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 …

We consider the attributes of a point cloud as samples of a vector-valued volumetric function
at discrete positions. To compress the attributes given the positions, we compress the …

Block-based compression scheme shows remarkable success in image and video coding.
However, existing tree-type block partition methods usually divide point clouds into clusters …

This work extends the multiscale structure originally developed for point cloud geometry
compression to point cloud attribute compression. To losslessly encode the attribute while …

3-D point clouds facilitate 3-D visual applications with detailed information of objects and
scenes but bring about enormous challenges to design efficient compression technologies …

This paper develops a Scalable Point Cloud Attribute Compression solution, termed
ScalablePCAC. In a two-layer example, ScalablePCAC uses the standard G-PCC at the …

LiDAR sensors are widely used in autonomous driving, and the growing storage and
transmission demands have made LiDAR point cloud compression (LPCC) a hot research …

随着元宇宙概念的兴起, 以6 自由度(six degree of freedom, 6DoF) 视频为代表的新一代交互式
媒体技术得到产业界和学术界的广泛关注. 6DoF 视频隶属于多媒体通信领域 …

Geometry-based point cloud compression (G-PCC) is a state-of-the-art point cloud
compression standard. While G-PCC achieves excellent performance, its reliance on the …

In point cloud geometry compression, context models usually use the one-hot encoding of
node occupancy as the label, and the cross-entropy between the one-hot encoding and the …