Interactive Room Capture on 3D-Aware Mobile Devices
UIST 2017Aditya Sankar - University of Washington
Steve Seitz - University of Washington & Google
Demo Video
Talk
Talk
Abstract -- We propose a novel interactive system to simplify the process of indoor 3D CAD room modeling. Traditional room modeling methods require users to measure room and furniture dimensions, and manually select models that match the scene from large catalogs. Users then employ a mouse and keyboard interface to construct walls and place the objects in their appropriate locations. In contrast, our system leverages the sensing capabilities of a 3D aware mobile device, recent advances in object recognition, and a novel augmented reality user interface, to capture indoor 3D room models in-situ. With a few taps, a user can mark the surface of an object, take a photo, and the system retrieves and places a matching 3D model into the scene, from a large online database. User studies indicate that this modality is significantly quicker, more accurate, and requires less effort than traditional desktop tools.
With our system, a casual user can generate a representative 3D CAD model of a room, in a few minutes, on a `3D-aware' mobile device. The generated model is editable and contains scene semantics, enabling interior design applications.
Paper: PDF (22.9 MB)
Citation: Aditya Sankar and Steven Seitz. 2017. "Interactive Room Capture on 3D-Aware Mobile Devices."
In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (UIST '17). (ACM DL) (BibTeX)
Contact Author: Aditya Sankar, aditya[at]cs[dot]washington[dot]edu
Acknowledgements: This work was supported in part by the National Science Foundation (IIS-1250793), the University of Washington Animation Research Labs, and Google.
Citation: Aditya Sankar and Steven Seitz. 2017. "Interactive Room Capture on 3D-Aware Mobile Devices."
In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (UIST '17). (ACM DL) (BibTeX)
Contact Author: Aditya Sankar, aditya[at]cs[dot]washington[dot]edu
Acknowledgements: This work was supported in part by the National Science Foundation (IIS-1250793), the University of Washington Animation Research Labs, and Google.
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