LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation

Anh Ngoc Phuong Duong, Alexandre Almin, Léo Lemarié, B. Ravi Kiran

2022

Abstract

Autonomous driving (AD) datasets have progressively grown in size in the past few years to enable better deep representation learning. Active learning (AL) has re-gained attention recently to address reduction of annotation costs and dataset size. AL has remained relatively unexplored for AD datasets, especially on point cloud data from LiDARs. This paper performs a principled evaluation of AL based dataset distillation on (1/4th) of the large Semantic-KITTI dataset. Further on, the gains in model performance due to data augmentation (DA) are demonstrated across different subsets of the AL loop. We also demonstrate how DA improves the selection of informative samples to annotate. We observe that data augmentation achieves full dataset accuracy using only 60% of samples from the selected dataset configuration. This provides faster training time and subsequent gains in annotation costs.

Paper Citation

in Harvard Style

Duong A., Almin A., Lemarié L. and Kiran B. (2022). LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 159-167. DOI: 10.5220/0010860800003124

in Bibtex Style

@conference{visapp22,
author={Anh Ngoc Phuong Duong and Alexandre Almin and Léo Lemarié and B. Ravi Kiran},
title={LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={159-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010860800003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - LiDAR Dataset Distillation within Bayesian Active Learning Framework Understanding the Effect of Data Augmentation
SN - 978-989-758-555-5
AU - Duong A.
AU - Almin A.
AU - Lemarié L.
AU - Kiran B.
PY - 2022
SP - 159
EP - 167
DO - 10.5220/0010860800003124
PB - SciTePress