3D Cad Models And Its Feature Similarity (original) (raw)
Related papers
3D CAD Model Retrieval and its Application: A Review
International Journal of Analytical, Experimental and Finite Element Analysis, 2023
The use of this model is particularly important, especially in the aerospace, shipbuilding, automotive, and other fields that are producing more and more three dimensional CAD models, as most existing 3D model retrieval methods such as are based on multilevel extraction, 3D CAD model semantics. Existing model retrieval methods are limited to the retrieval between similar models,granularity as the foundation for retrieving similarities The majority of these methods begin by extracting 3D model features, compare similarity based on those attributes, and subsequently accomplish retrieval goals. applications for retrieving 3D CAD models are numerous. One crucial aspect of product development that must be addressed before product realisation is the retrieval process. Different assembly joint information (liaison), including geometric and non-geometric information, is required in order to choose the most appropriate joining procedure. An active tool must be used to capture, represent, and reuse this knowledge across domains in order for the effective selection of model retrieval to take place.The designer needs ontology-based information to analyse these multiple design requirements, functional requirements, and production requirements for the choice of joining technique. This article suggests some uses for 3D CAD model retrieval.
Reusing engineering data has opened a new opportunity to improve product quality, shorten design lead-time and reduce costs using existing know-how within the design process. Geometrical aspects or 3D shape information of a product is an essential data which can be reused in CAD software. In order to compare and retrieve the existing 3D models, having a precise computational representation of a shape, so-called shape index or shape signature, is a main challenge. The shape signature is often used for the shape similarity comparison. There are several specifications for a shape signature like quick to compute, easy to index, invariant under transformation, independent of 3D representations, tessellation, genus or topology. The algorithms or the methods which decompose a shape into a signature can be classified into seven main classes. This paper aims to focus on the discussion of the first three methods, i.e., Invariant-based methods, Harmonics-based methods, and Graph-based methods,...
A CAD model based system for object recognition
1997
3D object recognition is a difficult and yet an important problem in computer vision. A 3D object recognition system has two major components, namely: an object modeller and a system that performs the matching of stored representations to those derived from the sensed image. The performance of systems wherein the construction of object models is done by training from one or more images of the objects, has not been very satisfactory. Although objects used in a robotic workcell or in assembly processes have been designed using a CAD system, the vision systems used for recognition of these objects are independent of the CAD database. This paper proposes a scheme for interfacing the CAD database of objects and the computer vision processes used for recognising these objects. CAD models of objects are processed to generate vision oriented features that appear in the different views of the object and the same features are extracted from images of the object to identify the object and its pose.
Automated extraction of features from cad models for 3d object recognition
2000
In this paper we report on our work on a CAD model-based object recognition system for industrial parts. We present a novel approach which uses information derived from the CAD model in the early process of range image segmentation. This approach gives an exact classification of the range image enabling the development of a CAD based object recognition system. We describe the feature extraction from CAD data and its use in the curvature based range image classification. We carried out experiments with data from multiple sources. The results obtained are presented and discussed.
A new methodology for extracting manufacturing features from CAD system
Computers & Industrial Engineering, 2006
In recent years, various researchers have come up with different ways and means to integrate CAD and CAM. Automatic feature recognition from CAD solid systems highly impacts the level of integration. CAD files contain detailed geometric information of a part, which are not suitable for using in the downstream applications such as process planning. Different CAD or geometric modeling packages store the information related to the design in their own databases. Structures of these databases are different from each other. As a result no common or standard structure has been developed so far, that can be used by all CAD packages. For that reason this paper proposes an intelligent feature recognition methodology (IFRM) to develop a feature recognition system which has the ability to communicate with various CAD/ CAM systems. The proposed methodology is developed for 3D prismatic parts that are created by using solid modeling package by using CSG technique as a drawing tool. The system takes a neutral file in Initial Graphics Exchange Specification (IGES) format as input and translates the information in the file to manufacturing information. The boundary (B-rep) geometrical information of the part design is then analyzed by a feature recognition program that is created specifically to extract the features from the geometrical information based on a geometric reasoning approach by using object oriented design software which is included in C++ language. A feature recognition algorithm is used to recognize different features of the part such as step, holes, etc. Finally, a sample application description for a workpiece is presented for demonstration purposes.
Generate a 3D CAD models for Pose Estimation
International Journal of Computer and Electrical Engineering, 2010
The pose estimation of 3D object of products in production line is needed beforehand. In order to perform shape measurement of objects corresponding to speed of the mass production lines before the contact measurement is done, the information of shape representation and matching of object is required. Objects are compared with its descriptor. The descriptor conceptually subtracted from each other to form a scalar metric. If the value difference is smaller, the object shape is considered closer to each other. The rotation object from its original pose can produce the information to estimate its rotation pose by using the boundary. In this paper, we proposed the method to measure 3D object pose from the designing stage of 3D CAD data.
Partial Pose Estimation Of Rigid Object System Using Cad Database
2017
Partial pose estimation identification is required for inspection in manufacturing industry. By knowing the partial pose estimation before inspection, overall inspection processing time can be reduced. System development for partial pose estimation identification consists of a few main parts which are image acquisition, pre-processing, processing and camera calibration. Image acquisition is divided into CAD image acquisition and Projection Real Image (PRI) acquisition. Image pre-processing consists of image rescaling, image segmentation and image registration. In image segmentation, high level feature of Outer Box object segmentation method was proposed. In pre-processing development section, the development of CAD model database imitates inspection environment was implemented. The object was represented by the area combined with the edge information of the object. Within this shape representation, partial pose estimation was identified by linking the CAD model database to the inspe...
Form features recognition as a technique for CAD-CAM integration
The paper points out how the recognition activity is a useful tool to realize the integration of design activities with manufacturing, handling, assembling etc…The concept on which the system is based is the possibility to utilize, in processes from the design to the manufacturing of a mechanical part, a common model for solid model representation and a set of tools which permits in different contexts, the extraction of information useful to carry out the activities within the considered context. The goal of the paper is to present a prototype of an expert system for the automatic recognition of form features. The system is founded on a rule-based architecture that allows a great flexibility and an easy adaptation to different contexts. The objects are described by using a boundary representation. The recognitions rules are formalized using a particular rewriting system called CAIL (Conditional Attributed Interactive Lindenmayer system).
A Study of 3D CAD Model and Feature Analysis for Casting Object
2012
When dealing with computer vision inspection testing parts in production line, the appearance of noise such as dust and inconsistent light distribution should be consider for further analysis on the parts image. In this paper, shape representation model using feature vector and Fourier descriptor were presented on the 3D CAD model image with the aim to gain the shape feature analysis for casting object. By adding light and salt & pepper noise on the CAD model image, the predicted database was compared to its original CAD image. In feature vector method, calculation on its Similarity, Correlation, Matching black and white points was carried out. Results observation show similarity of feature vector method performs 68% accuracy for light noise appearance, while correlation method performs 98% accuracy on disturbance of salt & pepper noise. Fourier Descriptor used to present the pose estimation of images on CCW and CW direction. Result shows matching sets similarity is value high since...
A Comparative Survey on 3D Models Retrieval Methods
REV Journal on Electronics and Communications, 2013
In computer vision many studies have been conducted in order to perform the matching and comparison of 3D models of objects. The main goal of matching is to group the models into different categories according to their similarity in order to allow their retrieval for recognition purposes and for further usage. So, in most of the cases, the comparison is run on a large dataset containing various models whether they belong to the same type of object or not and generally having similar or different shapes and poses. The objects’ nature and characteristics are important factors to be taken into consideration before performing the comparison step. We distinguish between two main categories of objects: rigid objects and deformable objects whose treatment and handling differ in the modeling as well as in the comparison phases. In this paper, we will be focusing on the comparison of deformable objects, and thus dealing with objects whose shapes might vary in different instances. For this pu...