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Inverse Rendering
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Background:
One of the main goals in the image processing research, from the early days until now, is the ability to describe the scene in terms of “intrinsic” characteristics like depth, shape, surface orientation (normals), incident light, and reflectance at each visible surface point. Each of these intrinsic characteristics provides us with valuable cues for scene understanding. Accurate intrinsic scene characterization is essential in various tasks including: photo forensics, segmentation, object classification and recognition, scene relighting and automatic white balance, shadow removal, augmented reality, object recoloring, and etc.
Inverse Rendering refers to estimation of intrinsic scene characteristics given a single photo or a set of photos of the scene. While predicting these characteristics from 2D projects is highly underconstrained, recent advances in this topic have made a big step in solving this problem. Inverse rendering methods have already made their way into end-user applications.
Our latest achievement in this field is our novel multi-illuminant dataset published in ICCV 2015.
Inverse Rendering refers to estimation of intrinsic scene characteristics given a single photo or a set of photos of the scene. While predicting these characteristics from 2D projects is highly underconstrained, recent advances in this topic have made a big step in solving this problem. Inverse rendering methods have already made their way into end-user applications.
Our latest achievement in this field is our novel multi-illuminant dataset published in ICCV 2015.
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Publications:
2015
Conference Papers
[bib] - Shida Beigpour, Andreas Kolb, Sven Kunz - A Comprehensive Multi-Illuminant Dataset for Benchmarking of Intrinsic Image Algorithms
In Proc. IEEE International Conference on Computer Vision (ICCV), 2015, pages 172-180 - [pdf]
2014
Articles/Journal Papers
[bib] - Imtiaz Masud Ziko, Shida Beigpour, Jon Yngve Hardeberg - Design and Creation of a Multi-Illuminant Scene Image Dataset
In Image and Signal Processing, Lecture Notes in Computer Science, 8509, 2014, pages 531-538 - [pdf]
Articles/Journal Papers
[bib] - Shida Beigpour, Christian Riess, Joost van de Weijer, Elli Angelopoulou - Multi-Illuminant Estimation with Conditional Random Fields
In IEEE Transactions on Image Processing (TIP), 23(1), 2014, pages 83--96 - [pdf]
2013
Conference Papers
[bib] - Shida Beigpour, Marc Serra, Joost van de Weijer, Robert Benavente, Maria Vanrell, Olivier Penacchio, Dimitris Samaras - Intrinsic Image Evaluation On Synthetic Complex Scenes
In IEEE International Conference on Image Processing (ICIP), 2013 - [pdf]
PhD Thesis
[bib] - Shida Beigpour - Illumination and object reflectance modeling
In Phd Thesis, Universitat Autonoma de Barcelona, 2013 - [pdf]