PROGRESSIVE POINT CLOUD COMPRESSION BASED ON BOTTOM-UP POINT CLUSTERING

摘要

In this work we propose an algorithm for compressing a three-dimensional point-based model. The proposed algorithm is based on the bottom-up clustering of the points. Starting from the input model, it performs clustering to the points to generate a coarser approximation, or a coarser level of detail (LOD). Iterating this clustering process, a sequence of LODs is generated, forming an LOD hierarchy tree. Then, the tree is traversed from the root down to the leaves. For each node encountered during the traversal, the corresponding updates associated with its children are encoded, leading to a progressive encoding of the original model. Further more, we design a method to give higher priority to the encoding of nodes with higher coding gains at each LOD.