Point Cloud Compression, Super-Resolving and Deblocking

Due to the increased popularity of augmented and virtual reality experiences, as well as 3D sensing for auto-driving, the interest in capturing high resolution real-world point clouds has grown significantly in recent years. Point cloud is a new class of signal that is non-uniform and sparse and this present unique challenges to the signal processing, compression and learning problems. In this talk, we present our multi-scale sparse convolutional learning and Graph Frourier Transform (GFT) based framework for large scale point cloud processing, with applications to the geometry and attributes super-resolution, and dynamic point cloud compression with latent space compensation. The architecture is memory efficient and can learn deep networks to handle large scale point cloud in real world applications. Initial results demonstrated that this framework achieved new state of the art results in geometry super-resolution, attributes deblocking and super-resolving, and dynamic point cloud sequence compression.