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Parsing IKEA Objects: Fine Pose Estimation

Author(s)
Pirsiavash, Hamed; Torralba, Antonio; Lim, Joseph Jaewhan
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Abstract
We address the problem of localizing and estimating the fine-pose of objects in the image with exact 3D models. Our main focus is to unify contributions from the 1970s with recent advances in object detection: use local keypoint detectors to find candidate poses and score global alignment of each candidate pose to the image. Moreover, we also provide a new dataset containing fine-aligned objects with their exactly matched 3D models, and a set of models for widely used objects. We also evaluate our algorithm both on object detection and fine pose estimation, and show that our method outperforms state-of-the art algorithms.
Date issued
2013-12
URI
http://hdl.handle.net/1721.1/90988
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 2013 IEEE International Conference on Computer Vision
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Lim, Joseph J., Hamed Pirsiavash, and Antonio Torralba. “Parsing IKEA Objects: Fine Pose Estimation.” 2013 IEEE International Conference on Computer Vision (December 2013).
Version: Author's final manuscript
ISBN
978-1-4799-2840-8
ISSN
1550-5499

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