We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. Traditional point-based 3D object detectors often employ architectures that rely on a progressive downsampling of points. While this method effectively reduces computational demands and increases receptive fields, it will compromise the preservation of crucial non-local information for accurate 3D object detection, especially in the complex driving scenarios. To address this, we introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector by efficiently modeling longer-range inter-dependency while including only a negligible overhead. Concretely, the Cross-Cluster Shifting operation enhances the conventional design by shifting partial channels from neighboring clusters, which enables richer interaction with non-local regions and thus enlarges the receptive field of clusters. We conduct extensive experiments on the KITTI, Waymo, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD in both detection accuracy and runtime efficiency.

翻译：本文提出一种新型三维点云目标检测模型Shift-SSD，用于自动驾驶场景中的精确三维目标检测。传统基于点的三维目标检测器常采用逐级下采样点的架构，虽能有效降低计算复杂度并扩大感受野，但在复杂驾驶场景中会损害精确三维目标检测所需的关键非局部信息的保留性。为此，我们引入一种创新的跨簇移位操作（Cross-Cluster Shifting），通过高效建模更长距离的相互依赖关系，仅引入可忽略的额外开销即可释放基于点检测器的表征能力。具体而言，该跨簇移位操作通过将邻近簇的部分通道进行移位，增强传统设计范式，实现与非局部区域的更丰富交互，从而扩大簇的感受野。我们在KITTI、Waymo和nuScenes数据集上进行了大量实验，结果表明Shift-SSD在检测精度和运行时效率方面均达到最先进水平。