Neural Clustering for Prefractured Mesh Generation in Real-time Object Destruction
This addresses the challenge of realistic real-time destruction in graphics and gaming, though it appears incremental by applying neural networks to an existing prefracture method.
The paper tackled the problem of unrealistic results in real-time object destruction by proposing a neural clustering approach for prefractured mesh generation, achieving remarkable quality in ready-to-use results.
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.