Non-uniform Point Cloud Upsampling via Local Manifold Distribution
This addresses a domain-specific challenge in point cloud processing for applications like 3D reconstruction and computer vision.
The paper tackles the problem of generating high-quality dense point clouds from sparse and non-uniform inputs by imposing manifold distribution constraints, achieving state-of-the-art performance with more uniformly distributed results.
Existing learning-based point cloud upsampling methods often overlook the intrinsic data distribution charac?teristics of point clouds, leading to suboptimal results when handling sparse and non-uniform point clouds. We propose a novel approach to point cloud upsampling by imposing constraints from the perspective of manifold distributions. Leveraging the strong fitting capability of Gaussian functions, our method employs a network to iteratively optimize Gaussian components and their weights, accurately representing local manifolds. By utilizing the probabilistic distribution properties of Gaussian functions, we construct a unified statistical manifold to impose distribution constraints on the point cloud. Experimental results on multiple datasets demonstrate that our method generates higher-quality and more uniformly distributed dense point clouds when processing sparse and non-uniform inputs, outperforming state-of-the-art point cloud upsampling techniques.