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FPM: Fine Pose Parts-Based Model with 3D CAD Models

Author(s)
Khosla, Aditya; Torralba, Antonio; Lim, Joseph Jaewhan
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Abstract
We introduce a novel approach to the problem of localizing objects in an image and estimating their fine-pose. Given exact CAD models, and a few real training images with aligned models, we propose to leverage the geometric information from CAD models and appearance information from real images to learn a model that can accurately estimate fine pose in real images. Specifically, we propose FPM, a fine pose parts-based model, that combines geometric information in the form of shared 3D parts in deformable part based models, and appearance information in the form of objectness to achieve both fast and accurate fine pose estimation. Our method significantly outperforms current state-of-the-art algorithms in both accuracy and speed.
Date issued
2014
URI
http://hdl.handle.net/1721.1/91008
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Computer Vision – ECCV 2014
Publisher
Springer-Verlag
Citation
Lim, Joseph J., Aditya Khosla, and Antonio Torralba. “FPM: Fine Pose Parts-Based Model with 3D CAD Models.” Lecture Notes in Computer Science (2014): 478–493.
Version: Author's final manuscript
ISBN
978-3-319-10598-7
978-3-319-10599-4
ISSN
0302-9743
1611-3349

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