  Place recognition plays an essential role in the field of autonomous driving
and robot navigation. Point cloud based methods mainly focus on extracting
global descriptors from local features of point clouds. Despite having achieved
promising results, existing solutions neglect the following aspects, which may
cause performance degradation: (1) huge size difference between objects in
outdoor scenes; (2) moving objects that are unrelated to place recognition; (3)
long-range contextual information. We illustrate that the above aspects bring
challenges to extracting discriminative global descriptors. To mitigate these
problems, we propose a novel method named TransLoc3D, utilizing adaptive
receptive fields with a point-wise reweighting scheme to handle objects of
different sizes while suppressing noises, and an external transformer to
capture long-range feature dependencies. As opposed to existing architectures
which adopt fixed and limited receptive fields, our method benefits from
size-adaptive receptive fields as well as global contextual information, and
outperforms current state-of-the-arts with significant improvements on popular
datasets.
