基于LiDAR的语义分割中不确定性估计的校准研究
On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation
AI总结：
提出一种针对语义分割模型的类别级置信度校准度量方法，通过稀疏化曲线评估不确定性估计，并可用于自动发现标注问题以提升数据集质量。
摘要：
基于深度学习的感知模型的置信度校准在其可靠性中起着至关重要的作用。特别是在自动驾驶的背景下，预测和规划等下游任务依赖于准确的置信度估计。在语义分割等逐点多类分类任务中，模型必须处理严重的类别不平衡问题。由于代表性不足，较小实例类别的置信度校准具有挑战性但必不可少，这不仅出于安全原因。我们提出了一种度量方法，用于衡量语义分割模型在单个类别上的置信度校准质量。该方法通过基于不确定性估计为每个类别计算稀疏化曲线来实现。我们使用该分类校准度量来评估不确定性估计方法在代表性不足类别上的置信度校准效果。此外，我们建议该方法的双重用途：自动发现标注问题以改进手工或自动标注数据集的质量。
英文摘要：
The confidence calibration of deep learning-based perception models plays a crucial role in their reliability. Especially in the context of autonomous driving, downstream tasks like prediction and planning depend on accurate confidence estimates. In point-wise multiclass classification tasks like sematic segmentation the model has to deal with heavy class imbalances. Due to their underrepresentation, the confidence calibration of classes with smaller instances is challenging but essential, not only for safety reasons. We propose a metric to measure the confidence calibration quality of a semantic segmentation model with respect to individual classes. It is calculated by computing sparsification curves for each class based on the uncertainty estimates. We use the classification calibration metric to evaluate uncertainty estimation methods with respect to their confidence calibration of underrepresented classes. We furthermore suggest a double use for the method to automatically find label problems to improve the quality of hand- or auto-annotated datasets.
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