The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration. (arXiv:2401.16634v1 [cs.CV])

Active learning strategies for 3D object detection in autonomous driving
datasets may help to address challenges of data imbalance, redundancy, and
high-dimensional data. We demonstrate the effectiveness of entropy querying to
select informative samples, aiming to reduce annotation costs and improve model
performance. We experiment using the BEVFusion model for 3D object detection on
the nuScenes dataset, comparing active learning to random sampling and
demonstrating that entropy querying outperforms in most cases. The method is
particularly effective in reducing the …

3d object 3d object detection active learning arxiv autonomous autonomous driving challenges cs.cv data data-driven datasets detection driving entropy exploration reduce redundancy samples strategies