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

MM		95.10 11	94.91 13	95.68 5	96.09 101	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

SF-MVS		94.97 12	94.90 15	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 13	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 14	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 15	94.69 18	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 16	94.56 19	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 17	95.11 10	93.50 69	95.79 115	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 18	94.81 16	93.98 53	94.62 170	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 18	94.38 25	95.23 11	95.41 130	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 20	94.40 23	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 21	94.28 30	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 22	94.32 26	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 22	94.20 35	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 24	94.27 32	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 24	94.28 30	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 26	94.22 33	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 27	94.21 34	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 28	94.46 21	93.79 63	95.28 134	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 29	94.07 39	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 30	94.07 39	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 31	94.17 37	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 32	93.97 43	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 32	94.00 42	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 34	94.40 23	93.60 67	95.29 133	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 34	94.31 28	93.88 57	92.46 250	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 34	94.77 17	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 37	94.44 22	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 38	93.79 46	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 39	94.29 29	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 39	93.88 44	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 41	93.78 47	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 42	93.72 50	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 43	93.65 54	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 44	93.82 45	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 45	93.77 48	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 46	94.06 41	92.86 94	95.62 123	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 47	94.32 26	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 48	94.08 38	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 49	93.41 56	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 50	93.76 49	93.00 86	95.02 146	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 51	93.20 61	94.55 42	95.65 121	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 52	94.19 36	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 53	93.66 53	92.85 95	93.75 212	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 54	93.31 57	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 55	93.55 55	93.10 79	93.67 216	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	96.32 145

train_agg		93.44 55	93.08 62	94.52 43	97.53 58	86.49 36	94.07 186	96.78 72	81.86 249	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 55	93.71 51	92.63 107	95.21 139	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 58	93.25 59	93.97 54	95.42 129	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 59	93.22 60	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 60	92.99 65	94.26 51	96.07 103	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 61	92.75 69	94.85 25	95.70 120	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 62	92.88 67	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 63	93.05 63	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 64	93.26 58	92.97 88	92.49 248	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 64	93.02 64	93.71 65	89.25 344	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 66	92.50 73	94.81 31	95.62 123	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 67	92.92 66	93.29 71	95.01 147	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 68	92.83 68	93.35 70	94.59 171	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 69	92.54 72	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 69	92.30 75	94.44 44	97.79 49	86.11 48	94.06 188	96.66 85	80.09 278	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 71	92.66 70	92.97 88	95.20 140	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	96.54 141

EI-MVSNet-UG-set		92.74 71	92.62 71	93.12 78	94.86 158	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 73	91.74 81	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 74	92.43 74	92.74 101	94.41 183	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 75	92.29 76	92.98 87	95.99 109	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 76	91.37 84	95.55 7	95.63 122	88.73 6	97.07 18	96.77 74	90.84 16	84.02 261	96.62 74	75.95 163	99.34 34	87.77 130	97.68 78	98.59 24

baseline		92.39 77	92.29 76	92.69 105	94.46 180	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 78	91.91 79	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 79	91.80 80	92.55 111	98.24 31	81.98 160	96.76 30	96.49 95	81.89 248	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 80	91.94 78	91.98 134	95.16 141	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 80	91.28 86	93.96 55	98.33 27	85.92 56	94.66 147	96.66 85	82.69 230	90.03 129	95.82 105	82.30 93	99.03 58	84.57 172	96.48 104	96.91 126

EPNet		91.79 82	91.02 92	94.10 52	90.10 332	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 83	91.70 82	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 84	91.23 87	93.29 71	95.32 132	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 85	90.82 96	94.44 44	94.59 171	86.37 40	97.18 12	97.02 47	89.20 60	84.31 257	96.66 69	73.74 199	99.17 47	86.74 146	97.96 68	97.79 85

EPP-MVSNet		91.70 86	91.56 83	92.13 129	95.88 112	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 87	91.30 85	92.80 97	93.86 206	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 88	91.11 89	93.01 85	94.35 188	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 89	91.09 91	92.46 115	95.87 114	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 90	90.91 94	92.80 97	96.39 90	83.17 122	94.87 133	96.66 85	83.29 215	89.27 137	94.46 158	80.29 113	99.17 47	87.57 134	95.37 123	96.05 161

diffmvs		91.37 91	91.23 87	91.77 150	93.09 231	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 92	91.11 89	91.93 138	94.37 184	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 93	90.62 98	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 94	90.78 97	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 95	90.92 93	91.96 136	95.26 137	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	94.29 235

xiu_mvs_v2_base		91.13 96	90.89 95	91.86 144	94.97 150	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	94.28 236

nrg030		91.08 97	90.39 99	93.17 76	93.07 232	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	82.95 292	94.96 199

lupinMVS		90.92 98	90.21 102	93.03 84	93.86 206	83.88 101	92.81 245	93.86 254	79.84 280	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 99	90.15 105	92.75 100	96.01 105	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	78.05 347	96.60 136

jason		90.80 99	90.10 106	92.90 92	93.04 234	83.53 112	93.08 235	94.15 243	80.22 275	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		90.74 101	89.92 113	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		90.73 102	90.32 101	91.98 134	96.12 97	81.25 179	92.55 252	96.83 66	82.04 242	89.10 139	92.56 227	81.04 109	98.85 86	86.72 148	95.91 110	95.84 168

test_yl		90.69 103	90.02 111	92.71 102	95.72 118	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		90.69 103	90.02 111	92.71 102	95.72 118	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		90.66 105	90.07 107	92.45 116	96.36 91	84.57 80	96.06 64	95.22 193	82.39 233	89.13 138	94.27 167	80.32 112	98.46 115	80.16 245	96.71 98	94.33 232

xiu_mvs_v1_base_debu		90.64 106	90.05 108	92.40 117	93.97 203	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	94.18 237

xiu_mvs_v1_base		90.64 106	90.05 108	92.40 117	93.97 203	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	94.18 237

xiu_mvs_v1_base_debi		90.64 106	90.05 108	92.40 117	93.97 203	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	94.18 237

HQP_MVS		90.60 109	90.19 103	91.82 147	94.70 166	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	89.99 203	94.63 211

FIs		90.51 110	90.35 100	90.99 186	93.99 202	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	88.31 239	94.76 209

MAR-MVS		90.30 111	89.37 124	93.07 83	96.61 81	84.48 85	95.68 85	95.67 160	82.36 235	87.85 159	92.85 216	76.63 157	98.80 90	80.01 246	96.68 99	95.91 164
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		90.27 112	90.18 104	90.53 198	93.71 213	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	88.09 242	94.71 210

CANet_DTU		90.26 113	89.41 123	92.81 96	93.46 222	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	96.38 144

SDMVSNet		90.19 114	89.61 117	91.93 138	96.00 106	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	89.85 209	95.63 178

OPM-MVS		90.12 115	89.56 118	91.82 147	93.14 229	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	90.18 202	93.65 271
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 116	89.13 130	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		90.05 117	89.43 122	91.90 143	95.16 141	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		90.02 118	89.27 129	92.29 125	95.78 116	80.95 189	92.68 247	96.22 115	81.91 246	86.66 188	93.75 192	82.23 95	98.44 121	79.40 257	94.79 132	97.48 97

PVSNet_BlendedMVS		89.98 119	89.70 115	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	87.86 246	92.35 317

PS-MVSNAJss		89.97 120	89.62 116	91.02 183	91.90 268	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	88.10 240	94.57 216

mvsmamba		89.96 121	89.50 119	91.33 168	92.90 241	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	88.63 230	94.51 221

XVG-OURS-SEG-HR		89.95 122	89.45 120	91.47 162	94.00 201	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	89.64 214	95.91 164

UGNet		89.95 122	88.95 134	92.95 90	94.51 177	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		89.92 124	89.29 127	91.81 149	93.39 224	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	81.02 321	94.49 226

AdaColmap		89.89 125	89.07 131	92.37 120	97.41 62	83.03 130	94.42 162	95.92 139	82.81 227	86.34 195	94.65 152	73.89 195	99.02 61	80.69 236	95.51 116	95.05 194

hse-mvs2		89.88 126	89.34 125	91.51 159	94.83 160	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	77.07 354	95.74 173

UniMVSNet (Re)		89.80 127	89.07 131	92.01 130	93.60 218	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	80.84 325	94.12 241

HQP-MVS		89.80 127	89.28 128	91.34 167	94.17 191	81.56 168	94.39 165	96.04 131	88.81 72	85.43 223	93.97 179	73.83 197	97.96 165	87.11 143	89.77 212	94.50 224

FA-MVS(test-final)		89.66 129	88.91 136	91.93 138	94.57 174	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		89.62 130	88.96 133	91.60 155	93.86 206	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	82.86 297	94.52 219

WTY-MVS		89.60 131	88.92 135	91.67 153	95.47 128	81.15 183	92.38 256	94.78 220	83.11 219	89.06 141	94.32 162	78.67 135	96.61 262	81.57 222	90.89 195	97.24 104

Vis-MVSNet (Re-imp)		89.59 132	89.44 121	90.03 225	95.74 117	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		89.56 133	88.49 151	92.76 99	95.07 145	82.09 157	96.30 43	93.19 267	81.05 270	91.88 98	96.86 59	61.16 330	98.33 131	88.43 123	92.49 179	97.84 82

114514_t		89.51 134	88.50 149	92.54 112	98.11 36	81.99 159	95.16 116	96.36 102	70.19 372	85.81 203	95.25 124	76.70 155	98.63 102	82.07 210	96.86 95	97.00 120

QAPM		89.51 134	88.15 160	93.59 68	94.92 154	84.58 79	96.82 29	96.70 83	78.43 303	83.41 276	96.19 90	73.18 206	99.30 40	77.11 278	96.54 101	96.89 127

CLD-MVS		89.47 136	88.90 137	91.18 173	94.22 190	82.07 158	92.13 267	96.09 126	87.90 105	85.37 229	92.45 230	74.38 185	97.56 190	87.15 141	90.43 198	93.93 250
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		89.45 137	88.90 137	91.12 175	94.47 178	81.49 172	95.30 103	96.14 120	86.73 135	85.45 220	95.16 130	69.89 245	98.10 146	87.70 132	89.23 221	93.77 264

CDS-MVSNet		89.45 137	88.51 148	92.29 125	93.62 217	83.61 111	93.01 238	94.68 225	81.95 244	87.82 161	93.24 205	78.69 134	96.99 243	80.34 242	93.23 167	96.28 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
iter_conf_final		89.42 139	88.69 142	91.60 155	95.12 144	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	89.01 225	94.32 233

Fast-Effi-MVS+		89.41 140	88.64 143	91.71 152	94.74 162	80.81 193	93.54 213	95.10 198	83.11 219	86.82 186	90.67 291	79.74 120	97.75 177	80.51 240	93.55 156	96.57 139

ab-mvs		89.41 140	88.35 153	92.60 108	95.15 143	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	89.37 217	96.94 123

XVG-OURS		89.40 142	88.70 141	91.52 158	94.06 195	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	89.96 205	95.83 169

test_vis1_n_1920		89.39 143	89.84 114	88.04 285	92.97 238	72.64 334	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	95.74 173

mvs_anonymous		89.37 144	89.32 126	89.51 248	93.47 221	74.22 317	91.65 280	94.83 216	82.91 225	85.45 220	93.79 188	81.23 108	96.36 282	86.47 150	94.09 147	97.94 74

DU-MVS		89.34 145	88.50 149	91.85 146	93.04 234	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	81.02 321	94.59 214

TAMVS		89.21 146	88.29 157	91.96 136	93.71 213	82.62 148	93.30 225	94.19 241	82.22 237	87.78 162	93.94 180	78.83 131	96.95 245	77.70 271	92.98 171	96.32 145

ACMM	84.12 9	89.14 147	88.48 152	91.12 175	94.65 169	81.22 181	95.31 101	96.12 123	85.31 169	85.92 202	94.34 160	70.19 243	98.06 158	85.65 159	88.86 228	94.08 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 148	88.64 143	90.48 204	95.53 127	74.97 309	96.08 61	84.89 375	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		89.10 148	88.86 139	89.80 237	91.84 270	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	87.36 255	93.88 253

ECVR-MVS		89.09 150	88.53 147	90.77 193	95.62 123	75.89 302	96.16 53	84.22 377	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		89.09 150	88.62 146	90.49 202	92.85 242	79.65 228	96.41 39	94.41 232	88.22 94	85.50 216	94.77 146	69.36 253	97.31 217	89.33 112	86.73 262	94.51 221

CNLPA		89.07 152	87.98 164	92.34 121	96.87 74	84.78 76	94.08 185	93.24 265	81.41 261	84.46 247	95.13 132	75.57 171	96.62 259	77.21 276	93.84 152	95.61 180

PLC	84.53 7	89.06 153	88.03 163	92.15 128	97.27 68	82.69 145	94.29 171	95.44 180	79.71 282	84.01 262	94.18 169	76.68 156	98.75 93	77.28 275	93.41 162	95.02 195
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 154	88.64 143	90.21 215	90.74 318	79.28 240	95.96 71	95.90 142	84.66 186	85.33 231	92.94 215	74.02 193	97.30 218	89.64 109	88.53 232	94.05 247

HY-MVS	83.01 12	89.03 154	87.94 166	92.29 125	94.86 158	82.77 138	92.08 270	94.49 228	81.52 260	86.93 178	92.79 222	78.32 141	98.23 137	79.93 247	90.55 196	95.88 166

ACMP	84.23 8	89.01 156	88.35 153	90.99 186	94.73 163	81.27 178	95.07 121	95.89 144	86.48 139	83.67 269	94.30 163	69.33 254	97.99 163	87.10 145	88.55 231	93.72 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 157	88.26 159	90.94 189	94.05 196	80.78 194	91.71 277	95.38 184	81.55 259	88.63 146	93.91 184	75.04 176	95.47 320	82.47 202	91.61 184	96.57 139

iter_conf05		88.85 158	88.08 162	91.17 174	94.27 189	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	89.00 226	94.32 233

TranMVSNet+NR-MVSNet		88.84 159	87.95 165	91.49 160	92.68 246	83.01 132	94.92 130	96.31 104	89.88 39	85.53 213	93.85 187	76.63 157	96.96 244	81.91 214	79.87 338	94.50 224

CHOSEN 1792x2688		88.84 159	87.69 170	92.30 124	96.14 96	81.42 176	90.01 313	95.86 146	74.52 342	87.41 168	93.94 180	75.46 172	98.36 126	80.36 241	95.53 115	97.12 113

MVSTER		88.84 159	88.29 157	90.51 201	92.95 239	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	87.36 255	93.87 254

test_cas_vis1_n_1920		88.83 162	88.85 140	88.78 264	91.15 299	76.72 290	93.85 202	94.93 208	83.23 218	92.81 72	96.00 96	61.17 329	94.45 330	91.67 83	94.84 131	95.17 191

OpenMVS	83.78 11	88.74 163	87.29 180	93.08 81	92.70 245	85.39 67	96.57 36	96.43 97	78.74 298	80.85 306	96.07 94	69.64 249	99.01 63	78.01 269	96.65 100	94.83 206

thisisatest0530		88.67 164	87.61 172	91.86 144	94.87 157	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		88.65 165	88.35 153	89.54 245	93.33 225	76.39 296	94.47 158	94.36 235	87.70 112	85.43 223	89.56 314	73.45 202	97.26 224	85.57 161	91.28 188	94.97 196

tttt0517		88.61 166	87.78 169	91.11 178	94.96 151	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		88.60 167	88.13 161	90.01 228	95.24 138	78.50 253	93.29 226	94.15 243	84.75 183	84.46 247	93.40 197	75.76 166	97.40 211	77.59 272	94.52 141	94.12 241

sd_testset		88.59 168	87.85 168	90.83 190	96.00 106	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	89.85 209	95.63 178

NR-MVSNet		88.58 169	87.47 176	91.93 138	93.04 234	84.16 95	94.77 140	96.25 112	89.05 65	80.04 319	93.29 203	79.02 130	97.05 240	81.71 221	80.05 335	94.59 214

1112_ss		88.42 170	87.33 179	91.72 151	94.92 154	80.98 187	92.97 240	94.54 227	78.16 309	83.82 265	93.88 185	78.78 133	97.91 169	79.45 253	89.41 216	96.26 149

WR-MVS		88.38 171	87.67 171	90.52 200	93.30 226	80.18 208	93.26 228	95.96 137	88.57 83	85.47 219	92.81 220	76.12 159	96.91 248	81.24 226	82.29 301	94.47 229

BH-RMVSNet		88.37 172	87.48 175	91.02 183	95.28 134	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	95.85 167

IterMVS-LS		88.36 173	87.91 167	89.70 241	93.80 209	78.29 260	93.73 206	95.08 200	85.73 157	84.75 239	91.90 253	79.88 117	96.92 247	83.83 182	82.51 298	93.89 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 174	86.13 220	94.85 25	98.54 13	86.60 33	96.93 22	97.19 35	90.66 24	92.85 69	23.41 399	85.02 59	99.49 26	91.99 74	98.56 48	98.47 33

LCM-MVSNet-Re		88.30 175	88.32 156	88.27 278	94.71 165	72.41 339	93.15 231	90.98 327	87.77 110	79.25 328	91.96 251	78.35 140	95.75 308	83.04 191	95.62 114	96.65 135

jajsoiax		88.24 176	87.50 174	90.48 204	90.89 312	80.14 210	95.31 101	95.65 164	84.97 177	84.24 258	94.02 175	65.31 298	97.42 204	88.56 121	88.52 233	93.89 251

VPNet		88.20 177	87.47 176	90.39 209	93.56 219	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	80.53 330	94.56 217

TAPA-MVS	84.62 6	88.16 178	87.01 188	91.62 154	96.64 80	80.65 196	94.39 165	96.21 118	76.38 322	86.19 198	95.44 117	79.75 119	98.08 156	62.75 366	95.29 125	96.13 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 179	87.28 181	90.57 196	94.96 151	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	79.31 343	95.44 182

Anonymous20240529		88.09 180	86.59 204	92.58 110	96.53 86	81.92 162	95.99 69	95.84 147	74.11 346	89.06 141	95.21 127	61.44 324	98.81 89	83.67 186	87.47 252	97.01 119

HyFIR lowres test		88.09 180	86.81 192	91.93 138	96.00 106	80.63 197	90.01 313	95.79 150	73.42 353	87.68 164	92.10 245	73.86 196	97.96 165	80.75 235	91.70 183	97.19 107

mvs_tets		88.06 182	87.28 181	90.38 211	90.94 308	79.88 222	95.22 110	95.66 162	85.10 174	84.21 259	93.94 180	63.53 310	97.40 211	88.50 122	88.40 237	93.87 254

F-COLMAP		87.95 183	86.80 193	91.40 164	96.35 92	80.88 191	94.73 142	95.45 178	79.65 283	82.04 293	94.61 153	71.13 226	98.50 110	76.24 287	91.05 193	94.80 208

LS3D		87.89 184	86.32 214	92.59 109	96.07 103	82.92 136	95.23 109	94.92 209	75.66 329	82.89 283	95.98 98	72.48 215	99.21 45	68.43 338	95.23 128	95.64 177

anonymousdsp		87.84 185	87.09 184	90.12 221	89.13 345	80.54 200	94.67 146	95.55 169	82.05 240	83.82 265	92.12 242	71.47 224	97.15 231	87.15 141	87.80 250	92.67 305

v2v482		87.84 185	87.06 185	90.17 217	90.99 304	79.23 243	94.00 194	95.13 195	84.87 179	85.53 213	92.07 248	74.45 184	97.45 200	84.71 171	81.75 309	93.85 257

WR-MVS_H		87.80 187	87.37 178	89.10 257	93.23 227	78.12 263	95.61 92	97.30 29	87.90 105	83.72 267	92.01 250	79.65 125	96.01 296	76.36 284	80.54 329	93.16 290

AUN-MVS		87.78 188	86.54 206	91.48 161	94.82 161	81.05 185	93.91 201	93.93 250	83.00 222	86.93 178	93.53 195	69.50 251	97.67 179	86.14 151	77.12 353	95.73 175

PCF-MVS	84.11 10	87.74 189	86.08 224	92.70 104	94.02 197	84.43 89	89.27 325	95.87 145	73.62 351	84.43 249	94.33 161	78.48 139	98.86 84	70.27 324	94.45 143	94.81 207
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 190	86.13 220	92.31 123	96.66 79	80.74 195	94.87 133	91.49 315	80.47 274	89.46 135	95.44 117	54.72 358	98.23 137	82.19 208	89.89 207	97.97 72

V42		87.68 190	86.86 190	90.15 219	90.58 323	80.14 210	94.24 175	95.28 189	83.66 203	85.67 207	91.33 268	74.73 181	97.41 209	84.43 175	81.83 307	92.89 300

thres600view7		87.65 192	86.67 199	90.59 195	96.08 102	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	91.10 189	94.96 199

XXY-MVS		87.65 192	86.85 191	90.03 225	92.14 258	80.60 199	93.76 205	95.23 191	82.94 224	84.60 242	94.02 175	74.27 186	95.49 319	81.04 228	83.68 285	94.01 249

Test_1112_low_res		87.65 192	86.51 207	91.08 179	94.94 153	79.28 240	91.77 275	94.30 237	76.04 327	83.51 274	92.37 232	77.86 146	97.73 178	78.69 261	89.13 223	96.22 150

thres100view900		87.63 195	86.71 197	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	91.10 189	94.48 227

CP-MVSNet		87.63 195	87.26 183	88.74 268	93.12 230	76.59 293	95.29 105	96.58 91	88.43 86	83.49 275	92.98 214	75.28 173	95.83 304	78.97 259	81.15 317	93.79 259

thres400		87.62 197	86.64 200	90.57 196	95.99 109	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	91.10 189	94.96 199

v1144		87.61 198	86.79 194	90.06 224	91.01 303	79.34 236	93.95 196	95.42 183	83.36 214	85.66 208	91.31 271	74.98 177	97.42 204	83.37 187	82.06 303	93.42 280

bld_raw_dy_0_64		87.60 199	86.73 195	90.21 215	91.72 275	80.26 207	95.09 120	88.61 357	85.68 159	85.55 210	94.38 159	63.93 308	96.66 256	87.73 131	87.84 247	93.72 268

tfpn200view9		87.58 200	86.64 200	90.41 208	95.99 109	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	91.10 189	94.48 227

BH-w/o		87.57 201	87.05 186	89.12 256	94.90 156	77.90 268	92.41 254	93.51 262	82.89 226	83.70 268	91.34 267	75.75 167	97.07 238	75.49 291	93.49 159	92.39 315

UniMVSNet_ETH3D		87.53 202	86.37 211	91.00 185	92.44 251	78.96 245	94.74 141	95.61 166	84.07 194	85.36 230	94.52 157	59.78 338	97.34 216	82.93 193	87.88 245	96.71 134

ET-MVSNet_ETH3D		87.51 203	85.91 232	92.32 122	93.70 215	83.93 99	92.33 260	90.94 328	84.16 191	72.09 368	92.52 228	69.90 244	95.85 303	89.20 114	88.36 238	97.17 108

1314		87.51 203	86.57 205	90.34 213	92.42 252	79.74 226	92.63 249	95.35 188	78.35 304	80.14 316	91.62 262	74.05 192	97.15 231	81.05 227	93.53 157	94.12 241

v8		87.50 205	86.71 197	89.89 231	91.37 289	79.40 233	94.50 154	95.38 184	84.81 182	83.60 272	91.33 268	76.05 160	97.42 204	82.84 196	80.51 332	92.84 302

Fast-Effi-MVS+-dtu		87.44 206	86.72 196	89.63 243	92.04 262	77.68 278	94.03 190	93.94 249	85.81 154	82.42 287	91.32 270	70.33 241	97.06 239	80.33 243	90.23 201	94.14 240

MVS		87.44 206	86.10 223	91.44 163	92.61 247	83.62 109	92.63 249	95.66 162	67.26 376	81.47 298	92.15 240	77.95 143	98.22 139	79.71 249	95.48 118	92.47 311

FE-MVS		87.40 208	86.02 226	91.57 157	94.56 175	79.69 227	90.27 302	93.72 259	80.57 273	88.80 144	91.62 262	65.32 297	98.59 106	74.97 299	94.33 146	96.44 142

FMVSNet3		87.40 208	86.11 222	91.30 169	93.79 211	83.64 108	94.20 177	94.81 218	83.89 198	84.37 250	91.87 254	68.45 269	96.56 267	78.23 266	85.36 270	93.70 270

test_fmvs1		87.34 210	87.56 173	86.68 318	90.59 322	71.80 343	94.01 192	94.04 248	78.30 305	91.97 94	95.22 125	56.28 350	93.71 345	92.89 49	94.71 133	94.52 219

thisisatest0515		87.33 211	85.99 227	91.37 166	93.49 220	79.55 229	90.63 298	89.56 354	80.17 276	87.56 166	90.86 284	67.07 279	98.28 135	81.50 223	93.02 170	96.29 147

PS-CasMVS		87.32 212	86.88 189	88.63 271	92.99 237	76.33 298	95.33 100	96.61 89	88.22 94	83.30 280	93.07 212	73.03 209	95.79 307	78.36 263	81.00 323	93.75 266

GBi-Net		87.26 213	85.98 228	91.08 179	94.01 198	83.10 125	95.14 117	94.94 204	83.57 205	84.37 250	91.64 258	66.59 287	96.34 283	78.23 266	85.36 270	93.79 259

test1		87.26 213	85.98 228	91.08 179	94.01 198	83.10 125	95.14 117	94.94 204	83.57 205	84.37 250	91.64 258	66.59 287	96.34 283	78.23 266	85.36 270	93.79 259

v1192		87.25 215	86.33 213	90.00 229	90.76 317	79.04 244	93.80 203	95.48 174	82.57 231	85.48 218	91.18 275	73.38 205	97.42 204	82.30 206	82.06 303	93.53 274

v10		87.25 215	86.38 210	89.85 232	91.19 295	79.50 230	94.48 155	95.45 178	83.79 201	83.62 271	91.19 273	75.13 174	97.42 204	81.94 213	80.60 327	92.63 307

DP-MVS		87.25 215	85.36 247	92.90 92	97.65 55	83.24 119	94.81 137	92.00 299	74.99 337	81.92 295	95.00 135	72.66 212	99.05 55	66.92 350	92.33 180	96.40 143

miper_ehance_all_eth		87.22 218	86.62 203	89.02 260	92.13 259	77.40 282	90.91 294	94.81 218	81.28 264	84.32 255	90.08 303	79.26 127	96.62 259	83.81 183	82.94 293	93.04 295

test2506		87.21 219	86.28 216	90.02 227	95.62 123	73.64 322	96.25 48	71.38 399	87.89 107	90.45 120	96.65 70	55.29 356	98.09 154	86.03 155	96.94 90	98.33 43

thres200		87.21 219	86.24 218	90.12 221	95.36 131	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	91.04 194	93.83 258

v144192		87.19 221	86.35 212	89.74 238	90.64 321	78.24 261	93.92 199	95.43 181	81.93 245	85.51 215	91.05 281	74.21 189	97.45 200	82.86 195	81.56 311	93.53 274

FMVSNet2		87.19 221	85.82 234	91.30 169	94.01 198	83.67 106	94.79 138	94.94 204	83.57 205	83.88 264	92.05 249	66.59 287	96.51 270	77.56 273	85.01 273	93.73 267

c3_l		87.14 223	86.50 208	89.04 259	92.20 256	77.26 283	91.22 289	94.70 224	82.01 243	84.34 254	90.43 295	78.81 132	96.61 262	83.70 185	81.09 318	93.25 285

Baseline_NR-MVSNet		87.07 224	86.63 202	88.40 274	91.44 284	77.87 270	94.23 176	92.57 282	84.12 193	85.74 206	92.08 246	77.25 149	96.04 293	82.29 207	79.94 336	91.30 338

v148		87.04 225	86.32 214	89.21 253	90.94 308	77.26 283	93.71 208	94.43 230	84.84 181	84.36 253	90.80 288	76.04 161	97.05 240	82.12 209	79.60 340	93.31 282

test_fmvs1_n		87.03 226	87.04 187	86.97 310	89.74 340	71.86 341	94.55 152	94.43 230	78.47 301	91.95 96	95.50 116	51.16 369	93.81 343	93.02 48	94.56 139	95.26 188

v1921920		86.97 227	86.06 225	89.69 242	90.53 326	78.11 264	93.80 203	95.43 181	81.90 247	85.33 231	91.05 281	72.66 212	97.41 209	82.05 211	81.80 308	93.53 274

tt0805		86.92 228	85.74 240	90.48 204	92.22 255	79.98 220	95.63 91	94.88 212	83.83 200	84.74 240	92.80 221	57.61 346	97.67 179	85.48 162	84.42 277	93.79 259

miper_enhance_ethall		86.90 229	86.18 219	89.06 258	91.66 280	77.58 280	90.22 308	94.82 217	79.16 289	84.48 246	89.10 319	79.19 129	96.66 256	84.06 178	82.94 293	92.94 298

v7n		86.81 230	85.76 238	89.95 230	90.72 319	79.25 242	95.07 121	95.92 139	84.45 189	82.29 288	90.86 284	72.60 214	97.53 194	79.42 256	80.52 331	93.08 294

PEN-MVS		86.80 231	86.27 217	88.40 274	92.32 254	75.71 305	95.18 113	96.38 101	87.97 102	82.82 284	93.15 208	73.39 204	95.92 299	76.15 288	79.03 345	93.59 272

cl22		86.78 232	85.98 228	89.18 255	92.34 253	77.62 279	90.84 295	94.13 245	81.33 263	83.97 263	90.15 301	73.96 194	96.60 264	84.19 177	82.94 293	93.33 281

v1240		86.78 232	85.85 233	89.56 244	90.45 327	77.79 274	93.61 211	95.37 186	81.65 255	85.43 223	91.15 277	71.50 223	97.43 203	81.47 224	82.05 305	93.47 278

TR-MVS		86.78 232	85.76 238	89.82 234	94.37 184	78.41 255	92.47 253	92.83 274	81.11 269	86.36 194	92.40 231	68.73 266	97.48 197	73.75 308	89.85 209	93.57 273

PatchMatch-RL		86.77 235	85.54 241	90.47 207	95.88 112	82.71 144	90.54 299	92.31 288	79.82 281	84.32 255	91.57 266	68.77 265	96.39 279	73.16 310	93.48 161	92.32 318

PAPM		86.68 236	85.39 245	90.53 198	93.05 233	79.33 239	89.79 316	94.77 221	78.82 295	81.95 294	93.24 205	76.81 152	97.30 218	66.94 348	93.16 168	94.95 202

pm-mvs1		86.61 237	85.54 241	89.82 234	91.44 284	80.18 208	95.28 107	94.85 214	83.84 199	81.66 296	92.62 225	72.45 217	96.48 272	79.67 250	78.06 346	92.82 303

GA-MVS		86.61 237	85.27 249	90.66 194	91.33 292	78.71 247	90.40 301	93.81 257	85.34 168	85.12 233	89.57 313	61.25 326	97.11 235	80.99 231	89.59 215	96.15 151

Anonymous20231211		86.59 239	85.13 251	90.98 188	96.52 87	81.50 170	96.14 57	96.16 119	73.78 349	83.65 270	92.15 240	63.26 313	97.37 215	82.82 197	81.74 310	94.06 246

test_vis1_n		86.56 240	86.49 209	86.78 317	88.51 350	72.69 331	94.68 145	93.78 258	79.55 284	90.70 117	95.31 121	48.75 374	93.28 351	93.15 45	93.99 148	94.38 231

DIV-MVS_self_test		86.53 241	85.78 235	88.75 266	92.02 264	76.45 295	90.74 296	94.30 237	81.83 251	83.34 278	90.82 287	75.75 167	96.57 265	81.73 220	81.52 313	93.24 286

cl____		86.52 242	85.78 235	88.75 266	92.03 263	76.46 294	90.74 296	94.30 237	81.83 251	83.34 278	90.78 289	75.74 169	96.57 265	81.74 219	81.54 312	93.22 287

eth_miper_zixun_eth		86.50 243	85.77 237	88.68 269	91.94 265	75.81 304	90.47 300	94.89 210	82.05 240	84.05 260	90.46 294	75.96 162	96.77 252	82.76 199	79.36 342	93.46 279

baseline2		86.50 243	85.39 245	89.84 233	91.12 300	76.70 291	91.88 272	88.58 358	82.35 236	79.95 320	90.95 283	73.42 203	97.63 186	80.27 244	89.95 206	95.19 190

EPNet_dtu		86.49 245	85.94 231	88.14 283	90.24 330	72.82 329	94.11 181	92.20 291	86.66 137	79.42 327	92.36 233	73.52 200	95.81 306	71.26 317	93.66 153	95.80 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		86.43 246	84.98 254	90.80 192	92.10 261	80.92 190	90.24 306	95.91 141	73.10 356	83.57 273	88.39 331	65.15 299	97.46 199	84.90 168	91.43 186	94.03 248

SCA		86.32 247	85.18 250	89.73 240	92.15 257	76.60 292	91.12 290	91.69 308	83.53 208	85.50 216	88.81 324	66.79 283	96.48 272	76.65 281	90.35 200	96.12 154

LTVRE_ROB	82.13 13	86.26 248	84.90 257	90.34 213	94.44 182	81.50 170	92.31 262	94.89 210	83.03 221	79.63 325	92.67 223	69.69 248	97.79 172	71.20 318	86.26 265	91.72 328
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		86.11 249	85.48 243	87.98 286	91.65 281	74.92 310	94.93 129	95.75 153	87.36 119	82.26 289	93.04 213	72.85 210	95.82 305	74.04 304	77.46 351	93.20 288

XVG-ACMP-BASELINE		86.00 250	84.84 259	89.45 249	91.20 294	78.00 265	91.70 278	95.55 169	85.05 176	82.97 282	92.25 238	54.49 359	97.48 197	82.93 193	87.45 254	92.89 300

MVP-Stereo		85.97 251	84.86 258	89.32 251	90.92 310	82.19 156	92.11 268	94.19 241	78.76 297	78.77 333	91.63 261	68.38 270	96.56 267	75.01 298	93.95 149	89.20 363
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 252	85.09 252	88.35 276	90.79 315	77.42 281	91.83 274	95.70 158	80.77 272	80.08 318	90.02 304	66.74 285	96.37 280	81.88 215	87.97 244	91.26 339

test-LLR		85.87 253	85.41 244	87.25 302	90.95 306	71.67 345	89.55 319	89.88 349	83.41 211	84.54 244	87.95 338	67.25 275	95.11 325	81.82 216	93.37 164	94.97 196

FMVSNet1		85.85 254	84.11 268	91.08 179	92.81 243	83.10 125	95.14 117	94.94 204	81.64 256	82.68 285	91.64 258	59.01 342	96.34 283	75.37 293	83.78 282	93.79 259

PatchmatchNet		85.85 254	84.70 261	89.29 252	91.76 274	75.54 306	88.49 337	91.30 319	81.63 257	85.05 234	88.70 328	71.71 220	96.24 287	74.61 302	89.05 224	96.08 157
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 256	84.94 256	88.26 279	91.16 298	72.58 337	89.47 323	91.04 326	76.26 325	86.45 192	89.97 306	70.74 233	96.86 251	82.35 205	87.07 260	95.34 187

PMMVS		85.71 257	84.96 255	87.95 287	88.90 348	77.09 285	88.68 335	90.06 343	72.32 363	86.47 189	90.76 290	72.15 218	94.40 332	81.78 218	93.49 159	92.36 316

PVSNet	78.82 18	85.55 258	84.65 262	88.23 281	94.72 164	71.93 340	87.12 354	92.75 277	78.80 296	84.95 236	90.53 293	64.43 303	96.71 255	74.74 300	93.86 151	96.06 160

IterMVS-SCA-FT		85.45 259	84.53 265	88.18 282	91.71 277	76.87 288	90.19 309	92.65 281	85.40 167	81.44 299	90.54 292	66.79 283	95.00 328	81.04 228	81.05 319	92.66 306

pmmvs4		85.43 260	83.86 273	90.16 218	90.02 335	82.97 134	90.27 302	92.67 280	75.93 328	80.73 307	91.74 257	71.05 227	95.73 310	78.85 260	83.46 289	91.78 327

mvsany_test1		85.42 261	85.30 248	85.77 328	87.95 361	75.41 308	87.61 351	80.97 385	76.82 319	88.68 145	95.83 104	77.44 148	90.82 373	85.90 156	86.51 263	91.08 346

ACMH	80.38 17	85.36 262	83.68 275	90.39 209	94.45 181	80.63 197	94.73 142	94.85 214	82.09 239	77.24 341	92.65 224	60.01 336	97.58 188	72.25 314	84.87 274	92.96 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 263	84.64 263	87.49 296	90.77 316	72.59 336	94.01 192	94.40 233	84.72 184	79.62 326	93.17 207	61.91 320	96.72 253	81.99 212	81.16 315	93.16 290

CR-MVSNet		85.35 263	83.76 274	90.12 221	90.58 323	79.34 236	85.24 367	91.96 303	78.27 306	85.55 210	87.87 341	71.03 228	95.61 312	73.96 306	89.36 218	95.40 184

tpmrst		85.35 263	84.99 253	86.43 320	90.88 313	67.88 367	88.71 334	91.43 317	80.13 277	86.08 200	88.80 326	73.05 207	96.02 295	82.48 201	83.40 291	95.40 184

miper_lstm_enhance		85.27 266	84.59 264	87.31 299	91.28 293	74.63 312	87.69 348	94.09 247	81.20 268	81.36 301	89.85 309	74.97 178	94.30 335	81.03 230	79.84 339	93.01 296

IB-MVS	80.51 15	85.24 267	83.26 281	91.19 172	92.13 259	79.86 223	91.75 276	91.29 320	83.28 216	80.66 309	88.49 330	61.28 325	98.46 115	80.99 231	79.46 341	95.25 189
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		85.15 268	83.99 271	88.65 270	92.47 249	78.40 256	79.68 387	92.76 276	74.90 339	81.41 300	89.59 312	69.85 247	95.51 316	79.92 248	95.29 125	92.03 323

RPSCF		85.07 269	84.27 266	87.48 297	92.91 240	70.62 356	91.69 279	92.46 283	76.20 326	82.67 286	95.22 125	63.94 306	97.29 221	77.51 274	85.80 267	94.53 218

MS-PatchMatch		85.05 270	84.16 267	87.73 290	91.42 287	78.51 252	91.25 287	93.53 261	77.50 312	80.15 315	91.58 264	61.99 319	95.51 316	75.69 290	94.35 145	89.16 364

ACMH+	81.04 14	85.05 270	83.46 278	89.82 234	94.66 168	79.37 234	94.44 160	94.12 246	82.19 238	78.04 336	92.82 219	58.23 344	97.54 193	73.77 307	82.90 296	92.54 308

IterMVS		84.88 272	83.98 272	87.60 292	91.44 284	76.03 300	90.18 310	92.41 284	83.24 217	81.06 305	90.42 296	66.60 286	94.28 336	79.46 252	80.98 324	92.48 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 273	83.09 284	90.14 220	93.80 209	80.05 215	89.18 328	93.09 268	78.89 293	78.19 334	91.91 252	65.86 296	97.27 222	68.47 337	88.45 235	93.11 292

testing222		84.84 274	83.32 279	89.43 250	94.15 194	75.94 301	91.09 291	89.41 355	84.90 178	85.78 204	89.44 315	52.70 366	96.28 286	70.80 323	91.57 185	96.07 158

tpm		84.73 275	84.02 270	86.87 315	90.33 328	68.90 363	89.06 330	89.94 346	80.85 271	85.75 205	89.86 308	68.54 268	95.97 297	77.76 270	84.05 281	95.75 172

tfpnnormal		84.72 276	83.23 282	89.20 254	92.79 244	80.05 215	94.48 155	95.81 148	82.38 234	81.08 304	91.21 272	69.01 262	96.95 245	61.69 368	80.59 328	90.58 353

CVMVSNet		84.69 277	84.79 260	84.37 340	91.84 270	64.92 376	93.70 209	91.47 316	66.19 378	86.16 199	95.28 122	67.18 277	93.33 350	80.89 233	90.42 199	94.88 204

test-mter		84.54 278	83.64 276	87.25 302	90.95 306	71.67 345	89.55 319	89.88 349	79.17 288	84.54 244	87.95 338	55.56 352	95.11 325	81.82 216	93.37 164	94.97 196

ETVMVS		84.43 279	82.92 287	88.97 262	94.37 184	74.67 311	91.23 288	88.35 360	83.37 213	86.06 201	89.04 320	55.38 354	95.67 311	67.12 346	91.34 187	96.58 138

TransMVSNet (Re)		84.43 279	83.06 285	88.54 272	91.72 275	78.44 254	95.18 113	92.82 275	82.73 229	79.67 324	92.12 242	73.49 201	95.96 298	71.10 322	68.73 376	91.21 340

pmmvs5		84.21 281	82.84 290	88.34 277	88.95 347	76.94 287	92.41 254	91.91 305	75.63 330	80.28 313	91.18 275	64.59 302	95.57 313	77.09 279	83.47 288	92.53 309

dmvs_re		84.20 282	83.22 283	87.14 308	91.83 272	77.81 272	90.04 312	90.19 339	84.70 185	81.49 297	89.17 318	64.37 304	91.13 371	71.58 316	85.65 269	92.46 312

tpm2		84.08 283	82.94 286	87.48 297	91.39 288	71.27 347	89.23 327	90.37 336	71.95 365	84.64 241	89.33 316	67.30 274	96.55 269	75.17 295	87.09 259	94.63 211

test_fmvs2		83.98 284	84.03 269	83.83 345	87.16 364	67.53 370	93.93 198	92.89 272	77.62 311	86.89 183	93.53 195	47.18 378	92.02 363	90.54 102	86.51 263	91.93 325

COLMAP_ROB	80.39 16	83.96 285	82.04 293	89.74 238	95.28 134	79.75 225	94.25 173	92.28 289	75.17 335	78.02 337	93.77 190	58.60 343	97.84 171	65.06 358	85.92 266	91.63 330
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 286	81.53 296	91.21 171	90.58 323	79.34 236	85.24 367	96.76 75	71.44 367	85.55 210	82.97 374	70.87 231	98.91 80	61.01 370	89.36 218	95.40 184

SixPastTwentyTwo		83.91 287	82.90 288	86.92 312	90.99 304	70.67 355	93.48 215	91.99 300	85.54 164	77.62 340	92.11 244	60.59 332	96.87 250	76.05 289	77.75 348	93.20 288

EPMVS		83.90 288	82.70 291	87.51 294	90.23 331	72.67 332	88.62 336	81.96 383	81.37 262	85.01 235	88.34 332	66.31 290	94.45 330	75.30 294	87.12 258	95.43 183

WB-MVSnew		83.77 289	83.28 280	85.26 334	91.48 283	71.03 351	91.89 271	87.98 361	78.91 291	84.78 238	90.22 298	69.11 261	94.02 339	64.70 359	90.44 197	90.71 348

TESTMET0.1,1		83.74 290	82.85 289	86.42 321	89.96 336	71.21 349	89.55 319	87.88 362	77.41 313	83.37 277	87.31 346	56.71 348	93.65 347	80.62 238	92.85 174	94.40 230

pmmvs6		83.42 291	81.60 295	88.87 263	88.01 359	77.87 270	94.96 127	94.24 240	74.67 341	78.80 332	91.09 280	60.17 335	96.49 271	77.06 280	75.40 360	92.23 320

AllTest		83.42 291	81.39 297	89.52 246	95.01 147	77.79 274	93.12 232	90.89 330	77.41 313	76.12 349	93.34 198	54.08 361	97.51 195	68.31 339	84.27 279	93.26 283

tpmvs		83.35 293	82.07 292	87.20 306	91.07 302	71.00 353	88.31 340	91.70 307	78.91 291	80.49 312	87.18 350	69.30 257	97.08 236	68.12 342	83.56 287	93.51 277

USDC		82.76 294	81.26 299	87.26 301	91.17 296	74.55 313	89.27 325	93.39 264	78.26 307	75.30 354	92.08 246	54.43 360	96.63 258	71.64 315	85.79 268	90.61 350

Patchmtry		82.71 295	80.93 301	88.06 284	90.05 334	76.37 297	84.74 372	91.96 303	72.28 364	81.32 302	87.87 341	71.03 228	95.50 318	68.97 334	80.15 334	92.32 318

PatchT		82.68 296	81.27 298	86.89 314	90.09 333	70.94 354	84.06 374	90.15 340	74.91 338	85.63 209	83.57 369	69.37 252	94.87 329	65.19 355	88.50 234	94.84 205

MIMVSNet		82.59 297	80.53 302	88.76 265	91.51 282	78.32 258	86.57 358	90.13 341	79.32 285	80.70 308	88.69 329	52.98 365	93.07 355	66.03 353	88.86 228	94.90 203

test0.0.03 1		82.41 298	81.69 294	84.59 338	88.23 356	72.89 328	90.24 306	87.83 363	83.41 211	79.86 322	89.78 310	67.25 275	88.99 381	65.18 356	83.42 290	91.90 326

EG-PatchMatch MVS		82.37 299	80.34 305	88.46 273	90.27 329	79.35 235	92.80 246	94.33 236	77.14 317	73.26 365	90.18 300	47.47 377	96.72 253	70.25 325	87.32 257	89.30 361

tpm cat1		81.96 300	80.27 306	87.01 309	91.09 301	71.02 352	87.38 352	91.53 314	66.25 377	80.17 314	86.35 356	68.22 271	96.15 291	69.16 333	82.29 301	93.86 256

our_test_3		81.93 301	80.46 304	86.33 322	88.46 353	73.48 324	88.46 338	91.11 322	76.46 320	76.69 345	88.25 334	66.89 281	94.36 333	68.75 335	79.08 344	91.14 342

ppachtmachnet_test		81.84 302	80.07 310	87.15 307	88.46 353	74.43 316	89.04 331	92.16 292	75.33 333	77.75 338	88.99 321	66.20 292	95.37 321	65.12 357	77.60 349	91.65 329

gg-mvs-nofinetune		81.77 303	79.37 318	88.99 261	90.85 314	77.73 277	86.29 359	79.63 388	74.88 340	83.19 281	69.05 389	60.34 333	96.11 292	75.46 292	94.64 137	93.11 292

CL-MVSNet_self_test		81.74 304	80.53 302	85.36 331	85.96 370	72.45 338	90.25 304	93.07 269	81.24 266	79.85 323	87.29 347	70.93 230	92.52 358	66.95 347	69.23 372	91.11 344

Patchmatch-RL test		81.67 305	79.96 311	86.81 316	85.42 375	71.23 348	82.17 381	87.50 366	78.47 301	77.19 342	82.50 376	70.81 232	93.48 348	82.66 200	72.89 364	95.71 176

ADS-MVSNet2		81.66 306	79.71 315	87.50 295	91.35 290	74.19 318	83.33 377	88.48 359	72.90 358	82.24 290	85.77 360	64.98 300	93.20 353	64.57 360	83.74 283	95.12 192

K. test v3		81.59 307	80.15 309	85.91 327	89.89 338	69.42 362	92.57 251	87.71 364	85.56 163	73.44 364	89.71 311	55.58 351	95.52 315	77.17 277	69.76 370	92.78 304

ADS-MVSNet		81.56 308	79.78 312	86.90 313	91.35 290	71.82 342	83.33 377	89.16 356	72.90 358	82.24 290	85.77 360	64.98 300	93.76 344	64.57 360	83.74 283	95.12 192

FMVSNet5		81.52 309	79.60 316	87.27 300	91.17 296	77.95 266	91.49 282	92.26 290	76.87 318	76.16 348	87.91 340	51.67 367	92.34 360	67.74 343	81.16 315	91.52 333

dp		81.47 310	80.23 307	85.17 335	89.92 337	65.49 374	86.74 356	90.10 342	76.30 324	81.10 303	87.12 351	62.81 315	95.92 299	68.13 341	79.88 337	94.09 244

Patchmatch-test		81.37 311	79.30 319	87.58 293	90.92 310	74.16 319	80.99 383	87.68 365	70.52 371	76.63 346	88.81 324	71.21 225	92.76 357	60.01 374	86.93 261	95.83 169

EU-MVSNet		81.32 312	80.95 300	82.42 352	88.50 352	63.67 380	93.32 221	91.33 318	64.02 381	80.57 311	92.83 218	61.21 328	92.27 361	76.34 285	80.38 333	91.32 337

test_0402		81.30 313	79.17 323	87.67 291	93.19 228	78.17 262	92.98 239	91.71 306	75.25 334	76.02 351	90.31 297	59.23 340	96.37 280	50.22 385	83.63 286	88.47 370

JIA-IIPM		81.04 314	78.98 326	87.25 302	88.64 349	73.48 324	81.75 382	89.61 353	73.19 355	82.05 292	73.71 386	66.07 295	95.87 302	71.18 320	84.60 276	92.41 314

Anonymous20231206		81.03 315	79.77 314	84.82 337	87.85 362	70.26 358	91.42 283	92.08 296	73.67 350	77.75 338	89.25 317	62.43 317	93.08 354	61.50 369	82.00 306	91.12 343

pmmvs-eth3d		80.97 316	78.72 328	87.74 289	84.99 377	79.97 221	90.11 311	91.65 309	75.36 332	73.51 363	86.03 357	59.45 339	93.96 342	75.17 295	72.21 365	89.29 362

testgi		80.94 317	80.20 308	83.18 346	87.96 360	66.29 371	91.28 285	90.70 334	83.70 202	78.12 335	92.84 217	51.37 368	90.82 373	63.34 363	82.46 299	92.43 313

CMPMVS	59.16 21	80.52 318	79.20 322	84.48 339	83.98 378	67.63 369	89.95 315	93.84 256	64.79 380	66.81 379	91.14 278	57.93 345	95.17 323	76.25 286	88.10 240	90.65 349
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 319	79.59 317	83.06 348	93.44 223	64.64 377	93.33 220	85.47 372	84.34 190	79.93 321	90.84 286	44.35 382	92.39 359	57.06 380	87.56 251	92.16 322

Anonymous20240521		80.44 320	79.21 321	84.11 343	85.75 373	67.89 366	92.86 244	93.23 266	75.61 331	75.59 353	87.47 345	50.03 370	94.33 334	71.14 321	81.21 314	90.12 355

LF4IMVS		80.37 321	79.07 325	84.27 342	86.64 366	69.87 361	89.39 324	91.05 325	76.38 322	74.97 356	90.00 305	47.85 376	94.25 337	74.55 303	80.82 326	88.69 368

KD-MVS_self_test		80.20 322	79.24 320	83.07 347	85.64 374	65.29 375	91.01 293	93.93 250	78.71 299	76.32 347	86.40 355	59.20 341	92.93 356	72.59 312	69.35 371	91.00 347

Syy-MVS		80.07 323	79.78 312	80.94 355	91.92 266	59.93 386	89.75 317	87.40 367	81.72 253	78.82 330	87.20 348	66.29 291	91.29 369	47.06 387	87.84 247	91.60 331

UnsupCasMVSNet_eth		80.07 323	78.27 329	85.46 330	85.24 376	72.63 335	88.45 339	94.87 213	82.99 223	71.64 371	88.07 337	56.34 349	91.75 366	73.48 309	63.36 383	92.01 324

test20.03		79.95 325	79.08 324	82.55 350	85.79 372	67.74 368	91.09 291	91.08 323	81.23 267	74.48 360	89.96 307	61.63 321	90.15 375	60.08 372	76.38 356	89.76 356

TDRefinement		79.81 326	77.34 331	87.22 305	79.24 389	75.48 307	93.12 232	92.03 298	76.45 321	75.01 355	91.58 264	49.19 373	96.44 276	70.22 327	69.18 373	89.75 357

TinyColmap		79.76 327	77.69 330	85.97 324	91.71 277	73.12 326	89.55 319	90.36 337	75.03 336	72.03 369	90.19 299	46.22 379	96.19 290	63.11 364	81.03 320	88.59 369

myMVS_eth3d		79.67 328	78.79 327	82.32 353	91.92 266	64.08 378	89.75 317	87.40 367	81.72 253	78.82 330	87.20 348	45.33 380	91.29 369	59.09 376	87.84 247	91.60 331

OpenMVS_ROB	74.94 19	79.51 329	77.03 336	86.93 311	87.00 365	76.23 299	92.33 260	90.74 333	68.93 374	74.52 359	88.23 335	49.58 372	96.62 259	57.64 378	84.29 278	87.94 373

MIMVSNet1		79.38 330	77.28 332	85.69 329	86.35 367	73.67 321	91.61 281	92.75 277	78.11 310	72.64 367	88.12 336	48.16 375	91.97 365	60.32 371	77.49 350	91.43 336

YYNet1		79.22 331	77.20 333	85.28 333	88.20 358	72.66 333	85.87 361	90.05 345	74.33 344	62.70 381	87.61 343	66.09 294	92.03 362	66.94 348	72.97 363	91.15 341

MDA-MVSNet_test_wron		79.21 332	77.19 334	85.29 332	88.22 357	72.77 330	85.87 361	90.06 343	74.34 343	62.62 383	87.56 344	66.14 293	91.99 364	66.90 351	73.01 362	91.10 345

MDA-MVSNet-bldmvs		78.85 333	76.31 338	86.46 319	89.76 339	73.88 320	88.79 333	90.42 335	79.16 289	59.18 385	88.33 333	60.20 334	94.04 338	62.00 367	68.96 374	91.48 335

KD-MVS_2432*1600		78.50 334	76.02 341	85.93 325	86.22 368	74.47 314	84.80 370	92.33 286	79.29 286	76.98 343	85.92 358	53.81 363	93.97 340	67.39 344	57.42 388	89.36 359

miper_refine_blended		78.50 334	76.02 341	85.93 325	86.22 368	74.47 314	84.80 370	92.33 286	79.29 286	76.98 343	85.92 358	53.81 363	93.97 340	67.39 344	57.42 388	89.36 359

PM-MVS		78.11 336	76.12 340	84.09 344	83.54 380	70.08 359	88.97 332	85.27 374	79.93 279	74.73 358	86.43 354	34.70 388	93.48 348	79.43 255	72.06 366	88.72 367

test_vis1_rt		77.96 337	76.46 337	82.48 351	85.89 371	71.74 344	90.25 304	78.89 389	71.03 370	71.30 372	81.35 378	42.49 384	91.05 372	84.55 173	82.37 300	84.65 376

test_fmvs3		77.67 338	77.16 335	79.22 358	79.52 388	61.14 384	92.34 259	91.64 310	73.98 347	78.86 329	86.59 352	27.38 392	87.03 383	88.12 127	75.97 358	89.50 358

PVSNet_0	73.20 20	77.22 339	74.83 345	84.37 340	90.70 320	71.10 350	83.09 379	89.67 352	72.81 360	73.93 362	83.13 371	60.79 331	93.70 346	68.54 336	50.84 392	88.30 371

DSMNet-mixed		76.94 340	76.29 339	78.89 359	83.10 381	56.11 395	87.78 345	79.77 387	60.65 384	75.64 352	88.71 327	61.56 323	88.34 382	60.07 373	89.29 220	92.21 321

new-patchmatchnet		76.41 341	75.17 344	80.13 356	82.65 383	59.61 387	87.66 349	91.08 323	78.23 308	69.85 375	83.22 370	54.76 357	91.63 368	64.14 362	64.89 381	89.16 364

UnsupCasMVSNet_bld		76.23 342	73.27 346	85.09 336	83.79 379	72.92 327	85.65 364	93.47 263	71.52 366	68.84 377	79.08 381	49.77 371	93.21 352	66.81 352	60.52 385	89.13 366

mvsany_test3		74.95 343	73.26 347	80.02 357	74.61 391	63.16 382	85.53 365	78.42 390	74.16 345	74.89 357	86.46 353	36.02 387	89.09 380	82.39 204	66.91 377	87.82 374

dmvs_testset		74.57 344	75.81 343	70.86 369	87.72 363	40.47 402	87.05 355	77.90 394	82.75 228	71.15 373	85.47 362	67.98 272	84.12 391	45.26 388	76.98 355	88.00 372

MVS-HIRNet		73.70 345	72.20 348	78.18 362	91.81 273	56.42 394	82.94 380	82.58 381	55.24 386	68.88 376	66.48 390	55.32 355	95.13 324	58.12 377	88.42 236	83.01 379

new_pmnet		72.15 346	70.13 350	78.20 361	82.95 382	65.68 372	83.91 375	82.40 382	62.94 383	64.47 380	79.82 380	42.85 383	86.26 387	57.41 379	74.44 361	82.65 381

test_f		71.95 347	70.87 349	75.21 365	74.21 393	59.37 388	85.07 369	85.82 370	65.25 379	70.42 374	83.13 371	23.62 393	82.93 393	78.32 264	71.94 367	83.33 378

pmmvs3		71.81 348	68.71 351	81.11 354	75.86 390	70.42 357	86.74 356	83.66 378	58.95 385	68.64 378	80.89 379	36.93 386	89.52 378	63.10 365	63.59 382	83.39 377

APD_test1		69.04 349	66.26 355	77.36 364	80.51 386	62.79 383	85.46 366	83.51 379	54.11 388	59.14 386	84.79 365	23.40 395	89.61 377	55.22 381	70.24 369	79.68 385

N_pmnet		68.89 350	68.44 352	70.23 370	89.07 346	28.79 407	88.06 341	19.50 407	69.47 373	71.86 370	84.93 363	61.24 327	91.75 366	54.70 382	77.15 352	90.15 354

WB-MVS		67.92 351	67.49 353	69.21 373	81.09 384	41.17 401	88.03 342	78.00 393	73.50 352	62.63 382	83.11 373	63.94 306	86.52 385	25.66 398	51.45 391	79.94 384

SSC-MVS		67.06 352	66.56 354	68.56 375	80.54 385	40.06 403	87.77 346	77.37 396	72.38 362	61.75 384	82.66 375	63.37 311	86.45 386	24.48 399	48.69 394	79.16 386

LCM-MVSNet		66.00 353	62.16 358	77.51 363	64.51 401	58.29 389	83.87 376	90.90 329	48.17 390	54.69 387	73.31 387	16.83 401	86.75 384	65.47 354	61.67 384	87.48 375

test_vis3_rt		65.12 354	62.60 356	72.69 367	71.44 394	60.71 385	87.17 353	65.55 400	63.80 382	53.22 388	65.65 392	14.54 402	89.44 379	76.65 281	65.38 379	67.91 391

FPMVS		64.63 355	62.55 357	70.88 368	70.80 395	56.71 390	84.42 373	84.42 376	51.78 389	49.57 389	81.61 377	23.49 394	81.48 394	40.61 394	76.25 357	74.46 387

EGC-MVSNET		61.97 356	56.37 360	78.77 360	89.63 342	73.50 323	89.12 329	82.79 380	0.21 404	1.24 405	84.80 364	39.48 385	90.04 376	44.13 389	75.94 359	72.79 388

PMMVS2		59.60 357	56.40 359	69.21 373	68.83 398	46.58 399	73.02 392	77.48 395	55.07 387	49.21 390	72.95 388	17.43 400	80.04 395	49.32 386	44.33 395	80.99 383

testf1		59.54 358	56.11 361	69.85 371	69.28 396	56.61 392	80.37 385	76.55 397	42.58 393	45.68 392	75.61 382	11.26 403	84.18 389	43.20 391	60.44 386	68.75 389

APD_test2		59.54 358	56.11 361	69.85 371	69.28 396	56.61 392	80.37 385	76.55 397	42.58 393	45.68 392	75.61 382	11.26 403	84.18 389	43.20 391	60.44 386	68.75 389

ANet_high		58.88 360	54.22 364	72.86 366	56.50 404	56.67 391	80.75 384	86.00 369	73.09 357	37.39 396	64.63 393	22.17 396	79.49 396	43.51 390	23.96 398	82.43 382

Gipuma		57.99 361	54.91 363	67.24 376	88.51 350	65.59 373	52.21 395	90.33 338	43.58 392	42.84 395	51.18 396	20.29 398	85.07 388	34.77 395	70.45 368	51.05 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 362	48.46 366	63.48 377	45.72 406	46.20 400	73.41 391	78.31 391	41.03 395	30.06 398	65.68 391	6.05 405	83.43 392	30.04 396	65.86 378	60.80 392
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 363	48.47 365	56.66 379	52.26 405	18.98 409	41.51 397	81.40 384	10.10 399	44.59 394	75.01 385	28.51 390	68.16 397	53.54 383	49.31 393	82.83 380

MVE	39.65 23	43.39 364	38.59 370	57.77 378	56.52 403	48.77 398	55.38 394	58.64 404	29.33 398	28.96 399	52.65 395	4.68 406	64.62 400	28.11 397	33.07 396	59.93 393
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 365	42.29 367	46.03 381	65.58 400	37.41 404	73.51 390	64.62 401	33.99 396	28.47 400	47.87 397	19.90 399	67.91 398	22.23 400	24.45 397	32.77 396

EMVS		42.07 366	41.12 368	44.92 382	63.45 402	35.56 406	73.65 389	63.48 402	33.05 397	26.88 401	45.45 398	21.27 397	67.14 399	19.80 401	23.02 399	32.06 397

tmp_tt		35.64 367	39.24 369	24.84 383	14.87 407	23.90 408	62.71 393	51.51 406	6.58 401	36.66 397	62.08 394	44.37 381	30.34 403	52.40 384	22.00 400	20.27 398

cdsmvs_eth3d_5k		22.14 368	29.52 371	0.00 387	0.00 410	0.00 412	0.00 398	95.76 152	0.00 405	0.00 406	94.29 164	75.66 170	0.00 406	0.00 405	0.00 404	0.00 402

wuyk23d		21.27 369	20.48 372	23.63 384	68.59 399	36.41 405	49.57 396	6.85 408	9.37 400	7.89 402	4.46 404	4.03 407	31.37 402	17.47 402	16.07 401	3.12 399

testmvs		8.92 370	11.52 373	1.12 386	1.06 408	0.46 411	86.02 360	0.65 409	0.62 402	2.74 403	9.52 402	0.31 409	0.45 405	2.38 403	0.39 402	2.46 401

test123		8.76 371	11.22 374	1.39 385	0.85 409	0.97 410	85.76 363	0.35 410	0.54 403	2.45 404	8.14 403	0.60 408	0.48 404	2.16 404	0.17 403	2.71 400

ab-mvs-re		7.82 372	10.43 375	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	93.88 185	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

pcd_1.5k_mvsjas		6.64 373	8.86 376	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	79.70 121	0.00 406	0.00 405	0.00 404	0.00 402

test_blank		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet_test		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

DCPMVS		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

sosnet-low-res		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

sosnet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uncertanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

Regformer		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

WAC-MVS						64.08 378								59.14 375

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									82.47 232	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					0.00 410

eth-test					0.00 410

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 52		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	81.26 265	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 20		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																96.12 154

test_part2					98.55 12	87.22 18				96.40 17

sam_mvs1												71.70 221				96.12 154

sam_mvs												70.60 234

ambc				83.06 348	79.99 387	63.51 381	77.47 388	92.86 273		74.34 361	84.45 366	28.74 389	95.06 327	73.06 311	68.89 375	90.61 350

MTGPA								96.97 50

test_post1							88.00 343				9.81 401	69.31 256	95.53 314	76.65 281

test_post											10.29 400	70.57 238	95.91 301

patchmatchnet-post											83.76 368	71.53 222	96.48 272

GG-mvs-BLEND				87.94 288	89.73 341	77.91 267	87.80 344	78.23 392		80.58 310	83.86 367	59.88 337	95.33 322	71.20 318	92.22 181	90.60 352

MTMP							96.16 53	60.64 403

gm-plane-assit					89.60 343	68.00 365			77.28 316		88.99 321		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	81.61 258	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	81.86 249	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	95.01 147	77.79 274		90.89 330	77.41 313	76.12 349	93.34 198	54.08 361	97.51 195	68.31 339	84.27 279	93.26 283

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		71.25 368	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	71.09 369	91.26 113	96.24 85	82.87 85	98.86 84	79.19 258	98.10 62	96.07 158

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	73.95 348				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	78.89 293	90.45 120	95.92 100	82.65 87	98.84 88	80.68 237	98.26 57	96.14 152

test222					96.55 84	81.70 166	92.22 264	95.01 201	68.36 375	90.20 124	96.14 92	80.26 114			97.80 74	96.05 161

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	72.51 361	93.63 52	96.69 66	82.08 99	97.65 182	83.08 190	97.39 82	95.94 163

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					94.70 166	82.74 141

plane_prior6					94.52 176	82.75 139						74.23 187

plane_prior5								96.22 115					98.12 144	88.15 124	89.99 203	94.63 211

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					94.59 171

plane_prior						82.73 142	95.21 111		89.66 48						89.88 208

n2								0.00 411

nn								0.00 411

door-mid								85.49 371

lessismore_v0				86.04 323	88.46 353	68.78 364		80.59 386		73.01 366	90.11 302	55.39 353	96.43 277	75.06 297	65.06 380	92.90 299

LGP-MVS_train				91.12 175	94.47 178	81.49 172		96.14 120	86.73 135	85.45 220	95.16 130	69.89 245	98.10 146	87.70 132	89.23 221	93.77 264

test11								96.57 92

door								85.33 373

HQP5-MVS						81.56 168

HQP-NCC					94.17 191		94.39 165		88.81 72	85.43 223

ACMP_Plane					94.17 191		94.39 165		88.81 72	85.43 223

BP-MVS														87.11 143

HQP4-MVS										85.43 223			97.96 165			94.51 221

HQP3-MVS								96.04 131							89.77 212

HQP2-MVS												73.83 197

NP-MVS					94.37 184	82.42 151					93.98 178

MDTV_nov1_ep13_2view						55.91 396	87.62 350		73.32 354	84.59 243		70.33 241		74.65 301		95.50 181

MDTV_nov1_ep13			83.56 277		91.69 279	69.93 360	87.75 347	91.54 313	78.60 300	84.86 237	88.90 323	69.54 250	96.03 294	70.25 325	88.93 227

ACMMP++_ref															87.47 252

ACMMP++															88.01 243

Test By Simon												80.02 116

ITE_SJBPF				88.24 280	91.88 269	77.05 286		92.92 271	85.54 164	80.13 317	93.30 202	57.29 347	96.20 288	72.46 313	84.71 275	91.49 334

DeepMVS_CX				56.31 380	74.23 392	51.81 397		56.67 405	44.85 391	48.54 391	75.16 384	27.87 391	58.74 401	40.92 393	52.22 390	58.39 394

High-resolution multi-view benchmark