  LiDAR-based 3D object detection plays a crucial role in modern autonomous
driving systems. LiDAR data often exhibit severe changes in properties across
different observation ranges. In this paper, we explore cross-range adaptation
for 3D object detection using LiDAR, i.e., far-range observations are adapted
to near-range. This way, far-range detection is optimized for similar
performance to near-range one. We adopt a bird-eyes view (BEV) detection
framework to perform the proposed model adaptation. Our model adaptation
consists of an adversarial global adaptation, and a fine-grained local
adaptation. The proposed cross range adaptation framework is validated on three
state-of-the-art LiDAR based object detection networks, and we consistently
observe performance improvement on the far-range objects, without adding any
auxiliary parameters to the model. To the best of our knowledge, this paper is
the first attempt to study cross-range LiDAR adaptation for object detection in
point clouds. To demonstrate the generality of the proposed adaptation
framework, experiments on more challenging cross-device adaptation are further
conducted, and a new LiDAR dataset with high-quality annotated point clouds is
released to promote future research.
