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Resolution-Enhanced Photometric Stereo

  • Ping Tan
  • Stephen Lin
  • Long Quan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

Conventional photometric stereo has a fundamental limitation that the scale of recovered geometry is limited to the resolution of the input images. However, surfaces that contain sub-pixel geometric structures are not well modelled by a single normal direction per pixel. In this work, we propose a technique for resolution-enhanced photometric stereo, in which surface geometry is computed at a resolution higher than that of the input images. To achieve this goal, our method first utilizes a generalized reflectance model to recover the distribution of surface normals inside each pixel. This normal distribution is then used to infer sub-pixel structures on a surface of uniform material by spatially arranging the normals among pixels at a higher resolution according to a minimum description length criterion on 3D textons over the surface. With the presented method, high resolution geometry that is lost in conventional photometric stereo can be recovered from low resolution input images.

Keywords

Gaussian Mixture Model Minimum Description Length Resolution Enhancement Integrability Constraint Photometric Stereo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ping Tan
    • 1
  • Stephen Lin
    • 2
  • Long Quan
    • 1
  1. 1.Computer Science DepartmentHong Kong University of Science and Technology 
  2. 2.Microsoft Research Asia 

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