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Building a database of 3D scenes from user annotations

Author(s)
Russell, Bryan C.; Torralba, Antonio
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
In this paper, we wish to build a high quality database of images depicting scenes, along with their real-world three-dimensional (3D) coordinates. Such a database is useful for a variety of applications, including training systems for object detection and validation of 3D output. We build such a database from images that have been annotated with only the identity of objects and their spatial extent in images. Important for this task is the recovery of geometric information that is implicit in the object labels, such as qualitative relationships between objects (attachment, support, occlusion) and quantitative ones (inferring camera parameters). We describe a model that integrates cues extracted from the object labels to infer the implicit geometric information. We show that we are able to obtain high quality 3D information by evaluating the proposed approach on a database obtained with a laser range scanner. Finally, given the database of 3D scenes, we show how it can find better scene matches for an unlabeled image by expanding the database through viewpoint interpolation to unseen views.
Date issued
2009-08
URI
http://hdl.handle.net/1721.1/60053
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Russell, B.C., and A. Torralba. “Building a database of 3D scenes from user annotations.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 2711-2718. © Copyright 2009 IEEE
Version: Final published version
Other identifiers
INSPEC Accession Number: 10835842
ISBN
978-1-4244-3992-8
ISSN
1063-6919

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