Self-Supervised Monocular Depth Estimation Model Based on Global Information Correlation Under Influence of Local Attention

Lei Xiao; Peng Hu; Junjie Ma

doi:10.3788/LOP241870

Laser & Optoelectronics Progress, Volume. 62, Issue 8, 0815010(2025)

Self-Supervised Monocular Depth Estimation Model Based on Global Information Correlation Under Influence of Local Attention

Lei Xiao, Peng Hu^*, and Junjie Ma

Author Affiliations

College of Artificial Intelligence, Anhui University of Science & Technology, Huainan 232001, Anhui , China

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Figures & Tables(9)

Fig. 1. Overall framework of proposed network

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Fig. 2. Internal structure of the DepthNet

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Fig. 3. Schematic diagram of the internal structure for the joint DSConv & Transformer block

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Fig. 4. Qualitative comparison between the proposed method and other methods on the KITTI dataset

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Fig. 5. Qualitative comparison of the proposed method and other methods on the Cityscapes dataset

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Fig. 6. Model complexity and speed evaluation. (a) Params; (b) FLOPs

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Table 1. Quantitative comparison between the proposed method and other monocular depth estimation methods on the KITTI dataset

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Table 1. Quantitative comparison between the proposed method and other monocular depth estimation methods on the KITTI dataset

Method	Data	Abs Rel	Sq Rel	RMSE	RMSE log	$δ_{1}$	$δ_{2}$	$δ_{3}$
Monodepth2^［22］	M	0.115	0.903	4.863	0.193	0.877	0.959	0.981
SGDepth^［32］	M+Se	0.113	0.835	4.693	0.191	0.879	0.961	0.981
SAFENet^［33］	M+Se	0.112	0.788	4.582	0.187	0.878	0.963	0.983
R-MSFM6^［24］	M	0.112	0.806	4.704	0.191	0.878	0.960	0.981
PackNet^［34］	M	0.108	0.727	4.426	0.184	0.885	0.963	0.983
HR-Depth^［9］	M	0.109	0.792	4.632	0.185	0.884	0.962	0.983
Ref. ［35］	M	0.106	0.861	4.699	0.185	0.889	0.962	0.982
CADepth^［23］	M	0.105	0.769	4.535	0.181	0.892	0.964	0.983
DIFFNet^［36］	M	0.102	0.749	4.445	0.179	0.897	0.965	0.983
Proposed	M	0.102	0.734	4.435	0.177	0.895	0.966	0.984

Table 2. Quantitative comparison between the proposed method and other monocular depth estimation methods on the Cityscapes dataset
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Table 2. Quantitative comparison between the proposed method and other monocular depth estimation methods on the Cityscapes dataset
Method Data Abs Rel Sq Rel RMSE RMSE log $δ_{1}$ $δ_{2}$ $δ_{3}$
Monodepth2^［22］ M 0.155 1.900 6.796 0.209 0.813 0.943 0.979
DIFFNet^［36］ M 0.140 1.571 6.298 0.192 0.837 0.950 0.983
Proposed M 0.125 1.300 5.790 0.177 0.861 0.958 0.986

Table 3. Ablation experimental results

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Table 3. Ablation experimental results

DSConv	SConv	Shuffle	Params /10⁶	Inference time /ms	Abs Rel	Sq Rel	RMSE	RMSE log	$δ_{1}$	$δ_{2}$	$δ_{3}$
			3.36	4.7	0.124	0.901	4.798	0.205	0.849	0.941	0.962
√			4.20	6.0	0.105	0.749	4.501	0.180	0.891	0.960	0.981
		√	3.36	4.8	0.120	0.850	4.602	0.195	0.860	0.953	0.972
	√		7.20	8.5	0.100	0.720	4.400	0.175	0.900	0.970	0.986
√		√	4.20	5.9	0.102	0.734	4.435	0.177	0.895	0.966	0.984

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Lei Xiao, Peng Hu, Junjie Ma. Self-Supervised Monocular Depth Estimation Model Based on Global Information Correlation Under Influence of Local Attention[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0815010

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Paper Information

Category: Machine Vision

Received: Aug. 19, 2024

Accepted: Oct. 8, 2024

Published Online: Mar. 25, 2025

The Author Email: Peng Hu (aust_hp@163.com)

DOI:10.3788/LOP241870

CSTR:32186.14.LOP241870

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Quantitative comparison between the proposed method and other monocular depth estimation methods on the KITTI dataset

Table 1. Quantitative comparison between the proposed method and other monocular depth estimation methods on the KITTI dataset

Table 2. Quantitative comparison between the proposed method and other monocular depth estimation methods on the Cityscapes dataset

Table 2. Quantitative comparison between the proposed method and other monocular depth estimation methods on the Cityscapes dataset

Table 3. Ablation experimental results

Table 3. Ablation experimental results