  With the development of range sensors such as LIDAR and time-of-flight
cameras, 3D point cloud scans have become ubiquitous in computer vision
applications, the most prominent ones being gesture recognition and autonomous
driving. Parsimony-based algorithms have shown great success on images and
videos where data points are sampled on a regular Cartesian grid. We propose an
adaptation of these techniques to irregularly sampled signals by using
continuous dictionaries. We present an example application in the form of point
cloud denoising.
