Mathematical Approach in Complex Surfaces Toolpaths

Popişter, Florin; Popescu, Daniela; Păcurar, Ancuţa; Păcurar, Răzvan

doi:10.3390/math9121360

Cited by 9 publications

(5 citation statements)

References 36 publications

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“…By point cloud voxelization, Biao Xiong et al [20] caught the keypoints from the voxel grid and registered point clouds with the four-point congruent set technology. Compared to the point features above, line features, which can be extracted by sliding a sectioning plane through the point cloud [21], are more robust since they capture quadrilateral structures of the windows or doors [22]. Chenglu Wen et al [22] used the iterative closest point (ICP) algorithm on the extracted 3D line segments.…”

Section: Related Workmentioning

confidence: 99%

Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor

et al. 2022

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Aligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor and outdoor models. However, the registration performance using the geometric features of windows and doors is limited due to the considerable number of extracted features and the mismatch of similar features. This paper proposed an indoor/outdoor alignment framework with a semantic feature matching method to solve the problem. After identifying the 3D window and door instances from the point clouds, a novel semantic–geometric descriptor (SGD) is proposed to describe the semantic information and the spatial distribution pattern of the instances. The best object match is identified with an improved Hungarian algorithm using indoor and outdoor SGDs. The matching method is effective even when the numbers of objects are not equal in the indoor and outdoor models, which is robust to measurement occlusions and feature outliers. The experimental results conducted in the collected dataset and the public dataset demonstrated that the proposed method could identify accurate object matches under complicated conditions, and the alignment accuracy reached the centimeter level.

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Section: Related Workmentioning

confidence: 99%

Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor

et al. 2022

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“…Three-dimensional reconstruction provides digital 3D models by presenting a realworld scene, promoting the development of augmented reality, such as autonomous driving and digital twins [1][2][3][4][5]. On the other hand, point cloud data is fundamental for building a digital 3D model because 3D point cloud registration is the core technology for achieving 3D reconstruction by providing stereoscopic models [6,7].…”

Section: Introductionmentioning

confidence: 99%

GraM: Geometric Structure Embedding into Attention Mechanisms for 3D Point Cloud Registration

Liu,

Zhong,

Wang

et al. 2024

Electronics

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3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. The 3D point cloud registration method achieves excellent performance only when fusing local and global features with geometric structure information. Based on these discoveries, we propose a 3D point cloud registration method based on geometric structure embedding into the attention mechanism (GraM), which can extract the local features of the non-critical point and global features of the corresponding point containing geometric structure information. According to the local and global features, the simple regression operation can obtain the transformation matrix of point cloud pairs, thereby eliminating the semantics that ignores the geometric structure relationship. GraM surpasses the state-of-the-art results by 0.548° and 0.915° regarding the relative rotation error on ModelNet40 and LowModelNet40, respectively.

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“…In the field of electric power, two-dimensional (2D) images have played an important role in safety production monitoring, defect detection, and other areas; however, they have limitations in spatial distance perception [5]. Taking into account the advantages of spatial distance measurement, three-dimensional (3D) point cloud technology is increasingly being recognized and effectively applied in various industrial domains [6]. In the power industry, there is a growing appreciation for the potential of 3D point cloud technology.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Transmission Corridor 3D Point Clouds Based on CA-PointNet++

Wang

et al. 2023

Electronics

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Automated extraction of key points from three-dimensional (3D) point clouds in transmission corridors provides technical support for digital twin construction and risk management of the power grid. However, accurately and efficiently segmenting the point clouds of transmission corridors remains a challenging problem. Traditional segmentation methods for transmission corridors suffer from low accuracy and poor generalization ability, and the potential of deep learning in this field has been overlooked. Therefore, the PointNet++ deep learning model is employed as the backbone network for the segmentation of 3D point clouds in transmission corridors. Additionally, given the distinct distribution of key components, an end-to-end CA-PointNet++ architecture is proposed by integrating the Coordinate Attention (CA) module with PointNet++. This approach captures long-distance spatial contextual features and improves feature saliency for more precise segmentation. Furthermore, CA-PointNet++ is evaluated on a dataset of 3D point clouds collected by unmanned aerial vehicles (UAV) equipped with Light Detection and Ranging (LiDAR) for inspecting transmission corridors. The results show that CA-PointNet++ achieved 93.7% overall accuracy (OA) and 67.4% mean Intersection over Union (mIoU). Comparative studies with established deep learning models confirm that our proposed CA-PointNet++ exhibits high accuracy and strong generalization ability for point cloud segmentation tasks in transmission corridors.

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Mathematical Approach in Complex Surfaces Toolpaths

Cited by 9 publications

References 36 publications

Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor

Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor

GraM: Geometric Structure Embedding into Attention Mechanisms for 3D Point Cloud Registration

Semantic Segmentation of Transmission Corridor 3D Point Clouds Based on CA-PointNet++

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