Point Cloud Attribute Compression using 3-D Intra Prediction and Shape-Adaptive Transforms

With the increased proliferation of applications using 3-D capture technologies for applications such as virtual reality, mobile mapping, scanning of historical artifacts, and 3-D printing, representing these kinds of data as 3-D point clouds has become a popular method for storing and conveying the data independently of how it was captured. A point cloud consists of a set of coordinates indicating the location of each point, along with one or more attributes such as color associated with each point. Because the size of point cloud data can be quite large, compression is needed to efficiently store or transmit this data. This paper, motivated by techniques currently being used for image and video coding, proposes methods using 3-D block-based prediction and transform coding to compress point cloud attributes. Experimental results using a modified shape-adaptive DCT tailored for use in 3-D point clouds and a benchmark using 3-D graph transforms are shown.