Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
This addresses the challenge of processing irregular point cloud data for applications like gesture recognition and autonomous driving, representing an incremental adaptation of existing techniques.
The paper tackled the problem of adapting parsimony-based algorithms, which are effective on regular grids, to irregularly sampled 3D point clouds by using continuous dictionaries, and demonstrated this with point cloud denoising as an example application.
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.