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: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.
By Ali Balapour, Faraz Hach
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
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...
By Abolfazl Babanazari, Carson Cramer, Tyler Summers, Carlos Nieto, Kaveh Fathian
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...
By Yefan Wang
The paper introduces two non‑contact, vision‑based methods for detecting and quantifying structural defects in steel components. The first method uses high‑resolution laser scanning to create 3D point clouds, comparing measured and reference data to locate surface damage, measure geometric loss, and map defect geometry into finite element models. The second method combines full‑field surface deformation data from 3D digital image correlation with finite element model updating and topology optimization to infer subsurface defects from their effect on structural response. Experimental validation on steel‑beam specimens with controlled defects shows both approaches can accurately identify and quantify defect geometry, offering complementary insights for visible and subsurface damage assessment.
By Mehrdad Shafiei Dizaji, Devin Harris
PointLAM introduces a new point-based 3D object detection architecture that addresses efficiency and fidelity trade-offs inherent in LiDAR point cloud processing. It employs a Laplacian Point Sampler (LPS) to accelerate downsampling while preserving foreground structure, and a Local Hadamard Aggregator (LHA) that replaces costly continuous interactions with a topology‑aware gating mechanism. Combined with Bi‑Directional Mamba layers, the resulting Local Attentive Mamba (LAM) block delivers competitive performance on nuScenes and Waymo datasets, outperforming voxel‑based competitors in detecting small objects and handling extreme sparsity with a smaller computational footprint.
By Xuanming Shang, Weijia Zhang, Chao Ma
arXiv:2605. 17131v2 Announce Type: replace-cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity.
By Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran
The paper introduces PointPiT, a partition‑invariant tuning framework designed for scene‑level point cloud understanding. It combines a Scene‑aware Structural Adapter (SSA) that fuses local geometry with global context, and Gradient Subspace Optimization (GSO) that selects stable update directions to reduce partition‑induced representation shifts. Experiments on multiple benchmarks show that PointPiT matches or surpasses full fine‑tuning while using less than 1% of the backbone’s parameters, achieving state‑of‑the‑art performance among parameter‑efficient fine‑tuning methods.
By Hongqiang Lin, Tianle Wang, Shuiwang Li, Dongxu Zhang, Yiding Sun, Zihao Guo, Dongfu Yin
arXiv:2609.18542v1 Announce Type: new
Abstract: 4D radar has emerged as a promising next-generation sensor for improving the robustness of autonomous driving perception systems because of its stable...
By Woo-Jin Jung, Dong-Hee Paek, Jeong-Su Park, Seung-Hyun Kong
arXiv:2606. 10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings.
By Ray Zhang, Marcus Greiff, Thomas Lew, John Subosits