Lightweight monocular depth estimation model based on multi-kernel grouped convolution and adaptive feature fusion

doi:10.13474/j.cnki.11-2246.2026.0810

Abstract

Abstract: [Purposes] To address the problems of insufficient extraction of distant object edge details，weak global consistency of depth prediction，and the difficulty of meeting real-time 3D perception requirements in autonomous driving for self-supervised monocular depth estimation，this paper proposes a lightweight self-supervised monocular depth estimation model. [Methods] Firstly，a multi-kernel grouped depthwise separable convolution module is designed to effectively fuse local detail and global structural information，significantly enhancing feature representation capability.In addition，an attention-guided adaptive feature fusion module is introduced to dynamically adjust multi-scale feature weighting and enhance the overall consistency of depth prediction. [Findings] Experimental results show that，compared with classical methods Monodepth2，the proposed approach reduces parameters by 10.4% and computation by 34.7%，achieving 0.112 and 0.878 on key performance metrics Abs Rel and δ₁，respectively，and demonstrating superior generalization performance in cross-scene testing. [Conclusions] The proposed method effectively improves depth estimation accuracy under a lightweight design，providing a reliable solution for real-time 3D perception in autonomous driving．

Key words: monocular depth estimation, autonomous driving, self-supervised learning, adaptive feature fusion, multi-kernel grouped convolution, depth separable

CLC Number:

P237

Liu Yao, Huang He, Ma Yanjie, Ma Chaowei, Ren Boyang, Yang Junxing. Lightweight monocular depth estimation model based on multi-kernel grouped convolution and adaptive feature fusion[J]. Bulletin of Surveying and Mapping, 2026, 0(8): 67-73,81.

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URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2026.0810

http://tb.chinasmp.com/EN/Y2026/V0/I8/67

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