Abstract
Geometry-based point cloud compression (G-PCC) has been rapidly evolving
in the context of international standards. Despite the inherent
scalability of octree-based geometry description, current G-PCC
attribute compression techniques prevent full scalability for compressed
point clouds. In this paper, we present a solution to add scalability to
attributes compressed using the region-adaptive hierarchical transform
(RAHT), enabling the reconstruction of the point cloud using only a
portion of the original bitstream. Without the full geometry
information, one cannot compute the weights in which the RAHT relies on
to calculate its coefficients for further levels of detail. In order to
overcome this problem, we propose a linear relationship approximation
relating the downsampled point cloud to the truncated inverse RAHT
coefficients at that same level. The linear relationship parameters are
sent as side information. After truncating the bitstream at a point
corresponding to a given octree level, we can, then, recreate the
attributes at that level. Tests were carried out and results attest the
good approximation quality of the proposed technique.