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arXiv · 2402.11735

LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection

Jingyu Song·Lingjun Zhao·Katherine A. Skinner
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Abstract

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.

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BibTeXRIS

Jingyu Song, Lingjun Zhao, Katherine A. Skinner. 2024-02-18. LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection. https://arxiv.org/abs/2402.11735

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