Neural Implicit Surfaces for Efficient and Accurate Collisions in Physically Based Simulations

TL;DR

This paper introduces a deep learning-based implicit surface representation that enables fast, accurate collision detection in physically based simulations, significantly reducing computational complexity and memory usage compared to traditional methods.

Contribution

The authors propose a novel neural implicit surface method for collision detection that achieves linear or constant query complexity, overcoming parallelization issues of traditional acceleration structures.

Findings

Querying 1 million points in 300 ms

Achieves O(n) or O(1) complexity for collision queries

Improves efficiency and accuracy in cloth simulation

Abstract

Current trends in the computer graphics community propose leveraging the massive parallel computational power of GPUs to accelerate physically based simulations. Collision detection and solving is a fundamental part of this process. It is also the most significant bottleneck on physically based simulations and it easily becomes intractable as the number of vertices in the scene increases. Brute force approaches carry a quadratic growth in both computational time and memory footprint. While their parallelization is trivial in GPUs, their complexity discourages from using such approaches. Acceleration structures -- such as BVH -- are often applied to increase performance, achieving logarithmic computational times for individual point queries. Nonetheless, their memory footprint also grows rapidly and their parallelization in a GPU is problematic due to their branching nature. We propose using implicit surface representations learnt through deep learning for collision handling in physically based simulations. Our proposed architecture has a complexity of O(n) -- or O(1) for a single point query -- and has no parallelization issues. We will show how this permits accurate and efficient collision handling in physically based simulations, more specifically, for cloth. In our experiments, we query up to 1M points in 300 milliseconds.