An incremental weighted least squares approach to surface lights fields

An Image-Based Rendering (IBR) approach to appearance modelling enables the capture of a wide variety of real physical surfaces with complex reflectance behaviour. The challenges with this approach are handling the large amount of data, rendering the data efficiently, and previewing the model as it is being constructed. In this paper, we introduce the Incremental Weighted Least Squares approach to the representation and rendering of spatially and directionally varying illumination. Each surface patch consists of a set of Weighted Least Squares (WLS) node centers, which are low-degree polynomial representations of the anisotropic exitant radiance. During rendering, the representations are combined in a non-linear fashion to generate a full reconstruction of the exitant radiance. The rendering algorithm is fast, efficient, and implemented entirely on the GPU. The construction algorithm is incremental, which means that images are processed as they arrive instead of in the traditional batch fashion. This human-in-the-loop process enables the user to preview the model as it is being constructed and to adapt to over-sampling and under-sampling of the surface appearance.

[1]  Todd E. Zickler,et al.  Image-based rendering from a sparse set of images , 2005, SIGGRAPH '05.

[2]  Katsushi Ikeuchi,et al.  Eigen-texture method: Appearance compression based on 3D model , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[4]  Wojciech Matusik,et al.  A data-driven reflectance model , 2003, ACM Trans. Graph..

[5]  Donald P. Greenberg,et al.  Non-linear approximation of reflectance functions , 1997, SIGGRAPH.

[6]  Holger Wendland,et al.  Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree , 1995, Adv. Comput. Math..

[7]  Andrew Gardner,et al.  Linear light source reflectometry , 2003, ACM Trans. Graph..

[8]  Roldan Pozo,et al.  NIST sparse BLAS user's guide , 2001 .

[9]  Wojciech Matusik,et al.  Progressively-Refined Reflectance Functions from Natural Illumination , 2004 .

[10]  Paul Debevec,et al.  Proceedings of the 13th Eurographics Workshop on Rendering Techniques, Pisa, Italy, June 26-28, 2002 , 2002, Rendering Techniques.

[11]  Richard K. Beatson,et al.  Reconstruction and representation of 3D objects with radial basis functions , 2001, SIGGRAPH.

[12]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[13]  H. Seidel,et al.  Multi-level partition of unity implicits , 2003 .

[14]  Anselmo Lastra,et al.  The spatial bi-directional reflectance distribution function , 2002, SIGGRAPH '02.

[15]  Hans-Peter Seidel,et al.  Adaptive Acquisition of Lumigraphs from Synthetic Scenes , 1999, Comput. Graph. Forum.

[16]  Holger Wendland,et al.  Scattered Data Approximation: Conditionally positive definite functions , 2004 .

[17]  Michael Bosse,et al.  Unstructured lumigraph rendering , 2001, SIGGRAPH.

[18]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

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

[20]  Ralf Sarlette,et al.  Acquisition, Synthesis, and Rendering of Bidirectional Texture Functions , 2005, Comput. Graph. Forum.

[21]  Peter Shirley,et al.  A Low Distortion Map Between Disk and Square , 1997, J. Graphics, GPU, & Game Tools.

[22]  Anselmo Lastra,et al.  Efficient rendering of spatial bi-directional reflectance distribution functions , 2002, HWWS '02.

[23]  Ralf Sarlette,et al.  Acquisition, Synthesis and Rendering of Bidirectional Texture Functions , 2004, Eurographics.

[24]  Richard Szeliski,et al.  The lumigraph , 1996, SIGGRAPH.

[25]  Yizhou Yu,et al.  Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping , 1998, Rendering Techniques.

[26]  D. Shepard A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[27]  Hans-Peter Seidel,et al.  Multi-level partition of unity implicits , 2005, SIGGRAPH Courses.

[28]  Ravi Ramamoorthi,et al.  Reflectance sharing: image-based rendering from a sparse set of images , 2005, EGSR '05.

[29]  David Salesin,et al.  Surface light fields for 3D photography , 2000, SIGGRAPH.

[30]  Katsushi Ikeuchi,et al.  Appearance Based Object Modeling using Texture Database: Acquisition Compression and Rendering , 2002, Rendering Techniques.

[31]  Jitendra Malik,et al.  Modeling and Rendering Architecture from Photographs: A hybrid geometry- and image-based approach , 1996, SIGGRAPH.

[32]  Wei-Chao Chen,et al.  Light field mapping: efficient representation and hardware rendering of surface light fields , 2002, SIGGRAPH.

[33]  Hans-Peter Seidel,et al.  Image-Based Reconstruction of Spatially Varying Materials , 2001 .

[34]  Thomas Malzbender,et al.  Polynomial texture maps , 2001, SIGGRAPH.

[35]  Anselmo Lastra,et al.  Online construction of surface light fields , 2005, EGSR '05.

[36]  Taku Komura,et al.  Topology matching for fully automatic similarity estimation of 3D shapes , 2001, SIGGRAPH.