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arXiv · 2410.10298

ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object Detection

Jiwei Chen·Yubao Sun·Laiyan Ding·Rui Huang
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Abstract

Vision-based Bird's-Eye-View (BEV) 3D object detection has recently become popular in autonomous driving. However, objects with a high similarity to the background from a camera perspective cannot be detected well by existing methods. In this paper, we propose a BEV-based 3D Object Detection Network with 2D Region-Oriented Attention (ROA-BEV), which enables the backbone to focus more on feature learning of the regions where objects exist. Moreover, our method further enhances the information feature learning ability of ROA through multi-scale structures. Each block of ROA utilizes a large kernel to ensure that the receptive field is large enough to catch information about large objects. Experiments on nuScenes show that ROA-BEV improves the performance based on BEVDepth. The source codes of this work will be available at https://github.com/DFLyan/ROA-BEV.

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Jiwei Chen, Yubao Sun, Laiyan Ding, Rui Huang. 2024-10-14. ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object Detection. https://arxiv.org/abs/2410.10298

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