by Peers, Pieter, Mahajan, Dhruv K., Lamond, Bruce, Ghosh, Abhijeet, Matusik, Wojciech, Ramamoorthi, Ravi and Debevec, Paul
Abstract:
In this paper we propose a new framework for capturing light trans- port data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid math- ematical framework to infer a sparse signal from a limited number of non-adaptive measurements. Besides introducing compressive sensing for fast acquisition of light transport to computer graphics, we develop several innovations that address specific challenges for image-based relighting, and which may have broader implications. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting inter-pixel coherency relations. Additionally, we design new non-adaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed high- resolution reflectance fields for image-based relighting.
Reference:
Compressive Light Transport Sensing (Peers, Pieter, Mahajan, Dhruv K., Lamond, Bruce, Ghosh, Abhijeet, Matusik, Wojciech, Ramamoorthi, Ravi and Debevec, Paul), Technical report ICT TR 05 2008, University of Southern California Institute for Creative Technologies, 2008.
Bibtex Entry:
@techreport{peers_compressive_2008,
type = {{ICT} {Technical} {Report}},
title = {Compressive {Light} {Transport} {Sensing}},
url = {http://ict.usc.edu/pubs/ICT%20TR%2005%202008.pdf},
abstract = {In this paper we propose a new framework for capturing light trans- port data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid math- ematical framework to infer a sparse signal from a limited number of non-adaptive measurements. Besides introducing compressive sensing for fast acquisition of light transport to computer graphics, we develop several innovations that address specific challenges for image-based relighting, and which may have broader implications. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting inter-pixel coherency relations. Additionally, we design new non-adaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed high- resolution reflectance fields for image-based relighting.},
number = {ICT TR 05 2008},
institution = {University of Southern California Institute for Creative Technologies},
author = {Peers, Pieter and Mahajan, Dhruv K. and Lamond, Bruce and Ghosh, Abhijeet and Matusik, Wojciech and Ramamoorthi, Ravi and Debevec, Paul},
year = {2008},
keywords = {Graphics}
}