GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild

Alexander Grabner, Peter M. Roth, and Vincent Lepetit

Abstract:

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 2D3D 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.