Lighting Estimation
in Outdoor Image Collections

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.


Jean-François Lalonde, and Iain Matthews
Lighting Estimation in Outdoor Image Collections
International Conference on 3-D Vision (3DV), 2014.
[PDF pre-print, 19.5MB] [BibTeX]


Download the poster that was presented at 3DV 2014: [PDF, 36.5MB]

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Supplementary results

Please download supplementary results [PDF, 2.9MB].


We provide some of the HDR sky maps that were used in this paper. Please see this website.

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