3D object detection is a crucial problem in environmental perception for autonomous driving. Currently, most works focused on LiDAR, camera, or their fusion, while very few algorithms involve a RaDAR sensor, especially 4D RaDAR providing 3D position and velocity information. 4D RaDAR can work well in bad weather and has a higher performance than traditional 3D RaDAR, but it also contains lots of noise information and suffers measurement ambiguities. Existing 3D object detection methods can't judge the heading of objects by focusing on local features in sparse point clouds. To better overcome this problem, we propose a new method named RPFA-Net only using a 4D RaDAR, which utilizes a self-attention mechanism instead of PointNet to extract point clouds' global features. These global features containing long-distance information can effectively improve the network's ability to regress the heading angle of objects and enhance detection accuracy. Our method's performance is enhanced by 8.13% of 3D mAP and 5.52% of BEV mAP compared with the baseline. Extensive experiments show that RPFA-Net surpasses state-of-the-art 3D detection methods on Astyx HiRes 2019 dataset. The code and pre-trained models are available at https://github.com/adept-thu/RPFA-Net.git.
RPFA-Net: a 4D RaDAR Pillar Feature Attention Network for 3D Object Detection
2021-09-19
944045 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Feature binding, attention and object perception
British Library Conference Proceedings | 1999
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