Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection

Understand

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.