Image-Based Models: Geometry and Reflectance Acquisition Systems
Image-Based Models: Geometry and Reflectance Acquisition Systems
Master's Thesis, UC Berkeley, Fall 2002
Christopher Damien Tchou
USC Institute for Creative Technologies

(Left) Standard correspondence from structured light Gray codes with no sub-pixel information. (Middle) Sub-pixel accurate scan derived assuming sinusoidal behavior.
(Right) Improved sub-pixel scan using localized models of the sub-pixel curve behavior.


This thesis describes two scanning devices for acquiring realistic computer models of real objects. The first device captures the reflectance properties of a human face by rapidly lighting the face from a large number of directions. With this data, it is possible to reproduce the appearance of the subject under any complex illumination environment. The second device acquires detailed geometric models of sculptures, suitable for polygonal rendering systems. This thesis discusses the design considerations and implementation of these two systems, including the calibration, data processing, and rendering algorithms.

Master's Thesis:



This thesis describes a technique for neglecting the contribution of indirect illumination in structured light patterns by taking the minimum of a stripe pattern and its inverse:

Since light from secondary diffuse bounces is usually very similar in intensity in both the Gray code and its inverse, subtracting the two should remove most of the effect of this light. This assumption is better for high-frequency Gray code patterns than low-frequency ones; diffusely reflected light is, in some sense, a weighted average of all visible points. High-frequency patterns are more likely to have several oscillation periods visible from any point, so the average will be more likely to be similar to its inverse than to low-frequency patterns. [pp. 46-47]

Similar techniques have since appeared and been used in the following publications:

  • Fast separation of direct and global components of a scene using high frequency illumination Shree K. Nayar, Gurunandan Krishnan, Michael D. Grossberg, Ramesh Raskar ACM Transactions on Graphics, 25(3), July 2006, pp. 935-944.

  • Sarah Tariq, Andrew Gardner, Ignacio Llamas, Andrew Jones, Paul Debevec, and Greg Turk. Efficient Estimation of Spatially Varying Subsurface Scattering Parameters, Vision, Modeling, and Visualzation (VMV) 2006, Aachen, Germany, November 2006.

  • Y. Xu, D. Aliaga. Robust Pixel Classification for 3D Modeling with Structured Light. Proceedings of Graphics Interface, 2007.

Related Projects: