Abstract
Separating an image into reflectance and shading layers poses a challenge for learning approaches because no
large corpus of precise and realistic ground truth decompositions exists. The Intrinsic Images in the Wild (IIW)
dataset provides a sparse set of relative human reflectance
judgments, which serves as a standard benchmark for intrinsic images. A number of methods use IIW to learn
statistical dependencies between the images and their re-
flectance layer. Although learning plays an important role
for high performance, we show that a standard signal processing technique achieves performance on par with current state-of-the-art. We propose a loss function for CNN
learning of dense reflectance predictions. Our results show
a simple pixel-wise decision, without any context or prior
knowledge, is sufficient to provide a strong baseline on IIW.
This sets a competitive baseline which only two other approaches surpass. We then develop a joint bilateral filtering
method that implements strong prior knowledge about re-
flectance constancy. This filtering operation can be applied
to any intrinsic image algorithm and we improve several
previous results achieving a new state-of-the-art on IIW.
Our findings suggest that the effect of learning-based approaches may have been over-estimated so far. Explicit
prior knowledge is still at least as important to obtain high
performance in intrinsic image decompositions