GLENet：利用生成式标签不确定性估计增强3D目标检测器
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
AI总结：
本文提出基于条件变分自编码器的GLENet框架，通过生成3D边界框标签不确定性并结合不确定性感知质量估计，持续提升多种3D检测器在KITTI和Waymo上的性能。
摘要：
由遮挡、信号缺失或人工标注错误导致的3D边界框真值标注中固有的模糊性，可能在训练过程中干扰深度3D目标检测器，从而降低检测精度。然而，现有方法在一定程度上忽视了这些问题，并将标签视为确定性的。在本文中，我们将标签不确定性问题表述为对象潜在合理边界框的多样性。随后，我们提出了GLENet，这是一种改编自条件变分自编码器的生成式框架，通过潜变量建模典型3D对象与其潜在真值边界框之间的一对多关系。GLENet生成的标签不确定性是一个即插即用模块，可以方便地集成到现有深度3D检测器中，以构建概率检测器并监督定位不确定性的学习。此外，我们在概率检测器中提出了一种不确定性感知质量估计器架构，利用预测的定位不确定性指导IoU分支的训练。我们将所提方法融入多种流行的基础3D检测器，并在KITTI和Waymo基准数据集上证明了显著且一致的性能提升。特别是，所提出的GLENet-VR大幅优于所有已发表的基于LiDAR的方法，并在具有挑战性的KITTI测试集上的单模态方法中取得第一名。源代码和预训练模型已公开发布于https://github.com/Eaphan/GLENet。
英文摘要：
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors during training, thus deteriorating detection accuracy. However, existing methods overlook such issues to some extent and treat the labels as deterministic. In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects. Then, we propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty. Besides, we propose an uncertainty-aware quality estimator architecture in probabilistic detectors to guide the training of the IoU-branch with predicted localization uncertainty. We incorporate the proposed methods into various popular base 3D detectors and demonstrate significant and consistent performance gains on both KITTI and Waymo benchmark datasets. Especially, the proposed GLENet-VR outperforms all published LiDAR-based approaches by a large margin and achieves the top rank among single-modal methods on the challenging KITTI test set. The source code and pre-trained models are publicly available at \url{https://github.com/Eaphan/GLENet}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
