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++.		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 33	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 5

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 51	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 9

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 49	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 80
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		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 42	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 64	94.67 19
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
APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 26

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 22	93.63 23	74.77 110	92.29 7	95.97 2	74.28 34	97.24 11	88.58 13	96.91 1	94.87 11
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 25	92.85 60	80.26 13	87.78 30	94.27 36	75.89 19	96.81 22	87.45 19	96.44 9	93.05 91

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 63	93.00 47	80.90 9	88.06 28	94.06 46	76.43 16	96.84 20	88.48 14	95.99 19	94.34 32

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 22	80.19 14	88.10 27	94.80 16	73.76 39	97.11 13	87.51 18	95.82 24	94.90 8
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 22	94.74 2	75.71 90	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 14

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 110	89.57 89	93.39 35	77.53 46	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 22

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 40	93.19 40	77.87 36	90.32 17	94.00 48	74.83 27	93.78 145	87.63 17	94.27 63	93.65 68
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
ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 46	94.70 3	74.47 117	88.86 22	94.61 21	75.23 24	95.84 58	86.62 26	95.92 21	94.78 16

9.14			88.26 15		92.84 68		91.52 45	94.75 1	73.93 130	88.57 24	94.67 19	75.57 23	95.79 59	86.77 23	95.76 28

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 47	92.35 80	74.62 114	88.90 21	93.85 52	75.75 20	96.00 53	87.80 15	94.63 53	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 56	93.59 25	76.27 81	88.14 26	95.09 15	71.06 59	96.67 28	87.67 16	96.37 14	94.09 41

NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 51	92.83 61	81.50 6	85.79 45	93.47 59	73.02 45	97.00 17	84.90 33	94.94 44	94.10 40

xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 66	89.69 168	74.31 120	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 14

ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 65	85.24 51	94.32 35	71.76 54	96.93 18	85.53 29	95.79 25	94.32 33

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 31	93.49 30	74.75 111	88.33 25	94.43 32	73.27 42	97.02 16	84.18 49	94.84 48	93.82 57

MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 83	93.50 28	75.17 103	86.34 40	95.29 12	70.86 60	96.00 53	88.78 12	96.04 16	94.58 22
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 21	92.65 70	77.57 42	83.84 80	94.40 34	72.24 50	96.28 41	85.65 28	95.30 40	93.62 70
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 39	92.83 61	73.01 148	88.58 23	94.52 23	73.36 40	96.49 37	84.26 46	95.01 42	92.70 102
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 67	84.91 56	94.44 30	70.78 61	96.61 32	84.53 41	94.89 46	93.66 63

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 61	94.05 15	70.80 179	87.59 33	93.51 56	77.57 14	96.63 31	83.31 55	95.77 26	94.72 18

zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 73	92.02 94	79.45 19	85.88 42	94.80 16	68.07 86	96.21 43	86.69 24	95.34 36	93.23 83

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 87	72.45 41	90.02 77	94.37 4	71.76 162	87.28 34	94.27 36	75.18 25	96.08 49	85.16 30	95.77 26	93.80 60

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 24	93.43 33	76.89 63	84.68 62	93.99 50	70.67 64	96.82 21	84.18 49	95.01 42	93.90 52

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 81	93.82 19	73.07 146	84.86 61	92.89 71	76.22 17	96.33 39	84.89 35	95.13 41	94.40 29

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 67	84.66 65	94.52 23	68.81 84	96.65 29	84.53 41	94.90 45	94.00 47

region2R		87.42 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 70	84.45 68	94.52 23	69.09 80	96.70 26	84.37 44	94.83 50	94.03 44

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 27	93.24 37	75.23 100	84.91 56	94.44 30	70.78 61	96.61 32	83.75 54	94.89 46	93.66 63

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 183	92.02 94	79.45 19	85.88 42	94.80 16	68.07 86	96.21 43	86.69 24	95.34 36	93.23 83

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 32	93.33 36	76.07 84	83.81 81	93.95 51	69.77 74	96.01 52	85.15 31	94.66 52	94.32 33
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 35	86.92 36	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 72	83.68 82	94.46 27	67.93 88	95.95 56	84.20 48	94.39 59	93.23 83

XVS		87.18 34	86.91 37	88.00 15	94.42 22	73.33 20	92.78 17	92.99 49	79.14 22	83.67 83	94.17 40	67.45 93	96.60 34	83.06 60	94.50 56	94.07 42

DeepC-MVS	79.81 2	87.08 37	86.88 38	87.69 36	91.16 91	72.32 47	90.31 71	93.94 17	77.12 57	82.82 94	94.23 39	72.13 52	97.09 14	84.83 36	95.37 35	93.65 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		86.73 39	86.86 39	86.33 63	92.61 74	69.59 101	88.85 108	92.97 54	75.41 96	84.91 56	93.54 54	74.28 34	95.48 71	83.31 55	95.86 22	93.91 50

SR-MVS		86.73 39	86.67 40	86.91 52	94.11 40	72.11 51	92.37 26	92.56 73	74.50 115	86.84 37	94.65 20	67.31 95	95.77 60	84.80 37	92.85 74	92.84 100

DeepC-MVS_fast	79.65 3	86.91 38	86.62 41	87.76 29	93.52 52	72.37 44	91.26 48	93.04 43	76.62 72	84.22 73	93.36 61	71.44 57	96.76 24	80.82 83	95.33 38	94.16 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		86.63 43	86.53 42	86.95 51	89.33 132	71.24 67	88.43 123	92.05 93	82.50 1	86.88 36	90.09 127	74.45 29	95.61 63	84.38 43	90.63 99	94.01 46

DROMVSNet		86.01 51	86.38 43	84.91 95	89.31 137	66.27 167	92.32 28	93.63 23	79.37 21	84.17 75	91.88 86	69.04 83	95.43 75	83.93 52	93.77 67	93.01 94

Regformer-1		86.41 47	86.33 44	86.64 58	89.33 132	70.93 75	88.43 123	91.39 122	82.14 3	86.65 38	90.09 127	74.39 32	95.01 97	83.97 51	90.63 99	93.97 48

mPP-MVS		86.67 42	86.32 45	87.72 32	94.41 24	73.55 13	92.74 19	92.22 87	76.87 64	82.81 95	94.25 38	66.44 102	96.24 42	82.88 64	94.28 62	93.38 77

PGM-MVS		86.68 41	86.27 46	87.90 21	94.22 35	73.38 19	90.22 74	93.04 43	75.53 94	83.86 79	94.42 33	67.87 90	96.64 30	82.70 69	94.57 55	93.66 63

test1172		86.20 50	86.22 47	86.12 70	93.95 42	69.89 96	91.79 41	92.28 82	75.07 104	86.40 39	94.58 22	65.00 120	95.56 66	84.34 45	92.60 77	92.90 98

train_agg		86.43 45	86.20 48	87.13 49	93.26 56	72.96 26	88.75 113	91.89 103	68.69 226	85.00 54	93.10 65	74.43 30	95.41 77	84.97 32	95.71 30	93.02 93

CSCG		86.41 47	86.19 49	87.07 50	92.91 65	72.48 38	90.81 57	93.56 26	73.95 128	83.16 88	91.07 108	75.94 18	95.19 88	79.94 92	94.38 60	93.55 73

PHI-MVS		86.43 45	86.17 50	87.24 46	90.88 97	70.96 72	92.27 30	94.07 11	72.45 151	85.22 52	91.90 85	69.47 76	96.42 38	83.28 58	95.94 20	94.35 31

CANet		86.45 44	86.10 51	87.51 40	90.09 111	70.94 74	89.70 87	92.59 72	81.78 4	81.32 111	91.43 99	70.34 66	97.23 12	84.26 46	93.36 70	94.37 30

agg_prior1		86.22 49	86.09 52	86.62 59	92.85 66	71.94 54	88.59 120	91.78 109	68.96 221	84.41 69	93.18 64	74.94 26	94.93 98	84.75 38	95.33 38	93.01 94

APD-MVS_3200maxsize		85.97 52	85.88 53	86.22 67	92.69 72	69.53 103	91.93 36	92.99 49	73.54 138	85.94 41	94.51 26	65.80 112	95.61 63	83.04 62	92.51 79	93.53 75

canonicalmvs		85.91 53	85.87 54	86.04 72	89.84 118	69.44 108	90.45 69	93.00 47	76.70 71	88.01 29	91.23 102	73.28 41	93.91 140	81.50 77	88.80 120	94.77 17

MSLP-MVS++		85.43 61	85.76 55	84.45 109	91.93 83	70.24 87	90.71 59	92.86 59	77.46 48	84.22 73	92.81 75	67.16 97	92.94 186	80.36 88	94.35 61	90.16 185

SR-MVS-dyc-post		85.77 55	85.61 56	86.23 66	93.06 62	70.63 82	91.88 37	92.27 83	73.53 139	85.69 46	94.45 28	65.00 120	95.56 66	82.75 65	91.87 84	92.50 109

RE-MVS-def			85.48 57		93.06 62	70.63 82	91.88 37	92.27 83	73.53 139	85.69 46	94.45 28	63.87 127		82.75 65	91.87 84	92.50 109

Regformer-4		85.68 58	85.45 58	86.35 62	88.95 151	69.67 100	88.29 133	91.29 124	81.73 5	85.36 49	90.01 130	72.62 47	95.35 84	83.28 58	87.57 132	94.03 44

ACMMP		85.89 54	85.39 59	87.38 44	93.59 51	72.63 34	92.74 19	93.18 41	76.78 67	80.73 120	93.82 53	64.33 123	96.29 40	82.67 70	90.69 98	93.23 83
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
TSAR-MVS + GP.		85.71 57	85.33 60	86.84 53	91.34 89	72.50 37	89.07 101	87.28 232	76.41 74	85.80 44	90.22 125	74.15 37	95.37 83	81.82 75	91.88 83	92.65 106

alignmvs		85.48 59	85.32 61	85.96 74	89.51 125	69.47 105	89.74 85	92.47 74	76.17 82	87.73 32	91.46 98	70.32 67	93.78 145	81.51 76	88.95 117	94.63 21

DELS-MVS		85.41 62	85.30 62	85.77 75	88.49 169	67.93 139	85.52 215	93.44 32	78.70 29	83.63 85	89.03 159	74.57 28	95.71 62	80.26 90	94.04 65	93.66 63
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
CDPH-MVS		85.76 56	85.29 63	87.17 48	93.49 53	71.08 68	88.58 121	92.42 78	68.32 231	84.61 66	93.48 57	72.32 49	96.15 48	79.00 94	95.43 34	94.28 35

CS-MVS-test		85.02 69	85.21 64	84.46 108	89.28 139	65.70 179	91.16 52	93.56 26	77.83 38	81.80 105	89.89 132	70.67 64	95.61 63	80.39 87	92.34 81	92.06 126

casdiffmvs		85.11 67	85.14 65	85.01 89	87.20 210	65.77 178	87.75 149	92.83 61	77.84 37	84.36 72	92.38 78	72.15 51	93.93 139	81.27 79	90.48 101	95.33 1

Regformer-3		85.23 64	85.07 66	85.70 76	88.95 151	69.01 112	88.29 133	89.91 162	80.95 8	85.01 53	90.01 130	72.45 48	94.19 126	82.50 71	87.57 132	93.90 52

baseline		84.93 70	84.98 67	84.80 100	87.30 208	65.39 186	87.30 161	92.88 58	77.62 40	84.04 78	92.26 79	71.81 53	93.96 133	81.31 78	90.30 103	95.03 4

CS-MVS		84.53 73	84.97 68	83.23 154	87.54 202	63.27 228	88.82 110	93.50 28	75.98 87	83.07 89	89.73 137	70.29 68	95.23 86	82.07 74	93.70 69	91.18 149

UA-Net		85.08 68	84.96 69	85.45 78	92.07 81	68.07 137	89.78 84	90.86 137	82.48 2	84.60 67	93.20 63	69.35 77	95.22 87	71.39 164	90.88 97	93.07 90

abl_6		85.23 64	84.95 70	86.07 71	92.23 79	70.48 86	90.80 58	92.08 92	73.51 141	85.26 50	94.16 41	62.75 144	95.92 57	82.46 72	91.30 93	91.81 133

HPM-MVS_fast		85.35 63	84.95 70	86.57 61	93.69 48	70.58 85	92.15 34	91.62 113	73.89 131	82.67 97	94.09 44	62.60 145	95.54 69	80.93 81	92.93 72	93.57 72

MVS_111021_HR		85.14 66	84.75 72	86.32 65	91.65 86	72.70 31	85.98 198	90.33 150	76.11 83	82.08 100	91.61 93	71.36 58	94.17 128	81.02 80	92.58 78	92.08 125

ETV-MVS		84.90 72	84.67 73	85.59 77	89.39 130	68.66 126	88.74 115	92.64 71	79.97 17	84.10 76	85.71 246	69.32 78	95.38 80	80.82 83	91.37 91	92.72 101

3Dnovator+	77.84 4	85.48 59	84.47 74	88.51 6	91.08 92	73.49 17	93.18 11	93.78 21	80.79 10	76.66 185	93.37 60	60.40 188	96.75 25	77.20 114	93.73 68	95.29 2

DPM-MVS		84.93 70	84.29 75	86.84 53	90.20 109	73.04 24	87.12 165	93.04 43	69.80 199	82.85 93	91.22 103	73.06 44	96.02 51	76.72 121	94.63 53	91.46 143

EI-MVSNet-Vis-set		84.19 74	83.81 76	85.31 80	88.18 178	67.85 140	87.66 151	89.73 167	80.05 16	82.95 90	89.59 143	70.74 63	94.82 106	80.66 86	84.72 168	93.28 82

nrg030		83.88 75	83.53 77	84.96 91	86.77 218	69.28 109	90.46 68	92.67 67	74.79 109	82.95 90	91.33 101	72.70 46	93.09 180	80.79 85	79.28 234	92.50 109

MG-MVS		83.41 83	83.45 78	83.28 149	92.74 71	62.28 244	88.17 138	89.50 172	75.22 101	81.49 110	92.74 77	66.75 98	95.11 91	72.85 154	91.58 88	92.45 112

EI-MVSNet-UG-set		83.81 76	83.38 79	85.09 87	87.87 187	67.53 146	87.44 157	89.66 169	79.74 18	82.23 99	89.41 152	70.24 69	94.74 109	79.95 91	83.92 176	92.99 96

CPTT-MVS		83.73 77	83.33 80	84.92 94	93.28 55	70.86 77	92.09 35	90.38 146	68.75 225	79.57 129	92.83 73	60.60 184	93.04 184	80.92 82	91.56 89	90.86 161

HQP_MVS		83.64 79	83.14 81	85.14 85	90.08 112	68.71 122	91.25 49	92.44 75	79.12 24	78.92 137	91.00 112	60.42 186	95.38 80	78.71 97	86.32 153	91.33 145

Effi-MVS+		83.62 80	83.08 82	85.24 83	88.38 174	67.45 147	88.89 106	89.15 185	75.50 95	82.27 98	88.28 178	69.61 75	94.45 116	77.81 108	87.84 130	93.84 56

MVS_Test		83.15 87	83.06 83	83.41 146	86.86 214	63.21 230	86.11 196	92.00 97	74.31 120	82.87 92	89.44 151	70.03 70	93.21 170	77.39 113	88.50 126	93.81 58

EPP-MVSNet		83.40 84	83.02 84	84.57 104	90.13 110	64.47 202	92.32 28	90.73 138	74.45 119	79.35 132	91.10 106	69.05 82	95.12 90	72.78 155	87.22 140	94.13 39

OPM-MVS		83.50 81	82.95 85	85.14 85	88.79 159	70.95 73	89.13 100	91.52 116	77.55 45	80.96 118	91.75 88	60.71 180	94.50 115	79.67 93	86.51 151	89.97 201
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EPNet		83.72 78	82.92 86	86.14 69	84.22 253	69.48 104	91.05 54	85.27 257	81.30 7	76.83 180	91.65 90	66.09 107	95.56 66	76.00 126	93.85 66	93.38 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 87	82.81 87	84.18 120	89.94 116	63.30 227	91.59 42	88.46 209	79.04 26	79.49 130	92.16 80	65.10 117	94.28 119	67.71 196	91.86 86	94.95 5

EIA-MVS		83.31 86	82.80 88	84.82 98	89.59 121	65.59 181	88.21 136	92.68 66	74.66 113	78.96 135	86.42 234	69.06 81	95.26 85	75.54 131	90.09 107	93.62 70

Vis-MVSNet		83.46 82	82.80 88	85.43 79	90.25 108	68.74 120	90.30 72	90.13 156	76.33 80	80.87 119	92.89 71	61.00 177	94.20 125	72.45 158	90.97 95	93.35 79
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FIs		82.07 103	82.42 90	81.04 217	88.80 158	58.34 282	88.26 135	93.49 30	76.93 62	78.47 147	91.04 109	69.92 72	92.34 203	69.87 179	84.97 165	92.44 113

VNet		82.21 100	82.41 91	81.62 199	90.82 98	60.93 258	84.47 234	89.78 164	76.36 79	84.07 77	91.88 86	64.71 122	90.26 255	70.68 169	88.89 118	93.66 63

PAPM_NR		83.02 91	82.41 91	84.82 98	92.47 77	66.37 165	87.93 146	91.80 107	73.82 132	77.32 170	90.66 117	67.90 89	94.90 102	70.37 172	89.48 114	93.19 87

VDD-MVS		83.01 92	82.36 93	84.96 91	91.02 94	66.40 164	88.91 105	88.11 212	77.57 42	84.39 71	93.29 62	52.19 243	93.91 140	77.05 116	88.70 122	94.57 24

3Dnovator	76.31 5	83.38 85	82.31 94	86.59 60	87.94 186	72.94 29	90.64 60	92.14 91	77.21 53	75.47 210	92.83 73	58.56 195	94.72 110	73.24 151	92.71 76	92.13 124

h-mvs33		83.15 87	82.19 95	86.02 73	90.56 102	70.85 78	88.15 140	89.16 184	76.02 85	84.67 63	91.39 100	61.54 163	95.50 70	82.71 67	75.48 277	91.72 135

MVS_111021_LR		82.61 97	82.11 96	84.11 121	88.82 156	71.58 59	85.15 218	86.16 249	74.69 112	80.47 123	91.04 109	62.29 152	90.55 253	80.33 89	90.08 108	90.20 184

DP-MVS Recon		83.11 90	82.09 97	86.15 68	94.44 21	70.92 76	88.79 111	92.20 88	70.53 186	79.17 133	91.03 111	64.12 125	96.03 50	68.39 193	90.14 106	91.50 140

test_part1		82.78 94	82.08 98	84.89 96	90.66 100	66.97 158	90.96 55	92.93 57	77.19 54	80.53 122	90.04 129	63.44 130	95.39 79	76.04 125	76.90 254	92.31 116

MVSFormer		82.85 93	82.05 99	85.24 83	87.35 203	70.21 88	90.50 64	90.38 146	68.55 228	81.32 111	89.47 146	61.68 160	93.46 163	78.98 95	90.26 104	92.05 127

FC-MVSNet-test		81.52 115	82.02 100	80.03 235	88.42 173	55.97 318	87.95 144	93.42 34	77.10 58	77.38 168	90.98 114	69.96 71	91.79 222	68.46 192	84.50 170	92.33 114

HQP-MVS		82.61 97	82.02 100	84.37 112	89.33 132	66.98 156	89.17 95	92.19 89	76.41 74	77.23 173	90.23 124	60.17 189	95.11 91	77.47 111	85.99 159	91.03 155

OMC-MVS		82.69 95	81.97 102	84.85 97	88.75 161	67.42 148	87.98 142	90.87 136	74.92 108	79.72 128	91.65 90	62.19 155	93.96 133	75.26 133	86.42 152	93.16 88

diffmvs		82.10 101	81.88 103	82.76 180	83.00 279	63.78 215	83.68 250	89.76 165	72.94 149	82.02 101	89.85 134	65.96 111	90.79 249	82.38 73	87.30 139	93.71 62

PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 91	90.80 99	69.76 98	88.74 115	91.70 112	69.39 206	78.96 135	88.46 173	65.47 114	94.87 105	74.42 136	88.57 123	90.24 183

CLD-MVS		82.31 99	81.65 105	84.29 117	88.47 170	67.73 143	85.81 206	92.35 80	75.78 89	78.33 150	86.58 229	64.01 126	94.35 117	76.05 124	87.48 137	90.79 162
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 169	88.46 171	63.46 223	87.13 164	92.37 79	80.19 14	78.38 148	89.14 154	71.66 56	93.05 182	70.05 175	76.46 262	92.25 119

PS-MVSNAJss		82.07 103	81.31 107	84.34 115	86.51 221	67.27 152	89.27 93	91.51 117	71.75 163	79.37 131	90.22 125	63.15 138	94.27 120	77.69 109	82.36 199	91.49 141

LPG-MVS_test		82.08 102	81.27 108	84.50 106	89.23 142	68.76 118	90.22 74	91.94 101	75.37 98	76.64 186	91.51 95	54.29 226	94.91 100	78.44 101	83.78 177	89.83 206

LFMVS		81.82 108	81.23 109	83.57 141	91.89 84	63.43 225	89.84 80	81.85 303	77.04 60	83.21 86	93.10 65	52.26 242	93.43 165	71.98 159	89.95 110	93.85 54

API-MVS		81.99 105	81.23 109	84.26 118	90.94 95	70.18 93	91.10 53	89.32 176	71.51 169	78.66 142	88.28 178	65.26 115	95.10 94	64.74 224	91.23 94	87.51 265

UniMVSNet (Re)		81.60 114	81.11 111	83.09 160	88.38 174	64.41 204	87.60 152	93.02 46	78.42 32	78.56 144	88.16 182	69.78 73	93.26 169	69.58 182	76.49 261	91.60 136

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 139	89.15 145	68.03 138	84.46 236	90.02 158	70.67 183	81.30 114	86.53 232	63.17 137	94.19 126	75.60 130	88.54 124	88.57 245

PS-MVSNAJ		81.69 111	81.02 113	83.70 138	89.51 125	68.21 135	84.28 242	90.09 157	70.79 180	81.26 115	85.62 250	63.15 138	94.29 118	75.62 129	88.87 119	88.59 244

GeoE		81.71 110	81.01 114	83.80 137	89.51 125	64.45 203	88.97 103	88.73 204	71.27 172	78.63 143	89.76 136	66.32 104	93.20 172	69.89 178	86.02 158	93.74 61

hse-mvs2		81.72 109	80.94 115	84.07 124	88.72 162	67.68 144	85.87 202	87.26 233	76.02 85	84.67 63	88.22 181	61.54 163	93.48 161	82.71 67	73.44 304	91.06 153

PAPR		81.66 113	80.89 116	83.99 132	90.27 107	64.00 210	86.76 179	91.77 111	68.84 224	77.13 178	89.50 144	67.63 91	94.88 104	67.55 198	88.52 125	93.09 89

MAR-MVS		81.84 107	80.70 117	85.27 82	91.32 90	71.53 60	89.82 81	90.92 134	69.77 200	78.50 145	86.21 238	62.36 151	94.52 114	65.36 218	92.05 82	89.77 209
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
VDDNet		81.52 115	80.67 118	84.05 126	90.44 105	64.13 209	89.73 86	85.91 252	71.11 174	83.18 87	93.48 57	50.54 266	93.49 160	73.40 148	88.25 128	94.54 25

ACMP	74.13 6	81.51 117	80.57 119	84.36 113	89.42 128	68.69 125	89.97 79	91.50 120	74.46 118	75.04 228	90.41 121	53.82 231	94.54 112	77.56 110	82.91 191	89.86 205
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 137	80.55 120	80.76 222	88.07 182	60.80 261	86.86 173	91.58 115	75.67 93	80.24 124	89.45 150	63.34 132	90.25 256	70.51 171	79.22 235	91.23 148

DU-MVS		81.12 122	80.52 121	82.90 170	87.80 190	63.46 223	87.02 168	91.87 105	79.01 27	78.38 148	89.07 157	65.02 118	93.05 182	70.05 175	76.46 262	92.20 121

test_yl		81.17 120	80.47 122	83.24 152	89.13 146	63.62 216	86.21 193	89.95 160	72.43 154	81.78 107	89.61 141	57.50 204	93.58 154	70.75 167	86.90 144	92.52 107

DCV-MVSNet		81.17 120	80.47 122	83.24 152	89.13 146	63.62 216	86.21 193	89.95 160	72.43 154	81.78 107	89.61 141	57.50 204	93.58 154	70.75 167	86.90 144	92.52 107

PVSNet_Blended		80.98 123	80.34 124	82.90 170	88.85 153	65.40 184	84.43 238	92.00 97	67.62 235	78.11 155	85.05 263	66.02 109	94.27 120	71.52 161	89.50 113	89.01 228

TranMVSNet+NR-MVSNet		80.84 126	80.31 125	82.42 185	87.85 188	62.33 242	87.74 150	91.33 123	80.55 11	77.99 158	89.86 133	65.23 116	92.62 192	67.05 206	75.24 287	92.30 117

jason		81.39 118	80.29 126	84.70 102	86.63 220	69.90 95	85.95 199	86.77 240	63.24 283	81.07 117	89.47 146	61.08 176	92.15 211	78.33 104	90.07 109	92.05 127
jason: jason.
lupinMVS		81.39 118	80.27 127	84.76 101	87.35 203	70.21 88	85.55 211	86.41 244	62.85 290	81.32 111	88.61 168	61.68 160	92.24 208	78.41 103	90.26 104	91.83 131

PVSNet_BlendedMVS		80.60 137	80.02 128	82.36 187	88.85 153	65.40 184	86.16 195	92.00 97	69.34 208	78.11 155	86.09 241	66.02 109	94.27 120	71.52 161	82.06 201	87.39 267

EI-MVSNet		80.52 140	79.98 129	82.12 188	84.28 251	63.19 232	86.41 187	88.95 195	74.18 125	78.69 140	87.54 197	66.62 99	92.43 198	72.57 157	80.57 218	90.74 165

Fast-Effi-MVS+		80.81 129	79.92 130	83.47 142	88.85 153	64.51 199	85.53 213	89.39 174	70.79 180	78.49 146	85.06 262	67.54 92	93.58 154	67.03 207	86.58 149	92.32 115

CANet_DTU		80.61 136	79.87 131	82.83 172	85.60 232	63.17 233	87.36 158	88.65 205	76.37 78	75.88 204	88.44 174	53.51 233	93.07 181	73.30 149	89.74 112	92.25 119

ACMM	73.20 8	80.78 134	79.84 132	83.58 140	89.31 137	68.37 130	89.99 78	91.60 114	70.28 190	77.25 171	89.66 139	53.37 234	93.53 159	74.24 139	82.85 192	88.85 236
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1121		80.84 126	79.77 133	84.05 126	93.11 60	70.78 79	84.66 228	85.42 256	57.37 333	81.76 109	92.02 82	63.41 131	94.12 129	67.28 201	92.93 72	87.26 272

XVG-OURS-SEG-HR		80.81 129	79.76 134	83.96 134	85.60 232	68.78 117	83.54 256	90.50 143	70.66 184	76.71 184	91.66 89	60.69 181	91.26 236	76.94 118	81.58 206	91.83 131

xiu_mvs_v1_base_debu		80.80 131	79.72 135	84.03 129	87.35 203	70.19 90	85.56 208	88.77 199	69.06 217	81.83 102	88.16 182	50.91 260	92.85 188	78.29 105	87.56 134	89.06 223

xiu_mvs_v1_base		80.80 131	79.72 135	84.03 129	87.35 203	70.19 90	85.56 208	88.77 199	69.06 217	81.83 102	88.16 182	50.91 260	92.85 188	78.29 105	87.56 134	89.06 223

xiu_mvs_v1_base_debi		80.80 131	79.72 135	84.03 129	87.35 203	70.19 90	85.56 208	88.77 199	69.06 217	81.83 102	88.16 182	50.91 260	92.85 188	78.29 105	87.56 134	89.06 223

UGNet		80.83 128	79.59 138	84.54 105	88.04 183	68.09 136	89.42 90	88.16 211	76.95 61	76.22 195	89.46 148	49.30 280	93.94 136	68.48 191	90.31 102	91.60 136
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
114514_t		80.68 135	79.51 139	84.20 119	94.09 41	67.27 152	89.64 88	91.11 131	58.75 324	74.08 238	90.72 116	58.10 197	95.04 96	69.70 180	89.42 115	90.30 181

QAPM		80.88 124	79.50 140	85.03 88	88.01 185	68.97 114	91.59 42	92.00 97	66.63 247	75.15 224	92.16 80	57.70 201	95.45 73	63.52 228	88.76 121	90.66 167

AdaColmap		80.58 139	79.42 141	84.06 125	93.09 61	68.91 115	89.36 91	88.97 194	69.27 209	75.70 207	89.69 138	57.20 209	95.77 60	63.06 234	88.41 127	87.50 266

mvs-test1		80.88 124	79.40 142	85.29 81	85.13 241	69.75 99	89.28 92	88.10 213	74.99 106	76.44 191	86.72 218	57.27 207	94.26 124	73.53 144	83.18 188	91.87 130

NR-MVSNet		80.23 145	79.38 143	82.78 178	87.80 190	63.34 226	86.31 190	91.09 132	79.01 27	72.17 256	89.07 157	67.20 96	92.81 191	66.08 213	75.65 273	92.20 121

IterMVS-LS		80.06 148	79.38 143	82.11 189	85.89 227	63.20 231	86.79 176	89.34 175	74.19 124	75.45 213	86.72 218	66.62 99	92.39 200	72.58 156	76.86 256	90.75 164
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 144	79.32 145	83.27 150	83.98 258	65.37 187	90.50 64	90.38 146	68.55 228	76.19 196	88.70 164	56.44 213	93.46 163	78.98 95	80.14 224	90.97 158

v2v482		80.23 145	79.29 146	83.05 163	83.62 263	64.14 208	87.04 167	89.97 159	73.61 135	78.18 154	87.22 206	61.10 175	93.82 143	76.11 123	76.78 259	91.18 149

XVG-OURS		80.41 141	79.23 147	83.97 133	85.64 231	69.02 111	83.03 264	90.39 145	71.09 175	77.63 164	91.49 97	54.62 225	91.35 234	75.71 127	83.47 184	91.54 138

WR-MVS		79.49 158	79.22 148	80.27 231	88.79 159	58.35 281	85.06 220	88.61 207	78.56 30	77.65 163	88.34 176	63.81 129	90.66 252	64.98 222	77.22 250	91.80 134

ECVR-MVS		79.43 161	79.18 149	80.15 233	89.99 114	53.31 336	87.33 160	77.05 337	75.04 105	80.23 125	92.77 76	48.97 284	92.33 204	68.87 189	92.40 80	94.81 13

mvs_anonymous		79.42 162	79.11 150	80.34 229	84.45 250	57.97 288	82.59 266	87.62 225	67.40 238	76.17 199	88.56 171	68.47 85	89.59 266	70.65 170	86.05 157	93.47 76

v1144		80.03 149	79.03 151	83.01 165	83.78 261	64.51 199	87.11 166	90.57 142	71.96 161	78.08 157	86.20 239	61.41 167	93.94 136	74.93 134	77.23 249	90.60 170

v8		79.97 151	79.02 152	82.80 175	84.09 255	64.50 201	87.96 143	90.29 153	74.13 127	75.24 222	86.81 215	62.88 143	93.89 142	74.39 137	75.40 281	90.00 197

ab-mvs		79.51 157	78.97 153	81.14 214	88.46 171	60.91 259	83.84 248	89.24 181	70.36 188	79.03 134	88.87 162	63.23 136	90.21 257	65.12 220	82.57 197	92.28 118

Anonymous20240529		80.19 147	78.89 154	84.10 122	90.60 101	64.75 196	88.95 104	90.90 135	65.97 255	80.59 121	91.17 105	49.97 271	93.73 151	69.16 186	82.70 196	93.81 58

PCF-MVS	73.52 7	80.38 142	78.84 155	85.01 89	87.71 194	68.99 113	83.65 251	91.46 121	63.00 287	77.77 162	90.28 122	66.10 106	95.09 95	61.40 250	88.22 129	90.94 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 154	78.67 156	82.97 168	84.06 256	64.95 193	87.88 148	90.62 140	73.11 145	75.11 225	86.56 230	61.46 166	94.05 132	73.68 142	75.55 275	89.90 203

VPNet		78.69 180	78.66 157	78.76 256	88.31 176	55.72 320	84.45 237	86.63 242	76.79 66	78.26 151	90.55 119	59.30 191	89.70 265	66.63 208	77.05 252	90.88 160

BH-untuned		79.47 159	78.60 158	82.05 191	89.19 144	65.91 174	86.07 197	88.52 208	72.18 157	75.42 214	87.69 192	61.15 174	93.54 158	60.38 257	86.83 146	86.70 286

Effi-MVS+-dtu		80.03 149	78.57 159	84.42 110	85.13 241	68.74 120	88.77 112	88.10 213	74.99 106	74.97 229	83.49 282	57.27 207	93.36 166	73.53 144	80.88 212	91.18 149

WR-MVS_H		78.51 184	78.49 160	78.56 259	88.02 184	56.38 313	88.43 123	92.67 67	77.14 56	73.89 239	87.55 196	66.25 105	89.24 272	58.92 270	73.55 302	90.06 195

Vis-MVSNet (Re-imp)		78.36 188	78.45 161	78.07 267	88.64 165	51.78 341	86.70 180	79.63 324	74.14 126	75.11 225	90.83 115	61.29 171	89.75 263	58.10 279	91.60 87	92.69 104

BH-RMVSNet		79.61 155	78.44 162	83.14 158	89.38 131	65.93 173	84.95 223	87.15 235	73.56 137	78.19 153	89.79 135	56.67 212	93.36 166	59.53 264	86.74 147	90.13 187

v1192		79.59 156	78.43 163	83.07 162	83.55 265	64.52 198	86.93 171	90.58 141	70.83 178	77.78 161	85.90 242	59.15 192	93.94 136	73.96 141	77.19 251	90.76 163

RRT_MVS		79.88 152	78.38 164	84.38 111	85.42 235	70.60 84	88.71 117	88.75 203	72.30 156	78.83 139	89.14 154	44.44 311	92.18 210	78.50 100	79.33 233	90.35 179

v144192		79.47 159	78.37 165	82.78 178	83.35 267	63.96 211	86.96 169	90.36 149	69.99 194	77.50 165	85.67 248	60.66 182	93.77 147	74.27 138	76.58 260	90.62 168

CP-MVSNet		78.22 190	78.34 166	77.84 269	87.83 189	54.54 325	87.94 145	91.17 129	77.65 39	73.48 241	88.49 172	62.24 154	88.43 285	62.19 241	74.07 295	90.55 172

Baseline_NR-MVSNet		78.15 194	78.33 167	77.61 274	85.79 228	56.21 316	86.78 177	85.76 253	73.60 136	77.93 159	87.57 195	65.02 118	88.99 276	67.14 205	75.33 283	87.63 261

OpenMVS	72.83 10	79.77 153	78.33 167	84.09 123	85.17 238	69.91 94	90.57 62	90.97 133	66.70 243	72.17 256	91.91 84	54.70 223	93.96 133	61.81 247	90.95 96	88.41 249

UniMVSNet_ETH3D		79.10 171	78.24 169	81.70 198	86.85 215	60.24 268	87.28 162	88.79 198	74.25 123	76.84 179	90.53 120	49.48 277	91.56 228	67.98 194	82.15 200	93.29 81

V42		79.38 165	78.24 169	82.83 172	81.10 314	65.50 183	85.55 211	89.82 163	71.57 168	78.21 152	86.12 240	60.66 182	93.18 175	75.64 128	75.46 279	89.81 208

PS-CasMVS		78.01 199	78.09 171	77.77 271	87.71 194	54.39 327	88.02 141	91.22 126	77.50 47	73.26 243	88.64 167	60.73 179	88.41 286	61.88 245	73.88 299	90.53 173

v1921920		79.22 167	78.03 172	82.80 175	83.30 269	63.94 212	86.80 175	90.33 150	69.91 197	77.48 166	85.53 251	58.44 196	93.75 149	73.60 143	76.85 257	90.71 166

jajsoiax		79.29 166	77.96 173	83.27 150	84.68 247	66.57 163	89.25 94	90.16 155	69.20 213	75.46 212	89.49 145	45.75 305	93.13 178	76.84 119	80.80 214	90.11 189

TAPA-MVS	73.13 9	79.15 169	77.94 174	82.79 177	89.59 121	62.99 237	88.16 139	91.51 117	65.77 256	77.14 177	91.09 107	60.91 178	93.21 170	50.26 319	87.05 142	92.17 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		79.40 163	77.91 175	83.90 136	88.10 181	63.84 213	88.37 130	84.05 273	71.45 170	76.78 182	89.12 156	49.93 274	94.89 103	70.18 174	83.18 188	92.96 97

c3_l		78.75 178	77.91 175	81.26 210	82.89 283	61.56 253	84.09 246	89.13 187	69.97 195	75.56 208	84.29 270	66.36 103	92.09 213	73.47 147	75.48 277	90.12 188

MVSTER		79.01 173	77.88 177	82.38 186	83.07 276	64.80 195	84.08 247	88.95 195	69.01 220	78.69 140	87.17 209	54.70 223	92.43 198	74.69 135	80.57 218	89.89 204

X-MVStestdata		80.37 143	77.83 178	88.00 15	94.42 22	73.33 20	92.78 17	92.99 49	79.14 22	83.67 83	12.47 368	67.45 93	96.60 34	83.06 60	94.50 56	94.07 42

v148		78.72 179	77.80 179	81.47 203	82.73 286	61.96 248	86.30 191	88.08 215	73.26 144	76.18 197	85.47 253	62.46 149	92.36 202	71.92 160	73.82 300	90.09 191

v1240		78.99 174	77.78 180	82.64 181	83.21 271	63.54 220	86.62 182	90.30 152	69.74 203	77.33 169	85.68 247	57.04 210	93.76 148	73.13 152	76.92 253	90.62 168

mvs_tets		79.13 170	77.77 181	83.22 155	84.70 246	66.37 165	89.17 95	90.19 154	69.38 207	75.40 215	89.46 148	44.17 313	93.15 176	76.78 120	80.70 216	90.14 186

miper_ehance_all_eth		78.59 183	77.76 182	81.08 216	82.66 288	61.56 253	83.65 251	89.15 185	68.87 223	75.55 209	83.79 278	66.49 101	92.03 214	73.25 150	76.39 264	89.64 212

thisisatest0530		79.40 163	77.76 182	84.31 116	87.69 196	65.10 192	87.36 158	84.26 271	70.04 193	77.42 167	88.26 180	49.94 272	94.79 108	70.20 173	84.70 169	93.03 92

CDS-MVSNet		79.07 172	77.70 184	83.17 157	87.60 197	68.23 134	84.40 240	86.20 248	67.49 237	76.36 192	86.54 231	61.54 163	90.79 249	61.86 246	87.33 138	90.49 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20231211		78.97 175	77.69 185	82.81 174	90.54 103	64.29 206	90.11 76	91.51 117	65.01 266	76.16 200	88.13 187	50.56 265	93.03 185	69.68 181	77.56 247	91.11 152

PEN-MVS		77.73 204	77.69 185	77.84 269	87.07 213	53.91 330	87.91 147	91.18 128	77.56 44	73.14 245	88.82 163	61.23 172	89.17 273	59.95 260	72.37 310	90.43 176

AUN-MVS		79.21 168	77.60 187	84.05 126	88.71 163	67.61 145	85.84 204	87.26 233	69.08 216	77.23 173	88.14 186	53.20 236	93.47 162	75.50 132	73.45 303	91.06 153

v7n		78.97 175	77.58 188	83.14 158	83.45 266	65.51 182	88.32 131	91.21 127	73.69 134	72.41 253	86.32 237	57.93 198	93.81 144	69.18 185	75.65 273	90.11 189

TAMVS		78.89 177	77.51 189	83.03 164	87.80 190	67.79 142	84.72 227	85.05 260	67.63 234	76.75 183	87.70 191	62.25 153	90.82 248	58.53 275	87.13 141	90.49 174

RRT_test8_iter05		78.38 187	77.40 190	81.34 208	86.00 226	58.86 277	86.55 185	91.26 125	72.13 160	75.91 202	87.42 200	44.97 308	93.73 151	77.02 117	75.30 284	91.45 144

GBi-Net		78.40 185	77.40 190	81.40 205	87.60 197	63.01 234	88.39 127	89.28 177	71.63 165	75.34 217	87.28 202	54.80 219	91.11 239	62.72 235	79.57 227	90.09 191

test1		78.40 185	77.40 190	81.40 205	87.60 197	63.01 234	88.39 127	89.28 177	71.63 165	75.34 217	87.28 202	54.80 219	91.11 239	62.72 235	79.57 227	90.09 191

BH-w/o		78.21 191	77.33 193	80.84 220	88.81 157	65.13 191	84.87 224	87.85 222	69.75 201	74.52 234	84.74 266	61.34 169	93.11 179	58.24 278	85.84 161	84.27 316

FMVSNet2		78.20 192	77.21 194	81.20 212	87.60 197	62.89 238	87.47 156	89.02 190	71.63 165	75.29 221	87.28 202	54.80 219	91.10 242	62.38 239	79.38 231	89.61 213

anonymousdsp		78.60 182	77.15 195	82.98 167	80.51 320	67.08 154	87.24 163	89.53 171	65.66 258	75.16 223	87.19 208	52.52 237	92.25 207	77.17 115	79.34 232	89.61 213

HY-MVS	69.67 12	77.95 200	77.15 195	80.36 228	87.57 201	60.21 269	83.37 258	87.78 223	66.11 251	75.37 216	87.06 213	63.27 134	90.48 254	61.38 251	82.43 198	90.40 178

cl22		78.07 196	77.01 197	81.23 211	82.37 295	61.83 250	83.55 255	87.98 217	68.96 221	75.06 227	83.87 274	61.40 168	91.88 221	73.53 144	76.39 264	89.98 200

Anonymous202405211		78.25 189	77.01 197	81.99 193	91.03 93	60.67 262	84.77 226	83.90 275	70.65 185	80.00 126	91.20 104	41.08 330	91.43 232	65.21 219	85.26 163	93.85 54

MVS		78.19 193	76.99 199	81.78 196	85.66 230	66.99 155	84.66 228	90.47 144	55.08 343	72.02 258	85.27 256	63.83 128	94.11 131	66.10 212	89.80 111	84.24 317

LCM-MVSNet-Re		77.05 216	76.94 200	77.36 277	87.20 210	51.60 342	80.06 290	80.46 316	75.20 102	67.69 297	86.72 218	62.48 148	88.98 277	63.44 230	89.25 116	91.51 139

miper_enhance_ethall		77.87 203	76.86 201	80.92 219	81.65 302	61.38 255	82.68 265	88.98 192	65.52 260	75.47 210	82.30 296	65.76 113	92.00 216	72.95 153	76.39 264	89.39 217

FMVSNet3		77.88 202	76.85 202	80.97 218	86.84 216	62.36 241	86.52 186	88.77 199	71.13 173	75.34 217	86.66 225	54.07 229	91.10 242	62.72 235	79.57 227	89.45 216

DTE-MVSNet		76.99 217	76.80 203	77.54 276	86.24 223	53.06 338	87.52 154	90.66 139	77.08 59	72.50 251	88.67 166	60.48 185	89.52 267	57.33 286	70.74 321	90.05 196

CNLPA		78.08 195	76.79 204	81.97 194	90.40 106	71.07 69	87.59 153	84.55 265	66.03 254	72.38 254	89.64 140	57.56 203	86.04 304	59.61 263	83.35 185	88.79 239

cl____		77.72 205	76.76 205	80.58 224	82.49 292	60.48 265	83.09 261	87.87 220	69.22 211	74.38 236	85.22 258	62.10 156	91.53 229	71.09 165	75.41 280	89.73 211

DIV-MVS_self_test		77.72 205	76.76 205	80.58 224	82.48 293	60.48 265	83.09 261	87.86 221	69.22 211	74.38 236	85.24 257	62.10 156	91.53 229	71.09 165	75.40 281	89.74 210

baseline1		76.98 218	76.75 207	77.66 272	88.13 179	55.66 321	85.12 219	81.89 301	73.04 147	76.79 181	88.90 160	62.43 150	87.78 293	63.30 232	71.18 319	89.55 215

eth_miper_zixun_eth		77.92 201	76.69 208	81.61 201	83.00 279	61.98 247	83.15 260	89.20 183	69.52 205	74.86 231	84.35 269	61.76 159	92.56 195	71.50 163	72.89 308	90.28 182

pm-mvs1		77.25 214	76.68 209	78.93 254	84.22 253	58.62 280	86.41 187	88.36 210	71.37 171	73.31 242	88.01 188	61.22 173	89.15 274	64.24 226	73.01 307	89.03 227

ET-MVSNet_ETH3D		78.63 181	76.63 210	84.64 103	86.73 219	69.47 105	85.01 221	84.61 264	69.54 204	66.51 313	86.59 227	50.16 269	91.75 223	76.26 122	84.24 174	92.69 104

Fast-Effi-MVS+-dtu		78.02 198	76.49 211	82.62 182	83.16 275	66.96 159	86.94 170	87.45 230	72.45 151	71.49 263	84.17 271	54.79 222	91.58 227	67.61 197	80.31 221	89.30 219

1112_ss		77.40 212	76.43 212	80.32 230	89.11 150	60.41 267	83.65 251	87.72 224	62.13 298	73.05 246	86.72 218	62.58 147	89.97 260	62.11 244	80.80 214	90.59 171

PAPM		77.68 207	76.40 213	81.51 202	87.29 209	61.85 249	83.78 249	89.59 170	64.74 268	71.23 264	88.70 164	62.59 146	93.66 153	52.66 306	87.03 143	89.01 228

PLC	70.83 11	78.05 197	76.37 214	83.08 161	91.88 85	67.80 141	88.19 137	89.46 173	64.33 274	69.87 282	88.38 175	53.66 232	93.58 154	58.86 271	82.73 194	87.86 257
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 210	76.18 215	81.20 212	88.24 177	63.24 229	84.61 232	86.40 245	67.55 236	77.81 160	86.48 233	54.10 228	93.15 176	57.75 282	82.72 195	87.20 273

FMVSNet1		77.44 210	76.12 216	81.40 205	86.81 217	63.01 234	88.39 127	89.28 177	70.49 187	74.39 235	87.28 202	49.06 283	91.11 239	60.91 254	78.52 237	90.09 191

CHOSEN 1792x2688		77.63 208	75.69 217	83.44 143	89.98 115	68.58 128	78.70 305	87.50 228	56.38 338	75.80 206	86.84 214	58.67 194	91.40 233	61.58 249	85.75 162	90.34 180

WTY-MVS		75.65 237	75.68 218	75.57 293	86.40 222	56.82 304	77.92 313	82.40 297	65.10 263	76.18 197	87.72 190	63.13 141	80.90 332	60.31 258	81.96 202	89.00 230

XXY-MVS		75.41 241	75.56 219	74.96 299	83.59 264	57.82 292	80.59 286	83.87 276	66.54 248	74.93 230	88.31 177	63.24 135	80.09 335	62.16 242	76.85 257	86.97 280

thres100view900		76.50 224	75.55 220	79.33 248	89.52 124	56.99 302	85.83 205	83.23 287	73.94 129	76.32 193	87.12 210	51.89 251	91.95 217	48.33 327	83.75 179	89.07 221

bset_n11_16_dypcd		77.12 215	75.47 221	82.06 190	81.12 313	65.99 171	81.37 280	83.20 289	69.94 196	76.09 201	83.38 284	47.75 289	92.26 206	78.51 99	77.91 243	87.95 253

thres600view7		76.50 224	75.44 222	79.68 242	89.40 129	57.16 299	85.53 213	83.23 287	73.79 133	76.26 194	87.09 211	51.89 251	91.89 220	48.05 332	83.72 182	90.00 197

Test_1112_low_res		76.40 228	75.44 222	79.27 249	89.28 139	58.09 284	81.69 275	87.07 236	59.53 317	72.48 252	86.67 224	61.30 170	89.33 270	60.81 256	80.15 223	90.41 177

HyFIR lowres test		77.53 209	75.40 224	83.94 135	89.59 121	66.62 161	80.36 287	88.64 206	56.29 339	76.45 188	85.17 259	57.64 202	93.28 168	61.34 252	83.10 190	91.91 129

thisisatest0515		77.33 213	75.38 225	83.18 156	85.27 237	63.80 214	82.11 271	83.27 286	65.06 264	75.91 202	83.84 276	49.54 276	94.27 120	67.24 203	86.19 155	91.48 142

tfpn200view9		76.42 227	75.37 226	79.55 247	89.13 146	57.65 294	85.17 216	83.60 278	73.41 142	76.45 188	86.39 235	52.12 244	91.95 217	48.33 327	83.75 179	89.07 221

thres400		76.50 224	75.37 226	79.86 238	89.13 146	57.65 294	85.17 216	83.60 278	73.41 142	76.45 188	86.39 235	52.12 244	91.95 217	48.33 327	83.75 179	90.00 197

1314		76.53 223	75.30 228	80.21 232	83.93 259	62.32 243	84.66 228	88.81 197	60.23 310	70.16 276	84.07 273	55.30 217	90.73 251	67.37 200	83.21 187	87.59 264

GA-MVS		76.87 220	75.17 229	81.97 194	82.75 285	62.58 239	81.44 279	86.35 247	72.16 159	74.74 232	82.89 288	46.20 300	92.02 215	68.85 190	81.09 210	91.30 147

EPNet_dtu		75.46 239	74.86 230	77.23 281	82.57 290	54.60 324	86.89 172	83.09 291	71.64 164	66.25 315	85.86 244	55.99 214	88.04 290	54.92 297	86.55 150	89.05 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 219	74.82 231	83.37 147	90.45 104	67.36 151	89.15 99	86.94 238	61.87 300	69.52 285	90.61 118	51.71 254	94.53 113	46.38 339	86.71 148	88.21 251

cascas		76.72 222	74.64 232	82.99 166	85.78 229	65.88 175	82.33 269	89.21 182	60.85 306	72.74 248	81.02 307	47.28 292	93.75 149	67.48 199	85.02 164	89.34 218

DP-MVS		76.78 221	74.57 233	83.42 144	93.29 54	69.46 107	88.55 122	83.70 277	63.98 280	70.20 273	88.89 161	54.01 230	94.80 107	46.66 336	81.88 204	86.01 298

TransMVSNet (Re)		75.39 242	74.56 234	77.86 268	85.50 234	57.10 301	86.78 177	86.09 251	72.17 158	71.53 262	87.34 201	63.01 142	89.31 271	56.84 290	61.83 343	87.17 274

LTVRE_ROB	69.57 13	76.25 230	74.54 235	81.41 204	88.60 166	64.38 205	79.24 298	89.12 188	70.76 182	69.79 284	87.86 189	49.09 282	93.20 172	56.21 294	80.16 222	86.65 287
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
thres200		75.55 238	74.47 236	78.82 255	87.78 193	57.85 291	83.07 263	83.51 281	72.44 153	75.84 205	84.42 268	52.08 246	91.75 223	47.41 334	83.64 183	86.86 282

MVP-Stereo		76.12 231	74.46 237	81.13 215	85.37 236	69.79 97	84.42 239	87.95 218	65.03 265	67.46 299	85.33 255	53.28 235	91.73 225	58.01 280	83.27 186	81.85 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		76.38 229	74.33 238	82.50 184	89.28 139	66.95 160	88.41 126	89.03 189	64.05 278	66.83 307	88.61 168	46.78 295	92.89 187	57.48 283	78.55 236	87.67 260

XVG-ACMP-BASELINE		76.11 232	74.27 239	81.62 199	83.20 272	64.67 197	83.60 254	89.75 166	69.75 201	71.85 259	87.09 211	32.78 352	92.11 212	69.99 177	80.43 220	88.09 252

ACMH+	68.96 14	76.01 233	74.01 240	82.03 192	88.60 166	65.31 188	88.86 107	87.55 226	70.25 191	67.75 296	87.47 199	41.27 328	93.19 174	58.37 276	75.94 270	87.60 262

ACMH	67.68 16	75.89 234	73.93 241	81.77 197	88.71 163	66.61 162	88.62 119	89.01 191	69.81 198	66.78 308	86.70 223	41.95 327	91.51 231	55.64 295	78.14 242	87.17 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 243	73.90 242	79.27 249	82.65 289	58.27 283	80.80 281	82.73 295	61.57 301	75.33 220	83.13 286	55.52 215	91.07 245	64.98 222	78.34 241	88.45 247

IterMVS-SCA-FT		75.43 240	73.87 243	80.11 234	82.69 287	64.85 194	81.57 277	83.47 283	69.16 214	70.49 270	84.15 272	51.95 249	88.15 288	69.23 184	72.14 313	87.34 269

baseline2		75.70 236	73.83 244	81.30 209	83.26 270	61.79 251	82.57 267	80.65 312	66.81 240	66.88 305	83.42 283	57.86 200	92.19 209	63.47 229	79.57 227	89.91 202

sss		73.60 255	73.64 245	73.51 310	82.80 284	55.01 323	76.12 319	81.69 304	62.47 295	74.68 233	85.85 245	57.32 206	78.11 342	60.86 255	80.93 211	87.39 267

pmmvs6		74.69 245	73.39 246	78.61 258	81.38 308	57.48 297	86.64 181	87.95 218	64.99 267	70.18 274	86.61 226	50.43 267	89.52 267	62.12 243	70.18 323	88.83 237

IB-MVS	68.01 15	75.85 235	73.36 247	83.31 148	84.76 245	66.03 169	83.38 257	85.06 259	70.21 192	69.40 286	81.05 306	45.76 304	94.66 111	65.10 221	75.49 276	89.25 220
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
D2MVS		74.82 244	73.21 248	79.64 244	79.81 327	62.56 240	80.34 288	87.35 231	64.37 273	68.86 289	82.66 292	46.37 297	90.10 259	67.91 195	81.24 209	86.25 291

tfpnnormal		74.39 246	73.16 249	78.08 266	86.10 225	58.05 285	84.65 231	87.53 227	70.32 189	71.22 265	85.63 249	54.97 218	89.86 261	43.03 348	75.02 288	86.32 290

miper_lstm_enhance		74.11 250	73.11 250	77.13 282	80.11 323	59.62 272	72.23 335	86.92 239	66.76 242	70.40 271	82.92 287	56.93 211	82.92 325	69.06 187	72.63 309	88.87 235

IterMVS		74.29 247	72.94 251	78.35 263	81.53 305	63.49 222	81.58 276	82.49 296	68.06 233	69.99 279	83.69 280	51.66 255	85.54 307	65.85 215	71.64 316	86.01 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		73.83 253	72.67 252	77.30 279	83.87 260	66.02 170	81.82 272	84.66 263	61.37 304	68.61 292	82.82 290	47.29 291	88.21 287	59.27 265	84.32 173	77.68 350

CVMVSNet		72.99 264	72.58 253	74.25 306	84.28 251	50.85 347	86.41 187	83.45 284	44.56 354	73.23 244	87.54 197	49.38 278	85.70 306	65.90 214	78.44 239	86.19 293

test-LLR		72.94 265	72.43 254	74.48 303	81.35 309	58.04 286	78.38 306	77.46 333	66.66 244	69.95 280	79.00 325	48.06 287	79.24 336	66.13 210	84.83 166	86.15 294

OurMVSNet-221017-0		74.26 248	72.42 255	79.80 240	83.76 262	59.59 273	85.92 201	86.64 241	66.39 249	66.96 304	87.58 194	39.46 334	91.60 226	65.76 216	69.27 325	88.22 250

SCA		74.22 249	72.33 256	79.91 237	84.05 257	62.17 245	79.96 292	79.29 326	66.30 250	72.38 254	80.13 316	51.95 249	88.60 283	59.25 266	77.67 246	88.96 232

tpmrst		72.39 268	72.13 257	73.18 314	80.54 319	49.91 350	79.91 293	79.08 327	63.11 285	71.69 261	79.95 318	55.32 216	82.77 326	65.66 217	73.89 298	86.87 281

pmmvs4		74.03 252	71.91 258	80.39 227	81.96 299	68.32 131	81.45 278	82.14 299	59.32 318	69.87 282	85.13 260	52.40 240	88.13 289	60.21 259	74.74 291	84.73 313

DWT-MVSNet_test		73.70 254	71.86 259	79.21 251	82.91 282	58.94 276	82.34 268	82.17 298	65.21 261	71.05 267	78.31 330	44.21 312	90.17 258	63.29 233	77.28 248	88.53 246

EG-PatchMatch MVS		74.04 251	71.82 260	80.71 223	84.92 244	67.42 148	85.86 203	88.08 215	66.04 253	64.22 327	83.85 275	35.10 348	92.56 195	57.44 284	80.83 213	82.16 335

tpm		72.37 270	71.71 261	74.35 305	82.19 297	52.00 339	79.22 299	77.29 335	64.56 270	72.95 247	83.68 281	51.35 256	83.26 324	58.33 277	75.80 271	87.81 258

CL-MVSNet_self_test		72.37 270	71.46 262	75.09 298	79.49 333	53.53 332	80.76 283	85.01 261	69.12 215	70.51 269	82.05 300	57.92 199	84.13 317	52.27 307	66.00 336	87.60 262

tpm2		73.26 260	71.46 262	78.63 257	83.34 268	56.71 307	80.65 285	80.40 317	56.63 337	73.55 240	82.02 301	51.80 253	91.24 237	56.35 293	78.42 240	87.95 253

RPSCF		73.23 261	71.46 262	78.54 260	82.50 291	59.85 270	82.18 270	82.84 294	58.96 321	71.15 266	89.41 152	45.48 307	84.77 314	58.82 272	71.83 315	91.02 157

PatchmatchNet		73.12 262	71.33 265	78.49 262	83.18 273	60.85 260	79.63 294	78.57 328	64.13 275	71.73 260	79.81 321	51.20 258	85.97 305	57.40 285	76.36 267	88.66 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 257	71.27 266	79.67 243	81.32 311	65.19 189	75.92 321	80.30 318	59.92 313	72.73 249	81.19 304	52.50 238	86.69 299	59.84 261	77.71 244	87.11 278

SixPastTwentyTwo		73.37 257	71.26 267	79.70 241	85.08 243	57.89 290	85.57 207	83.56 280	71.03 176	65.66 317	85.88 243	42.10 325	92.57 194	59.11 268	63.34 342	88.65 243

MSDG		73.36 259	70.99 268	80.49 226	84.51 249	65.80 176	80.71 284	86.13 250	65.70 257	65.46 318	83.74 279	44.60 309	90.91 247	51.13 312	76.89 255	84.74 312

PatchMatch-RL		72.38 269	70.90 269	76.80 285	88.60 166	67.38 150	79.53 295	76.17 340	62.75 292	69.36 287	82.00 302	45.51 306	84.89 313	53.62 302	80.58 217	78.12 349

MVS_0304		72.48 267	70.89 270	77.24 280	82.20 296	59.68 271	84.11 245	83.49 282	67.10 239	66.87 306	80.59 312	35.00 349	87.40 295	59.07 269	79.58 226	84.63 314

PVSNet	64.34 18	72.08 272	70.87 271	75.69 291	86.21 224	56.44 311	74.37 331	80.73 311	62.06 299	70.17 275	82.23 298	42.86 319	83.31 323	54.77 298	84.45 172	87.32 270

RPMNet		73.51 256	70.49 272	82.58 183	81.32 311	65.19 189	75.92 321	92.27 83	57.60 331	72.73 249	76.45 341	52.30 241	95.43 75	48.14 331	77.71 244	87.11 278

test_0402		72.79 266	70.44 273	79.84 239	88.13 179	65.99 171	85.93 200	84.29 269	65.57 259	67.40 301	85.49 252	46.92 294	92.61 193	35.88 357	74.38 294	80.94 341

COLMAP_ROB	66.92 17	73.01 263	70.41 274	80.81 221	87.13 212	65.63 180	88.30 132	84.19 272	62.96 288	63.80 331	87.69 192	38.04 340	92.56 195	46.66 336	74.91 289	84.24 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 274	70.39 275	74.48 303	81.35 309	58.04 286	78.38 306	77.46 333	60.32 309	69.95 280	79.00 325	36.08 346	79.24 336	66.13 210	84.83 166	86.15 294

pmmvs5		71.55 273	70.20 276	75.61 292	77.83 339	56.39 312	81.74 274	80.89 308	57.76 329	67.46 299	84.49 267	49.26 281	85.32 310	57.08 288	75.29 285	85.11 309

MDTV_nov1_ep13			69.97 277		83.18 273	53.48 333	77.10 317	80.18 321	60.45 307	69.33 288	80.44 313	48.89 285	86.90 298	51.60 310	78.51 238

MIMVSNet		70.69 279	69.30 278	74.88 300	84.52 248	56.35 314	75.87 323	79.42 325	64.59 269	67.76 295	82.41 294	41.10 329	81.54 330	46.64 338	81.34 207	86.75 285

tpmvs		71.09 276	69.29 279	76.49 286	82.04 298	56.04 317	78.92 303	81.37 307	64.05 278	67.18 303	78.28 331	49.74 275	89.77 262	49.67 322	72.37 310	83.67 322

Patchmtry		70.74 278	69.16 280	75.49 295	80.72 316	54.07 329	74.94 330	80.30 318	58.34 325	70.01 277	81.19 304	52.50 238	86.54 300	53.37 303	71.09 320	85.87 301

TESTMET0.1,1		69.89 288	69.00 281	72.55 315	79.27 336	56.85 303	78.38 306	74.71 346	57.64 330	68.09 294	77.19 338	37.75 341	76.70 347	63.92 227	84.09 175	84.10 320

PMMVS		69.34 290	68.67 282	71.35 322	75.67 347	62.03 246	75.17 325	73.46 348	50.00 352	68.68 290	79.05 323	52.07 247	78.13 341	61.16 253	82.77 193	73.90 353

K. test v3		71.19 275	68.51 283	79.21 251	83.04 278	57.78 293	84.35 241	76.91 338	72.90 150	62.99 334	82.86 289	39.27 335	91.09 244	61.65 248	52.66 355	88.75 240

USDC		70.33 283	68.37 284	76.21 288	80.60 318	56.23 315	79.19 300	86.49 243	60.89 305	61.29 338	85.47 253	31.78 355	89.47 269	53.37 303	76.21 268	82.94 332

tpm cat1		70.57 280	68.31 285	77.35 278	82.41 294	57.95 289	78.08 310	80.22 320	52.04 349	68.54 293	77.66 336	52.00 248	87.84 292	51.77 308	72.07 314	86.25 291

OpenMVS_ROB	64.09 19	70.56 281	68.19 286	77.65 273	80.26 321	59.41 275	85.01 221	82.96 293	58.76 323	65.43 319	82.33 295	37.63 342	91.23 238	45.34 344	76.03 269	82.32 333

EPMVS		69.02 292	68.16 287	71.59 318	79.61 331	49.80 352	77.40 315	66.93 360	62.82 291	70.01 277	79.05 323	45.79 303	77.86 344	56.58 291	75.26 286	87.13 277

CMPMVS	51.72 21	70.19 285	68.16 287	76.28 287	73.15 359	57.55 296	79.47 296	83.92 274	48.02 353	56.48 352	84.81 264	43.13 317	86.42 302	62.67 238	81.81 205	84.89 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 277	68.09 289	79.58 245	85.15 239	63.62 216	84.58 233	79.83 322	62.31 296	60.32 341	86.73 216	32.02 353	88.96 279	50.28 317	71.57 317	86.15 294

gg-mvs-nofinetune		69.95 287	67.96 290	75.94 289	83.07 276	54.51 326	77.23 316	70.29 353	63.11 285	70.32 272	62.33 354	43.62 315	88.69 282	53.88 301	87.76 131	84.62 315

FMVSNet5		69.50 289	67.96 290	74.15 307	82.97 281	55.35 322	80.01 291	82.12 300	62.56 294	63.02 332	81.53 303	36.92 343	81.92 328	48.42 326	74.06 296	85.17 308

PatchT		68.46 298	67.85 292	70.29 326	80.70 317	43.93 360	72.47 334	74.88 343	60.15 311	70.55 268	76.57 340	49.94 272	81.59 329	50.58 313	74.83 290	85.34 304

pmmvs-eth3d		70.50 282	67.83 293	78.52 261	77.37 342	66.18 168	81.82 272	81.51 305	58.90 322	63.90 330	80.42 314	42.69 320	86.28 303	58.56 274	65.30 338	83.11 328

Anonymous20231206		68.60 295	67.80 294	71.02 324	80.23 322	50.75 348	78.30 309	80.47 315	56.79 336	66.11 316	82.63 293	46.35 298	78.95 338	43.62 347	75.70 272	83.36 325

Patchmatch-RL test		70.24 284	67.78 295	77.61 274	77.43 341	59.57 274	71.16 337	70.33 352	62.94 289	68.65 291	72.77 348	50.62 264	85.49 308	69.58 182	66.58 334	87.77 259

test0.0.03 1		68.00 299	67.69 296	68.90 331	77.55 340	47.43 354	75.70 324	72.95 350	66.66 244	66.56 309	82.29 297	48.06 287	75.87 351	44.97 345	74.51 293	83.41 324

EU-MVSNet		68.53 297	67.61 297	71.31 323	78.51 338	47.01 356	84.47 234	84.27 270	42.27 355	66.44 314	84.79 265	40.44 332	83.76 319	58.76 273	68.54 330	83.17 326

KD-MVS_self_test		68.81 293	67.59 298	72.46 316	74.29 353	45.45 357	77.93 312	87.00 237	63.12 284	63.99 329	78.99 327	42.32 322	84.77 314	56.55 292	64.09 341	87.16 276

ppachtmachnet_test		70.04 286	67.34 299	78.14 265	79.80 328	61.13 256	79.19 300	80.59 313	59.16 320	65.27 320	79.29 322	46.75 296	87.29 296	49.33 323	66.72 332	86.00 300

Anonymous20240521		68.80 294	67.22 300	73.55 309	74.33 352	54.11 328	83.18 259	85.61 254	58.15 326	61.68 337	80.94 309	30.71 356	81.27 331	57.00 289	73.34 306	85.28 305

our_test_3		69.14 291	67.00 301	75.57 293	79.80 328	58.80 278	77.96 311	77.81 331	59.55 316	62.90 335	78.25 332	47.43 290	83.97 318	51.71 309	67.58 331	83.93 321

test20.03		67.45 301	66.95 302	68.94 330	75.48 349	44.84 359	77.50 314	77.67 332	66.66 244	63.01 333	83.80 277	47.02 293	78.40 340	42.53 350	68.86 329	83.58 323

MIMVSNet1		68.58 296	66.78 303	73.98 308	80.07 324	51.82 340	80.77 282	84.37 266	64.40 272	59.75 344	82.16 299	36.47 344	83.63 321	42.73 349	70.33 322	86.48 289

testgi		66.67 306	66.53 304	67.08 336	75.62 348	41.69 363	75.93 320	76.50 339	66.11 251	65.20 323	86.59 227	35.72 347	74.71 355	43.71 346	73.38 305	84.84 311

UnsupCasMVSNet_eth		67.33 302	65.99 305	71.37 320	73.48 356	51.47 344	75.16 326	85.19 258	65.20 262	60.78 340	80.93 311	42.35 321	77.20 346	57.12 287	53.69 354	85.44 303

dp		66.80 304	65.43 306	70.90 325	79.74 330	48.82 353	75.12 328	74.77 344	59.61 315	64.08 328	77.23 337	42.89 318	80.72 333	48.86 325	66.58 334	83.16 327

TinyColmap		67.30 303	64.81 307	74.76 302	81.92 300	56.68 308	80.29 289	81.49 306	60.33 308	56.27 353	83.22 285	24.77 359	87.66 294	45.52 342	69.47 324	79.95 345

CHOSEN 280x420		66.51 307	64.71 308	71.90 317	81.45 306	63.52 221	57.98 358	68.95 359	53.57 345	62.59 336	76.70 339	46.22 299	75.29 354	55.25 296	79.68 225	76.88 352

TDRefinement		67.49 300	64.34 309	76.92 283	73.47 357	61.07 257	84.86 225	82.98 292	59.77 314	58.30 347	85.13 260	26.06 358	87.89 291	47.92 333	60.59 347	81.81 337

PM-MVS		66.41 308	64.14 310	73.20 313	73.92 354	56.45 310	78.97 302	64.96 364	63.88 282	64.72 324	80.24 315	19.84 363	83.44 322	66.24 209	64.52 340	79.71 346

KD-MVS_2432*1600		66.22 310	63.89 311	73.21 311	75.47 350	53.42 334	70.76 340	84.35 267	64.10 276	66.52 311	78.52 328	34.55 350	84.98 311	50.40 315	50.33 358	81.23 339

miper_refine_blended		66.22 310	63.89 311	73.21 311	75.47 350	53.42 334	70.76 340	84.35 267	64.10 276	66.52 311	78.52 328	34.55 350	84.98 311	50.40 315	50.33 358	81.23 339

MDA-MVSNet-bldmvs		66.68 305	63.66 313	75.75 290	79.28 335	60.56 264	73.92 332	78.35 329	64.43 271	50.13 358	79.87 320	44.02 314	83.67 320	46.10 340	56.86 350	83.03 330

ADS-MVSNet2		66.20 312	63.33 314	74.82 301	79.92 325	58.75 279	67.55 350	75.19 342	53.37 346	65.25 321	75.86 342	42.32 322	80.53 334	41.57 351	68.91 327	85.18 306

Patchmatch-test		64.82 315	63.24 315	69.57 328	79.42 334	49.82 351	63.49 356	69.05 358	51.98 350	59.95 343	80.13 316	50.91 260	70.98 360	40.66 353	73.57 301	87.90 256

MDA-MVSNet_test_wron		65.03 313	62.92 316	71.37 320	75.93 345	56.73 305	69.09 348	74.73 345	57.28 334	54.03 355	77.89 333	45.88 301	74.39 357	49.89 321	61.55 344	82.99 331

YYNet1		65.03 313	62.91 317	71.38 319	75.85 346	56.60 309	69.12 347	74.66 347	57.28 334	54.12 354	77.87 334	45.85 302	74.48 356	49.95 320	61.52 345	83.05 329

ADS-MVSNet		64.36 316	62.88 318	68.78 333	79.92 325	47.17 355	67.55 350	71.18 351	53.37 346	65.25 321	75.86 342	42.32 322	73.99 358	41.57 351	68.91 327	85.18 306

JIA-IIPM		66.32 309	62.82 319	76.82 284	77.09 343	61.72 252	65.34 353	75.38 341	58.04 328	64.51 325	62.32 355	42.05 326	86.51 301	51.45 311	69.22 326	82.21 334

LF4IMVS		64.02 317	62.19 320	69.50 329	70.90 361	53.29 337	76.13 318	77.18 336	52.65 348	58.59 345	80.98 308	23.55 360	76.52 348	53.06 305	66.66 333	78.68 348

new-patchmatchnet		61.73 319	61.73 321	61.70 339	72.74 360	24.50 373	69.16 346	78.03 330	61.40 302	56.72 351	75.53 344	38.42 338	76.48 349	45.95 341	57.67 349	84.13 319

UnsupCasMVSNet_bld		63.70 318	61.53 322	70.21 327	73.69 355	51.39 345	72.82 333	81.89 301	55.63 341	57.81 348	71.80 350	38.67 337	78.61 339	49.26 324	52.21 356	80.63 342

PVSNet_0	57.27 20	61.67 320	59.27 323	68.85 332	79.61 331	57.44 298	68.01 349	73.44 349	55.93 340	58.54 346	70.41 351	44.58 310	77.55 345	47.01 335	35.91 361	71.55 355

MVS-HIRNet		59.14 321	57.67 324	63.57 338	81.65 302	43.50 361	71.73 336	65.06 363	39.59 359	51.43 357	57.73 358	38.34 339	82.58 327	39.53 354	73.95 297	64.62 358

DSMNet-mixed		57.77 323	56.90 325	60.38 340	67.70 363	35.61 366	69.18 345	53.97 368	32.30 364	57.49 349	79.88 319	40.39 333	68.57 362	38.78 355	72.37 310	76.97 351

pmmvs3		57.79 322	54.26 326	68.37 334	64.02 365	56.72 306	75.12 328	65.17 362	40.20 357	52.93 356	69.86 352	20.36 362	75.48 353	45.45 343	55.25 353	72.90 354

N_pmnet		52.79 326	53.26 327	51.40 345	78.99 337	7.68 376	69.52 343	3.89 376	51.63 351	57.01 350	74.98 345	40.83 331	65.96 363	37.78 356	64.67 339	80.56 344

FPMVS		53.68 325	51.64 328	59.81 341	65.08 364	51.03 346	69.48 344	69.58 356	41.46 356	40.67 360	72.32 349	16.46 366	70.00 361	24.24 363	65.42 337	58.40 360

new_pmnet		50.91 327	50.29 329	52.78 344	68.58 362	34.94 368	63.71 355	56.63 367	39.73 358	44.95 359	65.47 353	21.93 361	58.48 364	34.98 358	56.62 351	64.92 357

LCM-MVSNet		54.25 324	49.68 330	67.97 335	53.73 368	45.28 358	66.85 352	80.78 310	35.96 361	39.45 361	62.23 356	8.70 372	78.06 343	48.24 330	51.20 357	80.57 343

ANet_high		50.57 328	46.10 331	63.99 337	48.67 371	39.13 364	70.99 339	80.85 309	61.39 303	31.18 363	57.70 359	17.02 365	73.65 359	31.22 359	15.89 368	79.18 347

Gipuma		45.18 329	41.86 332	55.16 343	77.03 344	51.52 343	32.50 364	80.52 314	32.46 363	27.12 364	35.02 364	9.52 371	75.50 352	22.31 364	60.21 348	38.45 363
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 330	40.28 333	55.82 342	40.82 373	42.54 362	65.12 354	63.99 365	34.43 362	24.48 365	57.12 360	3.92 374	76.17 350	17.10 366	55.52 352	48.75 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 331	38.86 334	46.69 346	53.84 367	16.45 374	48.61 361	49.92 369	37.49 360	31.67 362	60.97 357	8.14 373	56.42 365	28.42 360	30.72 363	67.19 356

E-PMN		31.77 332	30.64 335	35.15 349	52.87 369	27.67 370	57.09 359	47.86 370	24.64 365	16.40 370	33.05 365	11.23 369	54.90 366	14.46 368	18.15 366	22.87 365

EMVS		30.81 334	29.65 336	34.27 350	50.96 370	25.95 372	56.58 360	46.80 371	24.01 366	15.53 371	30.68 366	12.47 368	54.43 367	12.81 369	17.05 367	22.43 366

test_method		31.52 333	29.28 337	38.23 348	27.03 375	6.50 377	20.94 366	62.21 366	4.05 370	22.35 368	52.50 361	13.33 367	47.58 368	27.04 362	34.04 362	60.62 359

cdsmvs_eth3d_5k		19.96 336	26.61 338	0.00 356	0.00 379	0.00 380	0.00 367	89.26 180	0.00 374	0.00 375	88.61 168	61.62 162	0.00 375	0.00 373	0.00 373	0.00 371

MVE	26.22 23	30.37 335	25.89 339	43.81 347	44.55 372	35.46 367	28.87 365	39.07 372	18.20 367	18.58 369	40.18 363	2.68 375	47.37 369	17.07 367	23.78 365	48.60 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 337	21.40 340	10.23 353	4.82 376	10.11 375	34.70 363	30.74 374	1.48 371	23.91 367	26.07 367	28.42 357	13.41 372	27.12 361	15.35 369	7.17 367

wuyk23d		16.82 338	15.94 341	19.46 352	58.74 366	31.45 369	39.22 362	3.74 377	6.84 369	6.04 372	2.70 371	1.27 376	24.29 371	10.54 370	14.40 370	2.63 368

ab-mvs-re		7.23 339	9.64 342	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	86.72 218	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

test123		6.12 340	8.11 343	0.14 354	0.06 378	0.09 378	71.05 338	0.03 379	0.04 373	0.25 374	1.30 373	0.05 377	0.03 374	0.21 372	0.01 372	0.29 369

testmvs		6.04 341	8.02 344	0.10 355	0.08 377	0.03 379	69.74 342	0.04 378	0.05 372	0.31 373	1.68 372	0.02 378	0.04 373	0.24 371	0.02 371	0.25 370

pcd_1.5k_mvsjas		5.26 342	7.02 345	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	63.15 138	0.00 375	0.00 373	0.00 373	0.00 371

test_blank		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

uanet_test		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

sosnet-low-res		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

sosnet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

uncertanet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

Regformer		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

uanet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 116	91.30 15

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 27

PC_three_1452									68.21 232	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 5

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 27

test_one_0601					95.07 7	71.46 61		94.14 7	78.27 35	92.05 11	95.74 6	80.83 11

eth-test2					0.00 379

eth-test					0.00 379

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 177	87.75 31	94.07 45	74.01 38	96.70 26	84.66 39	94.84 48

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 240	92.39 6				88.94 11	96.63 4	94.85 12

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 53	96.48 8	94.88 9

test_241102_TWO								94.06 12	77.24 51	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 36

test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 55	93.10 1	95.39 11	82.99 1	97.27 10

save fliter					93.80 44	72.35 45	90.47 66	91.17 129	74.31 120

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 19

test_0728_SECOND				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 37

test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 49	92.81 3	95.79 3	80.98 9

GSMVS																88.96 232

test_part2					95.06 8	72.65 33				91.80 13

sam_mvs1												51.32 257				88.96 232

sam_mvs												50.01 270

ambc				75.24 297	73.16 358	50.51 349	63.05 357	87.47 229		64.28 326	77.81 335	17.80 364	89.73 264	57.88 281	60.64 346	85.49 302

MTGPA								92.02 94

test_post1							78.90 304				5.43 370	48.81 286	85.44 309	59.25 266

test_post											5.46 369	50.36 268	84.24 316

patchmatchnet-post											74.00 346	51.12 259	88.60 283

GG-mvs-BLEND				75.38 296	81.59 304	55.80 319	79.32 297	69.63 355		67.19 302	73.67 347	43.24 316	88.90 281	50.41 314	84.50 170	81.45 338

MTMP							92.18 33	32.83 373

gm-plane-assit					81.40 307	53.83 331			62.72 293		80.94 309		92.39 200	63.40 231

test9_res														84.90 33	95.70 31	92.87 99

TEST9					93.26 56	72.96 26	88.75 113	91.89 103	68.44 230	85.00 54	93.10 65	74.36 33	95.41 77

test_8					93.13 58	72.57 36	88.68 118	91.84 106	68.69 226	84.87 60	93.10 65	74.43 30	95.16 89

agg_prior2														82.91 63	95.45 33	92.70 102

agg_prior					92.85 66	71.94 54		91.78 109		84.41 69			94.93 98

TestCases				79.58 245	85.15 239	63.62 216		79.83 322	62.31 296	60.32 341	86.73 216	32.02 353	88.96 279	50.28 317	71.57 317	86.15 294

test_prior4						72.60 35	89.01 102

test_prior2							88.85 108		75.41 96	84.91 56	93.54 54	74.28 34		83.31 55	95.86 22

test_prior				86.33 63	92.61 74	69.59 101		92.97 54					95.48 71			93.91 50

旧先验2							86.56 184		58.10 327	87.04 35			88.98 277	74.07 140

新几何2							86.29 192

新几何1				83.42 144	93.13 58	70.71 80		85.48 255	57.43 332	81.80 105	91.98 83	63.28 133	92.27 205	64.60 225	92.99 71	87.27 271

旧先验1					91.96 82	65.79 177		86.37 246			93.08 69	69.31 79			92.74 75	88.74 241

无先验							87.48 155	88.98 192	60.00 312				94.12 129	67.28 201		88.97 231

原ACMM2							86.86 173

原ACMM1				84.35 114	93.01 64	68.79 116		92.44 75	63.96 281	81.09 116	91.57 94	66.06 108	95.45 73	67.19 204	94.82 51	88.81 238

test222					91.50 88	68.26 133	84.16 243	83.20 289	54.63 344	79.74 127	91.63 92	58.97 193			91.42 90	86.77 284

testdata2													91.01 246	62.37 240

segment_acmp												73.08 43

testdata				79.97 236	90.90 96	64.21 207		84.71 262	59.27 319	85.40 48	92.91 70	62.02 158	89.08 275	68.95 188	91.37 91	86.63 288

testdata1							84.14 244		75.71 90

test12				86.80 55	92.63 73	70.70 81		91.79 108		82.71 96		71.67 55	96.16 47		94.50 56	93.54 74

plane_prior7					90.08 112	68.51 129

plane_prior6					89.84 118	68.70 124						60.42 186

plane_prior5								92.44 75					95.38 80	78.71 97	86.32 153	91.33 145

plane_prior4											91.00 112

plane_prior3						68.60 127			78.44 31	78.92 137

plane_prior2							91.25 49		79.12 24

plane_prior1					89.90 117

plane_prior						68.71 122	90.38 70		77.62 40						86.16 156

n2								0.00 380

nn								0.00 380

door-mid								69.98 354

lessismore_v0				78.97 253	81.01 315	57.15 300		65.99 361		61.16 339	82.82 290	39.12 336	91.34 235	59.67 262	46.92 360	88.43 248

LGP-MVS_train				84.50 106	89.23 142	68.76 118		91.94 101	75.37 98	76.64 186	91.51 95	54.29 226	94.91 100	78.44 101	83.78 177	89.83 206

test11								92.23 86

door								69.44 357

HQP5-MVS						66.98 156

HQP-NCC					89.33 132		89.17 95		76.41 74	77.23 173

ACMP_Plane					89.33 132		89.17 95		76.41 74	77.23 173

BP-MVS														77.47 111

HQP4-MVS										77.24 172			95.11 91			91.03 155

HQP3-MVS								92.19 89							85.99 159

HQP2-MVS												60.17 189

NP-MVS					89.62 120	68.32 131					90.24 123

MDTV_nov1_ep13_2view						37.79 365	75.16 326		55.10 342	66.53 310		49.34 279		53.98 300		87.94 255

ACMMP++_ref															81.95 203

ACMMP++															81.25 208

Test By Simon												64.33 123

ITE_SJBPF				78.22 264	81.77 301	60.57 263		83.30 285	69.25 210	67.54 298	87.20 207	36.33 345	87.28 297	54.34 299	74.62 292	86.80 283

DeepMVS_CX				27.40 351	40.17 374	26.90 371		24.59 375	17.44 368	23.95 366	48.61 362	9.77 370	26.48 370	18.06 365	24.47 364	28.83 364

High-resolution multi-view benchmark