将扩散模型用于三维姿态轨迹的异常检测，突破传统方法局限。
DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories
- 基于扩散模型构建统一的三维姿态序列异常检测框架
- 在多个基准数据集上优于当前最优检测方法
- 适用于机器人、视觉等需处理三维运动轨迹的场景
异常检测是机器学习中的基础任务，旨在识别异常样本。传统方法在面对不同正常分布时需重新训练模型。尽管近期研究已证明扩散模型可用于异常检测，但现有方法仅限于欧氏空间或图像隐空间。本文将异常检测拓展至三维特殊欧几里得群（SE(3)）中的轨迹空间，解决了计算机视觉、机器人和工程应用中对三维姿态序列处理的迫切需求。我们提出新型异常检测框架DOSE3，将扩散模型扩展至统一的SE(3)姿态序列样本空间。通过在多个基准数据集上的广泛验证，DOSE3表现出显著优于现有先进方法的性能。
原文摘要 · Abstract (English)
Out-of-Distribution(OOD) detection, a fundamental machine learning task aimed at identifying abnormal samples, traditionally requires model retraining for different inlier distributions. While recent research demonstrates the applicability of diffusion models to OOD detection, existing approaches are limited to Euclidean or latent image spaces. Our work extends OOD detection to trajectories in the Special Euclidean Group in 3D ($\mathbb{SE}(3)$), addressing a critical need in computer vision, robotics, and engineering applications that process object pose sequences in $\mathbb{SE}(3)$. We present $\textbf{D}$iffusion-based $\textbf{O}$ut-of-distribution detection on $\mathbb{SE}(3)$ ($\mathbf{DOSE3}$), a novel OOD framework that extends diffusion to a unified sample space of $\mathbb{SE}(3)$ pose sequences. Through extensive validation on multiple benchmark datasets, we demonstrate $\mathbf{DOSE3}$'s superior performance compared to state-of-the-art OOD detection frameworks.
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