Skip to main content

A 3D Imaging Framework Based on High-Resolution Photometric-Stereo and Low-Resolution Depth


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 mm\(^2\). 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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19


  1. Also see


  • Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., et al. (2004). Interactive digital photomontage. ACM Transactions on Graphics (SIGGRAPH), 23(3), 294–302.

    Google Scholar 

  • Agrawal, A., Raskar, R.,& Chellappa, R. (2006). What is the range of surface reconstructions from a gradient field? In European conference on computer vision (ECCV). Graz, Austria: Springer.

  • Agrawal, A., Xu, Y.,& Raskar, R. (2009). Invertible motion blur in video. ACM Transactions on Graphics (SIGGRAPH), 28(3), 1–8.

    Article  Google Scholar 

  • Anagramm and Digital Reproduction (1998). Accesed 1 August 2011.

  • Banerjee, S., Sastry, P.,& Venkatesh, Y. (1992). Surface reconstruction from disparate shading: An integration of shape-from-shading and stereopsis. In 11th IAPR International conference on pattern recognition. The Hague, The Netherlands: IEEE Computer Society.

  • Bernardini, F., Rushmeier, H., Martin, I. M., Mittleman, J.,& Taubin, G. (2002). Building a digital model of Michelangelo’s Florentine Pieta. IEEE Computer Graphics and Applications, 22(1), 59–67.

    Google Scholar 

  • Chen, C. Y., Klette, R.,& Chen, C. F. (2003). Shape from photometric stereo and contours. In Proceedings of computer analysis of images patterns (CAIP). Groningen, The Netherlands: Springer.

  • Cignoni, P., Rocchini, C.,& Scopigno, R. (1998). Metro: Measuring error on simplified surfaces. Computer Graphics Forum, 17(2), 167–174.

    Google Scholar 

  • Darrell, T.,& Wohn, K. (1988). Pyramid based depth from focus. In Computer vision and pattern recognition (CVPR). Ann Arbor, MI: IEEE Computer Society.

  • Fua, P.,& Leclerc, Y. G. (1994). Using 3-dimensional meshes to combine image-based and geometry-based constraints. In European conference on computer vision (ECCV). Stockholm, Sweden: Springer.

  • Hausler, G. (1972). A method to increase the depth of focus by two step image processing. Optics Communications, 6(1), 38–42.

    Article  Google Scholar 

  • Hernández, C., Vogiatzis, G.,& Cipolla, R. (2008). Multi-view photometric stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3), 548–554.

    Google Scholar 

  • Higo, T., Matsushita, Y., Joshi, N.,& Ikeuchi, K. (2009). A hand-held photometric stereo camera for 3D modeling. In Computer vision and pattern recognition (CVPR). Miami, FL: IEEE Computer Society.

  • Horn, B.,& Brooks, M. (1989). Shape from shading. Cambridge: MIT Press.

    Google Scholar 

  • Ikeuchi, K. (1987). Determining a depth map using a dual photometric stereo. International Journal of Robotics Research, 6(1), 15–31.

    Article  Google Scholar 

  • Lange, H. (1999). Advances in the cooperation of shape from shading and stereo vision. In Proceedings 3DIM. Ottawa, Canada: IEEE Computer Society.

  • Lu, Z., Tai, Y. W., Ben-Ezra, M.,& Brown, M. S. (2010). A framework for ultra high resolution 3D imaging. In Computer vision and pattern recognition (CVPR). San Francisco, CA: IEEE Computer Society.

  • Malik, A. S.,& Choi, T. S. (2008). A novel algorithm for estimation of depth map using image focus for 3d shape recovery in the presence of noise. Pattern Recognition, 41(7), 2200–2225.

    MATH  Article  Google Scholar 

  • Nayar, S. K.,& Nakagawa, Y. (1994). Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(8), 824–831.

    Article  Google Scholar 

  • Nayar, S. K., Fang, X. S.,& Boult, T. (1997). Separation of reflection components using color and polarization. International Journal of Computer Vision, 21(3), 163–186.

    Google Scholar 

  • Nehab, D., Rusinkiewicz, S., Davis, J.,& Ramamoorthi, R. (2005). Efficiently combining positions and normals for precise 3d geometry. ACM Transactions on Graphics (SIGGRAPH), 24(3), 536–543.

    Google Scholar 

  • Reid, J. K.,& Scott, J. A. (2009). An out-of-core sparse cholesky solver. ACM Transactions on Mathematical Software, 36(2), 1–33.

    MathSciNet  Article  Google Scholar 

  • Scharstein, D.,& Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(1–3), 7–42.

    Google Scholar 

  • Scharstein, D.,& Szeliski, R. (2003). High-accuracy stereo depth maps using structured light. In Computer vision and pattern recognition (CVPR). Madison, WI: IEEE Computer Society.

  • Seitz, S. M., Curless, B., Diebel, J., Scharstein, D.,& Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Computer vision and pattern recognition (CVPR). New York, NY: IEEE Computer Society.

  • Terzopoulos, D. (1988). The computation of visible-surface representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4), 417–438.

    Google Scholar 

  • Vlasic, D., Peers, P., Baran, I., Debevec, P., Popovi’c, J., Rusinkiewicz, S., et al. (2009). Dynamic shape capture using multi-view photometric stereo. ACM Transactions on Graphics (SIGGRAPH-ASIA), 28(5), 1–11.

    Google Scholar 

  • Wholer, C. (2009). 3D computer vision: efficient methods and applications. New York: Springer.

    Book  Google Scholar 

  • Woodham, R. J. (1980). Photometric method for determining surface orientation from multiple images. Optical Engineering, 19(1), 139–144.

    Article  Google Scholar 

  • Wu, T. P.,& Tang, C. K. (2006). Visible surface reconstruction from normals with discontinuity consideration. In: Computer Vision and Pattern Recognition (CVPR).

  • Wu, T. P., Sun, J., Tang, C. K.,& Shum, H. Y. (2008). Interactive normal reconstruction from a single image. ACM Transactions on Graphics (SIGGRAPH-ASIA), 27(5), 1–9.

    Google Scholar 

  • Xiong, Y.,& Shafer, S. (1993). Depth from focusing and defocusing. In Computer vision and pattern recognition (CVPR). New York, NY: IEEE Computer Society.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Zheng Lu.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lu, Z., Tai, YW., Deng, F. et al. A 3D Imaging Framework Based on High-Resolution Photometric-Stereo and Low-Resolution Depth. Int J Comput Vis 102, 18–32 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


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