TransPCC: Towards Deep Point Cloud Compression via Transformers

High-efficient point cloud compression (PCC) techniques are necessary for various 3D practical applications, such as autonomous driving, holographic transmission, virtual reality, etc. The sparsity and disorder nature make it challenging to design frameworks for point cloud compression. In this paper, we present a new model, called TransPCC that adopts a fully Transformer auto-encoder architecture for deep Point Cloud Compression. By taking the input point cloud as a set in continuous space with learnable position embeddings, we employ the self-attention layers and necessary point-wise operations for point cloud compression. The self-attention based architecture enables our model to better learn point-wise dependency information for point cloud compression. Experimental results show that our method outperforms state-of-the-art methods on large-scale point cloud dataset.