  While modern deep neural networks (DNNs) achieve state-of-the-art results for
illuminant estimation, it is currently necessary to train a separate DNN for
each type of camera sensor. This means when a camera manufacturer uses a new
sensor, it is necessary to retrain an existing DNN model with training images
captured by the new sensor. This paper addresses this problem by introducing a
novel sensor-independent illuminant estimation framework. Our method learns a
sensor-independent working space that can be used to canonicalize the RGB
values of any arbitrary camera sensor. Our learned space retains the linear
property of the original sensor raw-RGB space and allows unseen camera sensors
to be used on a single DNN model trained on this working space. We demonstrate
the effectiveness of this approach on several different camera sensors and show
it provides performance on par with state-of-the-art methods that were trained
per sensor.
