mlsanityData to 7 Oct 2026

LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation

arXiv 2202.02661 ↗First posted 6 Feb 2022cs.CV cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 6 Feb 2022.

Builds on

22 of 38 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehiclescited by 9
  2. 2023Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI datasetcited by 0