Attribute-aware Partitioning for Graph-based Point Cloud Attribute Coding

Abstract

The unstructured nature of point cloud data makes compression of their attributes very challenging. In this paper, the known approach of using the Graph Fourier Transform on partitions of the point cloud is improved. It is proposed to make the partitioning process both geometry and attribute-aware, taking all of the point cloud’s characteristics into account simultaneously. Additional information, that allows the decoder to reproduce the partitioning of the encoder, is added to the bitstream. Furthermore, a refinement algorithm which re-estimates the partitioning information at the encoder with the decoder in mind is proposed. Experiments show that the baseline method is outperformed in Bjøntegaard Delta rate reduction by 2.39%, reaching as much as 3.58% at high bitrates.

Authors 3

1. Thibaut Meyer Aachen r Nachrichtentechnik

   RWTH Aachen University

   Affiliation as printed

   Institut für Nachrichtentechnik, RWTH Aachen University,Germany

2. Dominik Mehlem Aachen r Nachrichtentechnik

   RWTH Aachen University

   Affiliation as printed

   Institut für Nachrichtentechnik, RWTH Aachen University,Germany

3. Christian Rohlfing Aachen r Nachrichtentechnik

   RWTH Aachen University

   Affiliation as printed

   Institut für Nachrichtentechnik, RWTH Aachen University,Germany