LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
<|reference_start|>LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection: 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.<|reference_end|>
@article{song2024lirafusion:,
    title={LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection},
    author={Jingyu Song, Lingjun Zhao, Katherine A. Skinner},
    journal={arXiv preprint arXiv:2402.11735},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11735},
    primaryClass={cs.RO cs.CV}
}