Diffuse Reflectance Imaging with Astronomical Applications
Samuel W. Hasinoff,
Philip R. Goode,
William T. Freeman
Samuel W. Hasinoff, Anat Levin, Philip R. Goode, and William T. Freeman,
Diffuse Reflectance Imaging with Astronomical Applications.
Proc. 13th IEEE International Conference on Computer Vision, ICCV 2011, pp. 185-192
Diffuse objects generally tell us little about the surrounding lighting,
since the radiance they reflect blurs together incident lighting from
many directions. In this paper we discuss how occlusion geometry
can help invert diffuse reflectance to recover lighting or surface
albedo. Self-occlusion in the scene can be regarded as a form of coding,
creating high frequencies that improve the conditioning of diffuse light
transport. Our analysis builds on a basic observation that diffuse
reflectors with sufficiently detailed geometry can fully resolve the
incident lighting. Using a Bayesian framework, we propose a novel
reconstruction method based on high-resolution photography, taking
advantage of visibility changes near occlusion boundaries. We also
explore the limits of single-pixel observations as the diffuse reflector
(and potentially the lighting) vary over time. Diffuse reflectance
imaging is particularly relevant for astronomy applications, where
diffuse reflectors arise naturally but the incident lighting and camera
position cannot be controlled. To test our approaches, we first study
the feasibility of using the moon as a diffuse reflector to observe the
earth as seen from space. Next we present a reconstruction of Mars using
historical photometry measurements not previously used for this purpose.
As our results suggest, diffuse reflectance imaging expands our notion
of what can qualify as a camera.
We would like to thank Bernhard Schölkopf and Frédo Durand
for helpful discussions, Livia Ilie for early participation as an MIT
UROP, and David Chen for help with data entry.
Atmospheric data were obtained from the NASA Langley Research Center Atmospheric Science Data Center.
This work was supported
in part by an NSERC Postdoctoral Fellowship, BSF Grant 2008155, NGA
NEGI-1582-04-0004, MURI Grant N00014-06-1-0734, the ISF, ERC, and
Quanta T-Party, and gifts from Microsoft, Google and Adobe.