Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction

  • Authors:

  • Todd E. Zickler;Peter N. Belhumeur;David J. Kriegman

  • Affiliations:

  • Electrical Engineering, Yale University, New Haven, CT 06520-8285, USA. zickler@yale.edu;Computer Science, Columbia University, New York, NY 10027, USA. belhumeur@cs.columbia.edu;Computer Science and Engineering, University of California, San Diego, LaJolla, CA 94143-1290, USA. kriegman@cs.ucsd.edu

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2002

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Abstract

We present a method—termed Helmholtz stereopsis—for reconstructing the geometry of objects from a collection of images. Unlike existing methods for surface reconstruction (e.g., stereo vision, structure from motion, photometric stereopsis), Helmholtz stereopsis makes no assumptions about the nature of the bidirectional reflectance distribution functions (BRDFs) of objects. This new method of multinocular stereopsis exploits Helmholtz reciprocity by choosing pairs of light source and camera positions that guarantee that the ratio of the emitted radiance to the incident irradiance is the same for corresponding points in the two images. The method provides direct estimates of both depth and surface normals, and consequently weds the advantages of both conventional stereopsis and photometric stereopsis. Results from our implementation lend empirical support to our technique.