Modeling point cloud data using hierarchies of gaussian mixture models
AssigneeNVIDIA Corporation
InventorsBenjamin David Eckart, Kihwan Kim, Alejandro Jose Troccoli, Jan Kautz
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Abstract
A method, computer readable medium, and system are disclosed for generating a Gaussian mixture model hierarchy. The method includes the steps of receiving point cloud data defining a plurality of points; defining a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map; and adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.
Claims20
A method, comprising:
- receiving point cloud data defining a plurality of points;
- defining a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map;
- and
- adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.
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 includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map;
- and
- adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.
A system comprising:
- a memory storing point cloud data defining a plurality of points;
- and
- a processor configured to:
- define a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map, and
- adjust the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.
Dependencies are inferred from the claim text.