Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning

Active learning has recently gained attention in deep learning tasks dedicated to autonomous driving, such as image classification. However, semantic segmentation for point clouds remains a largely unexplored task in active learning, mainly due to the heavy computational cost of such work. In this paper, we present an analysis to reduce data redundancy in the large-scale dataset Semantic-Kitti, thanks to active learning uncertainty-based methods and data augmentation. We are able to demonstrate that data augmentation techniques is helping our active learning cycles, and achieve baseline accuracy with only 60% of the dataset.