3D point cloud deformation based on moving least squares and control curves

2013 ◽  
Author(s):  
Rui Wang ◽  
Linfeng Du ◽  
Shuqiong Chen ◽  
Ren Xiao
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Moving Least Squares ◽  
3D Point Cloud ◽  
And Control ◽  
Control Curves

Adaptive Slicing of Moving Least Squares Surfaces: Toward Direct Manufacturing of Point Set Surfaces

2007 ◽  
Author(s):  
Pinghai Yang ◽  
Xiaoping Qian
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Moving Least Squares ◽  
Point Sets ◽  
New Approach ◽  
Adaptive Slicing ◽  
3D Sensing ◽  
Point Set ◽  
Point Set Surfaces ◽  
Model Conversion

Rapid advancement of 3D sensing techniques has lead to dense and accurate point cloud of an object to be readily available. The growing use of such scanned point sets in product design, analysis and manufacturing necessitates research on direct processing of point set surfaces. In this paper, we present an approach that enables the direct layered manufacturing of point set surfaces. This new approach is based on adaptive slicing of moving least squares (MLS) surfaces. Salient features of this new approach include: 1) it bypasses the laborious surface reconstruction and avoids model conversion induced accuracy loss; 2) the resulting layer thickness and layer contours are adaptive to local curvature and thus it leads to better surface quality and more efficient fabrication; 3) the MLS surface naturally smoothes the point cloud and allows up-sampling and down-sampling, and thus it is robust even for noisy or sparse point sets. Experimental results of the slicing algorithm on both synthetic and scanned point sets are presented.

scholarly journals Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jingli Wang ◽  
Huiyuan Zhang ◽  
Jingxiang Gao ◽  
Dong Xiao
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Cloud Model ◽  
Hollow Structure ◽  
Total Least Squares ◽  
3D Point Cloud ◽  
Denoising Method ◽  
Least Squares Fitting ◽  
Cloud Models ◽  
Point Cloud Model

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.

scholarly journals POINT CLOUD SMOOTH SAMPLING AND SURFACE RECONSTRUCTION BASED ON MOVING LEAST SQUARES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 145-151 ◽  
Author(s):  
C. L. Kang ◽  
T. N. Lu ◽  
M. M. Zong ◽  
F. Wang ◽  
Y. Cheng
Keyword(s):  
Least Squares ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Least Squares Method ◽  
Moving Least Squares ◽  
Cloud Data ◽  
Grid Sampling ◽  
Moving Least Squares Method ◽  
Feature Information ◽  
Voxel Grid

Abstract. In point cloud data processing, smooth sampling and surface reconstruction are important aspects of point cloud data processing. In view of the current point cloud sampling method, the point cloud distribution is not uniform, the point cloud feature information is incomplete, and the reconstructed model surface is not smooth. This paper proposes a method of smoothing sampling processing and surface reconstruction using point cloud using moving least squares method. This paper first introduces the traditional moving least squares method in detail, and then proposes an improved moving least squares method for point cloud smooth sampling and surface reconstruction. In this paper, the algorithm is designed for the proposed theory, combined with C++ and point cloud library PCL programming, using voxel grid sampling and uniform sampling and moving least squares smooth sampling comparison, after sampling, using greedy triangulation algorithm surface reconstruction. The experimental results show that the improved moving least squares method performs point cloud smooth sampling more uniformly than the voxel grid sampling and the feature information is more prominent. The surface reconstructed by the moving least squares method is smooth, the surface reconstructed by the voxel grid sampling and the uniformly sampled data surface is rough, and the surface has a rough triangular surface. Point cloud smooth sampling and surface reconstruction based on moving least squares method can better maintain point cloud feature information and smooth model smoothness. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of point cloud sampling and surface reconstruction.

Adaptive Slicing of Moving Least Squares Surfaces: Toward Direct Manufacturing of Point Set Surfaces

2008 ◽  
Vol 8 (3) ◽  
Author(s):  
Pinghai Yang ◽  
Xiaoping Qian
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Moving Least Squares ◽  
Point Sets ◽  
New Approach ◽  
Adaptive Slicing ◽  
3D Sensing ◽  
Point Set ◽  
Point Set Surfaces ◽  
Model Conversion

Rapid advancement of 3D sensing techniques has led to dense and accurate point cloud of an object to be readily available. The growing use of such scanned point sets in product design, analysis, and manufacturing necessitates research on direct processing of point set surfaces. In this paper, we present an approach that enables the direct layered manufacturing of point set surfaces. This new approach is based on adaptive slicing of moving least squares (MLS) surfaces. Salient features of this new approach include the following: (1) It bypasses the laborious surface reconstruction and avoids model conversion induced accuracy loss. (2) The resulting layer thickness and layer contours are adaptive to local curvatures, and thus it leads to better surface quality and more efficient fabrication. (3) The curvatures are computed from a set of closed formula based on the MLS surface. The MLS surface naturally smoothes the point cloud and allows upsampling and downsampling, and thus it is robust even for noisy or sparse point sets. Experimental results on both synthetic and scanned point sets are presented.

Adaptive Moving Least-Squares Surfaces for Multiple Point Clouds Registration

2011 ◽  
Author(s):  
Yunbao Huang ◽  
Linchi Zhang ◽  
Zhihui Tan ◽  
Qifu Wang ◽  
Liping Chen
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Synthetic Data ◽  
Point Clouds ◽  
Integral Invariant ◽  
Gaussian Kernel ◽  
Moving Least Squares ◽  
Multiple Point ◽  
Surface Model ◽  
Core Idea

In this paper, we propose an Adaptive Moving Least-Squares (AMLS) surface based approach for multi-view or multi-sensor point cloud ICP registration. The core idea of this approach is to reconstruct a smooth and accurate surface, e. s. AMLS surface, from a point cloud, without data segmentation and surface model selection, resulting in an accurate point-to-AMLS surface ICP registration. The major difference between AMLS and traditional MLS is that the width of Gaussian kernel is adaptively scaled with the principle curvature, which is defined through local integral invariant analysis. Experimental results of both synthetic data and scanned data from a mechanical part show that the presented approach is more accurate and robust on sensor noise and sample density.

3D point‐cloud spatial expansion by total least‐squares line fitting

2020 ◽  
Vol 35 (172) ◽  
pp. 509-527
Author(s):  
Paweł S. Dąbrowski ◽  
Marek H. Zienkiewicz
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Total Least Squares ◽  
3D Point Cloud ◽  
Spatial Expansion ◽  
Line Fitting

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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