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目标检测技术的进步需要大规模数据集的支持。然而，这一需求引入了人工标注的挑战，该过程通常既繁重又耗时。为解决该问题，学术界已涌现出多种弱监督3D目标检测框架，可自动为未标注数据生成伪标签。然而，这些生成的伪标签包含噪声，其准确性不及人工标注。本文首次提出一种通过引入基于证据深度学习（EDL）的不确定性估计框架来解决伪标签固有模糊性的方法。具体而言，我们提出MEDL-U——一种基于MTrans的EDL框架，该框架不仅生成伪标签，还能量化相关的不确定性。然而，将EDL应用于3D目标检测面临三大主要挑战：（1）与其他自动标注器相比伪标签质量相对较低；（2）证据不确定性估计值过高；（3）缺乏清晰的可解释性及有效利用不确定性进行下游任务的能力。我们通过引入基于不确定性感知交并比（IoU）的损失函数、证据感知多任务损失函数，以及实施不确定性精炼的后处理阶段来解决这些问题。实验结果表明，在KITTI验证集的所有难度等级上，使用MEDL-U输出训练的概率检测器性能均优于使用先前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。