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:点云异常检测稳步成为一个有前途的研究领域。本研究旨在通过结合手工点云描述符和强大的预训练2D神经网络来提高点云异常检测性能。为此,本研究提出了互补伪多模态特征(CPMF),它将手工点云描述符中的局部几何信息和在生成的伪2D模态中使用预训练2D神经网络提取的全局语义信息结合起来。为了提取全局语义信息,CPMF将原始点云投射到一个包含多视图图像的伪2D模态中。这些图像被输入到预训练2D神经网络中,以提取有信息的2D模态特征。3D和2D模态特征被聚合以获得用于点云异常检测的CPMF。广泛的实验表明了2D和3D模态特征之间的互补能力以及CPMF的有效性,MVTec3D基准测试上的图像级AU-ROC为95.15%,像素级PRO为92.93%。代码可在https://github.com/caoyunkang/CPMF上获得。