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キーワード:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
要旨:
Recovering natural illumination from a single Low-Dynamic Range (LDR) image
is a challenging task. To remedy this situation we exploit two properties often
found in everyday images. First, images rarely show a single material, but
rather multiple ones that all reflect the same illumination. However, the
appearance of each material is observed only for some surface orientations, not
all. Second, parts of the illumination are often directly observed in the
background, without being affected by reflection. Typically, this directly
observed part of the illumination is even smaller. We propose a deep
Convolutional Neural Network (CNN) that combines prior knowledge about the
statistics of illumination and reflectance with an input that makes explicit
use of these two observations. Our approach maps multiple partial LDR material
observations represented as reflectance maps and a background image to a
spherical High-Dynamic Range (HDR) illumination map. For training and testing
we propose a new data set comprising of synthetic and real images with multiple
materials observed under the same illumination. Qualitative and quantitative
evidence shows how both multi-material and using a background are essential to
improve illumination estimations.
