LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the computational costs introduced by the dense feature maps grow quadratically as the perception range increases, making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue; nevertheless, the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work, we propose SAFDNet, a straightforward yet highly effective architecture, tailored for fully sparse 3D object detection. In SAFDNet, an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset, which features long-range detection, verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://github.com/zhanggang001/HEDNet.

翻译：基于激光雷达的3D目标检测在自动驾驶中扮演着关键角色。现有高性能3D目标检测器通常会在主干网络和预测头中构建密集特征图。然而，密集特征图带来的计算成本会随着感知范围的扩大呈二次方增长，使得这些模型难以扩展至远距离检测场景。近期部分研究尝试构建全稀疏检测器来解决该问题，但现有模型要么依赖复杂的多阶段流水线架构，要么性能表现欠佳。本文提出SAFDNet——一种专为全稀疏3D目标检测设计的简洁高效架构。在SAFDNet中，我们设计了自适应特征扩散策略以解决中心特征缺失问题。在Waymo Open、nuScenes和Argoverse2数据集上开展的广泛实验表明：SAFDNet在前两个数据集上略优于先前最优方法，而在侧重远距离检测的Argoverse2数据集上表现显著提升。值得注意的是，在Argoverse2数据集上，SAFDNet以2.1倍的推理速度超越先前最优混合检测器HEDNet达2.6% mAP，同时以1.3倍推理速度较先前最优稀疏检测器FSDv2提升2.1% mAP。代码将开源至https://github.com/zhanggang001/HEDNet。