Algorithm for Stereo Matching Based on Multi-Task Learning

Table 1. Performance evaluation of algorithm under each number of DRM iterations
View table
Table 1. Performance evaluation of algorithm under each number of DRM iterations
Number of iterations E_ep /pixel E_D1 /% R₁ /% R₃ /% R₅ /% f_run /(frame·s^-1)
1 1.02 4.12 11.67 4.63 3.38 15.84
2 0.85 3.56 10.06 4.13 2.82 12.22
3 0.83 3.52 9.85 4.02 2.80 8.64
4 0.82 3.46 9.62 3.87 2.81 6.22

Table 2. Performance evaluation of algorithm under different FEM settings
View table
Table 2. Performance evaluation of algorithm under different FEM settings
Number of iterations E_ep /pixel E_D1 /% R₁ /% R₃ /% R₅ /% f_run /(frame·s^-1)
ResBlock 0.87 3.75 10.48 4.32 2.93 13.37
SPP 0.86 3.62 10.00 4.22 2.88 12.64
SPPSA 0.85 3.56 10.06 4.13 2.82 12.22

Table 3. Performance evaluation of algorithm under different auxiliary task settings

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Table 3. Performance evaluation of algorithm under different auxiliary task settings

Number of iterations	E_ep /pixel	E_D1 /%	R₁ /%	R₃ /%	R₅ /%	f_run /(frame·s^-1)
None	1.05	4.48	12.36	4.94	3.52	14.65
Con	1.00	4.17	11.65	4.80	3.32	13.58
edge 1+Con	0.88	3.84	10.71	4.35	2.76	12.46
edge 2+Con	0.89	3.85	10.65	4.37	2.98	13.23
edge 1+edge 2+Con	0.85	3.56	10.06	4.13	2.82	12.22

Table 4. Performance evaluation under different training losses
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Table 4. Performance evaluation under different training losses
Training loss E_ep /pixel E_D1 /% R₁ /% R₃ /% R₅ /%
ap 1.65 8.27 27.96 8.65 6.02
ap+edge 1.48 7.51 25.75 7.89 5.36
ap+Con 1.32 6.56 21.72 6.86 4.75
ap+edge+Con 1.28 6.04 19.95 6.23 4.09
SL1+edge+Con 0.71 2.56 13.25 2.87 1.69

Table 5. Performance evaluation of each algorithm on KITTI2015 test dataset

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Table 5. Performance evaluation of each algorithm on KITTI2015 test dataset

Algorithm	E_D1(All) /%			E_D1(Noc) /%			Runtime /s
Algorithm	bg	fg	All area	bg	fg	All area	Runtime /s
M2S_CSPN^[18]	1.51	2.88	1.74	1.40	2.67	1.61	0.50
EdgeStereo-V2^[21]	1.84	3.30	2.08	1.69	2.94	1.89	0.32
WSMCnet^[17]	1.72	4.19	2.13	1.51	3.57	1.85	0.39
SegStereo^[19]	1.88	4.07	2.25	1.76	3.70	2.08	0.60
PSMNet^[12]	1.86	4.62	2.32	1.71	4.31	2.14	0.41
iResNet-i2^[9]	2.25	3.40	2.44	4.11	3.72	4.05	0.12
CRL^[8]	2.48	3.59	2.67	2.32	3.12	2.45	0.47
GC-net^[11]	2.21	6.16	2.87	2.02	5.58	2.61	0.90
MBFnet^[22]	2.59	4.80	2.96	2.22	4.14	2.54	0.05
SGM-Net^[6]	2.66	8.64	3.66	2.23	7.44	3.09	67.00
MC-CNN-arct^[2]	2.89	8.88	3.89	2.48	7.64	3.33	67.00
DispNetC^[7]	4.32	4.41	4.34	4.11	3.72	4.05	0.06
Proposed algorithm	2.07	4.01	2.39	1.89	3.69	2.19	0.09

Table 6. Performance evaluation on each dataset

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Table 6. Performance evaluation on each dataset

Dataset	K-val		MQ-val		ETH-val
Dataset	E_ep /pixel	E_D1 /%	E_ep /pixel	E_D1 /%	E_ep /pixel	ED1 /%
Pretrained	1.48	7.05	0.72	4.99	0.65	4.77
K-train	0.71	2.56	1.06	7.20	0.51	1.66
MQ-train	1.72	9.38	0.47	2.58	0.59	2.69
ETH-train	1.84	10.85	1.32	8.36	0.32	1.26
KME-train	0.78	3.01	0.49	2.61	0.36	1.33

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Yufeng Wang, Hongwei Wang, Yu Liu, Mingquan Yang, Jicheng Quan. Algorithm for Stereo Matching Based on Multi-Task Learning[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415010

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Paper Information

Category: Machine Vision

Received: Apr. 16, 2020

Accepted: Apr. 25, 2020

Published Online: Feb. 26, 2021

The Author Email: Wang Hongwei (jicheng_quan@126.com), Quan Jicheng (jicheng_quan@126.com)

DOI:10.3788/LOP202158.0415010

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Performance evaluation of algorithm under each number of DRM iterations

Table 1. Performance evaluation of algorithm under each number of DRM iterations

Table 2. Performance evaluation of algorithm under different FEM settings

Table 2. Performance evaluation of algorithm under different FEM settings

Table 3. Performance evaluation of algorithm under different auxiliary task settings

Table 3. Performance evaluation of algorithm under different auxiliary task settings

Table 4. Performance evaluation under different training losses

Table 4. Performance evaluation under different training losses

Table 5. Performance evaluation of each algorithm on KITTI2015 test dataset

Table 5. Performance evaluation of each algorithm on KITTI2015 test dataset

Table 6. Performance evaluation on each dataset

Table 6. Performance evaluation on each dataset