The problem of identifying the k-Nearest Neighbors (kNNS) of a point has proven to be very useful both as a standalone application and as a subroutine in larger applications. Given its far-reaching applicability in areas such as machine learning and point clouds, extensive research has gone into leveraging GPU acceleration to solve this problem. Recent work has shown that using Ray Tracing cores in recent GPUs to accelerate kNNS is much more efficient compared to traditional acceleration using shader cores. However, the existing translation of kNNS to a ray tracing problem imposes a constraint on the search space for neighbors. Due to this, we can only use RT cores to accelerate fixed-radius kNNS, which requires the user to set a search radius a priori and hence can miss neighbors. In this work, we propose TrueKNN, the first unbounded RT-accelerated neighbor search. TrueKNN adopts an iterative approach where we incrementally grow the search space until all points have found their k neighbors. We show that our approach is orders of magnitude faster than existing approaches and can even be used to accelerate fixed-radius neighbor searches.

翻译：识别点的k近邻（kNNS）问题已证明在作为独立应用及更大应用的子程序时非常有用。鉴于其在机器学习和点云等领域的广泛应用，大量研究致力于利用GPU加速解决该问题。近期研究表明，与传统使用着色器核心的加速方法相比，利用新一代GPU中的光线追踪核心来加速kNNS效率更高。然而，现有将kNNS转化为光线追踪问题的方法对近邻搜索空间施加了限制。由于这一限制，我们只能使用RT核心加速固定半径kNNS，这要求用户预先设定搜索半径，因此可能遗漏近邻。在本工作中，我们提出TrueKNN——首个无界RT加速近邻搜索方法。TrueKNN采用迭代方式，逐步扩大搜索空间直至所有点找到其k个近邻。我们证明该方法比现有方法快数个数量级，甚至可用于加速固定半径近邻搜索。