Point cloud data reduction with tangent function method for nurbs curve and surface fitting

Reconstruction of B-spline curves and surfaces by adaptive group testing

Reverse engineering modelling of free-form surfaces from point clouds subject to boundary conditions

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

In a manufacturing area, design changes for an original surface model are frequently required: for example, when the physical parts are modified or when the parts are partially manufactured from analogous shapes. In this case, an efficient surface updating method by locally adding scan data for the modified area is highly desirable. For this purpose, this paper presents a new procedure to update an initial model that is composed of combinatorial triangular facets based on a set of locally added point data. Nowadays, many people are using surface models that are represented by triangular facets in reverse engineering, since it is fast and easy to create the triangular meshes directly from large amounts of point data. The initial surface model is first created from the initial point set by tight cocone, which is a water-tight surface reconstructor; and then the point cloud data for the updates is locally added onto the initial model maintaining the same coordinate system. In order to update the initial model, the special region on the initial surface that needs to be updated is recognized through the detection of the overlapping area between the initial model and the boundary of the newly added point cloud. After that, the initial surface model is eventually updated to the final output by replacing the recognized region with the newly added point cloud. The proposed method has been implemented and tested with several examples. This algorithm will be practically useful to modify the surface model with physical part changes and free-form surface design.

Tool Path Generation, for Complex Surface Machining, Using Point Cloud Data

The processes applied on the pixels by the image processing methods provide us information about images. Image processing is a computer programming area which can be used in computer integrated industrial applications and it is one of the research areas of reverse engineering. In this study, a new system in which color digital images are interpreted by using image processing method was developed. The purpose of this study is to improve a system to interpret the images, which are taken with a hand camcorder or digital camera, and to obtain point clouds by evaluating with image processing and to convert these point clouds surface or solid model in a computer aided design program. In accordance with these purposes, the three dimensional point clouds are obtained from a rectangular prism shaped object in the specified area. These point clouds can easily be converted into three dimensional solid or surface models in a Computer Aided Design (CAD) program. In this reverse engineering study, the developed system is explained and sample parts are given.

The non-contact 3D surface measurement theory and CAD technology integration, the reverse engineering means can be three-dimensional entity model accurately obtain irregular products. Application of reverse engineering method to establish CAD model and suitable to finite element analysis, the shape and size of surface reconstruction of entity can be guaranteed, through the component structure design and optimization, the realization of rapid prototyping and quality testing of new products. The point cloud data acquisition products surface using 3D laser scanner, the scanner itself, the measurement process vulnerable to the external environment and the surrounding objects of interference noise, therefore, the research take the suitable method of point cloud data denoising. The key research based on the mathematical model of NURBS curve and surface, revealing the control principle of curve and surface, the shape is easier to achieve the best effect, the completion of surface reconstruction and materialization process in CAD software. The final will be imported into the finite element software reverse molding analysis and optimization of the characteristics of the local, through the analysis of cloud drop test, stress generation, to provide a theoretical basis for the optimization design of structure. Keywords-reverse engineering; CAD; finite element analysis; quality detection; 3D entity model

As new functional requirements of products lead to the definition of more complicated shapes, reverse engineering is playing a more important role. The process consists in defining a CAD model of the object surfaces from the measurement of the real object. Reverse engineering takes advantage of new advances in noncontact measuring systems leading to a representation of the surfaces as large clouds of points. Nevertheless, scanning without path planning may affect completeness and accuracy of the measured data. This paper addresses the problem of intelligent scan planning within the context of reverse engineering. A measuring system allows us to acquire a cloud of points, which represents the first measurement of the free-form object. This incomplete and locally inaccurate cloud of points is used as a basis to generate an intelligent scan planning. A pretreatment of the point cloud is performed to determine the quality of the first scan and to find out the characteristic edges. The method relies on a voxel representation of the data. According to given thresholds of quality criteria (noise and completeness), unsatisfactory quality zones and digitizing gaps are identified. The new scan paths for an optimal digitizing are then calculated including optimal orientation search. An experimental application of the presented work is described through the digitizing of a face mask.

Ultrasonic scanning is the most commonly used method for detecting breast cancer. Traditional breast ultrasound mainly relies on manual examination, which requires doctors with rich clinical experience. The scanning path planning of the robot for breast ultrasound examination based on binocular vision and non-uniform rational B-Splines (NURBS) is proposed in this paper to solve the limitations of traditional examination methods. First, breast images are acquired by the binocular camera. The depth information of the images is obtained through stereo matching, and the 3D point cloud reconstruction of the breast model is completed. Second, the spatial index algorithm based on the K-dimension tree (Kd-tree) and uniform sampling method are selected to denoise and down-sample the point cloud model to complete the preliminary optimization of the model. The point cloud information for the scanning path is extracted according to the method of point cloud extraction and the principle of normal vector calculation. Then, the interpolation optimization of the scanning path based on the NURBS curve is completed. The optimized scanning path is transformed into the robot motion path. Finally, the robot motion is simulated and analyzed, and the experimental results show that the rationality of robot operation and the smoothness of robot motion meet the requirements of path planning defined in this paper. The error analysis experiment shows that the path planned by this method can ensure the efficiency and accuracy of ultrasound examination.

In view of the complex development process of mechanical equipment and high research and development costs, the virtual assembly technology can quickly and effectively discover the problems and risks that may occur in the assembly process of the structure. A set of general schemes for the reconstruction and updating of assembly geometric models, which are comprehensively used by three modeling methods: parametric modeling, synchronous modeling and reverse engineering modeling, are given. Taking a surface block structure with free-form surface as the measurement object, the surface of the surface block generated by 3D printing is scanned by laser, and the k-d tree topological relationship is established for the obtained point cloud data. Combining the search strategy based on k-d tree and ICP algorithm, the point cloud data scanned from different angles in the same area are spliced. Through the analysis of the surface point cloud data model of the surface block, the spatial point cloud data is mapped to the two-dimensional plane by using the projection plane method to realize triangulation, and finally complete the surface reconstruction of the surface block.

The issue of surface reconstruction and slicing from point clouds has been receiving extensive attention recently. When using the B-spline surface fitting technique, the difficulty of parameterization exists. At the same time, for interfacing between reverse engineering and rapid prototyping, the point clouds are usually converted to an stereolithography (STL) model. This leads to a huge file size and requires expert modeling skills. The objective of this work is to establish a base surface parameterization and direct slicing strategy for scattered data based on a cross-sectional design technique. We first present a new method of directly extracting sectional contours from point clouds. Then, we create a base surface by skinning the primary boundary curves and interior sectional curves. Based on a good parameterization, the final surface is achieved with tight tolerance. Several practical examples have demonstrated the feasibility of the proposed method. It can be widely used in Number Control (NC) machining and rapid prototyping.

Quadratic curve and surface fitting via squared distance minimization

It is important to ensure every component of the rail tanker with standard dimensions for guaranteeing transportation safety. Point clouds are widely used in the dimension measurement, and the key of the measurement is to segment the point cloud into different components. Currently, the segmentation is realized manually, which is labor-intensive and has high uncertainty. Different from this, we model this problem as semantic segmentation based on point clouds and present an automatic and accurate method for solving it. Previous semantic segmentation methods cannot capture characteristics of inherent geometric structures well, which leads to unexpected segmentation results. Therefore, we explore the inherent geometric structures hidden in the point cloud and propose a new two-stage framework to achieve automatic and accurate segmentation. Especially, the framework consists of structure modeling and semantic labeling. First, we model the inherent geometric structures of the tanker. To this end, the point cloud is partitioned into a few geometrically homogeneous regions, and each region contains points with semantic consistency and geometric continuity. An edge-informative graph is introduced to describe the characteristics of regions and the adjacency relationships between regions. Second, we design a new graph neural network to process the graph with modeling information for component segmentation. The network extracts the deep features for each region and models the interaction between regions. Besides, it obtains the final segmentation by labeling points at different granularity levels. We evaluate our method on the point clouds of different types of tankers. The results show that the overall accuracy of our method can reach 97.53%, and our method shows better performance compared with other baseline methods in almost all metrics.

Completing point clouds using structural constraints for large-scale points absence in 3D building reconstruction

Reconstructing DEM using TLS point cloud data and NURBS surface