Auto-BPA: An Enhanced Ball-Pivoting Algorithm With Adaptive Radius Using Contextual Bandits

The Ball-Pivoting Algorithm (BPA) is a notable technique for 3D surface reconstruction from point clouds, heavily reliant on the ball radius. In practical application, determining the optimal radius for BPA often necessitates iterative experimentation to achieve better reconstruction quality. BPA entails geometric computations like iterative pivoting, inherently lacking differentiability. In this paper, we tackle the dual challenges of radius selection and non-differentiability in BPA. Inspired by contextual bandits, we propose an innovative approach that learns the optimal radius based on local geometric features within point clouds. We validate our method on the ModelNet10 and ShapeNet datasets, showcasing superior surface reconstruction compared to manual tuning and other classic methods both for low and high point cloud densities. Our code is available at https://github.com/houda-pixel/Auto-BPA.