IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2022.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2023Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classificationcited by 4
- 2024Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimatorcited by 2
- 2025ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Codingcited by 0
- 2026TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinementcited by 0
- 2026TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement30 Apr 2026
- 2025ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding20 Oct 2025
- 2024Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator11 Apr 2024
- 2023Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification28 Oct 2023