  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.
