Spatially Varying Image Based Lighting using HDR-video

 
J. Unger, J. Kronander, P. Larsson, S. Gustavson, J. Löw, A. Ynnerman
  Media and Information Technology, Linköping University, Sweden
 
Computers & Graphics, Vol. 37, No. 7, 2013
 
 
Figure: (Left) shows a real photo studio that was captured and reconstructed (geometric model + radiometric model) using the HDR-video based method presented in this paper. (Middle) and (Right) displays photo-realistic global illumination renderings of virtual furnitures placed into the real scene.
 
Abstract:
 
Illumination is one of the key components in the creation of realistic renderings of scenes containing virtual objects. In this paper, we present a set of novel algorithms and data structures for visualization, processing and rendering with real world lighting conditions captured using High Dynamic Range (HDR) video. The presented algorithms enable rapid construction of general and editable representations of the lighting environment, as well as extraction and fitting of sampled reflectance to parametric BRDF models. For efficient representation and rendering of the sampled lighting environment function, we consider an adaptive (2D/4D) data structure for storage of light field data on proxy geometry describing the scene. To demonstrate the usefulness of the algorithms, they are presented in the context of a fully integrated framework for spatially varying image based lighting. We show reconstructions of example scenes and resulting production quality renderings of virtual furniture with spatially varying real world illumination including occlusions.
Keywords: High dynamic range video, Image based lighting, Scene capture and processing, Photo realistic rendering
 
Documents:
Paper preprint:Download open access pdf here
Supplementary video: Modeling and results (.mp4) (27MB)
 
Bibtex:
@article{Unger2013923,
title = {Spatially varying image based lighting using HDR-video},
journal = {Computers \& Graphics},
volume = {37},
number = {7},
pages = {923 -- 934},
year = {2013},
issn = {0097-8493},
author = {Jonas Unger and Joel Kronander and Per Larsson and Stefan Gustavson and Joakim L\"ow and Anders Ynnerman}
}
 
Acknowledgements
This project was funded by the Swedish Foundation for Strategic Research (SSF) through grant IIS11-0081, Linköping University Center for Industrial Information Technology (CENIIT), and the Swedish Research Council through the Linnaeus Environment CADICS.