 |
    |
FPM: Fine Pose Parts-Based Model with 3D CAD Models
Joseph J. Lim, Aditya Khosla, and Antonio Torralba
Massachusetts Institute of Technology, USAlim@csail.mit.edu
khosla@csail.mit.edu
torralba@csail.mit.edu
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.
LNCS 8694, p. 478 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014