Convex Hull 3D Filtering with GPU Ray Tracing and Tensor Cores

2026-01-27

AI Summary

This work proposes a GPU-accelerated preprocessing filtering method leveraging ray tracing and Tensor Cores to address the high computational cost and energy consumption of traditional 3D convex hull algorithms in real-time simulation and autonomous driving applications. By constructing a Manhattan-distance-based bounding polyhedron to eliminate redundant points, the approach substantially reduces the workload of subsequent convex hull computation. To the best of our knowledge, this is the first method to synergistically employ ray tracing and Tensor Cores for geometric preprocessing. Experimental results demonstrate up to a 200× speedup over CPU-based parallel implementations on both uniform and spherical point cloud distributions, while effectively constraining GPU energy consumption—thus achieving a balanced optimization of computational performance and energy efficiency.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In recent years, applications such as real-time simulations, autonomous systems, and video games increasingly demand the processing of complex geometric models under stringent time constraints. Traditional geometric algorithms, including the convex hull, are subject to these challenges. A common approach to improve performance is scaling computational resources, which often results in higher energy consumption. Given the growing global concern regarding sustainable use of energy, this becomes a critical limitation. This work presents a 3D preprocessing filter for the convex hull algorithm using ray tracing and tensor core technologies. The filter builds a delimiter polyhedron based on Manhattan distances that discards points from the original set. The filter is evaluated on two point distributions: uniform and sphere. Experimental results show that the proposed filter, combined with convex hull construction, accelerates the computation of the 3D convex hull by up to 200x with respect to a CPU parallel implementation. This research demonstrates that geometric algorithms can be accelerated through massive parallelism while maintaining efficient energy utilization. Beyond execution time and speedup evaluation, we also analyze GPU energy consumption, showing that the proposed preprocessing filter not only reduces the computational workload but also achieves performance gains with controlled energy usage. These results highlight the dual benefit of the method in terms of both speed and energy efficiency, reinforcing its applicability in modern high-performance scenarios.

Problem

• convex hull
• energy efficiency
• real-time processing
• geometric algorithms
• GPU acceleration

Innovation

• convex hull
• GPU ray tracing
• tensor cores
• energy-efficient computing
• geometric preprocessing