6 DoF head mounted display user navigation data for dynamic point cloud streaming
In recent years, the development of devices for acquisition and rendering of 3D contents have facilitated the diffusion of immersive virtual reality experiences. In particular, the point cloud representation has emerged as a popular format for volumetric photorealistic reconstructions of dynamic real world objects, due to its simplicity and versatility. To optimize the delivery of the large amount of data needed to provide these experiences, adaptive streaming over HTTP is a promising solution. In order to ensure the best quality of experience within the bandwidth constraints, adaptive streaming is combined with tiling to optimize the quality of what is being visualized by the user at a given moment; as such, it has been successfully used in the past for omnidirectional contents. However, its adoption to the point cloud streaming scenario has only been studied to optimize multi-object delivery. In this work, we present a low-complexity tiling approach to perform adaptive streaming of point cloud content. Tiles are defined by segmenting each point cloud object in several parts, which are then independently encoded. In order to evaluate the approach, we first collect real navigation paths, obtained through a user study in 6 degrees of freedom with 26 participants. The variation in movements and interaction behaviour among users indicate that a user-centered adaptive delivery could lead to sensible gains in terms of perceived quality. This dataset is made available here for future research. The point cloud dataset used in this work is - Eugene d’Eon, Bob Harrison, Taos Myers, and Philip A. Chou. 2017. 8i Voxelized Full Bodies - A Voxelized Point Cloud Dataset, ISO/IEC JTC1/SC29 JointWG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006, Geneva.(January 2017)
|Organisation||Distributed and Interactive Systems|
Subramanyam, S, Li, J, Viola, I, Hanjalic, A, & César Garcia, P.S. (2020). 6 DoF head mounted display user navigation data for dynamic point cloud streaming.
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