InterFusion: Interaction-based 4D Radar and LiDAR Fusion for 3D Object Detection
Li Wang, Xinyu Zhang, Baowei Xv, Jinzhao Zhang, Rong Fu, Xiaoyu Wang, Lei Zhu, Haibing Ren, Pingping Lu, Jun Li, Huaping Liu
Abstract
Many recent works detect 3D objects by several sensor modalities for autonomous driving, where high-resolution cameras and high-line LiDARs are mostly used but relatively expensive. To achieve a balance between overall cost and detection accuracy, many multi-modal fusion techniques have been suggested. In recent years, the fusion of LiDAR and Radar has gained ever-increasing attention, especially 4D Radar, which can adapt to bad weather conditions due to its penetrability. Although features have been fused from multiple sensing modalities, most methods cannot learn interactions from different modalities, which does not make for their best use. Inspired by the self-attention mechanism, we present InterFusion, an interaction-based fusion framework, to fuse 16-line LiDAR with 4D Radar. It aggregates features from two modalities and identifies cross-modal relations between Radar and LiDAR features. In experimental evaluations on the Astyx HiRes 2019 dataset, our method outperformed the baseline by 4.20% mAP in 3D and 10.76% BEV mAP for the car class at the moderate level.