Frequency-Space Decomposition and Acquisition of Light Transport under Spatially Varying Illumination

We show that, under spatially varying illumination, the light transport of diffuse scenes can be decomposed into direct, near-range (subsurface scattering and local inter-reflections) and far-range transports (diffuse inter-reflections). We show that these three component transports are redundant either in the spatial or the frequency domain and can be separated using appropriate illumination patterns. We propose a novel, efficient method to sequentially separate and acquire the component transports. First, we acquire the direct transport by extending the direct-global separation technique from floodlit images to full transport matrices. Next, we separate and acquire the near-range transport by illuminating patterns sampled uniformly in the frequency domain. Finally, we acquire the far-range transport by illuminating low-frequency patterns. We show that theoretically, our acquisition method achieves the lower bound our model places on the required number of patterns. We quantify the savings in number of patterns over the brute force approach. We validate our observations and acquisition method with rendered and real examples throughout.

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

[2]  Shree K. Nayar,et al.  Multiplexed illumination for scene recovery in the presence of global illumination , 2011, 2011 International Conference on Computer Vision.

[3]  Qionghai Dai,et al.  Decomposing Global Light Transport Using Time of Flight Imaging , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[5]  Matthew O'Toole,et al.  Primal-dual coding to probe light transport , 2012, ACM Trans. Graph..

[6]  Tian-Tsong Ng,et al.  On the Duality of Forward and Inverse Light Transport , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Hans-Peter Seidel,et al.  Adaptive sampling of reflectance fields , 2007, TOGS.

[8]  Aggelos K. Katsaggelos,et al.  Bayesian Compressive Sensing Using Laplace Priors , 2010, IEEE Transactions on Image Processing.

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

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

[11]  Zhouchen Lin,et al.  Kernel Nyström method for light transport , 2009, ACM Trans. Graph..

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

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

[14]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[15]  Pieter Peers,et al.  Compressive light transport sensing , 2009, ACM Trans. Graph..

[16]  Takeo Kanade,et al.  Shape from interreflections , 2004, International Journal of Computer Vision.

[17]  Steve Marschner,et al.  A practical model for subsurface light transport , 2001, SIGGRAPH.

[18]  J. Koenderink,et al.  Geometrical modes as a general method to treat diffuse interreflections in radiometry , 1983 .

[19]  Matthew O'Toole,et al.  Optical computing for fast light transport analysis , 2010, ACM Trans. Graph..

[20]  Kiriakos N. Kutulakos,et al.  Optical computing for fast light transport analysis , 2010, SIGGRAPH 2010.