  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
threefold. 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. Third, we explore the effect of different synthetic training data on
the performance of our method. Specifically, we investigate different
distributions used for sampling object's 6D pose and camera's focal length when
rendering the synthetic images, and show that parametric distribution fitted on
real training data works the best. 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.
