3DAC: Learning Attribute Compression for Point Clouds
This addresses storage efficiency for 3D point cloud data, which is incremental as it builds on existing compression methods with a novel deep learning approach.
The paper tackles attribute compression for large-scale unstructured 3D point clouds by introducing 3DAC, a deep compression network that reduces storage usage, achieving superior compression rates and reconstruction quality on datasets like ScanNet and SemanticKITTI.
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.