Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Curve-Skeleton Properties, Applications, and Algorithms
IEEE Transactions on Visualization and Computer Graphics
Skeleton extraction by mesh contraction
ACM SIGGRAPH 2008 papers
Web-based quotation system for stereolithography parts
Computers in Industry
Survey paper: Web-based rapid prototyping and manufacturing systems: A review
Computers in Industry
On visual similarity based 2D drawing retrieval
Computer-Aided Design
A survey on geometrical reconstruction as a core technology to sketch-based modeling
Computers and Graphics
3D CAD model matching from 2D local invariant features
Computers in Industry
"3D reconstruction problem": an automated procedure
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
Algorithms
A review of conventional and knowledge based systems for machining price quotation
Journal of Intelligent Manufacturing
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We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%.