3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, leading to poor performance on unfamiliar scenes. We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. These uncertainty estimates require minimal computational overhead and are generalizable across different architectures. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections; our framework consistently improves over baselines by 10-20% on average. Finally, we integrate this suite of tasks into a system where a 3D object detector auto-labels driving scenes and our uncertainty estimates verify label correctness before the labels are used to train a second model. Here, our uncertainty-driven verification results in a 1% improvement in mAP and a 1-2% improvement in NDS.

翻译：三维目标检测是自动驾驶汽车和机器人等计算机视觉应用中的关键任务。然而，模型往往难以量化检测的可靠性，导致在陌生场景下性能不佳。我们提出了一种量化三维目标检测不确定性的框架，该框架通过在三维检测器的鸟瞰图表示上应用证据学习损失来实现。这些不确定性估计仅需极少的计算开销，且能泛化至不同的架构。我们证明了这些不确定性估计在识别分布外场景、定位不佳的目标以及漏检（假阴性）方面的有效性和重要性；我们的框架相较于基线方法平均持续提升10-20%的性能。最后，我们将该系列任务集成到一个系统中：三维目标检测器自动标注驾驶场景，而我们的不确定性估计在标注被用于训练第二个模型之前验证其正确性。在此，基于不确定性的验证使mAP提升了1%，NDS提升了1-2%。