Automated learning of model classifications

Ip, Cheuk Yiu; Regli, William C.; Sieger, Leonard; Shokoufandeh, Ali

doi:10.1145/781606.781659

Cited by 46 publications

(18 citation statements)

References 14 publications

(8 reference statements)

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“…This experiment aimed to test if the proposed approach would only retrieve the matching model, with no false positives, among dissimilar shapes. This dataset was provided by the National Design Repository at Drexel University [15]. It consisted of 55 prismatic machined parts of various shapes.…”

Section: Resultsmentioning

confidence: 99%

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Retrieving Matching CAD Models by Using Partial 3D Point Clouds

Ip¹,

Gupta²

2007

Computer-Aided Design and Applications

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The ability to search for a CAD model that represents a specific physical part is a useful capability that can be used in many different applications. This paper presents an approach to use partial 3D point cloud of an artifact for retrieving the CAD model of the artifact. We assume that the information about the physical parts will be captured by a single 3D scan that produces dense point clouds. CAD models in our approach are represented as polygonal meshes. Our approach involves segmenting the point cloud and CAD mesh models into surface patches. The next step is to identify corresponding surface patches in point clouds and CAD models that could potentially match. Finally, we compute transformations to align the point cloud to the CAD model and compute distance between them. We also present experimental results to show that our approach can be used to retrieve CAD models of mechanical parts. INTRODUCTIONThe ability to search for a CAD model that represents a specific physical part is a useful capability that can be used in many different applications. The following scenario illustrates the usefulness of being able to search for a CAD model based on point cloud generated by a partial scan. Let us assume that a part needs to be replaced in a complex machine. There is no label on the part. Hence the user does not know the part number. The user scans the physical part using a 3D scanner and generates the point cloud. This point cloud is then used by the user to search the CAD database and find the CAD model of this part. The CAD model has the information about the part number and the user is able to order the replacement part using the part number.

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Section: Resultsmentioning

confidence: 99%

“…While these techniques target general 3D models, Ip et al [14,15] focused on comparing shape models of CAD with shape distributions. Iyer et al [17] presented a CAD oriented search system, based on shape, voxelization and other approaches.…”

Section: Comparing Shape Models Of Cadmentioning

confidence: 99%

Retrieving Matching CAD Models by Using Partial 3D Point Clouds

Ip¹,

Gupta²

2007

Computer-Aided Design and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…They applied this method to compare solid CAD models. They also extended this method to automatically categorize a large model database [53]. A 3D histogram is implemented in [54]: two dimensions are for local and global shape signature and the third one is for distance between local shape pairs.…”

Section: Related Workmentioning

confidence: 99%

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

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“…Therefore it can be applied to volume models, but not to polygonal soups. Recently, Ip et al [38] extend this approach with a technique to automatically categorize a large model database, given a categorization on a number of training examples from the database.…”

Section: Global Feature Distribution Based Similaritymentioning

confidence: 99%