5 accepted papers
Regional Adaptive Hierarchical Transform (RAHT) is an effective point cloud attribute compression (PCAC) method. However, its application in deep learning lacks research. In this paper, we propose an end-to-end RAHT framework for lossy PCAC based on the sparse tensor, called DeepRAHT. The RAHT trans
For mesh animation sequences with consistent topology, traditional compression methods are well established, but deep learning approaches are emerging. This paper presents a novel deep learning framework for vertex position reconstruction by encoding displacements. We propose LoD-Uniform Partitionin…
Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression. Recently, the octree-based auto-regressive entropy model which adopts the self-attention mechanism to explore dependencies in a large-scale context is proved to be promising. However, heavy g…
In this paper, we prove that it is feasible to perform mesh quality assessment by sampling it into point cloud. We propose a general and efficient surface-sampling based framework that can deal with various types and levels of distortions with less complexity. In this method, the original and distor…
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework ca…