3D shape recovery of smooth surfaces: Dropping the fixed-viewpoint assumption

Yael Moses, Ilan Shimshoni

Research output: Contribution to journalArticlepeer-review


We present a new method for recovering the 3D shape of a featureless smooth surface from three or more calibrated images illuminated by different light sources (three of them are independent). This method is unique in its ability to handle images taken from unconstrained perspective viewpoints and unconstrained illumination directions. The correspondence between such images is hard to compute and no other known method can handle this problem locally from a small number of images. Our method combines geometric and photometric information in order to recover dense correspondence between the images and accurately computes the 3D shape. Only a single pass starting at one point and local computation are used. This is in contrast to methods that use the occluding contours recovered from many images to initialize and constrain an optimization process. The output of our method can be used to initialize such processes. In the special case of fixed viewpoint, the proposed method becomes a new perspective photometric stereo algorithm. Nevertheless, the introduction of the multiview setup, self-occlusions, and regions close to the occluding boundaries are better handled, and the method is more robust to noise than photometric stereo. Experimental results are presented for simulated and real images.

Original languageEnglish
Pages (from-to)1310-1324
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
StatePublished - 2009

Bibliographical note

Funding Information:
This research was supported by the Israel Science Foundation (Grant 133/0-125). The authors would like to thank Avi Barliya, Gil Ben-Artzi, and Benjamin Neeman for working on the implementation of the method.


  • 3D shape reconstruction
  • Featureless objects

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics


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