Visual attention for point clouds in VR

In this page supplemental material of the study presented in [1] is made publicly available to enrich visual saliency datasets for 3D point cloud models. The given dataset consists of tracked behavioural data, post-processing results, saliency maps in form of importance weights, a sub-set of contents allowed for re-distribution, scripts to generate the models that were used in the study, and usage examples. 


You can download all related files from the following FTP address, using the provided credentials. Please use dedicated FTP clients, such as FileZilla or FireFTP:

FTP address:
User name:
Password: pc_visual_attention

The total size of the dataset is about 750 MB.

Please read carefully the README file for a detailed description about the structure of the provided material as well as how to use it. You may also want to read the related paper [1] for more information about the experiment, and details for the post-processing methodologies applied on the acquired data.

In case of any problems or questions, please send an email to evangelos.alexiou (at)

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 École 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 École Polytechnique Fédérale de Lausanne (EPFL) specifically disclaims any warranties. The data provided hereunder is on an “as is” basis and the École Polytechnique Fédérale de Lausanne (EPFL) has no obligation to provide maintenance, support, updates, enhancements, or modifications.

[1] Evangelos Alexiou, Peisen Xu, and Touradj Ebrahimi, “Towards modelling of visual saliency in point clouds for immersive applications,” in 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, September 2019.