Variational Graph Methods for Efficient Point Cloud Sparsification

TL;DR

This paper introduces a novel variational graph-based method for efficient point cloud sparsification, offering flexible control over the approximation and significant speed improvements over previous techniques.

Contribution

It presents a new coarse-to-fine optimization scheme inspired by Cut Pursuit, enabling faster and more adaptable point cloud compression compared to traditional methods.

Findings

The method effectively sparsifies point clouds with noise.

It achieves substantial speed-up over direct variational approaches.

Flexible regularization allows tailored approximations.

Abstract

In recent years new application areas have emerged in which one aims to capture the geometry of objects by means of three-dimensional point clouds. Often the obtained data consist of a dense sampling of the object's surface, containing many redundant 3D points. These unnecessary data samples lead to high computational effort in subsequent processing steps. Thus, point cloud sparsification or compression is often applied as a preprocessing step. The two standard methods to compress dense 3D point clouds are random subsampling and approximation schemes based on hierarchical tree structures, e.g., octree representations. However, both approaches give little flexibility for adjusting point cloud compression based on a-priori knowledge on the geometry of the scanned object. Furthermore, these methods lead to suboptimal approximations if the 3D point cloud data is prone to noise. In this paper we propose a variational method defined on finite weighted graphs, which allows to sparsify a given 3D point cloud while giving the flexibility to control the appearance of the resulting approximation based on the chosen regularization functional. The main contribution in this paper is a novel coarse-to-fine optimization scheme for point cloud sparsification, inspired by the efficiency of the recently proposed Cut Pursuit algorithm for total variation denoising. This strategy gives a substantial speed up in computing sparse point clouds compared to a direct application on all points as done in previous works and renders variational methods now applicable for this task. We compare different settings for our point cloud sparsification method both on unperturbed as well as noisy 3D point cloud data.