Shape from Second-Bounce of Light Transport

This paper describes a method to recover scene geometry from the second-bounce of light transport. We show that form factors (up to a scaling ambiguity) can be derived from the second-bounce component of light transport in a Lambertian case. The form factors carry information of the geometric relationship between every pair of scene points, i.e., distance between scene points and relative surface orientations. Modelling the scene as polygonal, we develop a method to recover the scene geometry up to a scaling ambiguity from the form factors by optimization. Unlike other shape-from-intensity methods, our method simultaneously estimates depth and surface normal; therefore, our method can handle discontinuous surfaces as it can avoid surface normal integration. Various simulation and real-world experiments demonstrate the correctness of the proposed theory of shape recovery from light transport.

[1]  Donald P. Greenberg,et al.  Modeling the interaction of light between diffuse surfaces , 1984, SIGGRAPH.

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

[3]  Steven K. Feiner,et al.  Computer graphics: principles and practice (2nd ed.) , 1990 .

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

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

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

[7]  Kim L. Boyer,et al.  Color-Encoded Structured Light for Rapid Active Ranging , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[10]  Yuandong Tian,et al.  (De) focusing on global light transport for active scene recovery , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[12]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[14]  Thomas Ertl,et al.  Computer Graphics - Principles and Practice, 3rd Edition , 2014 .

[15]  M. Carter Computer graphics: Principles and practice , 1997 .

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

[17]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

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

[19]  Naokazu Yokoya,et al.  Surface reflectance modeling of real objects with interreflections , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[21]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision: N-View Geometry , 2004 .

[22]  Steve Marschner,et al.  Dual photography , 2005, ACM Trans. Graph..

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

[24]  H. Seidel,et al.  DISCO: acquisition of translucent objects , 2004, ACM Trans. Graph..