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:本文提出了一种针对三维点云完成无监督异常检测的端到端方法。至我们所知,这是第一篇针对以三维点云表示的通用物体进行异常检测的工作。 我们提出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,并提出了一种特别适用于三维点云的异常得分指标。我们在ShapeNet数据集上进行了大量实验,通过定量和定性评估,证明了所提出方法的优越性。我们的代码可在 https://github.com/llien30/point_cloud_anomaly_detection 上获得。