24 lines
2.6 KiB
Plaintext
24 lines
2.6 KiB
Plaintext
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With the advances in 3D models editing and 3D reconstruction techniques, more and more 3D models are available and their quality is increasing.
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Furthermore, the support of 3D visualization on the web has become standard during the last years.
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A major challenge is thus to deliver these remote heavy models and to allow users to visualise and navigate in these virtual environments.
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This thesis focuses on 3D content streaming and interaction, and proposes three major contributions.
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First, *we develop a 3D scene navigation interface with bookmarks* -- small virtual objects added to the scene that the user can click on to ease reaching a recommended location.
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We describe a user study where participants navigate in 3D scenes with and without bookmarks.
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We show that users navigate (and accomplish a given task) faster when using bookmarks.
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However, this faster navigation has a drawback on the streaming performance: a user who moves faster in a scene requires higher streaming capabilities in order to enjoy the same quality of service.
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This drawback can be mitigated using the fact that bookmarks positions are known in advance: by ordering the faces of the 3D model according to their visibility at a bookmark, we optimize the streaming and thus, decrease the latency when users click on bookmarks.
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Secondly, *we propose an adaptation of Dynamic Adaptive Streaming over HTTP (DASH), the video streaming standard, to 3D textured meshes streaming*.
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To do so, we partition the scene into a k-d tree where each cell corresponds to a DASH adaptation set.
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Each cell is further divided into DASH segments of a fixed number of faces, grouping together faces of similar areas.
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Each texture is indexed in its own adaptation set, and multiple DASH representations are available for different resolutions of the textures.
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All the metadata (the cells of the k-d tree, the resolutions of the textures, etc.) is encoded in the Media Presentation Description (MPD): an XML file that DASH uses to index content.
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Thus, our framework inherits DASH scalability.
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We then propose clients capable of evaluating the usefulness of each chunk of data depending on their viewpoint, and streaming policies that decide which chunks to download.
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Finally, *we investigate the setting of 3D streaming and navigation on mobile devices*.
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We integrate bookmarks in our 3D version of DASH and propose an improved version of our DASH client that benefits from bookmarks.
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A user study shows that with our dedicated bookmark streaming policy, bookmarks are more likely to be clicked on, enhancing both users quality of service and quality of experience.
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