Two cloud-based cues for estimating scene structure and camera calibration

Nathan Jacobs, Austin Abrams, Robert Pless

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


We describe algorithms that use cloud shadows as a form of stochastically structured light to support 3D scene geometry estimation. Taking video captured from a static outdoor camera as input, we use the relationship of the time series of intensity values between pairs of pixels as the primary input to our algorithms. We describe two cues that relate the 3D distance between a pair of points to the pair of intensity time series. The first cue results from the fact that two pixels that are nearby in the world are more likely to be under a cloud at the same time than two distant points. We describe methods for using this cue to estimate focal length and scene structure. The second cue is based on the motion of cloud shadows across the scene; this cue results in a set of linear constraints on scene structure. These constraints have an inherent ambiguity, which we show how to overcome by combining the cloud motion cue with the spatial cue. We evaluate our method on several time lapses of real outdoor scenes.

Original languageEnglish
Article number6477050
Pages (from-to)2526-2538
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number10
StatePublished - 2013


  • Time lapse
  • clouds
  • depth map
  • image formation
  • nonmetric multidimensional scaling
  • shape from shadows

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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