用启发式规则提升3D目标检测主动学习效果，仅用24%数据达全监督精度。
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection
- 融合距离、点数等启发式特征评估不确定性，优化样本选择。
- 在KITTI上仅用24%数据即达到全监督基线的mAP性能。
- 适合自动驾驶场景下标注成本敏感的3D检测模型训练。
主动学习在自动驾驶3D目标检测的样本选择中展现出显著价值。然而，现有方法在复杂场景下的样本筛选仍具挑战性，且过度关注理论而忽视实际应用中的经验知识。本文提出HeAL（启发式增强的主动学习），将物体距离、点云数量等启发式特征与定位、分类能力结合，更精准地选出对模型训练贡献最大的样本。相比以往方法，该策略通过引入实用启发式信息提升所选样本的有效性。在KITTI数据集上的定量评估显示，HeAL在性能上媲美当前最优方法，并仅需24%的样本量即可达到全监督基线的mAP水平。
原文摘要 · Abstract (English)
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.
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