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Probabilistic Temporal Inference on Reconstructed 3D Scenes
This video was recorded at 23rd IEEE Conference on Computer Vision and Pattern Recognition 2010 - San Francisco. Modern structure from motion techniques are capable of building city-scale 3D reconstructions from large image collections, but have mostly ignored the problem of largescale structural changes over time. We present a general framework for estimating temporal variables in structure from motion problems, including an unknown date for each camera and an unknown time interval for each structural element. Given a collection of images with mostly unknown or uncertain dates, we use this framework to automatically recover the dates of all images by reasoning probabilistically about the visibility and existence of objects in the scene. We present results on a collection of over 100 historical images of a city taken over decades of time.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
