An optimization-based approach to the interpretation of single line drawings as 3D wire frames
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Regularity selection for effective 3D object reconstruction from a single line drawing
Pattern Recognition Letters
Frontal geometry from sketches of engineering objects: is line labelling necessary?
Computer-Aided Design
Technical Section: An optimisation-based reconstruction engine for 3D modelling by sketching
Computers and Graphics
A freehand-sketch environment for architectural design supported by a multi-agent system
Computers and Graphics
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This study compares different forms of compliance function, such as the linear, polynomial and Gaussian forms, in 3D reconstruction of polyhedral objects, under the framework of support vector machine for regression (SVR). The correlations among different regularities are considered in the proposed form. This study also provides a systematic method to calculate the corresponding coefficients of regularities based on the 2D line drawings and corresponding 3D objects library.