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

TL;DR

This paper introduces a GPU-accelerated 3D convex hull filtering method using ray tracing and tensor cores, significantly speeding up computation while maintaining energy efficiency for complex geometric models.

Contribution

It presents a novel GPU-based preprocessing filter that accelerates convex hull computation by up to 200x and reduces energy consumption through parallel processing and efficient filtering.

Findings

Up to 200x speedup over CPU implementations.

Effective point filtering reduces computational workload.

Energy consumption remains controlled despite acceleration.

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.