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 30	97.78 51	86.00 49	98.29 1	97.49 6	90.75 19	97.62 5	98.06 11	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 32	98.77 5	85.99 51	97.13 14	97.44 15	90.31 28	97.71 1	98.07 9	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 38	98.78 3	85.93 54	97.09 16	96.73 79	90.27 31	97.04 11	98.05 13	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 10	98.36 25	87.28 17	95.56 95	97.51 5	89.13 63	97.14 9	97.91 18	91.64 7	99.62 2	94.61 27	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 45	97.46 6	97.40 20	89.03 67	96.20 19	98.10 7	89.39 16	99.34 34	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 35	96.91 25	97.47 11	91.73 10	96.10 20	96.69 66	89.90 12	99.30 40	94.70 25	98.04 66	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	95.29 105	96.96 52	92.09 6	95.32 31	97.08 49	89.49 15	99.33 37	95.10 24	98.85 19	98.66 20

SMA-MVS		95.20 8	95.07 11	95.59 6	98.14 35	88.48 8	96.26 47	97.28 31	85.90 153	97.67 3	98.10 7	88.41 20	99.56 12	94.66 26	99.19 1	98.71 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
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 25	96.99 72	86.33 41	97.33 7	97.30 29	91.38 12	95.39 30	97.46 30	88.98 19	99.40 30	94.12 31	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 10	94.91 13	95.83 4	98.25 29	89.65 4	95.92 73	96.96 52	91.75 9	94.02 46	96.83 61	88.12 24	99.55 16	93.41 42	98.94 16	98.28 50

SF-MVS		94.97 11	94.90 14	95.20 12	97.84 47	87.76 10	96.65 35	97.48 10	87.76 111	95.71 27	97.70 25	88.28 23	99.35 33	93.89 35	98.78 25	98.48 30

SD-MVS		94.96 12	95.33 8	93.88 57	97.25 69	86.69 27	96.19 50	97.11 43	90.42 27	96.95 13	97.27 38	89.53 14	96.91 248	94.38 29	98.85 19	98.03 70
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 13	94.94 12	94.58 41	98.25 29	86.33 41	96.11 60	96.62 88	88.14 98	96.10 20	96.96 55	89.09 18	98.94 78	94.48 28	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
NCCC		94.81 14	94.69 17	95.17 14	97.83 48	87.46 16	95.66 88	96.93 56	92.34 4	93.94 47	96.58 76	87.74 27	99.44 29	92.83 50	98.40 52	98.62 21

ACMMP_NAP		94.74 15	94.56 18	95.28 9	98.02 41	87.70 11	95.68 85	97.34 23	88.28 91	95.30 32	97.67 26	85.90 47	99.54 20	93.91 34	98.95 15	98.60 23

test_fmvsm_n_1920		94.71 16	95.11 10	93.50 69	95.79 114	84.62 78	96.15 55	97.64 2	89.85 40	97.19 8	97.89 19	86.28 43	98.71 97	97.11 7	98.08 65	97.17 108

test_fmvsmconf_n		94.60 17	94.81 15	93.98 53	94.62 169	84.96 72	96.15 55	97.35 22	89.37 54	96.03 23	98.11 5	86.36 41	99.01 63	97.45 3	97.83 73	97.96 73

MVS_0304		94.60 17	94.38 24	95.23 11	95.41 129	87.49 15	96.53 38	92.75 277	93.82 2	93.07 65	97.84 22	83.66 74	99.59 8	97.61 2	98.76 28	98.61 22

HFP-MVS		94.52 19	94.40 22	94.86 24	98.61 10	86.81 24	96.94 20	97.34 23	88.63 80	93.65 51	97.21 42	86.10 45	99.49 26	92.35 61	98.77 27	98.30 47

ZNCC-MVS		94.47 20	94.28 29	95.03 16	98.52 15	86.96 19	96.85 28	97.32 27	88.24 92	93.15 61	97.04 52	86.17 44	99.62 2	92.40 59	98.81 22	98.52 26

XVS		94.45 21	94.32 25	94.85 25	98.54 13	86.60 33	96.93 22	97.19 35	90.66 24	92.85 69	97.16 47	85.02 59	99.49 26	91.99 74	98.56 48	98.47 33

MCST-MVS		94.45 21	94.20 34	95.19 13	98.46 19	87.50 14	95.00 125	97.12 41	87.13 123	92.51 83	96.30 83	89.24 17	99.34 34	93.46 39	98.62 44	98.73 17

region2R		94.43 23	94.27 31	94.92 20	98.65 8	86.67 29	96.92 24	97.23 34	88.60 82	93.58 53	97.27 38	85.22 54	99.54 20	92.21 64	98.74 31	98.56 25

ACMMPR		94.43 23	94.28 29	94.91 21	98.63 9	86.69 27	96.94 20	97.32 27	88.63 80	93.53 56	97.26 40	85.04 58	99.54 20	92.35 61	98.78 25	98.50 27

MTAPA		94.42 25	94.22 32	95.00 18	98.42 21	86.95 20	94.36 170	96.97 50	91.07 13	93.14 62	97.56 27	84.30 67	99.56 12	93.43 40	98.75 30	98.47 33

CP-MVS		94.34 26	94.21 33	94.74 36	98.39 23	86.64 31	97.60 4	97.24 32	88.53 84	92.73 77	97.23 41	85.20 55	99.32 38	92.15 67	98.83 21	98.25 55

fmvsm_l_conf0.5_n		94.29 27	94.46 20	93.79 63	95.28 133	85.43 66	95.68 85	96.43 97	86.56 138	96.84 14	97.81 23	87.56 32	98.77 92	97.14 6	96.82 96	97.16 112

MP-MVS		94.25 28	94.07 38	94.77 34	98.47 18	86.31 43	96.71 31	96.98 49	89.04 66	91.98 93	97.19 44	85.43 52	99.56 12	92.06 73	98.79 23	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 29	94.07 38	94.75 35	98.06 39	86.90 22	95.88 74	96.94 55	85.68 159	95.05 34	97.18 45	87.31 35	99.07 53	91.90 80	98.61 46	98.28 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 30	94.17 36	94.43 46	98.21 32	85.78 61	96.40 41	96.90 59	88.20 96	94.33 40	97.40 33	84.75 64	99.03 58	93.35 43	97.99 67	98.48 30

GST-MVS		94.21 31	93.97 42	94.90 23	98.41 22	86.82 23	96.54 37	97.19 35	88.24 92	93.26 58	96.83 61	85.48 51	99.59 8	91.43 87	98.40 52	98.30 47

MP-MVS-pluss		94.21 31	94.00 41	94.85 25	98.17 33	86.65 30	94.82 136	97.17 39	86.26 145	92.83 71	97.87 20	85.57 50	99.56 12	94.37 30	98.92 17	98.34 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 33	94.40 22	93.60 67	95.29 132	84.98 71	95.61 92	96.28 108	86.31 143	96.75 16	97.86 21	87.40 33	98.74 95	97.07 8	97.02 89	97.07 114

test_fmvsmconf0.1_n		94.20 33	94.31 27	93.88 57	92.46 248	84.80 75	96.18 52	96.82 68	89.29 57	95.68 28	98.11 5	85.10 56	98.99 70	97.38 4	97.75 77	97.86 80

DeepPCF-MVS	89.96 1	94.20 33	94.77 16	92.49 114	96.52 87	80.00 219	94.00 194	97.08 44	90.05 35	95.65 29	97.29 37	89.66 13	98.97 75	93.95 33	98.71 32	98.50 27

CS-MVS		94.12 36	94.44 21	93.17 76	96.55 84	83.08 129	97.63 3	96.95 54	91.71 11	93.50 57	96.21 86	85.61 48	98.24 136	93.64 37	98.17 58	98.19 58

DeepC-MVS_fast	89.43 2	94.04 37	93.79 45	94.80 32	97.48 61	86.78 25	95.65 90	96.89 60	89.40 53	92.81 72	96.97 54	85.37 53	99.24 43	90.87 97	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
CS-MVS-test		94.02 38	94.29 28	93.24 73	96.69 78	83.24 119	97.49 5	96.92 57	92.14 5	92.90 67	95.77 108	85.02 59	98.33 131	93.03 47	98.62 44	98.13 62

HPM-MVS		94.02 38	93.88 43	94.43 46	98.39 23	85.78 61	97.25 10	97.07 45	86.90 131	92.62 80	96.80 65	84.85 63	99.17 47	92.43 57	98.65 42	98.33 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 40	93.78 46	94.63 39	98.50 16	85.90 58	96.87 26	96.91 58	88.70 78	91.83 102	97.17 46	83.96 71	99.55 16	91.44 86	98.64 43	98.43 38

PGM-MVS		93.96 41	93.72 49	94.68 37	98.43 20	86.22 46	95.30 103	97.78 1	87.45 118	93.26 58	97.33 36	84.62 65	99.51 24	90.75 99	98.57 47	98.32 46

PHI-MVS		93.89 42	93.65 53	94.62 40	96.84 75	86.43 38	96.69 32	97.49 6	85.15 173	93.56 55	96.28 84	85.60 49	99.31 39	92.45 56	98.79 23	98.12 64

SR-MVS-dyc-post		93.82 43	93.82 44	93.82 60	97.92 43	84.57 80	96.28 45	96.76 75	87.46 116	93.75 49	97.43 31	84.24 68	99.01 63	92.73 51	97.80 74	97.88 78

APD-MVS_3200maxsize		93.78 44	93.77 47	93.80 62	97.92 43	84.19 94	96.30 43	96.87 62	86.96 127	93.92 48	97.47 29	83.88 72	98.96 77	92.71 54	97.87 71	98.26 54

fmvsm_s_conf0.5_n		93.76 45	94.06 40	92.86 94	95.62 122	83.17 122	96.14 57	96.12 123	88.13 99	95.82 26	98.04 16	83.43 75	98.48 111	96.97 9	96.23 107	96.92 125

patch_mono-2		93.74 46	94.32 25	92.01 130	97.54 57	78.37 257	93.40 218	97.19 35	88.02 101	94.99 35	97.21 42	88.35 21	98.44 121	94.07 32	98.09 63	99.23 1

MSLP-MVS++		93.72 47	94.08 37	92.65 106	97.31 65	83.43 114	95.79 79	97.33 25	90.03 36	93.58 53	96.96 55	84.87 62	97.76 174	92.19 66	98.66 40	96.76 131

TSAR-MVS + GP.		93.66 48	93.41 55	94.41 48	96.59 82	86.78 25	94.40 163	93.93 250	89.77 45	94.21 41	95.59 115	87.35 34	98.61 104	92.72 53	96.15 109	97.83 83

fmvsm_s_conf0.5_n_a		93.57 49	93.76 48	93.00 86	95.02 145	83.67 106	96.19 50	96.10 125	87.27 121	95.98 24	98.05 13	83.07 82	98.45 119	96.68 11	95.51 116	96.88 128

CANet		93.54 50	93.20 60	94.55 42	95.65 120	85.73 63	94.94 128	96.69 84	91.89 8	90.69 118	95.88 102	81.99 102	99.54 20	93.14 46	97.95 69	98.39 39

dcpmvs_2		93.49 51	94.19 35	91.38 165	97.69 54	76.78 289	94.25 173	96.29 105	88.33 88	94.46 38	96.88 58	88.07 25	98.64 100	93.62 38	98.09 63	98.73 17

fmvsm_s_conf0.1_n		93.46 52	93.66 52	92.85 95	93.75 210	83.13 124	96.02 67	95.74 154	87.68 113	95.89 25	98.17 2	82.78 86	98.46 115	96.71 10	96.17 108	96.98 121

MVS_111021_HR		93.45 53	93.31 56	93.84 59	96.99 72	84.84 73	93.24 230	97.24 32	88.76 75	91.60 107	95.85 103	86.07 46	98.66 98	91.91 78	98.16 59	98.03 70

test_fmvsmvis_n_1920		93.44 54	93.55 54	93.10 79	93.67 214	84.26 93	95.83 77	96.14 120	89.00 70	92.43 85	97.50 28	83.37 78	98.72 96	96.61 12	97.44 81	96.32 144

train_agg		93.44 54	93.08 61	94.52 43	97.53 58	86.49 36	94.07 186	96.78 72	81.86 248	92.77 74	96.20 87	87.63 29	99.12 51	92.14 68	98.69 35	97.94 74

EC-MVSNet		93.44 54	93.71 50	92.63 107	95.21 138	82.43 150	97.27 9	96.71 82	90.57 26	92.88 68	95.80 106	83.16 79	98.16 142	93.68 36	98.14 60	97.31 101

DELS-MVS		93.43 57	93.25 58	93.97 54	95.42 128	85.04 70	93.06 237	97.13 40	90.74 21	91.84 100	95.09 133	86.32 42	99.21 45	91.22 88	98.45 50	97.65 89
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 58	93.22 59	93.94 56	98.36 25	84.83 74	97.15 13	96.80 71	85.77 156	92.47 84	97.13 48	82.38 90	99.07 53	90.51 104	98.40 52	97.92 77

DeepC-MVS	88.79 3	93.31 59	92.99 64	94.26 51	96.07 102	85.83 59	94.89 131	96.99 48	89.02 69	89.56 132	97.37 35	82.51 89	99.38 31	92.20 65	98.30 55	97.57 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		93.27 60	92.75 68	94.85 25	95.70 119	87.66 12	96.33 42	96.41 99	90.00 37	94.09 44	94.60 154	82.33 92	98.62 103	92.40 59	92.86 173	98.27 52

ACMMP		93.24 61	92.88 66	94.30 50	98.09 38	85.33 68	96.86 27	97.45 14	88.33 88	90.15 127	97.03 53	81.44 105	99.51 24	90.85 98	95.74 112	98.04 69
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 62	93.05 62	93.76 64	98.04 40	84.07 96	96.22 49	97.37 21	84.15 192	90.05 128	95.66 112	87.77 26	99.15 50	89.91 107	98.27 56	98.07 66

fmvsm_s_conf0.1_n_a		93.19 63	93.26 57	92.97 88	92.49 246	83.62 109	96.02 67	95.72 157	86.78 133	96.04 22	98.19 1	82.30 93	98.43 123	96.38 13	95.42 122	96.86 129

test_fmvsmconf0.01_n		93.19 63	93.02 63	93.71 65	89.25 342	84.42 91	96.06 64	96.29 105	89.06 64	94.68 36	98.13 3	79.22 128	98.98 74	97.22 5	97.24 84	97.74 87

alignmvs		93.08 65	92.50 72	94.81 31	95.62 122	87.61 13	95.99 69	96.07 128	89.77 45	94.12 43	94.87 139	80.56 111	98.66 98	92.42 58	93.10 169	98.15 61

EI-MVSNet-Vis-set		93.01 66	92.92 65	93.29 71	95.01 146	83.51 113	94.48 155	95.77 151	90.87 15	92.52 82	96.67 68	84.50 66	99.00 68	91.99 74	94.44 144	97.36 100

casdiffmvs_mvg		92.96 67	92.83 67	93.35 70	94.59 170	83.40 116	95.00 125	96.34 103	90.30 30	92.05 91	96.05 95	83.43 75	98.15 143	92.07 70	95.67 113	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 68	92.54 71	93.68 66	96.10 100	84.71 77	95.66 88	96.39 100	91.92 7	93.22 60	96.49 79	83.16 79	98.87 82	84.47 174	95.47 119	97.45 99

CDPH-MVS		92.83 68	92.30 74	94.44 44	97.79 49	86.11 48	94.06 188	96.66 85	80.09 277	92.77 74	96.63 73	86.62 38	99.04 57	87.40 136	98.66 40	98.17 60

ETV-MVS		92.74 70	92.66 69	92.97 88	95.20 139	84.04 98	95.07 121	96.51 94	90.73 22	92.96 66	91.19 273	84.06 69	98.34 129	91.72 82	96.54 101	96.54 140

EI-MVSNet-UG-set		92.74 70	92.62 70	93.12 78	94.86 157	83.20 121	94.40 163	95.74 154	90.71 23	92.05 91	96.60 75	84.00 70	98.99 70	91.55 84	93.63 154	97.17 108

DPM-MVS		92.58 72	91.74 80	95.08 15	96.19 95	89.31 5	92.66 248	96.56 93	83.44 210	91.68 106	95.04 134	86.60 40	98.99 70	85.60 160	97.92 70	96.93 124

casdiffmvs		92.51 73	92.43 73	92.74 101	94.41 182	81.98 160	94.54 153	96.23 114	89.57 49	91.96 95	96.17 91	82.58 88	98.01 161	90.95 95	95.45 121	98.23 56
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 74	92.29 75	92.98 87	95.99 108	84.43 89	93.08 235	96.09 126	88.20 96	91.12 114	95.72 111	81.33 107	97.76 174	91.74 81	97.37 83	96.75 132

3Dnovator+	87.14 4	92.42 75	91.37 83	95.55 7	95.63 121	88.73 6	97.07 18	96.77 74	90.84 16	84.02 260	96.62 74	75.95 163	99.34 34	87.77 130	97.68 78	98.59 24

baseline		92.39 76	92.29 75	92.69 105	94.46 179	81.77 165	94.14 179	96.27 109	89.22 59	91.88 98	96.00 96	82.35 91	97.99 163	91.05 90	95.27 127	98.30 47

VNet		92.24 77	91.91 78	93.24 73	96.59 82	83.43 114	94.84 135	96.44 96	89.19 61	94.08 45	95.90 101	77.85 147	98.17 141	88.90 117	93.38 163	98.13 62

CPTT-MVS		91.99 78	91.80 79	92.55 111	98.24 31	81.98 160	96.76 30	96.49 95	81.89 247	90.24 123	96.44 81	78.59 136	98.61 104	89.68 108	97.85 72	97.06 115

EIA-MVS		91.95 79	91.94 77	91.98 134	95.16 140	80.01 218	95.36 98	96.73 79	88.44 85	89.34 136	92.16 239	83.82 73	98.45 119	89.35 111	97.06 87	97.48 97

DP-MVS Recon		91.95 79	91.28 85	93.96 55	98.33 27	85.92 56	94.66 147	96.66 85	82.69 229	90.03 129	95.82 105	82.30 93	99.03 58	84.57 172	96.48 104	96.91 126

EPNet		91.79 81	91.02 91	94.10 52	90.10 330	85.25 69	96.03 66	92.05 297	92.83 3	87.39 171	95.78 107	79.39 126	99.01 63	88.13 126	97.48 80	98.05 68
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 82	91.70 81	92.00 133	97.08 71	80.03 217	93.60 212	95.18 194	87.85 109	90.89 116	96.47 80	82.06 100	98.36 126	85.07 164	97.04 88	97.62 90

Vis-MVSNet		91.75 83	91.23 86	93.29 71	95.32 131	83.78 103	96.14 57	95.98 134	89.89 38	90.45 120	96.58 76	75.09 175	98.31 134	84.75 170	96.90 92	97.78 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 84	90.82 95	94.44 44	94.59 170	86.37 40	97.18 12	97.02 47	89.20 60	84.31 256	96.66 69	73.74 199	99.17 47	86.74 146	97.96 68	97.79 85

EPP-MVSNet		91.70 85	91.56 82	92.13 129	95.88 111	80.50 201	97.33 7	95.25 190	86.15 149	89.76 131	95.60 114	83.42 77	98.32 133	87.37 138	93.25 166	97.56 95

MVSFormer		91.68 86	91.30 84	92.80 97	93.86 204	83.88 101	95.96 71	95.90 142	84.66 186	91.76 103	94.91 137	77.92 144	97.30 218	89.64 109	97.11 85	97.24 104

Effi-MVS+		91.59 87	91.11 88	93.01 85	94.35 186	83.39 117	94.60 149	95.10 198	87.10 124	90.57 119	93.10 211	81.43 106	98.07 157	89.29 113	94.48 142	97.59 93

IS-MVSNet		91.43 88	91.09 90	92.46 115	95.87 113	81.38 177	96.95 19	93.69 260	89.72 47	89.50 134	95.98 98	78.57 137	97.77 173	83.02 192	96.50 103	98.22 57

PVSNet_Blended_VisFu		91.38 89	90.91 93	92.80 97	96.39 90	83.17 122	94.87 133	96.66 85	83.29 214	89.27 137	94.46 158	80.29 113	99.17 47	87.57 134	95.37 123	96.05 160

diffmvs		91.37 90	91.23 86	91.77 150	93.09 229	80.27 205	92.36 257	95.52 173	87.03 126	91.40 111	94.93 136	80.08 115	97.44 202	92.13 69	94.56 139	97.61 91
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 91	91.11 88	91.93 138	94.37 183	80.14 210	93.46 217	95.80 149	86.46 140	91.35 112	93.77 190	82.21 96	98.09 154	87.57 134	94.95 130	97.55 96

OMC-MVS		91.23 92	90.62 97	93.08 81	96.27 93	84.07 96	93.52 214	95.93 138	86.95 128	89.51 133	96.13 93	78.50 138	98.35 128	85.84 158	92.90 172	96.83 130

PAPM_NR		91.22 93	90.78 96	92.52 113	97.60 56	81.46 174	94.37 169	96.24 113	86.39 142	87.41 168	94.80 145	82.06 100	98.48 111	82.80 198	95.37 123	97.61 91

PS-MVSNAJ		91.18 94	90.92 92	91.96 136	95.26 136	82.60 149	92.09 269	95.70 158	86.27 144	91.84 100	92.46 229	79.70 121	98.99 70	89.08 115	95.86 111	94.29 234

xiu_mvs_v2_base		91.13 95	90.89 94	91.86 144	94.97 149	82.42 151	92.24 263	95.64 165	86.11 152	91.74 105	93.14 209	79.67 124	98.89 81	89.06 116	95.46 120	94.28 235

nrg030		91.08 96	90.39 98	93.17 76	93.07 230	86.91 21	96.41 39	96.26 110	88.30 90	88.37 151	94.85 142	82.19 97	97.64 185	91.09 89	82.95 291	94.96 198

lupinMVS		90.92 97	90.21 101	93.03 84	93.86 204	83.88 101	92.81 245	93.86 254	79.84 279	91.76 103	94.29 164	77.92 144	98.04 159	90.48 105	97.11 85	97.17 108

h-mvs33		90.80 98	90.15 104	92.75 100	96.01 104	82.66 146	95.43 97	95.53 172	89.80 41	93.08 63	95.64 113	75.77 164	99.00 68	92.07 70	78.05 346	96.60 136

jason		90.80 98	90.10 105	92.90 92	93.04 232	83.53 112	93.08 235	94.15 243	80.22 274	91.41 110	94.91 137	76.87 151	97.93 168	90.28 106	96.90 92	97.24 104
jason: jason.
VDD-MVS		90.74 100	89.92 112	93.20 75	96.27 93	83.02 131	95.73 82	93.86 254	88.42 87	92.53 81	96.84 60	62.09 318	98.64 100	90.95 95	92.62 176	97.93 76

PVSNet_Blended		90.73 101	90.32 100	91.98 134	96.12 97	81.25 179	92.55 252	96.83 66	82.04 241	89.10 139	92.56 227	81.04 109	98.85 86	86.72 148	95.91 110	95.84 167

test_yl		90.69 102	90.02 110	92.71 102	95.72 117	82.41 153	94.11 181	95.12 196	85.63 161	91.49 108	94.70 148	74.75 179	98.42 124	86.13 153	92.53 177	97.31 101

DCV-MVSNet		90.69 102	90.02 110	92.71 102	95.72 117	82.41 153	94.11 181	95.12 196	85.63 161	91.49 108	94.70 148	74.75 179	98.42 124	86.13 153	92.53 177	97.31 101

API-MVS		90.66 104	90.07 106	92.45 116	96.36 91	84.57 80	96.06 64	95.22 193	82.39 232	89.13 138	94.27 167	80.32 112	98.46 115	80.16 245	96.71 98	94.33 231

xiu_mvs_v1_base_debu		90.64 105	90.05 107	92.40 117	93.97 201	84.46 86	93.32 221	95.46 175	85.17 170	92.25 86	94.03 172	70.59 235	98.57 107	90.97 92	94.67 134	94.18 236

xiu_mvs_v1_base		90.64 105	90.05 107	92.40 117	93.97 201	84.46 86	93.32 221	95.46 175	85.17 170	92.25 86	94.03 172	70.59 235	98.57 107	90.97 92	94.67 134	94.18 236

xiu_mvs_v1_base_debi		90.64 105	90.05 107	92.40 117	93.97 201	84.46 86	93.32 221	95.46 175	85.17 170	92.25 86	94.03 172	70.59 235	98.57 107	90.97 92	94.67 134	94.18 236

HQP_MVS		90.60 108	90.19 102	91.82 147	94.70 165	82.73 142	95.85 75	96.22 115	90.81 17	86.91 180	94.86 140	74.23 187	98.12 144	88.15 124	89.99 202	94.63 210

FIs		90.51 109	90.35 99	90.99 186	93.99 200	80.98 187	95.73 82	97.54 4	89.15 62	86.72 187	94.68 150	81.83 104	97.24 226	85.18 163	88.31 238	94.76 208

MAR-MVS		90.30 110	89.37 123	93.07 83	96.61 81	84.48 85	95.68 85	95.67 160	82.36 234	87.85 159	92.85 216	76.63 157	98.80 90	80.01 246	96.68 99	95.91 163
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		90.27 111	90.18 103	90.53 198	93.71 211	79.85 224	95.77 80	97.59 3	89.31 56	86.27 196	94.67 151	81.93 103	97.01 242	84.26 176	88.09 241	94.71 209

CANet_DTU		90.26 112	89.41 122	92.81 96	93.46 220	83.01 132	93.48 215	94.47 229	89.43 52	87.76 163	94.23 168	70.54 239	99.03 58	84.97 165	96.39 105	96.38 143

SDMVSNet		90.19 113	89.61 116	91.93 138	96.00 105	83.09 128	92.89 242	95.98 134	88.73 76	86.85 184	95.20 128	72.09 219	97.08 236	88.90 117	89.85 208	95.63 177

OPM-MVS		90.12 114	89.56 117	91.82 147	93.14 227	83.90 100	94.16 178	95.74 154	88.96 71	87.86 158	95.43 119	72.48 215	97.91 169	88.10 128	90.18 201	93.65 270
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 115	89.13 129	92.95 90	96.71 77	82.32 155	96.08 61	89.91 347	86.79 132	92.15 90	96.81 63	62.60 316	98.34 129	87.18 140	93.90 150	98.19 58

GeoE		90.05 116	89.43 121	91.90 143	95.16 140	80.37 204	95.80 78	94.65 226	83.90 197	87.55 167	94.75 147	78.18 142	97.62 187	81.28 225	93.63 154	97.71 88

PAPR		90.02 117	89.27 128	92.29 125	95.78 115	80.95 189	92.68 247	96.22 115	81.91 245	86.66 188	93.75 192	82.23 95	98.44 121	79.40 257	94.79 132	97.48 97

PVSNet_BlendedMVS		89.98 118	89.70 114	90.82 191	96.12 97	81.25 179	93.92 199	96.83 66	83.49 209	89.10 139	92.26 237	81.04 109	98.85 86	86.72 148	87.86 245	92.35 316

PS-MVSNAJss		89.97 119	89.62 115	91.02 183	91.90 266	80.85 192	95.26 108	95.98 134	86.26 145	86.21 197	94.29 164	79.70 121	97.65 182	88.87 119	88.10 239	94.57 215

mvsmamba		89.96 120	89.50 118	91.33 168	92.90 239	81.82 163	96.68 33	92.37 285	89.03 67	87.00 176	94.85 142	73.05 207	97.65 182	91.03 91	88.63 229	94.51 220

XVG-OURS-SEG-HR		89.95 121	89.45 119	91.47 162	94.00 199	81.21 182	91.87 273	96.06 130	85.78 155	88.55 147	95.73 110	74.67 183	97.27 222	88.71 120	89.64 213	95.91 163

UGNet		89.95 121	88.95 133	92.95 90	94.51 176	83.31 118	95.70 84	95.23 191	89.37 54	87.58 165	93.94 180	64.00 305	98.78 91	83.92 181	96.31 106	96.74 133
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		89.92 123	89.29 126	91.81 149	93.39 222	83.72 104	94.43 161	97.12 41	89.80 41	86.46 190	93.32 200	83.16 79	97.23 227	84.92 166	81.02 320	94.49 225

AdaColmap		89.89 124	89.07 130	92.37 120	97.41 62	83.03 130	94.42 162	95.92 139	82.81 226	86.34 195	94.65 152	73.89 195	99.02 61	80.69 236	95.51 116	95.05 193

hse-mvs2		89.88 125	89.34 124	91.51 159	94.83 159	81.12 184	93.94 197	93.91 253	89.80 41	93.08 63	93.60 194	75.77 164	97.66 181	92.07 70	77.07 353	95.74 172

UniMVSNet (Re)		89.80 126	89.07 130	92.01 130	93.60 216	84.52 83	94.78 139	97.47 11	89.26 58	86.44 193	92.32 234	82.10 98	97.39 214	84.81 169	80.84 324	94.12 240

HQP-MVS		89.80 126	89.28 127	91.34 167	94.17 189	81.56 168	94.39 165	96.04 131	88.81 72	85.43 222	93.97 179	73.83 197	97.96 165	87.11 143	89.77 211	94.50 223

FA-MVS(test-final)		89.66 128	88.91 135	91.93 138	94.57 173	80.27 205	91.36 284	94.74 222	84.87 179	89.82 130	92.61 226	74.72 182	98.47 114	83.97 180	93.53 157	97.04 117

VPA-MVSNet		89.62 129	88.96 132	91.60 155	93.86 204	82.89 137	95.46 96	97.33 25	87.91 104	88.43 150	93.31 201	74.17 190	97.40 211	87.32 139	82.86 296	94.52 218

WTY-MVS		89.60 130	88.92 134	91.67 153	95.47 127	81.15 183	92.38 256	94.78 220	83.11 218	89.06 141	94.32 162	78.67 135	96.61 262	81.57 222	90.89 194	97.24 104

Vis-MVSNet (Re-imp)		89.59 131	89.44 120	90.03 225	95.74 116	75.85 303	95.61 92	90.80 332	87.66 115	87.83 160	95.40 120	76.79 153	96.46 275	78.37 262	96.73 97	97.80 84

VDDNet		89.56 132	88.49 150	92.76 99	95.07 144	82.09 157	96.30 43	93.19 267	81.05 269	91.88 98	96.86 59	61.16 330	98.33 131	88.43 123	92.49 179	97.84 82

114514_t		89.51 133	88.50 148	92.54 112	98.11 36	81.99 159	95.16 116	96.36 102	70.19 371	85.81 202	95.25 124	76.70 155	98.63 102	82.07 210	96.86 95	97.00 120

QAPM		89.51 133	88.15 159	93.59 68	94.92 153	84.58 79	96.82 29	96.70 83	78.43 302	83.41 275	96.19 90	73.18 206	99.30 40	77.11 278	96.54 101	96.89 127

CLD-MVS		89.47 135	88.90 136	91.18 173	94.22 188	82.07 158	92.13 267	96.09 126	87.90 105	85.37 228	92.45 230	74.38 185	97.56 190	87.15 141	90.43 197	93.93 249
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		89.45 136	88.90 136	91.12 175	94.47 177	81.49 172	95.30 103	96.14 120	86.73 135	85.45 219	95.16 130	69.89 245	98.10 146	87.70 132	89.23 220	93.77 263

CDS-MVSNet		89.45 136	88.51 147	92.29 125	93.62 215	83.61 111	93.01 238	94.68 225	81.95 243	87.82 161	93.24 205	78.69 134	96.99 243	80.34 242	93.23 167	96.28 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
iter_conf_final		89.42 138	88.69 141	91.60 155	95.12 143	82.93 135	95.75 81	92.14 294	87.32 120	87.12 175	94.07 170	67.09 278	97.55 191	90.61 101	89.01 224	94.32 232

Fast-Effi-MVS+		89.41 139	88.64 142	91.71 152	94.74 161	80.81 193	93.54 213	95.10 198	83.11 218	86.82 186	90.67 291	79.74 120	97.75 177	80.51 240	93.55 156	96.57 138

ab-mvs		89.41 139	88.35 152	92.60 108	95.15 142	82.65 147	92.20 265	95.60 167	83.97 196	88.55 147	93.70 193	74.16 191	98.21 140	82.46 203	89.37 216	96.94 123

XVG-OURS		89.40 141	88.70 140	91.52 158	94.06 193	81.46 174	91.27 286	96.07 128	86.14 150	88.89 143	95.77 108	68.73 266	97.26 224	87.39 137	89.96 204	95.83 168

test_vis1_n_1920		89.39 142	89.84 113	88.04 284	92.97 236	72.64 333	94.71 144	96.03 133	86.18 148	91.94 97	96.56 78	61.63 321	95.74 309	93.42 41	95.11 129	95.74 172

mvs_anonymous		89.37 143	89.32 125	89.51 248	93.47 219	74.22 316	91.65 280	94.83 216	82.91 224	85.45 219	93.79 188	81.23 108	96.36 282	86.47 150	94.09 147	97.94 74

DU-MVS		89.34 144	88.50 148	91.85 146	93.04 232	83.72 104	94.47 158	96.59 90	89.50 50	86.46 190	93.29 203	77.25 149	97.23 227	84.92 166	81.02 320	94.59 213

TAMVS		89.21 145	88.29 156	91.96 136	93.71 211	82.62 148	93.30 225	94.19 241	82.22 236	87.78 162	93.94 180	78.83 131	96.95 245	77.70 271	92.98 171	96.32 144

ACMM	84.12 9	89.14 146	88.48 151	91.12 175	94.65 168	81.22 181	95.31 101	96.12 123	85.31 169	85.92 201	94.34 160	70.19 243	98.06 158	85.65 159	88.86 227	94.08 244
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 147	88.64 142	90.48 204	95.53 126	74.97 309	96.08 61	84.89 374	88.13 99	90.16 126	96.65 70	63.29 312	98.10 146	86.14 151	96.90 92	98.39 39

EI-MVSNet		89.10 147	88.86 138	89.80 237	91.84 268	78.30 259	93.70 209	95.01 201	85.73 157	87.15 173	95.28 122	79.87 118	97.21 229	83.81 183	87.36 254	93.88 252

ECVR-MVS		89.09 149	88.53 146	90.77 193	95.62 122	75.89 302	96.16 53	84.22 376	87.89 107	90.20 124	96.65 70	63.19 314	98.10 146	85.90 156	96.94 90	98.33 43

RRT_MVS		89.09 149	88.62 145	90.49 202	92.85 240	79.65 228	96.41 39	94.41 232	88.22 94	85.50 215	94.77 146	69.36 253	97.31 217	89.33 112	86.73 261	94.51 220

CNLPA		89.07 151	87.98 163	92.34 121	96.87 74	84.78 76	94.08 185	93.24 265	81.41 260	84.46 246	95.13 132	75.57 171	96.62 259	77.21 276	93.84 152	95.61 179

PLC	84.53 7	89.06 152	88.03 162	92.15 128	97.27 68	82.69 145	94.29 171	95.44 180	79.71 281	84.01 261	94.18 169	76.68 156	98.75 93	77.28 275	93.41 162	95.02 194
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 153	88.64 142	90.21 215	90.74 316	79.28 240	95.96 71	95.90 142	84.66 186	85.33 230	92.94 215	74.02 193	97.30 218	89.64 109	88.53 231	94.05 246

HY-MVS	83.01 12	89.03 153	87.94 165	92.29 125	94.86 157	82.77 138	92.08 270	94.49 228	81.52 259	86.93 178	92.79 222	78.32 141	98.23 137	79.93 247	90.55 195	95.88 165

ACMP	84.23 8	89.01 155	88.35 152	90.99 186	94.73 162	81.27 178	95.07 121	95.89 144	86.48 139	83.67 268	94.30 163	69.33 254	97.99 163	87.10 145	88.55 230	93.72 267
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 156	88.26 158	90.94 189	94.05 194	80.78 194	91.71 277	95.38 184	81.55 258	88.63 146	93.91 184	75.04 176	95.47 319	82.47 202	91.61 184	96.57 138

iter_conf05		88.85 157	88.08 161	91.17 174	94.27 187	81.64 167	95.18 113	92.15 293	86.23 147	87.28 172	94.07 170	63.89 309	97.55 191	90.63 100	89.00 225	94.32 232

TranMVSNet+NR-MVSNet		88.84 158	87.95 164	91.49 160	92.68 244	83.01 132	94.92 130	96.31 104	89.88 39	85.53 212	93.85 187	76.63 157	96.96 244	81.91 214	79.87 337	94.50 223

CHOSEN 1792x2688		88.84 158	87.69 169	92.30 124	96.14 96	81.42 176	90.01 312	95.86 146	74.52 341	87.41 168	93.94 180	75.46 172	98.36 126	80.36 241	95.53 115	97.12 113

MVSTER		88.84 158	88.29 156	90.51 201	92.95 237	80.44 202	93.73 206	95.01 201	84.66 186	87.15 173	93.12 210	72.79 211	97.21 229	87.86 129	87.36 254	93.87 253

test_cas_vis1_n_1920		88.83 161	88.85 139	88.78 263	91.15 297	76.72 290	93.85 202	94.93 208	83.23 217	92.81 72	96.00 96	61.17 329	94.45 329	91.67 83	94.84 131	95.17 190

OpenMVS	83.78 11	88.74 162	87.29 179	93.08 81	92.70 243	85.39 67	96.57 36	96.43 97	78.74 297	80.85 305	96.07 94	69.64 249	99.01 63	78.01 269	96.65 100	94.83 205

thisisatest0530		88.67 163	87.61 171	91.86 144	94.87 156	80.07 213	94.63 148	89.90 348	84.00 195	88.46 149	93.78 189	66.88 282	98.46 115	83.30 188	92.65 175	97.06 115

Effi-MVS+-dtu		88.65 164	88.35 152	89.54 245	93.33 223	76.39 296	94.47 158	94.36 235	87.70 112	85.43 222	89.56 314	73.45 202	97.26 224	85.57 161	91.28 187	94.97 195

tttt0517		88.61 165	87.78 168	91.11 178	94.96 150	77.81 272	95.35 99	89.69 351	85.09 175	88.05 156	94.59 155	66.93 280	98.48 111	83.27 189	92.13 182	97.03 118

BH-untuned		88.60 166	88.13 160	90.01 228	95.24 137	78.50 253	93.29 226	94.15 243	84.75 183	84.46 246	93.40 197	75.76 166	97.40 211	77.59 272	94.52 141	94.12 240

sd_testset		88.59 167	87.85 167	90.83 190	96.00 105	80.42 203	92.35 258	94.71 223	88.73 76	86.85 184	95.20 128	67.31 273	96.43 277	79.64 251	89.85 208	95.63 177

NR-MVSNet		88.58 168	87.47 175	91.93 138	93.04 232	84.16 95	94.77 140	96.25 112	89.05 65	80.04 318	93.29 203	79.02 130	97.05 240	81.71 221	80.05 334	94.59 213

1112_ss		88.42 169	87.33 178	91.72 151	94.92 153	80.98 187	92.97 240	94.54 227	78.16 308	83.82 264	93.88 185	78.78 133	97.91 169	79.45 253	89.41 215	96.26 148

WR-MVS		88.38 170	87.67 170	90.52 200	93.30 224	80.18 208	93.26 228	95.96 137	88.57 83	85.47 218	92.81 220	76.12 159	96.91 248	81.24 226	82.29 300	94.47 228

BH-RMVSNet		88.37 171	87.48 174	91.02 183	95.28 133	79.45 232	92.89 242	93.07 269	85.45 166	86.91 180	94.84 144	70.35 240	97.76 174	73.97 305	94.59 138	95.85 166

IterMVS-LS		88.36 172	87.91 166	89.70 241	93.80 207	78.29 260	93.73 206	95.08 200	85.73 157	84.75 238	91.90 253	79.88 117	96.92 247	83.83 182	82.51 297	93.89 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 173	86.13 219	94.85 25	98.54 13	86.60 33	96.93 22	97.19 35	90.66 24	92.85 69	23.41 398	85.02 59	99.49 26	91.99 74	98.56 48	98.47 33

LCM-MVSNet-Re		88.30 174	88.32 155	88.27 277	94.71 164	72.41 338	93.15 231	90.98 327	87.77 110	79.25 327	91.96 251	78.35 140	95.75 308	83.04 191	95.62 114	96.65 135

jajsoiax		88.24 175	87.50 173	90.48 204	90.89 310	80.14 210	95.31 101	95.65 164	84.97 177	84.24 257	94.02 175	65.31 298	97.42 204	88.56 121	88.52 232	93.89 250

VPNet		88.20 176	87.47 175	90.39 209	93.56 217	79.46 231	94.04 189	95.54 171	88.67 79	86.96 177	94.58 156	69.33 254	97.15 231	84.05 179	80.53 329	94.56 216

TAPA-MVS	84.62 6	88.16 177	87.01 187	91.62 154	96.64 80	80.65 196	94.39 165	96.21 118	76.38 321	86.19 198	95.44 117	79.75 119	98.08 156	62.75 365	95.29 125	96.13 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 178	87.28 180	90.57 196	94.96 150	80.07 213	94.27 172	91.29 320	86.74 134	87.41 168	94.00 177	76.77 154	96.20 288	80.77 234	79.31 342	95.44 181

Anonymous20240529		88.09 179	86.59 203	92.58 110	96.53 86	81.92 162	95.99 69	95.84 147	74.11 345	89.06 141	95.21 127	61.44 324	98.81 89	83.67 186	87.47 251	97.01 119

HyFIR lowres test		88.09 179	86.81 191	91.93 138	96.00 105	80.63 197	90.01 312	95.79 150	73.42 352	87.68 164	92.10 245	73.86 196	97.96 165	80.75 235	91.70 183	97.19 107

mvs_tets		88.06 181	87.28 180	90.38 211	90.94 306	79.88 222	95.22 110	95.66 162	85.10 174	84.21 258	93.94 180	63.53 310	97.40 211	88.50 122	88.40 236	93.87 253

F-COLMAP		87.95 182	86.80 192	91.40 164	96.35 92	80.88 191	94.73 142	95.45 178	79.65 282	82.04 292	94.61 153	71.13 226	98.50 110	76.24 287	91.05 192	94.80 207

LS3D		87.89 183	86.32 213	92.59 109	96.07 102	82.92 136	95.23 109	94.92 209	75.66 328	82.89 282	95.98 98	72.48 215	99.21 45	68.43 338	95.23 128	95.64 176

anonymousdsp		87.84 184	87.09 183	90.12 221	89.13 343	80.54 200	94.67 146	95.55 169	82.05 239	83.82 264	92.12 242	71.47 224	97.15 231	87.15 141	87.80 249	92.67 304

v2v482		87.84 184	87.06 184	90.17 217	90.99 302	79.23 243	94.00 194	95.13 195	84.87 179	85.53 212	92.07 248	74.45 184	97.45 200	84.71 171	81.75 308	93.85 256

WR-MVS_H		87.80 186	87.37 177	89.10 257	93.23 225	78.12 263	95.61 92	97.30 29	87.90 105	83.72 266	92.01 250	79.65 125	96.01 296	76.36 284	80.54 328	93.16 289

AUN-MVS		87.78 187	86.54 205	91.48 161	94.82 160	81.05 185	93.91 201	93.93 250	83.00 221	86.93 178	93.53 195	69.50 251	97.67 179	86.14 151	77.12 352	95.73 174

PCF-MVS	84.11 10	87.74 188	86.08 223	92.70 104	94.02 195	84.43 89	89.27 324	95.87 145	73.62 350	84.43 248	94.33 161	78.48 139	98.86 84	70.27 324	94.45 143	94.81 206
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 189	86.13 219	92.31 123	96.66 79	80.74 195	94.87 133	91.49 315	80.47 273	89.46 135	95.44 117	54.72 357	98.23 137	82.19 208	89.89 206	97.97 72

V42		87.68 189	86.86 189	90.15 219	90.58 321	80.14 210	94.24 175	95.28 189	83.66 203	85.67 206	91.33 268	74.73 181	97.41 209	84.43 175	81.83 306	92.89 299

thres600view7		87.65 191	86.67 198	90.59 195	96.08 101	78.72 246	94.88 132	91.58 311	87.06 125	88.08 154	92.30 235	68.91 263	98.10 146	70.05 331	91.10 188	94.96 198

XXY-MVS		87.65 191	86.85 190	90.03 225	92.14 256	80.60 199	93.76 205	95.23 191	82.94 223	84.60 241	94.02 175	74.27 186	95.49 318	81.04 228	83.68 284	94.01 248

Test_1112_low_res		87.65 191	86.51 206	91.08 179	94.94 152	79.28 240	91.77 275	94.30 237	76.04 326	83.51 273	92.37 232	77.86 146	97.73 178	78.69 261	89.13 222	96.22 149

thres100view900		87.63 194	86.71 196	90.38 211	96.12 97	78.55 250	95.03 124	91.58 311	87.15 122	88.06 155	92.29 236	68.91 263	98.10 146	70.13 328	91.10 188	94.48 226

CP-MVSNet		87.63 194	87.26 182	88.74 267	93.12 228	76.59 293	95.29 105	96.58 91	88.43 86	83.49 274	92.98 214	75.28 173	95.83 304	78.97 259	81.15 316	93.79 258

thres400		87.62 196	86.64 199	90.57 196	95.99 108	78.64 248	94.58 150	91.98 301	86.94 129	88.09 152	91.77 255	69.18 259	98.10 146	70.13 328	91.10 188	94.96 198

v1144		87.61 197	86.79 193	90.06 224	91.01 301	79.34 236	93.95 196	95.42 183	83.36 213	85.66 207	91.31 271	74.98 177	97.42 204	83.37 187	82.06 302	93.42 279

bld_raw_dy_0_64		87.60 198	86.73 194	90.21 215	91.72 273	80.26 207	95.09 120	88.61 357	85.68 159	85.55 209	94.38 159	63.93 308	96.66 256	87.73 131	87.84 246	93.72 267

tfpn200view9		87.58 199	86.64 199	90.41 208	95.99 108	78.64 248	94.58 150	91.98 301	86.94 129	88.09 152	91.77 255	69.18 259	98.10 146	70.13 328	91.10 188	94.48 226

BH-w/o		87.57 200	87.05 185	89.12 256	94.90 155	77.90 268	92.41 254	93.51 262	82.89 225	83.70 267	91.34 267	75.75 167	97.07 238	75.49 291	93.49 159	92.39 314

UniMVSNet_ETH3D		87.53 201	86.37 210	91.00 185	92.44 249	78.96 245	94.74 141	95.61 166	84.07 194	85.36 229	94.52 157	59.78 338	97.34 216	82.93 193	87.88 244	96.71 134

ET-MVSNet_ETH3D		87.51 202	85.91 231	92.32 122	93.70 213	83.93 99	92.33 260	90.94 328	84.16 191	72.09 367	92.52 228	69.90 244	95.85 303	89.20 114	88.36 237	97.17 108

1314		87.51 202	86.57 204	90.34 213	92.42 250	79.74 226	92.63 249	95.35 188	78.35 303	80.14 315	91.62 262	74.05 192	97.15 231	81.05 227	93.53 157	94.12 240

v8		87.50 204	86.71 196	89.89 231	91.37 287	79.40 233	94.50 154	95.38 184	84.81 182	83.60 271	91.33 268	76.05 160	97.42 204	82.84 196	80.51 331	92.84 301

Fast-Effi-MVS+-dtu		87.44 205	86.72 195	89.63 243	92.04 260	77.68 278	94.03 190	93.94 249	85.81 154	82.42 286	91.32 270	70.33 241	97.06 239	80.33 243	90.23 200	94.14 239

MVS		87.44 205	86.10 222	91.44 163	92.61 245	83.62 109	92.63 249	95.66 162	67.26 375	81.47 297	92.15 240	77.95 143	98.22 139	79.71 249	95.48 118	92.47 310

FE-MVS		87.40 207	86.02 225	91.57 157	94.56 174	79.69 227	90.27 301	93.72 259	80.57 272	88.80 144	91.62 262	65.32 297	98.59 106	74.97 299	94.33 146	96.44 141

FMVSNet3		87.40 207	86.11 221	91.30 169	93.79 209	83.64 108	94.20 177	94.81 218	83.89 198	84.37 249	91.87 254	68.45 269	96.56 267	78.23 266	85.36 269	93.70 269

test_fmvs1		87.34 209	87.56 172	86.68 317	90.59 320	71.80 342	94.01 192	94.04 248	78.30 304	91.97 94	95.22 125	56.28 350	93.71 344	92.89 49	94.71 133	94.52 218

thisisatest0515		87.33 210	85.99 226	91.37 166	93.49 218	79.55 229	90.63 297	89.56 354	80.17 275	87.56 166	90.86 284	67.07 279	98.28 135	81.50 223	93.02 170	96.29 146

PS-CasMVS		87.32 211	86.88 188	88.63 270	92.99 235	76.33 298	95.33 100	96.61 89	88.22 94	83.30 279	93.07 212	73.03 209	95.79 307	78.36 263	81.00 322	93.75 265

GBi-Net		87.26 212	85.98 227	91.08 179	94.01 196	83.10 125	95.14 117	94.94 204	83.57 205	84.37 249	91.64 258	66.59 287	96.34 283	78.23 266	85.36 269	93.79 258

test1		87.26 212	85.98 227	91.08 179	94.01 196	83.10 125	95.14 117	94.94 204	83.57 205	84.37 249	91.64 258	66.59 287	96.34 283	78.23 266	85.36 269	93.79 258

v1192		87.25 214	86.33 212	90.00 229	90.76 315	79.04 244	93.80 203	95.48 174	82.57 230	85.48 217	91.18 275	73.38 205	97.42 204	82.30 206	82.06 302	93.53 273

v10		87.25 214	86.38 209	89.85 232	91.19 293	79.50 230	94.48 155	95.45 178	83.79 201	83.62 270	91.19 273	75.13 174	97.42 204	81.94 213	80.60 326	92.63 306

DP-MVS		87.25 214	85.36 246	92.90 92	97.65 55	83.24 119	94.81 137	92.00 299	74.99 336	81.92 294	95.00 135	72.66 212	99.05 55	66.92 349	92.33 180	96.40 142

miper_ehance_all_eth		87.22 217	86.62 202	89.02 260	92.13 257	77.40 282	90.91 293	94.81 218	81.28 263	84.32 254	90.08 303	79.26 127	96.62 259	83.81 183	82.94 292	93.04 294

test2506		87.21 218	86.28 215	90.02 227	95.62 122	73.64 321	96.25 48	71.38 398	87.89 107	90.45 120	96.65 70	55.29 355	98.09 154	86.03 155	96.94 90	98.33 43

thres200		87.21 218	86.24 217	90.12 221	95.36 130	78.53 251	93.26 228	92.10 295	86.42 141	88.00 157	91.11 279	69.24 258	98.00 162	69.58 332	91.04 193	93.83 257

v144192		87.19 220	86.35 211	89.74 238	90.64 319	78.24 261	93.92 199	95.43 181	81.93 244	85.51 214	91.05 281	74.21 189	97.45 200	82.86 195	81.56 310	93.53 273

FMVSNet2		87.19 220	85.82 233	91.30 169	94.01 196	83.67 106	94.79 138	94.94 204	83.57 205	83.88 263	92.05 249	66.59 287	96.51 270	77.56 273	85.01 272	93.73 266

c3_l		87.14 222	86.50 207	89.04 259	92.20 254	77.26 283	91.22 288	94.70 224	82.01 242	84.34 253	90.43 295	78.81 132	96.61 262	83.70 185	81.09 317	93.25 284

Baseline_NR-MVSNet		87.07 223	86.63 201	88.40 273	91.44 282	77.87 270	94.23 176	92.57 282	84.12 193	85.74 205	92.08 246	77.25 149	96.04 293	82.29 207	79.94 335	91.30 337

v148		87.04 224	86.32 213	89.21 253	90.94 306	77.26 283	93.71 208	94.43 230	84.84 181	84.36 252	90.80 288	76.04 161	97.05 240	82.12 209	79.60 339	93.31 281

test_fmvs1_n		87.03 225	87.04 186	86.97 309	89.74 338	71.86 340	94.55 152	94.43 230	78.47 300	91.95 96	95.50 116	51.16 368	93.81 342	93.02 48	94.56 139	95.26 187

v1921920		86.97 226	86.06 224	89.69 242	90.53 324	78.11 264	93.80 203	95.43 181	81.90 246	85.33 230	91.05 281	72.66 212	97.41 209	82.05 211	81.80 307	93.53 273

tt0805		86.92 227	85.74 239	90.48 204	92.22 253	79.98 220	95.63 91	94.88 212	83.83 200	84.74 239	92.80 221	57.61 346	97.67 179	85.48 162	84.42 276	93.79 258

miper_enhance_ethall		86.90 228	86.18 218	89.06 258	91.66 278	77.58 280	90.22 307	94.82 217	79.16 288	84.48 245	89.10 319	79.19 129	96.66 256	84.06 178	82.94 292	92.94 297

v7n		86.81 229	85.76 237	89.95 230	90.72 317	79.25 242	95.07 121	95.92 139	84.45 189	82.29 287	90.86 284	72.60 214	97.53 194	79.42 256	80.52 330	93.08 293

PEN-MVS		86.80 230	86.27 216	88.40 273	92.32 252	75.71 305	95.18 113	96.38 101	87.97 102	82.82 283	93.15 208	73.39 204	95.92 299	76.15 288	79.03 344	93.59 271

cl22		86.78 231	85.98 227	89.18 255	92.34 251	77.62 279	90.84 294	94.13 245	81.33 262	83.97 262	90.15 301	73.96 194	96.60 264	84.19 177	82.94 292	93.33 280

v1240		86.78 231	85.85 232	89.56 244	90.45 325	77.79 274	93.61 211	95.37 186	81.65 254	85.43 222	91.15 277	71.50 223	97.43 203	81.47 224	82.05 304	93.47 277

TR-MVS		86.78 231	85.76 237	89.82 234	94.37 183	78.41 255	92.47 253	92.83 274	81.11 268	86.36 194	92.40 231	68.73 266	97.48 197	73.75 308	89.85 208	93.57 272

PatchMatch-RL		86.77 234	85.54 240	90.47 207	95.88 111	82.71 144	90.54 298	92.31 288	79.82 280	84.32 254	91.57 266	68.77 265	96.39 279	73.16 310	93.48 161	92.32 317

PAPM		86.68 235	85.39 244	90.53 198	93.05 231	79.33 239	89.79 315	94.77 221	78.82 294	81.95 293	93.24 205	76.81 152	97.30 218	66.94 347	93.16 168	94.95 201

pm-mvs1		86.61 236	85.54 240	89.82 234	91.44 282	80.18 208	95.28 107	94.85 214	83.84 199	81.66 295	92.62 225	72.45 217	96.48 272	79.67 250	78.06 345	92.82 302

GA-MVS		86.61 236	85.27 248	90.66 194	91.33 290	78.71 247	90.40 300	93.81 257	85.34 168	85.12 232	89.57 313	61.25 326	97.11 235	80.99 231	89.59 214	96.15 150

Anonymous20231211		86.59 238	85.13 250	90.98 188	96.52 87	81.50 170	96.14 57	96.16 119	73.78 348	83.65 269	92.15 240	63.26 313	97.37 215	82.82 197	81.74 309	94.06 245

test_vis1_n		86.56 239	86.49 208	86.78 316	88.51 348	72.69 330	94.68 145	93.78 258	79.55 283	90.70 117	95.31 121	48.75 373	93.28 350	93.15 45	93.99 148	94.38 230

DIV-MVS_self_test		86.53 240	85.78 234	88.75 265	92.02 262	76.45 295	90.74 295	94.30 237	81.83 250	83.34 277	90.82 287	75.75 167	96.57 265	81.73 220	81.52 312	93.24 285

cl____		86.52 241	85.78 234	88.75 265	92.03 261	76.46 294	90.74 295	94.30 237	81.83 250	83.34 277	90.78 289	75.74 169	96.57 265	81.74 219	81.54 311	93.22 286

eth_miper_zixun_eth		86.50 242	85.77 236	88.68 268	91.94 263	75.81 304	90.47 299	94.89 210	82.05 239	84.05 259	90.46 294	75.96 162	96.77 252	82.76 199	79.36 341	93.46 278

baseline2		86.50 242	85.39 244	89.84 233	91.12 298	76.70 291	91.88 272	88.58 358	82.35 235	79.95 319	90.95 283	73.42 203	97.63 186	80.27 244	89.95 205	95.19 189

EPNet_dtu		86.49 244	85.94 230	88.14 282	90.24 328	72.82 328	94.11 181	92.20 291	86.66 137	79.42 326	92.36 233	73.52 200	95.81 306	71.26 317	93.66 153	95.80 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		86.43 245	84.98 253	90.80 192	92.10 259	80.92 190	90.24 305	95.91 141	73.10 355	83.57 272	88.39 330	65.15 299	97.46 199	84.90 168	91.43 186	94.03 247

SCA		86.32 246	85.18 249	89.73 240	92.15 255	76.60 292	91.12 289	91.69 308	83.53 208	85.50 215	88.81 323	66.79 283	96.48 272	76.65 281	90.35 199	96.12 153

LTVRE_ROB	82.13 13	86.26 247	84.90 256	90.34 213	94.44 181	81.50 170	92.31 262	94.89 210	83.03 220	79.63 324	92.67 223	69.69 248	97.79 172	71.20 318	86.26 264	91.72 327
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		86.11 248	85.48 242	87.98 285	91.65 279	74.92 310	94.93 129	95.75 153	87.36 119	82.26 288	93.04 213	72.85 210	95.82 305	74.04 304	77.46 350	93.20 287

XVG-ACMP-BASELINE		86.00 249	84.84 258	89.45 249	91.20 292	78.00 265	91.70 278	95.55 169	85.05 176	82.97 281	92.25 238	54.49 358	97.48 197	82.93 193	87.45 253	92.89 299

MVP-Stereo		85.97 250	84.86 257	89.32 251	90.92 308	82.19 156	92.11 268	94.19 241	78.76 296	78.77 332	91.63 261	68.38 270	96.56 267	75.01 298	93.95 149	89.20 362
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 251	85.09 251	88.35 275	90.79 313	77.42 281	91.83 274	95.70 158	80.77 271	80.08 317	90.02 304	66.74 285	96.37 280	81.88 215	87.97 243	91.26 338

test-LLR		85.87 252	85.41 243	87.25 301	90.95 304	71.67 344	89.55 318	89.88 349	83.41 211	84.54 243	87.95 337	67.25 275	95.11 324	81.82 216	93.37 164	94.97 195

FMVSNet1		85.85 253	84.11 267	91.08 179	92.81 241	83.10 125	95.14 117	94.94 204	81.64 255	82.68 284	91.64 258	59.01 342	96.34 283	75.37 293	83.78 281	93.79 258

PatchmatchNet		85.85 253	84.70 260	89.29 252	91.76 272	75.54 306	88.49 336	91.30 319	81.63 256	85.05 233	88.70 327	71.71 220	96.24 287	74.61 302	89.05 223	96.08 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 255	84.94 255	88.26 278	91.16 296	72.58 336	89.47 322	91.04 326	76.26 324	86.45 192	89.97 306	70.74 233	96.86 251	82.35 205	87.07 259	95.34 186

PMMVS		85.71 256	84.96 254	87.95 286	88.90 346	77.09 285	88.68 334	90.06 343	72.32 362	86.47 189	90.76 290	72.15 218	94.40 331	81.78 218	93.49 159	92.36 315

PVSNet	78.82 18	85.55 257	84.65 261	88.23 280	94.72 163	71.93 339	87.12 353	92.75 277	78.80 295	84.95 235	90.53 293	64.43 303	96.71 255	74.74 300	93.86 151	96.06 159

IterMVS-SCA-FT		85.45 258	84.53 264	88.18 281	91.71 275	76.87 288	90.19 308	92.65 281	85.40 167	81.44 298	90.54 292	66.79 283	95.00 327	81.04 228	81.05 318	92.66 305

pmmvs4		85.43 259	83.86 272	90.16 218	90.02 333	82.97 134	90.27 301	92.67 280	75.93 327	80.73 306	91.74 257	71.05 227	95.73 310	78.85 260	83.46 288	91.78 326

mvsany_test1		85.42 260	85.30 247	85.77 327	87.95 359	75.41 308	87.61 350	80.97 384	76.82 318	88.68 145	95.83 104	77.44 148	90.82 372	85.90 156	86.51 262	91.08 345

ACMH	80.38 17	85.36 261	83.68 274	90.39 209	94.45 180	80.63 197	94.73 142	94.85 214	82.09 238	77.24 340	92.65 224	60.01 336	97.58 188	72.25 314	84.87 273	92.96 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 262	84.64 262	87.49 295	90.77 314	72.59 335	94.01 192	94.40 233	84.72 184	79.62 325	93.17 207	61.91 320	96.72 253	81.99 212	81.16 314	93.16 289

CR-MVSNet		85.35 262	83.76 273	90.12 221	90.58 321	79.34 236	85.24 366	91.96 303	78.27 305	85.55 209	87.87 340	71.03 228	95.61 311	73.96 306	89.36 217	95.40 183

tpmrst		85.35 262	84.99 252	86.43 319	90.88 311	67.88 366	88.71 333	91.43 317	80.13 276	86.08 200	88.80 325	73.05 207	96.02 295	82.48 201	83.40 290	95.40 183

miper_lstm_enhance		85.27 265	84.59 263	87.31 298	91.28 291	74.63 311	87.69 347	94.09 247	81.20 267	81.36 300	89.85 309	74.97 178	94.30 334	81.03 230	79.84 338	93.01 295

IB-MVS	80.51 15	85.24 266	83.26 280	91.19 172	92.13 257	79.86 223	91.75 276	91.29 320	83.28 215	80.66 308	88.49 329	61.28 325	98.46 115	80.99 231	79.46 340	95.25 188
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		85.15 267	83.99 270	88.65 269	92.47 247	78.40 256	79.68 386	92.76 276	74.90 338	81.41 299	89.59 312	69.85 247	95.51 315	79.92 248	95.29 125	92.03 322

RPSCF		85.07 268	84.27 265	87.48 296	92.91 238	70.62 355	91.69 279	92.46 283	76.20 325	82.67 285	95.22 125	63.94 306	97.29 221	77.51 274	85.80 266	94.53 217

MS-PatchMatch		85.05 269	84.16 266	87.73 289	91.42 285	78.51 252	91.25 287	93.53 261	77.50 311	80.15 314	91.58 264	61.99 319	95.51 315	75.69 290	94.35 145	89.16 363

ACMH+	81.04 14	85.05 269	83.46 277	89.82 234	94.66 167	79.37 234	94.44 160	94.12 246	82.19 237	78.04 335	92.82 219	58.23 344	97.54 193	73.77 307	82.90 295	92.54 307

IterMVS		84.88 271	83.98 271	87.60 291	91.44 282	76.03 300	90.18 309	92.41 284	83.24 216	81.06 304	90.42 296	66.60 286	94.28 335	79.46 252	80.98 323	92.48 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 272	83.09 283	90.14 220	93.80 207	80.05 215	89.18 327	93.09 268	78.89 292	78.19 333	91.91 252	65.86 296	97.27 222	68.47 337	88.45 234	93.11 291

testing222		84.84 273	83.32 278	89.43 250	94.15 192	75.94 301	91.09 290	89.41 355	84.90 178	85.78 203	89.44 315	52.70 365	96.28 286	70.80 323	91.57 185	96.07 157

tpm		84.73 274	84.02 269	86.87 314	90.33 326	68.90 362	89.06 329	89.94 346	80.85 270	85.75 204	89.86 308	68.54 268	95.97 297	77.76 270	84.05 280	95.75 171

tfpnnormal		84.72 275	83.23 281	89.20 254	92.79 242	80.05 215	94.48 155	95.81 148	82.38 233	81.08 303	91.21 272	69.01 262	96.95 245	61.69 367	80.59 327	90.58 352

CVMVSNet		84.69 276	84.79 259	84.37 339	91.84 268	64.92 375	93.70 209	91.47 316	66.19 377	86.16 199	95.28 122	67.18 277	93.33 349	80.89 233	90.42 198	94.88 203

test-mter		84.54 277	83.64 275	87.25 301	90.95 304	71.67 344	89.55 318	89.88 349	79.17 287	84.54 243	87.95 337	55.56 352	95.11 324	81.82 216	93.37 164	94.97 195

TransMVSNet (Re)		84.43 278	83.06 284	88.54 271	91.72 273	78.44 254	95.18 113	92.82 275	82.73 228	79.67 323	92.12 242	73.49 201	95.96 298	71.10 322	68.73 375	91.21 339

pmmvs5		84.21 279	82.84 288	88.34 276	88.95 345	76.94 287	92.41 254	91.91 305	75.63 329	80.28 312	91.18 275	64.59 302	95.57 312	77.09 279	83.47 287	92.53 308

dmvs_re		84.20 280	83.22 282	87.14 307	91.83 270	77.81 272	90.04 311	90.19 339	84.70 185	81.49 296	89.17 318	64.37 304	91.13 370	71.58 316	85.65 268	92.46 311

tpm2		84.08 281	82.94 285	87.48 296	91.39 286	71.27 346	89.23 326	90.37 336	71.95 364	84.64 240	89.33 316	67.30 274	96.55 269	75.17 295	87.09 258	94.63 210

test_fmvs2		83.98 282	84.03 268	83.83 344	87.16 362	67.53 369	93.93 198	92.89 272	77.62 310	86.89 183	93.53 195	47.18 377	92.02 362	90.54 102	86.51 262	91.93 324

COLMAP_ROB	80.39 16	83.96 283	82.04 291	89.74 238	95.28 133	79.75 225	94.25 173	92.28 289	75.17 334	78.02 336	93.77 190	58.60 343	97.84 171	65.06 357	85.92 265	91.63 329
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 284	81.53 294	91.21 171	90.58 321	79.34 236	85.24 366	96.76 75	71.44 366	85.55 209	82.97 373	70.87 231	98.91 80	61.01 369	89.36 217	95.40 183

SixPastTwentyTwo		83.91 285	82.90 286	86.92 311	90.99 302	70.67 354	93.48 215	91.99 300	85.54 164	77.62 339	92.11 244	60.59 332	96.87 250	76.05 289	77.75 347	93.20 287

EPMVS		83.90 286	82.70 289	87.51 293	90.23 329	72.67 331	88.62 335	81.96 382	81.37 261	85.01 234	88.34 331	66.31 290	94.45 329	75.30 294	87.12 257	95.43 182

WB-MVSnew		83.77 287	83.28 279	85.26 333	91.48 281	71.03 350	91.89 271	87.98 360	78.91 290	84.78 237	90.22 298	69.11 261	94.02 338	64.70 358	90.44 196	90.71 347

TESTMET0.1,1		83.74 288	82.85 287	86.42 320	89.96 334	71.21 348	89.55 318	87.88 361	77.41 312	83.37 276	87.31 345	56.71 348	93.65 346	80.62 238	92.85 174	94.40 229

pmmvs6		83.42 289	81.60 293	88.87 262	88.01 357	77.87 270	94.96 127	94.24 240	74.67 340	78.80 331	91.09 280	60.17 335	96.49 271	77.06 280	75.40 359	92.23 319

AllTest		83.42 289	81.39 295	89.52 246	95.01 146	77.79 274	93.12 232	90.89 330	77.41 312	76.12 348	93.34 198	54.08 360	97.51 195	68.31 339	84.27 278	93.26 282

tpmvs		83.35 291	82.07 290	87.20 305	91.07 300	71.00 352	88.31 339	91.70 307	78.91 290	80.49 311	87.18 349	69.30 257	97.08 236	68.12 342	83.56 286	93.51 276

USDC		82.76 292	81.26 297	87.26 300	91.17 294	74.55 312	89.27 324	93.39 264	78.26 306	75.30 353	92.08 246	54.43 359	96.63 258	71.64 315	85.79 267	90.61 349

Patchmtry		82.71 293	80.93 299	88.06 283	90.05 332	76.37 297	84.74 371	91.96 303	72.28 363	81.32 301	87.87 340	71.03 228	95.50 317	68.97 334	80.15 333	92.32 317

PatchT		82.68 294	81.27 296	86.89 313	90.09 331	70.94 353	84.06 373	90.15 340	74.91 337	85.63 208	83.57 368	69.37 252	94.87 328	65.19 354	88.50 233	94.84 204

MIMVSNet		82.59 295	80.53 300	88.76 264	91.51 280	78.32 258	86.57 357	90.13 341	79.32 284	80.70 307	88.69 328	52.98 364	93.07 354	66.03 352	88.86 227	94.90 202

test0.0.03 1		82.41 296	81.69 292	84.59 337	88.23 354	72.89 327	90.24 305	87.83 362	83.41 211	79.86 321	89.78 310	67.25 275	88.99 380	65.18 355	83.42 289	91.90 325

EG-PatchMatch MVS		82.37 297	80.34 303	88.46 272	90.27 327	79.35 235	92.80 246	94.33 236	77.14 316	73.26 364	90.18 300	47.47 376	96.72 253	70.25 325	87.32 256	89.30 360

tpm cat1		81.96 298	80.27 304	87.01 308	91.09 299	71.02 351	87.38 351	91.53 314	66.25 376	80.17 313	86.35 355	68.22 271	96.15 291	69.16 333	82.29 300	93.86 255

our_test_3		81.93 299	80.46 302	86.33 321	88.46 351	73.48 323	88.46 337	91.11 322	76.46 319	76.69 344	88.25 333	66.89 281	94.36 332	68.75 335	79.08 343	91.14 341

ppachtmachnet_test		81.84 300	80.07 308	87.15 306	88.46 351	74.43 315	89.04 330	92.16 292	75.33 332	77.75 337	88.99 320	66.20 292	95.37 320	65.12 356	77.60 348	91.65 328

gg-mvs-nofinetune		81.77 301	79.37 316	88.99 261	90.85 312	77.73 277	86.29 358	79.63 387	74.88 339	83.19 280	69.05 388	60.34 333	96.11 292	75.46 292	94.64 137	93.11 291

CL-MVSNet_self_test		81.74 302	80.53 300	85.36 330	85.96 368	72.45 337	90.25 303	93.07 269	81.24 265	79.85 322	87.29 346	70.93 230	92.52 357	66.95 346	69.23 371	91.11 343

Patchmatch-RL test		81.67 303	79.96 309	86.81 315	85.42 373	71.23 347	82.17 380	87.50 365	78.47 300	77.19 341	82.50 375	70.81 232	93.48 347	82.66 200	72.89 363	95.71 175

ADS-MVSNet2		81.66 304	79.71 313	87.50 294	91.35 288	74.19 317	83.33 376	88.48 359	72.90 357	82.24 289	85.77 359	64.98 300	93.20 352	64.57 359	83.74 282	95.12 191

K. test v3		81.59 305	80.15 307	85.91 326	89.89 336	69.42 361	92.57 251	87.71 363	85.56 163	73.44 363	89.71 311	55.58 351	95.52 314	77.17 277	69.76 369	92.78 303

ADS-MVSNet		81.56 306	79.78 310	86.90 312	91.35 288	71.82 341	83.33 376	89.16 356	72.90 357	82.24 289	85.77 359	64.98 300	93.76 343	64.57 359	83.74 282	95.12 191

FMVSNet5		81.52 307	79.60 314	87.27 299	91.17 294	77.95 266	91.49 282	92.26 290	76.87 317	76.16 347	87.91 339	51.67 366	92.34 359	67.74 343	81.16 314	91.52 332

dp		81.47 308	80.23 305	85.17 334	89.92 335	65.49 373	86.74 355	90.10 342	76.30 323	81.10 302	87.12 350	62.81 315	95.92 299	68.13 341	79.88 336	94.09 243

Patchmatch-test		81.37 309	79.30 317	87.58 292	90.92 308	74.16 318	80.99 382	87.68 364	70.52 370	76.63 345	88.81 323	71.21 225	92.76 356	60.01 373	86.93 260	95.83 168

EU-MVSNet		81.32 310	80.95 298	82.42 351	88.50 350	63.67 379	93.32 221	91.33 318	64.02 380	80.57 310	92.83 218	61.21 328	92.27 360	76.34 285	80.38 332	91.32 336

test_0402		81.30 311	79.17 321	87.67 290	93.19 226	78.17 262	92.98 239	91.71 306	75.25 333	76.02 350	90.31 297	59.23 340	96.37 280	50.22 384	83.63 285	88.47 369

JIA-IIPM		81.04 312	78.98 324	87.25 301	88.64 347	73.48 323	81.75 381	89.61 353	73.19 354	82.05 291	73.71 385	66.07 295	95.87 302	71.18 320	84.60 275	92.41 313

Anonymous20231206		81.03 313	79.77 312	84.82 336	87.85 360	70.26 357	91.42 283	92.08 296	73.67 349	77.75 337	89.25 317	62.43 317	93.08 353	61.50 368	82.00 305	91.12 342

pmmvs-eth3d		80.97 314	78.72 326	87.74 288	84.99 375	79.97 221	90.11 310	91.65 309	75.36 331	73.51 362	86.03 356	59.45 339	93.96 341	75.17 295	72.21 364	89.29 361

testgi		80.94 315	80.20 306	83.18 345	87.96 358	66.29 370	91.28 285	90.70 334	83.70 202	78.12 334	92.84 217	51.37 367	90.82 372	63.34 362	82.46 298	92.43 312

CMPMVS	59.16 21	80.52 316	79.20 320	84.48 338	83.98 376	67.63 368	89.95 314	93.84 256	64.79 379	66.81 378	91.14 278	57.93 345	95.17 322	76.25 286	88.10 239	90.65 348
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 317	79.59 315	83.06 347	93.44 221	64.64 376	93.33 220	85.47 371	84.34 190	79.93 320	90.84 286	44.35 381	92.39 358	57.06 379	87.56 250	92.16 321

Anonymous20240521		80.44 318	79.21 319	84.11 342	85.75 371	67.89 365	92.86 244	93.23 266	75.61 330	75.59 352	87.47 344	50.03 369	94.33 333	71.14 321	81.21 313	90.12 354

LF4IMVS		80.37 319	79.07 323	84.27 341	86.64 364	69.87 360	89.39 323	91.05 325	76.38 321	74.97 355	90.00 305	47.85 375	94.25 336	74.55 303	80.82 325	88.69 367

KD-MVS_self_test		80.20 320	79.24 318	83.07 346	85.64 372	65.29 374	91.01 292	93.93 250	78.71 298	76.32 346	86.40 354	59.20 341	92.93 355	72.59 312	69.35 370	91.00 346

Syy-MVS		80.07 321	79.78 310	80.94 354	91.92 264	59.93 385	89.75 316	87.40 366	81.72 252	78.82 329	87.20 347	66.29 291	91.29 368	47.06 386	87.84 246	91.60 330

UnsupCasMVSNet_eth		80.07 321	78.27 327	85.46 329	85.24 374	72.63 334	88.45 338	94.87 213	82.99 222	71.64 370	88.07 336	56.34 349	91.75 365	73.48 309	63.36 382	92.01 323

test20.03		79.95 323	79.08 322	82.55 349	85.79 370	67.74 367	91.09 290	91.08 323	81.23 266	74.48 359	89.96 307	61.63 321	90.15 374	60.08 371	76.38 355	89.76 355

TDRefinement		79.81 324	77.34 329	87.22 304	79.24 387	75.48 307	93.12 232	92.03 298	76.45 320	75.01 354	91.58 264	49.19 372	96.44 276	70.22 327	69.18 372	89.75 356

TinyColmap		79.76 325	77.69 328	85.97 323	91.71 275	73.12 325	89.55 318	90.36 337	75.03 335	72.03 368	90.19 299	46.22 378	96.19 290	63.11 363	81.03 319	88.59 368

myMVS_eth3d		79.67 326	78.79 325	82.32 352	91.92 264	64.08 377	89.75 316	87.40 366	81.72 252	78.82 329	87.20 347	45.33 379	91.29 368	59.09 375	87.84 246	91.60 330

OpenMVS_ROB	74.94 19	79.51 327	77.03 334	86.93 310	87.00 363	76.23 299	92.33 260	90.74 333	68.93 373	74.52 358	88.23 334	49.58 371	96.62 259	57.64 377	84.29 277	87.94 372

MIMVSNet1		79.38 328	77.28 330	85.69 328	86.35 365	73.67 320	91.61 281	92.75 277	78.11 309	72.64 366	88.12 335	48.16 374	91.97 364	60.32 370	77.49 349	91.43 335

YYNet1		79.22 329	77.20 331	85.28 332	88.20 356	72.66 332	85.87 360	90.05 345	74.33 343	62.70 380	87.61 342	66.09 294	92.03 361	66.94 347	72.97 362	91.15 340

MDA-MVSNet_test_wron		79.21 330	77.19 332	85.29 331	88.22 355	72.77 329	85.87 360	90.06 343	74.34 342	62.62 382	87.56 343	66.14 293	91.99 363	66.90 350	73.01 361	91.10 344

MDA-MVSNet-bldmvs		78.85 331	76.31 336	86.46 318	89.76 337	73.88 319	88.79 332	90.42 335	79.16 288	59.18 384	88.33 332	60.20 334	94.04 337	62.00 366	68.96 373	91.48 334

KD-MVS_2432*1600		78.50 332	76.02 339	85.93 324	86.22 366	74.47 313	84.80 369	92.33 286	79.29 285	76.98 342	85.92 357	53.81 362	93.97 339	67.39 344	57.42 387	89.36 358

miper_refine_blended		78.50 332	76.02 339	85.93 324	86.22 366	74.47 313	84.80 369	92.33 286	79.29 285	76.98 342	85.92 357	53.81 362	93.97 339	67.39 344	57.42 387	89.36 358

PM-MVS		78.11 334	76.12 338	84.09 343	83.54 378	70.08 358	88.97 331	85.27 373	79.93 278	74.73 357	86.43 353	34.70 387	93.48 347	79.43 255	72.06 365	88.72 366

test_vis1_rt		77.96 335	76.46 335	82.48 350	85.89 369	71.74 343	90.25 303	78.89 388	71.03 369	71.30 371	81.35 377	42.49 383	91.05 371	84.55 173	82.37 299	84.65 375

test_fmvs3		77.67 336	77.16 333	79.22 357	79.52 386	61.14 383	92.34 259	91.64 310	73.98 346	78.86 328	86.59 351	27.38 391	87.03 382	88.12 127	75.97 357	89.50 357

PVSNet_0	73.20 20	77.22 337	74.83 343	84.37 339	90.70 318	71.10 349	83.09 378	89.67 352	72.81 359	73.93 361	83.13 370	60.79 331	93.70 345	68.54 336	50.84 391	88.30 370

DSMNet-mixed		76.94 338	76.29 337	78.89 358	83.10 379	56.11 394	87.78 344	79.77 386	60.65 383	75.64 351	88.71 326	61.56 323	88.34 381	60.07 372	89.29 219	92.21 320

new-patchmatchnet		76.41 339	75.17 342	80.13 355	82.65 381	59.61 386	87.66 348	91.08 323	78.23 307	69.85 374	83.22 369	54.76 356	91.63 367	64.14 361	64.89 380	89.16 363

UnsupCasMVSNet_bld		76.23 340	73.27 344	85.09 335	83.79 377	72.92 326	85.65 363	93.47 263	71.52 365	68.84 376	79.08 380	49.77 370	93.21 351	66.81 351	60.52 384	89.13 365

mvsany_test3		74.95 341	73.26 345	80.02 356	74.61 389	63.16 381	85.53 364	78.42 389	74.16 344	74.89 356	86.46 352	36.02 386	89.09 379	82.39 204	66.91 376	87.82 373

dmvs_testset		74.57 342	75.81 341	70.86 368	87.72 361	40.47 401	87.05 354	77.90 393	82.75 227	71.15 372	85.47 361	67.98 272	84.12 390	45.26 387	76.98 354	88.00 371

MVS-HIRNet		73.70 343	72.20 346	78.18 361	91.81 271	56.42 393	82.94 379	82.58 380	55.24 385	68.88 375	66.48 389	55.32 354	95.13 323	58.12 376	88.42 235	83.01 378

new_pmnet		72.15 344	70.13 348	78.20 360	82.95 380	65.68 371	83.91 374	82.40 381	62.94 382	64.47 379	79.82 379	42.85 382	86.26 386	57.41 378	74.44 360	82.65 380

test_f		71.95 345	70.87 347	75.21 364	74.21 391	59.37 387	85.07 368	85.82 369	65.25 378	70.42 373	83.13 370	23.62 392	82.93 392	78.32 264	71.94 366	83.33 377

pmmvs3		71.81 346	68.71 349	81.11 353	75.86 388	70.42 356	86.74 355	83.66 377	58.95 384	68.64 377	80.89 378	36.93 385	89.52 377	63.10 364	63.59 381	83.39 376

APD_test1		69.04 347	66.26 353	77.36 363	80.51 384	62.79 382	85.46 365	83.51 378	54.11 387	59.14 385	84.79 364	23.40 394	89.61 376	55.22 380	70.24 368	79.68 384

N_pmnet		68.89 348	68.44 350	70.23 369	89.07 344	28.79 406	88.06 340	19.50 406	69.47 372	71.86 369	84.93 362	61.24 327	91.75 365	54.70 381	77.15 351	90.15 353

WB-MVS		67.92 349	67.49 351	69.21 372	81.09 382	41.17 400	88.03 341	78.00 392	73.50 351	62.63 381	83.11 372	63.94 306	86.52 384	25.66 397	51.45 390	79.94 383

SSC-MVS		67.06 350	66.56 352	68.56 374	80.54 383	40.06 402	87.77 345	77.37 395	72.38 361	61.75 383	82.66 374	63.37 311	86.45 385	24.48 398	48.69 393	79.16 385

LCM-MVSNet		66.00 351	62.16 356	77.51 362	64.51 399	58.29 388	83.87 375	90.90 329	48.17 389	54.69 386	73.31 386	16.83 400	86.75 383	65.47 353	61.67 383	87.48 374

test_vis3_rt		65.12 352	62.60 354	72.69 366	71.44 392	60.71 384	87.17 352	65.55 399	63.80 381	53.22 387	65.65 391	14.54 401	89.44 378	76.65 281	65.38 378	67.91 390

FPMVS		64.63 353	62.55 355	70.88 367	70.80 393	56.71 389	84.42 372	84.42 375	51.78 388	49.57 388	81.61 376	23.49 393	81.48 393	40.61 393	76.25 356	74.46 386

EGC-MVSNET		61.97 354	56.37 358	78.77 359	89.63 340	73.50 322	89.12 328	82.79 379	0.21 403	1.24 404	84.80 363	39.48 384	90.04 375	44.13 388	75.94 358	72.79 387

PMMVS2		59.60 355	56.40 357	69.21 372	68.83 396	46.58 398	73.02 391	77.48 394	55.07 386	49.21 389	72.95 387	17.43 399	80.04 394	49.32 385	44.33 394	80.99 382

testf1		59.54 356	56.11 359	69.85 370	69.28 394	56.61 391	80.37 384	76.55 396	42.58 392	45.68 391	75.61 381	11.26 402	84.18 388	43.20 390	60.44 385	68.75 388

APD_test2		59.54 356	56.11 359	69.85 370	69.28 394	56.61 391	80.37 384	76.55 396	42.58 392	45.68 391	75.61 381	11.26 402	84.18 388	43.20 390	60.44 385	68.75 388

ANet_high		58.88 358	54.22 362	72.86 365	56.50 402	56.67 390	80.75 383	86.00 368	73.09 356	37.39 395	64.63 392	22.17 395	79.49 395	43.51 389	23.96 397	82.43 381

Gipuma		57.99 359	54.91 361	67.24 375	88.51 348	65.59 372	52.21 394	90.33 338	43.58 391	42.84 394	51.18 395	20.29 397	85.07 387	34.77 394	70.45 367	51.05 394
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 360	48.46 364	63.48 376	45.72 404	46.20 399	73.41 390	78.31 390	41.03 394	30.06 397	65.68 390	6.05 404	83.43 391	30.04 395	65.86 377	60.80 391
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 361	48.47 363	56.66 378	52.26 403	18.98 408	41.51 396	81.40 383	10.10 398	44.59 393	75.01 384	28.51 389	68.16 396	53.54 382	49.31 392	82.83 379

MVE	39.65 23	43.39 362	38.59 368	57.77 377	56.52 401	48.77 397	55.38 393	58.64 403	29.33 397	28.96 398	52.65 394	4.68 405	64.62 399	28.11 396	33.07 395	59.93 392
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 363	42.29 365	46.03 380	65.58 398	37.41 403	73.51 389	64.62 400	33.99 395	28.47 399	47.87 396	19.90 398	67.91 397	22.23 399	24.45 396	32.77 395

EMVS		42.07 364	41.12 366	44.92 381	63.45 400	35.56 405	73.65 388	63.48 401	33.05 396	26.88 400	45.45 397	21.27 396	67.14 398	19.80 400	23.02 398	32.06 396

tmp_tt		35.64 365	39.24 367	24.84 382	14.87 405	23.90 407	62.71 392	51.51 405	6.58 400	36.66 396	62.08 393	44.37 380	30.34 402	52.40 383	22.00 399	20.27 397

cdsmvs_eth3d_5k		22.14 366	29.52 369	0.00 386	0.00 408	0.00 411	0.00 397	95.76 152	0.00 404	0.00 405	94.29 164	75.66 170	0.00 405	0.00 404	0.00 403	0.00 401

wuyk23d		21.27 367	20.48 370	23.63 383	68.59 397	36.41 404	49.57 395	6.85 407	9.37 399	7.89 401	4.46 403	4.03 406	31.37 401	17.47 401	16.07 400	3.12 398

testmvs		8.92 368	11.52 371	1.12 385	1.06 406	0.46 410	86.02 359	0.65 408	0.62 401	2.74 402	9.52 401	0.31 408	0.45 404	2.38 402	0.39 401	2.46 400

test123		8.76 369	11.22 372	1.39 384	0.85 407	0.97 409	85.76 362	0.35 409	0.54 402	2.45 403	8.14 402	0.60 407	0.48 403	2.16 403	0.17 402	2.71 399

ab-mvs-re		7.82 370	10.43 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	93.88 185	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		6.64 371	8.86 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	79.70 121	0.00 405	0.00 404	0.00 403	0.00 401

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				95.68 5		88.34 9	96.68 33	94.37 234	95.08 1	94.68 36	97.72 24	82.94 83	99.64 1	97.85 1	98.76 28	99.06 7

WAC-MVS						64.08 377								59.14 374

FOURS1					98.86 1	85.54 65	98.29 1	97.49 6	89.79 44	96.29 18

MSC_two_6792asdad				96.52 1	97.78 51	90.86 1		96.85 63					99.61 4	96.03 14	99.06 9	99.07 5

PC_three_1452									82.47 231	97.09 10	97.07 51	92.72 1	98.04 159	92.70 55	99.02 12	98.86 11

No_MVS				96.52 1	97.78 51	90.86 1		96.85 63					99.61 4	96.03 14	99.06 9	99.07 5

test_one_0601					98.58 11	85.83 59		97.44 15	91.05 14	96.78 15	98.06 11	91.45 11

eth-test2					0.00 408

eth-test					0.00 408

ZD-MVS					98.15 34	86.62 32		97.07 45	83.63 204	94.19 42	96.91 57	87.57 31	99.26 42	91.99 74	98.44 51

RE-MVS-def			93.68 51		97.92 43	84.57 80	96.28 45	96.76 75	87.46 116	93.75 49	97.43 31	82.94 83		92.73 51	97.80 74	97.88 78

IU-MVS					98.77 5	86.00 49		96.84 65	81.26 264	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 49	92.59 2	98.94 78	92.25 63	98.99 14	98.84 14

test_241102_TWO								97.44 15	90.31 28	97.62 5	98.07 9	91.46 10	99.58 10	95.66 17	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 51		97.44 15	90.26 33	97.71 1	97.96 17	92.31 4	99.38 31

9.14			94.47 19		97.79 49		96.08 61	97.44 15	86.13 151	95.10 33	97.40 33	88.34 22	99.22 44	93.25 44	98.70 34

save fliter					97.85 46	85.63 64	95.21 111	96.82 68	89.44 51

test_0728_THIRD									90.75 19	97.04 11	98.05 13	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 38	97.09 16	97.49 6					99.61 4	95.62 21	99.08 7	98.99 9

test0726					98.78 3	85.93 54	97.19 11	97.47 11	90.27 31	97.64 4	98.13 3	91.47 8

GSMVS																96.12 153

test_part2					98.55 12	87.22 18				96.40 17

sam_mvs1												71.70 221				96.12 153

sam_mvs												70.60 234

ambc				83.06 347	79.99 385	63.51 380	77.47 387	92.86 273		74.34 360	84.45 365	28.74 388	95.06 326	73.06 311	68.89 374	90.61 349

MTGPA								96.97 50

test_post1							88.00 342				9.81 400	69.31 256	95.53 313	76.65 281

test_post											10.29 399	70.57 238	95.91 301

patchmatchnet-post											83.76 367	71.53 222	96.48 272

GG-mvs-BLEND				87.94 287	89.73 339	77.91 267	87.80 343	78.23 391		80.58 309	83.86 366	59.88 337	95.33 321	71.20 318	92.22 181	90.60 351

MTMP							96.16 53	60.64 402

gm-plane-assit					89.60 341	68.00 364			77.28 315		88.99 320		97.57 189	79.44 254

test9_res														91.91 78	98.71 32	98.07 66

TEST9					97.53 58	86.49 36	94.07 186	96.78 72	81.61 257	92.77 74	96.20 87	87.71 28	99.12 51

test_8					97.49 60	86.30 44	94.02 191	96.76 75	81.86 248	92.70 78	96.20 87	87.63 29	99.02 61

agg_prior2														90.54 102	98.68 37	98.27 52

agg_prior					97.38 63	85.92 56		96.72 81		92.16 89			98.97 75

TestCases				89.52 246	95.01 146	77.79 274		90.89 330	77.41 312	76.12 348	93.34 198	54.08 360	97.51 195	68.31 339	84.27 278	93.26 282

test_prior4						85.96 53	94.11 181

test_prior2							94.12 180		87.67 114	92.63 79	96.39 82	86.62 38		91.50 85	98.67 39

test_prior				93.82 60	97.29 67	84.49 84		96.88 61					98.87 82			98.11 65

旧先验2							93.36 219		71.25 367	94.37 39			97.13 234	86.74 146

新几何2							93.11 234

新几何1				93.10 79	97.30 66	84.35 92		95.56 168	71.09 368	91.26 113	96.24 85	82.87 85	98.86 84	79.19 258	98.10 62	96.07 157

旧先验1					96.79 76	81.81 164		95.67 160			96.81 63	86.69 37			97.66 79	96.97 122

无先验							93.28 227	96.26 110	73.95 347				99.05 55	80.56 239		96.59 137

原ACMM2							92.94 241

原ACMM1				92.01 130	97.34 64	81.05 185		96.81 70	78.89 292	90.45 120	95.92 100	82.65 87	98.84 88	80.68 237	98.26 57	96.14 151

test222					96.55 84	81.70 166	92.22 264	95.01 201	68.36 374	90.20 124	96.14 92	80.26 114			97.80 74	96.05 160

testdata2													98.75 93	78.30 265

segment_acmp												87.16 36

testdata				90.49 202	96.40 89	77.89 269		95.37 186	72.51 360	93.63 52	96.69 66	82.08 99	97.65 182	83.08 190	97.39 82	95.94 162

testdata1							92.15 266		87.94 103

test12				94.34 49	97.13 70	86.15 47		96.29 105		91.04 115		85.08 57	99.01 63		98.13 61	97.86 80

plane_prior7					94.70 165	82.74 141

plane_prior6					94.52 175	82.75 139						74.23 187

plane_prior5								96.22 115					98.12 144	88.15 124	89.99 202	94.63 210

plane_prior4											94.86 140

plane_prior3						82.75 139			90.26 33	86.91 180

plane_prior2							95.85 75		90.81 17

plane_prior1					94.59 170

plane_prior						82.73 142	95.21 111		89.66 48						89.88 207

n2								0.00 410

nn								0.00 410

door-mid								85.49 370

lessismore_v0				86.04 322	88.46 351	68.78 363		80.59 385		73.01 365	90.11 302	55.39 353	96.43 277	75.06 297	65.06 379	92.90 298

LGP-MVS_train				91.12 175	94.47 177	81.49 172		96.14 120	86.73 135	85.45 219	95.16 130	69.89 245	98.10 146	87.70 132	89.23 220	93.77 263

test11								96.57 92

door								85.33 372

HQP5-MVS						81.56 168

HQP-NCC					94.17 189		94.39 165		88.81 72	85.43 222

ACMP_Plane					94.17 189		94.39 165		88.81 72	85.43 222

BP-MVS														87.11 143

HQP4-MVS										85.43 222			97.96 165			94.51 220

HQP3-MVS								96.04 131							89.77 211

HQP2-MVS												73.83 197

NP-MVS					94.37 183	82.42 151					93.98 178

MDTV_nov1_ep13_2view						55.91 395	87.62 349		73.32 353	84.59 242		70.33 241		74.65 301		95.50 180

MDTV_nov1_ep13			83.56 276		91.69 277	69.93 359	87.75 346	91.54 313	78.60 299	84.86 236	88.90 322	69.54 250	96.03 294	70.25 325	88.93 226

ACMMP++_ref															87.47 251

ACMMP++															88.01 242

Test By Simon												80.02 116

ITE_SJBPF				88.24 279	91.88 267	77.05 286		92.92 271	85.54 164	80.13 316	93.30 202	57.29 347	96.20 288	72.46 313	84.71 274	91.49 333

DeepMVS_CX				56.31 379	74.23 390	51.81 396		56.67 404	44.85 390	48.54 390	75.16 383	27.87 390	58.74 400	40.92 392	52.22 389	58.39 393

High-resolution multi-view benchmark