Széchenyi Plusz RRF

Generative modeling of uniquely structured three dimensional set data, such as point clouds, requires capturing local and global geometric features. Utilization of multi-scale frameworks based on ordinary, grid-structured data to set data is nontrivial. Set structures require a permutation-invariant feature extraction process to capture multi-scale geometric signatures effectively. In this paper, we propose PCD-VAE, a permutation invariant Variational Auto-Encoder. Motivated by recent progress in irregular and unordered set-encoding we created PCD-VAE, built on attentive and convolutional modules that processes the input set derived from geometric localities within the spatial and the latent domain. Exploiting these modules our VAE learns a smaller, permutation invariant latent representation of the input data. We evaluate our model on point cloud generation tasks and achieve competitive results in both compression rate and reconstruction accuracy. Experimental results demonstrate the effectiveness of our proposed method.