My Ph.D. thesis is available as a 2.6M PDF file (two-sided format, 163 pages) that includes all the images at 75 dpi. If you don't already have it, you will need Adobe Acrobat Reader in order to read the PDF file. If you are going to print it out and you want to save half the paper, I have also prepared a 2.4M PDF file that has two pages per side (prints on 41 pieces of paper).

You can also download the full 9.1M gzipped PostScript file, which has all the images at around 150 dpi.

Creating realistic images has been a major focus in the study of computer
graphics for much of its history. This effort has led to mathematical models
and algorithms that can compute *predictive*, or *physically
realistic*, images from known camera positions and scene descriptions that
include the geometry of objects, the reflectance of surfaces, and the lighting
used to illuminate the scene. These images accurately describe the physical
quantities that would be measured from a real scene. Because these algorithms
can predict real images, they can also be used in inverse problems to
work backward from photographs to attributes of the scene.

Work on three such *inverse rendering* problems is described. The first,
* inverse lighting*, assumes knowledge of geometry, reflectance, and the
recorded photograph and solves for the lighting in the scene. A technique
using a linear least-squares system is proposed and demonstrated. Also
demonstrated is an application of inverse lighting, called re-lighting, which
modifies lighting in photographs.

The second two inverse rendering problems solve for unknown reflectance, given
images with known geometry, lighting, and camera positions. *Photographic
texture measurement* concentrates on capturing the spatial variation in an
object's reflectance. The resulting system begins with scanned 3D models of
real objects and uses photographs to construct accurate, high-resolution
textures suitable for physically realistic rendering. The system is
demonstrated on two complex natural objects with detailed surface textures.

*Image-based BRDF measurement* takes the opposite approach to reflectance
measurement, capturing the directional characteristics of a surface's
reflectance by measuring the *bidirectional reflectance distribution
function*, or BRDF. Using photographs of an object with spatially uniform
reflectance, the BRDFs of paints and papers are measured with completeness and
accuracy that rival that of measurements obtained using specialized devices.
The image-based approach and novel light source positioning technique require
only general-purpose equipment, so the cost of the apparatus is low compared to
conventional approaches. In addition, very densely sampled data can be
measured very quickly, when the wavelength spectrum of the BRDF does not need
to be measured in detail.

- Introduction
- Background
- Mathematical Preliminaries
- Radiometry
- The Bidirectional Reflectance Distribution Function
- Rendering

- Inverse Lighting
- Problem Statement
- Prior Work
- Basic Least-squares Solution
- Regularized Solution
- Re-lighting
- A Test with a Synthetic Photograph
- A Test with a Rigid Object
- Tests on Human Faces
- Conclusion

- Photographic Texture Measurement
- Prior Work
- Texture Representation
- Estimating Reflectance
- A Synthetic Example
- Measurement Setup
- Results
- Future Work
- Conclusion

- Image-based BRDF Measurement
- Overview of Method
- Prior Work
- Apparatus
- Data Processing
- Results
- Mapping the BRDF Domain to 3-space
- BRDF Resampling
- Conclusion

- Conclusion
- Appendices
- Camera Calibration
- Bundle Adjustment
- Calibration Targets
- Cameras
- The Cyberware Scanner
- BRDF Measurement Procedure

- Bibliography

Steve Marschner