Anomaly detection, which is a critical and popular topic in computer vision, aims to detect anomalous samples that are different from the normal (i.e., non-anomalous) ones. The current mainstream methods focus on anomaly detection for images, whereas little attention has been paid to 3D point cloud. In this paper, drawing inspiration from the knowledge transfer ability of teacher-student architecture and the impressive feature extraction capability of recent neural networks, we design a teacher-student structured model for 3D anomaly detection. Specifically, we use feature space alignment, dimension zoom, and max pooling to extract the features of the point cloud and then minimize a multi-scale loss between the feature vectors produced by the teacher and the student networks. Moreover, our method only requires very few normal samples to train the student network due to the teacher-student distillation mechanism. Once trained, the teacher-student network pair can be leveraged jointly to fulfill 3D point cloud anomaly detection based on the calculated anomaly score. For evaluation, we compare our method against the reconstruction-based method on the ShapeNet-Part dataset. The experimental results and ablation studies quantitatively and qualitatively confirm that our model can achieve higher performance compared with the state of the arts in 3D anomaly detection with very few training samples.

翻译：异常检测是计算机视觉中一项关键且热门的研究课题，旨在识别与正常（即非异常）样本不同的异常样本。当前主流方法主要聚焦于图像的异常检测，而对三维点云的关注较少。本文受师生架构的知识迁移能力以及近期神经网络卓越的特征提取能力启发，设计了一种用于三维异常检测的师生结构模型。具体而言，我们采用特征空间对齐、维度缩放和最大池化来提取点云特征，随后最小化教师网络与学生网络生成的特征向量之间的多尺度损失。此外，得益于师生蒸馏机制，我们的方法仅需极少量正常样本即可训练学生网络。训练完成后，师生网络对可联合用于基于异常分数的三维点云异常检测。为评估性能，我们在ShapeNet-Part数据集上将所提方法与基于重构的方法进行了比较。实验结果与消融研究从定量和定性两个角度证实，在训练样本极少的情况下，我们的模型在三维异常检测任务中能够取得优于现有最先进方法的性能。