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
this https URL.

本研究旨在通过将手工制作的 PCD 描述符与强大的预训练 2D 神经网络相结合，提高点云异常检测性能，提出了全面的伪多模特征（CPMF），通过使用手工制作的 PCD 描述符将 3D 模态中的局部几何信息与生成伪 2D 模态中的全局语义信息相结合，展示了 2D 和 3D 模态特征之间互补的能力和 CPMF 的有效性。