Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descriptions with powerful pre-trained 2D neural networks. To this end, this study proposes Complementary Pseudo Multimodal Feature (CPMF) that incorporates local geometrical information in 3D modality using handcrafted PCD descriptors and global semantic information in the generated pseudo 2D modality using pre-trained 2D neural networks. For global semantics extraction, CPMF projects the origin PCD into a pseudo 2D modality containing multi-view images. These images are delivered to pre-trained 2D neural networks for informative 2D modality feature extraction. The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection. Extensive experiments demonstrate the complementary capacity between 2D and 3D modality features and the effectiveness of CPMF, with 95.15% image-level AU-ROC and 92.93% pixel-level PRO on the MVTec3D benchmark. Code is available on https://github.com/caoyunkang/CPMF.

翻译：点云（PCD）异常检测逐渐成为一个前景广阔的研究领域。本研究旨在通过结合手工设计的点云描述与强大的预训练二维神经网络，提升点云异常检测性能。为此，本文提出互补伪多模态特征（CPMF），该特征利用手工设计的点云描述符整合三维模态下的局部几何信息，并借助预训练二维神经网络在生成的伪二维模态中提取全局语义信息。为提取全局语义，CPMF将原始点云投影至包含多视角图像的伪二维模态，并将这些图像输入预训练二维神经网络以获取信息丰富的二维模态特征。通过聚合三维与二维模态特征，获得用于点云异常检测的CPMF。大量实验证明了二维和三维模态特征之间的互补能力以及CPMF的有效性：在MVTec3D基准上，图像级AU-ROC达到95.15%，像素级PRO达到92.93%。代码开源地址：https://github.com/caoyunkang/CPMF。