Automatic coarse registration of point clouds using plane contour shape descriptor and topological graph voting

Wei, Pengcheng; Li, Yan; Xie, Hong; Huang, Ming

doi:10.1016/j.autcon.2021.104055

Cited by 27 publications

(17 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For image fusion, especially for pixel-level image fusion methods, it is required that the image first needs to be accurately registered. Image registration is the matching of pixel points at the same location in spatial dimension for two or more images of the same scene, these images may come from different times, different viewpoint locations, or different sensors 27 . Complex gyration class parts are often composed of multiple areas of different diameters.…”

Section: Proposed Methodsmentioning

confidence: 99%

Large depth-of-field image fusion method for complex gyration class mechanical parts

Zhang

Guo

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

.The local regions of complex gyration class mechanical parts are largely different, which results in the problem that their surface images are partially clear. This problem directly affects the effectiveness of vision methods on detecting surface defects. To tackle this problem, a large depth-of-field (DoF) surface image fusion method is proposed to obtain a full-focus image of complex mechanical parts, such as gyration class mechanical parts. We design an image acquisition platform based on the characteristics of gyration class parts to acquire their surface images accurately and clearly. We then propose a feature detection-based image registration method, by which the registered image can represent the surface information of the part completely and accurately. Additionally, we adopt a convolutional neural network-based image fusion method to achieve a fused large DoF surface image. Experimental studies were conducted to evaluate the performance of the deep-learning-based methods. The experimental results show that the proposed method can completely and clearly fuse the large DoF surface images of complex gyration class mechanical parts. The quality of the fused images has a significant improvement, and the proposed method is significantly more efficient than traditional fusion methods and other deep-learning methods.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Large depth-of-field image fusion method for complex gyration class mechanical parts

Zhang

Guo

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…In addition, line and planar primitives in the scene can also establish descriptors. For example, Wei et al [10] proposed a plane shape descriptor, which can be less affected by noise and occlusion in the point cloud than the point-based descriptor. Finally, correspondences are matched by matching score between each primitive descriptor histogram or directly use KD-Tree to conduct the neighbor query of multi-dimensional features histogram.…”

Section: Correspondence Matchingmentioning

confidence: 99%

“…First, we evaluate the registration results, adopting widely used quality evaluation metrics: rotation error δ R and translation error δ t [10]:…”

Section: Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Li¹,

Wei²,

Xie³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph. The method constructs the corresponding graph according to the given correspondences and designs the concept of the reliability degree of the graph node for optimal candidate selection and the reliability degree of the graph edge to obtain the global maximum consensus set. The presented method could achieve fast and accurate outliers removal along with gradual aligning parameters estimation. Extensive experiments on simulations and challenging real-world datasets demonstrate that the proposed method can still perform effective point cloud registration even the correspondence outlier ratio is over 99%, and the efficiency is better than the state-of-the-art.Code is available at https://github.com/WPC-WHU/GROR.

show abstract

“…Extensive work has been carried out on panoramic 3D measurement [3]. Most of the methods are achieved on the basis of the iterative closest point (ICP) algorithm or its variants.…”

Section: Introductionmentioning

confidence: 99%

Comparison between optical markers and physical markers-assisted panoramic 3D measurement

Zhao

Zheng

et al. 2023

International Conference on Optical and Photonic Engineering (icOPEN 2022)

View full text Add to dashboard Cite

Panoramic 3D measurement becomes increasingly important for fringe projection profilometry (FPP). Traditional physical markers-assisted (PMA) method suffers from inefficiencies and non-complete measurement. An optical markers-assisted (OMA) panoramic 3D method has been recently proposed, which enables accurate, efficient and non-destructive panoramic 3D measurement. In this paper, we give a comprehensive comparison between OMA and PMA, which provides reasonable suggestions for different panoramic 3D measurement applications.

show abstract

Automatic coarse registration of point clouds using plane contour shape descriptor and topological graph voting

Cited by 27 publications

References 46 publications

Large depth-of-field image fusion method for complex gyration class mechanical parts

Large depth-of-field image fusion method for complex gyration class mechanical parts

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Comparison between optical markers and physical markers-assisted panoramic 3D measurement

Contact Info

Product

Resources

About