Kim, YoungWooLee, JaehongKim, DuksuChristie, MarcPietroni, NicoWang, Yu-Shuen2025-10-072025-10-0720251467-8659https://doi.org/10.1111/cgf.70229https://diglib.eg.org/handle/10.1111/cgf70229The Hausdorff distance is a fundamental metric with widespread applications across various fields. However, its computation remains computationally expensive, especially for large-scale datasets. This work targets exact point-to-point Hausdorff distance on point sets. In this work, we present RT-HDIST, the first Hausdorff distance algorithm accelerated by ray-tracing cores (RT-cores). By reformulating the Hausdorff distance problem as a series of nearest-neighbor searches and introducing a novel quantized voxel-index space, RT-HDIST achieves significant reductions in computational overhead while maintaining exact results. Extensive benchmarks demonstrate up to a two-order-of-magnitude speedup over prior state-of-the-art methods, underscoring RT-HDIST's potential for real-time and large-scale applications.CCS Concepts: Computing methodologies → Shape analysis; Mesh geometry models; Parallel algorithmsComputing methodologies → Shape analysisMesh geometry modelsParallel algorithms10.1111/cgf.7022910 pages