XiDepth: a Lightweight and Efficient Network for Self-supervised Monocular Depth Estimation

2026-08-04

AI Summary

This work addresses the challenge of achieving efficient and low-power self-supervised monocular depth estimation on resource-constrained devices by proposing XiDepth, a novel network architecture built upon lightweight XiNet operator blocks. Departing from computationally expensive components such as depthwise separable convolutions and attention mechanisms, XiDepth leverages a self-supervised learning paradigm combined with an optimized feature extraction module to significantly reduce both parameter count and computational overhead while maintaining competitive accuracy. Experimental results demonstrate that XiDepth achieves state-of-the-art performance on the KITTI benchmark with only 0.8 million parameters. When deployed on a Raspberry Pi 4, it reduces FLOPs by 40% and energy consumption by 35% compared to existing approaches, offering high compatibility and deployment efficiency for edge platforms.

Abstract

Self-supervised monocular depth estimation has emerged as an appealing solution to design lightweight and effective models for deployment on computationally constrained devices due to its reduced reliance on expensive depth sensors. By eliminating the need for ground-truth annotations and leveraging the simplicity of monocular camera setups, this approach facilitates cost-effective data collection and broad applicability across fields such as computer vision and robotics. A critical challenge is achieving resource-efficient neural networks without compromising the overall performance. State-of-the-art models generally adopt depth-wise convolutions and attention mechanisms; however, these functions often incur high energy costs and face compatibility issues in embedded environments. To address this, we propose XiDepth, a lightweight architecture based on the XiNet operator block, designed to enhance feature extraction while maintaining low computational complexity and energy demand. On the KITTI dataset, XiDepth achieves state-of-the-art performance with only 0.8M parameters. Tests on a Raspberry Pi 4 further confirm its suitability for real-world embedded applications, reducing FLOPs by 40% and energy consumption by 35% compared to leading methods.

Problem

• self-supervised
• monocular depth estimation
• lightweight network
• embedded systems
• computational efficiency

Innovation

• XiDepth
• self-supervised monocular depth estimation
• lightweight architecture
• XiNet operator
• embedded deployment