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We address the task of predicting pose for objects of unannotated objectcategories from a small seed set of annotated object classes. We present ageneralized classifier that can reliably induce pose given a single instance ofa novel category. In case of availability of a large collection of novelinstances, our approach then jointly reasons over all instances to improve theinitial estimates. We empirically validate the various components of ouralgorithm and quantitatively show that our method produces reliable poseestimates. We also show qualitative results on a diverse set of classes andfurther demonstrate the applicability of our system for learning shape modelsof novel object classes.

Pose Induction for Novel Object Categories