Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Separation of an input image into its 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 dataset (IIW) provides a sparse set of relative human reflectance judgments, which serves as a standard benchmark for intrinsic images. This dataset led to an increase in methods that learn statistical dependencies between the images and their reflectance layer. Although learning plays a role in pushing state-of-the-art performance, we show that a standard signal processing technique achieves performance on par with recent developments. We propose a loss function that enables learning dense reflectance predictions with a CNN. 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 bar and we find that only two approaches surpass this result. We then develop a joint bilateral filtering method that implements strong prior knowledge about reflectance 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 be over-estimated and that it is still the use of explicit prior knowledge that drives performance on intrinsic image decompositions.