Feature-based similarity assessment of solid models
SMA '97 Proceedings of the fourth ACM symposium on Solid modeling and applications
Resolving non-uniqueness in design feature histories
Proceedings of the fifth ACM symposium on Solid modeling and applications
A discourse on geometric feature recognition from CAD models
Journal of Computing and Information Science in Engineering
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Using shape distributions to compare solid models
Proceedings of the seventh ACM symposium on Solid modeling and applications
ACM Transactions on Graphics (TOG)
Automated learning of model classifications
SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
An approach to a feature-based comparison of solid models of machined parts
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
SMI '04 Proceedings of the Shape Modeling International 2004
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
A 3D object classifier for discriminating manufacturing processes
Computers and Graphics
Shape-based clustering for 3D CAD objects: A comparative study of effectiveness
Computer-Aided Design
A 3D shape classifier with neural network supervision
International Journal of Computer Applications in Technology
Partial retrieval of CAD models based on the gradient flows in Lie group
Pattern Recognition
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While benchmark datasets have been proposed for testing computer vision and 3D shape retrieval algorithms, no such datasets have yet been put forward to assess the relevance of these techniques for engineering problems. This paper presents several distinctive benchmark datasets for evaluating techniques for automated classification and retrieval of CAD objects. These datasets include (1) a dataset of CAD primitives (such as those common in constructive solid geometry modeling); (2) two datasets consisting of classes generated by minor topological variation; (3) two datasets of industrial CAD models classified based on object function and manufacturing process, respectively; (4) and a dataset of LEGO© models from the Mindstorms© robotics kits. Each model in the datasets is available in three formats - ACIS SAT, ISO STEP, and as a VRML mesh (some models are available under several different fidelity settings). These are all available through the National Design Repository.Using these datasets, we present comprehensive empirical results for nińe (9) different shape and solid model matching and retrieval techniques. These experiments show, as expected, that the quality of precision-recall performance can significantly vary on different datasets. These experiments reveal that for certain object classes and classifications, such as those based on manufacturing processes, all existing techniques perform poorly. This study reveals the strengths and weaknesses of existing research in these areas, introduces open challenge problems, and provides meaningful datasets and metrics against which the success of current and future work can be measured.