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.
REF provides a short discussion on how the proposed graph-based static point cloud denoising naturally generalizes to time-varying inputs such as 3D dynamic point clouds.
Graph-based denoising for time-varying point clouds
3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) (2015) 1-4
Computer Science