  Object location prior is critical for the standard 6D object pose estimation
setting. The prior can be used to initialize the 3D object translation and
facilitate 3D object rotation estimation. Unfortunately, the object detectors
that are used for this purpose do not generalize to unseen objects. Therefore,
existing 6D pose estimation methods for unseen objects either assume the
ground-truth object location to be known or yield inaccurate results when it is
unavailable. In this paper, we address this problem by developing a method,
LocPoseNet, able to robustly learn location prior for unseen objects. Our
method builds upon a template matching strategy, where we propose to distribute
the reference kernels and convolve them with a query to efficiently compute
multi-scale correlations. We then introduce a novel translation estimator,
which decouples scale-aware and scale-robust features to predict different
object location parameters. Our method outperforms existing works by a large
margin on LINEMOD and GenMOP. We further construct a challenging synthetic
dataset, which allows us to highlight the better robustness of our method to
various noise sources. Our project website is at:
https://sailor-z.github.io/projects/3DV2024_LocPoseNet.html.
