  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.
