Graph-based denoising for time-varying point clouds
This addresses noise reduction in 3D point clouds for applications such as 3D modeling and depth sensing, but appears incremental as it builds on existing graph-based and convex optimization approaches.
The paper tackles the problem of denoising 3D point clouds, which are often noisy due to errors in model construction or depth sensors, by introducing a technique that uses graph structures and convex optimization methods, with a discussion on generalizing to time-varying inputs like 3D point cloud time series.
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.