Among them, over-sampling and data augmentation aim to balance the data distribution by oversampling or generating closely related minority classes.

As a result, a naïve ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes.

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

Despite a realistic problem, quantization on class-imbalanced data has not been studied.