Shape reconstruction of 3D bilaterally symmetric surfaces

Ilan Shimshoni, Yael Moses, Michael Lindenbaum

Research output: Contribution to journalArticlepeer-review


The paper presents a new approach for shape recovery based on integrating geometric and photometric information. We consider 3D bilaterally symmetric objects, that is, objects which are symmetric with respect to a plane (e.g., faces), and their reconstruction from a single image. Both the viewpoint and the illumination are not necessarily frontal. Furthermore, no correspondence between symmetric points is required. The basic idea is that an image taken from a general, non frontal viewpoint, under non-frontal illumination can be regarded as a pair of images. Each image of the pair is one half of the object, taken from different viewing positions and with different lighting directions. Thus, one-image-variants of geometric stereo and of photometric stereo can be used. Unlike the separate invocation of these approaches, which require point correspondence between the two images, we show that integrating the photometric and geometric information suffice to yield a dense correspondence between pairs of symmetric points, and as a result, a dense shape recovery of the object. Furthermore, the unknown lighting and viewing parameters, are also recovered in this process. Unknown distant point light source, Lambertian surfaces, unknown constant albedo, and weak perspective projection are assumed. The method has been implemented and tested experimentally on simulated and real data.

Original languageEnglish
Pages (from-to)97-110
Number of pages14
JournalInternational Journal of Computer Vision
Issue number2
StatePublished - Sep 2000
Externally publishedYes

Bibliographical note

Funding Information:
We would like to thank Dalit Ramon for donating the mannequin used in the experiments. Yael Moses would like to thank Mike Brady for useful discussion in early stages of this study. Ilan Shimshoni was supported in part by the Koret and Goldschmidt Foundations.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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