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}.

翻译：当前主流的 multimodal 3D 检测方法基于 LiDAR 检测器，通常采用稠密的鸟瞰图特征图，然而此类特征图的计算代价与检测范围呈二次关系，不适用于远距离检测任务。全稀疏架构因其在远距离感知中的高效性而备受关注。本文研究了如何在新兴的全稀疏架构中有效利用图像模态信息。具体而言，通过实例查询，本框架将成熟的2D实例分割与LiDAR端对齐，使其与全稀疏检测器中的3D实例分割部分并行运行。该设计在保持全稀疏特性的同时，实现了2D与3D两端统一的基于查询的融合框架。大量实验表明，本方法在广泛使用的nuScenes数据集及远距离Argoverse 2数据集上均取得了最优结果。值得注意的是，在远距离LiDAR感知设定下，本方法推理速度相较其他最优多模态3D检测方法提升2.7倍。代码将发布于 \url{https://github.com/BraveGroup/FullySparseFusion}。