Just Add $100 More, Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem

Abstract

Typical LiDAR-based 3D object detection models are trained with real-world data collection, which is often imbalanced over classes. To deal with it, augmentation techniques are commonly used, such as copying ground truth LiDAR points and pasting them into scenes. However, existing methods struggle with the lack of sample diversity for minority classes and the limitation of suitable placement. In this work, we introduce a novel approach that utilizes pseudo LiDAR point clouds generated from low-cost miniatures or real-world videos, which is called Pseudo Ground Truth augmentation (PGT-Aug). PGT-Aug involves three key steps, (i) volumetric 3D instance reconstruction using a 2D-to-3D view synthesis model, (ii) object-level domain alignment with LiDAR intensity simulation, 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 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.