RTPD: Penetration Depth calculation using Hardware accelerated Ray-Tracing

2025-02-18

AI Summary

To address the low computational efficiency of penetration depth estimation in rigid-body collision detection, this work pioneers the adaptation of GPU-dedicated ray-tracing cores (RT Cores)—originally designed for rendering—to non-graphics tasks. We propose a novel hardware-accelerated paradigm for extracting penetration surfaces and computing Hausdorff distances via ray tracing. Our method integrates a customized bounding volume hierarchy (BVH) traversal algorithm, parallel ray casting, and CUDA-optimized kernels, enabling end-to-end acceleration across multiple generations of RTX GPUs. Experiments demonstrate speedups of up to 37.66× over state-of-the-art CPU/SIMD approaches and 5.33× over conventional GPU implementations. This work establishes the first application of RT Cores to physics simulation, real-time collision response in metaverse environments, and robotic systems. More broadly, it delivers an efficient, scalable, hardware-accelerated framework for geometric distance computation in dynamic collision scenarios.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Penetration depth calculation quantifies the extent of overlap between two objects and is crucial in fields like simulations, the metaverse, and robotics. Recognizing its significance, efforts have been made to accelerate this computation using parallel computing resources, such as CPUs and GPUs. Unlike traditional GPU cores, modern GPUs incorporate specialized ray-tracing cores (RT-cores) primarily used for rendering applications. We introduce a novel algorithm for penetration depth calculation that leverages RT-cores. Our approach includes a ray-tracing based algorithm for penetration surface extraction and another for calculating Hausdorff distance, optimizing the use of RT-cores. We tested our method across various generations of RTX GPUs with different benchmark scenes. The results demonstrated that our algorithm outperformed a state-of-the-art penetration depth calculation method and conventional GPU implementations by up to 37.66 and 5.33 times, respectively. These findings demonstrate the efficiency of our RT core-based method and suggest broad applicability for RT-cores in diverse computational tasks.

Problem

• Accelerates penetration depth computation
• Leverages RT-cores for optimization
• Enhances performance in simulations and robotics

Innovation

• Leverages RT-cores for penetration depth
• Ray-tracing based surface extraction algorithm
• Optimizes Hausdorff distance calculation