  Existing techniques to compress point cloud attributes leverage either
geometric or video-based compression tools. We explore a radically different
approach inspired by recent advances in point cloud representation learning.
Point clouds can be interpreted as 2D manifolds in 3D space. Specifically, we
fold a 2D grid onto a point cloud and we map attributes from the point cloud
onto the folded 2D grid using a novel optimized mapping method. This mapping
results in an image, which opens a way to apply existing image processing
techniques on point cloud attributes. However, as this mapping process is lossy
in nature, we propose several strategies to refine it so that attributes can be
mapped to the 2D grid with minimal distortion. Moreover, this approach can be
flexibly applied to point cloud patches in order to better adapt to local
geometric complexity. In this work, we consider point cloud attribute
compression; thus, we compress this image with a conventional 2D image codec.
Our preliminary results show that the proposed folding-based coding scheme can
already reach performance similar to the latest MPEG Geometry-based PCC (G-PCC)
codec.
