LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
This work addresses the performance gap in LiDAR-radar fusion for autonomous driving, but it is incremental as it builds on existing fusion techniques.
The paper tackled 3D object detection by fusing LiDAR and radar data, achieving notable improvement over existing methods on the nuScenes dataset.
We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. We perform extensive evaluation on nuScenes to demonstrate that LiRaFusion leverages the complementary information of LiDAR and radar effectively and achieves notable improvement over existing methods.