  Object re-identification (ReID) from images plays a critical role in
application domains of image retrieval (surveillance, retail analytics, etc.)
and multi-object tracking (autonomous driving, robotics, etc.). However,
systems that additionally or exclusively perceive the world from depth sensors
are becoming more commonplace without any corresponding methods for object
ReID. In this work, we fill the gap by providing the first large-scale study of
object ReID from point clouds and establishing its performance relative to
image ReID. To enable such a study, we create two large-scale ReID datasets
with paired image and LiDAR observations and propose a lightweight matching
head that can be concatenated to any set or sequence processing backbone (e.g.,
PointNet or ViT), creating a family of comparable object ReID networks for both
modalities. Run in Siamese style, our proposed point cloud ReID networks can
make thousands of pairwise comparisons in real-time ($10$ Hz). Our findings
demonstrate that their performance increases with higher sensor resolution and
approaches that of image ReID when observations are sufficiently dense. Our
strongest network trained at the largest scale achieves ReID accuracy exceeding
$90\%$ for rigid objects and $85\%$ for deformable objects (without any
explicit skeleton normalization). To our knowledge, we are the first to study
object re-identification from real point cloud observations.
