Improved SFS 3D measurement based on BP neural network

  • Authors:

  • LiMei Song;XingHua Qu;ShengHua Ye

  • Affiliations:

  • Province Key Laboratory of Robot Technique and Application, Information Engineering College, South West University of Science and Technology, MianYang SiChuan 621010, China;State Key Laboratory of Precision Measuring Technology and Instrument, TianJin University, 300072, China;State Key Laboratory of Precision Measuring Technology and Instrument, TianJin University, 300072, China

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2007

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Abstract

3D surface measurement is an important requirement for modern industry. Non-contact optical method is a commonly used way to plot 2D and 3D measurement. Shape from shading (SFS) is a convenient 3D method because it can recover the 3D shape only from one image. But the existing SFS research has a lot of restriction, such as Lambertian illumination model. If the object is not under Lambertian model, the precision will decrease quickly. We propose an improved SFS based on BP neural network combining with genetic algorithm. This proposed SFS can provide the approximation of the reflectance model. So even the object is not under Lambertian light source, it can also be recovered and the precision has been greatly increased. It has been tested in the 3D reconstruction of synthetic vase, black and complex motorcar part, and really used in the online 3D measurement of work pieces.