ABSTRACT
Hybrid Bayesian Eigenobjects are a novel representation for 3D objects that leverage both convolutional (deep) inference and linear subspace methods to enable robust reasoning about novel 3D objects. HBEOs allow joint estimation of the pose, class, and full 3D geometry of a novel object observed from a single (depth-image) viewpoint in a unified practical framework. By combining both linear subspace methods and deep convolutional prediction, HBEOs offer improved runtime, data efficiency, and performance compared to preceding purely deep or purely linear methods. In this talk, I discuss the current state of 3D object perception, HBEOs (and their predecessor BEOs), and the path forward towards reliable perception in cluttered and fully unstructured environments.