Work

Non-uniform Point Cloud Upsampling via Local Manifold Distribution

Year: 2025

Type: article

Abstract: Existing learning-based point cloud upsampling methods often overlook the intrinsic data distribution characteristics 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.

Source: Proceedings of the ACM on Computer Graphics and Interactive Techniques

DOI: 10.1145/3728309

Authors: Yao Hui Fang, Xing Ce Wang

Institution: Beijing Normal University

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: Beijing Municipal Science and Technology Commission

Award: G1174456100