Reference
Distorted
Front View
Back View
Introduction
This research introduces two complementary eye-tracking datasets for studying visual attention in Dynamic Point Clouds (DPC) within Virtual Reality environments. We compare task-free viewing (24 participants, 19 DPCs) with task-dependent quality assessment (40 participants, 5 reference DPCs with multiple distortions). Our analysis reveals significant differences in visual attention between these paradigms, measured using Pearson correlation and an adapted Earth Mover’s Distance metric. This work establishes a crucial connection between quality assessment and visual attention in DPCs, providing valuable benchmark data for improving VR experiences and visual saliency models.
Apparatus

Experimental Design
Data Processing

Data Collection: Angular Error Estimation

Gaze Point Identification

Truncated Cone-Sector
Each stage of our data processing pipeline is carefully designed to maintain data integrity while extracting meaningful insights about visual attention patterns in dynamic point cloud environments.
Comparison

Task-dependent
Results

Comparison Consistency of Visual Saliency Map Higher value means two saliency maps are more similar
Publications
-
QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF.
In IEEE International Symposium on Mixed and Augmented Reality (ISMAR),
2023.
-
Comparison of Visual Saliency for Dynamic Point Clouds: Task-free vs. Task-dependent.
IEEE Transactions on Visualization and Computer Graphics,
31(5): pp. 2964 - 2974,
2025.
PDF
Github Repository
-
Visual-Saliency Guided Multi-modal Learning for No Reference Point Cloud Quality Assessment.
In Proceedings of the 3rd Workshop on Quality of Experience in Visual Multimedia Applications,
2024.
Datasets
-
Dynamic Point Cloud Quality Score & Eye-tracking Dataset (QAVQ-DPC) - Data from the 1st task-dependent user study
LinkDataset -
Dynamic Point Cloud Eye-tracking Dataset - Data from the 2nd task-free user study
LinkDataset
Contact
For questions about this research, please contact Xuemei Zhou (xuemei.zhou [at] cwi.nl)