Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
arXiv:2606. 29181v1 Announce Type: cross Abstract: 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.
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.
arXiv:2609.25375v1 Announce Type: cross Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
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.
arXiv:2609.13282v1 Announce Type: new Abstract: Industrial anomaly localization has advanced rapidly with feature-based, reconstruction-based, and distillation-based methods. Most of these methods sc...