HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection

TL;DR

HeAL3D introduces a heuristic-enhanced active learning approach for 3D object detection that effectively selects training samples, reducing labeling effort while maintaining high detection accuracy in autonomous driving scenarios.

Contribution

This work integrates heuristic features like object distance and point-quantity into active learning for 3D detection, improving sample selection effectiveness.

Findings

Achieves comparable mAP to full supervision with only 24% of samples.

Outperforms previous active learning methods in 3D detection.

Demonstrates effectiveness on KITTI dataset.

Abstract

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.