Real-Time Stereo Matching Algorithm with Hierarchical Refinement

Table 1. Operating rates of proposed method under different L and bframe·s^-1
View table
Table 1. Operating rates of proposed method under different L and bframe·s^-1
b L=2 L=3 L=4 L=5 L=6 L=7
1 1.6 8.5 20.8 26.1 24.0 23.7
2 1.6 8.5 20.8 24.2 24.4 22.0
3 1.6 8.3 20.7 22.7 23.0 19.9
4 1.6 8.1 19.7 21.8 21.2 20.5
5 1.6 8.1 19.5 21.0 20.6 20.5
6 1.6 8.0 18.5 20.4 20.6 19.5

Table 2. Performance evaluation of proposed method under different L(b=3)
View table
Table 2. Performance evaluation of proposed method under different L(b=3)
Parameter L=4 L=5 L=6 L=7
${E_{ep}}_{}$ /pixel 1.108 1.106 1.117 1.180
${E_{D 1}}_{}$ /% 4.922 4.826 4.772 4.999

Table 3. Performance evaluation of proposed method under different b (L=5)
View table
Table 3. Performance evaluation of proposed method under different b (L=5)
Parameter b=1 b=2 b=3 b=4 b=5 b=6
${E_{ep}}_{}$ /pixel 1.133 1.115 1.106 1.097 1.077 1.089
${E_{D 1}}_{}$ /% 4.870 4.859 4.826 4.668 4.636 4.724

Table 4. Performance evaluation of models with different versions (L=5,b=5)

View table

Table 4. Performance evaluation of models with different versions (L=5,b=5)

Prototype of DRM	FEM		${E_{ep}}_{}$ /pixel	${E_{D 1}}_{}$ /%	${f_{run}}_{}$ /(frame·s^-1)
Prototype of DRM	Fusion	SPP	${E_{ep}}_{}$ /pixel	${E_{D 1}}_{}$ /%	${f_{run}}_{}$ /(frame·s^-1)
Prototype 1	√	√	2.580	17.028	21.5
Prototype 2	√	√	1.517	6.753	19.1
Prototype 3	√	√	1.077	4.636	21.0
Prototype 3	√		1.105	4.707	21.9
Prototype 3		√	1.141	4.924	24.3

Table 5. Performance evaluation of proposed method under different α_F(L=5,b=5)
View table
Table 5. Performance evaluation of proposed method under different α_F(L=5,b=5)
α_F E_ep/pixel E_D1/%
0.000 0.776 2.926
0.001 0.773 2.879
0.010 0.785 2.940
0.100 0.758 2.822
1.000 0.766 2.853

Table 6. Performance evaluation of different methods on KITTI2015 test dataset

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Table 6. Performance evaluation of different methods on KITTI2015 test dataset

Method	All			Noc			Runtime /s
Method	${E_{D 1 ⁃ bg}}_{}$ /%	${E_{D 1 ⁃ fg}}_{}$ /%	${E_{D 1 ⁃ all}}_{}$ /%	${E_{D 1 ⁃ bg}}_{}$ /%	${E_{D 1 ⁃ fg}}_{}$ /%	${E_{D 1 ⁃ all}}_{}$ /%	Runtime /s
M2S_CSPN^[25]	1.51	2.88	1.74	1.40	2.67	1.61	0.50
GANet-deep^[26]	1.48	3.46	1.81	1.34	3.11	1.63	1.80
WSMCnetEB^[23]	1.72	4.19	2.13	1.51	3.57	1.85	0.39
DeepPruner(best)^[22]	1.87	3.56	2.15	1.71	3.18	1.95	0.18
PSMNet^[18]	1.86	4.62	2.32	1.71	4.31	2.14	0.41
iResNet-i2^[15]	2.25	3.40	2.44	4.11	3.72	4.05	0.12
CRL^[14]	2.48	3.59	2.67	2.32	3.12	2.45	0.47
GC-net^[17]	2.21	6.16	2.87	2.02	5.58	2.61	0.90
PBCP^[10]	2.58	8.74	3.61	2.27	7.71	3.17	68.00
SGM-Net^[11]	2.66	8.64	3.66	2.23	7.44	3.09	67.00
MC-CNN-arct^[4]	2.89	8.88	3.89	2.48	7.64	3.33	67.00
DeepPruner(fast)^[22]	2.32	3.91	2.59	2.13	3.43	2.35	0.06
DispNetC^[13]	4.32	4.41	4.34	4.11	3.72	4.05	0.06
MADnet^[28]	3.75	9.20	4.66	3.45	8.41	4.27	0.02
Proposed	2.59	4.80	2.96	2.22	4.14	2.54	0.05

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Yufeng Wang, Hongwei Wang, Yu Liu, Mingquan Yang, Jicheng Quan. Real-Time Stereo Matching Algorithm with Hierarchical Refinement[J]. Acta Optica Sinica, 2020, 40(9): 0915002

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