Article
Computer Science, Software Engineering
F. J. delaCalle, D. F. Garcia, R. Usamentiaga, P. Nuno, L. Magadan
Summary: The manufacturing industry often uses 3D scanning technologies for product inspection. This paper proposes a new registration method that aligns the point cloud with the product model in multiple steps, improving the measurement of surface defects.
Article
Computer Science, Information Systems
Chuanwang Wen, Shucheng Huang
Summary: This study proposes a LiDAR point cloud registration method that combines linear feature extraction and the trimmed iterative closest point algorithm to address the high initial position requirement of the iterative closest point algorithm. By adopting a registration strategy of combining coarse registration and accurate registration, the proposed method achieves accurate registration for point clouds with large differences in initial poses.
MULTIMEDIA SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Haiyuan Cao, Deng Chen, Zhaohui Zheng, Yanduo Zhang, Huabing Zhou, Jianping Ju
Summary: Point cloud registration plays a crucial role in various applications such as 3D reconstruction and pose estimation. However, traditional ICP algorithm has limitations in terms of dependence on initial position, robustness, and timeliness. To address these issues, a fast point cloud registration method that incorporates RGB image information is proposed. This method utilizes SIFT algorithm for feature point detection, RANSAC algorithm for outlier removal and initial transformation matrix calculation, and FR-ICP algorithm for precise registration.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Analytical
Ouk Choi, Wonjun Hwang
Summary: In this paper, a new algorithm with improved numerical stability is proposed for colored point cloud registration. The algorithm constructs a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. The extension allows all source points to be processed in a unified framework, irrespective of the existence of their corresponding points in the reference point cloud, and also improves the numerical stability of using point-to-plane distances.
Article
Chemistry, Multidisciplinary
Siyu Ren, Xiaodong Chen, Huaiyu Cai, Yi Wang, Haitao Liang, Haotian Li
Summary: The study introduces a color point cloud registration algorithm based on hue to address the issue of errors in similar structures using ICP method. By extracting hue component for robustness and optimizing error function for accuracy, the algorithm improves performance and achieves better results.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Guanglei Li, Yahui Cui, Lihua Wang, Lei Meng
Summary: The paper proposes a method for point cloud registration using binocular stereo cameras, dividing the registration process into two steps for coarse and exact registration, utilizing improved IWOA and IICP algorithms, resulting in high accuracy and speed in registration.
APPLIED SCIENCES-BASEL
(2022)
Article
Physics, Multidisciplinary
Zhao Hui, Zhang Yong-Jian, Zhang Lei, Jiao Xiao-Xue, Lang Li-Ying
Summary: This paper studies a fast registration technique for large-scale point clouds based on virtual viewpoint image generation. By generating the projection image of color point cloud, extracting features and calculating the rotation and translation matrix, the proposed method achieves fast and accurate registration. Experimental results show significant improvements compared to traditional ICP registration method in terms of point cloud size and registration time.
FRONTIERS IN PHYSICS
(2022)
Article
Geochemistry & Geophysics
Leping He, Shuaiqing Wang, Qijun Hu, Qijie Cai, Muyao Li, Yu Bai, Kai Wu, Bo Xiang
Summary: This article proposes a new method for three-dimensional point cloud registration, optimizing the geometric features to improve accuracy and speed. Experimental results demonstrate that this method significantly outperforms traditional ICP and other state-of-the-art registration methods in terms of accuracy and speed.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Agriculture, Multidisciplinary
Rui Gao, Shangqing Cui, HaoRan Xu, Qingming Kong, Zhongbin Su, Jinlong Li
Summary: This study develops a clustering algorithm for corn population point clouds to accurately extract the three-dimensional morphology of individual crops. The improved QuickShift method was used to segment the corn point cloud data, achieving the automated measurement of plant height and stem thickness with high accuracy.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Construction & Building Technology
Zicheng Zhu, Steve Rowlinson, Tianzhuo Chen, Alan Patching
Summary: This study investigates the impact of mainstream point cloud registration algorithms on point cloud models in the context of bridge engineering. The study also examines the influence of noise removal on these registration algorithms. Experimental outcomes reveal the precision, efficiency, and cost-benefit ratio of different algorithms, as well as the improvements achieved after noise removal.
Article
Environmental Sciences
Mengting Sang, Wei Wang, Yani Pan
Summary: With the rapid development of LiDAR technology, the improved RGB-ICP algorithm, by incorporating color information, enhances the alignment accuracy in describing surface deformation processes, especially in different terrain structures, providing a reliable basis for surface change interpretation.
Article
Engineering, Multidisciplinary
Yier Zhou, Xiaolu Li, Haixia Hu, Lixuan Su, Hang Du, Wenming Fu, Lijun Xu
Summary: This study proposes a method for feature points selection based on neighbor feature variance (NFV) to improve the accuracy of point clouds-based pose estimation for space targets, and applies corresponding algorithms in coarse and fine registration. Experimental results show that this method can effectively reduce errors.
Article
Computer Science, Artificial Intelligence
Yujie Wang, Chenggang Yan, Yutong Feng, Shaoyi Du, Qionghai Dai, Yue Gao
Summary: Partial point cloud registration transforms partial scans into a common coordinate system, which is crucial for generating complete 3D shapes. Traditional registration methods struggle with small point cloud overlaps, but the STORM method utilizes structure information to accurately detect overlap and generate precise partial correspondences, achieving superior performance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Environmental Sciences
Shiming Li, Xuming Ge, Shengfu Li, Bo Xu, Zhendong Wang
Summary: In this paper, a systematic incremental registration method is proposed for successfully registering point cloud data from different sources. The robustness of this method is attributed to the elimination of noise in extracted linear features and its 2D incremental registration strategy. Experimental results demonstrate that this method can efficiently achieve data registration and has good generality.
Article
Optics
Song Zhang, Qifeng Wang, Jianxin Zhang, Bin Liu
Summary: This study presents a learning-based registration method for sub models of mesh models obtained by optical scanning. It addresses the limitations of current registration methods in achieving sub model registration under poor initial position conditions and common adverse conditions.
OPTICS AND LASER TECHNOLOGY
(2023)
