通过证据学习提升3D检测的不确定性估计，增强模型对异常场景的识别能力。
Uncertainty Estimation for 3D Object Detection via Evidential Learning
- 在鸟瞰图表示上使用证据学习损失，量化检测置信度。
- 在异常场景、误检和漏检上表现优异，平均性能提升10%-20%。
- 可适配多种架构，适合自动驾驶中自标注系统的可靠性验证。
3D目标检测是自动驾驶与机器人领域的重要任务，但现有模型难以量化检测可靠性，导致在陌生场景下表现不佳。本文提出一种基于证据学习的不确定性估计框架，应用于3D检测器的鸟瞰图表示。该方法计算开销极低，且可跨不同架构泛化。实验表明，其能有效识别分布外场景、定位偏差目标及漏检情况，相较基线平均提升10%-20%。进一步地，将该框架集成至自标注系统：3D检测器自动标注驾驶场景，不确定性估计在标签用于训练第二阶段模型前验证其正确性。结果表明，该机制使mAP提升1%，NDS提升1%-2%。
原文摘要 · Abstract (English)
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.
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