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.

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

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

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

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

SMA-MVS		95.20 8	95.07 11	95.59 6	98.14 35	88.48 8	96.26 47	97.28 31	85.90 154	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
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	84.02 263	96.62 74	75.95 163	99.34 34	87.77 130	97.68 78	98.59 24

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

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

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
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

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

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

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

DPM-MVS		92.58 73	91.74 81	95.08 15	96.19 95	89.31 5	92.66 248	96.56 93	83.44 211	91.68 106	95.04 134	86.60 40	98.99 70	85.60 160	97.92 70	96.93 124

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

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

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

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
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

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
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

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

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

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
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

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

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	23.41 401	85.02 59	99.49 26	91.99 74	98.56 48	98.47 33

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.
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

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

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

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
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	85.68 160	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
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

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

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
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

PHI-MVS		93.89 43	93.65 54	94.62 40	96.84 75	86.43 38	96.69 32	97.49 6	85.15 174	93.56 55	96.28 84	85.60 49	99.31 39	92.45 56	98.79 23	98.12 64

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
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

train_agg		93.44 55	93.08 62	94.52 43	97.53 58	86.49 36	94.07 186	96.78 72	81.86 250	92.77 74	96.20 87	87.63 29	99.12 51	92.14 68	98.69 35	97.94 74

CDPH-MVS		92.83 69	92.30 75	94.44 44	97.79 49	86.11 48	94.06 188	96.66 85	80.09 279	92.77 74	96.63 73	86.62 38	99.04 57	87.40 136	98.66 40	98.17 60

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	84.31 259	96.66 69	73.74 199	99.17 47	86.74 146	97.96 68	97.79 85

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

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
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

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

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
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
EPNet		91.79 82	91.02 92	94.10 52	90.10 334	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
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

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
DP-MVS Recon		91.95 80	91.28 86	93.96 55	98.33 27	85.92 56	94.66 147	96.66 85	82.69 231	90.03 129	95.82 105	82.30 93	99.03 58	84.57 172	96.48 104	96.91 126

HPM-MVS_fast		93.40 59	93.22 60	93.94 56	98.36 25	84.83 74	97.15 13	96.80 71	85.77 157	92.47 84	97.13 48	82.38 90	99.07 53	90.51 104	98.40 52	97.92 77

test_fmvsmconf0.1_n		94.20 34	94.31 28	93.88 57	92.46 252	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

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	96.91 249	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
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

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

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

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_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

CSCG		93.23 63	93.05 63	93.76 64	98.04 40	84.07 96	96.22 49	97.37 21	84.15 193	90.05 128	95.66 112	87.77 26	99.15 50	89.91 107	98.27 56	98.07 66

test_fmvsmconf0.01_n		93.19 64	93.02 64	93.71 65	89.25 346	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

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

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

QAPM		89.51 134	88.15 160	93.59 68	94.92 154	84.58 79	96.82 29	96.70 83	78.43 305	83.41 278	96.19 90	73.18 206	99.30 40	77.11 279	96.54 101	96.89 127

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

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
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

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
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

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

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

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

nrg030		91.08 97	90.39 99	93.17 76	93.07 233	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	82.95 294	94.96 201

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

test_fmvsmvis_n_1920		93.44 55	93.55 55	93.10 79	93.67 217	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	96.32 146

新几何1				93.10 79	97.30 66	84.35 92		95.56 168	71.09 371	91.26 113	96.24 85	82.87 85	98.86 84	79.19 259	98.10 62	96.07 159

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

OpenMVS	83.78 11	88.74 163	87.29 180	93.08 81	92.70 247	85.39 67	96.57 36	96.43 97	78.74 300	80.85 308	96.07 94	69.64 249	99.01 63	78.01 270	96.65 100	94.83 208

MAR-MVS		90.30 111	89.37 124	93.07 83	96.61 81	84.48 85	95.68 85	95.67 160	82.36 236	87.85 159	92.85 216	76.63 157	98.80 90	80.01 247	96.68 99	95.91 165
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
lupinMVS		90.92 98	90.21 102	93.03 84	93.86 207	83.88 101	92.81 245	93.86 254	79.84 282	91.76 103	94.29 164	77.92 144	98.04 159	90.48 105	97.11 85	97.17 108

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

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

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

fmvsm_s_conf0.1_n_a		93.19 64	93.26 58	92.97 88	92.49 250	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

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	96.54 142

LFMVS		90.08 116	89.13 130	92.95 90	96.71 77	82.32 155	96.08 61	89.91 348	86.79 132	92.15 90	96.81 63	62.60 316	98.34 129	87.18 140	93.90 150	98.19 58

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
jason		90.80 99	90.10 106	92.90 92	93.04 236	83.53 112	93.08 235	94.15 243	80.22 276	91.41 110	94.91 137	76.87 151	97.93 168	90.28 106	96.90 92	97.24 104
jason: jason.
DP-MVS		87.25 215	85.36 248	92.90 92	97.65 55	83.24 119	94.81 137	92.00 299	74.99 339	81.92 297	95.00 135	72.66 212	99.05 55	66.92 352	92.33 181	96.40 144

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

fmvsm_s_conf0.1_n		93.46 53	93.66 53	92.85 95	93.75 213	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

CANet_DTU		90.26 113	89.41 123	92.81 96	93.46 223	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	96.38 145

MVSFormer		91.68 87	91.30 85	92.80 97	93.86 207	83.88 101	95.96 71	95.90 142	84.66 187	91.76 103	94.91 137	77.92 144	97.30 219	89.64 109	97.11 85	97.24 104

PVSNet_Blended_VisFu		91.38 90	90.91 94	92.80 97	96.39 90	83.17 122	94.87 133	96.66 85	83.29 216	89.27 137	94.46 158	80.29 113	99.17 47	87.57 134	95.37 123	96.05 162

VDDNet		89.56 133	88.49 151	92.76 99	95.07 145	82.09 157	96.30 43	93.19 267	81.05 271	91.88 98	96.86 59	61.16 330	98.33 131	88.43 123	92.49 180	97.84 82

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	78.05 349	96.60 137

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
test_yl		90.69 103	90.02 111	92.71 102	95.72 118	82.41 153	94.11 181	95.12 196	85.63 162	91.49 108	94.70 148	74.75 179	98.42 124	86.13 153	92.53 178	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	85.63 162	91.49 108	94.70 148	74.75 179	98.42 124	86.13 153	92.53 178	97.31 101

PCF-MVS	84.11 10	87.74 189	86.08 224	92.70 104	94.02 198	84.43 89	89.27 327	95.87 145	73.62 353	84.43 251	94.33 161	78.48 139	98.86 84	70.27 326	94.45 143	94.81 209
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
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

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

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

ab-mvs		89.41 140	88.35 153	92.60 108	95.15 143	82.65 147	92.20 265	95.60 167	83.97 197	88.55 147	93.70 193	74.16 191	98.21 140	82.46 203	89.37 219	96.94 123

LS3D		87.89 184	86.32 214	92.59 109	96.07 103	82.92 136	95.23 109	94.92 209	75.66 331	82.89 285	95.98 98	72.48 215	99.21 45	68.43 340	95.23 128	95.64 178

Anonymous20240529		88.09 180	86.59 204	92.58 110	96.53 86	81.92 162	95.99 69	95.84 147	74.11 348	89.06 141	95.21 127	61.44 324	98.81 89	83.67 186	87.47 254	97.01 119

CPTT-MVS		91.99 79	91.80 80	92.55 111	98.24 31	81.98 160	96.76 30	96.49 95	81.89 249	90.24 123	96.44 81	78.59 136	98.61 104	89.68 108	97.85 72	97.06 115

114514_t		89.51 134	88.50 149	92.54 112	98.11 36	81.99 159	95.16 116	96.36 102	70.19 374	85.81 205	95.25 124	76.70 155	98.63 102	82.07 211	96.86 95	97.00 120

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

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

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

API-MVS		90.66 105	90.07 107	92.45 116	96.36 91	84.57 80	96.06 64	95.22 193	82.39 234	89.13 138	94.27 167	80.32 112	98.46 115	80.16 246	96.71 98	94.33 234

xiu_mvs_v1_base_debu		90.64 106	90.05 108	92.40 117	93.97 204	84.46 86	93.32 221	95.46 175	85.17 171	92.25 86	94.03 172	70.59 235	98.57 107	90.97 92	94.67 134	94.18 239

xiu_mvs_v1_base		90.64 106	90.05 108	92.40 117	93.97 204	84.46 86	93.32 221	95.46 175	85.17 171	92.25 86	94.03 172	70.59 235	98.57 107	90.97 92	94.67 134	94.18 239

xiu_mvs_v1_base_debi		90.64 106	90.05 108	92.40 117	93.97 204	84.46 86	93.32 221	95.46 175	85.17 171	92.25 86	94.03 172	70.59 235	98.57 107	90.97 92	94.67 134	94.18 239

AdaColmap		89.89 125	89.07 131	92.37 120	97.41 62	83.03 130	94.42 162	95.92 139	82.81 228	86.34 196	94.65 152	73.89 195	99.02 61	80.69 237	95.51 116	95.05 196

CNLPA		89.07 152	87.98 164	92.34 121	96.87 74	84.78 76	94.08 185	93.24 265	81.41 262	84.46 249	95.13 132	75.57 171	96.62 260	77.21 277	93.84 152	95.61 181

ET-MVSNet_ETH3D		87.51 203	85.91 232	92.32 122	93.70 216	83.93 99	92.33 260	90.94 329	84.16 192	72.09 370	92.52 228	69.90 244	95.85 304	89.20 114	88.36 240	97.17 108

Anonymous202405211		87.68 190	86.13 220	92.31 123	96.66 79	80.74 195	94.87 133	91.49 315	80.47 275	89.46 135	95.44 117	54.72 360	98.23 137	82.19 209	89.89 209	97.97 72

CHOSEN 1792x2688		88.84 159	87.69 170	92.30 124	96.14 96	81.42 176	90.01 315	95.86 146	74.52 344	87.41 168	93.94 180	75.46 172	98.36 126	80.36 242	95.53 115	97.12 113

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	81.52 261	86.93 178	92.79 222	78.32 141	98.23 137	79.93 248	90.55 198	95.88 167

CDS-MVSNet		89.45 137	88.51 148	92.29 125	93.62 218	83.61 111	93.01 238	94.68 225	81.95 245	87.82 161	93.24 205	78.69 134	96.99 244	80.34 243	93.23 167	96.28 149
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 118	89.27 129	92.29 125	95.78 116	80.95 189	92.68 247	96.22 115	81.91 247	86.66 188	93.75 192	82.23 95	98.44 121	79.40 258	94.79 132	97.48 97

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	79.71 284	84.01 264	94.18 169	76.68 156	98.75 93	77.28 276	93.41 162	95.02 197
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
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

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

原ACMM1				92.01 130	97.34 64	81.05 185		96.81 70	78.89 295	90.45 120	95.92 100	82.65 87	98.84 88	80.68 238	98.26 57	96.14 153

UniMVSNet (Re)		89.80 127	89.07 131	92.01 130	93.60 219	84.52 83	94.78 139	97.47 11	89.26 58	86.44 193	92.32 234	82.10 98	97.39 215	84.81 169	80.84 327	94.12 243

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

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

PVSNet_Blended		90.73 102	90.32 101	91.98 134	96.12 97	81.25 179	92.55 252	96.83 66	82.04 243	89.10 139	92.56 227	81.04 109	98.85 86	86.72 148	95.91 110	95.84 169

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	94.29 237

TAMVS		89.21 146	88.29 157	91.96 136	93.71 214	82.62 148	93.30 225	94.19 241	82.22 238	87.78 162	93.94 180	78.83 131	96.95 246	77.70 272	92.98 171	96.32 146

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	97.08 237	88.90 117	89.85 211	95.63 179

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	84.87 180	89.82 130	92.61 226	74.72 182	98.47 114	83.97 180	93.53 157	97.04 117

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

NR-MVSNet		88.58 169	87.47 176	91.93 138	93.04 236	84.16 95	94.77 140	96.25 112	89.05 65	80.04 321	93.29 203	79.02 130	97.05 241	81.71 222	80.05 337	94.59 216

HyFIR lowres test		88.09 180	86.81 192	91.93 138	96.00 106	80.63 197	90.01 315	95.79 150	73.42 355	87.68 164	92.10 245	73.86 196	97.96 165	80.75 236	91.70 184	97.19 107

GeoE		90.05 117	89.43 122	91.90 143	95.16 141	80.37 204	95.80 78	94.65 226	83.90 198	87.55 167	94.75 147	78.18 142	97.62 187	81.28 226	93.63 154	97.71 88

thisisatest0530		88.67 164	87.61 172	91.86 144	94.87 157	80.07 213	94.63 148	89.90 349	84.00 196	88.46 149	93.78 189	66.88 282	98.46 115	83.30 188	92.65 175	97.06 115

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	86.11 153	91.74 105	93.14 209	79.67 124	98.89 81	89.06 116	95.46 120	94.28 238

DU-MVS		89.34 145	88.50 149	91.85 146	93.04 236	83.72 104	94.47 158	96.59 90	89.50 50	86.46 190	93.29 203	77.25 149	97.23 228	84.92 166	81.02 323	94.59 216

OPM-MVS		90.12 115	89.56 118	91.82 147	93.14 230	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	90.18 204	93.65 273
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
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	89.99 205	94.63 213

UniMVSNet_NR-MVSNet		89.92 124	89.29 127	91.81 149	93.39 225	83.72 104	94.43 161	97.12 41	89.80 41	86.46 190	93.32 200	83.16 79	97.23 228	84.92 166	81.02 323	94.49 228

diffmvs		91.37 91	91.23 87	91.77 150	93.09 232	80.27 205	92.36 257	95.52 173	87.03 126	91.40 111	94.93 136	80.08 115	97.44 203	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
1112_ss		88.42 170	87.33 179	91.72 151	94.92 154	80.98 187	92.97 240	94.54 227	78.16 311	83.82 267	93.88 185	78.78 133	97.91 169	79.45 254	89.41 218	96.26 150

Fast-Effi-MVS+		89.41 140	88.64 143	91.71 152	94.74 162	80.81 193	93.54 213	95.10 198	83.11 220	86.82 186	90.67 292	79.74 120	97.75 177	80.51 241	93.55 156	96.57 140

WTY-MVS		89.60 131	88.92 135	91.67 153	95.47 128	81.15 183	92.38 256	94.78 220	83.11 220	89.06 141	94.32 162	78.67 135	96.61 263	81.57 223	90.89 196	97.24 104

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	76.38 324	86.19 200	95.44 117	79.75 119	98.08 156	62.75 368	95.29 125	96.13 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
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	89.01 227	94.32 235

VPA-MVSNet		89.62 130	88.96 133	91.60 155	93.86 207	82.89 137	95.46 96	97.33 25	87.91 104	88.43 150	93.31 201	74.17 190	97.40 212	87.32 139	82.86 299	94.52 221

FE-MVS		87.40 208	86.02 226	91.57 157	94.56 175	79.69 227	90.27 304	93.72 259	80.57 274	88.80 144	91.62 262	65.32 297	98.59 106	74.97 300	94.33 146	96.44 143

XVG-OURS		89.40 142	88.70 141	91.52 158	94.06 196	81.46 174	91.27 287	96.07 128	86.14 150	88.89 143	95.77 108	68.73 266	97.26 225	87.39 137	89.96 207	95.83 170

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	77.07 356	95.74 174

TranMVSNet+NR-MVSNet		88.84 159	87.95 165	91.49 160	92.68 248	83.01 132	94.92 130	96.31 104	89.88 39	85.53 215	93.85 187	76.63 157	96.96 245	81.91 215	79.87 340	94.50 226

AUN-MVS		87.78 188	86.54 206	91.48 161	94.82 161	81.05 185	93.91 201	93.93 250	83.00 223	86.93 178	93.53 195	69.50 251	97.67 179	86.14 151	77.12 355	95.73 176

XVG-OURS-SEG-HR		89.95 122	89.45 120	91.47 162	94.00 202	81.21 182	91.87 273	96.06 130	85.78 156	88.55 147	95.73 110	74.67 183	97.27 223	88.71 120	89.64 216	95.91 165

MVS		87.44 206	86.10 223	91.44 163	92.61 249	83.62 109	92.63 249	95.66 162	67.26 378	81.47 300	92.15 240	77.95 143	98.22 139	79.71 250	95.48 118	92.47 313

F-COLMAP		87.95 183	86.80 193	91.40 164	96.35 92	80.88 191	94.73 142	95.45 178	79.65 285	82.04 295	94.61 153	71.13 226	98.50 110	76.24 288	91.05 194	94.80 210

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

thisisatest0515		87.33 211	85.99 227	91.37 166	93.49 221	79.55 229	90.63 300	89.56 355	80.17 277	87.56 166	90.86 284	67.07 279	98.28 135	81.50 224	93.02 170	96.29 148

HQP-MVS		89.80 127	89.28 128	91.34 167	94.17 192	81.56 168	94.39 165	96.04 131	88.81 72	85.43 225	93.97 179	73.83 197	97.96 165	87.11 143	89.77 214	94.50 226

mvsmamba		89.96 121	89.50 119	91.33 168	92.90 243	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	88.63 232	94.51 223

FMVSNet3		87.40 208	86.11 222	91.30 169	93.79 212	83.64 108	94.20 177	94.81 218	83.89 199	84.37 252	91.87 254	68.45 269	96.56 268	78.23 267	85.36 272	93.70 272

FMVSNet2		87.19 221	85.82 234	91.30 169	94.01 199	83.67 106	94.79 138	94.94 204	83.57 206	83.88 266	92.05 249	66.59 287	96.51 271	77.56 274	85.01 275	93.73 269

RPMNet		83.95 287	81.53 298	91.21 171	90.58 325	79.34 236	85.24 369	96.76 75	71.44 369	85.55 212	82.97 376	70.87 231	98.91 80	61.01 372	89.36 220	95.40 185

IB-MVS	80.51 15	85.24 268	83.26 282	91.19 172	92.13 261	79.86 223	91.75 276	91.29 320	83.28 217	80.66 311	88.49 332	61.28 325	98.46 115	80.99 232	79.46 343	95.25 191
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
CLD-MVS		89.47 136	88.90 137	91.18 173	94.22 191	82.07 158	92.13 267	96.09 126	87.90 105	85.37 231	92.45 230	74.38 185	97.56 190	87.15 141	90.43 200	93.93 252
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
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	89.00 228	94.32 235

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	85.45 222	95.16 130	69.89 245	98.10 146	87.70 132	89.23 223	93.77 266

LGP-MVS_train				91.12 175	94.47 178	81.49 172		96.14 120	86.73 135	85.45 222	95.16 130	69.89 245	98.10 146	87.70 132	89.23 223	93.77 266

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	85.31 170	85.92 204	94.34 160	70.19 243	98.06 158	85.65 159	88.86 230	94.08 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 166	87.78 169	91.11 178	94.96 151	77.81 272	95.35 99	89.69 352	85.09 176	88.05 156	94.59 155	66.93 280	98.48 111	83.27 189	92.13 183	97.03 118

GBi-Net		87.26 213	85.98 228	91.08 179	94.01 199	83.10 125	95.14 117	94.94 204	83.57 206	84.37 252	91.64 258	66.59 287	96.34 284	78.23 267	85.36 272	93.79 261

test1		87.26 213	85.98 228	91.08 179	94.01 199	83.10 125	95.14 117	94.94 204	83.57 206	84.37 252	91.64 258	66.59 287	96.34 284	78.23 267	85.36 272	93.79 261

FMVSNet1		85.85 255	84.11 269	91.08 179	92.81 245	83.10 125	95.14 117	94.94 204	81.64 257	82.68 287	91.64 258	59.01 343	96.34 284	75.37 294	83.78 284	93.79 261

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	76.04 329	83.51 276	92.37 232	77.86 146	97.73 178	78.69 262	89.13 225	96.22 151

PS-MVSNAJss		89.97 120	89.62 116	91.02 183	91.90 270	80.85 192	95.26 108	95.98 134	86.26 145	86.21 199	94.29 164	79.70 121	97.65 182	88.87 119	88.10 242	94.57 218

BH-RMVSNet		88.37 172	87.48 175	91.02 183	95.28 134	79.45 232	92.89 242	93.07 269	85.45 167	86.91 180	94.84 144	70.35 240	97.76 174	73.97 306	94.59 138	95.85 168

UniMVSNet_ETH3D		87.53 202	86.37 211	91.00 185	92.44 253	78.96 245	94.74 141	95.61 166	84.07 195	85.36 232	94.52 157	59.78 338	97.34 217	82.93 193	87.88 247	96.71 134

FIs		90.51 110	90.35 100	90.99 186	93.99 203	80.98 187	95.73 82	97.54 4	89.15 62	86.72 187	94.68 150	81.83 104	97.24 227	85.18 163	88.31 241	94.76 211

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	83.67 271	94.30 163	69.33 254	97.99 163	87.10 145	88.55 233	93.72 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 240	85.13 252	90.98 188	96.52 87	81.50 170	96.14 57	96.16 119	73.78 351	83.65 272	92.15 240	63.26 313	97.37 216	82.82 197	81.74 312	94.06 248

sss		88.93 157	88.26 159	90.94 189	94.05 197	80.78 194	91.71 277	95.38 184	81.55 260	88.63 146	93.91 184	75.04 176	95.47 322	82.47 202	91.61 185	96.57 140

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	96.43 278	79.64 252	89.85 211	95.63 179

PVSNet_BlendedMVS		89.98 119	89.70 115	90.82 191	96.12 97	81.25 179	93.92 199	96.83 66	83.49 210	89.10 139	92.26 237	81.04 109	98.85 86	86.72 148	87.86 248	92.35 319

cascas		86.43 247	84.98 255	90.80 192	92.10 263	80.92 190	90.24 308	95.91 141	73.10 358	83.57 275	88.39 333	65.15 299	97.46 200	84.90 168	91.43 187	94.03 250

ECVR-MVS		89.09 150	88.53 147	90.77 193	95.62 123	75.89 302	96.16 53	84.22 379	87.89 107	90.20 124	96.65 70	63.19 314	98.10 146	85.90 156	96.94 90	98.33 43

GA-MVS		86.61 238	85.27 250	90.66 194	91.33 294	78.71 247	90.40 303	93.81 257	85.34 169	85.12 235	89.57 315	61.25 326	97.11 236	80.99 232	89.59 217	96.15 152

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	70.05 333	91.10 190	94.96 201

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	70.13 330	91.10 190	94.96 201

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	96.20 289	80.77 235	79.31 345	95.44 183

FC-MVSNet-test		90.27 112	90.18 104	90.53 198	93.71 214	79.85 224	95.77 80	97.59 3	89.31 56	86.27 197	94.67 151	81.93 103	97.01 243	84.26 176	88.09 244	94.71 212

PAPM		86.68 237	85.39 246	90.53 198	93.05 235	79.33 239	89.79 318	94.77 221	78.82 297	81.95 296	93.24 205	76.81 152	97.30 219	66.94 350	93.16 168	94.95 204

WR-MVS		88.38 171	87.67 171	90.52 200	93.30 227	80.18 208	93.26 228	95.96 137	88.57 83	85.47 221	92.81 220	76.12 159	96.91 249	81.24 227	82.29 303	94.47 231

MVSTER		88.84 159	88.29 157	90.51 201	92.95 241	80.44 202	93.73 206	95.01 201	84.66 187	87.15 173	93.12 210	72.79 211	97.21 230	87.86 129	87.36 257	93.87 256

RRT_MVS		89.09 150	88.62 146	90.49 202	92.85 244	79.65 228	96.41 39	94.41 232	88.22 94	85.50 218	94.77 146	69.36 253	97.31 218	89.33 112	86.73 264	94.51 223

testdata				90.49 202	96.40 89	77.89 269		95.37 186	72.51 363	93.63 52	96.69 66	82.08 99	97.65 182	83.08 190	97.39 82	95.94 164

test1111		89.10 148	88.64 143	90.48 204	95.53 127	74.97 309	96.08 61	84.89 377	88.13 99	90.16 126	96.65 70	63.29 312	98.10 146	86.14 151	96.90 92	98.39 39

tt0805		86.92 228	85.74 240	90.48 204	92.22 257	79.98 220	95.63 91	94.88 212	83.83 201	84.74 242	92.80 221	57.61 347	97.67 179	85.48 162	84.42 279	93.79 261

jajsoiax		88.24 176	87.50 174	90.48 204	90.89 314	80.14 210	95.31 101	95.65 164	84.97 178	84.24 260	94.02 175	65.31 298	97.42 205	88.56 121	88.52 235	93.89 253

PatchMatch-RL		86.77 235	85.54 242	90.47 207	95.88 112	82.71 144	90.54 301	92.31 288	79.82 283	84.32 257	91.57 266	68.77 265	96.39 280	73.16 311	93.48 161	92.32 320

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	70.13 330	91.10 190	94.48 229

VPNet		88.20 177	87.47 176	90.39 209	93.56 220	79.46 231	94.04 189	95.54 171	88.67 79	86.96 177	94.58 156	69.33 254	97.15 232	84.05 179	80.53 332	94.56 219

ACMH	80.38 17	85.36 263	83.68 276	90.39 209	94.45 181	80.63 197	94.73 142	94.85 214	82.09 240	77.24 343	92.65 224	60.01 336	97.58 188	72.25 315	84.87 276	92.96 299
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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	70.13 330	91.10 190	94.48 229

mvs_tets		88.06 182	87.28 181	90.38 211	90.94 310	79.88 222	95.22 110	95.66 162	85.10 175	84.21 261	93.94 180	63.53 310	97.40 212	88.50 122	88.40 239	93.87 256

1314		87.51 203	86.57 205	90.34 213	92.42 254	79.74 226	92.63 249	95.35 188	78.35 306	80.14 318	91.62 262	74.05 192	97.15 232	81.05 228	93.53 157	94.12 243

LTVRE_ROB	82.13 13	86.26 249	84.90 258	90.34 213	94.44 182	81.50 170	92.31 262	94.89 210	83.03 222	79.63 327	92.67 223	69.69 248	97.79 172	71.20 319	86.26 267	91.72 330
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
bld_raw_dy_0_64		87.60 199	86.73 195	90.21 215	91.72 277	80.26 207	95.09 120	88.61 358	85.68 160	85.55 212	94.38 159	63.93 308	96.66 257	87.73 131	87.84 249	93.72 270

test_djsdf		89.03 154	88.64 143	90.21 215	90.74 320	79.28 240	95.96 71	95.90 142	84.66 187	85.33 233	92.94 215	74.02 193	97.30 219	89.64 109	88.53 234	94.05 249

v2v482		87.84 185	87.06 185	90.17 217	90.99 306	79.23 243	94.00 194	95.13 195	84.87 180	85.53 215	92.07 248	74.45 184	97.45 201	84.71 171	81.75 311	93.85 259

pmmvs4		85.43 261	83.86 274	90.16 218	90.02 337	82.97 134	90.27 304	92.67 280	75.93 330	80.73 309	91.74 257	71.05 227	95.73 312	78.85 261	83.46 291	91.78 329

V42		87.68 190	86.86 190	90.15 219	90.58 325	80.14 210	94.24 175	95.28 189	83.66 204	85.67 209	91.33 268	74.73 181	97.41 210	84.43 175	81.83 309	92.89 302

MSDG		84.86 274	83.09 285	90.14 220	93.80 210	80.05 215	89.18 330	93.09 268	78.89 295	78.19 336	91.91 252	65.86 296	97.27 223	68.47 339	88.45 237	93.11 294

anonymousdsp		87.84 185	87.09 184	90.12 221	89.13 347	80.54 200	94.67 146	95.55 169	82.05 241	83.82 267	92.12 242	71.47 224	97.15 232	87.15 141	87.80 252	92.67 307

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	69.58 334	91.04 195	93.83 260

CR-MVSNet		85.35 264	83.76 275	90.12 221	90.58 325	79.34 236	85.24 369	91.96 303	78.27 308	85.55 212	87.87 343	71.03 228	95.61 314	73.96 307	89.36 220	95.40 185

v1144		87.61 198	86.79 194	90.06 224	91.01 305	79.34 236	93.95 196	95.42 183	83.36 215	85.66 210	91.31 271	74.98 177	97.42 205	83.37 187	82.06 305	93.42 282

XXY-MVS		87.65 192	86.85 191	90.03 225	92.14 260	80.60 199	93.76 205	95.23 191	82.94 225	84.60 244	94.02 175	74.27 186	95.49 321	81.04 229	83.68 287	94.01 251

Vis-MVSNet (Re-imp)		89.59 132	89.44 121	90.03 225	95.74 117	75.85 303	95.61 92	90.80 333	87.66 115	87.83 160	95.40 120	76.79 153	96.46 276	78.37 263	96.73 97	97.80 84

test2506		87.21 219	86.28 216	90.02 227	95.62 123	73.64 323	96.25 48	71.38 401	87.89 107	90.45 120	96.65 70	55.29 358	98.09 154	86.03 155	96.94 90	98.33 43

BH-untuned		88.60 167	88.13 161	90.01 228	95.24 138	78.50 253	93.29 226	94.15 243	84.75 184	84.46 249	93.40 197	75.76 166	97.40 212	77.59 273	94.52 141	94.12 243

v1192		87.25 215	86.33 213	90.00 229	90.76 319	79.04 244	93.80 203	95.48 174	82.57 232	85.48 220	91.18 275	73.38 205	97.42 205	82.30 206	82.06 305	93.53 276

v7n		86.81 230	85.76 238	89.95 230	90.72 321	79.25 242	95.07 121	95.92 139	84.45 190	82.29 290	90.86 284	72.60 214	97.53 194	79.42 257	80.52 333	93.08 296

v8		87.50 205	86.71 197	89.89 231	91.37 291	79.40 233	94.50 154	95.38 184	84.81 183	83.60 274	91.33 268	76.05 160	97.42 205	82.84 196	80.51 334	92.84 304

v10		87.25 215	86.38 210	89.85 232	91.19 297	79.50 230	94.48 155	95.45 178	83.79 202	83.62 273	91.19 273	75.13 174	97.42 205	81.94 214	80.60 329	92.63 309

baseline2		86.50 244	85.39 246	89.84 233	91.12 302	76.70 291	91.88 272	88.58 359	82.35 237	79.95 322	90.95 283	73.42 203	97.63 186	80.27 245	89.95 208	95.19 192

pm-mvs1		86.61 238	85.54 242	89.82 234	91.44 286	80.18 208	95.28 107	94.85 214	83.84 200	81.66 298	92.62 225	72.45 217	96.48 273	79.67 251	78.06 348	92.82 305

TR-MVS		86.78 232	85.76 238	89.82 234	94.37 184	78.41 255	92.47 253	92.83 274	81.11 270	86.36 194	92.40 231	68.73 266	97.48 197	73.75 309	89.85 211	93.57 275

ACMH+	81.04 14	85.05 271	83.46 279	89.82 234	94.66 168	79.37 234	94.44 160	94.12 246	82.19 239	78.04 338	92.82 219	58.23 345	97.54 193	73.77 308	82.90 298	92.54 310

EI-MVSNet		89.10 148	88.86 139	89.80 237	91.84 272	78.30 259	93.70 209	95.01 201	85.73 158	87.15 173	95.28 122	79.87 118	97.21 230	83.81 183	87.36 257	93.88 255

v144192		87.19 221	86.35 212	89.74 238	90.64 323	78.24 261	93.92 199	95.43 181	81.93 246	85.51 217	91.05 281	74.21 189	97.45 201	82.86 195	81.56 313	93.53 276

COLMAP_ROB	80.39 16	83.96 286	82.04 295	89.74 238	95.28 134	79.75 225	94.25 173	92.28 289	75.17 337	78.02 339	93.77 190	58.60 344	97.84 171	65.06 360	85.92 268	91.63 332
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 248	85.18 251	89.73 240	92.15 259	76.60 292	91.12 291	91.69 308	83.53 209	85.50 218	88.81 326	66.79 283	96.48 273	76.65 282	90.35 202	96.12 155

IterMVS-LS		88.36 173	87.91 167	89.70 241	93.80 210	78.29 260	93.73 206	95.08 200	85.73 158	84.75 241	91.90 253	79.88 117	96.92 248	83.83 182	82.51 300	93.89 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing99		86.72 236	85.73 241	89.69 242	94.23 190	74.91 311	91.35 285	90.97 328	86.14 150	86.36 194	90.22 299	59.41 340	97.48 197	82.24 208	90.66 197	96.69 135

v1921920		86.97 227	86.06 225	89.69 242	90.53 328	78.11 264	93.80 203	95.43 181	81.90 248	85.33 233	91.05 281	72.66 212	97.41 210	82.05 212	81.80 310	93.53 276

Fast-Effi-MVS+-dtu		87.44 206	86.72 196	89.63 244	92.04 264	77.68 278	94.03 190	93.94 249	85.81 155	82.42 289	91.32 270	70.33 241	97.06 240	80.33 244	90.23 203	94.14 242

v1240		86.78 232	85.85 233	89.56 245	90.45 329	77.79 274	93.61 211	95.37 186	81.65 256	85.43 225	91.15 277	71.50 223	97.43 204	81.47 225	82.05 307	93.47 280

Effi-MVS+-dtu		88.65 165	88.35 153	89.54 246	93.33 226	76.39 296	94.47 158	94.36 235	87.70 112	85.43 225	89.56 316	73.45 202	97.26 225	85.57 161	91.28 189	94.97 198

AllTest		83.42 293	81.39 299	89.52 247	95.01 147	77.79 274	93.12 232	90.89 331	77.41 315	76.12 351	93.34 198	54.08 363	97.51 195	68.31 341	84.27 281	93.26 285

TestCases				89.52 247	95.01 147	77.79 274		90.89 331	77.41 315	76.12 351	93.34 198	54.08 363	97.51 195	68.31 341	84.27 281	93.26 285

mvs_anonymous		89.37 144	89.32 126	89.51 249	93.47 222	74.22 318	91.65 280	94.83 216	82.91 226	85.45 222	93.79 188	81.23 108	96.36 283	86.47 150	94.09 147	97.94 74

XVG-ACMP-BASELINE		86.00 251	84.84 260	89.45 250	91.20 296	78.00 265	91.70 278	95.55 169	85.05 177	82.97 284	92.25 238	54.49 361	97.48 197	82.93 193	87.45 256	92.89 302

testing222		84.84 275	83.32 280	89.43 251	94.15 195	75.94 301	91.09 292	89.41 356	84.90 179	85.78 206	89.44 317	52.70 368	96.28 287	70.80 325	91.57 186	96.07 159

MVP-Stereo		85.97 252	84.86 259	89.32 252	90.92 312	82.19 156	92.11 268	94.19 241	78.76 299	78.77 335	91.63 261	68.38 270	96.56 268	75.01 299	93.95 149	89.20 365
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 255	84.70 262	89.29 253	91.76 276	75.54 306	88.49 339	91.30 319	81.63 258	85.05 236	88.70 330	71.71 220	96.24 288	74.61 303	89.05 226	96.08 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 225	86.32 214	89.21 254	90.94 310	77.26 283	93.71 208	94.43 230	84.84 182	84.36 255	90.80 288	76.04 161	97.05 241	82.12 210	79.60 342	93.31 284

tfpnnormal		84.72 277	83.23 283	89.20 255	92.79 246	80.05 215	94.48 155	95.81 148	82.38 235	81.08 306	91.21 272	69.01 262	96.95 246	61.69 370	80.59 330	90.58 355

cl22		86.78 232	85.98 228	89.18 256	92.34 255	77.62 279	90.84 297	94.13 245	81.33 264	83.97 265	90.15 303	73.96 194	96.60 265	84.19 177	82.94 295	93.33 283

BH-w/o		87.57 201	87.05 186	89.12 257	94.90 156	77.90 268	92.41 254	93.51 262	82.89 227	83.70 270	91.34 267	75.75 167	97.07 239	75.49 292	93.49 159	92.39 317

WR-MVS_H		87.80 187	87.37 178	89.10 258	93.23 228	78.12 263	95.61 92	97.30 29	87.90 105	83.72 269	92.01 250	79.65 125	96.01 297	76.36 285	80.54 331	93.16 292

miper_enhance_ethall		86.90 229	86.18 219	89.06 259	91.66 282	77.58 280	90.22 310	94.82 217	79.16 291	84.48 248	89.10 321	79.19 129	96.66 257	84.06 178	82.94 295	92.94 300

c3_l		87.14 223	86.50 208	89.04 260	92.20 258	77.26 283	91.22 290	94.70 224	82.01 244	84.34 256	90.43 296	78.81 132	96.61 263	83.70 185	81.09 320	93.25 287

miper_ehance_all_eth		87.22 218	86.62 203	89.02 261	92.13 261	77.40 282	90.91 296	94.81 218	81.28 265	84.32 257	90.08 305	79.26 127	96.62 260	83.81 183	82.94 295	93.04 297

gg-mvs-nofinetune		81.77 305	79.37 320	88.99 262	90.85 316	77.73 277	86.29 361	79.63 390	74.88 342	83.19 283	69.05 391	60.34 333	96.11 293	75.46 293	94.64 137	93.11 294

ETVMVS		84.43 280	82.92 289	88.97 263	94.37 184	74.67 312	91.23 289	88.35 361	83.37 214	86.06 203	89.04 322	55.38 356	95.67 313	67.12 348	91.34 188	96.58 139

pmmvs6		83.42 293	81.60 297	88.87 264	88.01 361	77.87 270	94.96 127	94.24 240	74.67 343	78.80 334	91.09 280	60.17 335	96.49 272	77.06 281	75.40 362	92.23 322

test_cas_vis1_n_1920		88.83 162	88.85 140	88.78 265	91.15 301	76.72 290	93.85 202	94.93 208	83.23 219	92.81 72	96.00 96	61.17 329	94.45 332	91.67 83	94.84 131	95.17 193

MIMVSNet		82.59 299	80.53 304	88.76 266	91.51 284	78.32 258	86.57 360	90.13 342	79.32 287	80.70 310	88.69 331	52.98 367	93.07 357	66.03 355	88.86 230	94.90 205

cl____		86.52 243	85.78 235	88.75 267	92.03 265	76.46 294	90.74 298	94.30 237	81.83 252	83.34 280	90.78 289	75.74 169	96.57 266	81.74 220	81.54 314	93.22 289

DIV-MVS_self_test		86.53 242	85.78 235	88.75 267	92.02 266	76.45 295	90.74 298	94.30 237	81.83 252	83.34 280	90.82 287	75.75 167	96.57 266	81.73 221	81.52 315	93.24 288

CP-MVSNet		87.63 195	87.26 183	88.74 269	93.12 231	76.59 293	95.29 105	96.58 91	88.43 86	83.49 277	92.98 214	75.28 173	95.83 305	78.97 260	81.15 319	93.79 261

eth_miper_zixun_eth		86.50 244	85.77 237	88.68 270	91.94 267	75.81 304	90.47 302	94.89 210	82.05 241	84.05 262	90.46 295	75.96 162	96.77 253	82.76 199	79.36 344	93.46 281

CHOSEN 280x420		85.15 269	83.99 272	88.65 271	92.47 251	78.40 256	79.68 389	92.76 276	74.90 341	81.41 302	89.59 314	69.85 247	95.51 318	79.92 249	95.29 125	92.03 325

PS-CasMVS		87.32 212	86.88 189	88.63 272	92.99 239	76.33 298	95.33 100	96.61 89	88.22 94	83.30 282	93.07 212	73.03 209	95.79 309	78.36 264	81.00 325	93.75 268

TransMVSNet (Re)		84.43 280	83.06 287	88.54 273	91.72 277	78.44 254	95.18 113	92.82 275	82.73 230	79.67 326	92.12 242	73.49 201	95.96 299	71.10 323	68.73 378	91.21 342

EG-PatchMatch MVS		82.37 301	80.34 307	88.46 274	90.27 331	79.35 235	92.80 246	94.33 236	77.14 319	73.26 367	90.18 302	47.47 379	96.72 254	70.25 327	87.32 259	89.30 363

PEN-MVS		86.80 231	86.27 217	88.40 275	92.32 256	75.71 305	95.18 113	96.38 101	87.97 102	82.82 286	93.15 208	73.39 204	95.92 300	76.15 289	79.03 347	93.59 274

Baseline_NR-MVSNet		87.07 224	86.63 202	88.40 275	91.44 286	77.87 270	94.23 176	92.57 282	84.12 194	85.74 208	92.08 246	77.25 149	96.04 294	82.29 207	79.94 338	91.30 340

D2MVS		85.90 253	85.09 253	88.35 277	90.79 317	77.42 281	91.83 274	95.70 158	80.77 273	80.08 320	90.02 306	66.74 285	96.37 281	81.88 216	87.97 246	91.26 341

pmmvs5		84.21 282	82.84 292	88.34 278	88.95 349	76.94 287	92.41 254	91.91 305	75.63 332	80.28 315	91.18 275	64.59 302	95.57 315	77.09 280	83.47 290	92.53 311

LCM-MVSNet-Re		88.30 175	88.32 156	88.27 279	94.71 165	72.41 340	93.15 231	90.98 327	87.77 110	79.25 330	91.96 251	78.35 140	95.75 310	83.04 191	95.62 114	96.65 136

CostFormer		85.77 257	84.94 257	88.26 280	91.16 300	72.58 338	89.47 325	91.04 326	76.26 327	86.45 192	89.97 308	70.74 233	96.86 252	82.35 205	87.07 262	95.34 189

ITE_SJBPF				88.24 281	91.88 271	77.05 286		92.92 271	85.54 165	80.13 319	93.30 202	57.29 348	96.20 289	72.46 314	84.71 277	91.49 336

PVSNet	78.82 18	85.55 259	84.65 263	88.23 282	94.72 164	71.93 341	87.12 356	92.75 277	78.80 298	84.95 238	90.53 294	64.43 303	96.71 256	74.74 301	93.86 151	96.06 161

IterMVS-SCA-FT		85.45 260	84.53 266	88.18 283	91.71 279	76.87 288	90.19 311	92.65 281	85.40 168	81.44 301	90.54 293	66.79 283	95.00 330	81.04 229	81.05 321	92.66 308

EPNet_dtu		86.49 246	85.94 231	88.14 284	90.24 332	72.82 330	94.11 181	92.20 291	86.66 137	79.42 329	92.36 233	73.52 200	95.81 307	71.26 318	93.66 153	95.80 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 297	80.93 303	88.06 285	90.05 336	76.37 297	84.74 374	91.96 303	72.28 366	81.32 304	87.87 343	71.03 228	95.50 320	68.97 336	80.15 336	92.32 320

test_vis1_n_1920		89.39 143	89.84 114	88.04 286	92.97 240	72.64 335	94.71 144	96.03 133	86.18 148	91.94 97	96.56 78	61.63 321	95.74 311	93.42 41	95.11 129	95.74 174

DTE-MVSNet		86.11 250	85.48 244	87.98 287	91.65 283	74.92 310	94.93 129	95.75 153	87.36 119	82.26 291	93.04 213	72.85 210	95.82 306	74.04 305	77.46 353	93.20 290

PMMVS		85.71 258	84.96 256	87.95 288	88.90 350	77.09 285	88.68 337	90.06 344	72.32 365	86.47 189	90.76 290	72.15 218	94.40 334	81.78 219	93.49 159	92.36 318

GG-mvs-BLEND				87.94 289	89.73 343	77.91 267	87.80 346	78.23 394		80.58 312	83.86 369	59.88 337	95.33 324	71.20 319	92.22 182	90.60 354

pmmvs-eth3d		80.97 318	78.72 330	87.74 290	84.99 379	79.97 221	90.11 313	91.65 309	75.36 334	73.51 365	86.03 359	59.45 339	93.96 344	75.17 296	72.21 367	89.29 364

MS-PatchMatch		85.05 271	84.16 268	87.73 291	91.42 289	78.51 252	91.25 288	93.53 261	77.50 314	80.15 317	91.58 264	61.99 319	95.51 318	75.69 291	94.35 145	89.16 366

test_0402		81.30 315	79.17 325	87.67 292	93.19 229	78.17 262	92.98 239	91.71 306	75.25 336	76.02 353	90.31 298	59.23 341	96.37 281	50.22 387	83.63 288	88.47 372

IterMVS		84.88 273	83.98 273	87.60 293	91.44 286	76.03 300	90.18 312	92.41 284	83.24 218	81.06 307	90.42 297	66.60 286	94.28 338	79.46 253	80.98 326	92.48 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 313	79.30 321	87.58 294	90.92 312	74.16 320	80.99 385	87.68 366	70.52 373	76.63 348	88.81 326	71.21 225	92.76 359	60.01 376	86.93 263	95.83 170

EPMVS		83.90 289	82.70 293	87.51 295	90.23 333	72.67 333	88.62 338	81.96 385	81.37 263	85.01 237	88.34 334	66.31 290	94.45 332	75.30 295	87.12 260	95.43 184

ADS-MVSNet2		81.66 308	79.71 317	87.50 296	91.35 292	74.19 319	83.33 379	88.48 360	72.90 360	82.24 292	85.77 362	64.98 300	93.20 355	64.57 362	83.74 285	95.12 194

OurMVSNet-221017-0		85.35 264	84.64 264	87.49 297	90.77 318	72.59 337	94.01 192	94.40 233	84.72 185	79.62 328	93.17 207	61.91 320	96.72 254	81.99 213	81.16 317	93.16 292

tpm2		84.08 284	82.94 288	87.48 298	91.39 290	71.27 348	89.23 329	90.37 337	71.95 367	84.64 243	89.33 318	67.30 274	96.55 270	75.17 296	87.09 261	94.63 213

RPSCF		85.07 270	84.27 267	87.48 298	92.91 242	70.62 357	91.69 279	92.46 283	76.20 328	82.67 288	95.22 125	63.94 306	97.29 222	77.51 275	85.80 269	94.53 220

miper_lstm_enhance		85.27 267	84.59 265	87.31 300	91.28 295	74.63 313	87.69 350	94.09 247	81.20 269	81.36 303	89.85 311	74.97 178	94.30 337	81.03 231	79.84 341	93.01 298

FMVSNet5		81.52 311	79.60 318	87.27 301	91.17 298	77.95 266	91.49 282	92.26 290	76.87 320	76.16 350	87.91 342	51.67 369	92.34 362	67.74 345	81.16 317	91.52 335

USDC		82.76 296	81.26 301	87.26 302	91.17 298	74.55 314	89.27 327	93.39 264	78.26 309	75.30 356	92.08 246	54.43 362	96.63 259	71.64 316	85.79 270	90.61 352

test-LLR		85.87 254	85.41 245	87.25 303	90.95 308	71.67 346	89.55 321	89.88 350	83.41 212	84.54 246	87.95 340	67.25 275	95.11 327	81.82 217	93.37 164	94.97 198

test-mter		84.54 279	83.64 277	87.25 303	90.95 308	71.67 346	89.55 321	89.88 350	79.17 290	84.54 246	87.95 340	55.56 354	95.11 327	81.82 217	93.37 164	94.97 198

JIA-IIPM		81.04 316	78.98 328	87.25 303	88.64 351	73.48 325	81.75 384	89.61 354	73.19 357	82.05 294	73.71 388	66.07 295	95.87 303	71.18 321	84.60 278	92.41 316

TDRefinement		79.81 328	77.34 333	87.22 306	79.24 391	75.48 307	93.12 232	92.03 298	76.45 323	75.01 357	91.58 264	49.19 375	96.44 277	70.22 329	69.18 375	89.75 359

tpmvs		83.35 295	82.07 294	87.20 307	91.07 304	71.00 354	88.31 342	91.70 307	78.91 293	80.49 314	87.18 352	69.30 257	97.08 237	68.12 344	83.56 289	93.51 279

ppachtmachnet_test		81.84 304	80.07 312	87.15 308	88.46 355	74.43 317	89.04 333	92.16 292	75.33 335	77.75 340	88.99 323	66.20 292	95.37 323	65.12 359	77.60 351	91.65 331

dmvs_re		84.20 283	83.22 284	87.14 309	91.83 274	77.81 272	90.04 314	90.19 340	84.70 186	81.49 299	89.17 320	64.37 304	91.13 373	71.58 317	85.65 271	92.46 314

tpm cat1		81.96 302	80.27 308	87.01 310	91.09 303	71.02 353	87.38 354	91.53 314	66.25 379	80.17 316	86.35 358	68.22 271	96.15 292	69.16 335	82.29 303	93.86 258

test_fmvs1_n		87.03 226	87.04 187	86.97 311	89.74 342	71.86 342	94.55 152	94.43 230	78.47 303	91.95 96	95.50 116	51.16 371	93.81 345	93.02 48	94.56 139	95.26 190

OpenMVS_ROB	74.94 19	79.51 331	77.03 338	86.93 312	87.00 367	76.23 299	92.33 260	90.74 334	68.93 376	74.52 361	88.23 337	49.58 374	96.62 260	57.64 380	84.29 280	87.94 375

SixPastTwentyTwo		83.91 288	82.90 290	86.92 313	90.99 306	70.67 356	93.48 215	91.99 300	85.54 165	77.62 342	92.11 244	60.59 332	96.87 251	76.05 290	77.75 350	93.20 290

ADS-MVSNet		81.56 310	79.78 314	86.90 314	91.35 292	71.82 343	83.33 379	89.16 357	72.90 360	82.24 292	85.77 362	64.98 300	93.76 346	64.57 362	83.74 285	95.12 194

PatchT		82.68 298	81.27 300	86.89 315	90.09 335	70.94 355	84.06 376	90.15 341	74.91 340	85.63 211	83.57 371	69.37 252	94.87 331	65.19 357	88.50 236	94.84 207

tpm		84.73 276	84.02 271	86.87 316	90.33 330	68.90 364	89.06 332	89.94 347	80.85 272	85.75 207	89.86 310	68.54 268	95.97 298	77.76 271	84.05 283	95.75 173

Patchmatch-RL test		81.67 307	79.96 313	86.81 317	85.42 377	71.23 349	82.17 383	87.50 367	78.47 303	77.19 344	82.50 378	70.81 232	93.48 350	82.66 200	72.89 366	95.71 177

test_vis1_n		86.56 241	86.49 209	86.78 318	88.51 352	72.69 332	94.68 145	93.78 258	79.55 286	90.70 117	95.31 121	48.75 376	93.28 353	93.15 45	93.99 148	94.38 233

test_fmvs1		87.34 210	87.56 173	86.68 319	90.59 324	71.80 344	94.01 192	94.04 248	78.30 307	91.97 94	95.22 125	56.28 352	93.71 347	92.89 49	94.71 133	94.52 221

MDA-MVSNet-bldmvs		78.85 335	76.31 340	86.46 320	89.76 341	73.88 321	88.79 335	90.42 336	79.16 291	59.18 387	88.33 335	60.20 334	94.04 340	62.00 369	68.96 376	91.48 337

tpmrst		85.35 264	84.99 254	86.43 321	90.88 315	67.88 368	88.71 336	91.43 317	80.13 278	86.08 202	88.80 328	73.05 207	96.02 296	82.48 201	83.40 293	95.40 185

TESTMET0.1,1		83.74 291	82.85 291	86.42 322	89.96 338	71.21 350	89.55 321	87.88 363	77.41 315	83.37 279	87.31 348	56.71 350	93.65 349	80.62 239	92.85 174	94.40 232

our_test_3		81.93 303	80.46 306	86.33 323	88.46 355	73.48 325	88.46 340	91.11 322	76.46 322	76.69 347	88.25 336	66.89 281	94.36 335	68.75 337	79.08 346	91.14 344

lessismore_v0				86.04 324	88.46 355	68.78 365		80.59 388		73.01 368	90.11 304	55.39 355	96.43 278	75.06 298	65.06 382	92.90 301

TinyColmap		79.76 329	77.69 332	85.97 325	91.71 279	73.12 327	89.55 321	90.36 338	75.03 338	72.03 371	90.19 301	46.22 381	96.19 291	63.11 366	81.03 322	88.59 371

KD-MVS_2432*1600		78.50 336	76.02 343	85.93 326	86.22 370	74.47 315	84.80 372	92.33 286	79.29 288	76.98 345	85.92 360	53.81 365	93.97 342	67.39 346	57.42 390	89.36 361

miper_refine_blended		78.50 336	76.02 343	85.93 326	86.22 370	74.47 315	84.80 372	92.33 286	79.29 288	76.98 345	85.92 360	53.81 365	93.97 342	67.39 346	57.42 390	89.36 361

K. test v3		81.59 309	80.15 311	85.91 328	89.89 340	69.42 363	92.57 251	87.71 365	85.56 164	73.44 366	89.71 313	55.58 353	95.52 317	77.17 278	69.76 372	92.78 306

mvsany_test1		85.42 262	85.30 249	85.77 329	87.95 363	75.41 308	87.61 353	80.97 387	76.82 321	88.68 145	95.83 104	77.44 148	90.82 375	85.90 156	86.51 265	91.08 348

MIMVSNet1		79.38 332	77.28 334	85.69 330	86.35 369	73.67 322	91.61 281	92.75 277	78.11 312	72.64 369	88.12 338	48.16 377	91.97 367	60.32 373	77.49 352	91.43 338

UWE-MVS		83.69 292	83.09 285	85.48 331	93.06 234	65.27 377	90.92 295	86.14 370	79.90 281	86.26 198	90.72 291	57.17 349	95.81 307	71.03 324	92.62 176	95.35 188

UnsupCasMVSNet_eth		80.07 325	78.27 331	85.46 332	85.24 378	72.63 336	88.45 341	94.87 213	82.99 224	71.64 373	88.07 339	56.34 351	91.75 368	73.48 310	63.36 385	92.01 326

CL-MVSNet_self_test		81.74 306	80.53 304	85.36 333	85.96 372	72.45 339	90.25 306	93.07 269	81.24 267	79.85 325	87.29 349	70.93 230	92.52 360	66.95 349	69.23 374	91.11 346

MDA-MVSNet_test_wron		79.21 334	77.19 336	85.29 334	88.22 359	72.77 331	85.87 363	90.06 344	74.34 345	62.62 385	87.56 346	66.14 293	91.99 366	66.90 353	73.01 364	91.10 347

YYNet1		79.22 333	77.20 335	85.28 335	88.20 360	72.66 334	85.87 363	90.05 346	74.33 346	62.70 383	87.61 345	66.09 294	92.03 364	66.94 350	72.97 365	91.15 343

WB-MVSnew		83.77 290	83.28 281	85.26 336	91.48 285	71.03 352	91.89 271	87.98 362	78.91 293	84.78 240	90.22 299	69.11 261	94.02 341	64.70 361	90.44 199	90.71 350

dp		81.47 312	80.23 309	85.17 337	89.92 339	65.49 375	86.74 358	90.10 343	76.30 326	81.10 305	87.12 353	62.81 315	95.92 300	68.13 343	79.88 339	94.09 246

UnsupCasMVSNet_bld		76.23 344	73.27 348	85.09 338	83.79 381	72.92 328	85.65 366	93.47 263	71.52 368	68.84 379	79.08 383	49.77 373	93.21 354	66.81 354	60.52 387	89.13 368

Anonymous20231206		81.03 317	79.77 316	84.82 339	87.85 364	70.26 359	91.42 283	92.08 296	73.67 352	77.75 340	89.25 319	62.43 317	93.08 356	61.50 371	82.00 308	91.12 345

test0.0.03 1		82.41 300	81.69 296	84.59 340	88.23 358	72.89 329	90.24 308	87.83 364	83.41 212	79.86 324	89.78 312	67.25 275	88.99 383	65.18 358	83.42 292	91.90 328

CMPMVS	59.16 21	80.52 320	79.20 324	84.48 341	83.98 380	67.63 370	89.95 317	93.84 256	64.79 382	66.81 381	91.14 278	57.93 346	95.17 325	76.25 287	88.10 242	90.65 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 278	84.79 261	84.37 342	91.84 272	64.92 378	93.70 209	91.47 316	66.19 380	86.16 201	95.28 122	67.18 277	93.33 352	80.89 234	90.42 201	94.88 206

PVSNet_0	73.20 20	77.22 341	74.83 347	84.37 342	90.70 322	71.10 351	83.09 381	89.67 353	72.81 362	73.93 364	83.13 373	60.79 331	93.70 348	68.54 338	50.84 394	88.30 373

LF4IMVS		80.37 323	79.07 327	84.27 344	86.64 368	69.87 362	89.39 326	91.05 325	76.38 324	74.97 358	90.00 307	47.85 378	94.25 339	74.55 304	80.82 328	88.69 370

Anonymous20240521		80.44 322	79.21 323	84.11 345	85.75 375	67.89 367	92.86 244	93.23 266	75.61 333	75.59 355	87.47 347	50.03 372	94.33 336	71.14 322	81.21 316	90.12 357

PM-MVS		78.11 338	76.12 342	84.09 346	83.54 382	70.08 360	88.97 334	85.27 376	79.93 280	74.73 360	86.43 356	34.70 390	93.48 350	79.43 256	72.06 368	88.72 369

test_fmvs2		83.98 285	84.03 270	83.83 347	87.16 366	67.53 371	93.93 198	92.89 272	77.62 313	86.89 183	93.53 195	47.18 380	92.02 365	90.54 102	86.51 265	91.93 327

testgi		80.94 319	80.20 310	83.18 348	87.96 362	66.29 372	91.28 286	90.70 335	83.70 203	78.12 337	92.84 217	51.37 370	90.82 375	63.34 365	82.46 301	92.43 315

KD-MVS_self_test		80.20 324	79.24 322	83.07 349	85.64 376	65.29 376	91.01 294	93.93 250	78.71 301	76.32 349	86.40 357	59.20 342	92.93 358	72.59 313	69.35 373	91.00 349

testing3		80.46 321	79.59 319	83.06 350	93.44 224	64.64 379	93.33 220	85.47 374	84.34 191	79.93 323	90.84 286	44.35 384	92.39 361	57.06 382	87.56 253	92.16 324

ambc				83.06 350	79.99 389	63.51 383	77.47 390	92.86 273		74.34 363	84.45 368	28.74 391	95.06 329	73.06 312	68.89 377	90.61 352

test20.03		79.95 327	79.08 326	82.55 352	85.79 374	67.74 369	91.09 292	91.08 323	81.23 268	74.48 362	89.96 309	61.63 321	90.15 377	60.08 374	76.38 358	89.76 358

test_vis1_rt		77.96 339	76.46 339	82.48 353	85.89 373	71.74 345	90.25 306	78.89 391	71.03 372	71.30 374	81.35 380	42.49 386	91.05 374	84.55 173	82.37 302	84.65 378

EU-MVSNet		81.32 314	80.95 302	82.42 354	88.50 354	63.67 382	93.32 221	91.33 318	64.02 383	80.57 313	92.83 218	61.21 328	92.27 363	76.34 286	80.38 335	91.32 339

myMVS_eth3d		79.67 330	78.79 329	82.32 355	91.92 268	64.08 380	89.75 319	87.40 368	81.72 254	78.82 332	87.20 350	45.33 382	91.29 371	59.09 378	87.84 249	91.60 333

pmmvs3		71.81 350	68.71 353	81.11 356	75.86 392	70.42 358	86.74 358	83.66 380	58.95 387	68.64 380	80.89 381	36.93 388	89.52 380	63.10 367	63.59 384	83.39 379

Syy-MVS		80.07 325	79.78 314	80.94 357	91.92 268	59.93 388	89.75 319	87.40 368	81.72 254	78.82 332	87.20 350	66.29 291	91.29 371	47.06 389	87.84 249	91.60 333

new-patchmatchnet		76.41 343	75.17 346	80.13 358	82.65 385	59.61 389	87.66 351	91.08 323	78.23 310	69.85 377	83.22 372	54.76 359	91.63 370	64.14 364	64.89 383	89.16 366

mvsany_test3		74.95 345	73.26 349	80.02 359	74.61 393	63.16 384	85.53 367	78.42 392	74.16 347	74.89 359	86.46 355	36.02 389	89.09 382	82.39 204	66.91 379	87.82 376

test_fmvs3		77.67 340	77.16 337	79.22 360	79.52 390	61.14 386	92.34 259	91.64 310	73.98 349	78.86 331	86.59 354	27.38 394	87.03 385	88.12 127	75.97 360	89.50 360

DSMNet-mixed		76.94 342	76.29 341	78.89 361	83.10 383	56.11 397	87.78 347	79.77 389	60.65 386	75.64 354	88.71 329	61.56 323	88.34 384	60.07 375	89.29 222	92.21 323

EGC-MVSNET		61.97 358	56.37 362	78.77 362	89.63 344	73.50 324	89.12 331	82.79 382	0.21 406	1.24 407	84.80 366	39.48 387	90.04 378	44.13 391	75.94 361	72.79 390

new_pmnet		72.15 348	70.13 352	78.20 363	82.95 384	65.68 373	83.91 377	82.40 384	62.94 385	64.47 382	79.82 382	42.85 385	86.26 389	57.41 381	74.44 363	82.65 383

MVS-HIRNet		73.70 347	72.20 350	78.18 364	91.81 275	56.42 396	82.94 382	82.58 383	55.24 388	68.88 378	66.48 392	55.32 357	95.13 326	58.12 379	88.42 238	83.01 381

LCM-MVSNet		66.00 355	62.16 360	77.51 365	64.51 403	58.29 391	83.87 378	90.90 330	48.17 392	54.69 389	73.31 389	16.83 403	86.75 386	65.47 356	61.67 386	87.48 377

APD_test1		69.04 351	66.26 357	77.36 366	80.51 388	62.79 385	85.46 368	83.51 381	54.11 390	59.14 388	84.79 367	23.40 397	89.61 379	55.22 383	70.24 371	79.68 387

test_f		71.95 349	70.87 351	75.21 367	74.21 395	59.37 390	85.07 371	85.82 372	65.25 381	70.42 376	83.13 373	23.62 395	82.93 395	78.32 265	71.94 369	83.33 380

ANet_high		58.88 362	54.22 366	72.86 368	56.50 406	56.67 393	80.75 386	86.00 371	73.09 359	37.39 398	64.63 395	22.17 398	79.49 398	43.51 392	23.96 400	82.43 384

test_vis3_rt		65.12 356	62.60 358	72.69 369	71.44 396	60.71 387	87.17 355	65.55 402	63.80 384	53.22 390	65.65 394	14.54 404	89.44 381	76.65 282	65.38 381	67.91 393

FPMVS		64.63 357	62.55 359	70.88 370	70.80 397	56.71 392	84.42 375	84.42 378	51.78 391	49.57 391	81.61 379	23.49 396	81.48 396	40.61 396	76.25 359	74.46 389

dmvs_testset		74.57 346	75.81 345	70.86 371	87.72 365	40.47 404	87.05 357	77.90 396	82.75 229	71.15 375	85.47 364	67.98 272	84.12 393	45.26 390	76.98 357	88.00 374

N_pmnet		68.89 352	68.44 354	70.23 372	89.07 348	28.79 409	88.06 343	19.50 409	69.47 375	71.86 372	84.93 365	61.24 327	91.75 368	54.70 384	77.15 354	90.15 356

testf1		59.54 360	56.11 363	69.85 373	69.28 398	56.61 394	80.37 387	76.55 399	42.58 395	45.68 394	75.61 384	11.26 405	84.18 391	43.20 393	60.44 388	68.75 391

APD_test2		59.54 360	56.11 363	69.85 373	69.28 398	56.61 394	80.37 387	76.55 399	42.58 395	45.68 394	75.61 384	11.26 405	84.18 391	43.20 393	60.44 388	68.75 391

WB-MVS		67.92 353	67.49 355	69.21 375	81.09 386	41.17 403	88.03 344	78.00 395	73.50 354	62.63 384	83.11 375	63.94 306	86.52 387	25.66 400	51.45 393	79.94 386

PMMVS2		59.60 359	56.40 361	69.21 375	68.83 400	46.58 401	73.02 394	77.48 397	55.07 389	49.21 392	72.95 390	17.43 402	80.04 397	49.32 388	44.33 397	80.99 385

SSC-MVS		67.06 354	66.56 356	68.56 377	80.54 387	40.06 405	87.77 348	77.37 398	72.38 364	61.75 386	82.66 377	63.37 311	86.45 388	24.48 401	48.69 396	79.16 388

Gipuma		57.99 363	54.91 365	67.24 378	88.51 352	65.59 374	52.21 397	90.33 339	43.58 394	42.84 397	51.18 398	20.29 400	85.07 390	34.77 397	70.45 370	51.05 397
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 364	48.46 368	63.48 379	45.72 408	46.20 402	73.41 393	78.31 393	41.03 397	30.06 400	65.68 393	6.05 407	83.43 394	30.04 398	65.86 380	60.80 394
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 366	38.59 372	57.77 380	56.52 405	48.77 400	55.38 396	58.64 406	29.33 400	28.96 401	52.65 397	4.68 408	64.62 402	28.11 399	33.07 398	59.93 395
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 365	48.47 367	56.66 381	52.26 407	18.98 411	41.51 399	81.40 386	10.10 401	44.59 396	75.01 387	28.51 392	68.16 399	53.54 385	49.31 395	82.83 382

DeepMVS_CX				56.31 382	74.23 394	51.81 399		56.67 407	44.85 393	48.54 393	75.16 386	27.87 393	58.74 403	40.92 395	52.22 392	58.39 396

E-PMN		43.23 367	42.29 369	46.03 383	65.58 402	37.41 406	73.51 392	64.62 403	33.99 398	28.47 402	47.87 399	19.90 401	67.91 400	22.23 402	24.45 399	32.77 398

EMVS		42.07 368	41.12 370	44.92 384	63.45 404	35.56 408	73.65 391	63.48 404	33.05 399	26.88 403	45.45 400	21.27 399	67.14 401	19.80 403	23.02 401	32.06 399

tmp_tt		35.64 369	39.24 371	24.84 385	14.87 409	23.90 410	62.71 395	51.51 408	6.58 403	36.66 399	62.08 396	44.37 383	30.34 405	52.40 386	22.00 402	20.27 400

wuyk23d		21.27 371	20.48 374	23.63 386	68.59 401	36.41 407	49.57 398	6.85 410	9.37 402	7.89 404	4.46 406	4.03 409	31.37 404	17.47 404	16.07 403	3.12 401

test123		8.76 373	11.22 376	1.39 387	0.85 411	0.97 412	85.76 365	0.35 412	0.54 405	2.45 406	8.14 405	0.60 410	0.48 406	2.16 406	0.17 405	2.71 402

testmvs		8.92 372	11.52 375	1.12 388	1.06 410	0.46 413	86.02 362	0.65 411	0.62 404	2.74 405	9.52 404	0.31 411	0.45 407	2.38 405	0.39 404	2.46 403

test_blank		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uanet_test		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

DCPMVS		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

cdsmvs_eth3d_5k		22.14 370	29.52 373	0.00 389	0.00 412	0.00 414	0.00 400	95.76 152	0.00 407	0.00 408	94.29 164	75.66 170	0.00 408	0.00 407	0.00 406	0.00 404

pcd_1.5k_mvsjas		6.64 375	8.86 378	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	79.70 121	0.00 408	0.00 407	0.00 406	0.00 404

sosnet-low-res		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

sosnet		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uncertanet		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

Regformer		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

ab-mvs-re		7.82 374	10.43 377	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	93.88 185	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uanet		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

WAC-MVS						64.08 380								59.14 377

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

PC_three_1452									82.47 233	97.09 10	97.07 51	92.72 1	98.04 159	92.70 55	99.02 12	98.86 11

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					0.00 412

eth-test					0.00 412

ZD-MVS					98.15 34	86.62 32		97.07 45	83.63 205	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	81.26 266	97.26 7				95.50 23	99.13 3	99.03 8

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	86.13 152	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

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																96.12 155

test_part2					98.55 12	87.22 18				96.40 17

sam_mvs1												71.70 221				96.12 155

sam_mvs												70.60 234

MTGPA								96.97 50

test_post1							88.00 345				9.81 403	69.31 256	95.53 316	76.65 282

test_post											10.29 402	70.57 238	95.91 302

patchmatchnet-post											83.76 370	71.53 222	96.48 273

MTMP							96.16 53	60.64 405

gm-plane-assit					89.60 345	68.00 366			77.28 318		88.99 323		97.57 189	79.44 255

test9_res														91.91 78	98.71 32	98.07 66

TEST9					97.53 58	86.49 36	94.07 186	96.78 72	81.61 259	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	81.86 250	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

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

旧先验2							93.36 219		71.25 370	94.37 39			97.13 235	86.74 146

新几何2							93.11 234

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	73.95 350				99.05 55	80.56 240		96.59 138

原ACMM2							92.94 241

test222					96.55 84	81.70 166	92.22 264	95.01 201	68.36 377	90.20 124	96.14 92	80.26 114			97.80 74	96.05 162

testdata2													98.75 93	78.30 266

segment_acmp												87.16 36

testdata1							92.15 266		87.94 103

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	89.99 205	94.63 213

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						89.88 210

n2								0.00 413

nn								0.00 413

door-mid								85.49 373

test11								96.57 92

door								85.33 375

HQP5-MVS						81.56 168

HQP-NCC					94.17 192		94.39 165		88.81 72	85.43 225

ACMP_Plane					94.17 192		94.39 165		88.81 72	85.43 225

BP-MVS														87.11 143

HQP4-MVS										85.43 225			97.96 165			94.51 223

HQP3-MVS								96.04 131							89.77 214

HQP2-MVS												73.83 197

NP-MVS					94.37 184	82.42 151					93.98 178

MDTV_nov1_ep13_2view						55.91 398	87.62 352		73.32 356	84.59 245		70.33 241		74.65 302		95.50 182

MDTV_nov1_ep13			83.56 278		91.69 281	69.93 361	87.75 349	91.54 313	78.60 302	84.86 239	88.90 325	69.54 250	96.03 295	70.25 327	88.93 229

ACMMP++_ref															87.47 254

ACMMP++															88.01 245

Test By Simon												80.02 116

High-resolution multi-view benchmark