BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems

TL;DR

BoRe-Depth is a lightweight, self-supervised monocular depth estimation model optimized for embedded systems, featuring boundary refinement and semantic integration to improve accuracy and boundary quality.

Contribution

The paper introduces BoRe-Depth, a novel depth estimation model with only 8.7M parameters that enhances boundary detail and runs efficiently on embedded hardware.

Findings

Outperforms previous lightweight models on multiple datasets

Runs at 50.7 FPS on NVIDIA Jetson Orin

Significantly improves boundary quality in depth maps

Abstract

Depth estimation is one of the key technologies for realizing 3D perception in unmanned systems. Monocular depth estimation has been widely researched because of its low-cost advantage, but the existing methods face the challenges of poor depth estimation performance and blurred object boundaries on embedded systems. In this paper, we propose a novel monocular depth estimation model, BoRe-Depth, which contains only 8.7M parameters. It can accurately estimate depth maps on embedded systems and significantly improves boundary quality. Firstly, we design an Enhanced Feature Adaptive Fusion Module (EFAF) which adaptively fuses depth features to enhance boundary detail representation. Secondly, we integrate semantic knowledge into the encoder to improve the object recognition and boundary perception capabilities. Finally, BoRe-Depth is deployed on NVIDIA Jetson Orin, and runs efficiently at 50.7 FPS. We demonstrate that the proposed model significantly outperforms previous lightweight models on multiple challenging datasets, and we provide detailed ablation studies for the proposed methods. The code is available at https://github.com/liangxiansheng093/BoRe-Depth.