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.

翻译：本文提出了一种面向三维点云的端到端无监督异常检测框架。据我们所知，这是首个针对三维点云表示的一般物体进行异常检测任务的研究工作。我们设计了一种基于深度变分自编码器的三维点云无监督异常检测网络，并提出了专门适用于三维点云的异常评分机制。为验证模型有效性，我们在ShapeNet数据集上开展了大量实验。通过定量与定性评估，我们证明了所提方法优于基线方法。相关代码已开源至 https://github.com/llien30/point_cloud_anomaly_detection。