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We propose SeedAL, a method to seed active learning for efficient annotationof 3D point clouds for semantic segmentation. Active Learning (AL) iterativelyselects relevant data fractions to annotate within a given budget, but requiresa first fraction of the dataset (a 'seed') to be already annotated to estimatethe benefit of annotating other data fractions. We first show that the choiceof the seed can significantly affect the performance of many AL methods. Wethen propose a method for automatically constructing a seed that will ensuregood performance for AL. Assuming that images of the point clouds areavailable, which is common, our method relies on powerful unsupervised imagefeatures to measure the diversity of the point clouds. It selects the pointclouds for the seed by optimizing the diversity under an annotation budget,which can be done by solving a linear optimization problem. Our experimentsdemonstrate the effectiveness of our approach compared to random seeding andexisting methods on both the S3DIS and SemanticKitti datasets. Code isavailable at https://github.com/nerminsamet/seedal.

You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation