Research on Variable Scale Registration Algorithm for Scattered Point Clouds in Reverse Engineering

2013 ◽  
Vol 49 (02) ◽  
pp. 20 ◽  
Author(s):  
Hongbin LIN
Keyword(s):  
Reverse Engineering ◽  
Point Clouds ◽  
Registration Algorithm

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

2021 ◽  
pp. 1-16
Author(s):  
Ghazanfar Ali Shah ◽  
Jean-Philippe Pernot ◽  
Arnaud Polette ◽  
Franca Giannini ◽  
Marina Monti
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Computer Assisted ◽  
Reconstruction Process ◽  
Mechanical Parts ◽  
Starting Point ◽  
Cad Models ◽  
Incomplete Datasets

Abstract This paper introduces a novel reverse engineering technique for the reconstruction of editable CAD models of mechanical parts' assemblies. The input is a point cloud of a mechanical parts' assembly that has been acquired as a whole, i.e. without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized geometries (i.e 2D sketches, 3D parts or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the reconstructed geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the updates and satisfies the geometric constraints as the fitting process goes on. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

scholarly journals A HYBRID MODEL FOR THE REVERSE ENGINEERING OF THE MILAN CATHEDRAL. CHALLENGES AND LESSON LEARNT

2021 ◽  
Author(s):  
Franco Spettu ◽  
Simone Teruggi ◽  
Francesco Canali ◽  
Cristiana Achille ◽  
Francesco Fassi
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Learning Algorithm ◽  
A Priori ◽  
Point Clouds ◽  
Logical Structure ◽  
Cloud Data ◽  
Survey Techniques ◽  
Periodic Maintenance ◽  
Maintenance And Inspection

Cultural Heritage (CH) 3D digitisation is getting increasing attention and importance. Advanced survey techniques provide as output a 3D point cloud, wholly and accurately describing even the most complex architectural geometry with a priori established accuracy. These 3D point models are generally used as the base for the realisation of 2D technical drawings and 3D advanced representations. During the last 12 years, the 3DSurveyGroup (3DSG, Politecnico di Milano) conduced an omni-comprehensive, multi-technique survey, obtaining the full point cloud of Milan Cathedral, from which were produced the 2D technical drawings and the 3D model of the Main Spire used by the Veneranda Fabbrica del Duomo di Milano (VF) to plan its periodic maintenance and inspection activities on the Cathedral. Using the survey product directly to plan VF activities would help to skip a long-lasting, uneconomical and manual process of 2D and 3D technical elaboration extraction. In order to do so, the unstructured point cloud data must be enriched with semantics, providing a hierarchical structure that can communicate with a powerful, flexible information system able to effectively manage both point clouds and 3D geometries as hybrid models. For this purpose, the point cloud was segmented using a machine-learning algorithm with multi-level multi-resolution (MLMR) approach in order to obtain a manageable, reliable and repeatable dataset. This reverse engineering process allowed to identify directly on the point cloud the main architectonic elements that are then re-organised in a logical structure inserted inside the informative system built inside the 3DExperience environment, developed by Dassault Systémes.

Planar Feature-Constrained, Quaternion-Based Registration Algorithm for LiDAR Point Clouds

2020 ◽  
Vol 40 (23) ◽  
pp. 2310001
Author(s):  
王永波 Wang Yongbo ◽  
郑南山 Zheng Nanshan ◽  
卞正富 Bian Zhengfu
Keyword(s):  
Point Clouds ◽  
Registration Algorithm ◽  
Planar Feature

A Compared Approach on How Deep Learning May Support Reverse Engineering for Tolerance Inspection

2019 ◽  
Author(s):  
Michele Bici ◽  
Saber Seyed Mohammadi ◽  
Francesca Campana
Keyword(s):  
Deep Learning ◽  
Reverse Engineering ◽  
Shape Control ◽  
Feature Recognition ◽  
Point Clouds ◽  
Smart Manufacturing ◽  
Post Processing ◽  
Die Set ◽  
Set Up ◽  
Voxel Grid

Abstract Reverse Engineering (RE) may help tolerance inspection during production by digitalization of analyzed components and their comparison with design requirements. RE techniques are already applied for geometrical and tolerance shape control. Plastic injection molding is one of the fields where it may be applied, in particular for die set-up of multi-cavities, since no severe accuracy is required for the acquisition system. In this field, RE techniques integrated with Computer-Aided tools for tolerancing and inspection may contribute to the so-called “Smart Manufacturing”. Their integration with PLM and suppliers’ incoming components may set the information necessary to evaluate each component and die. Intensive application of shape digitalization has to front several issues: accuracy of data acquisition hardware and software; automation of experimental and post-processing steps; update of industrial protocol and workers knowledge among others. Concerning post-processing automation, many advantages arise from computer vision, considering that it is based on the same concepts developed in a RE post-processing (detection, segmentation and classification). Recently, deep learning has been applied to classify point clouds, considering object and/or feature recognition. This can be made in two ways: with a 3D voxel grid, increasing regularity, before feeding data to a deep net architecture; or acting directly on point cloud. Literature data demonstrate high accuracy according to net training quality. In this paper, a preliminary study about CNN for 3D points segmentation is provided. Their characteristics have been compared to an automatic approach that has been already implemented by the authors in the past. VoxNet and PointNet architectures have been compared according to the specific task of feature recognition for tolerance inspection and some investigations on test cases are discussed to understand their performance.

Study on Parametric Reverse Modeling

2014 ◽  
Vol 721 ◽  
pp. 230-233
Author(s):  
Shuang Xi Hu
Keyword(s):  
Reverse Engineering ◽  
3D Modeling ◽  
Point Clouds ◽  
Hybrid Modeling ◽  
Design Intent ◽  
Engineering Software ◽  
Modeling Software

Methods about parametric reverse modeling have been studied based on products’ point clouds by comparing model rebuilding processes in different ways, such as collaborative reconstruction based on both reverse engineering software Geomagic and 3D modeling software, reverse and forward hybrid modeling based on Rapidform. It‘s concluded that reverse and forward hybrid modeling based on Rapidform takes more advantages in parametric reverse modeling, It is more rapid , accurate, and closer to the design intent.

Converting Point Cloud to Revolved Solids

2013 ◽  
Vol 371 ◽  
pp. 468-472
Author(s):  
Mircea Viorel Drăgoi ◽  
Slobodan Navalušić
Keyword(s):  
Reverse Engineering ◽  
Cross Section ◽  
Software Package ◽  
Point Cloud ◽  
3D Model ◽  
Point Clouds ◽  
3D Scanning ◽  
Main Drawback ◽  
Mass Properties

3D scanning is one of the basic methods to gather data for reverse engineering. The main drawback of 3D scanning is that its output - the point cloud - can never be used directly to define surfaces or solids useful to reconstruct the electronic 3D model of the scanned part.The paper presents a piece of software designed in VisualLISP for AutoCAD, software that acts as a point cloud to 3D primitives converter. The novelty consists of the method used to find the parameters of the primitive that best fits to the point cloud: the mass properties of regions are used to find the center of a cones cross section. Parts have been scanned and the point clouds processed. The results obtained prove the correctness of the algorithm and of the method applied. A piece of software that processes the point cloud in order to find the 3D primitive that it fits the best has been developed. The output is the 3D primitive that successfully and accurate replaces the point cloud. Some adjacent tools were designed, so the entire software package becomes a useful tool for the reverse engineering user. The ways the researches can be continued and developed are foreseen, as well

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