Abstract
We present a method — termed Helmholtz stereopsis — for reconstructing the geometry of objects from a collection of images. Unlike most existing methods for surface reconstruction (e.g., stereo vision, structure from motion, photometric stereo), 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 field of surface normals, and consequently weds the advantages of both conventional and photometric stereopsis. Results from our implementations lend empirical support to our technique.
P. N. Belhumeur was supported by a Presidential Early Career Award IIS-9703134, NSF KDI-9980058, NIH R01-EY 12691-01, and NSF ITR ITS-00-85864. D. J. Kriegman was supported by NSF IIS 00-85864, NSF CCR 00-86094, and NIH R01-EY 12691-01.
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Keywords
- Surface Reconstruction
- Bidirectional Reflectance Distribution Function
- Photometric Stereo
- Incident Irradiance
- Surface Depth
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Zickler, T., Belhumeur, P.N., Kriegman, D.J. (2002). Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47977-5_57
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