Recent advances in point cloud capture and applications in VR/AR sparked new interests in the point cloud data compression. Point Clouds are often organized and compressed with octree based structures. The octree subdivision sequence is often serialized in a sequence of bytes that are subsequently entropy encoded using range coding, arithmetic coding or other methods. Such octree based algorithms are efficient only up to a certain level of detail as they have an exponential run-time in the number of subdivision levels. In addition, the compression efficiency diminishes when the number of subdivision levels increases. Therefore, in this work we present an alternative enhancement layer to the coarse octree coded point cloud. In this case, the base layer of the point cloud is coded in known octree based fashion, but the higher level of details are coded in a different way in an enhancement layer bit-stream. The enhancement layer coding method takes the distribution of the points into account and projects points to geometric primitives, i.e. planes. It then stores residuals and applies entropy encoding with a learning based technique. The plane projection method is used for both geometry compression and color attribute compression. For color coding the method is used to enable efficient raster scanning of the color attributes on the plane to map them to an image grid. Results show that both improved compression performance and faster run-times are achieved for geometry and color attribute compression in point clouds.

Date Published: 27 September 2016
PDF: 9 pages
Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99710R (27 September 2016); doi: 10.1117/12.2237753

Published in SPIE Proceedings Vol. 9971:
Applications of Digital Image Processing XXXIX
Andrew G. Tescher, Editor(s)