  We propose SeedAL, a method to seed active learning for efficient annotation
of 3D point clouds for semantic segmentation. Active Learning (AL) iteratively
selects relevant data fractions to annotate within a given budget, but requires
a first fraction of the dataset (a 'seed') to be already annotated to estimate
the benefit of annotating other data fractions. We first show that the choice
of the seed can significantly affect the performance of many AL methods. We
then propose a method for automatically constructing a seed that will ensure
good performance for AL. Assuming that images of the point clouds are
available, which is common, our method relies on powerful unsupervised image
features to measure the diversity of the point clouds. It selects the point
clouds for the seed by optimizing the diversity under an annotation budget,
which can be done by solving a linear optimization problem. Our experiments
demonstrate the effectiveness of our approach compared to random seeding and
existing methods on both the S3DIS and SemanticKitti datasets. Code is
available at https://github.com/nerminsamet/seedal.
