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Optimizing LBVH‐Construction and Hierarchy‐Traversal to accelerate kNN Queries on Point Clouds using the GPU
Computer Graphics Forum ( IF 2.7 ) Pub Date : 2020-10-28 , DOI: 10.1111/cgf.14177 J. Jakob 1 , M. Guthe 1
Computer Graphics Forum ( IF 2.7 ) Pub Date : 2020-10-28 , DOI: 10.1111/cgf.14177 J. Jakob 1 , M. Guthe 1
Affiliation
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Processing point clouds often requires information about the point neighbourhood in order to extract, calculate and determine characteristics. We continue the tradition of developing increasingly faster neighbourhood query algorithms and present a highly efficient algorithm for solving the exact neighbourhood problem in point clouds using the GPU. Both, the required data structures and the kNN query, are calculated entirely on the GPU. This enables real‐time performance for large queries in extremely large point clouds. Our experiments show a more than threefold acceleration, compared to state‐of‐the‐art GPU based methods including all memory transfers. In terms of pure query performance, we achieve over 
answered neighbourhood queries per millisecond for 16 nearest neighbours on common graphics hardware.
中文翻译：
使用GPU优化LBVH-Construction和Hierarchy-Traversal以加速点云上的kNN查询
处理点云通常需要有关点邻域的信息，以便提取，计算和确定特征。我们继续开发越来越快的邻域查询算法的传统，并提出了一种高效的算法，用于使用GPU解决点云中的精确邻域问题。所需的数据结构和k NN查询都完全在GPU上计算。这可为超大型点云中的大型查询提供实时性能。与基于GPU的最先进方法（包括所有内存传输）相比，我们的实验显示出超过三倍的加速。在纯查询性能方面，我们实现了 常见图形硬件上每毫秒对16个最近邻居的邻域查询的响应数。
更新日期：2020-10-28
answered neighbourhood queries per millisecond for 16 nearest neighbours on common graphics hardware.
中文翻译：
使用GPU优化LBVH-Construction和Hierarchy-Traversal以加速点云上的kNN查询
处理点云通常需要有关点邻域的信息，以便提取，计算和确定特征。我们继续开发越来越快的邻域查询算法的传统，并提出了一种高效的算法，用于使用GPU解决点云中的精确邻域问题。所需的数据结构和k NN查询都完全在GPU上计算。这可为超大型点云中的大型查询提供实时性能。与基于GPU的最先进方法（包括所有内存传输）相比，我们的实验显示出超过三倍的加速。在纯查询性能方面，我们实现了
常见图形硬件上每毫秒对16个最近邻居的邻域查询的响应数。
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