  We study the problem of attribute compression for large-scale unstructured 3D
point clouds. Through an in-depth exploration of the relationships between
different encoding steps and different attribute channels, we introduce a deep
compression network, termed 3DAC, to explicitly compress the attributes of 3D
point clouds and reduce storage usage in this paper. Specifically, the point
cloud attributes such as color and reflectance are firstly converted to
transform coefficients. We then propose a deep entropy model to model the
probabilities of these coefficients by considering information hidden in
attribute transforms and previous encoded attributes. Finally, the estimated
probabilities are used to further compress these transform coefficients to a
final attributes bitstream. Extensive experiments conducted on both indoor and
outdoor large-scale open point cloud datasets, including ScanNet and
SemanticKITTI, demonstrated the superior compression rates and reconstruction
quality of the proposed 3DAC.
