Paper Info
Title
Lighting Estimation in Outdoor Image Collections
Abstract
Large scale structure-from-motion (SfM) algorithms have recently enabled the reconstruction of highly detailed 3-D models of our surroundings simply by taking photographs. In this paper, we propose to leverage these reconstruction techniques to automatically estimate the outdoor illumination conditions for each image in a SfM photo collection. We introduce a novel dataset of outdoor photo collections, where the ground truth lighting conditions are known at each image. We also present an inverse rendering approach that recovers a high dynamic range estimate of the lighting conditions for each low dynamic range input image. Our novel database is used to quantitatively evaluate the performance of our algorithm. Results show that physically plausible lighting estimates can faithfully be recovered, both in terms of light direction and intensity.
Year
DOI
Venue
2014
10.1109/3DV.2014.112
3DV), 2014 2nd International Conference  
Keywords
Field
DocType
image reconstruction,lighting,motion estimation,solid modelling,3D model reconstruction,SfM algorithms,automatic outdoor illumination condition estimation,large scale structure-from-motion algorithms,light direction,light intensity,lighting estimation,outdoor image collections,outdoor photo collection dataset,3-D reconstruction,high dynamic range,lighting estimation
Computer vision,Computer graphics (images),Computer science,Image-based lighting,Low dynamic range,Ground truth,Artificial intelligence,High dynamic range,Inverse rendering
Conference
Volume
Citations 
PageRank 
1
10
0.51
References 
Authors
8
2
Name
Order
Citations
PageRank
Jean-françois Lalonde159037.69
Iain Matthews24900253.61