Abstract

We introduce a novel approach to the problem of localizing ob jects 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 esti- mate 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 infor- mation in the form of ob jectness to achieve both fast and accurate fine pose estimation. Our method significantly outperforms current state-of- the-art algorithms in both accuracy and speed.