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 …

Due to the increased popularity of augmented and virtual reality experiences, the interest in
capturing the real world in multiple dimensions and in presenting it to users in an immersible …

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

We present a generic and real-time time-varying point cloud codec for 3D immersive video.
This codec is suitable for mixed reality applications in which 3D point clouds are acquired at …

3D point clouds constitute an emerging multimedia content, now used in a wide range of
applications. The main drawback of this representation is the size of the data since typical …

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 …

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather
than using multiple depth maps. Point clouds have been used in computer graphics for a …

We present a novel deep compression algorithm to reduce the memory footprint of LiDAR
point clouds. Our method exploits the sparsity and structural redundancy between points to …

In recent years, 3D point clouds have enjoyed a great popularity for representing both static
and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of …

This paper addresses the problem of compression of 3D point cloud sequences that are
characterized by moving 3D positions and color attributes. As temporally successive point …