GRASP: Enhancing Zero-Shot Multi-Modal Anomaly Detection via Geometric Refinement and Semantic Prompting

Abstract

Zero-shot multimodal anomaly detection is critical for identifying structural defects that are often invisible to traditional RGB images, particularly in scenarios lacking target domain samples. However, existing methodologies face two significant impediments: the prohibitive computational overhead caused by relying on multi-view rendering for depth processing, and the inability of static text prompts to adapt to fine-grained local anomalies. To address these challenges, a unified zero-shot multimodal anomaly detection framework GRASP is proposed. Firstly, a Frequency Domain Enhancement module is introduced to replace costly rendering with spectral transformations, directly synthesizing high-fidelity depth images for efficient utilization. Secondly, a Prompt-Conditioned Variational module is designed to bridge the semantic gap by grounding global textual descriptions into local visual nuances. Finally, a Dual Cross-Injection Alignment module is proposed to enable robust feature fusion for enhancing anomaly classification performance, while a Pyramid Anomaly Map Recalibration module further refines anomaly localization across multiple scales. Extensive experiments on MVTec 3D-AD and Eyecandies demonstrate that GRASP establishes a new state-of-the-art, yielding substantial improvements of 3.0 points in I-AUROC and 2.7 points in AUPRO compared to the SOTA method.