Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder
This addresses the problem of detecting anomalies in 3D point clouds for applications like quality control or robotics, but it is incremental as it adapts existing methods to a new data type.
The paper tackles unsupervised anomaly detection for 3D point clouds, proposing a variational autoencoder-based framework and achieving superior performance over baselines on the ShapeNet dataset.
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.