User-assisted intrinsic images

For many computational photography applications, the lighting and materials in the scene are critical pieces of information. We seek to obtain intrinsic images, which decompose a photo into the product of an illumination component that represents lighting effects and a reflectance component that is...

Full description

Bibliographic Details
Main Authors: Bousseau, Adrien (Author), Paris, Sylvain (Author), Durand, Fredo (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery, 2012-07-26T19:51:43Z.
Subjects:
Online Access:Get fulltext
LEADER 02207 am a22002773u 4500
001 71855
042 |a dc 
100 1 0 |a Bousseau, Adrien  |e author 
100 1 0 |a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Durand, Fredo  |e contributor 
100 1 0 |a Durand, Fredo  |e contributor 
700 1 0 |a Paris, Sylvain  |e author 
700 1 0 |a Durand, Fredo  |e author 
245 0 0 |a User-assisted intrinsic images 
260 |b Association for Computing Machinery,   |c 2012-07-26T19:51:43Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/71855 
520 |a For many computational photography applications, the lighting and materials in the scene are critical pieces of information. We seek to obtain intrinsic images, which decompose a photo into the product of an illumination component that represents lighting effects and a reflectance component that is the color of the observed material. This is an under-constrained problem and automatic methods are challenged by complex natural images. We describe a new approach that enables users to guide an optimization with simple indications such as regions of constant reflectance or illumination. Based on a simple assumption on local reflectance distributions, we derive a new propagation energy that enables a closed form solution using linear least-squares. We achieve fast performance by introducing a novel downsampling that preserves local color distributions. We demonstrate intrinsic image decomposition on a variety of images and show applications. 
520 |a National Science Foundation (U.S.) (NSF CAREER award 0447561) 
520 |a Institut national de recherche en informatique et en automatique (France) (Associate Research Team "Flexible Rendering") 
520 |a Microsoft Research (New Faculty Fellowship) 
520 |a Alfred P. Sloan Foundation (Research Fellowship) 
520 |a Quanta Computer, Inc. (MIT-Quanta T Party) 
546 |a en_US 
655 7 |a Article 
773 |t Proceedings of ACM SIGGRAPH Asia 2009, ACM Transactions on Graphics (TOG)