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

• URL: http://arxiv.org/abs/2410.10298v1
• Date: Mon, 14 Oct 2024 08:51:56 GMT
• Title: ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object
• Authors: Jiwei Chen, Laiyan Ding, Chi Zhang, Feifei Li, Rui Huang,
• Abstract summary: We propose 2D Region-oriented Attention for a BEV-based 3D Object Detection Network (ROA-BEV) Our method increases the information content of ROA through a multi-scale structure. Experiments on nuScenes show that ROA-BEV improves the performance based on BEVDet and BEVDepth.
• Score: 14.219472370221029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-based BEV (Bird-Eye-View) 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 2D Region-oriented Attention for a BEV-based 3D Object Detection Network (ROA-BEV), which can make the backbone focus more on feature learning in areas where objects may exist. Moreover, our method increases the information content of ROA through a multi-scale structure. In addition, every block of ROA utilizes a large kernel to ensure that the receptive field is large enough to catch large objects' information. Experiments on nuScenes show that ROA-BEV improves the performance based on BEVDet and BEVDepth. The code will be released soon.

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