Fundamentals of matrix computations
Fundamentals of matrix computations
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Machine interpretation of CAD data for manufacturing applications
ACM Computing Surveys (CSUR)
Shape Spectrum Based View Grouping and Matching of 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based 3D object recognition using Bayesian indexing
Computer Vision and Image Understanding
A discourse on geometric feature recognition from CAD models
Journal of Computing and Information Science in Engineering
ACM Transactions on Graphics (TOG)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
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
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching and clustering using spectral partitions
Pattern Recognition
A spectral approach to shape-based retrieval of articulated 3D models
Computer-Aided Design
Introduction to Information Retrieval
Introduction to Information Retrieval
Surface Mesh Smoothing, Regularization, and Feature Detection
SIAM Journal on Scientific Computing
Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids
Computer-Aided Design
3-D Object Recognition Using 2-D Views
IEEE Transactions on Image Processing
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3D model matching has been widely studied in computer vision, graphics and robotics. While there is much success made in the matching of natural objects, most of these approaches consider smooth surfaces and are not suitable for computer aided design (CAD) models because of their complex topology and singular structures. This paper presents a novel spectral approach for the 3D CAD model matching in the framework of manifold learning. The 3D models are treated as undirected graphs. A regularized Laplacian spectrum approach is applied to solve this problem where the regularization term is used to characterize the shape geometries. Spectral distributions of different models are obtained and then compared by their divergence for model retrieval. The proposed approach is tested with models from known 3D CAD database for verification.