arXiv Computer Vision
Aug 25

3D Point Cloud from Close-Range Photogrammetry for Defect Characterisation of Rubberised Concrete

The paper presents a close‑range photogrammetry workflow using Structure‑from‑Motion and Multi‑View Stereo to generate high‑resolution 3D point clouds of rubberised concrete. By capturing images with a Canon DSLR and an iPhone 16, the authors achieved sub‑millimetre reconstruction accuracy, outperforming traditional LiDAR for fine‑scale defect analysis. An RGB‑guided crack extraction method and deformation analysis further demonstrate the method’s utility for detailed surface monitoring and material performance evaluation.

By Jiacheng Liu, Mohammed Alnahhal, Ailar Hajimohammadi, Sara Gonizzi Barsanti, Jinling Wang, Mohsen Kalantari
arXiv Computer Vision
Sep 23

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

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.

By Chenglin Ye, Lupeng Liu, Dongbo Yu, Jun Xiao, Yunbiao Wang
Hugging Face Trending Papers
Jun 28

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection

Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies.