A 3D imaging framework based on high-resolution photometric-stereo and low-resolution depth

Zheng Lu, Yu Wing Tai, Fanbo Deng, Moshe Ben-Ezra, Michael S. Brown

Research output: Journal PublicationArticlepeer-review

17 Citations (Scopus)


This paper introduces a 3D imaging framework that combines high-resolution photometric stereo and low-resolution depth. Our approach targets imaging scenarios based on either macro-lens photography combined with focal stacking or a large-format camera that are able to image objects with more than 600 samples per mm2. These imaging techniques allow photometric stereo algorithms to obtain surface normals at resolutions that far surpass corresponding depth values obtained with traditional approaches such as structured-light, passive stereo, or depth-from-focus. Our work offers two contributions for 3D imaging based on these scenarios. The first is a multi-resolution, patched-based surface reconstruction scheme that can robustly handle the significant resolution difference between our surface normals and depth samples. The second is a method to improve the initial normal estimation by using all the available focal information for images obtained using a focal stacking technique.

Original languageEnglish
Pages (from-to)18-32
Number of pages15
JournalInternational Journal of Computer Vision
Issue number1-3
Publication statusPublished - Mar 2013
Externally publishedYes


  • 3D Reconstruction
  • Focal stack
  • High resolution
  • Photometric stereo

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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