Modeling point cloud data using hierarchies of Gaussian mixture models

A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform steps comprising:

• receiving point cloud data defining a plurality of points;
• defining a Gaussian Mixture Model (GMM) hierarchy that represents the point cloud data, wherein the GMM hierarchy is stored in a tree data structure in a memory and each node in the GMM hierarchy comprises a mixel encoding parameters for a probabilistic occupancy map corresponding to a sub-population of the points in the point cloud;
• and
• adjusting the parameters for one or more probabilistic occupancy maps in the GMM hierarchy by executing a number of iterations of an Expectation-Maximum (EM) algorithm to fit the one or more probabilistic occupancy maps to the point cloud data.

A system comprising:

• a memory storing point cloud data defining a plurality of points;
• and
• a parallel processing unit-configured to:
• define a Gaussian Mixture Model (GMM) hierarchy that represents the point cloud data, wherein the GMM hierarchy is stored in a tree data structure in the memory and each node in the GMM hierarchy comprises a mixel encoding parameters for a probabilistic occupancy map corresponding to a sub-population of the points in the point cloud, and
• adjust the parameters for one or more probabilistic occupancy maps by executing a number of iterations of an Expectation-Maximum (EM) algorithm to fit the one or more probabilistic occupancy maps to the point cloud data.