  We present a joint 3D pose and focal length estimation approach for object
categories in the wild. In contrast to previous methods that predict 3D poses
independently of the focal length or assume a constant focal length, we
explicitly estimate and integrate the focal length into the 3D pose estimation.
For this purpose, we combine deep learning techniques and geometric algorithms
in a two-stage approach: First, we estimate an initial focal length and
establish 2D-3D correspondences from a single RGB image using a deep network.
Second, we recover 3D poses and refine the focal length by minimizing the
reprojection error of the predicted correspondences. In this way, we exploit
the geometric prior given by the focal length for 3D pose estimation. This
results in two advantages: First, we achieve significantly improved 3D
translation and 3D pose accuracy compared to existing methods. Second, our
approach finds a geometric consensus between the individual projection
parameters, which is required for precise 2D-3D alignment. We evaluate our
proposed approach on three challenging real-world datasets (Pix3D, Comp, and
Stanford) with different object categories and significantly outperform the
state-of-the-art by up to 20% absolute in multiple different metrics.
