DAFDeTr: Deformable Attention Fusion Based 3D Detection Transformer

Gopi Krishna Erabati, Helder Araújo

Abstract: Existing approaches fuse the LiDAR points and image pixels by hard association relying on highly accurate calibration matrices. We propose Deformable Attention Fusion based 3D Detection Transformer (DAFDeTr) to attentively and adaptively fuse the image features to the LiDAR features with soft association using deformable attention mechanism. Specifically, our detection head consists of two decoders for sequential fusion: LiDAR and image decoder powered by deformable cross-attention to link the multi-modal features to the 3D object predictions leveraging a sparse set of object queries. The refined object queries from the LiDAR decoder attentively fuse with the corresponding and required image features establishing a soft association, thereby making our model robust for any camera malfunction. We conduct extensive experiments and analysis on nuScenes and Waymo datasets. Our DAFDeTr-L achieves 63.4 mAP and outperforms well established networks on the nuScenes dataset and obtains competitive performance on the Waymo dataset. Our fusion model DAFDeTr achieves 64.6 mAP on the nuScenes dataset. We also extend our model to the 3D tracking task and our model outperforms state-of-the-art methods on 3D tracking.