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

Nermin Samet    Oriane Siméoni   Gilles Puy   Georgy Ponimatkin   Renaud Marlet   Vincent Lepetit

ICCV 2023

Abstract

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 this https URL.

BibTeX

@inproceedings{samet2023seedal,
  title={You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation},
  author={Samet, Nermin and Sim\'eoni, Oriane and Puy, Gilles and Ponimatkin, Georgy and Marlet, Renaud and Lepetit, Vincent},
  booktitle={ICCV},
  year={2023}
}