  This study addresses the challenge of accurate 6D pose estimation in
Augmented Reality (AR), a critical component for seamlessly integrating virtual
objects into real-world environments. Our research primarily addresses the
difficulty of estimating 6D poses from uncontrolled RGB images, a common
scenario in AR applications, which lacks metadata such as focal length. We
propose a novel approach that strategically decomposes the estimation of z-axis
translation and focal length, leveraging the neural-render and compare strategy
inherent in the FocalPose architecture. This methodology not only streamlines
the 6D pose estimation process but also significantly enhances the accuracy of
3D object overlaying in AR settings. Our experimental results demonstrate a
marked improvement in 6D pose estimation accuracy, with promising applications
in manufacturing and robotics. Here, the precise overlay of AR visualizations
and the advancement of robotic vision systems stand to benefit substantially
from our findings.
