Graph-based denoising for time-varying point clouds

TL;DR

This paper presents a graph-based convex optimization approach to denoise noisy 3D point clouds, effectively handling both static and time-varying data, with potential applications in various 3D modeling scenarios.

Contribution

It introduces a novel graph-based denoising method using convex optimization that extends naturally to time-varying point cloud sequences.

Findings

Effective noise reduction in static point clouds.

Extension to time-varying point cloud sequences.

Potential improvements in 3D reconstruction accuracy.

Abstract

Noisy 3D point clouds arise in many applications. They may be due to errors when constructing a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a technique that uses this graph structure and convex optimization methods to denoise 3D point clouds. A short discussion presents how those methods naturally generalize to time-varying inputs such as 3D point cloud time series.