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, our framework integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the fully sparse detector. This design achieves a uniform query-based fusion framework in both the 2D and 3D sides while maintaining the fully sparse characteristic. Extensive experiments showcase state-of-the-art results on the widely used nuScenes dataset and the long-range Argoverse 2 dataset. Notably, the inference speed of the proposed method under the long-range LiDAR perception setting is 2.7 $\times$ faster than that of other state-of-the-art multimodal 3D detection methods. Code will be released at \url{https://github.com/BraveGroup/FullySparseFusion}.

本文研究如何在全稀疏架构中有效利用图像模态，并利用实例查询将2D实例分割与3D实例分割部分并行集成到LiDAR侧，从而实现了统一的基于查询的2D和3D融合框架，同时保持全稀疏特性。广泛的实验证明了在广泛使用的nuScenes数据集和长距离Argoverse 2数据集上的最先进性能。值得注意的是，所提出的方法在长距离LiDAR感知设置下的推理速度比其他最先进的多模式3D检测方法快2.7倍。

3D物体检测的全稀疏融合