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 9	98.67 61	95.39 11	99.29 1	98.28 36	94.78 44	98.93 9	98.87 18	96.04 2	99.86 9	97.45 32	99.58 23	99.59 24

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 35	98.27 39	95.13 26	99.19 4	98.89 16	95.54 5	99.85 18	97.52 28	99.66 10	99.56 31

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 42	97.85 119	94.92 35	98.73 18	98.87 18	95.08 8	99.84 23	97.52 28	99.67 6	99.48 47
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 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 165	98.35 27	95.16 25	98.71 20	98.80 25	95.05 10	99.89 3	96.70 49	99.73 1	99.73 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 28	98.81 7	98.30 32	94.76 46	98.30 26	98.90 15	93.77 17	99.68 57	97.93 16	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 6	97.44 16	98.37 7	98.90 53	95.86 6	97.27 157	98.08 77	95.81 9	97.87 40	98.31 63	94.26 13	99.68 57	97.02 40	99.49 38	99.57 28

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 55	98.21 95	92.75 82	97.83 86	98.73 9	95.04 31	99.30 1	98.84 23	93.34 22	99.78 38	99.32 2	99.13 83	99.50 43

fmvsm_l_conf0.5_n_a		97.63 8	97.76 5	97.26 62	98.25 89	92.59 88	97.81 90	98.68 13	94.93 33	99.24 3	98.87 18	93.52 20	99.79 36	99.32 2	99.21 74	99.40 57

SteuartSystems-ACMMP		97.62 9	97.53 11	97.87 24	98.39 80	94.25 40	98.43 22	98.27 39	95.34 20	98.11 29	98.56 33	94.53 12	99.71 49	96.57 53	99.62 17	99.65 17
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		97.59 10	97.54 10	97.73 37	99.40 11	93.77 56	98.53 14	98.29 34	95.55 16	98.56 22	97.81 103	93.90 15	99.65 61	96.62 50	99.21 74	99.77 2
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
test_fmvsm_n_1920		97.55 11	97.89 3	96.53 87	98.41 77	91.73 115	98.01 60	99.02 1	96.37 4	99.30 1	98.92 13	92.39 41	99.79 36	99.16 4	99.46 41	98.08 178

reproduce-ours		97.53 12	97.51 13	97.60 46	98.97 47	93.31 68	97.71 103	98.20 53	95.80 10	97.88 37	98.98 9	92.91 27	99.81 30	97.68 20	99.43 48	99.67 13

our_new_method		97.53 12	97.51 13	97.60 46	98.97 47	93.31 68	97.71 103	98.20 53	95.80 10	97.88 37	98.98 9	92.91 27	99.81 30	97.68 20	99.43 48	99.67 13

reproduce_model		97.51 14	97.51 13	97.50 49	98.99 46	93.01 76	97.79 92	98.21 51	95.73 13	97.99 33	99.03 6	92.63 36	99.82 28	97.80 18	99.42 50	99.67 13

test_fmvsmconf_n		97.49 15	97.56 9	97.29 58	97.44 147	92.37 94	97.91 76	98.88 4	95.83 8	98.92 12	99.05 5	91.45 57	99.80 33	99.12 5	99.46 41	99.69 12

TSAR-MVS + MP.		97.42 16	97.33 18	97.69 41	99.25 27	94.24 41	98.07 55	97.85 119	93.72 82	98.57 21	98.35 54	93.69 18	99.40 113	97.06 39	99.46 41	99.44 52
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 17	97.53 11	97.06 72	98.57 72	94.46 34	97.92 75	98.14 67	94.82 41	99.01 6	98.55 35	94.18 14	97.41 336	96.94 41	99.64 14	99.32 65
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 18	97.13 19	98.17 15	99.02 42	95.28 19	98.23 39	98.27 39	92.37 134	98.27 27	98.65 31	93.33 23	99.72 48	96.49 55	99.52 30	99.51 40

SMA-MVS		97.35 19	97.03 27	98.30 8	99.06 38	95.42 10	97.94 73	98.18 60	90.57 200	98.85 15	98.94 12	93.33 23	99.83 26	96.72 48	99.68 4	99.63 19
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 20	96.97 30	98.47 5	99.08 36	96.16 4	97.55 127	97.97 104	95.59 14	96.61 78	97.89 93	92.57 38	99.84 23	95.95 77	99.51 33	99.40 57

NCCC		97.30 21	97.03 27	98.11 17	98.77 56	95.06 25	97.34 150	98.04 92	95.96 6	97.09 61	97.88 95	93.18 25	99.71 49	95.84 82	99.17 78	99.56 31

MM		97.29 22	96.98 29	98.23 11	98.01 111	95.03 26	98.07 55	95.76 293	97.78 1	97.52 44	98.80 25	88.09 107	99.86 9	99.44 1	99.37 61	99.80 1

ACMMP_NAP		97.20 23	96.86 35	98.23 11	99.09 34	95.16 22	97.60 119	98.19 58	92.82 125	97.93 36	98.74 28	91.60 55	99.86 9	96.26 58	99.52 30	99.67 13

XVS		97.18 24	96.96 31	97.81 28	99.38 14	94.03 50	98.59 12	98.20 53	94.85 37	96.59 80	98.29 66	91.70 52	99.80 33	95.66 86	99.40 55	99.62 20

MCST-MVS		97.18 24	96.84 37	98.20 14	99.30 24	95.35 15	97.12 172	98.07 82	93.54 90	96.08 102	97.69 110	93.86 16	99.71 49	96.50 54	99.39 57	99.55 34

HFP-MVS		97.14 26	96.92 33	97.83 26	99.42 7	94.12 46	98.52 15	98.32 30	93.21 102	97.18 55	98.29 66	92.08 46	99.83 26	95.63 91	99.59 19	99.54 36

test_fmvsmconf0.1_n		97.09 27	97.06 22	97.19 67	95.67 252	92.21 101	97.95 72	98.27 39	95.78 12	98.40 25	99.00 7	89.99 84	99.78 38	99.06 6	99.41 53	99.59 24

MTAPA		97.08 28	96.78 43	97.97 23	99.37 16	94.42 36	97.24 159	98.08 77	95.07 30	96.11 100	98.59 32	90.88 74	99.90 2	96.18 70	99.50 35	99.58 27

region2R		97.07 29	96.84 37	97.77 33	99.46 2	93.79 54	98.52 15	98.24 47	93.19 105	97.14 58	98.34 57	91.59 56	99.87 7	95.46 97	99.59 19	99.64 18

ACMMPR		97.07 29	96.84 37	97.79 30	99.44 6	93.88 52	98.52 15	98.31 31	93.21 102	97.15 57	98.33 60	91.35 61	99.86 9	95.63 91	99.59 19	99.62 20

CP-MVS		97.02 31	96.81 41	97.64 44	99.33 21	93.54 59	98.80 8	98.28 36	92.99 114	96.45 88	98.30 65	91.90 49	99.85 18	95.61 93	99.68 4	99.54 36

SR-MVS		97.01 32	96.86 35	97.47 51	99.09 34	93.27 70	97.98 63	98.07 82	93.75 81	97.45 46	98.48 43	91.43 59	99.59 77	96.22 61	99.27 67	99.54 36

ZNCC-MVS		96.96 33	96.67 48	97.85 25	99.37 16	94.12 46	98.49 19	98.18 60	92.64 130	96.39 90	98.18 73	91.61 54	99.88 4	95.59 96	99.55 26	99.57 28

APD-MVS		96.95 34	96.60 50	98.01 20	99.03 41	94.93 27	97.72 101	98.10 75	91.50 158	98.01 32	98.32 62	92.33 42	99.58 80	94.85 109	99.51 33	99.53 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 35	97.06 22	96.59 84	98.72 58	91.86 113	97.67 107	98.49 19	94.66 51	97.24 54	98.41 49	92.31 44	98.94 170	96.61 51	99.46 41	98.96 98

DeepC-MVS_fast	93.89 2	96.93 36	96.64 49	97.78 31	98.64 67	94.30 37	97.41 140	98.04 92	94.81 42	96.59 80	98.37 52	91.24 64	99.64 69	95.16 102	99.52 30	99.42 56
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 37	97.04 26	96.45 98	98.29 85	91.66 121	99.03 4	97.85 119	95.84 7	96.90 65	97.97 89	91.24 64	98.75 191	96.92 42	99.33 63	98.94 101

SR-MVS-dyc-post		96.88 38	96.80 42	97.11 70	99.02 42	92.34 95	97.98 63	98.03 94	93.52 92	97.43 49	98.51 38	91.40 60	99.56 88	96.05 72	99.26 69	99.43 54

CS-MVS		96.86 39	97.06 22	96.26 114	98.16 101	91.16 147	99.09 3	97.87 114	95.30 21	97.06 62	98.03 83	91.72 50	98.71 198	97.10 38	99.17 78	98.90 108

mPP-MVS		96.86 39	96.60 50	97.64 44	99.40 11	93.44 61	98.50 18	98.09 76	93.27 101	95.95 108	98.33 60	91.04 69	99.88 4	95.20 100	99.57 25	99.60 23

fmvsm_s_conf0.5_n		96.85 41	97.13 19	96.04 127	98.07 108	90.28 174	97.97 69	98.76 8	94.93 33	98.84 16	99.06 4	88.80 97	99.65 61	99.06 6	98.63 106	98.18 167

GST-MVS		96.85 41	96.52 54	97.82 27	99.36 18	94.14 45	98.29 29	98.13 68	92.72 127	96.70 72	98.06 80	91.35 61	99.86 9	94.83 111	99.28 66	99.47 49

balanced_conf03		96.84 43	96.89 34	96.68 78	97.63 136	92.22 100	98.17 48	97.82 124	94.44 61	98.23 28	97.36 135	90.97 71	99.22 130	97.74 19	99.66 10	98.61 131

patch_mono-2		96.83 44	97.44 16	95.01 180	99.05 39	85.39 307	96.98 184	98.77 7	94.70 48	97.99 33	98.66 29	93.61 19	99.91 1	97.67 24	99.50 35	99.72 11

APD-MVS_3200maxsize		96.81 45	96.71 47	97.12 69	99.01 45	92.31 97	97.98 63	98.06 85	93.11 111	97.44 47	98.55 35	90.93 72	99.55 90	96.06 71	99.25 71	99.51 40

PGM-MVS		96.81 45	96.53 53	97.65 42	99.35 20	93.53 60	97.65 110	98.98 2	92.22 136	97.14 58	98.44 46	91.17 67	99.85 18	94.35 123	99.46 41	99.57 28

MP-MVS		96.77 47	96.45 61	97.72 38	99.39 13	93.80 53	98.41 23	98.06 85	93.37 97	95.54 123	98.34 57	90.59 78	99.88 4	94.83 111	99.54 28	99.49 45
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 47	96.46 60	97.71 40	98.40 78	94.07 48	98.21 42	98.45 22	89.86 217	97.11 60	98.01 86	92.52 39	99.69 55	96.03 75	99.53 29	99.36 63

fmvsm_s_conf0.5_n_a		96.75 49	96.93 32	96.20 119	97.64 134	90.72 162	98.00 61	98.73 9	94.55 55	98.91 13	99.08 3	88.22 106	99.63 70	98.91 9	98.37 118	98.25 162

MVS_0304		96.74 50	96.31 64	98.02 19	96.87 174	94.65 30	97.58 120	94.39 353	96.47 3	97.16 56	98.39 50	87.53 121	99.87 7	98.97 8	99.41 53	99.55 34

test_fmvsmvis_n_1920		96.70 51	96.84 37	96.31 108	96.62 193	91.73 115	97.98 63	98.30 32	96.19 5	96.10 101	98.95 11	89.42 88	99.76 41	98.90 10	99.08 87	97.43 212

MP-MVS-pluss		96.70 51	96.27 66	97.98 22	99.23 30	94.71 29	96.96 186	98.06 85	90.67 191	95.55 121	98.78 27	91.07 68	99.86 9	96.58 52	99.55 26	99.38 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 53	96.49 55	97.27 61	98.31 84	93.39 62	96.79 199	96.72 244	94.17 69	97.44 47	97.66 114	92.76 31	99.33 118	96.86 44	97.76 139	99.08 87

HPM-MVS		96.69 53	96.45 61	97.40 53	99.36 18	93.11 74	98.87 6	98.06 85	91.17 174	96.40 89	97.99 87	90.99 70	99.58 80	95.61 93	99.61 18	99.49 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 55	96.58 52	96.99 74	98.46 73	92.31 97	96.20 253	98.90 3	94.30 68	95.86 110	97.74 108	92.33 42	99.38 116	96.04 74	99.42 50	99.28 68

DELS-MVS		96.61 56	96.38 63	97.30 57	97.79 125	93.19 72	95.96 264	98.18 60	95.23 22	95.87 109	97.65 115	91.45 57	99.70 54	95.87 78	99.44 47	99.00 96
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 56	97.09 21	95.15 172	98.09 104	86.63 283	96.00 262	98.15 65	95.43 17	97.95 35	98.56 33	93.40 21	99.36 117	96.77 45	99.48 39	99.45 50

fmvsm_s_conf0.1_n		96.58 58	96.77 44	96.01 131	96.67 191	90.25 175	97.91 76	98.38 23	94.48 59	98.84 16	99.14 1	88.06 108	99.62 71	98.82 11	98.60 108	98.15 171

MVSMamba_PlusPlus		96.51 59	96.48 56	96.59 84	98.07 108	91.97 110	98.14 49	97.79 126	90.43 204	97.34 52	97.52 128	91.29 63	99.19 133	98.12 15	99.64 14	98.60 132

EI-MVSNet-Vis-set		96.51 59	96.47 57	96.63 81	98.24 90	91.20 142	96.89 190	97.73 132	94.74 47	96.49 84	98.49 40	90.88 74	99.58 80	96.44 56	98.32 120	99.13 80

HPM-MVS_fast		96.51 59	96.27 66	97.22 64	99.32 22	92.74 83	98.74 9	98.06 85	90.57 200	96.77 69	98.35 54	90.21 81	99.53 94	94.80 114	99.63 16	99.38 61

EC-MVSNet		96.42 62	96.47 57	96.26 114	97.01 169	91.52 127	98.89 5	97.75 129	94.42 62	96.64 77	97.68 111	89.32 89	98.60 208	97.45 32	99.11 86	98.67 129

fmvsm_s_conf0.1_n_a		96.40 63	96.47 57	96.16 121	95.48 260	90.69 163	97.91 76	98.33 29	94.07 71	98.93 9	99.14 1	87.44 125	99.61 72	98.63 13	98.32 120	98.18 167

CANet		96.39 64	96.02 70	97.50 49	97.62 137	93.38 63	97.02 178	97.96 105	95.42 18	94.86 134	97.81 103	87.38 127	99.82 28	96.88 43	99.20 76	99.29 66

dcpmvs_2		96.37 65	97.05 25	94.31 222	98.96 49	84.11 328	97.56 123	97.51 161	93.92 76	97.43 49	98.52 37	92.75 32	99.32 120	97.32 37	99.50 35	99.51 40

EI-MVSNet-UG-set		96.34 66	96.30 65	96.47 95	98.20 96	90.93 154	96.86 192	97.72 134	94.67 50	96.16 99	98.46 44	90.43 79	99.58 80	96.23 60	97.96 133	98.90 108

train_agg		96.30 67	95.83 75	97.72 38	98.70 59	94.19 42	96.41 232	98.02 97	88.58 262	96.03 103	97.56 125	92.73 34	99.59 77	95.04 104	99.37 61	99.39 59

ACMMP		96.27 68	95.93 71	97.28 60	99.24 28	92.62 86	98.25 35	98.81 5	92.99 114	94.56 141	98.39 50	88.96 94	99.85 18	94.57 122	97.63 140	99.36 63
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 69	96.19 68	96.39 103	98.23 94	91.35 135	96.24 251	98.79 6	93.99 74	95.80 112	97.65 115	89.92 86	99.24 128	95.87 78	99.20 76	98.58 134

test_fmvsmconf0.01_n		96.15 70	95.85 74	97.03 73	92.66 368	91.83 114	97.97 69	97.84 123	95.57 15	97.53 43	99.00 7	84.20 169	99.76 41	98.82 11	99.08 87	99.48 47

DeepC-MVS	93.07 3	96.06 71	95.66 76	97.29 58	97.96 114	93.17 73	97.30 155	98.06 85	93.92 76	93.38 170	98.66 29	86.83 133	99.73 45	95.60 95	99.22 73	98.96 98
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 72	95.91 72	96.46 97	99.24 28	90.47 169	98.30 28	98.57 18	89.01 245	93.97 157	97.57 123	92.62 37	99.76 41	94.66 117	99.27 67	99.15 78

sasdasda		96.02 73	95.45 82	97.75 35	97.59 140	95.15 23	98.28 30	97.60 148	94.52 57	96.27 94	96.12 205	87.65 116	99.18 136	96.20 66	94.82 204	98.91 105

ETV-MVS		96.02 73	95.89 73	96.40 101	97.16 155	92.44 92	97.47 136	97.77 128	94.55 55	96.48 85	94.51 284	91.23 66	98.92 172	95.65 89	98.19 125	97.82 193

canonicalmvs		96.02 73	95.45 82	97.75 35	97.59 140	95.15 23	98.28 30	97.60 148	94.52 57	96.27 94	96.12 205	87.65 116	99.18 136	96.20 66	94.82 204	98.91 105

CDPH-MVS		95.97 76	95.38 87	97.77 33	98.93 50	94.44 35	96.35 240	97.88 112	86.98 307	96.65 76	97.89 93	91.99 48	99.47 105	92.26 158	99.46 41	99.39 59

UA-Net		95.95 77	95.53 78	97.20 66	97.67 130	92.98 78	97.65 110	98.13 68	94.81 42	96.61 78	98.35 54	88.87 95	99.51 99	90.36 200	97.35 150	99.11 84

MGCFI-Net		95.94 78	95.40 86	97.56 48	97.59 140	94.62 31	98.21 42	97.57 153	94.41 63	96.17 98	96.16 203	87.54 120	99.17 138	96.19 68	94.73 209	98.91 105

BP-MVS1		95.89 79	95.49 79	97.08 71	96.67 191	93.20 71	98.08 53	96.32 268	94.56 54	96.32 91	97.84 101	84.07 172	99.15 141	96.75 46	98.78 100	98.90 108

VNet		95.89 79	95.45 82	97.21 65	98.07 108	92.94 79	97.50 130	98.15 65	93.87 78	97.52 44	97.61 121	85.29 153	99.53 94	95.81 83	95.27 195	99.16 76

alignmvs		95.87 81	95.23 91	97.78 31	97.56 145	95.19 21	97.86 80	97.17 203	94.39 65	96.47 86	96.40 191	85.89 146	99.20 132	96.21 65	95.11 200	98.95 100

casdiffmvs_mvg		95.81 82	95.57 77	96.51 91	96.87 174	91.49 128	97.50 130	97.56 157	93.99 74	95.13 130	97.92 92	87.89 112	98.78 186	95.97 76	97.33 151	99.26 70
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 83	94.92 97	98.01 20	98.08 107	95.71 9	95.27 301	97.62 147	90.43 204	95.55 121	97.07 151	91.72 50	99.50 102	89.62 216	98.94 95	98.82 119

DP-MVS Recon		95.68 84	95.12 95	97.37 54	99.19 31	94.19 42	97.03 176	98.08 77	88.35 271	95.09 131	97.65 115	89.97 85	99.48 104	92.08 167	98.59 109	98.44 151

casdiffmvs		95.64 85	95.49 79	96.08 123	96.76 189	90.45 170	97.29 156	97.44 179	94.00 73	95.46 125	97.98 88	87.52 123	98.73 194	95.64 90	97.33 151	99.08 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MG-MVS		95.61 86	95.38 87	96.31 108	98.42 76	90.53 167	96.04 259	97.48 165	93.47 94	95.67 118	98.10 76	89.17 91	99.25 127	91.27 185	98.77 101	99.13 80

baseline		95.58 87	95.42 85	96.08 123	96.78 184	90.41 172	97.16 169	97.45 175	93.69 85	95.65 119	97.85 99	87.29 128	98.68 200	95.66 86	97.25 156	99.13 80

CPTT-MVS		95.57 88	95.19 92	96.70 77	99.27 26	91.48 129	98.33 26	98.11 73	87.79 288	95.17 129	98.03 83	87.09 131	99.61 72	93.51 138	99.42 50	99.02 90

EIA-MVS		95.53 89	95.47 81	95.71 147	97.06 163	89.63 191	97.82 88	97.87 114	93.57 86	93.92 158	95.04 258	90.61 77	98.95 168	94.62 119	98.68 104	98.54 136

3Dnovator+	91.43 4	95.40 90	94.48 113	98.16 16	96.90 173	95.34 16	98.48 20	97.87 114	94.65 52	88.53 296	98.02 85	83.69 176	99.71 49	93.18 145	98.96 94	99.44 52

PS-MVSNAJ		95.37 91	95.33 89	95.49 160	97.35 149	90.66 165	95.31 298	97.48 165	93.85 79	96.51 83	95.70 230	88.65 100	99.65 61	94.80 114	98.27 122	96.17 250

MVSFormer		95.37 91	95.16 93	95.99 132	96.34 221	91.21 140	98.22 40	97.57 153	91.42 162	96.22 96	97.32 136	86.20 143	97.92 288	94.07 126	99.05 89	98.85 115

xiu_mvs_v2_base		95.32 93	95.29 90	95.40 165	97.22 151	90.50 168	95.44 292	97.44 179	93.70 84	96.46 87	96.18 200	88.59 103	99.53 94	94.79 116	97.81 136	96.17 250

PVSNet_Blended_VisFu		95.27 94	94.91 98	96.38 104	98.20 96	90.86 156	97.27 157	98.25 45	90.21 208	94.18 151	97.27 140	87.48 124	99.73 45	93.53 137	97.77 138	98.55 135

diffmvs		95.25 95	95.13 94	95.63 150	96.43 216	89.34 207	95.99 263	97.35 192	92.83 124	96.31 92	97.37 134	86.44 138	98.67 201	96.26 58	97.19 158	98.87 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		95.23 96	94.81 99	96.51 91	97.18 154	91.58 125	98.26 34	98.12 70	94.38 66	94.90 133	98.15 75	82.28 209	98.92 172	91.45 182	98.58 110	99.01 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 97	95.04 96	95.76 140	97.49 146	89.56 195	98.67 10	97.00 222	90.69 189	94.24 149	97.62 120	89.79 87	98.81 183	93.39 143	96.49 173	98.92 104

EPNet		95.20 98	94.56 107	97.14 68	92.80 365	92.68 85	97.85 83	94.87 341	96.64 2	92.46 186	97.80 105	86.23 140	99.65 61	93.72 136	98.62 107	99.10 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 99	94.44 115	97.44 52	96.56 200	93.36 65	98.65 11	98.36 24	94.12 70	89.25 280	98.06 80	82.20 211	99.77 40	93.41 142	99.32 64	99.18 75

OMC-MVS		95.09 100	94.70 103	96.25 117	98.46 73	91.28 136	96.43 230	97.57 153	92.04 145	94.77 137	97.96 90	87.01 132	99.09 151	91.31 184	96.77 165	98.36 158

xiu_mvs_v1_base_debu		95.01 101	94.76 100	95.75 142	96.58 197	91.71 117	96.25 248	97.35 192	92.99 114	96.70 72	96.63 178	82.67 199	99.44 109	96.22 61	97.46 143	96.11 255

xiu_mvs_v1_base		95.01 101	94.76 100	95.75 142	96.58 197	91.71 117	96.25 248	97.35 192	92.99 114	96.70 72	96.63 178	82.67 199	99.44 109	96.22 61	97.46 143	96.11 255

xiu_mvs_v1_base_debi		95.01 101	94.76 100	95.75 142	96.58 197	91.71 117	96.25 248	97.35 192	92.99 114	96.70 72	96.63 178	82.67 199	99.44 109	96.22 61	97.46 143	96.11 255

PAPM_NR		95.01 101	94.59 105	96.26 114	98.89 54	90.68 164	97.24 159	97.73 132	91.80 150	92.93 183	96.62 181	89.13 92	99.14 144	89.21 229	97.78 137	98.97 97

lupinMVS		94.99 105	94.56 107	96.29 112	96.34 221	91.21 140	95.83 271	96.27 272	88.93 250	96.22 96	96.88 161	86.20 143	98.85 179	95.27 99	99.05 89	98.82 119

Effi-MVS+		94.93 106	94.45 114	96.36 106	96.61 194	91.47 130	96.41 232	97.41 184	91.02 180	94.50 143	95.92 214	87.53 121	98.78 186	93.89 132	96.81 164	98.84 118

IS-MVSNet		94.90 107	94.52 111	96.05 126	97.67 130	90.56 166	98.44 21	96.22 275	93.21 102	93.99 155	97.74 108	85.55 151	98.45 220	89.98 205	97.86 134	99.14 79

MVS_Test		94.89 108	94.62 104	95.68 148	96.83 179	89.55 196	96.70 208	97.17 203	91.17 174	95.60 120	96.11 209	87.87 113	98.76 190	93.01 153	97.17 159	98.72 124

PVSNet_Blended		94.87 109	94.56 107	95.81 139	98.27 86	89.46 202	95.47 291	98.36 24	88.84 253	94.36 146	96.09 210	88.02 109	99.58 80	93.44 140	98.18 126	98.40 154

jason		94.84 110	94.39 116	96.18 120	95.52 258	90.93 154	96.09 257	96.52 259	89.28 236	96.01 106	97.32 136	84.70 160	98.77 189	95.15 103	98.91 97	98.85 115
jason: jason.
API-MVS		94.84 110	94.49 112	95.90 134	97.90 120	92.00 109	97.80 91	97.48 165	89.19 239	94.81 135	96.71 167	88.84 96	99.17 138	88.91 236	98.76 102	96.53 239

test_yl		94.78 112	94.23 118	96.43 99	97.74 127	91.22 138	96.85 193	97.10 208	91.23 171	95.71 115	96.93 156	84.30 166	99.31 122	93.10 146	95.12 198	98.75 121

DCV-MVSNet		94.78 112	94.23 118	96.43 99	97.74 127	91.22 138	96.85 193	97.10 208	91.23 171	95.71 115	96.93 156	84.30 166	99.31 122	93.10 146	95.12 198	98.75 121

WTY-MVS		94.71 114	94.02 121	96.79 76	97.71 129	92.05 107	96.59 223	97.35 192	90.61 197	94.64 139	96.93 156	86.41 139	99.39 114	91.20 187	94.71 210	98.94 101

mamv4		94.66 115	96.10 69	90.37 351	98.01 111	73.41 399	96.82 197	97.78 127	89.95 215	94.52 142	97.43 132	92.91 27	99.09 151	98.28 14	99.16 80	98.60 132

mvsmamba		94.57 116	94.14 120	95.87 135	97.03 167	89.93 186	97.84 84	95.85 289	91.34 165	94.79 136	96.80 163	80.67 235	98.81 183	94.85 109	98.12 129	98.85 115

RRT-MVS		94.51 117	94.35 117	94.98 183	96.40 217	86.55 286	97.56 123	97.41 184	93.19 105	94.93 132	97.04 153	79.12 264	99.30 124	96.19 68	97.32 153	99.09 86

sss		94.51 117	93.80 125	96.64 79	97.07 160	91.97 110	96.32 243	98.06 85	88.94 249	94.50 143	96.78 164	84.60 161	99.27 126	91.90 168	96.02 178	98.68 128

test_cas_vis1_n_1920		94.48 119	94.55 110	94.28 224	96.78 184	86.45 288	97.63 116	97.64 144	93.32 100	97.68 42	98.36 53	73.75 324	99.08 154	96.73 47	99.05 89	97.31 219

CANet_DTU		94.37 120	93.65 129	96.55 86	96.46 214	92.13 105	96.21 252	96.67 251	94.38 66	93.53 166	97.03 154	79.34 260	99.71 49	90.76 193	98.45 116	97.82 193

AdaColmap		94.34 121	93.68 128	96.31 108	98.59 69	91.68 120	96.59 223	97.81 125	89.87 216	92.15 197	97.06 152	83.62 179	99.54 92	89.34 223	98.07 130	97.70 198

CNLPA		94.28 122	93.53 134	96.52 88	98.38 81	92.55 89	96.59 223	96.88 235	90.13 212	91.91 204	97.24 142	85.21 154	99.09 151	87.64 261	97.83 135	97.92 185

MAR-MVS		94.22 123	93.46 139	96.51 91	98.00 113	92.19 104	97.67 107	97.47 168	88.13 278	93.00 178	95.84 218	84.86 159	99.51 99	87.99 248	98.17 127	97.83 192
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		94.18 124	93.42 143	96.48 94	97.64 134	91.42 133	95.55 286	97.71 138	88.99 246	92.34 193	95.82 220	89.19 90	99.11 147	86.14 287	97.38 148	98.90 108

SDMVSNet		94.17 125	93.61 130	95.86 137	98.09 104	91.37 134	97.35 149	98.20 53	93.18 107	91.79 208	97.28 138	79.13 263	98.93 171	94.61 120	92.84 239	97.28 220

test_vis1_n_1920		94.17 125	94.58 106	92.91 286	97.42 148	82.02 353	97.83 86	97.85 119	94.68 49	98.10 30	98.49 40	70.15 347	99.32 120	97.91 17	98.82 98	97.40 214

h-mvs33		94.15 127	93.52 136	96.04 127	97.81 124	90.22 176	97.62 118	97.58 152	95.19 23	96.74 70	97.45 129	83.67 177	99.61 72	95.85 80	79.73 375	98.29 161

CHOSEN 1792x2688		94.15 127	93.51 137	96.06 125	98.27 86	89.38 205	95.18 307	98.48 21	85.60 330	93.76 161	97.11 149	83.15 187	99.61 72	91.33 183	98.72 103	99.19 74

Vis-MVSNet (Re-imp)		94.15 127	93.88 124	94.95 187	97.61 138	87.92 251	98.10 51	95.80 292	92.22 136	93.02 177	97.45 129	84.53 163	97.91 291	88.24 244	97.97 132	99.02 90

CDS-MVSNet		94.14 130	93.54 133	95.93 133	96.18 228	91.46 131	96.33 242	97.04 218	88.97 248	93.56 163	96.51 185	87.55 119	97.89 292	89.80 210	95.95 180	98.44 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 131	93.43 141	96.13 122	98.58 71	91.15 148	96.69 210	97.39 186	87.29 302	91.37 218	96.71 167	88.39 104	99.52 98	87.33 268	97.13 160	97.73 196
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 132	93.70 127	95.27 168	95.70 250	92.03 108	98.10 51	98.68 13	93.36 99	90.39 239	96.70 169	87.63 118	97.94 285	92.25 160	90.50 280	95.84 263

PVSNet_BlendedMVS		94.06 133	93.92 123	94.47 211	98.27 86	89.46 202	96.73 204	98.36 24	90.17 209	94.36 146	95.24 252	88.02 109	99.58 80	93.44 140	90.72 276	94.36 346

nrg030		94.05 134	93.31 145	96.27 113	95.22 282	94.59 32	98.34 25	97.46 170	92.93 121	91.21 228	96.64 174	87.23 130	98.22 239	94.99 107	85.80 323	95.98 259

UGNet		94.04 135	93.28 146	96.31 108	96.85 176	91.19 143	97.88 79	97.68 139	94.40 64	93.00 178	96.18 200	73.39 326	99.61 72	91.72 174	98.46 115	98.13 172
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		94.01 136	93.46 139	95.64 149	96.16 230	90.45 170	96.71 207	96.89 234	89.27 237	93.46 168	96.92 159	87.29 128	97.94 285	88.70 240	95.74 185	98.53 137

114514_t		93.95 137	93.06 150	96.63 81	99.07 37	91.61 122	97.46 138	97.96 105	77.99 393	93.00 178	97.57 123	86.14 145	99.33 118	89.22 228	99.15 81	98.94 101

FC-MVSNet-test		93.94 138	93.57 131	95.04 178	95.48 260	91.45 132	98.12 50	98.71 11	93.37 97	90.23 242	96.70 169	87.66 115	97.85 294	91.49 180	90.39 281	95.83 264

mvsany_test1		93.93 139	93.98 122	93.78 252	94.94 298	86.80 276	94.62 319	92.55 384	88.77 259	96.85 66	98.49 40	88.98 93	98.08 257	95.03 105	95.62 189	96.46 244

GeoE		93.89 140	93.28 146	95.72 146	96.96 172	89.75 190	98.24 38	96.92 231	89.47 230	92.12 199	97.21 144	84.42 164	98.39 227	87.71 255	96.50 172	99.01 93

HY-MVS	89.66 9	93.87 141	92.95 153	96.63 81	97.10 159	92.49 91	95.64 284	96.64 252	89.05 244	93.00 178	95.79 224	85.77 149	99.45 108	89.16 232	94.35 212	97.96 183

XVG-OURS-SEG-HR		93.86 142	93.55 132	94.81 193	97.06 163	88.53 233	95.28 299	97.45 175	91.68 154	94.08 154	97.68 111	82.41 207	98.90 175	93.84 134	92.47 245	96.98 227

VDD-MVS		93.82 143	93.08 149	96.02 129	97.88 121	89.96 185	97.72 101	95.85 289	92.43 132	95.86 110	98.44 46	68.42 361	99.39 114	96.31 57	94.85 202	98.71 126

mvs_anonymous		93.82 143	93.74 126	94.06 232	96.44 215	85.41 305	95.81 272	97.05 216	89.85 219	90.09 252	96.36 193	87.44 125	97.75 306	93.97 128	96.69 169	99.02 90

HQP_MVS		93.78 145	93.43 141	94.82 191	96.21 225	89.99 181	97.74 96	97.51 161	94.85 37	91.34 219	96.64 174	81.32 225	98.60 208	93.02 151	92.23 248	95.86 260

PS-MVSNAJss		93.74 146	93.51 137	94.44 213	93.91 336	89.28 212	97.75 95	97.56 157	92.50 131	89.94 255	96.54 184	88.65 100	98.18 244	93.83 135	90.90 274	95.86 260

XVG-OURS		93.72 147	93.35 144	94.80 196	97.07 160	88.61 228	94.79 316	97.46 170	91.97 148	93.99 155	97.86 98	81.74 220	98.88 176	92.64 157	92.67 244	96.92 231

HyFIR lowres test		93.66 148	92.92 154	95.87 135	98.24 90	89.88 187	94.58 321	98.49 19	85.06 340	93.78 160	95.78 225	82.86 195	98.67 201	91.77 173	95.71 187	99.07 89

LFMVS		93.60 149	92.63 167	96.52 88	98.13 103	91.27 137	97.94 73	93.39 373	90.57 200	96.29 93	98.31 63	69.00 354	99.16 140	94.18 125	95.87 182	99.12 83

F-COLMAP		93.58 150	92.98 152	95.37 166	98.40 78	88.98 221	97.18 167	97.29 197	87.75 291	90.49 237	97.10 150	85.21 154	99.50 102	86.70 278	96.72 168	97.63 200

ab-mvs		93.57 151	92.55 171	96.64 79	97.28 150	91.96 112	95.40 293	97.45 175	89.81 221	93.22 176	96.28 196	79.62 257	99.46 106	90.74 194	93.11 236	98.50 141

LS3D		93.57 151	92.61 169	96.47 95	97.59 140	91.61 122	97.67 107	97.72 134	85.17 338	90.29 241	98.34 57	84.60 161	99.73 45	83.85 321	98.27 122	98.06 179

FA-MVS(test-final)		93.52 153	92.92 154	95.31 167	96.77 186	88.54 232	94.82 315	96.21 277	89.61 225	94.20 150	95.25 251	83.24 184	99.14 144	90.01 204	96.16 177	98.25 162

Fast-Effi-MVS+		93.46 154	92.75 162	95.59 153	96.77 186	90.03 178	96.81 198	97.13 205	88.19 274	91.30 222	94.27 301	86.21 142	98.63 205	87.66 260	96.46 175	98.12 173

hse-mvs2		93.45 155	92.99 151	94.81 193	97.02 168	88.59 229	96.69 210	96.47 262	95.19 23	96.74 70	96.16 203	83.67 177	98.48 219	95.85 80	79.13 379	97.35 217

QAPM		93.45 155	92.27 181	96.98 75	96.77 186	92.62 86	98.39 24	98.12 70	84.50 348	88.27 304	97.77 106	82.39 208	99.81 30	85.40 300	98.81 99	98.51 140

UniMVSNet_NR-MVSNet		93.37 157	92.67 166	95.47 163	95.34 271	92.83 80	97.17 168	98.58 17	92.98 119	90.13 247	95.80 221	88.37 105	97.85 294	91.71 175	83.93 352	95.73 274

1112_ss		93.37 157	92.42 178	96.21 118	97.05 165	90.99 150	96.31 244	96.72 244	86.87 310	89.83 259	96.69 171	86.51 137	99.14 144	88.12 245	93.67 230	98.50 141

UniMVSNet (Re)		93.31 159	92.55 171	95.61 152	95.39 265	93.34 66	97.39 145	98.71 11	93.14 110	90.10 251	94.83 268	87.71 114	98.03 268	91.67 178	83.99 351	95.46 283

OPM-MVS		93.28 160	92.76 160	94.82 191	94.63 314	90.77 160	96.65 214	97.18 201	93.72 82	91.68 212	97.26 141	79.33 261	98.63 205	92.13 164	92.28 247	95.07 309
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 161	92.48 176	95.51 158	95.70 250	92.39 93	97.86 80	98.66 16	92.30 135	92.09 201	95.37 244	80.49 239	98.40 223	93.95 129	85.86 322	95.75 272

test_fmvs1		93.21 162	93.53 134	92.25 307	96.55 202	81.20 360	97.40 144	96.96 224	90.68 190	96.80 67	98.04 82	69.25 353	98.40 223	97.58 27	98.50 111	97.16 224

MVSTER		93.20 163	92.81 159	94.37 216	96.56 200	89.59 194	97.06 175	97.12 206	91.24 170	91.30 222	95.96 212	82.02 214	98.05 264	93.48 139	90.55 278	95.47 282

test1111		93.19 164	92.82 158	94.30 223	97.58 144	84.56 322	98.21 42	89.02 403	93.53 91	94.58 140	98.21 70	72.69 327	99.05 161	93.06 149	98.48 114	99.28 68

ECVR-MVS		93.19 164	92.73 164	94.57 208	97.66 132	85.41 305	98.21 42	88.23 405	93.43 95	94.70 138	98.21 70	72.57 328	99.07 158	93.05 150	98.49 112	99.25 71

HQP-MVS		93.19 164	92.74 163	94.54 209	95.86 242	89.33 208	96.65 214	97.39 186	93.55 87	90.14 243	95.87 216	80.95 229	98.50 216	92.13 164	92.10 253	95.78 268

CHOSEN 280x420		93.12 167	92.72 165	94.34 219	96.71 190	87.27 264	90.29 393	97.72 134	86.61 314	91.34 219	95.29 246	84.29 168	98.41 222	93.25 144	98.94 95	97.35 217

sd_testset		93.10 168	92.45 177	95.05 177	98.09 104	89.21 214	96.89 190	97.64 144	93.18 107	91.79 208	97.28 138	75.35 310	98.65 203	88.99 234	92.84 239	97.28 220

Effi-MVS+-dtu		93.08 169	93.21 148	92.68 297	96.02 239	83.25 338	97.14 171	96.72 244	93.85 79	91.20 229	93.44 338	83.08 189	98.30 234	91.69 177	95.73 186	96.50 241

test_djsdf		93.07 170	92.76 160	94.00 236	93.49 350	88.70 227	98.22 40	97.57 153	91.42 162	90.08 253	95.55 238	82.85 196	97.92 288	94.07 126	91.58 260	95.40 288

VDDNet		93.05 171	92.07 185	96.02 129	96.84 177	90.39 173	98.08 53	95.85 289	86.22 322	95.79 113	98.46 44	67.59 364	99.19 133	94.92 108	94.85 202	98.47 146

thisisatest0530		93.03 172	92.21 183	95.49 160	97.07 160	89.11 219	97.49 135	92.19 386	90.16 210	94.09 153	96.41 190	76.43 301	99.05 161	90.38 199	95.68 188	98.31 160

EI-MVSNet		93.03 172	92.88 156	93.48 266	95.77 248	86.98 273	96.44 228	97.12 206	90.66 193	91.30 222	97.64 118	86.56 135	98.05 264	89.91 207	90.55 278	95.41 285

CLD-MVS		92.98 174	92.53 173	94.32 220	96.12 235	89.20 215	95.28 299	97.47 168	92.66 128	89.90 256	95.62 234	80.58 237	98.40 223	92.73 156	92.40 246	95.38 290
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 175	92.33 180	94.87 190	97.11 158	87.16 270	97.97 69	92.09 387	90.63 195	93.88 159	97.01 155	76.50 298	99.06 160	90.29 202	95.45 192	98.38 156

ACMM	89.79 8	92.96 175	92.50 175	94.35 217	96.30 223	88.71 226	97.58 120	97.36 191	91.40 164	90.53 236	96.65 173	79.77 253	98.75 191	91.24 186	91.64 258	95.59 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 177	92.56 170	94.10 230	96.16 230	88.26 240	97.65 110	97.46 170	91.29 166	90.12 249	97.16 146	79.05 266	98.73 194	92.25 160	91.89 256	95.31 295

BH-untuned		92.94 177	92.62 168	93.92 246	97.22 151	86.16 296	96.40 236	96.25 274	90.06 213	89.79 260	96.17 202	83.19 185	98.35 230	87.19 271	97.27 155	97.24 222

DU-MVS		92.90 179	92.04 186	95.49 160	94.95 296	92.83 80	97.16 169	98.24 47	93.02 113	90.13 247	95.71 228	83.47 180	97.85 294	91.71 175	83.93 352	95.78 268

PatchMatch-RL		92.90 179	92.02 188	95.56 154	98.19 98	90.80 158	95.27 301	97.18 201	87.96 280	91.86 207	95.68 231	80.44 240	98.99 166	84.01 316	97.54 142	96.89 232

PMMVS		92.86 181	92.34 179	94.42 215	94.92 299	86.73 279	94.53 323	96.38 266	84.78 345	94.27 148	95.12 257	83.13 188	98.40 223	91.47 181	96.49 173	98.12 173

OpenMVS	89.19 12	92.86 181	91.68 200	96.40 101	95.34 271	92.73 84	98.27 32	98.12 70	84.86 343	85.78 344	97.75 107	78.89 273	99.74 44	87.50 265	98.65 105	96.73 236

Test_1112_low_res		92.84 183	91.84 194	95.85 138	97.04 166	89.97 184	95.53 288	96.64 252	85.38 333	89.65 265	95.18 253	85.86 147	99.10 148	87.70 256	93.58 235	98.49 143

baseline1		92.82 184	91.90 192	95.55 156	97.20 153	90.77 160	97.19 166	94.58 347	92.20 138	92.36 190	96.34 194	84.16 170	98.21 240	89.20 230	83.90 355	97.68 199

1314		92.81 185	92.03 187	95.14 173	95.33 274	89.52 199	96.04 259	97.44 179	87.72 292	86.25 341	95.33 245	83.84 174	98.79 185	89.26 226	97.05 161	97.11 225

DP-MVS		92.76 186	91.51 208	96.52 88	98.77 56	90.99 150	97.38 147	96.08 281	82.38 369	89.29 277	97.87 96	83.77 175	99.69 55	81.37 343	96.69 169	98.89 112

test_fmvs1_n		92.73 187	92.88 156	92.29 305	96.08 238	81.05 361	97.98 63	97.08 211	90.72 188	96.79 68	98.18 73	63.07 386	98.45 220	97.62 26	98.42 117	97.36 215

BH-RMVSNet		92.72 188	91.97 190	94.97 185	97.16 155	87.99 249	96.15 255	95.60 303	90.62 196	91.87 206	97.15 148	78.41 279	98.57 212	83.16 323	97.60 141	98.36 158

ACMP	89.59 10	92.62 189	92.14 184	94.05 233	96.40 217	88.20 243	97.36 148	97.25 200	91.52 157	88.30 302	96.64 174	78.46 278	98.72 197	91.86 171	91.48 262	95.23 302
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 190	92.52 174	92.44 299	96.82 181	81.89 354	96.92 188	93.71 370	92.41 133	84.30 357	94.60 279	85.08 156	97.03 349	91.51 179	97.36 149	98.40 154

TranMVSNet+NR-MVSNet		92.50 190	91.63 201	95.14 173	94.76 307	92.07 106	97.53 128	98.11 73	92.90 123	89.56 268	96.12 205	83.16 186	97.60 319	89.30 224	83.20 361	95.75 272

thres600view7		92.49 192	91.60 202	95.18 171	97.91 119	89.47 200	97.65 110	94.66 344	92.18 142	93.33 171	94.91 263	78.06 286	99.10 148	81.61 337	94.06 225	96.98 227

thres100view900		92.43 193	91.58 203	94.98 183	97.92 118	89.37 206	97.71 103	94.66 344	92.20 138	93.31 172	94.90 264	78.06 286	99.08 154	81.40 340	94.08 221	96.48 242

jajsoiax		92.42 194	91.89 193	94.03 235	93.33 356	88.50 234	97.73 98	97.53 159	92.00 147	88.85 288	96.50 186	75.62 308	98.11 251	93.88 133	91.56 261	95.48 280

thres400		92.42 194	91.52 206	95.12 175	97.85 122	89.29 210	97.41 140	94.88 338	92.19 140	93.27 174	94.46 289	78.17 282	99.08 154	81.40 340	94.08 221	96.98 227

tfpn200view9		92.38 196	91.52 206	94.95 187	97.85 122	89.29 210	97.41 140	94.88 338	92.19 140	93.27 174	94.46 289	78.17 282	99.08 154	81.40 340	94.08 221	96.48 242

test_vis1_n		92.37 197	92.26 182	92.72 294	94.75 308	82.64 343	98.02 59	96.80 241	91.18 173	97.77 41	97.93 91	58.02 395	98.29 235	97.63 25	98.21 124	97.23 223

WR-MVS		92.34 198	91.53 205	94.77 198	95.13 289	90.83 157	96.40 236	97.98 103	91.88 149	89.29 277	95.54 239	82.50 204	97.80 300	89.79 211	85.27 331	95.69 275

NR-MVSNet		92.34 198	91.27 216	95.53 157	94.95 296	93.05 75	97.39 145	98.07 82	92.65 129	84.46 355	95.71 228	85.00 157	97.77 304	89.71 212	83.52 358	95.78 268

mvs_tets		92.31 200	91.76 196	93.94 243	93.41 353	88.29 238	97.63 116	97.53 159	92.04 145	88.76 291	96.45 188	74.62 316	98.09 256	93.91 131	91.48 262	95.45 284

TAPA-MVS	90.10 7	92.30 201	91.22 219	95.56 154	98.33 83	89.60 193	96.79 199	97.65 142	81.83 373	91.52 214	97.23 143	87.94 111	98.91 174	71.31 395	98.37 118	98.17 170
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 202	91.30 214	95.25 169	96.60 195	88.90 223	94.36 331	92.32 385	87.92 281	93.43 169	94.57 280	77.28 293	99.00 165	89.42 221	95.86 183	97.86 189

Fast-Effi-MVS+-dtu		92.29 202	91.99 189	93.21 277	95.27 278	85.52 303	97.03 176	96.63 255	92.09 143	89.11 283	95.14 255	80.33 243	98.08 257	87.54 264	94.74 208	96.03 258

IterMVS-LS		92.29 202	91.94 191	93.34 271	96.25 224	86.97 274	96.57 226	97.05 216	90.67 191	89.50 271	94.80 270	86.59 134	97.64 314	89.91 207	86.11 321	95.40 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 205	91.74 199	93.73 253	97.77 126	83.69 335	92.88 373	96.72 244	87.91 282	93.00 178	94.86 266	78.51 277	99.05 161	86.53 279	97.45 147	98.47 146

VPNet		92.23 206	91.31 213	94.99 181	95.56 256	90.96 152	97.22 164	97.86 118	92.96 120	90.96 230	96.62 181	75.06 311	98.20 241	91.90 168	83.65 357	95.80 266

thres200		92.23 206	91.39 209	94.75 200	97.61 138	89.03 220	96.60 222	95.09 328	92.08 144	93.28 173	94.00 315	78.39 280	99.04 164	81.26 346	94.18 217	96.19 249

anonymousdsp		92.16 208	91.55 204	93.97 239	92.58 370	89.55 196	97.51 129	97.42 183	89.42 233	88.40 298	94.84 267	80.66 236	97.88 293	91.87 170	91.28 266	94.48 341

XXY-MVS		92.16 208	91.23 218	94.95 187	94.75 308	90.94 153	97.47 136	97.43 182	89.14 240	88.90 285	96.43 189	79.71 254	98.24 237	89.56 217	87.68 305	95.67 276

BH-w/o		92.14 210	91.75 197	93.31 272	96.99 171	85.73 300	95.67 279	95.69 298	88.73 260	89.26 279	94.82 269	82.97 193	98.07 261	85.26 302	96.32 176	96.13 254

Anonymous202405211		92.07 211	90.83 234	95.76 140	98.19 98	88.75 225	97.58 120	95.00 331	86.00 325	93.64 162	97.45 129	66.24 376	99.53 94	90.68 196	92.71 242	99.01 93

FE-MVS		92.05 212	91.05 224	95.08 176	96.83 179	87.93 250	93.91 349	95.70 296	86.30 319	94.15 152	94.97 259	76.59 297	99.21 131	84.10 314	96.86 162	98.09 177

WR-MVS_H		92.00 213	91.35 210	93.95 241	95.09 291	89.47 200	98.04 58	98.68 13	91.46 160	88.34 300	94.68 275	85.86 147	97.56 321	85.77 295	84.24 349	94.82 326

Anonymous20240529		91.98 214	90.73 240	95.73 145	98.14 102	89.40 204	97.99 62	97.72 134	79.63 387	93.54 165	97.41 133	69.94 349	99.56 88	91.04 190	91.11 269	98.22 164

MonoMVSNet		91.92 215	91.77 195	92.37 301	92.94 362	83.11 339	97.09 174	95.55 306	92.91 122	90.85 232	94.55 281	81.27 227	96.52 361	93.01 153	87.76 304	97.47 211

PatchmatchNet		91.91 216	91.35 210	93.59 261	95.38 266	84.11 328	93.15 368	95.39 311	89.54 227	92.10 200	93.68 328	82.82 197	98.13 247	84.81 306	95.32 194	98.52 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 217	91.02 225	94.53 210	96.54 203	86.55 286	95.86 269	95.64 302	91.77 151	91.89 205	93.47 337	69.94 349	98.86 177	90.23 203	93.86 228	98.18 167

CP-MVSNet		91.89 218	91.24 217	93.82 249	95.05 292	88.57 230	97.82 88	98.19 58	91.70 153	88.21 306	95.76 226	81.96 215	97.52 327	87.86 250	84.65 340	95.37 291

SCA		91.84 219	91.18 221	93.83 248	95.59 254	84.95 318	94.72 317	95.58 305	90.82 183	92.25 195	93.69 326	75.80 305	98.10 252	86.20 285	95.98 179	98.45 148

FMVSNet3		91.78 220	90.69 243	95.03 179	96.53 205	92.27 99	97.02 178	96.93 227	89.79 222	89.35 274	94.65 277	77.01 294	97.47 330	86.12 288	88.82 293	95.35 292

AUN-MVS		91.76 221	90.75 238	94.81 193	97.00 170	88.57 230	96.65 214	96.49 261	89.63 224	92.15 197	96.12 205	78.66 275	98.50 216	90.83 191	79.18 378	97.36 215

X-MVStestdata		91.71 222	89.67 286	97.81 28	99.38 14	94.03 50	98.59 12	98.20 53	94.85 37	96.59 80	32.69 419	91.70 52	99.80 33	95.66 86	99.40 55	99.62 20

MVS		91.71 222	90.44 250	95.51 158	95.20 284	91.59 124	96.04 259	97.45 175	73.44 403	87.36 323	95.60 235	85.42 152	99.10 148	85.97 292	97.46 143	95.83 264

EPNet_dtu		91.71 222	91.28 215	92.99 283	93.76 341	83.71 334	96.69 210	95.28 318	93.15 109	87.02 331	95.95 213	83.37 183	97.38 338	79.46 358	96.84 163	97.88 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 225	90.75 238	94.47 211	96.53 205	86.56 285	95.76 276	94.51 350	91.10 178	91.24 227	93.59 332	68.59 358	98.86 177	91.10 188	94.29 214	98.00 182

baseline2		91.63 226	90.86 230	93.94 243	94.33 325	86.32 290	95.92 266	91.64 391	89.37 234	86.94 334	94.69 274	81.62 222	98.69 199	88.64 241	94.57 211	96.81 234

testing99		91.62 227	90.72 241	94.32 220	96.48 211	86.11 297	95.81 272	94.76 342	91.55 156	91.75 210	93.44 338	68.55 359	98.82 181	90.43 197	93.69 229	98.04 180

test2506		91.60 228	90.78 235	94.04 234	97.66 132	83.81 331	98.27 32	75.53 420	93.43 95	95.23 127	98.21 70	67.21 367	99.07 158	93.01 153	98.49 112	99.25 71

miper_ehance_all_eth		91.59 229	91.13 222	92.97 284	95.55 257	86.57 284	94.47 325	96.88 235	87.77 289	88.88 287	94.01 314	86.22 141	97.54 323	89.49 218	86.93 313	94.79 331

v2v482		91.59 229	90.85 232	93.80 250	93.87 338	88.17 245	96.94 187	96.88 235	89.54 227	89.53 269	94.90 264	81.70 221	98.02 269	89.25 227	85.04 337	95.20 303

V42		91.58 231	90.87 229	93.73 253	94.05 333	88.50 234	97.32 153	96.97 223	88.80 258	89.71 261	94.33 296	82.54 203	98.05 264	89.01 233	85.07 335	94.64 339

PCF-MVS	89.48 11	91.56 232	89.95 274	96.36 106	96.60 195	92.52 90	92.51 378	97.26 198	79.41 388	88.90 285	96.56 183	84.04 173	99.55 90	77.01 372	97.30 154	97.01 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 233	90.76 236	93.94 243	96.52 207	85.06 314	95.22 304	94.54 348	90.47 203	91.98 203	92.71 348	72.02 331	98.74 193	88.10 246	95.26 196	98.01 181

PS-CasMVS		91.55 233	90.84 233	93.69 257	94.96 295	88.28 239	97.84 84	98.24 47	91.46 160	88.04 310	95.80 221	79.67 255	97.48 329	87.02 275	84.54 346	95.31 295

miper_enhance_ethall		91.54 235	91.01 226	93.15 278	95.35 270	87.07 272	93.97 344	96.90 232	86.79 311	89.17 281	93.43 341	86.55 136	97.64 314	89.97 206	86.93 313	94.74 335

PAPM		91.52 236	90.30 256	95.20 170	95.30 277	89.83 188	93.38 364	96.85 238	86.26 321	88.59 294	95.80 221	84.88 158	98.15 246	75.67 377	95.93 181	97.63 200

ET-MVSNet_ETH3D		91.49 237	90.11 266	95.63 150	96.40 217	91.57 126	95.34 295	93.48 372	90.60 199	75.58 395	95.49 241	80.08 247	96.79 358	94.25 124	89.76 286	98.52 138

TR-MVS		91.48 238	90.59 246	94.16 228	96.40 217	87.33 261	95.67 279	95.34 317	87.68 293	91.46 216	95.52 240	76.77 296	98.35 230	82.85 328	93.61 233	96.79 235

tpmrst		91.44 239	91.32 212	91.79 321	95.15 287	79.20 384	93.42 363	95.37 313	88.55 265	93.49 167	93.67 329	82.49 205	98.27 236	90.41 198	89.34 290	97.90 186

test-LLR		91.42 240	91.19 220	92.12 309	94.59 315	80.66 364	94.29 336	92.98 377	91.11 176	90.76 234	92.37 356	79.02 268	98.07 261	88.81 237	96.74 166	97.63 200

MSDG		91.42 240	90.24 260	94.96 186	97.15 157	88.91 222	93.69 356	96.32 268	85.72 329	86.93 335	96.47 187	80.24 244	98.98 167	80.57 349	95.05 201	96.98 227

c3_l		91.38 242	90.89 228	92.88 288	95.58 255	86.30 291	94.68 318	96.84 239	88.17 275	88.83 290	94.23 304	85.65 150	97.47 330	89.36 222	84.63 341	94.89 321

GA-MVS		91.38 242	90.31 255	94.59 203	94.65 313	87.62 259	94.34 332	96.19 278	90.73 187	90.35 240	93.83 319	71.84 333	97.96 280	87.22 270	93.61 233	98.21 165

v1144		91.37 244	90.60 245	93.68 258	93.89 337	88.23 242	96.84 195	97.03 220	88.37 270	89.69 263	94.39 291	82.04 213	97.98 273	87.80 252	85.37 328	94.84 323

GBi-Net		91.35 245	90.27 258	94.59 203	96.51 208	91.18 144	97.50 130	96.93 227	88.82 255	89.35 274	94.51 284	73.87 320	97.29 342	86.12 288	88.82 293	95.31 295

test1		91.35 245	90.27 258	94.59 203	96.51 208	91.18 144	97.50 130	96.93 227	88.82 255	89.35 274	94.51 284	73.87 320	97.29 342	86.12 288	88.82 293	95.31 295

UniMVSNet_ETH3D		91.34 247	90.22 263	94.68 201	94.86 303	87.86 254	97.23 163	97.46 170	87.99 279	89.90 256	96.92 159	66.35 374	98.23 238	90.30 201	90.99 272	97.96 183

FMVSNet2		91.31 248	90.08 267	94.99 181	96.51 208	92.21 101	97.41 140	96.95 225	88.82 255	88.62 293	94.75 272	73.87 320	97.42 335	85.20 303	88.55 298	95.35 292

reproduce_monomvs		91.30 249	91.10 223	91.92 313	96.82 181	82.48 347	97.01 181	97.49 164	94.64 53	88.35 299	95.27 249	70.53 342	98.10 252	95.20 100	84.60 343	95.19 306

D2MVS		91.30 249	90.95 227	92.35 302	94.71 311	85.52 303	96.18 254	98.21 51	88.89 251	86.60 338	93.82 321	79.92 251	97.95 284	89.29 225	90.95 273	93.56 359

v8		91.29 251	90.53 249	93.57 263	94.15 329	88.12 247	97.34 150	97.06 215	88.99 246	88.32 301	94.26 303	83.08 189	98.01 270	87.62 262	83.92 354	94.57 340

CVMVSNet		91.23 252	91.75 197	89.67 359	95.77 248	74.69 395	96.44 228	94.88 338	85.81 327	92.18 196	97.64 118	79.07 265	95.58 378	88.06 247	95.86 183	98.74 123

cl22		91.21 253	90.56 248	93.14 279	96.09 237	86.80 276	94.41 329	96.58 258	87.80 287	88.58 295	93.99 316	80.85 234	97.62 317	89.87 209	86.93 313	94.99 312

PEN-MVS		91.20 254	90.44 250	93.48 266	94.49 319	87.91 253	97.76 94	98.18 60	91.29 166	87.78 314	95.74 227	80.35 242	97.33 340	85.46 299	82.96 362	95.19 306

Baseline_NR-MVSNet		91.20 254	90.62 244	92.95 285	93.83 339	88.03 248	97.01 181	95.12 327	88.42 269	89.70 262	95.13 256	83.47 180	97.44 333	89.66 215	83.24 360	93.37 363

cascas		91.20 254	90.08 267	94.58 207	94.97 294	89.16 218	93.65 358	97.59 151	79.90 386	89.40 272	92.92 346	75.36 309	98.36 229	92.14 163	94.75 207	96.23 246

CostFormer		91.18 257	90.70 242	92.62 298	94.84 304	81.76 355	94.09 342	94.43 351	84.15 351	92.72 185	93.77 323	79.43 259	98.20 241	90.70 195	92.18 251	97.90 186

tt0805		91.09 258	90.07 270	94.16 228	95.61 253	88.31 237	97.56 123	96.51 260	89.56 226	89.17 281	95.64 233	67.08 371	98.38 228	91.07 189	88.44 299	95.80 266

v1192		91.07 259	90.23 261	93.58 262	93.70 342	87.82 256	96.73 204	97.07 213	87.77 289	89.58 266	94.32 298	80.90 233	97.97 276	86.52 280	85.48 326	94.95 313

v144192		91.06 260	90.28 257	93.39 269	93.66 345	87.23 267	96.83 196	97.07 213	87.43 298	89.69 263	94.28 300	81.48 223	98.00 271	87.18 272	84.92 339	94.93 317

v10		91.04 261	90.23 261	93.49 265	94.12 330	88.16 246	97.32 153	97.08 211	88.26 273	88.29 303	94.22 306	82.17 212	97.97 276	86.45 282	84.12 350	94.33 347

eth_miper_zixun_eth		91.02 262	90.59 246	92.34 304	95.33 274	84.35 324	94.10 341	96.90 232	88.56 264	88.84 289	94.33 296	84.08 171	97.60 319	88.77 239	84.37 348	95.06 310

v148		90.99 263	90.38 252	92.81 291	93.83 339	85.80 299	96.78 201	96.68 249	89.45 232	88.75 292	93.93 318	82.96 194	97.82 298	87.83 251	83.25 359	94.80 329

LTVRE_ROB	88.41 13	90.99 263	89.92 276	94.19 226	96.18 228	89.55 196	96.31 244	97.09 210	87.88 283	85.67 345	95.91 215	78.79 274	98.57 212	81.50 338	89.98 283	94.44 344
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		90.97 265	90.33 253	92.88 288	95.36 269	86.19 295	94.46 327	96.63 255	87.82 285	88.18 307	94.23 304	82.99 191	97.53 325	87.72 253	85.57 325	94.93 317

cl____		90.96 266	90.32 254	92.89 287	95.37 268	86.21 294	94.46 327	96.64 252	87.82 285	88.15 308	94.18 307	82.98 192	97.54 323	87.70 256	85.59 324	94.92 319

pmmvs4		90.93 267	89.85 278	94.17 227	93.34 355	90.79 159	94.60 320	96.02 282	84.62 346	87.45 319	95.15 254	81.88 218	97.45 332	87.70 256	87.87 303	94.27 351

XVG-ACMP-BASELINE		90.93 267	90.21 264	93.09 280	94.31 327	85.89 298	95.33 296	97.26 198	91.06 179	89.38 273	95.44 243	68.61 357	98.60 208	89.46 219	91.05 270	94.79 331

v1921920		90.85 269	90.03 272	93.29 273	93.55 346	86.96 275	96.74 203	97.04 218	87.36 300	89.52 270	94.34 295	80.23 245	97.97 276	86.27 283	85.21 332	94.94 315

CR-MVSNet		90.82 270	89.77 282	93.95 241	94.45 321	87.19 268	90.23 394	95.68 300	86.89 309	92.40 187	92.36 359	80.91 231	97.05 348	81.09 347	93.95 226	97.60 205

v7n		90.76 271	89.86 277	93.45 268	93.54 347	87.60 260	97.70 106	97.37 189	88.85 252	87.65 316	94.08 312	81.08 228	98.10 252	84.68 308	83.79 356	94.66 338

RPSCF		90.75 272	90.86 230	90.42 350	96.84 177	76.29 393	95.61 285	96.34 267	83.89 354	91.38 217	97.87 96	76.45 299	98.78 186	87.16 273	92.23 248	96.20 248

MVP-Stereo		90.74 273	90.08 267	92.71 295	93.19 358	88.20 243	95.86 269	96.27 272	86.07 324	84.86 353	94.76 271	77.84 289	97.75 306	83.88 320	98.01 131	92.17 384
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 274	89.65 288	93.96 240	94.29 328	89.63 191	97.79 92	96.82 240	89.07 242	86.12 343	95.48 242	78.61 276	97.78 302	86.97 276	81.67 367	94.46 342

v1240		90.70 275	89.85 278	93.23 275	93.51 349	86.80 276	96.61 220	97.02 221	87.16 305	89.58 266	94.31 299	79.55 258	97.98 273	85.52 298	85.44 327	94.90 320

EPMVS		90.70 275	89.81 280	93.37 270	94.73 310	84.21 326	93.67 357	88.02 406	89.50 229	92.38 189	93.49 335	77.82 290	97.78 302	86.03 291	92.68 243	98.11 176

WBMVS		90.69 277	89.99 273	92.81 291	96.48 211	85.00 315	95.21 306	96.30 270	89.46 231	89.04 284	94.05 313	72.45 330	97.82 298	89.46 219	87.41 310	95.61 277

Anonymous20231211		90.63 278	89.42 293	94.27 225	98.24 90	89.19 217	98.05 57	97.89 110	79.95 385	88.25 305	94.96 260	72.56 329	98.13 247	89.70 213	85.14 333	95.49 279

DTE-MVSNet		90.56 279	89.75 284	93.01 282	93.95 334	87.25 265	97.64 114	97.65 142	90.74 186	87.12 326	95.68 231	79.97 250	97.00 352	83.33 322	81.66 368	94.78 333

ACMH	87.59 16	90.53 280	89.42 293	93.87 247	96.21 225	87.92 251	97.24 159	96.94 226	88.45 268	83.91 365	96.27 197	71.92 332	98.62 207	84.43 311	89.43 289	95.05 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 281	89.14 300	94.67 202	96.81 183	87.85 255	95.91 267	93.97 364	89.71 223	92.34 193	92.48 354	65.41 381	97.96 280	81.37 343	94.27 215	98.21 165

OurMVSNet-221017-0		90.51 282	90.19 265	91.44 330	93.41 353	81.25 358	96.98 184	96.28 271	91.68 154	86.55 339	96.30 195	74.20 319	97.98 273	88.96 235	87.40 311	95.09 308

miper_lstm_enhance		90.50 283	90.06 271	91.83 318	95.33 274	83.74 332	93.86 350	96.70 248	87.56 296	87.79 313	93.81 322	83.45 182	96.92 354	87.39 266	84.62 342	94.82 326

COLMAP_ROB	87.81 15	90.40 284	89.28 296	93.79 251	97.95 115	87.13 271	96.92 188	95.89 288	82.83 366	86.88 337	97.18 145	73.77 323	99.29 125	78.44 363	93.62 232	94.95 313
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 285	88.96 302	94.35 217	96.54 203	87.29 262	95.50 289	93.84 368	90.97 181	91.75 210	92.96 345	62.18 391	98.00 271	82.86 326	94.08 221	97.76 195

IterMVS-SCA-FT		90.31 285	89.81 280	91.82 319	95.52 258	84.20 327	94.30 335	96.15 279	90.61 197	87.39 322	94.27 301	75.80 305	96.44 362	87.34 267	86.88 317	94.82 326

MS-PatchMatch		90.27 287	89.77 282	91.78 322	94.33 325	84.72 321	95.55 286	96.73 243	86.17 323	86.36 340	95.28 248	71.28 337	97.80 300	84.09 315	98.14 128	92.81 369

tpm		90.25 288	89.74 285	91.76 324	93.92 335	79.73 378	93.98 343	93.54 371	88.28 272	91.99 202	93.25 342	77.51 292	97.44 333	87.30 269	87.94 302	98.12 173

AllTest		90.23 289	88.98 301	93.98 237	97.94 116	86.64 280	96.51 227	95.54 307	85.38 333	85.49 347	96.77 165	70.28 344	99.15 141	80.02 353	92.87 237	96.15 252

dmvs_re		90.21 290	89.50 291	92.35 302	95.47 263	85.15 311	95.70 278	94.37 355	90.94 182	88.42 297	93.57 333	74.63 315	95.67 375	82.80 329	89.57 288	96.22 247

ACMH+	87.92 14	90.20 291	89.18 298	93.25 274	96.48 211	86.45 288	96.99 183	96.68 249	88.83 254	84.79 354	96.22 199	70.16 346	98.53 214	84.42 312	88.04 301	94.77 334

test-mter		90.19 292	89.54 290	92.12 309	94.59 315	80.66 364	94.29 336	92.98 377	87.68 293	90.76 234	92.37 356	67.67 363	98.07 261	88.81 237	96.74 166	97.63 200

IterMVS		90.15 293	89.67 286	91.61 326	95.48 260	83.72 333	94.33 333	96.12 280	89.99 214	87.31 325	94.15 309	75.78 307	96.27 365	86.97 276	86.89 316	94.83 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 294	89.42 293	91.97 312	94.41 323	80.62 366	94.29 336	91.97 389	87.28 303	90.44 238	92.47 355	68.79 355	97.67 311	88.50 243	96.60 171	97.61 204

tpm2		89.96 295	89.21 297	92.23 308	94.91 301	81.25 358	93.78 352	94.42 352	80.62 383	91.56 213	93.44 338	76.44 300	97.94 285	85.60 297	92.08 255	97.49 209

UWE-MVS		89.91 296	89.48 292	91.21 334	95.88 241	78.23 389	94.91 314	90.26 399	89.11 241	92.35 192	94.52 283	68.76 356	97.96 280	83.95 318	95.59 190	97.42 213

IB-MVS	87.33 17	89.91 296	88.28 312	94.79 197	95.26 281	87.70 258	95.12 309	93.95 365	89.35 235	87.03 330	92.49 353	70.74 341	99.19 133	89.18 231	81.37 369	97.49 209
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		89.89 298	88.68 307	93.53 264	95.86 242	84.89 319	90.93 389	95.07 329	83.23 364	91.28 225	91.81 368	79.01 270	97.85 294	79.52 355	91.39 264	97.84 190

WB-MVSnew		89.88 299	89.56 289	90.82 342	94.57 318	83.06 340	95.65 283	92.85 379	87.86 284	90.83 233	94.10 310	79.66 256	96.88 355	76.34 373	94.19 216	92.54 375

FMVSNet1		89.88 299	88.31 311	94.59 203	95.41 264	91.18 144	97.50 130	96.93 227	86.62 313	87.41 321	94.51 284	65.94 379	97.29 342	83.04 325	87.43 308	95.31 295

pmmvs5		89.86 301	88.87 305	92.82 290	92.86 363	86.23 293	96.26 247	95.39 311	84.24 350	87.12 326	94.51 284	74.27 318	97.36 339	87.61 263	87.57 306	94.86 322

tpmvs		89.83 302	89.15 299	91.89 316	94.92 299	80.30 371	93.11 369	95.46 310	86.28 320	88.08 309	92.65 349	80.44 240	98.52 215	81.47 339	89.92 284	96.84 233

test_fmvs2		89.77 303	89.93 275	89.31 365	93.68 344	76.37 392	97.64 114	95.90 286	89.84 220	91.49 215	96.26 198	58.77 394	97.10 346	94.65 118	91.13 268	94.46 342

mmtdpeth		89.70 304	88.96 302	91.90 315	95.84 247	84.42 323	97.46 138	95.53 309	90.27 207	94.46 145	90.50 376	69.74 352	98.95 168	97.39 36	69.48 401	92.34 378

tfpnnormal		89.70 304	88.40 310	93.60 260	95.15 287	90.10 177	97.56 123	98.16 64	87.28 303	86.16 342	94.63 278	77.57 291	98.05 264	74.48 381	84.59 344	92.65 372

ADS-MVSNet2		89.45 306	88.59 308	92.03 311	95.86 242	82.26 351	90.93 389	94.32 358	83.23 364	91.28 225	91.81 368	79.01 270	95.99 367	79.52 355	91.39 264	97.84 190

Patchmatch-test		89.42 307	87.99 314	93.70 256	95.27 278	85.11 312	88.98 401	94.37 355	81.11 377	87.10 329	93.69 326	82.28 209	97.50 328	74.37 383	94.76 206	98.48 145

test0.0.03 1		89.37 308	88.70 306	91.41 331	92.47 372	85.63 301	95.22 304	92.70 382	91.11 176	86.91 336	93.65 330	79.02 268	93.19 400	78.00 365	89.18 291	95.41 285

SixPastTwentyTwo		89.15 309	88.54 309	90.98 338	93.49 350	80.28 372	96.70 208	94.70 343	90.78 184	84.15 360	95.57 236	71.78 334	97.71 309	84.63 309	85.07 335	94.94 315

RPMNet		88.98 310	87.05 324	94.77 198	94.45 321	87.19 268	90.23 394	98.03 94	77.87 395	92.40 187	87.55 399	80.17 246	99.51 99	68.84 400	93.95 226	97.60 205

TransMVSNet (Re)		88.94 311	87.56 317	93.08 281	94.35 324	88.45 236	97.73 98	95.23 322	87.47 297	84.26 358	95.29 246	79.86 252	97.33 340	79.44 359	74.44 392	93.45 362

USDC		88.94 311	87.83 316	92.27 306	94.66 312	84.96 317	93.86 350	95.90 286	87.34 301	83.40 367	95.56 237	67.43 365	98.19 243	82.64 333	89.67 287	93.66 358

dp		88.90 313	88.26 313	90.81 343	94.58 317	76.62 391	92.85 374	94.93 335	85.12 339	90.07 254	93.07 343	75.81 304	98.12 250	80.53 350	87.42 309	97.71 197

PatchT		88.87 314	87.42 318	93.22 276	94.08 332	85.10 313	89.51 399	94.64 346	81.92 372	92.36 190	88.15 395	80.05 248	97.01 351	72.43 391	93.65 231	97.54 208

our_test_3		88.78 315	87.98 315	91.20 336	92.45 373	82.53 345	93.61 360	95.69 298	85.77 328	84.88 352	93.71 324	79.99 249	96.78 359	79.47 357	86.24 318	94.28 350

EU-MVSNet		88.72 316	88.90 304	88.20 369	93.15 359	74.21 396	96.63 219	94.22 360	85.18 337	87.32 324	95.97 211	76.16 302	94.98 384	85.27 301	86.17 319	95.41 285

Patchmtry		88.64 317	87.25 320	92.78 293	94.09 331	86.64 280	89.82 398	95.68 300	80.81 381	87.63 317	92.36 359	80.91 231	97.03 349	78.86 361	85.12 334	94.67 337

MIMVSNet		88.50 318	86.76 328	93.72 255	94.84 304	87.77 257	91.39 384	94.05 361	86.41 317	87.99 311	92.59 352	63.27 385	95.82 372	77.44 366	92.84 239	97.57 207

tpm cat1		88.36 319	87.21 322	91.81 320	95.13 289	80.55 367	92.58 377	95.70 296	74.97 399	87.45 319	91.96 366	78.01 288	98.17 245	80.39 351	88.74 296	96.72 237

ppachtmachnet_test		88.35 320	87.29 319	91.53 327	92.45 373	83.57 336	93.75 353	95.97 283	84.28 349	85.32 350	94.18 307	79.00 272	96.93 353	75.71 376	84.99 338	94.10 352

JIA-IIPM		88.26 321	87.04 325	91.91 314	93.52 348	81.42 357	89.38 400	94.38 354	80.84 380	90.93 231	80.74 407	79.22 262	97.92 288	82.76 330	91.62 259	96.38 245

testgi		87.97 322	87.21 322	90.24 353	92.86 363	80.76 362	96.67 213	94.97 333	91.74 152	85.52 346	95.83 219	62.66 389	94.47 388	76.25 374	88.36 300	95.48 280

LF4IMVS		87.94 323	87.25 320	89.98 356	92.38 375	80.05 376	94.38 330	95.25 321	87.59 295	84.34 356	94.74 273	64.31 383	97.66 313	84.83 305	87.45 307	92.23 381

gg-mvs-nofinetune		87.82 324	85.61 336	94.44 213	94.46 320	89.27 213	91.21 388	84.61 414	80.88 379	89.89 258	74.98 410	71.50 335	97.53 325	85.75 296	97.21 157	96.51 240

pmmvs6		87.81 325	86.19 332	92.69 296	91.32 380	86.30 291	97.34 150	96.41 265	80.59 384	84.05 364	94.37 293	67.37 366	97.67 311	84.75 307	79.51 377	94.09 354

testing3		87.67 326	86.88 327	90.05 355	96.14 233	80.71 363	97.10 173	92.85 379	90.15 211	87.54 318	94.55 281	55.70 400	94.10 391	73.77 387	94.10 220	95.35 292

K. test v3		87.64 327	86.75 329	90.32 352	93.02 361	79.48 382	96.61 220	92.08 388	90.66 193	80.25 384	94.09 311	67.21 367	96.65 360	85.96 293	80.83 371	94.83 324

Patchmatch-RL test		87.38 328	86.24 331	90.81 343	88.74 398	78.40 388	88.12 406	93.17 375	87.11 306	82.17 375	89.29 387	81.95 216	95.60 377	88.64 241	77.02 383	98.41 153

FMVSNet5		87.29 329	85.79 335	91.78 322	94.80 306	87.28 263	95.49 290	95.28 318	84.09 352	83.85 366	91.82 367	62.95 387	94.17 390	78.48 362	85.34 330	93.91 356

myMVS_eth3d		87.18 330	86.38 330	89.58 360	95.16 285	79.53 379	95.00 311	93.93 366	88.55 265	86.96 332	91.99 364	56.23 399	94.00 392	75.47 379	94.11 218	95.20 303

Syy-MVS		87.13 331	87.02 326	87.47 372	95.16 285	73.21 400	95.00 311	93.93 366	88.55 265	86.96 332	91.99 364	75.90 303	94.00 392	61.59 406	94.11 218	95.20 303

Anonymous20231206		87.09 332	86.14 333	89.93 357	91.22 381	80.35 369	96.11 256	95.35 314	83.57 361	84.16 359	93.02 344	73.54 325	95.61 376	72.16 392	86.14 320	93.84 357

EG-PatchMatch MVS		87.02 333	85.44 337	91.76 324	92.67 367	85.00 315	96.08 258	96.45 263	83.41 363	79.52 386	93.49 335	57.10 397	97.72 308	79.34 360	90.87 275	92.56 374

TinyColmap		86.82 334	85.35 340	91.21 334	94.91 301	82.99 341	93.94 346	94.02 363	83.58 360	81.56 376	94.68 275	62.34 390	98.13 247	75.78 375	87.35 312	92.52 376

mvs5depth		86.53 335	85.08 342	90.87 340	88.74 398	82.52 346	91.91 382	94.23 359	86.35 318	87.11 328	93.70 325	66.52 372	97.76 305	81.37 343	75.80 388	92.31 380

TDRefinement		86.53 335	84.76 347	91.85 317	82.23 413	84.25 325	96.38 238	95.35 314	84.97 342	84.09 362	94.94 261	65.76 380	98.34 233	84.60 310	74.52 391	92.97 366

test_0402		86.46 337	84.79 346	91.45 329	95.02 293	85.55 302	96.29 246	94.89 337	80.90 378	82.21 374	93.97 317	68.21 362	97.29 342	62.98 404	88.68 297	91.51 389

Anonymous20240521		86.42 338	85.44 337	89.34 364	90.33 385	79.79 377	96.73 204	95.92 284	83.71 359	83.25 369	91.36 372	63.92 384	96.01 366	78.39 364	85.36 329	92.22 382

DSMNet-mixed		86.34 339	86.12 334	87.00 376	89.88 389	70.43 402	94.93 313	90.08 400	77.97 394	85.42 349	92.78 347	74.44 317	93.96 394	74.43 382	95.14 197	96.62 238

CL-MVSNet_self_test		86.31 340	85.15 341	89.80 358	88.83 396	81.74 356	93.93 347	96.22 275	86.67 312	85.03 351	90.80 375	78.09 285	94.50 386	74.92 380	71.86 397	93.15 365

pmmvs-eth3d		86.22 341	84.45 349	91.53 327	88.34 400	87.25 265	94.47 325	95.01 330	83.47 362	79.51 387	89.61 385	69.75 351	95.71 373	83.13 324	76.73 386	91.64 386

test_vis1_rt		86.16 342	85.06 343	89.46 361	93.47 352	80.46 368	96.41 232	86.61 411	85.22 336	79.15 388	88.64 390	52.41 403	97.06 347	93.08 148	90.57 277	90.87 394

test20.03		86.14 343	85.40 339	88.35 367	90.12 386	80.06 375	95.90 268	95.20 323	88.59 261	81.29 377	93.62 331	71.43 336	92.65 401	71.26 396	81.17 370	92.34 378

UnsupCasMVSNet_eth		85.99 344	84.45 349	90.62 347	89.97 388	82.40 350	93.62 359	97.37 189	89.86 217	78.59 390	92.37 356	65.25 382	95.35 382	82.27 335	70.75 398	94.10 352

KD-MVS_self_test		85.95 345	84.95 344	88.96 366	89.55 392	79.11 385	95.13 308	96.42 264	85.91 326	84.07 363	90.48 377	70.03 348	94.82 385	80.04 352	72.94 395	92.94 367

ttmdpeth		85.91 346	84.76 347	89.36 363	89.14 393	80.25 373	95.66 282	93.16 376	83.77 357	83.39 368	95.26 250	66.24 376	95.26 383	80.65 348	75.57 389	92.57 373

YYNet1		85.87 347	84.23 351	90.78 346	92.38 375	82.46 349	93.17 366	95.14 326	82.12 371	67.69 403	92.36 359	78.16 284	95.50 380	77.31 368	79.73 375	94.39 345

MDA-MVSNet_test_wron		85.87 347	84.23 351	90.80 345	92.38 375	82.57 344	93.17 366	95.15 325	82.15 370	67.65 405	92.33 362	78.20 281	95.51 379	77.33 367	79.74 374	94.31 349

CMPMVS	62.92 21	85.62 349	84.92 345	87.74 371	89.14 393	73.12 401	94.17 339	96.80 241	73.98 400	73.65 399	94.93 262	66.36 373	97.61 318	83.95 318	91.28 266	92.48 377
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 350	83.64 353	90.92 339	95.27 278	79.49 381	90.55 392	95.60 303	83.76 358	83.00 372	89.95 382	71.09 338	97.97 276	82.75 331	60.79 412	95.31 295

MDA-MVSNet-bldmvs		85.00 351	82.95 356	91.17 337	93.13 360	83.33 337	94.56 322	95.00 331	84.57 347	65.13 409	92.65 349	70.45 343	95.85 370	73.57 388	77.49 382	94.33 347

MIMVSNet1		84.93 352	83.05 354	90.56 348	89.56 391	84.84 320	95.40 293	95.35 314	83.91 353	80.38 382	92.21 363	57.23 396	93.34 398	70.69 398	82.75 365	93.50 360

KD-MVS_2432*1600		84.81 353	82.64 357	91.31 332	91.07 382	85.34 309	91.22 386	95.75 294	85.56 331	83.09 370	90.21 380	67.21 367	95.89 368	77.18 370	62.48 410	92.69 370

miper_refine_blended		84.81 353	82.64 357	91.31 332	91.07 382	85.34 309	91.22 386	95.75 294	85.56 331	83.09 370	90.21 380	67.21 367	95.89 368	77.18 370	62.48 410	92.69 370

OpenMVS_ROB	81.14 20	84.42 355	82.28 361	90.83 341	90.06 387	84.05 330	95.73 277	94.04 362	73.89 402	80.17 385	91.53 371	59.15 393	97.64 314	66.92 402	89.05 292	90.80 395

mvsany_test3		83.59 356	82.44 360	87.03 375	83.80 408	73.82 397	93.70 354	90.92 397	86.42 316	82.51 373	90.26 379	46.76 408	95.71 373	90.82 192	76.76 385	91.57 388

PM-MVS		83.48 357	81.86 363	88.31 368	87.83 402	77.59 390	93.43 362	91.75 390	86.91 308	80.63 380	89.91 383	44.42 409	95.84 371	85.17 304	76.73 386	91.50 390

test_fmvs3		83.21 358	83.02 355	83.78 381	86.77 405	68.34 407	96.76 202	94.91 336	86.49 315	84.14 361	89.48 386	36.04 413	91.73 403	91.86 171	80.77 372	91.26 393

new-patchmatchnet		83.18 359	81.87 362	87.11 374	86.88 404	75.99 394	93.70 354	95.18 324	85.02 341	77.30 393	88.40 392	65.99 378	93.88 395	74.19 385	70.18 399	91.47 391

new_pmnet		82.89 360	81.12 365	88.18 370	89.63 390	80.18 374	91.77 383	92.57 383	76.79 397	75.56 396	88.23 394	61.22 392	94.48 387	71.43 394	82.92 363	89.87 398

MVS-HIRNet		82.47 361	81.21 364	86.26 378	95.38 266	69.21 405	88.96 402	89.49 401	66.28 407	80.79 379	74.08 412	68.48 360	97.39 337	71.93 393	95.47 191	92.18 383

MVStest1		82.38 362	80.04 366	89.37 362	87.63 403	82.83 342	95.03 310	93.37 374	73.90 401	73.50 400	94.35 294	62.89 388	93.25 399	73.80 386	65.92 407	92.04 385

UnsupCasMVSNet_bld		82.13 363	79.46 368	90.14 354	88.00 401	82.47 348	90.89 391	96.62 257	78.94 390	75.61 394	84.40 405	56.63 398	96.31 364	77.30 369	66.77 406	91.63 387

dmvs_testset		81.38 364	82.60 359	77.73 387	91.74 379	51.49 422	93.03 371	84.21 415	89.07 242	78.28 391	91.25 373	76.97 295	88.53 410	56.57 410	82.24 366	93.16 364

test_f		80.57 365	79.62 367	83.41 382	83.38 411	67.80 409	93.57 361	93.72 369	80.80 382	77.91 392	87.63 398	33.40 414	92.08 402	87.14 274	79.04 380	90.34 397

pmmvs3		79.97 366	77.50 371	87.39 373	82.80 412	79.38 383	92.70 376	90.75 398	70.69 404	78.66 389	87.47 400	51.34 404	93.40 397	73.39 389	69.65 400	89.38 399

APD_test1		79.31 367	77.70 370	84.14 380	89.11 395	69.07 406	92.36 381	91.50 392	69.07 405	73.87 398	92.63 351	39.93 411	94.32 389	70.54 399	80.25 373	89.02 400

N_pmnet		78.73 368	78.71 369	78.79 386	92.80 365	46.50 425	94.14 340	43.71 427	78.61 391	80.83 378	91.66 370	74.94 313	96.36 363	67.24 401	84.45 347	93.50 360

WB-MVS		76.77 369	76.63 372	77.18 388	85.32 406	56.82 420	94.53 323	89.39 402	82.66 368	71.35 401	89.18 388	75.03 312	88.88 408	35.42 417	66.79 405	85.84 402

SSC-MVS		76.05 370	75.83 373	76.72 392	84.77 407	56.22 421	94.32 334	88.96 404	81.82 374	70.52 402	88.91 389	74.79 314	88.71 409	33.69 418	64.71 408	85.23 403

test_vis3_rt		72.73 371	70.55 374	79.27 385	80.02 414	68.13 408	93.92 348	74.30 422	76.90 396	58.99 413	73.58 413	20.29 422	95.37 381	84.16 313	72.80 396	74.31 410

LCM-MVSNet		72.55 372	69.39 376	82.03 383	70.81 423	65.42 412	90.12 396	94.36 357	55.02 413	65.88 407	81.72 406	24.16 421	89.96 404	74.32 384	68.10 404	90.71 396

FPMVS		71.27 373	69.85 375	75.50 393	74.64 418	59.03 418	91.30 385	91.50 392	58.80 410	57.92 414	88.28 393	29.98 417	85.53 413	53.43 411	82.84 364	81.95 406

PMMVS2		70.19 374	66.92 378	80.01 384	76.35 417	65.67 411	86.22 407	87.58 408	64.83 409	62.38 410	80.29 409	26.78 419	88.49 411	63.79 403	54.07 414	85.88 401

dongtai		69.99 375	69.33 377	71.98 396	88.78 397	61.64 416	89.86 397	59.93 426	75.67 398	74.96 397	85.45 402	50.19 405	81.66 415	43.86 414	55.27 413	72.63 411

testf1		69.31 376	66.76 379	76.94 390	78.61 415	61.93 414	88.27 404	86.11 412	55.62 411	59.69 411	85.31 403	20.19 423	89.32 405	57.62 407	69.44 402	79.58 407

APD_test2		69.31 376	66.76 379	76.94 390	78.61 415	61.93 414	88.27 404	86.11 412	55.62 411	59.69 411	85.31 403	20.19 423	89.32 405	57.62 407	69.44 402	79.58 407

EGC-MVSNET		68.77 378	63.01 384	86.07 379	92.49 371	82.24 352	93.96 345	90.96 396	0.71 424	2.62 425	90.89 374	53.66 401	93.46 396	57.25 409	84.55 345	82.51 405

Gipuma		67.86 379	65.41 381	75.18 394	92.66 368	73.45 398	66.50 415	94.52 349	53.33 414	57.80 415	66.07 415	30.81 415	89.20 407	48.15 413	78.88 381	62.90 415
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 380	64.89 382	69.79 397	72.62 421	35.23 429	65.19 416	92.83 381	20.35 419	65.20 408	88.08 396	43.14 410	82.70 414	73.12 390	63.46 409	91.45 392

kuosan		65.27 381	64.66 383	67.11 399	83.80 408	61.32 417	88.53 403	60.77 425	68.22 406	67.67 404	80.52 408	49.12 406	70.76 421	29.67 420	53.64 415	69.26 413

ANet_high		63.94 382	59.58 385	77.02 389	61.24 425	66.06 410	85.66 409	87.93 407	78.53 392	42.94 417	71.04 414	25.42 420	80.71 416	52.60 412	30.83 418	84.28 404

PMVS	53.92 22	58.58 383	55.40 386	68.12 398	51.00 426	48.64 423	78.86 412	87.10 410	46.77 415	35.84 421	74.28 411	8.76 425	86.34 412	42.07 415	73.91 393	69.38 412
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 384	52.56 388	55.43 401	74.43 419	47.13 424	83.63 411	76.30 419	42.23 416	42.59 418	62.22 417	28.57 418	74.40 418	31.53 419	31.51 417	44.78 416

MVE	50.73 23	53.25 385	48.81 390	66.58 400	65.34 424	57.50 419	72.49 414	70.94 423	40.15 418	39.28 420	63.51 416	6.89 427	73.48 420	38.29 416	42.38 416	68.76 414
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 386	51.31 389	54.39 402	72.62 421	45.39 426	83.84 410	75.51 421	41.13 417	40.77 419	59.65 418	30.08 416	73.60 419	28.31 421	29.90 419	44.18 417

tmp_tt		51.94 387	53.82 387	46.29 403	33.73 427	45.30 427	78.32 413	67.24 424	18.02 420	50.93 416	87.05 401	52.99 402	53.11 422	70.76 397	25.29 420	40.46 418

wuyk23d		25.11 388	24.57 392	26.74 404	73.98 420	39.89 428	57.88 417	9.80 428	12.27 421	10.39 422	6.97 424	7.03 426	36.44 423	25.43 422	17.39 421	3.89 421

cdsmvs_eth3d_5k		23.24 389	30.99 391	0.00 407	0.00 430	0.00 432	0.00 418	97.63 146	0.00 425	0.00 426	96.88 161	84.38 165	0.00 426	0.00 425	0.00 424	0.00 422

testmvs		13.36 390	16.33 393	4.48 406	5.04 428	2.26 431	93.18 365	3.28 429	2.70 422	8.24 423	21.66 420	2.29 429	2.19 424	7.58 423	2.96 422	9.00 420

test123		13.04 391	15.66 394	5.18 405	4.51 429	3.45 430	92.50 379	1.81 430	2.50 423	7.58 424	20.15 421	3.67 428	2.18 425	7.13 424	1.07 423	9.90 419

ab-mvs-re		8.06 392	10.74 395	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	96.69 171	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

pcd_1.5k_mvsjas		7.39 393	9.85 396	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	88.65 100	0.00 426	0.00 425	0.00 424	0.00 422

mmdepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

monomultidepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

test_blank		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uanet_test		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

DCPMVS		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

sosnet-low-res		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

sosnet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uncertanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

Regformer		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

WAC-MVS						79.53 379								75.56 378

FOURS1					99.55 1	93.34 66	99.29 1	98.35 27	94.98 32	98.49 23

MSC_two_6792asdad				98.86 1	98.67 61	96.94 1		97.93 108					99.86 9	97.68 20	99.67 6	99.77 2

PC_three_1452									90.77 185	98.89 14	98.28 68	96.24 1	98.35 230	95.76 84	99.58 23	99.59 24

No_MVS				98.86 1	98.67 61	96.94 1		97.93 108					99.86 9	97.68 20	99.67 6	99.77 2

test_one_0601					99.32 22	95.20 20		98.25 45	95.13 26	98.48 24	98.87 18	95.16 7

eth-test2					0.00 430

eth-test					0.00 430

ZD-MVS					99.05 39	94.59 32		98.08 77	89.22 238	97.03 63	98.10 76	92.52 39	99.65 61	94.58 121	99.31 65

RE-MVS-def			96.72 46		99.02 42	92.34 95	97.98 63	98.03 94	93.52 92	97.43 49	98.51 38	90.71 76		96.05 72	99.26 69	99.43 54

IU-MVS					99.42 7	95.39 11		97.94 107	90.40 206	98.94 8				97.41 35	99.66 10	99.74 8

OPU-MVS				98.55 3	98.82 55	96.86 3	98.25 35				98.26 69	96.04 2	99.24 128	95.36 98	99.59 19	99.56 31

test_241102_TWO								98.27 39	95.13 26	98.93 9	98.89 16	94.99 11	99.85 18	97.52 28	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 39	95.09 29	99.19 4	98.81 24	95.54 5	99.65 61

9.14			96.75 45		98.93 50		97.73 98	98.23 50	91.28 169	97.88 37	98.44 46	93.00 26	99.65 61	95.76 84	99.47 40

save fliter					98.91 52	94.28 38	97.02 178	98.02 97	95.35 19

test_0728_THIRD									94.78 44	98.73 18	98.87 18	95.87 4	99.84 23	97.45 32	99.72 2	99.77 2

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 42	98.28 36					99.86 9	97.52 28	99.67 6	99.75 6

test0726					99.45 3	95.36 13	98.31 27	98.29 34	94.92 35	98.99 7	98.92 13	95.08 8

GSMVS																98.45 148

test_part2					99.28 25	95.74 8				98.10 30

sam_mvs1												82.76 198				98.45 148

sam_mvs												81.94 217

ambc				86.56 377	83.60 410	70.00 404	85.69 408	94.97 333		80.60 381	88.45 391	37.42 412	96.84 357	82.69 332	75.44 390	92.86 368

MTGPA								98.08 77

test_post1							92.81 375				16.58 423	80.53 238	97.68 310	86.20 285

test_post											17.58 422	81.76 219	98.08 257

patchmatchnet-post											90.45 378	82.65 202	98.10 252

GG-mvs-BLEND				93.62 259	93.69 343	89.20 215	92.39 380	83.33 416		87.98 312	89.84 384	71.00 339	96.87 356	82.08 336	95.40 193	94.80 329

MTMP							97.86 80	82.03 417

gm-plane-assit					93.22 357	78.89 387			84.82 344		93.52 334		98.64 204	87.72 253

test9_res														94.81 113	99.38 58	99.45 50

TEST9					98.70 59	94.19 42	96.41 232	98.02 97	88.17 275	96.03 103	97.56 125	92.74 33	99.59 77

test_8					98.67 61	94.06 49	96.37 239	98.01 100	88.58 262	95.98 107	97.55 127	92.73 34	99.58 80

agg_prior2														93.94 130	99.38 58	99.50 43

agg_prior					98.67 61	93.79 54		98.00 101		95.68 117			99.57 87

TestCases				93.98 237	97.94 116	86.64 280		95.54 307	85.38 333	85.49 347	96.77 165	70.28 344	99.15 141	80.02 353	92.87 237	96.15 252

test_prior4						93.66 57	96.42 231

test_prior2							96.35 240		92.80 126	96.03 103	97.59 122	92.01 47		95.01 106	99.38 58

test_prior				97.23 63	98.67 61	92.99 77		98.00 101					99.41 112			99.29 66

旧先验2							95.94 265		81.66 375	97.34 52			98.82 181	92.26 158

新几何2							95.79 274

新几何1				97.32 56	98.60 68	93.59 58		97.75 129	81.58 376	95.75 114	97.85 99	90.04 83	99.67 59	86.50 281	99.13 83	98.69 127

旧先验1					98.38 81	93.38 63		97.75 129			98.09 78	92.30 45			99.01 92	99.16 76

无先验							95.79 274	97.87 114	83.87 356				99.65 61	87.68 259		98.89 112

原ACMM2							95.67 279

原ACMM1				96.38 104	98.59 69	91.09 149		97.89 110	87.41 299	95.22 128	97.68 111	90.25 80	99.54 92	87.95 249	99.12 85	98.49 143

test222					98.24 90	92.21 101	95.33 296	97.60 148	79.22 389	95.25 126	97.84 101	88.80 97			99.15 81	98.72 124

testdata2													99.67 59	85.96 293

segment_acmp												92.89 30

testdata				95.46 164	98.18 100	88.90 223		97.66 140	82.73 367	97.03 63	98.07 79	90.06 82	98.85 179	89.67 214	98.98 93	98.64 130

testdata1							95.26 303		93.10 112

test12				97.65 42	98.46 73	94.26 39		97.66 140		95.52 124		90.89 73	99.46 106		99.25 71	99.22 73

plane_prior7					96.21 225	89.98 183

plane_prior6					96.10 236	90.00 179						81.32 225

plane_prior5								97.51 161					98.60 208	93.02 151	92.23 248	95.86 260

plane_prior4											96.64 174

plane_prior3						90.00 179			94.46 60	91.34 219

plane_prior2							97.74 96		94.85 37

plane_prior1					96.14 233

plane_prior						89.99 181	97.24 159		94.06 72						92.16 252

n2								0.00 431

nn								0.00 431

door-mid								91.06 395

lessismore_v0				90.45 349	91.96 378	79.09 386		87.19 409		80.32 383	94.39 291	66.31 375	97.55 322	84.00 317	76.84 384	94.70 336

LGP-MVS_train				94.10 230	96.16 230	88.26 240		97.46 170	91.29 166	90.12 249	97.16 146	79.05 266	98.73 194	92.25 160	91.89 256	95.31 295

test11								97.88 112

door								91.13 394

HQP5-MVS						89.33 208

HQP-NCC					95.86 242		96.65 214		93.55 87	90.14 243

ACMP_Plane					95.86 242		96.65 214		93.55 87	90.14 243

BP-MVS														92.13 164

HQP4-MVS										90.14 243			98.50 216			95.78 268

HQP3-MVS								97.39 186							92.10 253

HQP2-MVS												80.95 229

NP-MVS					95.99 240	89.81 189					95.87 216

MDTV_nov1_ep13_2view						70.35 403	93.10 370		83.88 355	93.55 164		82.47 206		86.25 284		98.38 156

MDTV_nov1_ep13			90.76 236		95.22 282	80.33 370	93.03 371	95.28 318	88.14 277	92.84 184	93.83 319	81.34 224	98.08 257	82.86 326	94.34 213

ACMMP++_ref															90.30 282

ACMMP++															91.02 271

Test By Simon												88.73 99

ITE_SJBPF				92.43 300	95.34 271	85.37 308		95.92 284	91.47 159	87.75 315	96.39 192	71.00 339	97.96 280	82.36 334	89.86 285	93.97 355

DeepMVS_CX				74.68 395	90.84 384	64.34 413		81.61 418	65.34 408	67.47 406	88.01 397	48.60 407	80.13 417	62.33 405	73.68 394	79.58 407

High-resolution multi-view benchmark