LB-PCCD: Learning-Based Point Cloud Compression Dataset

This dataset is the result of a crowdsourced experiment conducted to obtain subjective ratings for point cloud models whose geometry and color attributes are encoded by both conventional and learning-based methods. The QualityCrowd framework [1] was employed to display rendered videos of the assessed point clouds and allow for rating following a simultaneous Double Stimulus Impairment Scale (DSIS) protocol, while the subjects were recruited through the Amazon Mechanical Turk platform. A total of five different codecs were selected in this experiment, namely V-PCC [2], two distinct configurations of G-PCC [3], and two learning-based compression schemes [4,5]. All point cloud models were also assessed through a large pool of objective metrics, which were benchmarked against the subjective scores in order to rank the metrics by correlation with human perception. A complete description of the experiment is provided in [6].


The dataset can be downloaded as a zip file from the following FTP by using dedicated FTP clients, such as FileZilla or FireFTP (we recommend to use FileZilla):

Protocol: FTP
FTP address:
Username: [email protected]
Password: ohsh9jah4T
FTP port: 21

After you connect, choose the LB-PCCD folder from the remote site, and download the relevant material. The total size of the provided data is ~658 MB. 

Please refer to the README file for further information on the structure and the usage of the material.

Conditions of use

Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute the data provided and its documentation for research purpose only. The data provided may not be commercially distributed. In no event shall the Ecole Polytechnique Fédérale de Lausanne (EPFL) be liable to any party for direct, indirect, special, incidental, or consequential damages arising out of the use of the data and its documentation. The Ecole Polytechnique Fédérale de Lausanne (EPFL) specifically disclaims any warranties. The data provided hereunder is on an “as is” basis and the Ecole Polytechnique Fédérale de Lausanne (EPFL) has no obligation to provide maintenance, support, updates, enhancements, or modifications.

If you wish to use the subjective scores in your research, we kindly ask you to cite [6].


[1] C. Keimel, J. Habigt, C. Horch, and K. Diepold, “QualityCrowd –
A Framework for Crowd-based Quality Evaluation,” in Picture Coding
Symposium 2012 (PCS 2012), May 2012, pp. 245-248

[2] MPEG 3D Graphics Coding, “Text of ISO/IEC CD 23090-5 Visual Volumetric Video-based Coding and Video-based Point Cloud Compression 2nd Edition,” ISO/IEC JTC1/SC29/WG07 Doc. N0003, Nov. 2020.

[3] MPEG Systems, “Text of ISO/IEC DIS 23090-18 Carriage of Geometry-based Point Cloud Compression Data,” ISO/IEC JTC1/SC29/WG03 Doc. N0075, Nov. 2020.

[4] M. Quach, G. Valenzise, and F. Dufaux, “Improved Deep Point Cloud Geometry Compression,” in IEEE International Workshop on Multimedia Signal Processing (MMSP’2020), Sep. 2020.

[5] M. Quach, G. Valenzise, and F. Dufaux, “Folding-based compression of point cloud attributes,” in 2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 3309–3313.

[6] D. Lazzarotto, E. Alexiou, T. Ebrahimi, “Benchmarking of objective quality metrics for point cloud compression,” in 2021 IEEE International Workshop on Multimedia Signal Processing (MMSP), 2021.