Point cloud registration considering safety nets during scaffold installation using sensor fusion and deep learning

Kim, Juhyeon; Kim, Jeehoon; Paik, Sunwoong; Kim, Hyoungkwan

doi:10.1016/j.autcon.2024.105277

Automation in Construction

2024

DOI: 10.1016/j.autcon.2024.105277

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Point cloud registration considering safety nets during scaffold installation using sensor fusion and deep learning

Juhyeon Kim,

Jeehoon Kim,

Sunwoong Paik

et al.

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Cited by 3 publications

References 58 publications

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Automated system of scaffold point cloud data acquisition using a robot dog

Chung,

Kim,

Paik

et al. 2025

Automation in Construction

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Automated system of scaffold point cloud data acquisition using a robot dog

Chung,

Kim,

Paik

et al. 2025

Automation in Construction

View full text Add to dashboard Cite

Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors

Wang,

Zhou,

Geng

et al. 2024

Herit Sci

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Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization.

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Feature Selection-Based Method for Scaffolding Assembly Quality Inspection Using Point Cloud Data

Zhao,

Chen,

Liang

et al. 2024

Buildings

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The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process.

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Point cloud registration considering safety nets during scaffold installation using sensor fusion and deep learning

Cited by 3 publications

References 58 publications

Automated system of scaffold point cloud data acquisition using a robot dog

Automated system of scaffold point cloud data acquisition using a robot dog

Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors

Feature Selection-Based Method for Scaffolding Assembly Quality Inspection Using Point Cloud Data

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