  Current LiDAR-based 3D object detectors for autonomous driving are almost
entirely trained on human-annotated data collected in specific geographical
domains with specific sensor setups, making it difficult to adapt to a
different domain. MODEST is the first work to train 3D object detectors without
any labels. Our work, HyperMODEST, proposes a universal method implemented on
top of MODEST that can largely accelerate the self-training process and does
not require tuning on a specific dataset. We filter intermediate pseudo-labels
used for data augmentation with low confidence scores. On the nuScenes dataset,
we observe a significant improvement of 1.6% in AP BEV in 0-80m range at
IoU=0.25 and an improvement of 1.7% in AP BEV in 0-80m range at IoU=0.5 while
only using one-fifth of the training time in the original approach by MODEST.
On the Lyft dataset, we also observe an improvement over the baseline during
the first round of iterative self-training. We explore the trade-off between
high precision and high recall in the early stage of the self-training process
by comparing our proposed method with two other score filtering methods:
confidence score filtering for pseudo-labels with and without static label
retention. The code and models of this work are available at
https://github.com/TRAILab/HyperMODEST
