Point Cloud Attribute Compression with Graph Transfrom

Cha Zhang, Dinei Florencio, and Charles Loop

Abstract

Compressing attributes on 3D point clouds such as colors or normal directions has been a challenging problem, since these attribute signals are unstructured. In this paper, we propose to compress such attributes with graph transform. We construct graphs on small neighborhoods of the point cloud by connecting nearby points, and treat the attributes as signals over the graph. The graph transform, which is equivalent to Kahunen-Loeve Transform on such graphs, is then adopted to decorrelate the signal. Experimental results on a number of point clouds representing human upper bodies demonstrate that our method is much more efficient than traditional schemes such as octree-based methods.

Details

Publication typeInproceedings
PublisherIEEE – Institute of Electrical and Electronics Engineers
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