Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

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 …

Recent deep networks that directly handle points in a point set, eg, PointNet, have been
state-of-the-art for supervised learning tasks on point clouds such as classification and …

Unlike on images, semantic learning on 3D point clouds using a deep network is
challenging due to the naturally unordered data structure. Among existing works, PointNet …

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

Abstract Moving Object Segmentation (MOS) is an important topic in computer vision. MOS
becomes a challenging problem in the presence of dynamic background and moving …

In rate-distortion optimization, the encoder settings are determined by maximizing a
reconstruction quality measure subject to a constraint on the bitrate. One of the main …

We have recently seen tremendous progress in photo-real human modeling and rendering.
Yet efficiently rendering realistic human performance and integrating it into the rasterization …

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 …