用神经网络自动识别物体弱点，实时生成逼真破碎效果。
Neural Clustering for Prefractured Mesh Generation in Real-time Object Destruction
- 将破碎点聚类转化为点云无序分割问题
- 在物理仿真数据上训练模型，提升预测准确率
- 适合游戏与实时渲染场景的高效破碎生成
预破碎方法是实现实时物体破碎的实用方案，但受限于性能要求，常因启发式规则导致结果不真实。本文将预破碎网格生成的聚类问题建模为点云数据上的无序分割任务，利用在物理驱动数据集上训练的深度神经网络进行求解。该新范式能有效预测物体结构薄弱区域，生成质量显著提升、可直接使用的破碎结果，具备良好的实时性与真实性。
原文摘要 · Abstract (English)
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.
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