Print Email Facebook Twitter ComPEQ-MR Title ComPEQ-MR: Compressed Point Cloud Dataset with Eye Tracking and Quality Assessment in Mixed Reality Author Nguyen, Minh (Fraunhofer FOKUS; University of Klagenfurt) Vats, Shivi (University of Klagenfurt) Zhou, X. (TU Delft Multimedia Computing) Viola, Irene (Centrum Wiskunde & Informatica (CWI)) Cesar, Pablo (TU Delft Multimedia Computing) Timmerer, Christian (University of Klagenfurt) Hellwagner, Hermann (University of Klagenfurt) Date 2024 Abstract Point clouds (PCs) have attracted researchers and developers due to their ability to provide immersive experiences with six degrees of freedom (6DoF). However, there are still several open issues in understanding the Quality of Experience (QoE) and visual attention of end users while experiencing 6DoF volumetric videos. First, encoding and decoding point clouds require a significant amount of both time and computational resources. Second, QoE prediction models for dynamic point clouds in 6DoF have not yet been developed due to the lack of visual quality databases. Third, visual attention in 6DoF is hardly explored, which impedes research into more sophisticated approaches for adaptive streaming of dynamic point clouds. In this work, we provide an open-source Compressed Point cloud dataset with Eye-tracking and Quality assessment in Mixed Reality (ComPEQ - MR). The dataset comprises four compressed dynamic point clouds processed by Moving Picture Experts Group (MPEG) reference tools (i.e., VPCC and GPCC), each with 12 distortion levels. We also conducted subjective tests to assess the quality of the compressed point clouds with different levels of distortion. The rating scores are attached to ComPEQ - MR so that they can be used to develop QoE prediction models in the context of MR environments. Additionally, eye-tracking data for visual saliency is included in this dataset, which is necessary to predict where people look when watching 3D videos in MR experiences. We collected opinion scores and eye-tracking data from 41 participants, resulting in 2132 responses and 164 visual attention maps in total. The dataset is available at https://ftp.itec.aau.at/datasets/ComPEQ-MR/. Subject Adaptive Video StreamingAugmented RealityDatasetMetaversePoint Cloud To reference this document use: http://resolver.tudelft.nl/uuid:1aa7992e-cc41-49c0-b83f-963909e3878b DOI https://doi.org/10.1145/3625468.3652182 Publisher Association for Computing Machinery (ACM) ISBN 9798400704123 Source MMSys 2024 - Proceedings of the 2024 ACM Multimedia Systems Conference Event 15th ACM Multimedia Systems Conference, MMSys 2024, 2024-04-15 → 2024-04-18, Bari, Italy Series MMSys 2024 - Proceedings of the 2024 ACM Multimedia Systems Conference Part of collection Institutional Repository Document type conference paper Rights © 2024 Minh Nguyen, Shivi Vats, X. Zhou, Irene Viola, Pablo Cesar, Christian Timmerer, Hermann Hellwagner Files PDF 3625468.3652182.pdf 1.39 MB Close viewer /islandora/object/uuid:1aa7992e-cc41-49c0-b83f-963909e3878b/datastream/OBJ/view