HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection
This work addresses the problem of efficient training data selection for 3D object detection models in autonomous driving, offering a practical improvement over theoretical approaches.
The paper tackles the challenge of sample selection in active learning for 3D object detection in autonomous driving by integrating heuristical features like object distance and point-quantity to estimate uncertainty, achieving competitive mAP on KITTI and matching full-supervised baseline performance with only 24% of samples.
Active Learning has proved to be a relevant approach to perform sample selection for training models for Autonomous Driving. Particularly, previous works on active learning for 3D object detection have shown that selection of samples in uncontrolled scenarios is challenging. Furthermore, current approaches focus exclusively on the theoretical aspects of the sample selection problem but neglect the practical insights that can be obtained from the extensive literature and application of 3D detection models. In this paper, we introduce HeAL (Heuristical-enhanced Active Learning for 3D Object Detection) which integrates those heuristical features together with Localization and Classification to deliver the most contributing samples to the model's training. In contrast to previous works, our approach integrates heuristical features such as object distance and point-quantity to estimate the uncertainty, which enhance the usefulness of selected samples to train detection models. Our quantitative evaluation on KITTI shows that HeAL presents competitive mAP with respect to the State-of-the-Art, and achieves the same mAP as the full-supervised baseline with only 24% of the samples.