  Existing industrial anomaly detection methods primarily concentrate on
unsupervised learning with pristine RGB images. Yet, both RGB and 3D data are
crucial for anomaly detection, and the datasets are seldom completely clean in
practical scenarios. To address above challenges, this paper initially delves
into the RGB-3D multi-modal noisy anomaly detection, proposing a novel
noise-resistant M3DM-NR framework to leveraging strong multi-modal
discriminative capabilities of CLIP. M3DM-NR consists of three stages: Stage-I
introduces the Suspected References Selection module to filter a few normal
samples from the training dataset, using the multimodal features extracted by
the Initial Feature Extraction, and a Suspected Anomaly Map Computation module
to generate a suspected anomaly map to focus on abnormal regions as reference.
Stage-II uses the suspected anomaly maps of the reference samples as reference,
and inputs image, point cloud, and text information to achieve denoising of the
training samples through intra-modal comparison and multi-scale aggregation
operations. Finally, Stage-III proposes the Point Feature Alignment,
Unsupervised Feature Fusion, Noise Discriminative Coreset Selection, and
Decision Layer Fusion modules to learn the pattern of the training dataset,
enabling anomaly detection and segmentation while filtering out noise.
Extensive experiments show that M3DM-NR outperforms state-of-the-art methods in
3D-RGB multi-modal noisy anomaly detection.
