We propose a learning algorithm for estimating the 3-D orientation of objects. Orientation learning is a difficult problem because the space of orientations is non-Euclidean, and in some cases (such as quaternions) the representation is ambiguous, in that multiple representations exist for the same physical orientation. Learning is further complicated by the fact that most man-made objects exhibit symmetry, so that there are multiple “correct” orientations. In this paper, we propose a new representation for orientations—and a class of learning and inference algorithms using this representation— that allows us to learn orientations for symmetric or asymmetric objects as a function of a single image. We extensively evaluate our algorithm for learning orientations of objects from six categories.1

**Authors:**Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng (2009)**AUTHORED BY**

Ashutosh Saxena

Justin Driemeyer

Andrew Y. Ng

Justin Driemeyer

Andrew Y. Ng

### Abstract

We propose a learning algorithm for estimating the 3-D orientation of objects. Orientation learning is a difficult problem because the space of orientations is non-Euclidean, and in some cases (such as quaternions) the representation is ambiguous, in that multiple representations exist for the same physical orientation. Learning is further complicated by the fact that most man-made objects exhibit symmetry, so that there are multiple “correct” orientations. In this paper, we propose a new representation for orientations—and a class of learning and inference algorithms using this representation— that allows us to learn orientations for symmetric or asymmetric objects as a function of a single image. We extensively evaluate our algorithm for learning orientations of objects from six categories.Download PDF

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