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.

翻译：预分割方法是一种在性能约束下难以实现实时物体破碎的实用技术，但由于其启发式特性可能导致不真实的结果。为改善此问题，我们将预分割网格生成的聚类问题视为点云数据的无序分割任务，并提出利用基于物理数据集训练的深度神经网络。我们提出的新范式成功预测了以往受限物体的结构薄弱区域，展现出可直接使用且质量优异的结果。