Object location priors have been shown to be critical for the standard 6D
object pose estimation setting, where the training and testing objects are the
same. Specifically, they 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,
i.e., objects from new categories at test time. Therefore, existing 6D pose
estimation methods for previously-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