Kuiyang Lou

Three-dimensional shape searching: state-of-the-art review and future trends

Three-dimensional shape searching is a problem of current interest in several different fields. Most techniques have been developed for a particular domain and reduce a shape into a simpler shape representation. The techniques developed for a particular domain will also find applications in other domains. We classify and compare various 3D shape searching techniques based on their shape representations. A brief description of each technique is provided followed by a detailed survey of the state-of-the-art. The paper concludes by identifying gaps in current shape search techniques and identifies directions for future research.

Shape-based searching for product lifecycle applications

Estimates suggest that more than 75% of engineering design activity comprises reuse of previous design knowledge to address a new design problem. Reusing design knowledge has great potential to improve product quality, shorten lead time, and reduce cost. However, PLM systems, which address the issue of reuse by searching for keywords in filenames, part numbers or context attached to CAD models, do not provide a robust tool to search reusable knowledge. This paper presents a brief overview of a novel approach to search for 3D models. The system is built on a client-server-database architecture. The client takes in the query input from the user along with his search preferences and passes it to the server. The server converts the shape input into feature vectors and a unique skeletal graph representation. Details of the algorithms to perform these steps are presented here. Principal advantages of our graph representation are: (i) it preserves geometry and topology of the query model, (ii) it is considerably smaller than the B-Rep graph, and (iii) it is insensitive to minor perturbations in shape, but sensitive enough to capture the major features of a shape. The combined distance of feature vectors and skeletal graphs in the database provide an indirect measure of shape similarity between models. Critical database issues such as search system efficiency, semantic gap reduction and the subjectivity of the similarity definition are addressed. This paper reports our initial results in designing, implementing and running the shape search system.

A 2D Sketch-Based User Interface for 3D CAD Model Retrieval

AbstractThis paper describes a sketch user interface enhanced by feedback for 3D CAD model retrieval. Users can express their intent by sketching 2D shape in the way as engineers draw three views of 3D models. It not only supports users’ free form sketches, but also accepts users’ further editing operations and feedbacks. The interaction paradigm proposed in this paper is supported by a 3D shape matching method, in which three problems are solved: determination of projecting planes and directions, 2D view generation, and similarity measuring between views. In addition, experiments are conducted to evaluate the performance of this sketch user interface by 3D model retrieval.

SVM-based Semantic Clustering and Retrieval of a 3D Model Database

AbstractIn this paper, we present a semi-supervised semantic clustering method based on Support Vector Machines (SVM) to organize the 3D models semantically. Ground truth data is used to identify the pattern of each semantic category by supervised learning. The unknown data is then automatically classified and clustered based on the resulting pattern. We also propose a unified search strategy which applies semantic constraints to the retrieval by using the resulting clusters. A query is first labeled with its semantic concept therefore shape-based search is only conducted in the corresponding cluster. Experiments are performed to evaluate the effects of the semantic clustering and retrieval respectively by using our prototypical 3D Engineering Shape Search System (3DESS).

A Reconfigurable 3D Engineering Shape Search System: Part I — Shape Representation

This paper presents an approach for a reconfigurable shape search system for 3D engineering models using a client-serverdatabase architecture. The current paper focuses on the server functionality, while a subsequent paper will focus on the database issues. The server takes the shape query as input from the client and converts it into feature vectors and a new skeletal graph representation which we have developed. The algorithms such as voxelization, skeletonization, and skeletal graph extraction for accomplishing these are described in detail. The principal advantages of the skeletal graph representation are that (i) it preserves the geometry and topology of the query model, (ii) it is considerably smaller than the B-Rep graph, and (iii) it is insensitive to minor perturbations in shape, while sensitive enough to capture the major features of a shape. Our representation is also synergistic with the human cognitive representation of shape. The results indicate that the skeletal graph is considerably smaller than the B-Rep graph even for complicated shapes.Copyright

A Reconfigurable 3D Engineering Shape Search System: Part II — Database Indexing, Retrieval, and Clustering

This paper introduces database and related techniques for a reconfigurable, intelligent 3D engineering shape search system, which retrieves similar 3D models based on their shape content. Feature vectors, which are numeric “fingerprints” of 3D models, and skeletal graphs, which are the “minimal representations of the shape content” of a 3D model, represent the shape content. The Euclidean distance of the feature vectors, as well as the distance between skeletal graphs, provides indirect measures of shape similarity between the 3D models. Critical database issues regarding 3D shape search systems are discussed: (a) database indexing, (b) semantic gap, (c) subjectivity of similarity, and (d) database clustering. An Rtree based multidimensional index is used to speed up the feature-vector based search operation, while a decision treebased approach is used for efficiently indexing/searching skeletal graphs. Interactions among users and the search system, such as relevance feedback and feature vector reconfiguration, are used to bridge the semantic gap and to customize the system for different users. Database clustering of the R-tree index is compared with that generated by a selforganizing map (SOM). Synthetic databases and real 3D model databases are employed to investigate the efficiency of the multidimensional index and the effectiveness of relevance feedback.Copyright

Content-based three-dimensional engineering shape search

We discuss the design and implementation of a prototype 3D engineering shape search system. The system incorporates multiple feature vectors, relevance feedback, and query by example and browsing, flexible definition of shape similarity, and efficient execution through multidimensional indexing and clustering. In order to offer more information for a user to determine similarity of 3D engineering shape, a 3D interface that allows users to manipulate shapes is proposed and implemented to present the search results. The system allows users to specify which feature vectors should be used to perform the search. The system is used to conduct extensive experimentation real data to test the effectiveness of various feature vectors for shape - the first such comparison of this type. The test results show that the descending order of the average precision of feature vectors is: principal moments, moment invariants, geometric parameters, and eigenvalues. In addition, a multistep similarity search strategy is proposed and tested to improve the effectiveness of 3D engineering shape search. It is shown that the multistep approach is more effective than the one-shot search approach, when a fixed number of shapes are retrieved.

Effectiveness and efficiency of three-dimensional shape retrieval

The effectiveness and efficiency of a content-based three-dimensional shape search system are investigated for supporting the re-use of engineering designs. Search effectiveness is characterized by precision and recall values from repeated search experiments. We extract four feature vectors from each three-dimensional shape and compare them for effectiveness. The performance of an R-tree-based index structure is also evaluated to characterize search efficiency. Search efficiency is evaluated by the ratio of the number of visited nodes in a search operation to the number of nodes in a database index. A multi-step refinement approach is proposed to improve search effectiveness. Based on the results of the experiments with a database of real models, the effectiveness of using multi-step refinement is predicted to be 51% higher than that of one-shot search using a single feature vector, although the difference becomes smaller when the number of retrieved shapes is larger. The R-tree index significantly improves the efficiency of our search system. Search efficiency decreases with the dimensionality of the data records and the capacity of database nodes. Based on our experiments with the synthetic database, the efficiency is predicted to be stable when the size of a database increases.