  Typical LiDAR-based 3D object detection models are trained in a supervised
manner with real-world data collection, which is often imbalanced over classes
(or long-tailed). To deal with it, augmenting minority-class examples by
sampling ground truth (GT) LiDAR points from a database and pasting them into a
scene of interest is often used, but challenges still remain: inflexibility in
locating GT samples and limited sample diversity. In this work, we propose to
leverage pseudo-LiDAR point clouds generated (at a low cost) from videos
capturing a surround view of miniatures or real-world objects of minor classes.
Our method, called Pseudo Ground Truth Augmentation (PGT-Aug), consists of
three main steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D
view synthesis model, (ii) object-level domain alignment with LiDAR intensity
estimation and (iii) a hybrid context-aware placement method from ground and
map information. We demonstrate the superiority and generality of our method
through performance improvements in extensive experiments conducted on three
popular benchmarks, i.e., nuScenes, KITTI, and Lyft, especially for the
datasets with large domain gaps captured by different LiDAR configurations. Our
code and data will be publicly available upon publication.
