Advances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenes

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dc.contributor.authorPatil, Akshay Gadien_US
dc.contributor.authorPatil, Supriya Gadien_US
dc.contributor.authorLi, Manyien_US
dc.contributor.authorFisher, Matthewen_US
dc.contributor.authorSavva, Manolisen_US
dc.contributor.authorZhang, Haoen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWimmer, Michaelen_US
dc.date.accessioned2024-03-23T10:14:48Z
dc.date.available2024-03-23T10:14:48Z
dc.date.issued2024
dc.description.abstractThis report surveys advances in deep learning‐based modelling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modelling tasks based on these representations. Specifically, we focus on the and of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks – 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model‐driven methods that allow for human‐centric, progressive scene synthesis. We identify the challenges involved in modelling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state‐of‐the‐art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output and so on, providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.en_US
dc.description.number1
dc.description.sectionheadersARTICLES
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.14927
dc.identifier.issn1467-8659
dc.identifier.pages32 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14927
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf14927
dc.publisher© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectmethods and applications
dc.subjectmethods and applications – computer games
dc.subjectmodelling
dc.subjectgeometric modelling
dc.subjectvirtual environments
dc.titleAdvances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenesen_US
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