用通用特征提取器的隐空间表示，实现无需重训练的3D点云异常检测。
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor
- 用图自编码器构建通用特征提取器，一次性训练后可复用。
- 在隐空间进行单类分类，比直接在坐标空间检测更有效。
- 适用于未知类别异常检测，计算开销小，适合实际部署。
我们提出一种高效的无监督3D点云异常检测方法，利用通用点云特征提取器与单类分类器。该通用特征提取器由基于图的自编码器构成，在如数学生成的分形3D点云数据集等独立于正常/异常类别的数据集上一次性训练完成。输入点云首先被转换为隐向量，再在隐向量空间中执行单类分类。相比现有在3D坐标空间测量重建误差的方法，本方法利用形状信息高度凝聚的隐表示，实现更直接、有效的异常检测。实验表明，该通用特征提取器能有效提取未见类别的形状特征，避免了自编码器重训练，显著降低计算开销。我们在ShapeNet多个子集上验证了方法性能，结果表明基于隐空间的方法优于现有方法。
原文摘要 · Abstract (English)
We propose an effective unsupervised 3D point cloud novelty detection approach, leveraging a general point cloud feature extractor and a one-class classifier. The general feature extractor consists of a graph-based autoencoder and is trained once on a point cloud dataset such as a mathematically generated fractal 3D point cloud dataset that is independent of normal/abnormal categories. The input point clouds are first converted into latent vectors by the general feature extractor, and then one-class classification is performed on the latent vectors. Compared to existing methods measuring the reconstruction error in 3D coordinate space, our approach utilizes latent representations where the shape information is condensed, which allows more direct and effective novelty detection. We confirm that our general feature extractor can extract shape features of unseen categories, eliminating the need for autoencoder re-training and reducing the computational burden. We validate the performance of our method through experiments on several subsets of the ShapeNet dataset and demonstrate that our latent-based approach outperforms the existing methods.
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