Point clouds are becoming essential in key applications with advances in capture
technologies leading to large volumes of data. Compression is thus essential for storage …

This study develops a unified Point Cloud Geometry (PCG) compression method through the
processing of multiscale sparse tensor-based voxelized PCG. We call this compression …

Point cloud is emerged as a promising media format to represent realistic 3D objects or
scenes in applications, such as virtual reality, teleportation, etc. How to accurately quantify …

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (aka,
Learned-PCGC) system, leveraging stacked Deep Neural Networks (DNN) based …

The state-of-the-art geometry-based point cloud compression (G-PCC)(Octree) is the fine-
grained approach, which uses the octree to partition point clouds into voxels and predicts …

There is a critical requirement for efficiently compressing point cloud geometries
representing 3-D moving objects in various applications. The Moving Picture Experts Group …

Compressing point cloud geometry (PCG) efficiently is of great interests for enabling
abundant networked applications, because PCG is a promising representation to precisely …

Holographic-type communication (HTC) permits new levels of engagement between remote
users. It is anticipated that it will give a very immersive experience while enhancing the …

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected
into patches, and then the patches are padded into 2D images suitable for the video …

Deep neural networks designed for point clouds, also called point cloud neural networks
(PCNNs), are attracting increasing attention in recent years. In this work, we propose the first …