Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent years. Particularly uncertainty estimation is a crucial component for down-stream tasks and deep neural networks remain error-prone even for predictions with high confidence. Previously proposed methods for quantifying prediction uncertainty tend to alter the training scheme of the detector or rely on prediction sampling which results in vastly increased inference time. In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation. Our method can easily be added to any pre-trained Lidar object detector without altering anything about the base model and is purely based on post-processing, therefore, only leading to a negligible computational overhead. Our experiments show a significant increase of statistical reliability in separating true from false predictions. We propose and evaluate an additional application of our method leading to the detection of annotation errors. Explicit samples and a conservative count of annotation error proposals indicates the viability of our method for large-scale datasets like KITTI and nuScenes. On the widely-used nuScenes test dataset, 43 out of the top 100 proposals of our method indicate, in fact, erroneous annotations.

翻译：摘要：基于激光雷达点云数据的目标检测是自动驾驶与机器人领域的一项前景广阔的技术，近年来其性能与准确性显著提升。不确定性估计对于下游任务至关重要，而深度神经网络即使在高置信度预测中仍易出错。此前提出的预测不确定性量化方法往往需要修改检测器的训练方案或依赖预测采样，导致推理时间大幅增加。为解决这两个问题，我们提出LidarMetaDetect（LMD），一种用于预测质量估计的轻量级后处理方案。该方法可轻松集成至任何预训练的激光雷达目标检测器，无需对基础模型进行任何修改，且完全基于后处理，因此仅引入微不足道的计算开销。实验表明，该方法在区分正确预测与错误预测方面显著提升了统计可靠性。我们进一步提出并评估了该方法的一个附加应用——检测标注错误。明确的样本案例及保守的标注错误提议计数表明，该方法对KITTI和nuScenes等大规模数据集具有可行性。在广泛使用的nuScenes测试数据集中，该方法排名前100的提议中有43个实际上指向了错误标注。