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:异常探测是计算机视觉中一个关键和流行的主题,目的是探测不同于正常(即非非海洋)的异常样本。目前的主流方法侧重于图像异常探测,而很少注意3D点云。在本文中,从教师-学生结构的知识转让能力和最近神经网络令人印象深刻的特征提取能力中,我们设计了一个教师-学生3D异常探测结构模型。具体地说,我们使用地貌空间对齐、尺寸缩放和最大限度集中来提取点云的特征,然后尽量减少教师和学生网络生成的特征矢量器之间的多尺度损失。此外,我们的方法只需要很少的正常样本来培训学生网络,因为教师-学生结构的蒸馏机制。经过培训后,师-学生网络配对可以联合使用3D点云异常探测模型。在评估中,我们用基于重建的方法来比较显示点云云云云云的特征,然后尽量减少教师与学生网络网络生成的特性矢量向器之间的损失。实验结果和定性和定性的抽样可以确认我们高端数据元数据库中的几度和定性研究。