Fully Sparse Fusion for 3D Object Detection

arXiv:2304.12310v3 Announce Type: replace
Abstract: Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not suitable for long-range detection. Fully sparse architecture is gaining attention as they are highly efficient in long-range perception. In this paper, we study how to effectively leverage image modality in the emerging fully sparse architecture. Particularly, utilizing instance queries, …

3d object 3d object detection arxiv cs.cv detection fusion object type