Feature Preserving and Uniformity-controllable Point Cloud Simplification on Graph

TL;DR

This paper introduces a graph spectral-based point cloud simplification method that balances feature preservation and uniform density, improving efficiency in processing large-scale 3D data.

Contribution

It presents a novel graph filter-based formulation for point cloud simplification that effectively balances feature retention and density uniformity.

Findings

Outperforms existing methods in feature preservation and uniformity

Reduces computation time and storage for large-scale point clouds

Enhances accuracy in point cloud registration

Abstract

With the development of 3D sensing technologies, point clouds have attracted increasing attention in a variety of applications for 3D object representation, such as autonomous driving, 3D immersive tele-presence and heritage reconstruction. However, it is challenging to process large-scale point clouds in terms of both computation time and storage due to the tremendous amounts of data. Hence, we propose a point cloud simplification algorithm, aiming to strike a balance between preserving sharp features and keeping uniform density during resampling. In particular, leveraging on graph spectral processing, we represent irregular point clouds naturally on graphs, and propose concise formulations of feature preservation and density uniformity based on graph filters. The problem of point cloud simplification is finally formulated as a trade-off between the two factors and efficiently solved by our proposed algorithm. Experimental results demonstrate the superiority of our method, as well as its efficient application in point cloud registration.