From a 3D point cloud to an engineering CAD model: a knowledge-product-based approach for reverse engineering

2008 ◽  
Vol 3 (2) ◽  
pp. 51-59 ◽  
Author(s):  
A Durupt ◽  
S. Remy ◽  
G. Ducellier ◽  
B. Eynard
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Cad Model ◽  
3D Point Cloud

scholarly journals LARGE SCALE 3D POINT CLOUD MODELING FROM CAD DATABASE IN COMPLEX INDUSTRIAL ENVIRONMENTS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 391-398
Author(s):  
M. Corsia ◽  
T. Chabardès ◽  
H. Bouchiba ◽  
A. Serna
Keyword(s):  
Real World ◽  
Computer Aided Design ◽  
Point Cloud ◽  
Large Scale ◽  
Region Growing ◽  
Cad Model ◽  
3D Point Cloud ◽  
Multi Scale ◽  
Cad Models ◽  
Cloud Modeling

Abstract. In this paper, we present a method to build Computer Aided Design (CAD) representations of dense 3D point cloud scenes by queries in a large CAD model database. This method is applied to real world industrial scenes for infrastructure modeling. The proposed method firstly relies on a region growing algorithm based on novel edge detection method. This algorithm is able to produce geometrically coherent regions which can be agglomerated in order to extract the objects of interest of an industrial environment. Each segment is then processed to compute relevant keypoints and multi-scale features in order to be compared to all CAD models from the database. The best fitting model is estimated together with the rigid six degree of freedom (6 DOF) transformation for positioning the CAD model on the 3D scene. The proposed novel keypoints extractor achieves robust and repeatable results that captures both thin geometrical details and global shape of objects. Our new multi-scale descriptor stacks geometrical information around each keypoint at short and long range, allowing non-ambiguous matching for object recognition and positioning. We illustrate the efficiency of our method in a real-world application on 3D segmentation and modeling of electrical substations.

Data Reduction Based on Bi-Directional Point Cloud Slicing for Reverse Engineering

2010 ◽  
Vol 437 ◽  
pp. 492-496 ◽  
Author(s):  
Lei Chen ◽  
Zhuang De Jiang ◽  
Bing Li ◽  
Jian Jun Ding ◽  
Fei Zhang
Keyword(s):  
Reverse Engineering ◽  
Data Reduction ◽  
Point Cloud ◽  
Free Form ◽  
Cad Model ◽  
Dense Point ◽  
Long Time ◽  
Point Data ◽  
Data Reduction Method ◽  
Method Point

In reverse engineering, complex free-form shaped parts are usually digitized quickly and accurately using the newly arisen non-contact measuring methods. However, they produce extremely dense point data at great rate. Not all the point data are necessary for generating a surface CAD model. Moreover, owing to inefficiencies in storing and manipulating them it takes a long time to generate a surface CAD model from the measured data. Therefore, an important task is to reduce the large amount of data. After analyzing the existing methods developed by other researchers, a new data reduction method, which based on bi-directional point cloud slicing, is presented in this paper. Using the proposed method, point cloud can be reduced while considering geometric features in both two parametric directions. Finally, a face model is used to verify the effectiveness of the proposed method and experimental results are given.

scholarly journals Application of Reverse Engineering Technology in Part Design for Shipbuilding Industry

2019 ◽  
Vol 26 (2) ◽  
pp. 126-133
Author(s):  
Mariusz Deja ◽  
Michał Dobrzyński ◽  
Marcin Rymkiewicz
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
3D Model ◽  
Cad Model ◽  
Engineering Technology ◽  
Freeform Surfaces ◽  
Shipbuilding Industry ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Cad Models

Abstract In the shipbuilding industry, it is difficult to create CAD models of existing or prototype parts, especially with many freeform surfaces. The paper presents the creation of the CAD 3D model of a shipbuilding component with the application of the reverse engineering technology. Based on the data obtained from the digitization process, the component is reconstructed in point cloud processing programs and the CAD model is created. Finally, the accuracy of the digital model is estimated.

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