# IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding

arXiv: https://arxiv.org/abs/2208.02716 · HTML: https://mlsanity.com/paper/2208.02716/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2022.

## Built on by (most cited first, 4 of 4)

- [Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification](https://mlsanity.com/paper/2310.18849/) (2023)
- [Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator](https://mlsanity.com/paper/2404.07698/) (2024)
- [ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding](https://mlsanity.com/paper/2510.17068/) (2025)
- [TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement](https://mlsanity.com/paper/2604.28045/) (2026)

## Abstract (arXiv)

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.