  Active learning strives to reduce the need for costly data annotation, by
repeatedly querying an annotator to label the most informative samples from a
pool of unlabeled data, and then training a model from these samples. We
identify two problems with existing active learning methods for LiDAR semantic
segmentation. First, they overlook the severe class imbalance inherent in LiDAR
semantic segmentation datasets. Second, to bootstrap the active learning loop
when there is no labeled data available, they train their initial model from
randomly selected data samples, leading to low performance. This situation is
referred to as the cold start problem. To address these problems we propose
BaSAL, a size-balanced warm start active learning model, based on the
observation that each object class has a characteristic size. By sampling
object clusters according to their size, we can thus create a size-balanced
dataset that is also more class-balanced. Furthermore, in contrast to existing
information measures like entropy or CoreSet, size-based sampling does not
require a pretrained model, thus addressing the cold start problem effectively.
Results show that we are able to improve the performance of the initial model
by a large margin. Combining warm start and size-balanced sampling with
established information measures, our approach achieves comparable performance
to training on the entire SemanticKITTI dataset, despite using only 5% of the
annotations, outperforming existing active learning methods. We also match the
existing state-of-the-art in active learning on nuScenes. Our code is available
at: https://github.com/Tony-WJR/BaSAL.
