Publikation: ComPEQ-MR: Compressed Point Cloud Datas...
Stammdaten
Titel: | ComPEQ-MR: Compressed Point Cloud Dataset with Eye Tracking and Quality Assessment in Mixed Reality |
Untertitel: | |
Kurzfassung: | 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/. |
Schlagworte: | Point Cloud, Augmented Reality, Dataset, Adaptive Video Streaming, Metaverse |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 15.04.2024 (Print) |
Erschienen in: |
MMSys'24 Proceedings of the 15th ACM Multimedia Systems Conference
MMSys'24 Proceedings of the 15th ACM Multimedia Systems Conference
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ACM Digital Library;
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Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 367 - 373 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 15.04.2024 |
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ISSN: | - |
Homepage: | https://dl.acm.org/doi/10.1145/3625468.3652182 |
Erscheinungsdatum: | 17.04.2024 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1145/3625468.3652182 |
Homepage: | https://dl.acm.org/doi/10.1145/3625468.3652182 |
Open Access |
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AutorInnen
Minh Nguyen (intern) | ||||
Shivi Vats (intern) | ||||
Xuemei Zhou (extern) | ||||
Irene Viola (extern) | ||||
Pablo Cesar
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Christian Timmerer (intern) | ||||
Hermann Hellwagner (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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TECHNISCHE UNIVERSITEIT DELFT
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NL - 2628CN Delft |
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(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
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