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.

本研究主要解决在增强现实（AR）中准确估计六自由度姿态的挑战，我们提出了一种新颖的方法，通过分解 z 轴平移和焦距的估计，利用 FocalPose 架构中内置的神经渲染和比较策略，从无控制的 RGB 图像中准确估计六自由度姿态，并显著提高了 AR 环境中三维物体叠加的准确性，实验结果表明在六自由度姿态估计准确度上有明显改进，该研究成果对制造业和机器人领域具有潜在的重要应用。