  In autonomous driving, a LiDAR-based object detector should perform reliably
at different geographic locations and under various weather conditions. While
recent 3D detection research focuses on improving performance within a single
domain, our study reveals that the performance of modern detectors can drop
drastically cross-domain. In this paper, we investigate unsupervised domain
adaptation (UDA) for LiDAR-based 3D object detection. On the Waymo Domain
Adaptation dataset, we identify the deteriorating point cloud quality as the
root cause of the performance drop. To address this issue, we present Semantic
Point Generation (SPG), a general approach to enhance the reliability of LiDAR
detectors against domain shifts. Specifically, SPG generates semantic points at
the predicted foreground regions and faithfully recovers missing parts of the
foreground objects, which are caused by phenomena such as occlusions, low
reflectance or weather interference. By merging the semantic points with the
original points, we obtain an augmented point cloud, which can be directly
consumed by modern LiDAR-based detectors. To validate the wide applicability of
SPG, we experiment with two representative detectors, PointPillars and PV-RCNN.
On the UDA task, SPG significantly improves both detectors across all object
categories of interest and at all difficulty levels. SPG can also benefit
object detection in the original domain. On the Waymo Open Dataset and KITTI,
SPG improves 3D detection results of these two methods across all categories.
Combined with PV-RCNN, SPG achieves state-of-the-art 3D detection results on
KITTI.
