Towards uniform point distribution in feature-preserving point cloud filtering

Abstract While a popular representation of 3D data, point clouds may contain noise and need filtering before use. Existing cloud methods either cannot preserve sharp features or result in uneven distributions the filtered output. To address this problem, paper introduces method that considers both distribution feature preservation during filtering. The key idea is to incorporate repulsion term with data energy minimization. responsible for distribution, while aims approximate noisy surfaces preserving geometric features. This capable handling models fine-scale Extensive experiments show our quickly yields good results relatively uniform distribution.