Real-time scene analysis for 3D interaction

Abstract : This PhD thesis focuses on the problem of visual scene analysis captured by commodity depth sensors to convert their data into high level understanding of the scene. It explores the use of 3D geometry analysis tools on visual depth data in terms of enhancement, registration and consolidation. In particular, we aim to show how shape abstraction can generate lightweight representations of the data for fast analysis with low hardware requirements. This last property is important as one of our goals is to design algorithms suitable for live embedded operation in e.g., wearable devices, smartphones or mobile robots. The context of this thesis is the live operation of 3D interaction on a mobile device, which raises numerous issues including placing 3D interaction zones with relation to real surrounding objects, tracking the interaction zones in space when the sensor moves and providing a meaningful and understandable experience to non-expert users. Towards solving these problems, we make contributions where scene abstraction leads to fast and robust sensor localization as well as efficient frame data representation, enhancement and consolidation. While simple geometric surface shapes are not as faithful as heavy point sets or volumes to represent observed scenes, we show that they are an acceptable approximation and their light weight makes them well balanced between accuracy and performance.
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Adrien Kaiser. Real-time scene analysis for 3D interaction. Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Saclay, 2019. English. ⟨NNT : 2019SACLT025⟩. ⟨tel-02202007⟩

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