The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as

This document contains the draft version of the following paper: A. Cardone, S.K. Gupta, and M. Karnik. A survey of shape similarity assessment algorithms for product design and manufacturing applications. ASME Journal of

Methods for content-based similarity search are fundamental for managing large multimedia repositories, as they make it possible to conduct queries for similar content, and to organize the repositories into classes of similar objects. 3D objects are an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in 3D similarity search has arisen, and a growing number of competing algorithms for the retrieval of 3D objects have been proposed. The contributions of this paper are to survey a body of recently proposed methods for 3D similarity search, to organize them along a descriptor extraction process model, and to present an extensive experimental effectiveness and efficiency evaluation of these methods, using several 3D databases.

Our recent work has described a framework for matching solid of mechanical artifacts models based on scale-space feature decomposition. In this work we adopt a method of comparing solid models based on Multiresolutional Reeb Graphs (MRG) similarity computations. This method was originally proposed by Hilaga etal. in [1]. Reeb Graph technique applies MRG structure to comparisons of approximate models found in the graphics community, such as polygonal meshes, faceted representation and Virtual Reality Modeling Language (VRML) models. First, we provide a brief review of shape matching using Multiresolutional Reeb Graphs and present an approach to matching solid models. Second, we show the performance of the Reeb Graph technique when handling primitive CAD models, such as cubes and spheres; then we perform experiments with more complex models, such as LEGO models and mechanical parts, and we discuss Reeb Graph technique's performance on complex CAD models. Third, we emphasize several problems with the existing technique. Finally, we conclude with discussion of future work.

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this report we describe our framework of constructing and using a shape prior in estimation problems. The key novelty of our technique is a new way to use high level, global shape knowledge to derive a local driving force in a curve evolution context. We capture information about shape in the form of a family of shape distributions (cumulative distribution functions) of features related to the shape. We design a prior objective function that penalizes the di#erences between model shape distributions and those of an estimate. We incorporate this prior in a curve evolution formulation for function minimization. Shape distribution-based representations are shown to satisfy several desired properties, such as robustness and invariance. They also have good discriminative and generalizing properties. To our knowledge, shape distribution-based representations have only been used for shape classification. Our work represents the development of a tractable framework for their incorporation in estimation problems. We apply our framework to three applications: shape morphing, average shape calculation, and image segmentation

It is very often necessary to search for similar parts during designing a new product because modifying existing similar parts is a commonly used way of creating new parts with ease. In this way, the design time and cost can be reduced. Thus it would be nice to have an efficient similarity comparison algorithm that can be used anytime in the design process. In this research, the solid parts are represented by B-rep and similarity comparison is performed in two steps from overall appearances to detail features. First, geometric information is used in low level of detail for easy and fast pre-classification by overall appearances. Then feature information is used to compare the detail shape in high level of detail to find more similar design. To realize the idea above, a multi resolution algorithm is proposed so that a given solid is described by a low resolution appearance and detail features in high resolution. Using this multiresolution representation, parts can be compared based on the overall appearance first to reduce the number of parts to be compared in high resolution, and then detail features are evaluated to retrieve the most similar part. In this way, computational time can be saved by fast classification in the first step and reliability can be preserved by detail comparison in the second step.

Abstract. With fast evolving resources for 3D objects such as the Protein Data Bank (PDB) or the World Wide Web, new techniques, so-called similarity models to efficiently and effectively search for these 3D objects become indispensible. Invariances w.r.t. specific geometric transformations such as scaling, translation, and rotation are important features of similarity models. In this paper, we focus on rotation invariance. We first propose a new method of representing objects more accurately in the context of rotation invariance than the well-known voxelization technique.In addition, we extend existing feature-based similarity models by proposing a new spherical partitioning of the data objects based on proportionality and redundancy 1, and generalizing an existing method for feature extraction. A broad experimental evaluation compares our method with existing methods in terms of accuracy and efficiency. In particular, we experimentally confirm that our point sampling method is better suited to represent 3D objects in the context of rotation invariance than voxelized representations. In addition, we empirically show that our new similarity model significantly outperfoms competitive rotation invariant models in terms of accuracy as well as efficiency. 1

C. Ip and S.K. Gupta. Retrieving matching CAD models by using partial 3D point

This paper used surface curvatures and support vector machines for the identification of manufacturing processes in a database of mesh based CAD artifacts. The target is to classify prismatic machined and cast-then-machined parts into their respective classification to assist the manufacturing cost estimation. Current research on shape matching techniques have used shape functions to match the gross shapes of mesh models. However, they do not adequately discrimate artifacts manufactured by different processes. Our approach shows how different kinds of surfaces can distinguish different manufacturing processes. Statistics on surface curvatures are used to construct shape descriptors; then supervised machine learning classifier support vector machines are applied to separate the two classifications. 1.