This dataset contains the data related to the experiment described in . In this experiment, point clouds distorted with compression artifacts from both G-PCC and a learning-based codec  were subjectively evaluated on two visualization devices: an Eye-Sensing Light Field Display (ELFD) and a flat monitor. The Simultaneous DSIS protocol was used, and subjects were asked to assess the impairment between the distorted and the original point cloud in a 5-rating scale. The reference point clouds were also evaluated according to the naturalness of their renderings in each device. Each subject performed the experiment on each visualization device in two consecutive days. After the second day, they were asked to answer to a survey about their experience.
The point clouds and subjective scores from this experiment were later used to benchmark objective quality metrics in .
In this dataset are released all the collected subjective opinions from the experiments with both devices as well as the results of the survey and scripts to compress the dataset with the settings used in the experiment. Moreover, the two point clouds fruits_vox10 and wooden_dragon_vox10 are provided under the
The platform used to conduct the experiment was developed in Unity and is made available in the following Github repository:
The dataset can be downloaded from the following FTP by using dedicated FTP clients, such as FileZilla or FireFTP (we recommend to use
FTP address: tremplin.epfl.ch
Username: [email protected]
FTP port: 21
After you connect, choose the SR-PCD folder from the remote site, and download the relevant 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 provided script in your research, we kindly ask you to cite .
 Lazzarotto, Davi, Michela Testolina, and Touradj Ebrahimi. “On the impact of spatial rendering on point cloud subjective visual quality assessment.” 2022 14th International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2022.
 Frank, Nicolas, Davi Lazzarotto, and Touradj Ebrahimi. “Latent Space Slicing for Enhanced Entropy Modeling In Learning-Based Point Cloud Geometry Compression.” ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022.
 Lazzarotto, Davi, Michela Testolina, and Touradj Ebrahimi. “Influence of Spatial Rendering on the Performance of Point Cloud Objective Quality Metrics.” 2022 10th European Workshop on Visual Information Processing (EUVIP). IEEE, 2022.
In case of questions, feel free to contact the following email address: