Neural Clustering for Prefractured Mesh Generation in Real-time Object Destruction

TL;DR

This paper introduces a neural network-based method for real-time prefractured mesh generation that improves the realism and quality of object destruction simulations by predicting structural weaknesses from point cloud data.

Contribution

It presents a novel deep learning approach for clustering in prefractured mesh generation, enhancing realism in real-time object destruction.

Findings

Successfully predicts structural weaknesses in objects

Produces high-quality, realistic destruction results

Operates efficiently within real-time constraints

Abstract

Prefracture method is a practical implementation for real-time object destruction that is hardly achievable within performance constraints, but can produce unrealistic results due to its heuristic nature. To mitigate it, we approach the clustering of prefractured mesh generation as an unordered segmentation on point cloud data, and propose leveraging the deep neural network trained on a physics-based dataset. Our novel paradigm successfully predicts the structural weakness of object that have been limited, exhibiting ready-to-use results with remarkable quality.