A Dual Theory of Inverse and Forward Light Transport

Inverse light transport seeks to undo global illumination effects, such as interreflections, that pervade images of most scenes. This paper presents the theoretical and computational foundations for inverse light transport as a dual of forward rendering. Mathematically, this duality is established through the existence of underlying Neumann series expansions. Physically, we show that each term of our inverse series cancels an interreflection bounce, just as the forward series adds them. While the convergence properties of the forward series are well-known, we show that the oscillatory convergence of the inverse series leads to more interesting conditions on material reflectance. Conceptually, the inverse problem requires the inversion of a large transport matrix, which is impractical for realistic resolutions. A natural consequence of our theoretical framework is a suite of fast computational algorithms for light transport inversion - analogous to finite element radiosity, Monte Carlo and wavelet-based methods in forward rendering - that rely at most on matrix-vector multiplications. We demonstrate two practical applications, namely, separation of individual bounces of the light transport and fast projector radiometric compensation to display images free of global illumination artifacts in real-world environments.

[1]  H. Jensen Realistic Image Synthesis Using Photon Mapping , 2001 .

[2]  Shree K. Nayar,et al.  A projector-camera system with real-time photometric adaptation for dynamic environments , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Pat Hanrahan,et al.  All-frequency shadows using non-linear wavelet lighting approximation , 2003, ACM Trans. Graph..

[4]  Tony Q. S. Quek,et al.  Radiometric compensation using stratified inverses , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Gene H. Golub,et al.  Matrix computations , 1983 .

[6]  James Arvo,et al.  A framework for the analysis of error in global illumination algorithms , 1994, SIGGRAPH.

[7]  Leonidas J. Guibas,et al.  Robust Monte Carlo methods for light transport simulation , 1997 .

[8]  Paul Debevec,et al.  Inverse global illumination: Recovering re?ectance models of real scenes from photographs , 1998 .

[9]  R. A. Leibler,et al.  Matrix inversion by a Monte Carlo method , 1950 .

[10]  Ramesh Raskar,et al.  Fast separation of direct and global components of a scene using high frequency illumination , 2006, SIGGRAPH 2006.

[11]  Gordon Wetzstein,et al.  Radiometric Compensation through Inverse Light Transport , 2007 .

[12]  Pat Hanrahan,et al.  On the form factor between two polygons , 1993, SIGGRAPH.

[13]  Greg Welch,et al.  Shader Lamps , 2001 .

[14]  Kiriakos N. Kutulakos,et al.  A theory of inverse light transport , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  Yuichi Ohta,et al.  Analytical compensation of inter-reflection for pattern projection , 2006, VRST '06.

[16]  Jan Kautz,et al.  Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments , 2002 .

[17]  Kavita Bala,et al.  Direct-to-indirect transfer for cinematic relighting , 2006, ACM Trans. Graph..

[18]  Michael F. Cohen,et al.  Radiosity and realistic image synthesis , 1993 .

[19]  Jingyi Yu,et al.  Catadioptric projectors , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  James T. Kajiya,et al.  The rendering equation , 1986, SIGGRAPH.

[21]  Jyh-Ming Lien Point-Based Minkowski Sum Boundary , 2007, 15th Pacific Conference on Computer Graphics and Applications (PG'07).

[22]  Pat Hanrahan,et al.  Wavelet radiosity , 1993, SIGGRAPH.

[23]  Pieter Peers,et al.  A compact factored representation of heterogeneous subsurface scattering , 2006, ACM Trans. Graph..

[24]  Marc Levoy,et al.  Dual photography , 2005, SIGGRAPH 2005.

[25]  Marc Levoy,et al.  Symmetric photography: exploiting data-sparseness in reflectance fields , 2006, EGSR '06.

[26]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[27]  Sato Imari,et al.  Inverse Rendering for Computer Graphics , 2010 .

[28]  James Demmel,et al.  Applied Numerical Linear Algebra , 1997 .

[29]  Gordon Wetzstein,et al.  Radiometric Compensation through Inverse Light Transport , 2007, 15th Pacific Conference on Computer Graphics and Applications (PG'07).

[30]  Shree K. Nayar,et al.  A Projection System with Radiometric Compensation for Screen Imperfections , 2003 .

[31]  Katsushi Ikeuchi,et al.  Illumination distribution from shadows , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[32]  Pieter Peers,et al.  Relighting with 4D incident light fields , 2003, ACM Trans. Graph..

[33]  Oliver Bimber,et al.  Compensating Indirect Scattering for Immersive and Semi-Immersive Projection Displays , 2006, IEEE Virtual Reality Conference (VR 2006).

[34]  S. Marschner,et al.  Inverse Rendering for Computer Graphics , 1998 .

[35]  Pat Hanrahan,et al.  A signal-processing framework for inverse rendering , 2001, SIGGRAPH.

[36]  Paul E. Debevec,et al.  Acquiring the reflectance field of a human face , 2000, SIGGRAPH.

[37]  Pat Hanrahan,et al.  A rapid hierarchical radiosity algorithm , 1991, SIGGRAPH.