用分层高斯混合模型检测激光雷达语义分割中的未知物体，提升可靠性。
Out-of-Distribution Detection in LiDAR Semantic Segmentation Using Epistemic Uncertainty from Hierarchical GMMs
- 通过分层贝叶斯建模特征空间的高斯混合参数，分离模型不确定性。
- 在SemanticKITTI上比传统方法提升18% AUROC、22% AUPRC，FPR95降低36%。
- 无需额外数据或训练，适合自动驾驶等安全敏感场景使用。
除了通过精确的激光雷达点云语义分割实现场景理解外，检测训练中未见过的分布外（OOD）物体至关重要，以避免将未知物体错误分配到已知类别。尽管监督式OOD检测方法依赖辅助的OOD数据集，无监督方法则无需此要求，但通常依赖预测熵，即通过对模型集成或多重后验权重采样获得的预测分布熵。然而，这些方法常混淆认知不确定性（模型）与随机不确定性（数据），将分布内模糊区域误判为OOD。为此，我们提出一种无监督的OOD检测方法，利用深度神经网络特征空间中高斯混合模型（GMM）参数的分层贝叶斯建模所得到的认知不确定性。该方法无需额外数据或额外训练阶段，在SemanticKITTI数据集上表现优于现有基于不确定性的方法，相比先前工作使用的预测熵方法，实现了18%的AUROC提升、22%的AUPRC增长，以及FPR95从76%降至40%，降幅达36%。
原文摘要 · Abstract (English)
In addition to accurate scene understanding through precise semantic segmentation of LiDAR point clouds, detecting out-of-distribution (OOD) objects, instances not encountered during training, is essential to prevent the incorrect assignment of unknown objects to known classes. While supervised OOD detection methods depend on auxiliary OOD datasets, unsupervised methods avoid this requirement but typically rely on predictive entropy, the entropy of the predictive distribution obtained by averaging over an ensemble or multiple posterior weight samples. However, these methods often conflate epistemic (model) and aleatoric (data) uncertainties, misclassifying ambiguous in distribution regions as OOD. To address this issue, we present an unsupervised OOD detection approach that employs epistemic uncertainty derived from hierarchical Bayesian modeling of Gaussian Mixture Model (GMM) parameters in the feature space of a deep neural network. Without requiring auxiliary data or additional training stages, our approach outperforms existing uncertainty-based methods on the SemanticKITTI dataset, achieving an 18\% improvement in AUROC, 22\% increase in AUPRC, and 36\% reduction in FPR95 (from 76\% to 40\%), compared to the predictive entropy approach used in prior works.
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