Over the last decade, deep learning has shown great success at performing computer vision
tasks, including classification, super-resolution, and style transfer. Now, we apply it to data …

When compressing point clouds, point-based deep learning models operate points in a
continuous space, which has a chance to minimize the geometric fidelity loss introduced by …

Deep learning-based lossy signal compression methods have achieved substantial
progress and significantly enriched signal compression methodologies in recent years …

Deep learning is increasingly being used to perform machine vision tasks such as
classification, object detection, and segmentation on 3D point cloud data. However, deep …

In this paper, we will introduce the recent progress in deep learning based visual data
compression, including image compression, video compression and point cloud …

Due to the limited computational capabilities of edge devices, deep learning inference can
be quite expensive. One remedy is to compress and transmit point cloud data over the …

Point clouds are among popular visual representations for immersive media. However, the
vast amount of information generated during their acquisition requires effective compression …

Point cloud imaging has emerged as an efficient and popular solution to represent
immersive visual information. However, the large volume of data generated in the …

High-resolution point cloud videos combined with 3D scenes can create creative viewing
modes. However, their enormous data volume demands effective compression techniques …

Learned image compression methods generally optimize a rate-distortion loss, trading off
improvements in visual distortion for added bitrate. Increasingly, however, compressed …