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++		95.98 1	96.36 1	94.82 31	97.78 54	86.00 50	98.29 1	97.49 6	90.75 19	97.62 5	98.06 14	92.59 2	99.61 4	95.64 19	99.02 12	98.86 11

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 52	97.13 14	97.44 15	90.31 28	97.71 1	98.07 12	92.31 4	99.58 10	95.66 17	99.13 3	98.84 14

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 55	97.09 16	96.73 82	90.27 31	97.04 11	98.05 16	91.47 8	99.55 16	95.62 21	99.08 7	98.45 36
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		95.57 4	95.67 4	95.25 11	98.36 25	87.28 18	95.56 99	97.51 5	89.13 69	97.14 9	97.91 21	91.64 7	99.62 2	94.61 33	99.17 2	98.86 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 28	86.29 46	97.46 6	97.40 20	89.03 74	96.20 19	98.10 8	89.39 16	99.34 37	95.88 16	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 25	97.47 11	91.73 10	96.10 20	96.69 69	89.90 12	99.30 43	94.70 31	98.04 71	99.13 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
CNVR-MVS		95.40 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 109	96.96 55	92.09 6	95.32 32	97.08 52	89.49 15	99.33 40	95.10 28	98.85 20	98.66 21

SMA-MVS		95.20 8	95.07 11	95.59 6	98.14 35	88.48 8	96.26 46	97.28 31	85.90 160	97.67 3	98.10 8	88.41 20	99.56 12	94.66 32	99.19 1	98.71 20
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
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 25	96.99 75	86.33 42	97.33 7	97.30 29	91.38 12	95.39 31	97.46 32	88.98 19	99.40 30	94.12 37	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 10	94.91 16	95.83 4	98.25 29	89.65 4	95.92 76	96.96 55	91.75 9	94.02 53	96.83 64	88.12 24	99.55 16	93.41 48	98.94 16	98.28 54

MM		95.10 11	94.91 16	95.68 5	96.09 106	88.34 9	96.68 33	94.37 243	95.08 1	94.68 40	97.72 26	82.94 88	99.64 1	97.85 1	98.76 29	99.06 7

SF-MVS		94.97 12	94.90 18	95.20 12	97.84 50	87.76 10	96.65 34	97.48 10	87.76 118	95.71 27	97.70 27	88.28 23	99.35 36	93.89 41	98.78 26	98.48 30

SD-MVS		94.96 13	95.33 8	93.88 62	97.25 72	86.69 28	96.19 49	97.11 46	90.42 27	96.95 13	97.27 40	89.53 14	96.91 259	94.38 35	98.85 20	98.03 76
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
TSAR-MVS + MP.		94.85 14	94.94 14	94.58 42	98.25 29	86.33 42	96.11 59	96.62 91	88.14 104	96.10 20	96.96 58	89.09 18	98.94 81	94.48 34	98.68 37	98.48 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
reproduce-ours		94.82 15	94.97 12	94.38 50	97.91 47	85.46 68	95.86 78	97.15 41	89.82 43	95.23 35	98.10 8	87.09 37	99.37 33	95.30 25	98.25 60	98.30 49

our_new_method		94.82 15	94.97 12	94.38 50	97.91 47	85.46 68	95.86 78	97.15 41	89.82 43	95.23 35	98.10 8	87.09 37	99.37 33	95.30 25	98.25 60	98.30 49

NCCC		94.81 17	94.69 21	95.17 14	97.83 51	87.46 17	95.66 92	96.93 59	92.34 4	93.94 54	96.58 79	87.74 27	99.44 29	92.83 56	98.40 54	98.62 22

reproduce_model		94.76 18	94.92 15	94.29 54	97.92 43	85.18 74	95.95 75	97.19 35	89.67 53	95.27 34	98.16 3	86.53 43	99.36 35	95.42 24	98.15 64	98.33 44

ACMMP_NAP		94.74 19	94.56 22	95.28 10	98.02 41	87.70 11	95.68 89	97.34 23	88.28 98	95.30 33	97.67 28	85.90 50	99.54 20	93.91 40	98.95 15	98.60 23

test_fmvsm_n_1920		94.71 20	95.11 10	93.50 74	95.79 120	84.62 83	96.15 54	97.64 2	89.85 42	97.19 8	97.89 22	86.28 46	98.71 102	97.11 6	98.08 70	97.17 118

test_fmvsmconf_n		94.60 21	94.81 19	93.98 58	94.62 178	84.96 77	96.15 54	97.35 22	89.37 60	96.03 23	98.11 6	86.36 44	99.01 66	97.45 2	97.83 78	97.96 79

HFP-MVS		94.52 22	94.40 26	94.86 24	98.61 10	86.81 25	96.94 20	97.34 23	88.63 86	93.65 59	97.21 44	86.10 48	99.49 26	92.35 69	98.77 28	98.30 49

ZNCC-MVS		94.47 23	94.28 32	95.03 16	98.52 15	86.96 20	96.85 28	97.32 27	88.24 99	93.15 69	97.04 55	86.17 47	99.62 2	92.40 66	98.81 23	98.52 26

XVS		94.45 24	94.32 28	94.85 25	98.54 13	86.60 34	96.93 22	97.19 35	90.66 24	92.85 76	97.16 50	85.02 63	99.49 26	91.99 83	98.56 50	98.47 33

MCST-MVS		94.45 24	94.20 38	95.19 13	98.46 19	87.50 16	95.00 128	97.12 44	87.13 129	92.51 90	96.30 86	89.24 17	99.34 37	93.46 45	98.62 46	98.73 18

region2R		94.43 26	94.27 34	94.92 20	98.65 8	86.67 30	96.92 24	97.23 34	88.60 89	93.58 61	97.27 40	85.22 58	99.54 20	92.21 73	98.74 31	98.56 25

ACMMPR		94.43 26	94.28 32	94.91 21	98.63 9	86.69 28	96.94 20	97.32 27	88.63 86	93.53 64	97.26 42	85.04 62	99.54 20	92.35 69	98.78 26	98.50 27

MTAPA		94.42 28	94.22 35	95.00 18	98.42 21	86.95 21	94.36 175	96.97 53	91.07 13	93.14 70	97.56 29	84.30 73	99.56 12	93.43 46	98.75 30	98.47 33

CP-MVS		94.34 29	94.21 37	94.74 37	98.39 23	86.64 32	97.60 4	97.24 32	88.53 91	92.73 84	97.23 43	85.20 59	99.32 41	92.15 76	98.83 22	98.25 61

fmvsm_l_conf0.5_n		94.29 30	94.46 24	93.79 68	95.28 141	85.43 70	95.68 89	96.43 102	86.56 144	96.84 14	97.81 25	87.56 32	98.77 96	97.14 5	96.82 101	97.16 122

MP-MVS		94.25 31	94.07 42	94.77 35	98.47 18	86.31 44	96.71 31	96.98 52	89.04 72	91.98 100	97.19 47	85.43 56	99.56 12	92.06 82	98.79 24	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 32	94.07 42	94.75 36	98.06 39	86.90 23	95.88 77	96.94 58	85.68 166	95.05 38	97.18 48	87.31 35	99.07 56	91.90 89	98.61 48	98.28 54
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 33	94.17 40	94.43 47	98.21 32	85.78 63	96.40 38	96.90 62	88.20 102	94.33 44	97.40 35	84.75 69	99.03 61	93.35 49	97.99 72	98.48 30

GST-MVS		94.21 34	93.97 46	94.90 23	98.41 22	86.82 24	96.54 36	97.19 35	88.24 99	93.26 66	96.83 64	85.48 55	99.59 8	91.43 97	98.40 54	98.30 49

MP-MVS-pluss		94.21 34	94.00 45	94.85 25	98.17 33	86.65 31	94.82 140	97.17 40	86.26 152	92.83 78	97.87 23	85.57 54	99.56 12	94.37 36	98.92 17	98.34 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 36	94.40 26	93.60 72	95.29 140	84.98 76	95.61 96	96.28 115	86.31 150	96.75 16	97.86 24	87.40 33	98.74 99	97.07 7	97.02 94	97.07 124

test_fmvsmconf0.1_n		94.20 36	94.31 30	93.88 62	92.46 264	84.80 80	96.18 51	96.82 71	89.29 63	95.68 28	98.11 6	85.10 60	98.99 73	97.38 3	97.75 82	97.86 86

DeepPCF-MVS	89.96 1	94.20 36	94.77 20	92.49 121	96.52 91	80.00 223	94.00 200	97.08 47	90.05 35	95.65 29	97.29 39	89.66 13	98.97 78	93.95 39	98.71 32	98.50 27

MVS_0304		94.18 39	93.80 49	95.34 9	94.91 163	87.62 14	95.97 72	93.01 282	92.58 3	94.22 45	97.20 46	80.56 118	99.59 8	97.04 8	98.68 37	98.81 17

CS-MVS		94.12 40	94.44 25	93.17 82	96.55 88	83.08 135	97.63 3	96.95 57	91.71 11	93.50 65	96.21 89	85.61 52	98.24 144	93.64 43	98.17 62	98.19 64

DeepC-MVS_fast	89.43 2	94.04 41	93.79 50	94.80 33	97.48 64	86.78 26	95.65 94	96.89 63	89.40 59	92.81 79	96.97 57	85.37 57	99.24 46	90.87 106	98.69 35	98.38 41
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		94.02 42	94.29 31	93.24 79	96.69 81	83.24 125	97.49 5	96.92 60	92.14 5	92.90 74	95.77 111	85.02 63	98.33 139	93.03 53	98.62 46	98.13 68

HPM-MVS		94.02 42	93.88 47	94.43 47	98.39 23	85.78 63	97.25 10	97.07 48	86.90 137	92.62 87	96.80 68	84.85 68	99.17 50	92.43 64	98.65 44	98.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 44	93.78 51	94.63 40	98.50 16	85.90 60	96.87 26	96.91 61	88.70 84	91.83 109	97.17 49	83.96 77	99.55 16	91.44 96	98.64 45	98.43 38

balanced_conf03		93.98 45	94.22 35	93.26 78	96.13 101	83.29 124	96.27 45	96.52 97	89.82 43	95.56 30	95.51 120	84.50 71	98.79 94	94.83 30	98.86 19	97.72 94

PGM-MVS		93.96 46	93.72 54	94.68 38	98.43 20	86.22 47	95.30 107	97.78 1	87.45 125	93.26 66	97.33 38	84.62 70	99.51 24	90.75 108	98.57 49	98.32 48

PHI-MVS		93.89 47	93.65 58	94.62 41	96.84 78	86.43 39	96.69 32	97.49 6	85.15 179	93.56 63	96.28 87	85.60 53	99.31 42	92.45 63	98.79 24	98.12 70

SR-MVS-dyc-post		93.82 48	93.82 48	93.82 65	97.92 43	84.57 85	96.28 43	96.76 78	87.46 123	93.75 57	97.43 33	84.24 74	99.01 66	92.73 57	97.80 79	97.88 84

APD-MVS_3200maxsize		93.78 49	93.77 52	93.80 67	97.92 43	84.19 99	96.30 41	96.87 65	86.96 133	93.92 55	97.47 31	83.88 78	98.96 80	92.71 60	97.87 76	98.26 60

fmvsm_s_conf0.5_n		93.76 50	94.06 44	92.86 101	95.62 130	83.17 128	96.14 56	96.12 131	88.13 105	95.82 26	98.04 19	83.43 80	98.48 119	96.97 9	96.23 112	96.92 135

patch_mono-2		93.74 51	94.32 28	92.01 138	97.54 60	78.37 260	93.40 225	97.19 35	88.02 107	94.99 39	97.21 44	88.35 21	98.44 129	94.07 38	98.09 68	99.23 1

MSLP-MVS++		93.72 52	94.08 41	92.65 113	97.31 68	83.43 119	95.79 84	97.33 25	90.03 36	93.58 61	96.96 58	84.87 67	97.76 182	92.19 75	98.66 41	96.76 142

TSAR-MVS + GP.		93.66 53	93.41 61	94.41 49	96.59 85	86.78 26	94.40 167	93.93 259	89.77 50	94.21 46	95.59 118	87.35 34	98.61 111	92.72 59	96.15 114	97.83 89

fmvsm_s_conf0.5_n_a		93.57 54	93.76 53	93.00 93	95.02 153	83.67 111	96.19 49	96.10 133	87.27 127	95.98 24	98.05 16	83.07 87	98.45 127	96.68 11	95.51 122	96.88 138

CANet		93.54 55	93.20 66	94.55 43	95.65 128	85.73 65	94.94 131	96.69 87	91.89 8	90.69 125	95.88 105	81.99 109	99.54 20	93.14 52	97.95 74	98.39 39

dcpmvs_2		93.49 56	94.19 39	91.38 172	97.69 57	76.78 293	94.25 178	96.29 112	88.33 95	94.46 42	96.88 61	88.07 25	98.64 106	93.62 44	98.09 68	98.73 18

fmvsm_s_conf0.1_n		93.46 57	93.66 57	92.85 102	93.75 225	83.13 130	96.02 68	95.74 163	87.68 120	95.89 25	98.17 2	82.78 91	98.46 123	96.71 10	96.17 113	96.98 131

MVS_111021_HR		93.45 58	93.31 62	93.84 64	96.99 75	84.84 78	93.24 237	97.24 32	88.76 81	91.60 114	95.85 106	86.07 49	98.66 104	91.91 87	98.16 63	98.03 76

MVSMamba_PlusPlus		93.44 59	93.54 60	93.14 84	96.58 87	83.05 136	96.06 64	96.50 99	84.42 199	94.09 49	95.56 119	85.01 66	98.69 103	94.96 29	98.66 41	97.67 97

test_fmvsmvis_n_1920		93.44 59	93.55 59	93.10 86	93.67 229	84.26 98	95.83 82	96.14 127	89.00 76	92.43 92	97.50 30	83.37 83	98.72 100	96.61 12	97.44 86	96.32 157

train_agg		93.44 59	93.08 67	94.52 44	97.53 61	86.49 37	94.07 192	96.78 75	81.86 261	92.77 81	96.20 90	87.63 29	99.12 54	92.14 77	98.69 35	97.94 80

EC-MVSNet		93.44 59	93.71 55	92.63 114	95.21 146	82.43 156	97.27 9	96.71 85	90.57 26	92.88 75	95.80 109	83.16 84	98.16 150	93.68 42	98.14 65	97.31 110

DELS-MVS		93.43 63	93.25 64	93.97 59	95.42 137	85.04 75	93.06 244	97.13 43	90.74 21	91.84 107	95.09 139	86.32 45	99.21 48	91.22 98	98.45 52	97.65 98
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
HPM-MVS_fast		93.40 64	93.22 65	93.94 61	98.36 25	84.83 79	97.15 13	96.80 74	85.77 163	92.47 91	97.13 51	82.38 95	99.07 56	90.51 111	98.40 54	97.92 83

DeepC-MVS	88.79 3	93.31 65	92.99 70	94.26 55	96.07 108	85.83 61	94.89 134	96.99 51	89.02 75	89.56 140	97.37 37	82.51 94	99.38 31	92.20 74	98.30 57	97.57 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
sasdasda		93.27 66	92.75 74	94.85 25	95.70 125	87.66 12	96.33 39	96.41 104	90.00 37	94.09 49	94.60 160	82.33 97	98.62 109	92.40 66	92.86 181	98.27 56

canonicalmvs		93.27 66	92.75 74	94.85 25	95.70 125	87.66 12	96.33 39	96.41 104	90.00 37	94.09 49	94.60 160	82.33 97	98.62 109	92.40 66	92.86 181	98.27 56

ACMMP		93.24 68	92.88 72	94.30 53	98.09 38	85.33 72	96.86 27	97.45 14	88.33 95	90.15 135	97.03 56	81.44 112	99.51 24	90.85 107	95.74 118	98.04 75
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
CSCG		93.23 69	93.05 68	93.76 69	98.04 40	84.07 101	96.22 48	97.37 21	84.15 202	90.05 136	95.66 115	87.77 26	99.15 53	89.91 115	98.27 58	98.07 72

fmvsm_s_conf0.1_n_a		93.19 70	93.26 63	92.97 95	92.49 262	83.62 114	96.02 68	95.72 166	86.78 139	96.04 22	98.19 1	82.30 99	98.43 131	96.38 13	95.42 128	96.86 139

test_fmvsmconf0.01_n		93.19 70	93.02 69	93.71 70	89.25 358	84.42 96	96.06 64	96.29 112	89.06 70	94.68 40	98.13 4	79.22 136	98.98 77	97.22 4	97.24 89	97.74 93

alignmvs		93.08 72	92.50 80	94.81 32	95.62 130	87.61 15	95.99 70	96.07 136	89.77 50	94.12 48	94.87 146	80.56 118	98.66 104	92.42 65	93.10 177	98.15 67

MGCFI-Net		93.03 73	92.63 77	94.23 56	95.62 130	85.92 57	96.08 60	96.33 110	89.86 41	93.89 56	94.66 157	82.11 104	98.50 117	92.33 71	92.82 184	98.27 56

EI-MVSNet-Vis-set		93.01 74	92.92 71	93.29 76	95.01 154	83.51 118	94.48 159	95.77 160	90.87 15	92.52 89	96.67 71	84.50 71	99.00 71	91.99 83	94.44 152	97.36 109

casdiffmvs_mvg		92.96 75	92.83 73	93.35 75	94.59 179	83.40 121	95.00 128	96.34 109	90.30 30	92.05 98	96.05 98	83.43 80	98.15 151	92.07 79	95.67 119	98.49 29
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		92.83 76	92.54 79	93.68 71	96.10 105	84.71 82	95.66 92	96.39 106	91.92 7	93.22 68	96.49 82	83.16 84	98.87 85	84.47 182	95.47 125	97.45 108

CDPH-MVS		92.83 76	92.30 82	94.44 45	97.79 52	86.11 49	94.06 194	96.66 88	80.09 292	92.77 81	96.63 76	86.62 40	99.04 60	87.40 143	98.66 41	98.17 66

ETV-MVS		92.74 78	92.66 76	92.97 95	95.20 147	84.04 103	95.07 124	96.51 98	90.73 22	92.96 73	91.19 280	84.06 75	98.34 137	91.72 92	96.54 106	96.54 153

EI-MVSNet-UG-set		92.74 78	92.62 78	93.12 85	94.86 166	83.20 127	94.40 167	95.74 163	90.71 23	92.05 98	96.60 78	84.00 76	98.99 73	91.55 94	93.63 162	97.17 118

DPM-MVS		92.58 80	91.74 89	95.08 15	96.19 99	89.31 5	92.66 256	96.56 96	83.44 220	91.68 113	95.04 140	86.60 42	98.99 73	85.60 168	97.92 75	96.93 134

casdiffmvs		92.51 81	92.43 81	92.74 108	94.41 192	81.98 166	94.54 157	96.23 121	89.57 55	91.96 102	96.17 94	82.58 93	98.01 169	90.95 104	95.45 127	98.23 62
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_111021_LR		92.47 82	92.29 83	92.98 94	95.99 114	84.43 94	93.08 242	96.09 134	88.20 102	91.12 121	95.72 114	81.33 114	97.76 182	91.74 91	97.37 88	96.75 143

3Dnovator+	87.14 4	92.42 83	91.37 92	95.55 7	95.63 129	88.73 6	97.07 18	96.77 77	90.84 16	84.02 274	96.62 77	75.95 171	99.34 37	87.77 138	97.68 83	98.59 24

baseline		92.39 84	92.29 83	92.69 112	94.46 188	81.77 170	94.14 184	96.27 116	89.22 65	91.88 105	96.00 99	82.35 96	97.99 171	91.05 100	95.27 133	98.30 49

VNet		92.24 85	91.91 86	93.24 79	96.59 85	83.43 119	94.84 139	96.44 101	89.19 67	94.08 52	95.90 104	77.85 155	98.17 149	88.90 125	93.38 171	98.13 68

CPTT-MVS		91.99 86	91.80 87	92.55 118	98.24 31	81.98 166	96.76 30	96.49 100	81.89 260	90.24 130	96.44 84	78.59 144	98.61 111	89.68 116	97.85 77	97.06 125

EIA-MVS		91.95 87	91.94 85	91.98 142	95.16 149	80.01 222	95.36 102	96.73 82	88.44 92	89.34 145	92.16 244	83.82 79	98.45 127	89.35 119	97.06 92	97.48 106

DP-MVS Recon		91.95 87	91.28 94	93.96 60	98.33 27	85.92 57	94.66 151	96.66 88	82.69 240	90.03 137	95.82 108	82.30 99	99.03 61	84.57 180	96.48 109	96.91 136

EPNet		91.79 89	91.02 100	94.10 57	90.10 345	85.25 73	96.03 67	92.05 308	92.83 2	87.39 182	95.78 110	79.39 134	99.01 66	88.13 134	97.48 85	98.05 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 90	91.70 90	92.00 141	97.08 74	80.03 221	93.60 218	95.18 203	87.85 115	90.89 123	96.47 83	82.06 107	98.36 134	85.07 172	97.04 93	97.62 99

Vis-MVSNet		91.75 91	91.23 95	93.29 76	95.32 139	83.78 108	96.14 56	95.98 143	89.89 39	90.45 127	96.58 79	75.09 183	98.31 142	84.75 178	96.90 97	97.78 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 92	90.82 104	94.44 45	94.59 179	86.37 41	97.18 12	97.02 50	89.20 66	84.31 269	96.66 72	73.74 207	99.17 50	86.74 153	97.96 73	97.79 91

EPP-MVSNet		91.70 93	91.56 91	92.13 137	95.88 117	80.50 206	97.33 7	95.25 199	86.15 155	89.76 139	95.60 117	83.42 82	98.32 141	87.37 145	93.25 174	97.56 104

MVSFormer		91.68 94	91.30 93	92.80 104	93.86 219	83.88 106	95.96 73	95.90 151	84.66 195	91.76 110	94.91 143	77.92 152	97.30 226	89.64 117	97.11 90	97.24 114

Effi-MVS+		91.59 95	91.11 97	93.01 92	94.35 197	83.39 122	94.60 153	95.10 207	87.10 130	90.57 126	93.10 216	81.43 113	98.07 165	89.29 121	94.48 150	97.59 102

IS-MVSNet		91.43 96	91.09 99	92.46 122	95.87 119	81.38 182	96.95 19	93.69 269	89.72 52	89.50 143	95.98 101	78.57 145	97.77 181	83.02 200	96.50 108	98.22 63

PVSNet_Blended_VisFu		91.38 97	90.91 102	92.80 104	96.39 94	83.17 128	94.87 136	96.66 88	83.29 225	89.27 147	94.46 165	80.29 121	99.17 50	87.57 141	95.37 129	96.05 175

diffmvs		91.37 98	91.23 95	91.77 158	93.09 246	80.27 210	92.36 265	95.52 182	87.03 132	91.40 118	94.93 142	80.08 123	97.44 210	92.13 78	94.56 147	97.61 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		91.31 99	91.11 97	91.93 146	94.37 193	80.14 214	93.46 223	95.80 158	86.46 147	91.35 119	93.77 195	82.21 102	98.09 162	87.57 141	94.95 137	97.55 105

OMC-MVS		91.23 100	90.62 107	93.08 88	96.27 97	84.07 101	93.52 220	95.93 147	86.95 134	89.51 141	96.13 96	78.50 146	98.35 136	85.84 166	92.90 180	96.83 141

PAPM_NR		91.22 101	90.78 105	92.52 120	97.60 59	81.46 179	94.37 173	96.24 120	86.39 149	87.41 179	94.80 151	82.06 107	98.48 119	82.80 206	95.37 129	97.61 100

PS-MVSNAJ		91.18 102	90.92 101	91.96 144	95.26 144	82.60 155	92.09 277	95.70 167	86.27 151	91.84 107	92.46 234	79.70 129	98.99 73	89.08 123	95.86 117	94.29 247

xiu_mvs_v2_base		91.13 103	90.89 103	91.86 152	94.97 157	82.42 157	92.24 271	95.64 174	86.11 159	91.74 112	93.14 214	79.67 132	98.89 84	89.06 124	95.46 126	94.28 248

nrg030		91.08 104	90.39 108	93.17 82	93.07 247	86.91 22	96.41 37	96.26 117	88.30 97	88.37 161	94.85 149	82.19 103	97.64 192	91.09 99	82.95 303	94.96 215

mamv4		90.92 105	91.78 88	88.33 286	95.67 127	70.75 369	92.92 249	96.02 142	81.90 258	88.11 162	95.34 126	85.88 51	96.97 254	95.22 27	95.01 136	97.26 113

lupinMVS		90.92 105	90.21 111	93.03 91	93.86 219	83.88 106	92.81 253	93.86 263	79.84 295	91.76 110	94.29 170	77.92 152	98.04 167	90.48 112	97.11 90	97.17 118

RRT-MVS		90.85 107	90.70 106	91.30 175	94.25 199	76.83 292	94.85 138	96.13 130	89.04 72	90.23 131	94.88 145	70.15 251	98.72 100	91.86 90	94.88 138	98.34 42

h-mvs33		90.80 108	90.15 114	92.75 107	96.01 110	82.66 152	95.43 101	95.53 181	89.80 46	93.08 71	95.64 116	75.77 172	99.00 71	92.07 79	78.05 360	96.60 148

jason		90.80 108	90.10 115	92.90 99	93.04 250	83.53 117	93.08 242	94.15 252	80.22 289	91.41 117	94.91 143	76.87 159	97.93 176	90.28 113	96.90 97	97.24 114
jason: jason.
VDD-MVS		90.74 110	89.92 122	93.20 81	96.27 97	83.02 138	95.73 86	93.86 263	88.42 94	92.53 88	96.84 63	62.09 324	98.64 106	90.95 104	92.62 186	97.93 82

PVSNet_Blended		90.73 111	90.32 110	91.98 142	96.12 102	81.25 184	92.55 260	96.83 69	82.04 253	89.10 149	92.56 232	81.04 116	98.85 89	86.72 155	95.91 116	95.84 182

test_yl		90.69 112	90.02 120	92.71 109	95.72 123	82.41 159	94.11 187	95.12 205	85.63 167	91.49 115	94.70 153	74.75 187	98.42 132	86.13 161	92.53 188	97.31 110

DCV-MVSNet		90.69 112	90.02 120	92.71 109	95.72 123	82.41 159	94.11 187	95.12 205	85.63 167	91.49 115	94.70 153	74.75 187	98.42 132	86.13 161	92.53 188	97.31 110

API-MVS		90.66 114	90.07 116	92.45 123	96.36 95	84.57 85	96.06 64	95.22 202	82.39 243	89.13 148	94.27 173	80.32 120	98.46 123	80.16 256	96.71 103	94.33 246

xiu_mvs_v1_base_debu		90.64 115	90.05 117	92.40 124	93.97 216	84.46 91	93.32 228	95.46 184	85.17 176	92.25 93	94.03 177	70.59 242	98.57 114	90.97 101	94.67 142	94.18 249

xiu_mvs_v1_base		90.64 115	90.05 117	92.40 124	93.97 216	84.46 91	93.32 228	95.46 184	85.17 176	92.25 93	94.03 177	70.59 242	98.57 114	90.97 101	94.67 142	94.18 249

xiu_mvs_v1_base_debi		90.64 115	90.05 117	92.40 124	93.97 216	84.46 91	93.32 228	95.46 184	85.17 176	92.25 93	94.03 177	70.59 242	98.57 114	90.97 101	94.67 142	94.18 249

HQP_MVS		90.60 118	90.19 112	91.82 155	94.70 174	82.73 148	95.85 80	96.22 122	90.81 17	86.91 188	94.86 147	74.23 195	98.12 152	88.15 132	89.99 218	94.63 227

FIs		90.51 119	90.35 109	90.99 192	93.99 215	80.98 192	95.73 86	97.54 4	89.15 68	86.72 195	94.68 155	81.83 111	97.24 234	85.18 171	88.31 251	94.76 225

mvsmamba		90.33 120	89.69 125	92.25 135	95.17 148	81.64 172	95.27 112	93.36 274	84.88 186	89.51 141	94.27 173	69.29 266	97.42 212	89.34 120	96.12 115	97.68 96

MAR-MVS		90.30 121	89.37 133	93.07 90	96.61 84	84.48 90	95.68 89	95.67 169	82.36 245	87.85 170	92.85 221	76.63 165	98.80 93	80.01 257	96.68 104	95.91 178
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
FC-MVSNet-test		90.27 122	90.18 113	90.53 204	93.71 226	79.85 228	95.77 85	97.59 3	89.31 62	86.27 206	94.67 156	81.93 110	97.01 252	84.26 184	88.09 254	94.71 226

CANet_DTU		90.26 123	89.41 132	92.81 103	93.46 236	83.01 139	93.48 221	94.47 239	89.43 58	87.76 174	94.23 175	70.54 246	99.03 61	84.97 173	96.39 110	96.38 156

SDMVSNet		90.19 124	89.61 127	91.93 146	96.00 111	83.09 134	92.89 250	95.98 143	88.73 82	86.85 192	95.20 134	72.09 226	97.08 245	88.90 125	89.85 224	95.63 192

OPM-MVS		90.12 125	89.56 128	91.82 155	93.14 243	83.90 105	94.16 183	95.74 163	88.96 77	87.86 169	95.43 124	72.48 222	97.91 177	88.10 136	90.18 217	93.65 283
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 126	89.13 139	92.95 97	96.71 80	82.32 161	96.08 60	89.91 361	86.79 138	92.15 97	96.81 66	62.60 322	98.34 137	87.18 147	93.90 158	98.19 64

GeoE		90.05 127	89.43 131	91.90 151	95.16 149	80.37 209	95.80 83	94.65 236	83.90 207	87.55 178	94.75 152	78.18 150	97.62 194	81.28 236	93.63 162	97.71 95

PAPR		90.02 128	89.27 138	92.29 132	95.78 121	80.95 194	92.68 255	96.22 122	81.91 257	86.66 196	93.75 197	82.23 101	98.44 129	79.40 268	94.79 140	97.48 106

PVSNet_BlendedMVS		89.98 129	89.70 124	90.82 197	96.12 102	81.25 184	93.92 205	96.83 69	83.49 219	89.10 149	92.26 242	81.04 116	98.85 89	86.72 155	87.86 258	92.35 330

PS-MVSNAJss		89.97 130	89.62 126	91.02 189	91.90 282	80.85 197	95.26 113	95.98 143	86.26 152	86.21 208	94.29 170	79.70 129	97.65 190	88.87 127	88.10 252	94.57 232

XVG-OURS-SEG-HR		89.95 131	89.45 129	91.47 169	94.00 214	81.21 187	91.87 281	96.06 138	85.78 162	88.55 157	95.73 113	74.67 191	97.27 230	88.71 128	89.64 229	95.91 178

UGNet		89.95 131	88.95 143	92.95 97	94.51 185	83.31 123	95.70 88	95.23 200	89.37 60	87.58 176	93.94 185	64.00 313	98.78 95	83.92 189	96.31 111	96.74 144
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
UniMVSNet_NR-MVSNet		89.92 133	89.29 136	91.81 157	93.39 238	83.72 109	94.43 165	97.12 44	89.80 46	86.46 199	93.32 205	83.16 84	97.23 235	84.92 174	81.02 333	94.49 240

AdaColmap		89.89 134	89.07 140	92.37 127	97.41 65	83.03 137	94.42 166	95.92 148	82.81 237	86.34 205	94.65 158	73.89 203	99.02 64	80.69 247	95.51 122	95.05 210

hse-mvs2		89.88 135	89.34 134	91.51 166	94.83 168	81.12 189	93.94 203	93.91 262	89.80 46	93.08 71	93.60 199	75.77 172	97.66 189	92.07 79	77.07 367	95.74 187

UniMVSNet (Re)		89.80 136	89.07 140	92.01 138	93.60 232	84.52 88	94.78 143	97.47 11	89.26 64	86.44 202	92.32 239	82.10 105	97.39 223	84.81 177	80.84 337	94.12 253

HQP-MVS		89.80 136	89.28 137	91.34 174	94.17 203	81.56 173	94.39 169	96.04 139	88.81 78	85.43 233	93.97 184	73.83 205	97.96 173	87.11 150	89.77 227	94.50 238

FA-MVS(test-final)		89.66 138	88.91 145	91.93 146	94.57 182	80.27 210	91.36 293	94.74 232	84.87 187	89.82 138	92.61 231	74.72 190	98.47 122	83.97 188	93.53 165	97.04 127

VPA-MVSNet		89.62 139	88.96 142	91.60 163	93.86 219	82.89 143	95.46 100	97.33 25	87.91 110	88.43 160	93.31 206	74.17 198	97.40 220	87.32 146	82.86 308	94.52 235

WTY-MVS		89.60 140	88.92 144	91.67 161	95.47 136	81.15 188	92.38 264	94.78 230	83.11 229	89.06 151	94.32 168	78.67 143	96.61 273	81.57 233	90.89 208	97.24 114

Vis-MVSNet (Re-imp)		89.59 141	89.44 130	90.03 229	95.74 122	75.85 307	95.61 96	90.80 345	87.66 122	87.83 171	95.40 125	76.79 161	96.46 287	78.37 274	96.73 102	97.80 90

VDDNet		89.56 142	88.49 158	92.76 106	95.07 152	82.09 163	96.30 41	93.19 277	81.05 283	91.88 105	96.86 62	61.16 340	98.33 139	88.43 131	92.49 190	97.84 88

114514_t		89.51 143	88.50 156	92.54 119	98.11 36	81.99 165	95.16 120	96.36 108	70.19 388	85.81 215	95.25 130	76.70 163	98.63 108	82.07 221	96.86 100	97.00 130

QAPM		89.51 143	88.15 167	93.59 73	94.92 161	84.58 84	96.82 29	96.70 86	78.43 318	83.41 289	96.19 93	73.18 214	99.30 43	77.11 290	96.54 106	96.89 137

CLD-MVS		89.47 145	88.90 146	91.18 180	94.22 201	82.07 164	92.13 275	96.09 134	87.90 111	85.37 239	92.45 235	74.38 193	97.56 197	87.15 148	90.43 213	93.93 262
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 146	88.90 146	91.12 181	94.47 186	81.49 177	95.30 107	96.14 127	86.73 141	85.45 230	95.16 136	69.89 253	98.10 154	87.70 139	89.23 236	93.77 277

CDS-MVSNet		89.45 146	88.51 155	92.29 132	93.62 231	83.61 116	93.01 245	94.68 235	81.95 255	87.82 172	93.24 210	78.69 142	96.99 253	80.34 253	93.23 175	96.28 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		89.41 148	88.64 151	91.71 160	94.74 170	80.81 198	93.54 219	95.10 207	83.11 229	86.82 194	90.67 301	79.74 128	97.75 185	80.51 251	93.55 164	96.57 151

ab-mvs		89.41 148	88.35 160	92.60 115	95.15 151	82.65 153	92.20 273	95.60 176	83.97 206	88.55 157	93.70 198	74.16 199	98.21 148	82.46 211	89.37 232	96.94 133

XVG-OURS		89.40 150	88.70 150	91.52 165	94.06 208	81.46 179	91.27 297	96.07 136	86.14 156	88.89 153	95.77 111	68.73 275	97.26 232	87.39 144	89.96 220	95.83 183

test_vis1_n_1920		89.39 151	89.84 123	88.04 294	92.97 254	72.64 346	94.71 148	96.03 141	86.18 154	91.94 104	96.56 81	61.63 328	95.74 323	93.42 47	95.11 135	95.74 187

mvs_anonymous		89.37 152	89.32 135	89.51 255	93.47 235	74.22 325	91.65 288	94.83 226	82.91 235	85.45 230	93.79 193	81.23 115	96.36 294	86.47 157	94.09 155	97.94 80

DU-MVS		89.34 153	88.50 156	91.85 154	93.04 250	83.72 109	94.47 162	96.59 93	89.50 56	86.46 199	93.29 208	77.25 157	97.23 235	84.92 174	81.02 333	94.59 230

TAMVS		89.21 154	88.29 164	91.96 144	93.71 226	82.62 154	93.30 232	94.19 250	82.22 248	87.78 173	93.94 185	78.83 139	96.95 256	77.70 283	92.98 179	96.32 157

ACMM	84.12 9	89.14 155	88.48 159	91.12 181	94.65 177	81.22 186	95.31 105	96.12 131	85.31 175	85.92 213	94.34 166	70.19 250	98.06 166	85.65 167	88.86 241	94.08 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 156	88.64 151	90.48 209	95.53 135	74.97 316	96.08 60	84.89 392	88.13 105	90.16 134	96.65 73	63.29 318	98.10 154	86.14 159	96.90 97	98.39 39

EI-MVSNet		89.10 156	88.86 148	89.80 242	91.84 284	78.30 262	93.70 215	95.01 211	85.73 164	87.15 183	95.28 128	79.87 126	97.21 237	83.81 191	87.36 266	93.88 266

ECVR-MVS		89.09 158	88.53 154	90.77 199	95.62 130	75.89 306	96.16 52	84.22 394	87.89 113	90.20 132	96.65 73	63.19 320	98.10 154	85.90 164	96.94 95	98.33 44

CNLPA		89.07 159	87.98 170	92.34 128	96.87 77	84.78 81	94.08 191	93.24 275	81.41 274	84.46 259	95.13 138	75.57 179	96.62 270	77.21 288	93.84 160	95.61 194

PLC	84.53 7	89.06 160	88.03 169	92.15 136	97.27 71	82.69 151	94.29 176	95.44 189	79.71 297	84.01 275	94.18 176	76.68 164	98.75 97	77.28 287	93.41 170	95.02 211
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 161	88.64 151	90.21 220	90.74 331	79.28 243	95.96 73	95.90 151	84.66 195	85.33 241	92.94 220	74.02 201	97.30 226	89.64 117	88.53 244	94.05 259

HY-MVS	83.01 12	89.03 161	87.94 172	92.29 132	94.86 166	82.77 144	92.08 278	94.49 238	81.52 273	86.93 186	92.79 227	78.32 149	98.23 145	79.93 258	90.55 211	95.88 180

ACMP	84.23 8	89.01 163	88.35 160	90.99 192	94.73 171	81.27 183	95.07 124	95.89 153	86.48 145	83.67 282	94.30 169	69.33 262	97.99 171	87.10 152	88.55 243	93.72 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 164	88.26 166	90.94 195	94.05 209	80.78 199	91.71 285	95.38 193	81.55 272	88.63 156	93.91 189	75.04 184	95.47 334	82.47 210	91.61 196	96.57 151

TranMVSNet+NR-MVSNet		88.84 165	87.95 171	91.49 167	92.68 260	83.01 139	94.92 133	96.31 111	89.88 40	85.53 224	93.85 192	76.63 165	96.96 255	81.91 225	79.87 350	94.50 238

CHOSEN 1792x2688		88.84 165	87.69 176	92.30 131	96.14 100	81.42 181	90.01 327	95.86 155	74.52 357	87.41 179	93.94 185	75.46 180	98.36 134	80.36 252	95.53 121	97.12 123

MVSTER		88.84 165	88.29 164	90.51 207	92.95 255	80.44 207	93.73 212	95.01 211	84.66 195	87.15 183	93.12 215	72.79 218	97.21 237	87.86 137	87.36 266	93.87 267

test_cas_vis1_n_1920		88.83 168	88.85 149	88.78 271	91.15 312	76.72 294	93.85 208	94.93 218	83.23 228	92.81 79	96.00 99	61.17 339	94.45 345	91.67 93	94.84 139	95.17 206

OpenMVS	83.78 11	88.74 169	87.29 186	93.08 88	92.70 259	85.39 71	96.57 35	96.43 102	78.74 313	80.85 321	96.07 97	69.64 257	99.01 66	78.01 281	96.65 105	94.83 222

thisisatest0530		88.67 170	87.61 178	91.86 152	94.87 165	80.07 217	94.63 152	89.90 362	84.00 205	88.46 159	93.78 194	66.88 290	98.46 123	83.30 196	92.65 185	97.06 125

Effi-MVS+-dtu		88.65 171	88.35 160	89.54 252	93.33 239	76.39 300	94.47 162	94.36 244	87.70 119	85.43 233	89.56 329	73.45 210	97.26 232	85.57 169	91.28 200	94.97 212

tttt0517		88.61 172	87.78 175	91.11 184	94.96 158	77.81 275	95.35 103	89.69 365	85.09 181	88.05 167	94.59 162	66.93 288	98.48 119	83.27 197	92.13 193	97.03 128

BH-untuned		88.60 173	88.13 168	90.01 232	95.24 145	78.50 256	93.29 233	94.15 252	84.75 192	84.46 259	93.40 202	75.76 174	97.40 220	77.59 284	94.52 149	94.12 253

sd_testset		88.59 174	87.85 174	90.83 196	96.00 111	80.42 208	92.35 266	94.71 233	88.73 82	86.85 192	95.20 134	67.31 282	96.43 289	79.64 262	89.85 224	95.63 192

NR-MVSNet		88.58 175	87.47 182	91.93 146	93.04 250	84.16 100	94.77 144	96.25 119	89.05 71	80.04 334	93.29 208	79.02 138	97.05 250	81.71 232	80.05 347	94.59 230

1112_ss		88.42 176	87.33 185	91.72 159	94.92 161	80.98 192	92.97 247	94.54 237	78.16 324	83.82 278	93.88 190	78.78 141	97.91 177	79.45 264	89.41 231	96.26 161

WR-MVS		88.38 177	87.67 177	90.52 206	93.30 240	80.18 212	93.26 235	95.96 146	88.57 90	85.47 229	92.81 225	76.12 167	96.91 259	81.24 237	82.29 313	94.47 243

BH-RMVSNet		88.37 178	87.48 181	91.02 189	95.28 141	79.45 235	92.89 250	93.07 280	85.45 172	86.91 188	94.84 150	70.35 247	97.76 182	73.97 318	94.59 146	95.85 181

IterMVS-LS		88.36 179	87.91 173	89.70 246	93.80 222	78.29 263	93.73 212	95.08 209	85.73 164	84.75 251	91.90 259	79.88 125	96.92 258	83.83 190	82.51 309	93.89 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 180	86.13 227	94.85 25	98.54 13	86.60 34	96.93 22	97.19 35	90.66 24	92.85 76	23.41 418	85.02 63	99.49 26	91.99 83	98.56 50	98.47 33

LCM-MVSNet-Re		88.30 181	88.32 163	88.27 287	94.71 173	72.41 351	93.15 238	90.98 339	87.77 117	79.25 343	91.96 256	78.35 148	95.75 322	83.04 199	95.62 120	96.65 147

jajsoiax		88.24 182	87.50 180	90.48 209	90.89 325	80.14 214	95.31 105	95.65 173	84.97 184	84.24 270	94.02 180	65.31 306	97.42 212	88.56 129	88.52 245	93.89 263

VPNet		88.20 183	87.47 182	90.39 214	93.56 233	79.46 234	94.04 195	95.54 180	88.67 85	86.96 185	94.58 163	69.33 262	97.15 239	84.05 187	80.53 342	94.56 233

TAPA-MVS	84.62 6	88.16 184	87.01 194	91.62 162	96.64 83	80.65 201	94.39 169	96.21 125	76.38 337	86.19 209	95.44 122	79.75 127	98.08 164	62.75 382	95.29 131	96.13 167
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 185	87.28 187	90.57 202	94.96 158	80.07 217	94.27 177	91.29 332	86.74 140	87.41 179	94.00 182	76.77 162	96.20 300	80.77 245	79.31 356	95.44 196

Anonymous20240529		88.09 186	86.59 209	92.58 117	96.53 90	81.92 168	95.99 70	95.84 156	74.11 361	89.06 151	95.21 133	61.44 332	98.81 92	83.67 194	87.47 263	97.01 129

HyFIR lowres test		88.09 186	86.81 198	91.93 146	96.00 111	80.63 202	90.01 327	95.79 159	73.42 368	87.68 175	92.10 250	73.86 204	97.96 173	80.75 246	91.70 195	97.19 117

mvs_tets		88.06 188	87.28 187	90.38 216	90.94 321	79.88 226	95.22 115	95.66 171	85.10 180	84.21 271	93.94 185	63.53 316	97.40 220	88.50 130	88.40 249	93.87 267

F-COLMAP		87.95 189	86.80 199	91.40 171	96.35 96	80.88 196	94.73 146	95.45 187	79.65 298	82.04 308	94.61 159	71.13 233	98.50 117	76.24 300	91.05 206	94.80 224

LS3D		87.89 190	86.32 220	92.59 116	96.07 108	82.92 142	95.23 114	94.92 219	75.66 344	82.89 296	95.98 101	72.48 222	99.21 48	68.43 353	95.23 134	95.64 191

anonymousdsp		87.84 191	87.09 190	90.12 225	89.13 359	80.54 205	94.67 150	95.55 178	82.05 251	83.82 278	92.12 247	71.47 231	97.15 239	87.15 148	87.80 261	92.67 318

v2v482		87.84 191	87.06 191	90.17 221	90.99 317	79.23 246	94.00 200	95.13 204	84.87 187	85.53 224	92.07 253	74.45 192	97.45 207	84.71 179	81.75 321	93.85 270

WR-MVS_H		87.80 193	87.37 184	89.10 264	93.23 241	78.12 266	95.61 96	97.30 29	87.90 111	83.72 280	92.01 255	79.65 133	96.01 308	76.36 297	80.54 341	93.16 303

AUN-MVS		87.78 194	86.54 212	91.48 168	94.82 169	81.05 190	93.91 207	93.93 259	83.00 232	86.93 186	93.53 200	69.50 260	97.67 187	86.14 159	77.12 366	95.73 189

PCF-MVS	84.11 10	87.74 195	86.08 231	92.70 111	94.02 210	84.43 94	89.27 340	95.87 154	73.62 366	84.43 261	94.33 167	78.48 147	98.86 87	70.27 339	94.45 151	94.81 223
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 196	86.13 227	92.31 130	96.66 82	80.74 200	94.87 136	91.49 327	80.47 288	89.46 144	95.44 122	54.72 373	98.23 145	82.19 217	89.89 222	97.97 78

V42		87.68 196	86.86 196	90.15 223	90.58 336	80.14 214	94.24 180	95.28 198	83.66 213	85.67 219	91.33 275	74.73 189	97.41 218	84.43 183	81.83 319	92.89 313

thres600view7		87.65 198	86.67 204	90.59 201	96.08 107	78.72 249	94.88 135	91.58 323	87.06 131	88.08 165	92.30 240	68.91 272	98.10 154	70.05 346	91.10 201	94.96 215

XXY-MVS		87.65 198	86.85 197	90.03 229	92.14 272	80.60 204	93.76 211	95.23 200	82.94 234	84.60 254	94.02 180	74.27 194	95.49 333	81.04 239	83.68 296	94.01 261

Test_1112_low_res		87.65 198	86.51 213	91.08 185	94.94 160	79.28 243	91.77 283	94.30 246	76.04 342	83.51 287	92.37 237	77.86 154	97.73 186	78.69 273	89.13 238	96.22 162

thres100view900		87.63 201	86.71 202	90.38 216	96.12 102	78.55 253	95.03 127	91.58 323	87.15 128	88.06 166	92.29 241	68.91 272	98.10 154	70.13 343	91.10 201	94.48 241

CP-MVSNet		87.63 201	87.26 189	88.74 275	93.12 244	76.59 297	95.29 109	96.58 94	88.43 93	83.49 288	92.98 219	75.28 181	95.83 317	78.97 270	81.15 329	93.79 272

thres400		87.62 203	86.64 205	90.57 202	95.99 114	78.64 251	94.58 154	91.98 312	86.94 135	88.09 163	91.77 261	69.18 268	98.10 154	70.13 343	91.10 201	94.96 215

v1144		87.61 204	86.79 200	90.06 228	91.01 316	79.34 239	93.95 202	95.42 192	83.36 224	85.66 220	91.31 278	74.98 185	97.42 212	83.37 195	82.06 315	93.42 292

tfpn200view9		87.58 205	86.64 205	90.41 213	95.99 114	78.64 251	94.58 154	91.98 312	86.94 135	88.09 163	91.77 261	69.18 268	98.10 154	70.13 343	91.10 201	94.48 241

BH-w/o		87.57 206	87.05 192	89.12 263	94.90 164	77.90 271	92.41 262	93.51 271	82.89 236	83.70 281	91.34 274	75.75 175	97.07 247	75.49 304	93.49 167	92.39 328

UniMVSNet_ETH3D		87.53 207	86.37 217	91.00 191	92.44 265	78.96 248	94.74 145	95.61 175	84.07 204	85.36 240	94.52 164	59.78 348	97.34 225	82.93 201	87.88 257	96.71 145

ET-MVSNet_ETH3D		87.51 208	85.91 239	92.32 129	93.70 228	83.93 104	92.33 268	90.94 341	84.16 201	72.09 384	92.52 233	69.90 252	95.85 316	89.20 122	88.36 250	97.17 118

1314		87.51 208	86.57 210	90.34 218	92.42 266	79.74 230	92.63 257	95.35 197	78.35 319	80.14 331	91.62 269	74.05 200	97.15 239	81.05 238	93.53 165	94.12 253

v8		87.50 210	86.71 202	89.89 236	91.37 302	79.40 236	94.50 158	95.38 193	84.81 190	83.60 285	91.33 275	76.05 168	97.42 212	82.84 204	80.51 344	92.84 315

Fast-Effi-MVS+-dtu		87.44 211	86.72 201	89.63 250	92.04 276	77.68 281	94.03 196	93.94 258	85.81 161	82.42 301	91.32 277	70.33 248	97.06 248	80.33 254	90.23 216	94.14 252

MVS		87.44 211	86.10 230	91.44 170	92.61 261	83.62 114	92.63 257	95.66 171	67.26 393	81.47 313	92.15 245	77.95 151	98.22 147	79.71 260	95.48 124	92.47 324

FE-MVS		87.40 213	86.02 233	91.57 164	94.56 183	79.69 231	90.27 314	93.72 268	80.57 286	88.80 154	91.62 269	65.32 305	98.59 113	74.97 312	94.33 154	96.44 154

FMVSNet3		87.40 213	86.11 229	91.30 175	93.79 224	83.64 113	94.20 182	94.81 228	83.89 208	84.37 262	91.87 260	68.45 278	96.56 278	78.23 278	85.36 280	93.70 282

test_fmvs1		87.34 215	87.56 179	86.68 331	90.59 335	71.80 355	94.01 198	94.04 257	78.30 320	91.97 101	95.22 131	56.28 364	93.71 360	92.89 55	94.71 141	94.52 235

thisisatest0515		87.33 216	85.99 234	91.37 173	93.49 234	79.55 232	90.63 310	89.56 369	80.17 290	87.56 177	90.86 291	67.07 287	98.28 143	81.50 234	93.02 178	96.29 159

PS-CasMVS		87.32 217	86.88 195	88.63 278	92.99 253	76.33 302	95.33 104	96.61 92	88.22 101	83.30 293	93.07 217	73.03 216	95.79 321	78.36 275	81.00 335	93.75 279

GBi-Net		87.26 218	85.98 235	91.08 185	94.01 211	83.10 131	95.14 121	94.94 214	83.57 215	84.37 262	91.64 265	66.59 295	96.34 295	78.23 278	85.36 280	93.79 272

test1		87.26 218	85.98 235	91.08 185	94.01 211	83.10 131	95.14 121	94.94 214	83.57 215	84.37 262	91.64 265	66.59 295	96.34 295	78.23 278	85.36 280	93.79 272

v1192		87.25 220	86.33 219	90.00 233	90.76 330	79.04 247	93.80 209	95.48 183	82.57 241	85.48 228	91.18 282	73.38 213	97.42 212	82.30 214	82.06 315	93.53 286

v10		87.25 220	86.38 216	89.85 237	91.19 308	79.50 233	94.48 159	95.45 187	83.79 211	83.62 284	91.19 280	75.13 182	97.42 212	81.94 224	80.60 339	92.63 320

DP-MVS		87.25 220	85.36 256	92.90 99	97.65 58	83.24 125	94.81 141	92.00 310	74.99 352	81.92 310	95.00 141	72.66 219	99.05 58	66.92 365	92.33 191	96.40 155

miper_ehance_all_eth		87.22 223	86.62 208	89.02 267	92.13 273	77.40 285	90.91 306	94.81 228	81.28 277	84.32 267	90.08 317	79.26 135	96.62 270	83.81 191	82.94 304	93.04 308

test2506		87.21 224	86.28 222	90.02 231	95.62 130	73.64 332	96.25 47	71.38 416	87.89 113	90.45 127	96.65 73	55.29 370	98.09 162	86.03 163	96.94 95	98.33 44

thres200		87.21 224	86.24 224	90.12 225	95.36 138	78.53 254	93.26 235	92.10 306	86.42 148	88.00 168	91.11 286	69.24 267	98.00 170	69.58 347	91.04 207	93.83 271

v144192		87.19 226	86.35 218	89.74 243	90.64 334	78.24 264	93.92 205	95.43 190	81.93 256	85.51 226	91.05 288	74.21 197	97.45 207	82.86 203	81.56 323	93.53 286

FMVSNet2		87.19 226	85.82 242	91.30 175	94.01 211	83.67 111	94.79 142	94.94 214	83.57 215	83.88 277	92.05 254	66.59 295	96.51 282	77.56 285	85.01 283	93.73 280

c3_l		87.14 228	86.50 214	89.04 266	92.20 270	77.26 286	91.22 300	94.70 234	82.01 254	84.34 266	90.43 306	78.81 140	96.61 273	83.70 193	81.09 330	93.25 297

testing91		87.11 229	86.18 225	89.92 235	94.43 191	75.38 315	91.53 290	92.27 302	86.48 145	86.50 197	90.24 309	61.19 338	97.53 199	82.10 219	90.88 209	96.84 140

Baseline_NR-MVSNet		87.07 230	86.63 207	88.40 281	91.44 297	77.87 273	94.23 181	92.57 294	84.12 203	85.74 218	92.08 251	77.25 157	96.04 305	82.29 215	79.94 348	91.30 351

v148		87.04 231	86.32 220	89.21 260	90.94 321	77.26 286	93.71 214	94.43 240	84.84 189	84.36 265	90.80 295	76.04 169	97.05 250	82.12 218	79.60 353	93.31 294

test_fmvs1_n		87.03 232	87.04 193	86.97 323	89.74 353	71.86 353	94.55 156	94.43 240	78.47 316	91.95 103	95.50 121	51.16 384	93.81 358	93.02 54	94.56 147	95.26 203

v1921920		86.97 233	86.06 232	89.69 247	90.53 339	78.11 267	93.80 209	95.43 190	81.90 258	85.33 241	91.05 288	72.66 219	97.41 218	82.05 222	81.80 320	93.53 286

tt0805		86.92 234	85.74 248	90.48 209	92.22 269	79.98 224	95.63 95	94.88 222	83.83 210	84.74 252	92.80 226	57.61 359	97.67 187	85.48 170	84.42 287	93.79 272

miper_enhance_ethall		86.90 235	86.18 225	89.06 265	91.66 293	77.58 283	90.22 320	94.82 227	79.16 304	84.48 258	89.10 334	79.19 137	96.66 268	84.06 186	82.94 304	92.94 311

MonoMVSNet		86.89 236	86.55 211	87.92 298	89.46 357	73.75 329	94.12 185	93.10 278	87.82 116	85.10 244	90.76 297	69.59 258	94.94 343	86.47 157	82.50 310	95.07 209

v7n		86.81 237	85.76 246	89.95 234	90.72 332	79.25 245	95.07 124	95.92 148	84.45 198	82.29 302	90.86 291	72.60 221	97.53 199	79.42 267	80.52 343	93.08 307

PEN-MVS		86.80 238	86.27 223	88.40 281	92.32 268	75.71 310	95.18 118	96.38 107	87.97 108	82.82 297	93.15 213	73.39 212	95.92 312	76.15 301	79.03 358	93.59 284

cl22		86.78 239	85.98 235	89.18 262	92.34 267	77.62 282	90.84 307	94.13 254	81.33 276	83.97 276	90.15 314	73.96 202	96.60 275	84.19 185	82.94 304	93.33 293

v1240		86.78 239	85.85 241	89.56 251	90.45 340	77.79 277	93.61 217	95.37 195	81.65 267	85.43 233	91.15 284	71.50 230	97.43 211	81.47 235	82.05 317	93.47 290

TR-MVS		86.78 239	85.76 246	89.82 239	94.37 193	78.41 258	92.47 261	92.83 286	81.11 282	86.36 203	92.40 236	68.73 275	97.48 203	73.75 322	89.85 224	93.57 285

PatchMatch-RL		86.77 242	85.54 250	90.47 212	95.88 117	82.71 150	90.54 311	92.31 300	79.82 296	84.32 267	91.57 273	68.77 274	96.39 291	73.16 324	93.48 169	92.32 331

testing99		86.72 243	85.73 249	89.69 247	94.23 200	74.91 318	91.35 294	90.97 340	86.14 156	86.36 203	90.22 310	59.41 350	97.48 203	82.24 216	90.66 210	96.69 146

PAPM		86.68 244	85.39 254	90.53 204	93.05 249	79.33 242	89.79 330	94.77 231	78.82 310	81.95 309	93.24 210	76.81 160	97.30 226	66.94 363	93.16 176	94.95 218

pm-mvs1		86.61 245	85.54 250	89.82 239	91.44 297	80.18 212	95.28 111	94.85 224	83.84 209	81.66 311	92.62 230	72.45 224	96.48 284	79.67 261	78.06 359	92.82 316

GA-MVS		86.61 245	85.27 259	90.66 200	91.33 305	78.71 250	90.40 313	93.81 266	85.34 174	85.12 243	89.57 328	61.25 335	97.11 244	80.99 242	89.59 230	96.15 165

Anonymous20231211		86.59 247	85.13 261	90.98 194	96.52 91	81.50 175	96.14 56	96.16 126	73.78 364	83.65 283	92.15 245	63.26 319	97.37 224	82.82 205	81.74 322	94.06 258

test_vis1_n		86.56 248	86.49 215	86.78 330	88.51 364	72.69 343	94.68 149	93.78 267	79.55 299	90.70 124	95.31 127	48.75 389	93.28 366	93.15 51	93.99 156	94.38 245

DIV-MVS_self_test		86.53 249	85.78 243	88.75 273	92.02 278	76.45 299	90.74 308	94.30 246	81.83 263	83.34 291	90.82 294	75.75 175	96.57 276	81.73 231	81.52 325	93.24 298

cl____		86.52 250	85.78 243	88.75 273	92.03 277	76.46 298	90.74 308	94.30 246	81.83 263	83.34 291	90.78 296	75.74 177	96.57 276	81.74 230	81.54 324	93.22 299

eth_miper_zixun_eth		86.50 251	85.77 245	88.68 276	91.94 279	75.81 308	90.47 312	94.89 220	82.05 251	84.05 273	90.46 305	75.96 170	96.77 263	82.76 207	79.36 355	93.46 291

baseline2		86.50 251	85.39 254	89.84 238	91.12 313	76.70 295	91.88 280	88.58 372	82.35 246	79.95 335	90.95 290	73.42 211	97.63 193	80.27 255	89.95 221	95.19 205

EPNet_dtu		86.49 253	85.94 238	88.14 292	90.24 343	72.82 341	94.11 187	92.20 304	86.66 143	79.42 342	92.36 238	73.52 208	95.81 319	71.26 331	93.66 161	95.80 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		86.44 254	85.35 257	89.69 247	94.29 198	75.40 314	91.30 295	90.53 348	84.76 191	85.06 245	90.13 315	58.95 354	97.45 207	82.08 220	91.09 205	96.21 164

cascas		86.43 255	84.98 264	90.80 198	92.10 275	80.92 195	90.24 318	95.91 150	73.10 371	83.57 286	88.39 347	65.15 307	97.46 206	84.90 176	91.43 198	94.03 260

reproduce_monomvs		86.37 256	85.87 240	87.87 299	93.66 230	73.71 330	93.44 224	95.02 210	88.61 88	82.64 300	91.94 257	57.88 358	96.68 267	89.96 114	79.71 352	93.22 299

SCA		86.32 257	85.18 260	89.73 245	92.15 271	76.60 296	91.12 301	91.69 319	83.53 218	85.50 227	88.81 340	66.79 291	96.48 284	76.65 293	90.35 215	96.12 168

LTVRE_ROB	82.13 13	86.26 258	84.90 267	90.34 218	94.44 190	81.50 175	92.31 270	94.89 220	83.03 231	79.63 340	92.67 228	69.69 256	97.79 180	71.20 332	86.26 275	91.72 341
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
DTE-MVSNet		86.11 259	85.48 252	87.98 295	91.65 294	74.92 317	94.93 132	95.75 162	87.36 126	82.26 303	93.04 218	72.85 217	95.82 318	74.04 317	77.46 364	93.20 301

XVG-ACMP-BASELINE		86.00 260	84.84 269	89.45 256	91.20 307	78.00 268	91.70 286	95.55 178	85.05 182	82.97 295	92.25 243	54.49 374	97.48 203	82.93 201	87.45 265	92.89 313

MVP-Stereo		85.97 261	84.86 268	89.32 258	90.92 323	82.19 162	92.11 276	94.19 250	78.76 312	78.77 348	91.63 268	68.38 279	96.56 278	75.01 311	93.95 157	89.20 379
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 262	85.09 262	88.35 283	90.79 328	77.42 284	91.83 282	95.70 167	80.77 285	80.08 333	90.02 318	66.74 293	96.37 292	81.88 226	87.97 256	91.26 352

test-LLR		85.87 263	85.41 253	87.25 315	90.95 319	71.67 358	89.55 334	89.88 363	83.41 221	84.54 256	87.95 354	67.25 284	95.11 339	81.82 227	93.37 172	94.97 212

FMVSNet1		85.85 264	84.11 281	91.08 185	92.81 257	83.10 131	95.14 121	94.94 214	81.64 268	82.68 298	91.64 265	59.01 353	96.34 295	75.37 306	83.78 293	93.79 272

PatchmatchNet		85.85 264	84.70 271	89.29 259	91.76 288	75.54 311	88.49 352	91.30 331	81.63 269	85.05 246	88.70 344	71.71 227	96.24 299	74.61 315	89.05 239	96.08 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 266	84.94 266	88.26 288	91.16 311	72.58 349	89.47 338	91.04 338	76.26 340	86.45 201	89.97 320	70.74 240	96.86 262	82.35 213	87.07 271	95.34 202

PMMVS		85.71 267	84.96 265	87.95 296	88.90 362	77.09 288	88.68 350	90.06 357	72.32 378	86.47 198	90.76 297	72.15 225	94.40 347	81.78 229	93.49 167	92.36 329

PVSNet	78.82 18	85.55 268	84.65 272	88.23 290	94.72 172	71.93 352	87.12 371	92.75 290	78.80 311	84.95 248	90.53 303	64.43 311	96.71 266	74.74 313	93.86 159	96.06 174

UBG		85.51 269	84.57 275	88.35 283	94.21 202	71.78 356	90.07 325	89.66 367	82.28 247	85.91 214	89.01 336	61.30 333	97.06 248	76.58 296	92.06 194	96.22 162

IterMVS-SCA-FT		85.45 270	84.53 276	88.18 291	91.71 290	76.87 291	90.19 322	92.65 293	85.40 173	81.44 314	90.54 302	66.79 291	95.00 342	81.04 239	81.05 331	92.66 319

pmmvs4		85.43 271	83.86 286	90.16 222	90.02 348	82.97 141	90.27 314	92.67 292	75.93 343	80.73 322	91.74 263	71.05 234	95.73 324	78.85 272	83.46 300	91.78 340

mvsany_test1		85.42 272	85.30 258	85.77 342	87.95 375	75.41 313	87.61 368	80.97 402	76.82 334	88.68 155	95.83 107	77.44 156	90.82 388	85.90 164	86.51 273	91.08 359

ACMH	80.38 17	85.36 273	83.68 288	90.39 214	94.45 189	80.63 202	94.73 146	94.85 224	82.09 250	77.24 356	92.65 229	60.01 346	97.58 195	72.25 328	84.87 284	92.96 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 274	84.64 273	87.49 308	90.77 329	72.59 348	94.01 198	94.40 242	84.72 193	79.62 341	93.17 212	61.91 326	96.72 264	81.99 223	81.16 327	93.16 303

CR-MVSNet		85.35 274	83.76 287	90.12 225	90.58 336	79.34 239	85.24 384	91.96 314	78.27 321	85.55 222	87.87 357	71.03 235	95.61 326	73.96 319	89.36 233	95.40 198

tpmrst		85.35 274	84.99 263	86.43 334	90.88 326	67.88 382	88.71 349	91.43 329	80.13 291	86.08 211	88.80 342	73.05 215	96.02 307	82.48 209	83.40 302	95.40 198

miper_lstm_enhance		85.27 277	84.59 274	87.31 312	91.28 306	74.63 320	87.69 365	94.09 256	81.20 281	81.36 316	89.85 323	74.97 186	94.30 350	81.03 241	79.84 351	93.01 309

IB-MVS	80.51 15	85.24 278	83.26 294	91.19 179	92.13 273	79.86 227	91.75 284	91.29 332	83.28 226	80.66 324	88.49 346	61.28 334	98.46 123	80.99 242	79.46 354	95.25 204
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
CHOSEN 280x420		85.15 279	83.99 284	88.65 277	92.47 263	78.40 259	79.68 406	92.76 289	74.90 354	81.41 315	89.59 327	69.85 255	95.51 330	79.92 259	95.29 131	92.03 336

RPSCF		85.07 280	84.27 277	87.48 309	92.91 256	70.62 371	91.69 287	92.46 295	76.20 341	82.67 299	95.22 131	63.94 314	97.29 229	77.51 286	85.80 277	94.53 234

MS-PatchMatch		85.05 281	84.16 279	87.73 301	91.42 300	78.51 255	91.25 298	93.53 270	77.50 327	80.15 330	91.58 271	61.99 325	95.51 330	75.69 303	94.35 153	89.16 380

ACMH+	81.04 14	85.05 281	83.46 291	89.82 239	94.66 176	79.37 237	94.44 164	94.12 255	82.19 249	78.04 351	92.82 224	58.23 356	97.54 198	73.77 321	82.90 307	92.54 321

mmtdpeth		85.04 283	84.15 280	87.72 302	93.11 245	75.74 309	94.37 173	92.83 286	84.98 183	89.31 146	86.41 371	61.61 330	97.14 242	92.63 62	62.11 399	90.29 367

WBMVS		84.97 284	84.18 278	87.34 311	94.14 207	71.62 360	90.20 321	92.35 297	81.61 270	84.06 272	90.76 297	61.82 327	96.52 281	78.93 271	83.81 292	93.89 263

IterMVS		84.88 285	83.98 285	87.60 304	91.44 297	76.03 304	90.18 323	92.41 296	83.24 227	81.06 320	90.42 307	66.60 294	94.28 351	79.46 263	80.98 336	92.48 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 286	83.09 297	90.14 224	93.80 222	80.05 219	89.18 343	93.09 279	78.89 308	78.19 349	91.91 258	65.86 304	97.27 230	68.47 352	88.45 247	93.11 305

testing222		84.84 287	83.32 292	89.43 257	94.15 206	75.94 305	91.09 302	89.41 370	84.90 185	85.78 216	89.44 330	52.70 381	96.28 298	70.80 338	91.57 197	96.07 172

tpm		84.73 288	84.02 283	86.87 328	90.33 341	68.90 378	89.06 345	89.94 360	80.85 284	85.75 217	89.86 322	68.54 277	95.97 309	77.76 282	84.05 291	95.75 186

tfpnnormal		84.72 289	83.23 295	89.20 261	92.79 258	80.05 219	94.48 159	95.81 157	82.38 244	81.08 319	91.21 279	69.01 271	96.95 256	61.69 384	80.59 340	90.58 366

CVMVSNet		84.69 290	84.79 270	84.37 355	91.84 284	64.92 393	93.70 215	91.47 328	66.19 395	86.16 210	95.28 128	67.18 286	93.33 365	80.89 244	90.42 214	94.88 220

test-mter		84.54 291	83.64 289	87.25 315	90.95 319	71.67 358	89.55 334	89.88 363	79.17 303	84.54 256	87.95 354	55.56 366	95.11 339	81.82 227	93.37 172	94.97 212

ETVMVS		84.43 292	82.92 301	88.97 269	94.37 193	74.67 319	91.23 299	88.35 374	83.37 223	86.06 212	89.04 335	55.38 368	95.67 325	67.12 361	91.34 199	96.58 150

TransMVSNet (Re)		84.43 292	83.06 299	88.54 279	91.72 289	78.44 257	95.18 118	92.82 288	82.73 239	79.67 339	92.12 247	73.49 209	95.96 310	71.10 336	68.73 389	91.21 353

pmmvs5		84.21 294	82.84 304	88.34 285	88.95 361	76.94 290	92.41 262	91.91 316	75.63 345	80.28 328	91.18 282	64.59 310	95.57 327	77.09 291	83.47 299	92.53 322

dmvs_re		84.20 295	83.22 296	87.14 321	91.83 286	77.81 275	90.04 326	90.19 353	84.70 194	81.49 312	89.17 333	64.37 312	91.13 386	71.58 330	85.65 279	92.46 325

tpm2		84.08 296	82.94 300	87.48 309	91.39 301	71.27 361	89.23 342	90.37 350	71.95 380	84.64 253	89.33 331	67.30 283	96.55 280	75.17 308	87.09 270	94.63 227

test_fmvs2		83.98 297	84.03 282	83.83 360	87.16 378	67.53 386	93.93 204	92.89 284	77.62 326	86.89 191	93.53 200	47.18 393	92.02 378	90.54 109	86.51 273	91.93 338

COLMAP_ROB	80.39 16	83.96 298	82.04 307	89.74 243	95.28 141	79.75 229	94.25 178	92.28 301	75.17 350	78.02 352	93.77 195	58.60 355	97.84 179	65.06 374	85.92 276	91.63 343
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 299	81.53 310	91.21 178	90.58 336	79.34 239	85.24 384	96.76 78	71.44 382	85.55 222	82.97 391	70.87 238	98.91 83	61.01 386	89.36 233	95.40 198

SixPastTwentyTwo		83.91 300	82.90 302	86.92 325	90.99 317	70.67 370	93.48 221	91.99 311	85.54 170	77.62 355	92.11 249	60.59 342	96.87 261	76.05 302	77.75 361	93.20 301

EPMVS		83.90 301	82.70 305	87.51 306	90.23 344	72.67 344	88.62 351	81.96 400	81.37 275	85.01 247	88.34 348	66.31 298	94.45 345	75.30 307	87.12 269	95.43 197

WB-MVSnew		83.77 302	83.28 293	85.26 349	91.48 296	71.03 365	91.89 279	87.98 375	78.91 306	84.78 250	90.22 310	69.11 270	94.02 354	64.70 375	90.44 212	90.71 361

TESTMET0.1,1		83.74 303	82.85 303	86.42 335	89.96 349	71.21 363	89.55 334	87.88 376	77.41 328	83.37 290	87.31 362	56.71 362	93.65 362	80.62 249	92.85 183	94.40 244

UWE-MVS		83.69 304	83.09 297	85.48 344	93.06 248	65.27 392	90.92 305	86.14 384	79.90 294	86.26 207	90.72 300	57.17 361	95.81 319	71.03 337	92.62 186	95.35 201

pmmvs6		83.42 305	81.60 309	88.87 270	88.01 373	77.87 273	94.96 130	94.24 249	74.67 356	78.80 347	91.09 287	60.17 345	96.49 283	77.06 292	75.40 373	92.23 333

AllTest		83.42 305	81.39 311	89.52 253	95.01 154	77.79 277	93.12 239	90.89 343	77.41 328	76.12 364	93.34 203	54.08 376	97.51 201	68.31 354	84.27 289	93.26 295

tpmvs		83.35 307	82.07 306	87.20 319	91.07 315	71.00 367	88.31 355	91.70 318	78.91 306	80.49 327	87.18 366	69.30 265	97.08 245	68.12 357	83.56 298	93.51 289

USDC		82.76 308	81.26 313	87.26 314	91.17 309	74.55 321	89.27 340	93.39 273	78.26 322	75.30 370	92.08 251	54.43 375	96.63 269	71.64 329	85.79 278	90.61 363

Patchmtry		82.71 309	80.93 315	88.06 293	90.05 347	76.37 301	84.74 389	91.96 314	72.28 379	81.32 317	87.87 357	71.03 235	95.50 332	68.97 349	80.15 346	92.32 331

PatchT		82.68 310	81.27 312	86.89 327	90.09 346	70.94 368	84.06 391	90.15 354	74.91 353	85.63 221	83.57 386	69.37 261	94.87 344	65.19 371	88.50 246	94.84 221

MIMVSNet		82.59 311	80.53 316	88.76 272	91.51 295	78.32 261	86.57 375	90.13 355	79.32 300	80.70 323	88.69 345	52.98 380	93.07 370	66.03 369	88.86 241	94.90 219

test0.0.03 1		82.41 312	81.69 308	84.59 353	88.23 370	72.89 340	90.24 318	87.83 377	83.41 221	79.86 337	89.78 324	67.25 284	88.99 397	65.18 372	83.42 301	91.90 339

EG-PatchMatch MVS		82.37 313	80.34 319	88.46 280	90.27 342	79.35 238	92.80 254	94.33 245	77.14 332	73.26 381	90.18 313	47.47 392	96.72 264	70.25 340	87.32 268	89.30 376

tpm cat1		81.96 314	80.27 320	87.01 322	91.09 314	71.02 366	87.38 369	91.53 326	66.25 394	80.17 329	86.35 373	68.22 280	96.15 303	69.16 348	82.29 313	93.86 269

our_test_3		81.93 315	80.46 318	86.33 336	88.46 367	73.48 334	88.46 353	91.11 334	76.46 335	76.69 360	88.25 350	66.89 289	94.36 348	68.75 350	79.08 357	91.14 355

ppachtmachnet_test		81.84 316	80.07 324	87.15 320	88.46 367	74.43 324	89.04 346	92.16 305	75.33 348	77.75 353	88.99 337	66.20 300	95.37 335	65.12 373	77.60 362	91.65 342

gg-mvs-nofinetune		81.77 317	79.37 332	88.99 268	90.85 327	77.73 280	86.29 376	79.63 405	74.88 355	83.19 294	69.05 407	60.34 343	96.11 304	75.46 305	94.64 145	93.11 305

CL-MVSNet_self_test		81.74 318	80.53 316	85.36 346	85.96 384	72.45 350	90.25 316	93.07 280	81.24 279	79.85 338	87.29 363	70.93 237	92.52 373	66.95 362	69.23 385	91.11 357

Patchmatch-RL test		81.67 319	79.96 325	86.81 329	85.42 389	71.23 362	82.17 399	87.50 380	78.47 316	77.19 357	82.50 393	70.81 239	93.48 363	82.66 208	72.89 377	95.71 190

ADS-MVSNet2		81.66 320	79.71 329	87.50 307	91.35 303	74.19 326	83.33 394	88.48 373	72.90 373	82.24 304	85.77 377	64.98 308	93.20 368	64.57 376	83.74 294	95.12 207

K. test v3		81.59 321	80.15 323	85.91 341	89.89 351	69.42 377	92.57 259	87.71 378	85.56 169	73.44 380	89.71 326	55.58 365	95.52 329	77.17 289	69.76 383	92.78 317

ADS-MVSNet		81.56 322	79.78 326	86.90 326	91.35 303	71.82 354	83.33 394	89.16 371	72.90 373	82.24 304	85.77 377	64.98 308	93.76 359	64.57 376	83.74 294	95.12 207

FMVSNet5		81.52 323	79.60 330	87.27 313	91.17 309	77.95 269	91.49 291	92.26 303	76.87 333	76.16 363	87.91 356	51.67 382	92.34 375	67.74 358	81.16 327	91.52 346

dp		81.47 324	80.23 321	85.17 350	89.92 350	65.49 390	86.74 373	90.10 356	76.30 339	81.10 318	87.12 367	62.81 321	95.92 312	68.13 356	79.88 349	94.09 256

Patchmatch-test		81.37 325	79.30 333	87.58 305	90.92 323	74.16 327	80.99 401	87.68 379	70.52 386	76.63 361	88.81 340	71.21 232	92.76 372	60.01 390	86.93 272	95.83 183

EU-MVSNet		81.32 326	80.95 314	82.42 368	88.50 366	63.67 397	93.32 228	91.33 330	64.02 398	80.57 326	92.83 223	61.21 337	92.27 376	76.34 298	80.38 345	91.32 350

test_0402		81.30 327	79.17 337	87.67 303	93.19 242	78.17 265	92.98 246	91.71 317	75.25 349	76.02 366	90.31 308	59.23 351	96.37 292	50.22 402	83.63 297	88.47 387

JIA-IIPM		81.04 328	78.98 341	87.25 315	88.64 363	73.48 334	81.75 400	89.61 368	73.19 370	82.05 307	73.71 403	66.07 303	95.87 315	71.18 334	84.60 286	92.41 327

Anonymous20231206		81.03 329	79.77 328	84.82 352	87.85 376	70.26 373	91.42 292	92.08 307	73.67 365	77.75 353	89.25 332	62.43 323	93.08 369	61.50 385	82.00 318	91.12 356

mvs5depth		80.98 330	79.15 338	86.45 333	84.57 392	73.29 336	87.79 361	91.67 320	80.52 287	82.20 306	89.72 325	55.14 371	95.93 311	73.93 320	66.83 391	90.12 369

pmmvs-eth3d		80.97 331	78.72 343	87.74 300	84.99 391	79.97 225	90.11 324	91.65 321	75.36 347	73.51 379	86.03 374	59.45 349	93.96 357	75.17 308	72.21 378	89.29 378

testgi		80.94 332	80.20 322	83.18 361	87.96 374	66.29 387	91.28 296	90.70 347	83.70 212	78.12 350	92.84 222	51.37 383	90.82 388	63.34 379	82.46 311	92.43 326

CMPMVS	59.16 21	80.52 333	79.20 336	84.48 354	83.98 393	67.63 385	89.95 329	93.84 265	64.79 397	66.81 395	91.14 285	57.93 357	95.17 337	76.25 299	88.10 252	90.65 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 334	79.59 331	83.06 363	93.44 237	64.64 394	93.33 227	85.47 389	84.34 200	79.93 336	90.84 293	44.35 399	92.39 374	57.06 397	87.56 262	92.16 335

Anonymous20240521		80.44 335	79.21 335	84.11 358	85.75 387	67.89 381	92.86 252	93.23 276	75.61 346	75.59 369	87.47 361	50.03 385	94.33 349	71.14 335	81.21 326	90.12 369

LF4IMVS		80.37 336	79.07 340	84.27 357	86.64 380	69.87 376	89.39 339	91.05 337	76.38 337	74.97 372	90.00 319	47.85 391	94.25 352	74.55 316	80.82 338	88.69 385

KD-MVS_self_test		80.20 337	79.24 334	83.07 362	85.64 388	65.29 391	91.01 304	93.93 259	78.71 314	76.32 362	86.40 372	59.20 352	92.93 371	72.59 326	69.35 384	91.00 360

Syy-MVS		80.07 338	79.78 326	80.94 372	91.92 280	59.93 403	89.75 332	87.40 381	81.72 265	78.82 345	87.20 364	66.29 299	91.29 384	47.06 404	87.84 259	91.60 344

UnsupCasMVSNet_eth		80.07 338	78.27 344	85.46 345	85.24 390	72.63 347	88.45 354	94.87 223	82.99 233	71.64 387	88.07 353	56.34 363	91.75 381	73.48 323	63.36 397	92.01 337

test20.03		79.95 340	79.08 339	82.55 365	85.79 386	67.74 384	91.09 302	91.08 335	81.23 280	74.48 376	89.96 321	61.63 328	90.15 390	60.08 388	76.38 369	89.76 371

TDRefinement		79.81 341	77.34 346	87.22 318	79.24 406	75.48 312	93.12 239	92.03 309	76.45 336	75.01 371	91.58 271	49.19 388	96.44 288	70.22 342	69.18 386	89.75 372

TinyColmap		79.76 342	77.69 345	85.97 338	91.71 290	73.12 337	89.55 334	90.36 351	75.03 351	72.03 385	90.19 312	46.22 396	96.19 302	63.11 380	81.03 332	88.59 386

myMVS_eth3d		79.67 343	78.79 342	82.32 369	91.92 280	64.08 395	89.75 332	87.40 381	81.72 265	78.82 345	87.20 364	45.33 397	91.29 384	59.09 392	87.84 259	91.60 344

OpenMVS_ROB	74.94 19	79.51 344	77.03 351	86.93 324	87.00 379	76.23 303	92.33 268	90.74 346	68.93 390	74.52 375	88.23 351	49.58 387	96.62 270	57.64 395	84.29 288	87.94 390

MIMVSNet1		79.38 345	77.28 347	85.69 343	86.35 381	73.67 331	91.61 289	92.75 290	78.11 325	72.64 383	88.12 352	48.16 390	91.97 380	60.32 387	77.49 363	91.43 349

YYNet1		79.22 346	77.20 348	85.28 348	88.20 372	72.66 345	85.87 378	90.05 359	74.33 359	62.70 398	87.61 359	66.09 302	92.03 377	66.94 363	72.97 376	91.15 354

MDA-MVSNet_test_wron		79.21 347	77.19 349	85.29 347	88.22 371	72.77 342	85.87 378	90.06 357	74.34 358	62.62 400	87.56 360	66.14 301	91.99 379	66.90 366	73.01 375	91.10 358

MDA-MVSNet-bldmvs		78.85 348	76.31 353	86.46 332	89.76 352	73.88 328	88.79 348	90.42 349	79.16 304	59.18 403	88.33 349	60.20 344	94.04 353	62.00 383	68.96 387	91.48 348

KD-MVS_2432*1600		78.50 349	76.02 356	85.93 339	86.22 382	74.47 322	84.80 387	92.33 298	79.29 301	76.98 358	85.92 375	53.81 378	93.97 355	67.39 359	57.42 404	89.36 374

miper_refine_blended		78.50 349	76.02 356	85.93 339	86.22 382	74.47 322	84.80 387	92.33 298	79.29 301	76.98 358	85.92 375	53.81 378	93.97 355	67.39 359	57.42 404	89.36 374

PM-MVS		78.11 351	76.12 355	84.09 359	83.54 395	70.08 374	88.97 347	85.27 391	79.93 293	74.73 374	86.43 370	34.70 407	93.48 363	79.43 266	72.06 379	88.72 384

test_vis1_rt		77.96 352	76.46 352	82.48 367	85.89 385	71.74 357	90.25 316	78.89 406	71.03 385	71.30 388	81.35 395	42.49 401	91.05 387	84.55 181	82.37 312	84.65 393

test_fmvs3		77.67 353	77.16 350	79.22 375	79.52 405	61.14 401	92.34 267	91.64 322	73.98 362	78.86 344	86.59 368	27.38 411	87.03 399	88.12 135	75.97 371	89.50 373

PVSNet_0	73.20 20	77.22 354	74.83 360	84.37 355	90.70 333	71.10 364	83.09 396	89.67 366	72.81 375	73.93 378	83.13 388	60.79 341	93.70 361	68.54 351	50.84 409	88.30 388

DSMNet-mixed		76.94 355	76.29 354	78.89 376	83.10 397	56.11 412	87.78 362	79.77 404	60.65 402	75.64 368	88.71 343	61.56 331	88.34 398	60.07 389	89.29 235	92.21 334

ttmdpeth		76.55 356	74.64 361	82.29 370	82.25 400	67.81 383	89.76 331	85.69 387	70.35 387	75.76 367	91.69 264	46.88 394	89.77 392	66.16 368	63.23 398	89.30 376

new-patchmatchnet		76.41 357	75.17 359	80.13 373	82.65 399	59.61 404	87.66 366	91.08 335	78.23 323	69.85 391	83.22 387	54.76 372	91.63 383	64.14 378	64.89 395	89.16 380

UnsupCasMVSNet_bld		76.23 358	73.27 362	85.09 351	83.79 394	72.92 339	85.65 381	93.47 272	71.52 381	68.84 393	79.08 398	49.77 386	93.21 367	66.81 367	60.52 401	89.13 382

mvsany_test3		74.95 359	73.26 363	80.02 374	74.61 410	63.16 399	85.53 382	78.42 407	74.16 360	74.89 373	86.46 369	36.02 406	89.09 396	82.39 212	66.91 390	87.82 391

dmvs_testset		74.57 360	75.81 358	70.86 386	87.72 377	40.47 421	87.05 372	77.90 411	82.75 238	71.15 389	85.47 379	67.98 281	84.12 408	45.26 405	76.98 368	88.00 389

MVS-HIRNet		73.70 361	72.20 364	78.18 379	91.81 287	56.42 411	82.94 397	82.58 398	55.24 405	68.88 392	66.48 408	55.32 369	95.13 338	58.12 394	88.42 248	83.01 396

MVStest1		72.91 362	69.70 367	82.54 366	78.14 407	73.05 338	88.21 356	86.21 383	60.69 401	64.70 396	90.53 303	46.44 395	85.70 404	58.78 393	53.62 406	88.87 383

new_pmnet		72.15 363	70.13 366	78.20 378	82.95 398	65.68 388	83.91 392	82.40 399	62.94 400	64.47 397	79.82 397	42.85 400	86.26 403	57.41 396	74.44 374	82.65 398

test_f		71.95 364	70.87 365	75.21 382	74.21 412	59.37 405	85.07 386	85.82 386	65.25 396	70.42 390	83.13 388	23.62 412	82.93 410	78.32 276	71.94 380	83.33 395

pmmvs3		71.81 365	68.71 368	81.11 371	75.86 409	70.42 372	86.74 373	83.66 395	58.95 404	68.64 394	80.89 396	36.93 405	89.52 394	63.10 381	63.59 396	83.39 394

APD_test1		69.04 366	66.26 372	77.36 381	80.51 403	62.79 400	85.46 383	83.51 396	54.11 407	59.14 404	84.79 382	23.40 414	89.61 393	55.22 398	70.24 382	79.68 402

N_pmnet		68.89 367	68.44 369	70.23 387	89.07 360	28.79 426	88.06 357	19.50 426	69.47 389	71.86 386	84.93 380	61.24 336	91.75 381	54.70 399	77.15 365	90.15 368

WB-MVS		67.92 368	67.49 370	69.21 390	81.09 401	41.17 420	88.03 358	78.00 410	73.50 367	62.63 399	83.11 390	63.94 314	86.52 401	25.66 416	51.45 408	79.94 401

SSC-MVS		67.06 369	66.56 371	68.56 392	80.54 402	40.06 422	87.77 363	77.37 413	72.38 377	61.75 401	82.66 392	63.37 317	86.45 402	24.48 417	48.69 411	79.16 403

LCM-MVSNet		66.00 370	62.16 375	77.51 380	64.51 420	58.29 406	83.87 393	90.90 342	48.17 409	54.69 406	73.31 404	16.83 420	86.75 400	65.47 370	61.67 400	87.48 392

test_vis3_rt		65.12 371	62.60 373	72.69 384	71.44 413	60.71 402	87.17 370	65.55 417	63.80 399	53.22 407	65.65 410	14.54 421	89.44 395	76.65 293	65.38 393	67.91 408

FPMVS		64.63 372	62.55 374	70.88 385	70.80 414	56.71 407	84.42 390	84.42 393	51.78 408	49.57 408	81.61 394	23.49 413	81.48 411	40.61 411	76.25 370	74.46 404

EGC-MVSNET		61.97 373	56.37 378	78.77 377	89.63 355	73.50 333	89.12 344	82.79 397	0.21 423	1.24 424	84.80 381	39.48 402	90.04 391	44.13 406	75.94 372	72.79 405

PMMVS2		59.60 374	56.40 377	69.21 390	68.83 417	46.58 416	73.02 411	77.48 412	55.07 406	49.21 409	72.95 405	17.43 419	80.04 412	49.32 403	44.33 412	80.99 400

testf1		59.54 375	56.11 379	69.85 388	69.28 415	56.61 409	80.37 403	76.55 414	42.58 412	45.68 411	75.61 399	11.26 422	84.18 406	43.20 408	60.44 402	68.75 406

APD_test2		59.54 375	56.11 379	69.85 388	69.28 415	56.61 409	80.37 403	76.55 414	42.58 412	45.68 411	75.61 399	11.26 422	84.18 406	43.20 408	60.44 402	68.75 406

ANet_high		58.88 377	54.22 382	72.86 383	56.50 423	56.67 408	80.75 402	86.00 385	73.09 372	37.39 415	64.63 411	22.17 415	79.49 413	43.51 407	23.96 417	82.43 399

dongtai		58.82 378	58.24 376	60.56 395	83.13 396	45.09 419	82.32 398	48.22 425	67.61 392	61.70 402	69.15 406	38.75 403	76.05 414	32.01 413	41.31 413	60.55 410

Gipuma		57.99 379	54.91 381	67.24 393	88.51 364	65.59 389	52.21 414	90.33 352	43.58 411	42.84 414	51.18 415	20.29 417	85.07 405	34.77 412	70.45 381	51.05 414
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		53.51 380	53.30 383	54.13 399	76.06 408	45.36 418	80.11 405	48.36 424	59.63 403	54.84 405	63.43 412	37.41 404	62.07 419	20.73 419	39.10 414	54.96 413

PMVS	47.18 22	52.22 381	48.46 385	63.48 394	45.72 425	46.20 417	73.41 410	78.31 408	41.03 414	30.06 417	65.68 409	6.05 424	83.43 409	30.04 414	65.86 392	60.80 409
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 382	48.47 384	56.66 397	52.26 424	18.98 428	41.51 416	81.40 401	10.10 418	44.59 413	75.01 402	28.51 409	68.16 415	53.54 400	49.31 410	82.83 397

MVE	39.65 23	43.39 383	38.59 389	57.77 396	56.52 422	48.77 415	55.38 413	58.64 421	29.33 417	28.96 418	52.65 414	4.68 425	64.62 418	28.11 415	33.07 415	59.93 411
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 384	42.29 386	46.03 400	65.58 419	37.41 423	73.51 409	64.62 418	33.99 415	28.47 419	47.87 416	19.90 418	67.91 416	22.23 418	24.45 416	32.77 415

EMVS		42.07 385	41.12 387	44.92 401	63.45 421	35.56 425	73.65 408	63.48 419	33.05 416	26.88 420	45.45 417	21.27 416	67.14 417	19.80 420	23.02 418	32.06 416

tmp_tt		35.64 386	39.24 388	24.84 402	14.87 426	23.90 427	62.71 412	51.51 423	6.58 420	36.66 416	62.08 413	44.37 398	30.34 422	52.40 401	22.00 419	20.27 417

cdsmvs_eth3d_5k		22.14 387	29.52 390	0.00 406	0.00 429	0.00 431	0.00 417	95.76 161	0.00 424	0.00 425	94.29 170	75.66 178	0.00 425	0.00 424	0.00 423	0.00 421

wuyk23d		21.27 388	20.48 391	23.63 403	68.59 418	36.41 424	49.57 415	6.85 427	9.37 419	7.89 421	4.46 423	4.03 426	31.37 421	17.47 421	16.07 420	3.12 418

testmvs		8.92 389	11.52 392	1.12 405	1.06 427	0.46 430	86.02 377	0.65 428	0.62 421	2.74 422	9.52 421	0.31 428	0.45 424	2.38 422	0.39 421	2.46 420

test123		8.76 390	11.22 393	1.39 404	0.85 428	0.97 429	85.76 380	0.35 429	0.54 422	2.45 423	8.14 422	0.60 427	0.48 423	2.16 423	0.17 422	2.71 419

ab-mvs-re		7.82 391	10.43 394	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	93.88 190	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

pcd_1.5k_mvsjas		6.64 392	8.86 395	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	79.70 129	0.00 425	0.00 424	0.00 423	0.00 421

mmdepth		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

monomultidepth		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

test_blank		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet_test		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

DCPMVS		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

sosnet-low-res		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

sosnet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uncertanet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

Regformer		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

WAC-MVS						64.08 395								59.14 391

FOURS1					98.86 1	85.54 67	98.29 1	97.49 6	89.79 49	96.29 18

MSC_two_6792asdad				96.52 1	97.78 54	90.86 1		96.85 66					99.61 4	96.03 14	99.06 9	99.07 5

PC_three_1452									82.47 242	97.09 10	97.07 54	92.72 1	98.04 167	92.70 61	99.02 12	98.86 11

No_MVS				96.52 1	97.78 54	90.86 1		96.85 66					99.61 4	96.03 14	99.06 9	99.07 5

test_one_0601					98.58 11	85.83 61		97.44 15	91.05 14	96.78 15	98.06 14	91.45 11

eth-test2					0.00 429

eth-test					0.00 429

ZD-MVS					98.15 34	86.62 33		97.07 48	83.63 214	94.19 47	96.91 60	87.57 31	99.26 45	91.99 83	98.44 53

RE-MVS-def			93.68 56		97.92 43	84.57 85	96.28 43	96.76 78	87.46 123	93.75 57	97.43 33	82.94 88		92.73 57	97.80 79	97.88 84

IU-MVS					98.77 5	86.00 50		96.84 68	81.26 278	97.26 7				95.50 23	99.13 3	99.03 8

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 52	92.59 2	98.94 81	92.25 72	98.99 14	98.84 14

test_241102_TWO								97.44 15	90.31 28	97.62 5	98.07 12	91.46 10	99.58 10	95.66 17	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 52		97.44 15	90.26 33	97.71 1	97.96 20	92.31 4	99.38 31

9.14			94.47 23		97.79 52		96.08 60	97.44 15	86.13 158	95.10 37	97.40 35	88.34 22	99.22 47	93.25 50	98.70 34

save fliter					97.85 49	85.63 66	95.21 116	96.82 71	89.44 57

test_0728_THIRD									90.75 19	97.04 11	98.05 16	92.09 6	99.55 16	95.64 19	99.13 3	99.13 2

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 16	97.49 6					99.61 4	95.62 21	99.08 7	98.99 9

test0726					98.78 3	85.93 55	97.19 11	97.47 11	90.27 31	97.64 4	98.13 4	91.47 8

GSMVS																96.12 168

test_part2					98.55 12	87.22 19				96.40 17

sam_mvs1												71.70 228				96.12 168

sam_mvs												70.60 241

ambc				83.06 363	79.99 404	63.51 398	77.47 407	92.86 285		74.34 377	84.45 383	28.74 408	95.06 341	73.06 325	68.89 388	90.61 363

MTGPA								96.97 53

test_post1							88.00 359				9.81 420	69.31 264	95.53 328	76.65 293

test_post											10.29 419	70.57 245	95.91 314

patchmatchnet-post											83.76 385	71.53 229	96.48 284

GG-mvs-BLEND				87.94 297	89.73 354	77.91 270	87.80 360	78.23 409		80.58 325	83.86 384	59.88 347	95.33 336	71.20 332	92.22 192	90.60 365

MTMP							96.16 52	60.64 420

gm-plane-assit					89.60 356	68.00 380			77.28 331		88.99 337		97.57 196	79.44 265

test9_res														91.91 87	98.71 32	98.07 72

TEST9					97.53 61	86.49 37	94.07 192	96.78 75	81.61 270	92.77 81	96.20 90	87.71 28	99.12 54

test_8					97.49 63	86.30 45	94.02 197	96.76 78	81.86 261	92.70 85	96.20 90	87.63 29	99.02 64

agg_prior2														90.54 109	98.68 37	98.27 56

agg_prior					97.38 66	85.92 57		96.72 84		92.16 96			98.97 78

TestCases				89.52 253	95.01 154	77.79 277		90.89 343	77.41 328	76.12 364	93.34 203	54.08 376	97.51 201	68.31 354	84.27 289	93.26 295

test_prior4						85.96 54	94.11 187

test_prior2							94.12 185		87.67 121	92.63 86	96.39 85	86.62 40		91.50 95	98.67 40

test_prior				93.82 65	97.29 70	84.49 89		96.88 64					98.87 85			98.11 71

旧先验2							93.36 226		71.25 383	94.37 43			97.13 243	86.74 153

新几何2							93.11 241

新几何1				93.10 86	97.30 69	84.35 97		95.56 177	71.09 384	91.26 120	96.24 88	82.87 90	98.86 87	79.19 269	98.10 67	96.07 172

旧先验1					96.79 79	81.81 169		95.67 169			96.81 66	86.69 39			97.66 84	96.97 132

无先验							93.28 234	96.26 117	73.95 363				99.05 58	80.56 250		96.59 149

原ACMM2							92.94 248

原ACMM1				92.01 138	97.34 67	81.05 190		96.81 73	78.89 308	90.45 127	95.92 103	82.65 92	98.84 91	80.68 248	98.26 59	96.14 166

test222					96.55 88	81.70 171	92.22 272	95.01 211	68.36 391	90.20 132	96.14 95	80.26 122			97.80 79	96.05 175

testdata2													98.75 97	78.30 277

segment_acmp												87.16 36

testdata				90.49 208	96.40 93	77.89 272		95.37 195	72.51 376	93.63 60	96.69 69	82.08 106	97.65 190	83.08 198	97.39 87	95.94 177

testdata1							92.15 274		87.94 109

test12				94.34 52	97.13 73	86.15 48		96.29 112		91.04 122		85.08 61	99.01 66		98.13 66	97.86 86

plane_prior7					94.70 174	82.74 147

plane_prior6					94.52 184	82.75 145						74.23 195

plane_prior5								96.22 122					98.12 152	88.15 132	89.99 218	94.63 227

plane_prior4											94.86 147

plane_prior3						82.75 145			90.26 33	86.91 188

plane_prior2							95.85 80		90.81 17

plane_prior1					94.59 179

plane_prior						82.73 148	95.21 116		89.66 54						89.88 223

n2								0.00 430

nn								0.00 430

door-mid								85.49 388

lessismore_v0				86.04 337	88.46 367	68.78 379		80.59 403		73.01 382	90.11 316	55.39 367	96.43 289	75.06 310	65.06 394	92.90 312

LGP-MVS_train				91.12 181	94.47 186	81.49 177		96.14 127	86.73 141	85.45 230	95.16 136	69.89 253	98.10 154	87.70 139	89.23 236	93.77 277

test11								96.57 95

door								85.33 390

HQP5-MVS						81.56 173

HQP-NCC					94.17 203		94.39 169		88.81 78	85.43 233

ACMP_Plane					94.17 203		94.39 169		88.81 78	85.43 233

BP-MVS														87.11 150

HQP4-MVS										85.43 233			97.96 173			94.51 237

HQP3-MVS								96.04 139							89.77 227

HQP2-MVS												73.83 205

NP-MVS					94.37 193	82.42 157					93.98 183

MDTV_nov1_ep13_2view						55.91 413	87.62 367		73.32 369	84.59 255		70.33 248		74.65 314		95.50 195

MDTV_nov1_ep13			83.56 290		91.69 292	69.93 375	87.75 364	91.54 325	78.60 315	84.86 249	88.90 339	69.54 259	96.03 306	70.25 340	88.93 240

ACMMP++_ref															87.47 263

ACMMP++															88.01 255

Test By Simon												80.02 124

ITE_SJBPF				88.24 289	91.88 283	77.05 289		92.92 283	85.54 170	80.13 332	93.30 207	57.29 360	96.20 300	72.46 327	84.71 285	91.49 347

DeepMVS_CX				56.31 398	74.23 411	51.81 414		56.67 422	44.85 410	48.54 410	75.16 401	27.87 410	58.74 420	40.92 410	52.22 407	58.39 412

High-resolution multi-view benchmark