轻量级单目深度估计模型，提升嵌入式系统边界清晰度
BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems
- 自适应融合特征增强边界细节表达
- 引入语义知识提升物体识别与边界感知
- 在Jetson Orin上达50.7帧/秒，适合边缘部署
深度估计是无人系统实现三维感知的关键技术。单目深度估计因成本低而被广泛研究，但现有方法在嵌入式系统上仍存在精度差、边界模糊的问题。本文提出一种新型单目深度估计模型BoRe-Depth，仅含8.7M参数，可在嵌入式系统上准确生成深度图并显著提升边界质量。首先设计了增强型特征自适应融合模块（EFAF），自适应融合深度特征以增强边界细节表示；其次将语义知识融入编码器，提升物体识别与边界感知能力；最后在NVIDIA Jetson Orin上部署，运行效率达50.7 FPS。实验表明，该模型在多个挑战性数据集上显著优于以往轻量级模型，并提供了详细的消融实验。代码已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。