无需采样即可高效校准激光雷达语义分割的置信度。
Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation
- 提出无采样方法，直接计算校准置信度。
- 相比采样法速度更快，且置信度更贴近真实准确率。
- 适合自动驾驶等对安全性要求高的实时场景。
可靠的深度学习模型不仅需要准确预测，还需校准良好的置信度估计以确保不确定性评估的可靠性。这对自动驾驶等安全关键应用至关重要，其依赖于对激光雷达点云进行快速精准的语义分割以实现实时三维场景理解。本文提出一种无采样方法，用于分类任务的置信度估计，实现了与真实分类准确率一致的校准，并显著降低推理时间。基于自适应校准误差（ACE）指标的评估表明，该方法在保持良好校准性的同时，处理速度优于采样基线。此外，可靠性图显示该方法产生保守置信度而非过度自信预测，这对安全关键应用具有优势。所提方法为激光雷达场景语义分割提供了校准良好且高效的预测方案。
原文摘要 · Abstract (English)
Reliable deep learning models require not only accurate predictions but also well-calibrated confidence estimates to ensure dependable uncertainty estimation. This is crucial in safety-critical applications like autonomous driving, which depend on rapid and precise semantic segmentation of LiDAR point clouds for real-time 3D scene understanding. In this work, we introduce a sampling-free approach for estimating well-calibrated confidence values for classification tasks, achieving alignment with true classification accuracy and significantly reducing inference time compared to sampling-based methods. Our evaluation using the Adaptive Calibration Error (ACE) metric for LiDAR semantic segmentation shows that our approach maintains well-calibrated confidence values while achieving increased processing speed compared to a sampling baseline. Additionally, reliability diagrams reveal that our method produces underconfidence rather than overconfident predictions, an advantage for safety-critical applications. Our sampling-free approach offers well-calibrated and time-efficient predictions for LiDAR scene semantic segmentation.
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