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arXiv Computer Vision By Chenglin Ye, Lupeng Liu, Dongbo Yu, Jun Xiao, Yunbiao Wang

When Point Clouds Outperform Pixels: Rethinking Zero-Shot Multimodal Anomaly Detection

Read the original on arXiv Computer Vision →

The paper challenges the common assumption that RGB and point cloud data contribute equally to zero‑shot multimodal anomaly detection. It shows that point clouds are more reliable under category shift and introduces WOOPS, a framework that enhances point cloud features with a Multi‑view Information Decoupling module and calibrates modality contributions via a Modality Reliability Calibration module. Experiments demonstrate that WOOPS achieves top performance on new stringent metrics in both unimodal and multimodal settings, and that point cloud information also benefits RGB‑only inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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