We present ASH, a modern and high-performance framework for parallel spatial hashing on GPU. Compared to existing GPU hash map implementations, ASH achieves higher performance, supports richer functionality, and requires fewer lines of code (LoC) when used for implementing spatially varying operations from volumetric geometry reconstruction to differentiable appearance reconstruction. Unlike existing GPU hash maps, the ASH framework provides a versatile tensor interface, hiding low-level details from the users. In addition, by decoupling the internal hashing data structures and key-value data in buffers, we offer direct access to spatially varying data via indices, enabling seamless integration to modern libraries such as PyTorch. To achieve this, we 1) detach stored key-value data from the low-level hash map implementation; 2) bridge the pointer-first low level data structures to index-first high-level tensor interfaces via an index heap; 3) adapt both generic and non-generic integer-only hash map implementations as backends to operate on multi-dimensional keys. We first profile our hash map against state-of-the-art hash maps on synthetic data to show the performance gain from this architecture. We then show that ASH can consistently achieve higher performance on various large-scale 3D perception tasks with fewer LoC by showcasing several applications, including 1) point cloud voxelization, 2) retargetable volumetric scene reconstruction, 3) non-rigid point cloud registration and volumetric deformation, and 4) spatially varying geometry and appearance refinement. ASH and its example applications are open sourced in Open3D (http://www.open3d.org).
翻译:我们展示了ASASH,这是GPU上平行空间散列的现代和高性能框架。与现有的GPU散列地图执行相比,ASH实现了更高的性能,支持了更丰富的功能,并需要更少的代码线(LOC)用于执行从体积几何重建到不同外观重建的空间性不同操作。与现有的GPU散列地图不同,ASH框架提供了一个多功能的感应接口,向用户隐藏了低层次的细节。此外,通过将内部散列数据结构和缓冲中的关键值数据脱钩,我们通过指数,直接获取空间变化的数据,通过不易变异的表情地图执行,使得像PyTorrch这样的现代图书馆能够实现无缝的整合。为了实现这一点,我们1)将关键值数据从低层次的几度几行存储到不同的外观重建。3,我们通过一个指数高压直径直径直的数据结构在多维维维度键上可以调整普通和非直径直的直径直径的地图执行过程。我们第一次剖面的SASASASA 3,然后在一系列的地图上可以显示大规模的成绩。