IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding
This work addresses compression efficiency for point cloud data, relevant for applications like 3D imaging and virtual reality, but it is incremental as it builds on existing deep learning methods.
The authors developed deep learning-based codecs for point cloud geometry and joint geometry/color compression, submitted to a JPEG Pleno call, with the geometry codec outperforming MPEG G-PCC and being competitive with V-PCC Intra on test data, but the joint codec faced quality saturation issues.
This document describes a deep learning-based point cloud geometry codec and a deep learning-based point cloud joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022. The proposed codecs are based on recent developments in deep learning-based PC geometry coding and offer some of the key functionalities targeted by the Call for Proposals. The proposed geometry codec offers a compression efficiency that outperforms the MPEG G-PCC standard and outperforms or is competitive with the V-PCC Intra standard for the JPEG Call for Proposals test set; however, the same does not happen for the joint geometry and colour codec due to a quality saturation effect that needs to be overcome.