  In this paper, we present an end-to-end unsupervised anomaly detection
framework for 3D point clouds. To the best of our knowledge, this is the first
work to tackle the anomaly detection task on a general object represented by a
3D point cloud. We propose a deep variational autoencoder-based unsupervised
anomaly detection network adapted to the 3D point cloud and an anomaly score
specifically for 3D point clouds. To verify the effectiveness of the model, we
conducted extensive experiments on the ShapeNet dataset. Through quantitative
and qualitative evaluation, we demonstrate that the proposed method outperforms
the baseline method. Our code is available at
https://github.com/llien30/point_cloud_anomaly_detection.
