RT-HDIST: Ray-Tracing Core-based Hausdorff Distance Computation

2025-04-18

AI Summary

This paper addresses the low computational efficiency of Hausdorff distance estimation between large-scale point clouds, which hinders real-time applications. We propose the first GPU-accelerated framework leveraging dedicated ray-tracing cores (RT Cores). Methodologically, we reformulate the Hausdorff distance as a bidirectional nearest-neighbor search problem and design a quantized index space tailored to RT Core hardware characteristics, enabling geometry-aware efficient querying and optimized CUDA-level scheduling. Our key contributions are: (1) the first algorithm to exploit RT Cores for accelerating a classical geometric metric; (2) a novel quantized indexing paradigm that balances mathematical precision with hardware efficiency; and (3) up to 100× speedup on multiple large-scale benchmarks while preserving floating-point accuracy. The method enables real-time and industrial-grade point cloud analysis.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

The Hausdorff distance is a fundamental metric with widespread applications across various fields. However, its computation remains computationally expensive, especially for large-scale datasets. 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 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.

Problem

• Accelerates Hausdorff distance computation using ray-tracing cores
• Reduces computational overhead for large-scale datasets
• Enables real-time applications with exact distance results

Innovation

• Accelerates Hausdorff distance with ray-tracing cores
• Reformulates problem as nearest-neighbor searches
• Introduces quantized index space for efficiency