Neural networks based recognition of 3D freeform surface from 2D Sketch

Guangmin Sun, Sheng-feng Qin, David Wright

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this paper, the back propagation (BP) network and radial basis function (RBF) neural network are employed to recognize and reconstruct 3D freeform surface from 2D freehand sketch. Some tests and comparison experiments have been made to evaluate the performance for the reconstruction of freeform surfaces of both networks using simulation data. The experimental results show that both BP and RBF based freeform surface reconstruction methods are feasible; and the RBF network performed better. The RBF average point error between the reconstructed 3D surface data and the desired 3D surface data is less than 0.05 over all our 75 test sample data
Original languageEnglish
Title of host publicationProceedings of EUROCON 2005 - The International Conference on Computer as a Tool
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1378-1381
ISBN (Print)142440049X
DOIs
Publication statusPublished - 2005
EventThe International Conference on Computer as a Tool, 2005. (EUROCON 2005) - Belgrade
Duration: 1 Jan 2005 → …

Conference

ConferenceThe International Conference on Computer as a Tool, 2005. (EUROCON 2005)
Period1/01/05 → …

Fingerprint Dive into the research topics of 'Neural networks based recognition of 3D freeform surface from 2D Sketch'. Together they form a unique fingerprint.

Cite this