STL file generation from measured point data by segmentation and Delaunay triangulation

For the generation of the models in RP, a laser scanner is currently used a lot due to the fast measuring speed and high precision. Direct generation of STL file from the scanned data has a great advantage in that it can reduce the time and error in modeling process. The reduction of the number of point data is important while generating STL file directly from the measured point data with maintaining their precision.A new approach is addressed to reduce the amount of data by segmentation and Delaunay triangulation. Basic experiments are performed to apply the algorithm developed to real models. Different results are incurred by user-defined criteria, and some dominant output characteristics according to the user input are reviewed and analyzed. The program developed in the research generates an STL files efficiently with automated data reduction. Several criteria are specified in order to maintain the characteristics of the feature and satisfy the needs in real workshops.     

一种基于映射法的散乱点云Delaunay三角剖分算法

针对直接在三维空间构建海量点云的Delaunay三角网格效率低下,提出一种新的基于映射法的Delau-nay三角网格构建算法.首先提出一种基于区域增长法的点云分片方法,能够保证对分片后的点云数据进行映射而不产生重叠;然后保持空间点云之间的距离特性,将三维点云映射到二维平面;在二维平面内进行Delaunay三角剖分,再将结果返回到三维空间内.实验结果表明,算法能够构建质量较好的三角网格.由于该算法将点云的三角剖分转换到低维空间,通过实验结果对比本算法与其他算法效果,证明该方法能够更快地完成重构.     

Adjacency for grid generation and grid adaptation in Delaunay triangulation

One of the characteristics of Delaunay method for mesh generation is its local remeshing ability. The main part of the process is to identify remeshing block out of the whole domain and to execute remeshing on the block. Adjacency, adjacent element array, is introduced with an accompanying algorithm to make the process so simple and versatile that it will be used in generating the initial mesh, in applying mesh adaptation, in mesh revision for moving boundary problems, and in transforming 3-node base mesh to 6-node mesh. These features are demonstrated in the example problems of heat conduction with point sink, crack propagation, and simple upsetting of a circular cylinder. Proposition is made to take utility array ‘adjacency’ as basic element data.     

Surface reconstruction using Delaunay triangulation for applications in life sciences

The Delaunay triangulation is an established method to define neighborhood relations in multi-particle systems. In particular, this method was employed for interacting multi-cellular systems in Biology. The extension of this method to a sub-cellular level that considers the membrane and the inner structure of cells is not straight forward and subject of this article. It is the objective to use a three-dimensional Delaunay-triangulation as a basis for the definition of a triangulation of a subset of particles that form a surface.An essential problem of this objective is the conservation of the number of particles belonging to the surface. This excludes established surface reconstruction algorithms. The presented algorithm allows the definition of a triangulation within a subset of particles attributed to a surface without the deletion of particles. A particular challenge is the deletion of connection that infer three-dimensional structures in the surface. The presented method is suitable for many configurations. Its performance and its limitations are analyzed and discussed.The developed algorithm for the reconstruction of connections in a surface is suitable to be used for simulations of biological cells because of the inherent conservation of the number of particles attributed to the membrane.     

Automatic mesh generation for complex three-dimensional regions using a constrained Delaunay triangulation

This paper describes a method for generating tetrahedral meshes. The algorithm, based on the Delaunay triangulation, can treat objects of essentially arbitrary complexity. In order to preserve the surface triangulation of solid objects, it is necessary to override the Delaunay property and redefine the triangulation when points are introduced that are close to solid boundaries. Details of this constrained Delaunay algorithm are presented and an efficient implementation of the triangulation method is described. Techniques for controlling the distribution of mesh points and tetrahedron quality are also discussed.     

Non-parametric free-form optimization method for frame structures

In this paper, we propose a parameter-free shape optimization method based on the variational method for designing the smooth optimal free-form of a spatial frame structure. A stiffness design problem where the compliance is minimized under a volume constraint is solved as an example of shape design problems of frame structures. The optimum design problem is formulated as a distributed-parameter shape optimization problem under the assumptions that each member is varied in the out-of-plane direction to the centroidal axis and that the cross section is prismatic. The shape gradient function and the optimality conditions are then theoretically derived. The optimal curvature distribution is determined by applying the derived shape gradient function to each member as a fictitious distributed force both to vary the member in the optimum direction and to minimize the objective functional without shape parametrization, while maintaining the members’ smoothness. The validity and practical utility of this method were verified through several design examples. It was confirmed that axial-force-carrying structures were obtained by this method.     

A non-parametric free-form optimization method for shell structures

This paper presents a numerical shape optimization method for the optimum free-form design of shell structures. It is assumed that the shell is varied in the out-of-plane direction to the surface to determine the optimal free-form. A compliance minimization problem subject to a volume constraint is treated here as an example of free-form design problem of shell structures. This problem is formulated as a distributed-parameter, or non-parametric, shape optimization problem. The shape gradient function and the optimality conditions are theoretically derived using the material derivative formulae, the Lagrange multiplier method and the adjoint variable method. The negative shape gradient function is applied to the shell surface as a fictitious distributed traction force to vary the shell. Mathematically, this method is a gradient method with a Laplacian smoother in the Hilbert space. Therefore, this shape variation makes it possible both to reduce the objective functional and to maintain the mesh regularity simultaneously. With this method, the optimal smooth curvature distribution of a shell structure can be determined without shape parameterization. The calculated results show the effectiveness of the proposed method for the optimum free-form design of shell structures.     

An adaptive learning approach for 3-D surface reconstruction from point clouds.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.     

An effective quad-dominant meshing method for unorganized point clouds

To perform quad meshing on raw point clouds, existing algorithms usually require a time-consuming parameterization or Voronoi space partition process. In this paper, we propose an effective method to generate quad-dominant meshes directly from unorganized point clouds. In the proposed method, we first apply Marinov’s curvature tensor optimization to the input point cloud to reduce the umbilical regions in order to obtain a smooth curvature tensor. We then propose an efficient marching scheme to extract the curvature lines with controllable density from the point cloud. Finally, we apply a specialized K-Dimension (KD) tree structure, which converts the nearest neighbor searching problem into a sorting problem, to efficiently estimate the intersections of curvature lines and recover the topology of the quad-dominant meshes. We have tested the proposed method on different point clouds. Our results show that the proposed method produces good quality meshes with high computational efficiency and low memory requirement.     

A multigrain Delaunay mesh generation method for multicore SMT-based architectures

Given the proliferation of layered, multicore- and SMT-based architectures, it is imperative to deploy and evaluate important, multi-level, scientific computing codes, such as meshing algorithms, on these systems. We focus on Parallel Constrained Delaunay Mesh (PCDM) generation. We exploit coarse-grain parallelism at the subdomain level, medium-grain at the cavity level and fine-grain at the element level. This multi-grain data parallel approach targets clusters built from commercially available SMTs and multicore processors. The exploitation of the coarser degree of granularity facilitates scalability both in terms of execution time and problem size on loosely-coupled clusters. The exploitation of medium-grain parallelism allows performance improvement at the single node level. Our experimental evaluation shows that the first generation of SMT cores is not capable of taking advantage of fine-grain parallelism in PCDM. Many of our experimental findings with PCDM extend to other adaptive and irregular multigrain parallel algorithms as well.     

A high-accuracy method for fine registration of overlapping point clouds

This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-to-surface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations.     

3D object recognition method with multiple feature extraction from LiDAR point clouds

During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.

     

Multi-scale tensor voting for feature extraction from unstructured point clouds

Identifying sharp features in a 3D model is essential for shape analysis, matching and a wide range of geometry processing applications. This paper presents a new method based on the tensor voting theory to extract sharp features from an unstructured point cloud which may contain random noise, outliers and artifacts. Our method first takes the voting tensors at every point using the corresponding neighborhoods and computes the feature weight to infer the local structure via eigenvalue analysis of the tensor. The optimal scale for a point is automatically determined by observing the feature weight variation in order to deal with both a noisy smooth region and a sharp edge. We finally extract the points at sharp features using adaptive thresholding of the feature weight and the feature completion process. The multi-scale tensor voting of a given point set improves noise sensitivity and scale dependency of an input model. We demonstrate the strength of the proposed method in terms of efficiency and robustness by comparing it with other feature detection algorithms.     

Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds

Accurate detection and extraction of individual trees is one of hottest topics, which can be widely used in vehicles navigation, tree modeling, tree growth monitoring and urban green quantity estimation. The difficulty associated with individual trees extraction is the occlusion with other objects in cluttered point clouds of urban scenes, which inhibits the automatic extraction of individual trees. In this paper, we present a comprehensive framework that can be used to extract individual trees from terrestrial scanned outdoor scene. In our framework, a bottom-up method by shape-guided classification is achieved to select the candidate tree crowns and tree trunks, and a novel three-stage shape merging rule containing localization, filtering, and matching (LFM) are proposed to generate a complete individual tree. The primary advantage of the proposed method is that it is independent of the quality of data and different shapes. We made comparison experiments of classification methods of support vector machine and random forest on the accuracy assessment. The effectiveness of the proposed framework was tested in five street scenarios in point clouds from Oakland outdoor MLS dataset. The results for the five test sites achieved tree detection rates higher than 97%; the overall accuracy was approximately 98%, and the completion quality of both procedures was 96%. Non-detected trees are always sparse which come from occlusions in the point cloud data; most misclassifications occurred in man-made pillars adjacent to trees and have the same height with tree trunk. Comparison experiments to the existing methods are made to illustrate the effectiveness of our method.

     

An adaptive normal estimation method for scanned point clouds with sharp features

Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a robust normal estimation method is developed in order to effectively establish a proper neighborhood for each point in the scanned point cloud. In particular, for a point near sharp features, an anisotropic neighborhood is formed to only enclose neighboring points located on the same surface patch as the point. Neighboring points on the other surface patches are discarded. The developed method has been demonstrated to be robust towards noise and outliers in the scanned point cloud and capable of dealing with sparse point clouds. Some parameters are involved in the developed method. An automatic procedure is devised to adaptively evaluate the values of these parameters according to the varying local geometry. Numerous case studies using both synthetic and measured point cloud data have been carried out to compare the reliability and robustness of the proposed method against various existing methods.     

An accurate approach to roller path generation for robotic fibre placement of free-form surface composites

Due to their high stiffness and strength, composites are widely used in the aerospace industry. To manufacture composites, especially composites of free-form surface structure, process of robotic fibre placement (RFP) is widely used in industry. However, due to the complex geometry of the free-form surface, it is quite challenging to generate accurate roller paths for placing fibre on the surface for high composites quality. To address this problem, this work proposes an accurate roller path planning method using the differential geometry. The roller paths can ensure the specified small gaps and overlaps between two tows for high composite quality. This approach is applied to several examples, and their results verify the validity of this approach. It has great potential to be adopted in industry.     

Two point exponential approximation method for structural optimization

This paper examines various first order approximation methods commonly used in structural optimization. It considers several attempts at improving the approximation by using previous analytical results and introduces an adaptation of a first order approximation method using an exponent adjusted to better fit the constraints and reduce the overall number of iterations needed to attain the optimum.