Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotation, which is often both burdensome and time-consuming. To tackle this issue, the literature has seen the emergence of several weakly supervised frameworks for 3D object detection which can automatically generate pseudo labels for unlabeled data. Nevertheless, these generated pseudo labels contain noise and are not as accurate as those labeled by humans. In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework. Specifically, we propose MEDL-U, an EDL framework based on MTrans, which not only generates pseudo labels but also quantifies the associated uncertainties. However, applying EDL to 3D object detection presents three primary challenges: (1) relatively lower pseudolabel quality in comparison to other autolabelers; (2) excessively high evidential uncertainty estimates; and (3) lack of clear interpretability and effective utilization of uncertainties for downstream tasks. We tackle these issues through the introduction of an uncertainty-aware IoU-based loss, an evidence-aware multi-task loss function, and the implementation of a post-processing stage for uncertainty refinement. Our experimental results demonstrate that probabilistic detectors trained using the outputs of MEDL-U surpass deterministic detectors trained using outputs from previous 3D annotators on the KITTI val set for all difficulty levels. Moreover, MEDL-U achieves state-of-the-art results on the KITTI official test set compared to existing 3D automatic annotators.

翻译：基于深度学习的3D目标检测的进步依赖于大规模数据集的可获得性。然而，这一需求带来了人工标注的挑战——这一过程往往既繁重又耗时。为解决此问题，已有文献提出了若干弱监督3D目标检测框架，能够自动为未标注数据生成伪标签。但所生成的伪标签存在噪声，且准确性不及人工标注。本文首次提出通过引入基于证据深度学习的不确定性估计框架来处理伪标签中固有的歧义性。具体而言，我们提出MEDL-U——一种基于MTrans的EDL框架，它不仅能生成伪标签，还能量化相关的不确定性。然而，将EDL应用于3D目标检测面临三大挑战：(1) 伪标签质量低于其他自动标注器；(2) 证据不确定性估计过高；(3) 缺乏对不确定性的清晰解释及在下游任务中的有效利用。我们通过引入基于不确定性感知的交并比损失函数、证据感知的多任务损失函数，以及实现不确定性优化的后处理阶段来应对这些问题。实验结果表明，在KITTI验证集的所有难度级别上，使用MEDL-U输出训练的基于概率的检测器优于使用先前3D标注器输出训练的确定性检测器。此外，与现有3D自动标注器相比，MEDL-U在KITTI官方测试集上取得了最先进的结果。