US Patent Application 17661193. APPROXIMATE HIERARCHICAL CONVEX DECOMPOSITION simplified abstract
Contents
APPROXIMATE HIERARCHICAL CONVEX DECOMPOSITION
Organization Name
Inventor(s)
Khaled Mammou of Danville CA (US)
Adrian A Biagioli of Sunnyvale CA (US)
Deepak S Tolani of Sunnyvale CA (US)
APPROXIMATE HIERARCHICAL CONVEX DECOMPOSITION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17661193 titled 'APPROXIMATE HIERARCHICAL CONVEX DECOMPOSITION
Simplified Explanation
- The patent application describes a method for decomposing a three-dimensional object into multiple convex hulls. - The method involves using a cluster priority queue in a computing system memory to store clusters corresponding to the object. - A concavity measure is computed for each cluster in the queue to determine the cluster with the highest concavity measure. - The cluster with the highest concavity measure is then divided into two new clusters using a cut plane, with each new cluster having a corresponding convex hull. - The cut plane is computed through a hierarchical search of potential cut planes. - The original cluster is removed from the queue, and the two new clusters are added to the queue.
Original Abstract Submitted
A method of decomposing a three-dimensional representation of an object into a plurality of convex hulls can include instantiating a cluster priority queue in a computing system memory that initially contains a cluster corresponding to the three-dimensional representation of the object, computing with a processor of the computing system a concavity measure for each cluster in the cluster priority queue, and, for the cluster with the highest concavity measure: (1) computing with the processor a cut plane that divides the cluster corresponding to the three-dimensional representation of the object into two new clusters, each of the two new clusters having a corresponding convex hull, wherein computing a cut plane includes performing a hierarchical search of potential cut planes, (2) removing the cluster corresponding to the three-dimensional representation of the object from the cluster priority queue, and (3) adding the two new clusters to the cluster priority queue.
