Intrinsic decomposition of image sequences from local temporal variations

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We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variations observed over time to infer constraints on the reflectance and solve the ill-posed image decomposition problem. In particular, we derive an adaptive local energy from the observations of each local neighborhood over time, and integrate distant pairwise constraints to enforce coherent decomposition across all surfaces with consistent shading changes. Our method is solely based on multiple observations of a Lambertian scene under varying illumination and does not require user interaction, scene geometry, or an explicit lighting model. We compare our results with several intrinsic decomposition methods on a number of synthetic and captured datasets.
Publisher
IEEE Computer Society and the Computer Vision Foundation (CVF)
Issue Date
2015-12-11
Language
English
Citation

15th IEEE International Conference on Computer Vision, ICCV 2015, pp.433 - 441

DOI
10.1109/ICCV.2015.57
URI
http://hdl.handle.net/10203/225736
Appears in Collection
GCT-Conference Papers(학술회의논문)
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