Radiometric compensation using stratified inverses

Tian Tsong Ng, Ramanpreet S. Pahwa, Jiamin Bai, Tony Q.S. Quek, Kar Han Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)


Through radiometric compensation, a projector-camera system can project a desired image onto a non-flat and non-white surface. This can be achieved by computing the inverse light transport of a scene. A light transport matrix is in general large and on the order of 106 x 106 elements. Therefore, computing the inverse light transport matrix is computationally and memory intensive. Two prior methods were proposed to simplify matrix inversion by ignoring scene inter-reflection between individual or clusters of camera pixels. However, compromising scene inter-reflection in spatial domain introduces spatial artifacts and how to systematically adjust the compensation quality is not obvious. In this work, we show how scene inter-reflection can be systematically approximated by stratifying the light transport of a scene. The stratified light transport enables a similar stratification in the inverse light transport. We can show that the stratified inverse light transport converges to the true inverse. For radiometric compensation, the set of stratified inverse light transport provides a systematic way of quantifying the tradeoff between computational efficiency and accuracy. The framework of stratified matrix inversion is general and can have other applications, especially for applications that involve large-size sparse matrices.

Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Number of pages6
Publication statusPublished - 2009
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: 2009 Sep 292009 Oct 2

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Conference12th International Conference on Computer Vision, ICCV 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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