  Point cloud compression is a key enabler for the emerging applications of
immersive visual communication, autonomous driving and smart cities, etc. In
this paper, we propose a hybrid point cloud attribute compression scheme built
on an original layered data structure. First, a slice-partition scheme and
geometry-adaptive k dimensional-tree (kd-tree) method are devised to generate
the four-layer structure. Second, we introduce an efficient block-based intra
prediction scheme containing a DC prediction mode and several angular modes, in
order to exploit the spatial correlation between adjacent points. Third, an
adaptive transform scheme based on Graph Fourier Transform (GFT) is Lagrangian
optimized to achieve better transform efficiency. The Lagrange multiplier is
off-line derived based on the statistics of color attribute coding. Last but
not least, multiple reordering scan modes are dedicated to improve coding
efficiency for entropy coding. In intra-frame compression of point cloud color
attributes, results demonstrate that our method performs better than the
state-of-the-art region-adaptive hierarchical transform (RAHT) system, and on
average a 29.37$\%$ BD-rate gain is achieved. Comparing with the test model for
category 1 (TMC1) anchor's coding results, which were recently published by
MPEG-3DG group on 121st meeting, a 16.37$\%$ BD-rate gain is obtained.
