This thesis presents a generative three-dimensional (3D) representation and recognition framework for classes of objects. The framework uses probabilistic grammars to represent object classes recursively in terms of their parts, thereby exploiting the hierarchical and substitutive structure inherent to many types of objects. The framework models the 3D geometric characteristics of object parts using multivariate conditional Gaussians over dimensions, position, and rotation. I present algorithms… CONTINUE READING