ADAE: ADVERSARIAL DISTRIBUTED SOURCE AUTOENCODER FOR POINT CLOUD COMPRESSION

Simone Milani

Abstract:

The current paper presents an adversarial autoencoding strategy for
voxelized point cloud geometry based on the principles of distributed
source coding. The encoder characterizes the input voxel blocks with
an array of hash bytes while the decoder combines them with side
information blocks in order to reconstruct the original data. The reconstruction
process is optimized by classifying the reconstructed
block with an adversarial discriminator in order to make the recovered
data as close as possible to an original block. Experimental results
show that the proposed solution generalizes well while obtaining
better coding performance with respect to other state-of-the-art
solutions and allowing high flexibility in rate shaping and decoding
operations.