	89.58 316	94.31 352	79.55 304	97.98 320	85.52 349	85.44 381	94.90 375

EPMVS		90.70 325	89.81 331	93.37 320	94.73 363	84.21 380	93.67 416	88.02 465	89.50 281	92.38 237	93.49 389	77.82 336	97.78 351	86.03 342	92.68 293	98.11 221

WBMVS		90.69 327	89.99 324	92.81 342	96.48 250	85.00 368	95.21 361	96.30 320	89.46 283	89.04 334	94.05 367	72.45 379	97.82 346	89.46 269	87.41 363	95.61 330

Anonymous20231211		90.63 328	89.42 344	94.27 269	98.24 100	89.19 254	98.05 62	97.89 128	79.95 442	88.25 356	94.96 311	72.56 378	98.13 294	89.70 263	85.14 387	95.49 332

DTE-MVSNet		90.56 329	89.75 335	93.01 333	93.95 387	87.25 307	97.64 133	97.65 162	90.74 236	87.12 379	95.68 280	79.97 296	97.00 403	83.33 375	81.66 422	94.78 388

ACMH	87.59 16	90.53 330	89.42 344	93.87 295	96.21 266	87.92 291	97.24 186	96.94 272	88.45 321	83.91 420	96.27 246	71.92 381	98.62 251	84.43 363	89.43 339	95.05 366
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 331	89.14 352	94.67 244	96.81 213	87.85 295	95.91 319	93.97 422	89.71 275	92.34 241	92.48 409	65.41 435	97.96 327	81.37 398	94.27 263	98.21 206

OurMVSNet-221017-0		90.51 332	90.19 315	91.44 384	93.41 409	81.25 414	96.98 213	96.28 321	91.68 193	86.55 393	96.30 243	74.20 368	97.98 320	88.96 285	87.40 364	95.09 363

miper_lstm_enhance		90.50 333	90.06 321	91.83 372	95.33 326	83.74 386	93.86 407	96.70 295	87.56 351	87.79 365	93.81 376	83.45 218	96.92 405	87.39 317	84.62 396	94.82 381

COLMAP_ROB	87.81 15	90.40 334	89.28 347	93.79 299	97.95 129	87.13 313	96.92 219	95.89 339	82.83 423	86.88 390	97.18 186	73.77 372	99.29 146	78.44 420	93.62 282	94.95 368
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 335	88.96 354	94.35 261	96.54 241	87.29 304	95.50 343	93.84 426	90.97 229	91.75 259	92.96 400	62.18 446	98.00 318	82.86 379	94.08 270	97.76 246

IterMVS-SCA-FT		90.31 335	89.81 331	91.82 373	95.52 309	84.20 381	94.30 392	96.15 330	90.61 247	87.39 374	94.27 354	75.80 352	96.44 416	87.34 318	86.88 370	94.82 381

MS-PatchMatch		90.27 337	89.77 333	91.78 376	94.33 378	84.72 375	95.55 340	96.73 290	86.17 378	86.36 395	95.28 298	71.28 386	97.80 349	84.09 368	98.14 147	92.81 426

tpm		90.25 338	89.74 336	91.76 378	93.92 388	79.73 435	93.98 400	93.54 429	88.28 325	91.99 250	93.25 397	77.51 338	97.44 383	87.30 320	87.94 355	98.12 215

AllTest		90.23 339	88.98 353	93.98 284	97.94 130	86.64 323	96.51 268	95.54 359	85.38 388	85.49 402	96.77 213	70.28 394	99.15 163	80.02 410	92.87 287	96.15 305

dmvs_re		90.21 340	89.50 342	92.35 353	95.47 315	85.15 364	95.70 331	94.37 412	90.94 232	88.42 348	93.57 387	74.63 364	95.67 430	82.80 382	89.57 338	96.22 299

ACMH+	87.92 14	90.20 341	89.18 350	93.25 324	96.48 250	86.45 332	96.99 212	96.68 296	88.83 307	84.79 409	96.22 248	70.16 396	98.53 260	84.42 364	88.04 354	94.77 389

test-mter		90.19 342	89.54 341	92.12 363	94.59 368	80.66 420	94.29 393	92.98 436	87.68 348	90.76 283	92.37 411	67.67 416	98.07 308	88.81 287	96.74 201	97.63 251

IterMVS		90.15 343	89.67 337	91.61 380	95.48 311	83.72 387	94.33 390	96.12 331	89.99 266	87.31 377	94.15 362	75.78 354	96.27 420	86.97 327	86.89 369	94.83 379
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 344	89.42 344	91.97 366	94.41 376	80.62 422	94.29 393	91.97 448	87.28 358	90.44 287	92.47 410	68.79 408	97.67 361	88.50 294	96.60 207	97.61 255

SD_0403		90.01 345	90.02 323	89.96 414	95.65 303	76.76 449	95.76 328	96.46 310	90.58 250	86.59 392	96.29 244	82.12 253	94.78 443	73.00 448	93.76 278	98.35 194

tpm2		89.96 346	89.21 349	92.23 361	94.91 354	81.25 414	93.78 410	94.42 408	80.62 440	91.56 262	93.44 392	76.44 347	97.94 333	85.60 348	92.08 305	97.49 260

UWE-MVS		89.91 347	89.48 343	91.21 389	95.88 291	78.23 447	94.91 370	90.26 458	89.11 293	92.35 240	94.52 335	68.76 409	97.96 327	83.95 371	95.59 233	97.42 264

IB-MVS	87.33 17	89.91 347	88.28 364	94.79 238	95.26 333	87.70 298	95.12 365	93.95 423	89.35 287	87.03 383	92.49 408	70.74 391	99.19 154	89.18 281	81.37 423	97.49 260
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
ADS-MVSNet		89.89 349	88.68 359	93.53 314	95.86 292	84.89 373	90.93 448	95.07 381	83.23 421	91.28 274	91.81 424	79.01 316	97.85 342	79.52 412	91.39 314	97.84 241

WB-MVSnew		89.88 350	89.56 340	90.82 399	94.57 371	83.06 396	95.65 336	92.85 438	87.86 339	90.83 282	94.10 363	79.66 302	96.88 407	76.34 430	94.19 265	92.54 432

FMVSNet1		89.88 350	88.31 363	94.59 245	95.41 316	91.18 163	97.50 153	96.93 273	86.62 368	87.41 373	94.51 336	65.94 432	97.29 392	83.04 378	87.43 361	95.31 350

pmmvs5		89.86 352	88.87 357	92.82 341	92.86 420	86.23 337	96.26 293	95.39 363	84.24 406	87.12 379	94.51 336	74.27 367	97.36 389	87.61 314	87.57 359	94.86 377

tpmvs		89.83 353	89.15 351	91.89 370	94.92 352	80.30 427	93.11 428	95.46 362	86.28 375	88.08 361	92.65 404	80.44 286	98.52 261	81.47 394	89.92 334	96.84 285

test_fmvs2		89.77 354	89.93 326	89.31 424	93.68 397	76.37 451	97.64 133	95.90 337	89.84 272	91.49 264	96.26 247	58.77 449	97.10 396	94.65 147	91.13 318	94.46 397

SSC-MVS3.2		89.74 355	89.26 348	91.19 392	95.16 337	80.29 428	94.53 379	97.03 265	91.79 189	88.86 338	94.10 363	69.94 399	97.82 346	85.29 352	86.66 371	95.45 338

mmtdpeth		89.70 356	88.96 354	91.90 369	95.84 297	84.42 377	97.46 164	95.53 361	90.27 259	94.46 182	90.50 433	69.74 403	98.95 193	97.39 53	69.48 461	92.34 435

tfpnnormal		89.70 356	88.40 362	93.60 309	95.15 340	90.10 206	97.56 144	98.16 80	87.28 358	86.16 397	94.63 330	77.57 337	98.05 311	74.48 438	84.59 398	92.65 429

ADS-MVSNet2		89.45 358	88.59 360	92.03 365	95.86 292	82.26 407	90.93 448	94.32 415	83.23 421	91.28 274	91.81 424	79.01 316	95.99 422	79.52 412	91.39 314	97.84 241

Patchmatch-test		89.42 359	87.99 366	93.70 304	95.27 330	85.11 365	88.98 460	94.37 412	81.11 434	87.10 382	93.69 380	82.28 249	97.50 378	74.37 440	94.76 253	98.48 179

test0.0.03 1		89.37 360	88.70 358	91.41 385	92.47 429	85.63 352	95.22 359	92.70 441	91.11 224	86.91 389	93.65 384	79.02 314	93.19 460	78.00 422	89.18 341	95.41 340

SixPastTwentyTwo		89.15 361	88.54 361	90.98 394	93.49 404	80.28 429	96.70 249	94.70 398	90.78 234	84.15 415	95.57 285	71.78 383	97.71 359	84.63 361	85.07 389	94.94 370

RPMNet		88.98 362	87.05 376	94.77 239	94.45 374	87.19 310	90.23 453	98.03 110	77.87 452	92.40 235	87.55 459	80.17 292	99.51 117	68.84 459	93.95 275	97.60 256

TransMVSNet (Re)		88.94 363	87.56 369	93.08 332	94.35 377	88.45 274	97.73 115	95.23 374	87.47 352	84.26 413	95.29 296	79.86 298	97.33 390	79.44 416	74.44 452	93.45 419

USDC		88.94 363	87.83 368	92.27 358	94.66 365	84.96 371	93.86 407	95.90 337	87.34 356	83.40 422	95.56 286	67.43 418	98.19 290	82.64 386	89.67 337	93.66 415

dp		88.90 365	88.26 365	90.81 400	94.58 370	76.62 450	92.85 433	94.93 388	85.12 394	90.07 303	93.07 398	75.81 351	98.12 297	80.53 406	87.42 362	97.71 248

PatchT		88.87 366	87.42 370	93.22 326	94.08 385	85.10 366	89.51 458	94.64 401	81.92 429	92.36 238	88.15 455	80.05 294	97.01 402	72.43 449	93.65 281	97.54 259

our_test_3		88.78 367	87.98 367	91.20 391	92.45 430	82.53 401	93.61 419	95.69 349	85.77 383	84.88 407	93.71 378	79.99 295	96.78 412	79.47 414	86.24 372	94.28 405

EU-MVSNet		88.72 368	88.90 356	88.20 428	93.15 415	74.21 456	96.63 260	94.22 417	85.18 392	87.32 376	95.97 260	76.16 349	94.98 441	85.27 353	86.17 373	95.41 340

Patchmtry		88.64 369	87.25 372	92.78 344	94.09 384	86.64 323	89.82 457	95.68 351	80.81 438	87.63 369	92.36 414	80.91 274	97.03 400	78.86 418	85.12 388	94.67 392

MIMVSNet		88.50 370	86.76 380	93.72 303	94.84 357	87.77 297	91.39 443	94.05 419	86.41 372	87.99 363	92.59 407	63.27 440	95.82 427	77.44 423	92.84 289	97.57 258

tpm cat1		88.36 371	87.21 374	91.81 374	95.13 342	80.55 423	92.58 436	95.70 347	74.97 456	87.45 371	91.96 422	78.01 334	98.17 292	80.39 407	88.74 348	96.72 289

ppachtmachnet_test		88.35 372	87.29 371	91.53 381	92.45 430	83.57 390	93.75 411	95.97 334	84.28 404	85.32 405	94.18 360	79.00 318	96.93 404	75.71 433	84.99 392	94.10 407

JIA-IIPM		88.26 373	87.04 377	91.91 368	93.52 402	81.42 413	89.38 459	94.38 411	80.84 437	90.93 280	80.74 467	79.22 308	97.92 336	82.76 383	91.62 309	96.38 297

testgi		87.97 374	87.21 374	90.24 410	92.86 420	80.76 418	96.67 254	94.97 385	91.74 191	85.52 401	95.83 268	62.66 444	94.47 446	76.25 431	88.36 352	95.48 333

LF4IMVS		87.94 375	87.25 372	89.98 413	92.38 432	80.05 433	94.38 387	95.25 373	87.59 350	84.34 411	94.74 324	64.31 438	97.66 363	84.83 357	87.45 360	92.23 438

gg-mvs-nofinetune		87.82 376	85.61 389	94.44 257	94.46 373	89.27 250	91.21 447	84.61 474	80.88 436	89.89 307	74.98 470	71.50 384	97.53 375	85.75 347	97.21 182	96.51 292

pmmvs6		87.81 377	86.19 385	92.69 347	91.32 437	86.30 335	97.34 177	96.41 313	80.59 441	84.05 419	94.37 345	67.37 419	97.67 361	84.75 359	79.51 431	94.09 409

testing3		87.67 378	86.88 379	90.05 412	96.14 279	80.71 419	97.10 201	92.85 438	90.15 263	87.54 370	94.55 333	55.70 456	94.10 449	73.77 444	94.10 269	95.35 347

K. test v3		87.64 379	86.75 381	90.32 409	93.02 417	79.48 440	96.61 261	92.08 447	90.66 243	80.25 441	94.09 365	67.21 420	96.65 414	85.96 344	80.83 425	94.83 379

Patchmatch-RL test		87.38 380	86.24 384	90.81 400	88.74 455	78.40 446	88.12 467	93.17 433	87.11 361	82.17 431	89.29 445	81.95 257	95.60 432	88.64 292	77.02 440	98.41 187

FMVSNet5		87.29 381	85.79 388	91.78 376	94.80 359	87.28 305	95.49 344	95.28 370	84.09 408	83.85 421	91.82 423	62.95 442	94.17 448	78.48 419	85.34 384	93.91 413

myMVS_eth3d		87.18 382	86.38 383	89.58 418	95.16 337	79.53 437	95.00 367	93.93 424	88.55 318	86.96 385	91.99 420	56.23 455	94.00 450	75.47 436	94.11 267	95.20 358

Syy-MVS		87.13 383	87.02 378	87.47 432	95.16 337	73.21 460	95.00 367	93.93 424	88.55 318	86.96 385	91.99 420	75.90 350	94.00 450	61.59 466	94.11 267	95.20 358

Anonymous20231206		87.09 384	86.14 386	89.93 415	91.22 438	80.35 425	96.11 306	95.35 366	83.57 417	84.16 414	93.02 399	73.54 374	95.61 431	72.16 450	86.14 374	93.84 414

EG-PatchMatch MVS		87.02 385	85.44 390	91.76 378	92.67 424	85.00 368	96.08 308	96.45 311	83.41 420	79.52 443	93.49 389	57.10 453	97.72 358	79.34 417	90.87 325	92.56 431

TinyColmap		86.82 386	85.35 393	91.21 389	94.91 354	82.99 397	93.94 403	94.02 421	83.58 416	81.56 433	94.68 326	62.34 445	98.13 294	75.78 432	87.35 365	92.52 433

UWE-MVS-28		86.81 387	86.41 382	88.02 430	92.87 419	74.60 455	95.38 349	86.70 470	88.17 328	87.28 378	94.67 328	70.83 390	93.30 458	67.45 460	94.31 261	96.17 302

mvs5depth		86.53 388	85.08 395	90.87 396	88.74 455	82.52 402	91.91 441	94.23 416	86.35 373	87.11 381	93.70 379	66.52 425	97.76 354	81.37 398	75.80 446	92.31 437

TDRefinement		86.53 388	84.76 400	91.85 371	82.23 473	84.25 379	96.38 281	95.35 366	84.97 397	84.09 417	94.94 312	65.76 433	98.34 280	84.60 362	74.52 451	92.97 423

sc_t1		86.48 390	84.10 407	93.63 307	93.45 407	85.76 350	96.79 237	94.71 397	73.06 461	86.45 394	94.35 346	55.13 457	97.95 331	84.38 365	78.55 436	97.18 275

test_0402		86.46 391	84.79 399	91.45 383	95.02 346	85.55 353	96.29 292	94.89 390	80.90 435	82.21 430	93.97 371	68.21 415	97.29 392	62.98 464	88.68 349	91.51 448

Anonymous20240521		86.42 392	85.44 390	89.34 423	90.33 442	79.79 434	96.73 245	95.92 335	83.71 415	83.25 424	91.36 429	63.92 439	96.01 421	78.39 421	85.36 383	92.22 439

FE-MVSNET2		86.36 393	84.68 402	91.39 386	87.67 460	86.47 331	96.21 298	96.41 313	87.87 338	79.31 445	89.64 442	65.29 436	95.58 433	82.42 387	77.28 439	92.14 442

DSMNet-mixed		86.34 394	86.12 387	87.00 436	89.88 446	70.43 462	94.93 369	90.08 459	77.97 451	85.42 404	92.78 402	74.44 366	93.96 452	74.43 439	95.14 244	96.62 290

CL-MVSNet_self_test		86.31 395	85.15 394	89.80 416	88.83 453	81.74 412	93.93 404	96.22 325	86.67 367	85.03 406	90.80 432	78.09 331	94.50 444	74.92 437	71.86 457	93.15 422

pmmvs-eth3d		86.22 396	84.45 403	91.53 381	88.34 457	87.25 307	94.47 382	95.01 382	83.47 418	79.51 444	89.61 443	69.75 402	95.71 428	83.13 377	76.73 444	91.64 445

test_vis1_rt		86.16 397	85.06 396	89.46 420	93.47 406	80.46 424	96.41 275	86.61 471	85.22 391	79.15 446	88.64 450	52.41 462	97.06 398	93.08 187	90.57 327	90.87 454

test20.03		86.14 398	85.40 392	88.35 426	90.12 443	80.06 432	95.90 320	95.20 375	88.59 314	81.29 434	93.62 385	71.43 385	92.65 461	71.26 454	81.17 424	92.34 435

UnsupCasMVSNet_eth		85.99 399	84.45 403	90.62 404	89.97 445	82.40 406	93.62 418	97.37 220	89.86 269	78.59 449	92.37 411	65.25 437	95.35 439	82.27 389	70.75 458	94.10 407

KD-MVS_self_test		85.95 400	84.95 397	88.96 425	89.55 449	79.11 443	95.13 364	96.42 312	85.91 381	84.07 418	90.48 434	70.03 398	94.82 442	80.04 409	72.94 455	92.94 424

ttmdpeth		85.91 401	84.76 400	89.36 422	89.14 450	80.25 430	95.66 335	93.16 435	83.77 413	83.39 423	95.26 300	66.24 429	95.26 440	80.65 404	75.57 447	92.57 430

YYNet1		85.87 402	84.23 405	90.78 403	92.38 432	82.46 405	93.17 425	95.14 378	82.12 428	67.69 463	92.36 414	78.16 330	95.50 436	77.31 425	79.73 429	94.39 400

MDA-MVSNet_test_wron		85.87 402	84.23 405	90.80 402	92.38 432	82.57 400	93.17 425	95.15 377	82.15 427	67.65 465	92.33 417	78.20 327	95.51 435	77.33 424	79.74 428	94.31 404

CMPMVS	62.92 21	85.62 404	84.92 398	87.74 431	89.14 450	73.12 461	94.17 396	96.80 288	73.98 457	73.65 459	94.93 313	66.36 426	97.61 368	83.95 371	91.28 316	92.48 434
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 405	83.64 408	90.92 395	95.27 330	79.49 439	90.55 451	95.60 354	83.76 414	83.00 427	89.95 439	71.09 387	97.97 323	82.75 384	60.79 472	95.31 350

tt0320		85.39 406	83.12 409	92.19 362	93.44 408	85.79 349	96.19 301	94.87 394	71.19 463	82.92 428	91.76 426	58.43 450	96.81 410	81.03 403	78.26 437	93.98 411

MDA-MVSNet-bldmvs		85.00 407	82.95 412	91.17 393	93.13 416	83.33 391	94.56 378	95.00 383	84.57 402	65.13 469	92.65 404	70.45 393	95.85 425	73.57 445	77.49 438	94.33 402

MIMVSNet1		84.93 408	83.05 410	90.56 405	89.56 448	84.84 374	95.40 347	95.35 366	83.91 409	80.38 439	92.21 419	57.23 452	93.34 457	70.69 456	82.75 419	93.50 417

tt0320-xc		84.83 409	82.33 418	92.31 356	93.66 398	86.20 339	96.17 303	94.06 418	71.26 462	82.04 432	92.22 418	55.07 458	96.72 413	81.49 393	75.04 450	94.02 410

KD-MVS_2432*1600		84.81 410	82.64 413	91.31 387	91.07 439	85.34 362	91.22 445	95.75 345	85.56 386	83.09 425	90.21 437	67.21 420	95.89 423	77.18 427	62.48 470	92.69 427

miper_refine_blended		84.81 410	82.64 413	91.31 387	91.07 439	85.34 362	91.22 445	95.75 345	85.56 386	83.09 425	90.21 437	67.21 420	95.89 423	77.18 427	62.48 470	92.69 427

FE-MVSNET1		84.51 412	82.41 417	90.83 397	86.25 465	84.98 370	96.17 303	96.32 317	84.25 405	77.85 452	89.16 447	54.50 459	95.42 437	80.39 407	76.81 442	92.09 443

OpenMVS_ROB	81.14 20	84.42 413	82.28 419	90.83 397	90.06 444	84.05 384	95.73 330	94.04 420	73.89 459	80.17 442	91.53 428	59.15 448	97.64 364	66.92 462	89.05 342	90.80 455

FE-MVSNET		83.85 414	81.97 420	89.51 419	87.19 462	83.19 394	95.21 361	93.17 433	83.45 419	78.90 447	89.05 448	65.46 434	93.84 454	69.71 458	75.56 448	91.51 448

mvsany_test3		83.59 415	82.44 416	87.03 435	83.80 468	73.82 457	93.70 413	90.92 456	86.42 371	82.51 429	90.26 436	46.76 467	95.71 428	90.82 236	76.76 443	91.57 447

PM-MVS		83.48 416	81.86 422	88.31 427	87.83 459	77.59 448	93.43 421	91.75 449	86.91 363	80.63 437	89.91 440	44.42 468	95.84 426	85.17 356	76.73 444	91.50 450

test_fmvs3		83.21 417	83.02 411	83.78 441	86.77 464	68.34 467	96.76 243	94.91 389	86.49 370	84.14 416	89.48 444	36.04 472	91.73 463	91.86 213	80.77 426	91.26 453

new-patchmatchnet		83.18 418	81.87 421	87.11 434	86.88 463	75.99 453	93.70 413	95.18 376	85.02 396	77.30 453	88.40 452	65.99 431	93.88 453	74.19 442	70.18 459	91.47 451

new_pmnet		82.89 419	81.12 424	88.18 429	89.63 447	80.18 431	91.77 442	92.57 442	76.79 454	75.56 456	88.23 454	61.22 447	94.48 445	71.43 452	82.92 417	89.87 458

MVS-HIRNet		82.47 420	81.21 423	86.26 438	95.38 318	69.21 465	88.96 461	89.49 460	66.28 467	80.79 436	74.08 472	68.48 413	97.39 387	71.93 451	95.47 238	92.18 440

MVStest1		82.38 421	80.04 425	89.37 421	87.63 461	82.83 398	95.03 366	93.37 432	73.90 458	73.50 460	94.35 346	62.89 443	93.25 459	73.80 443	65.92 467	92.04 444

UnsupCasMVSNet_bld		82.13 422	79.46 427	90.14 411	88.00 458	82.47 404	90.89 450	96.62 304	78.94 447	75.61 454	84.40 465	56.63 454	96.31 419	77.30 426	66.77 466	91.63 446

dmvs_testset		81.38 423	82.60 415	77.73 447	91.74 436	51.49 482	93.03 430	84.21 475	89.07 294	78.28 450	91.25 430	76.97 341	88.53 470	56.57 470	82.24 420	93.16 421

test_f		80.57 424	79.62 426	83.41 442	83.38 471	67.80 469	93.57 420	93.72 427	80.80 439	77.91 451	87.63 458	33.40 473	92.08 462	87.14 325	79.04 434	90.34 457

pmmvs3		79.97 425	77.50 430	87.39 433	82.80 472	79.38 441	92.70 435	90.75 457	70.69 464	78.66 448	87.47 460	51.34 463	93.40 456	73.39 446	69.65 460	89.38 459

APD_test1		79.31 426	77.70 429	84.14 440	89.11 452	69.07 466	92.36 440	91.50 451	69.07 465	73.87 458	92.63 406	39.93 470	94.32 447	70.54 457	80.25 427	89.02 460

N_pmnet		78.73 427	78.71 428	78.79 446	92.80 422	46.50 485	94.14 397	43.71 487	78.61 448	80.83 435	91.66 427	74.94 362	96.36 418	67.24 461	84.45 401	93.50 417

WB-MVS		76.77 428	76.63 431	77.18 448	85.32 466	56.82 480	94.53 379	89.39 461	82.66 425	71.35 461	89.18 446	75.03 359	88.88 468	35.42 477	66.79 465	85.84 462

SSC-MVS		76.05 429	75.83 432	76.72 452	84.77 467	56.22 481	94.32 391	88.96 463	81.82 431	70.52 462	88.91 449	74.79 363	88.71 469	33.69 478	64.71 468	85.23 463

test_vis3_rt		72.73 430	70.55 433	79.27 445	80.02 474	68.13 468	93.92 405	74.30 482	76.90 453	58.99 473	73.58 473	20.29 481	95.37 438	84.16 366	72.80 456	74.31 470

LCM-MVSNet		72.55 431	69.39 435	82.03 443	70.81 483	65.42 472	90.12 455	94.36 414	55.02 473	65.88 467	81.72 466	24.16 480	89.96 464	74.32 441	68.10 464	90.71 456

FPMVS		71.27 432	69.85 434	75.50 453	74.64 478	59.03 478	91.30 444	91.50 451	58.80 470	57.92 474	88.28 453	29.98 476	85.53 473	53.43 471	82.84 418	81.95 466

PMMVS2		70.19 433	66.92 437	80.01 444	76.35 477	65.67 471	86.22 468	87.58 467	64.83 469	62.38 470	80.29 469	26.78 478	88.49 471	63.79 463	54.07 474	85.88 461

dongtai		69.99 434	69.33 436	71.98 456	88.78 454	61.64 476	89.86 456	59.93 486	75.67 455	74.96 457	85.45 462	50.19 464	81.66 475	43.86 474	55.27 473	72.63 471

testf1		69.31 435	66.76 438	76.94 450	78.61 475	61.93 474	88.27 465	86.11 472	55.62 471	59.69 471	85.31 463	20.19 482	89.32 465	57.62 467	69.44 462	79.58 467

APD_test2		69.31 435	66.76 438	76.94 450	78.61 475	61.93 474	88.27 465	86.11 472	55.62 471	59.69 471	85.31 463	20.19 482	89.32 465	57.62 467	69.44 462	79.58 467

EGC-MVSNET		68.77 437	63.01 443	86.07 439	92.49 428	82.24 408	93.96 402	90.96 455	0.71 484	2.62 485	90.89 431	53.66 460	93.46 455	57.25 469	84.55 399	82.51 465

Gipuma		67.86 438	65.41 440	75.18 454	92.66 425	73.45 458	66.50 476	94.52 405	53.33 474	57.80 475	66.07 475	30.81 474	89.20 467	48.15 473	78.88 435	62.90 475
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 439	64.89 441	69.79 457	72.62 481	35.23 489	65.19 477	92.83 440	20.35 479	65.20 468	88.08 456	43.14 469	82.70 474	73.12 447	63.46 469	91.45 452

kuosan		65.27 440	64.66 442	67.11 459	83.80 468	61.32 477	88.53 464	60.77 485	68.22 466	67.67 464	80.52 468	49.12 465	70.76 481	29.67 480	53.64 475	69.26 473

ANet_high		63.94 441	59.58 444	77.02 449	61.24 485	66.06 470	85.66 470	87.93 466	78.53 449	42.94 477	71.04 474	25.42 479	80.71 476	52.60 472	30.83 478	84.28 464

PMVS	53.92 22	58.58 442	55.40 445	68.12 458	51.00 486	48.64 483	78.86 473	87.10 469	46.77 475	35.84 481	74.28 471	8.76 484	86.34 472	42.07 475	73.91 453	69.38 472
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 443	52.56 447	55.43 461	74.43 479	47.13 484	83.63 472	76.30 479	42.23 476	42.59 478	62.22 477	28.57 477	74.40 478	31.53 479	31.51 477	44.78 476

MVE	50.73 23	53.25 444	48.81 449	66.58 460	65.34 484	57.50 479	72.49 475	70.94 483	40.15 478	39.28 480	63.51 476	6.89 486	73.48 480	38.29 476	42.38 476	68.76 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 445	51.31 448	54.39 462	72.62 481	45.39 486	83.84 471	75.51 481	41.13 477	40.77 479	59.65 478	30.08 475	73.60 479	28.31 481	29.90 479	44.18 477

tmp_tt		51.94 446	53.82 446	46.29 463	33.73 487	45.30 487	78.32 474	67.24 484	18.02 480	50.93 476	87.05 461	52.99 461	53.11 482	70.76 455	25.29 480	40.46 478

wuyk23d		25.11 447	24.57 451	26.74 464	73.98 480	39.89 488	57.88 478	9.80 488	12.27 481	10.39 482	6.97 484	7.03 485	36.44 483	25.43 482	17.39 481	3.89 481

cdsmvs_eth3d_5k		23.24 448	30.99 450	0.00 467	0.00 490	0.00 492	0.00 479	97.63 166	0.00 485	0.00 486	96.88 208	84.38 201	0.00 486	0.00 485	0.00 484	0.00 482

testmvs		13.36 449	16.33 452	4.48 466	5.04 488	2.26 491	93.18 424	3.28 489	2.70 482	8.24 483	21.66 480	2.29 488	2.19 484	7.58 483	2.96 482	9.00 480

test123		13.04 450	15.66 453	5.18 465	4.51 489	3.45 490	92.50 438	1.81 490	2.50 483	7.58 484	20.15 481	3.67 487	2.18 485	7.13 484	1.07 483	9.90 479

ab-mvs-re		8.06 451	10.74 454	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	96.69 219	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		7.39 452	9.85 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	88.65 109	0.00 486	0.00 485	0.00 484	0.00 482

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

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

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

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

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

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

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

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

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

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

MED-MVS test				98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51		99.86 9	97.40 50	99.58 23	99.65 20

TestfortrainingZip							98.69 11

WAC-MVS						79.53 437								75.56 435

FOURS1					99.55 4	93.34 71	99.29 1	98.35 42	94.98 46	98.49 39

MSC_two_6792asdad				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 33	99.67 6	99.77 3

PC_three_1452									90.77 235	98.89 26	98.28 86	96.24 1	98.35 277	95.76 106	99.58 23	99.59 32

No_MVS				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 33	99.67 6	99.77 3

test_one_0601					99.32 27	95.20 21		98.25 61	95.13 40	98.48 40	98.87 31	95.16 9

eth-test2					0.00 490

eth-test					0.00 490

ZD-MVS					99.05 45	94.59 33		98.08 93	89.22 290	97.03 81	98.10 94	92.52 42	99.65 79	94.58 151	99.31 72

RE-MVS-def			96.72 62		99.02 48	92.34 108	97.98 71	98.03 110	93.52 115	97.43 67	98.51 56	90.71 81		96.05 94	99.26 78	99.43 63

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 258	98.94 19				97.41 49	99.66 10	99.74 9

OPU-MVS				98.55 4	98.82 61	96.86 3	98.25 40				98.26 87	96.04 2	99.24 149	95.36 120	99.59 19	99.56 40

test_241102_TWO								98.27 55	95.13 40	98.93 20	98.89 28	94.99 13	99.85 21	97.52 42	99.65 13	99.74 9

test_241102_ONE					99.42 10	95.30 18		98.27 55	95.09 43	99.19 13	98.81 37	95.54 5	99.65 79

9.14			96.75 61		98.93 56		97.73 115	98.23 66	91.28 212	97.88 55	98.44 64	93.00 29	99.65 79	95.76 106	99.47 45

save fliter					98.91 58	94.28 42	97.02 206	98.02 113	95.35 31

test_0728_THIRD									94.78 61	98.73 30	98.87 31	95.87 4	99.84 26	97.45 46	99.72 2	99.77 3

test_0728_SECOND				98.51 5	99.45 6	95.93 6	98.21 47	98.28 52					99.86 9	97.52 42	99.67 6	99.75 7

test0726					99.45 6	95.36 14	98.31 32	98.29 50	94.92 50	98.99 18	98.92 23	95.08 10

GSMVS																98.45 182

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 237				98.45 182

sam_mvs												81.94 258

ambc				86.56 437	83.60 470	70.00 464	85.69 469	94.97 385		80.60 438	88.45 451	37.42 471	96.84 409	82.69 385	75.44 449	92.86 425

MTGPA								98.08 93

test_post1							92.81 434				16.58 483	80.53 284	97.68 360	86.20 336

test_post											17.58 482	81.76 261	98.08 304

patchmatchnet-post											90.45 435	82.65 242	98.10 299

GG-mvs-BLEND				93.62 308	93.69 396	89.20 252	92.39 439	83.33 476		87.98 364	89.84 441	71.00 388	96.87 408	82.08 390	95.40 240	94.80 384

MTMP							97.86 91	82.03 477

gm-plane-assit					93.22 413	78.89 445			84.82 399		93.52 388		98.64 247	87.72 304

test9_res														94.81 139	99.38 64	99.45 59

TEST9					98.70 65	94.19 46	96.41 275	98.02 113	88.17 328	96.03 126	97.56 161	92.74 36	99.59 95

test_8					98.67 67	94.06 53	96.37 283	98.01 116	88.58 315	95.98 130	97.55 163	92.73 37	99.58 98

agg_prior2														93.94 165	99.38 64	99.50 52

agg_prior					98.67 67	93.79 59		98.00 117		95.68 143			99.57 105

TestCases				93.98 284	97.94 130	86.64 323		95.54 359	85.38 388	85.49 402	96.77 213	70.28 394	99.15 163	80.02 410	92.87 287	96.15 305

test_prior4						93.66 62	96.42 274

test_prior2							96.35 284		92.80 155	96.03 126	97.59 158	92.01 50		95.01 128	99.38 64

test_prior				97.23 69	98.67 67	92.99 83		98.00 117					99.41 132			99.29 75

旧先验2							95.94 316		81.66 432	97.34 70			98.82 209	92.26 198

新几何2							95.79 326

新几何1				97.32 62	98.60 74	93.59 63		97.75 149	81.58 433	95.75 138	97.85 123	90.04 88	99.67 77	86.50 332	99.13 98	98.69 159

旧先验1					98.38 89	93.38 68		97.75 149			98.09 96	92.30 48			99.01 108	99.16 85

无先验							95.79 326	97.87 132	83.87 412				99.65 79	87.68 310		98.89 135

原ACMM2							95.67 332

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 354	95.22 160	97.68 145	90.25 85	99.54 110	87.95 300	99.12 100	98.49 177

test222					98.24 100	92.21 114	95.33 351	97.60 171	79.22 446	95.25 158	97.84 125	88.80 106			99.15 95	98.72 156

testdata2													99.67 77	85.96 344

segment_acmp												92.89 33

testdata				95.46 201	98.18 111	88.90 261		97.66 160	82.73 424	97.03 81	98.07 97	90.06 87	98.85 205	89.67 264	98.98 109	98.64 162

testdata1							95.26 358		93.10 137

test12				97.65 47	98.46 79	94.26 43		97.66 160		95.52 150		90.89 78	99.46 126		99.25 80	99.22 82

plane_prior7					96.21 266	89.98 212

plane_prior6					96.10 284	90.00 208						81.32 268

plane_prior5								97.51 188					98.60 252	93.02 190	92.23 298	95.86 313

plane_prior4											96.64 222

plane_prior3						90.00 208			94.46 78	91.34 268

plane_prior2							97.74 113		94.85 53

plane_prior1					96.14 279

plane_prior						89.99 210	97.24 186		94.06 92						92.16 302

n2								0.00 491

nn								0.00 491

door-mid								91.06 454

lessismore_v0				90.45 406	91.96 435	79.09 444		87.19 468		80.32 440	94.39 343	66.31 428	97.55 372	84.00 370	76.84 441	94.70 391

LGP-MVS_train				94.10 276	96.16 276	88.26 279		97.46 199	91.29 209	90.12 298	97.16 187	79.05 312	98.73 230	92.25 200	91.89 306	95.31 350

test11								97.88 130

door								91.13 453

HQP5-MVS						89.33 245

HQP-NCC					95.86 292		96.65 255		93.55 109	90.14 292

ACMP_Plane					95.86 292		96.65 255		93.55 109	90.14 292

BP-MVS														92.13 206

HQP4-MVS										90.14 292			98.50 262			95.78 321

HQP3-MVS								97.39 216							92.10 303

HQP2-MVS												80.95 272

NP-MVS					95.99 290	89.81 220					95.87 265

MDTV_nov1_ep13_2view						70.35 463	93.10 429		83.88 411	93.55 209		82.47 246		86.25 335		98.38 190

MDTV_nov1_ep13			90.76 286		95.22 334	80.33 426	93.03 430	95.28 370	88.14 331	92.84 232	93.83 373	81.34 267	98.08 304	82.86 379	94.34 260

ACMMP++_ref															90.30 332

ACMMP++															91.02 321

Test By Simon												88.73 108

ITE_SJBPF				92.43 351	95.34 323	85.37 361		95.92 335	91.47 202	87.75 367	96.39 240	71.00 388	97.96 327	82.36 388	89.86 335	93.97 412

DeepMVS_CX				74.68 455	90.84 441	64.34 473		81.61 478	65.34 468	67.47 466	88.01 457	48.60 466	80.13 477	62.33 465	73.68 454	79.58 467

High-resolution multi-view benchmark