  Estimating 2D-3D correspondences between RGB images and 3D space is a
fundamental problem in 6D object pose estimation. Recent pose estimators use
dense correspondence maps and Point-to-Point algorithms to estimate object
poses. The accuracy of pose estimation depends heavily on the quality of the
dense correspondence maps and their ability to withstand occlusion, clutter,
and challenging material properties. Currently, dense correspondence maps are
estimated using image-to-image translation models based on GANs, Autoencoders,
or direct regression models. However, recent advancements in image-to-image
translation have led to diffusion models being the superior choice when
evaluated on benchmarking datasets. In this study, we compare image-to-image
translation networks based on GANs and diffusion models for the downstream task
of 6D object pose estimation. Our results demonstrate that the diffusion-based
image-to-image translation model outperforms the GAN, revealing potential for
further improvements in 6D object pose estimation models.
