Light field constancy within natural scenes.

The structure of light fields of natural scenes is highly complex due to high frequencies in the radiance distribution function. However it is the low-order properties of light that determine the appearance of common matte materials. We describe the local light field in terms of spherical harmonics and analyze the qualitative properties and physical meaning of the low-order components. We take a first step in the further development of Gershun's classical work on the light field by extending his description beyond the 3D vector field, toward a more complete description of the illumination using tensors. We show that the three first components, namely, the monopole (density of light), the dipole (light vector), and the quadrupole (squash tensor) suffice to describe a wide range of qualitatively different light fields. In this paper we address a related issue, namely, the spatial properties of light fields within natural scenes. We want to find out to what extent local light fields change from point to point and how different orders behave. We found experimentally that the low-order components of the light field are rather constant over the scenes whereas high-order components are not. Using very simple models, we found a strong relationship between the low-order components and the geometrical layouts of the scenes.

[1]  M. Levoy,et al.  The light field , 1939 .

[2]  E. Adelson,et al.  The Plenoptic Function and the Elements of Early Vision , 1991 .

[3]  J. Endler The Color of Light in Forests and Its Implications , 1993 .

[4]  Richard Szeliski,et al.  The lumigraph , 1996, SIGGRAPH.

[5]  Mel Siegel,et al.  Orientation invariant light source parameters , 1996 .

[6]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[7]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[8]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  Edward H. Adelson,et al.  Statistics of real-world illumination , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Roland W Fleming,et al.  Real-world illumination and the perception of surface reflectance properties. , 2003, Journal of vision.

[11]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

[12]  Michael Bosse,et al.  Calibrated, Registered Images of an Extended Urban Area , 2003, International Journal of Computer Vision.

[13]  Ron O Dror,et al.  Statistical characterization of real-world illumination. , 2004, Journal of vision.

[14]  David J. Kriegman,et al.  What Is the Set of Images of an Object Under All Possible Illumination Conditions? , 1998, International Journal of Computer Vision.

[15]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.