Spatial join is a fundamental operation in spatial databases. With the rapid growth of 3D data in applications such as LiDAR-based object detection and 3D digital pathology, there is an increasing need to support spatial join over 3D datasets. However, existing techniques are largely designed for 2D data, leaving 3D spatial join underexplored and computationally expensive. We present 3DPipe, a pipelined GPU framework for scalable spatial join over polyhedral objects. 3DPipe exploits GPU parallelism across both filtering and refinement stages, incorporates a multi-level pruning strategy for efficient candidate reduction, and employs chunked streaming to handle datasets exceeding GPU memory. Its pipelined execution overlaps CPU data preparation, host-device data transfer, and GPU computation to improve throughput. Experiments show that 3DPipe achieves up to 9.0$\times$ speedup over the state-of-the-art GPU solution, TDBase, while maintaining excellent scalability. 3DPipe is open-sourced at https://github.com/lyuheng/3dpipe.

翻译：空间连接是空间数据库中的基本操作。随着激光雷达目标检测、三维数字病理等应用中三维数据的快速增长，对三维数据集空间连接的支持需求日益增加。然而，现有技术主要面向二维数据设计，导致三维空间连接尚未得到充分探索且计算成本高昂。我们提出3DPipe，一个面向多面体对象可扩展空间连接的分流水线GPU框架。3DPipe在过滤和精化两个阶段均利用GPU并行性，采用多级剪枝策略实现高效的候选对缩减，并运用分块流式处理以处理超出GPU内存的数据集。其流水线执行机制将CPU数据准备、主机-设备数据传输和GPU计算进行重叠，从而提升吞吐量。实验表明，与最先进的GPU解决方案TDBase相比，3DPipe在保持出色可扩展性的同时实现了最高9.0倍加速。3DPipe已在https://github.com/lyuheng/3dpipe开源。