LATENT SPACE SLICING FOR ENHANCED ENTROPY MODELING IN LEARNING-BASED POINT CLOUD GEOMETRY COMPRESSION

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research community and standardisation groups such as JPEG and MPEG. Learned autoencoder architectures based on 3D convolutional layers are popular solutions and have demonstrated higher performance when adopting latent space entropy modeling based on learned hyperpriors. We propose an enhanced entropy model that takes into account both the hyperprior and previously encoded latent features to estimate the mean and scale of compressed features. The obtained results show a large increase in performance, with a BD PSNR gain of 5.75dB when compared to the Octree coding module in G-PCC for the D2 PSNR metric. We also perform an ablation study to quantify the impact of network parameters in the performance of the model, drawing useful insights for future research.