  We introduce FocalPose, a neural render-and-compare method for jointly
estimating the camera-object 6D pose and camera focal length given a single RGB
input image depicting a known object. The contributions of this work are
twofold. First, we derive a focal length update rule that extends an existing
state-of-the-art render-and-compare 6D pose estimator to address the joint
estimation task. Second, we investigate several different loss functions for
jointly estimating the object pose and focal length. We find that a combination
of direct focal length regression with a reprojection loss disentangling the
contribution of translation, rotation, and focal length leads to improved
results. We show results on three challenging benchmark datasets that depict
known 3D models in uncontrolled settings. We demonstrate that our focal length
and 6D pose estimates have lower error than the existing state-of-the-art
methods.
