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

arXiv: https://arxiv.org/abs/2502.04615 · HTML: https://mlsanity.com/paper/2502.04615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Breaking Bad: A Dataset for Geometric Fracture and Reassembly](https://mlsanity.com/paper/2210.11463/) (2022)
- [Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts](https://mlsanity.com/paper/2303.14152/) (2023)
- [DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning](https://mlsanity.com/paper/2310.13344/) (2023)

## Abstract (arXiv)

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.