Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

TL;DR

This paper demonstrates that simple pixel-wise decisions and explicit prior knowledge through bilateral filtering can achieve state-of-the-art results in intrinsic image decomposition, challenging the perceived dominance of learning-based methods.

Contribution

It introduces a bilateral filtering approach that incorporates strong prior knowledge, improving intrinsic image estimation and establishing a new state-of-the-art benchmark on IIW.

Findings

A standard signal processing technique matches state-of-the-art performance.

A CNN loss function for dense reflectance prediction provides a strong baseline.

Explicit priors via bilateral filtering enhance intrinsic image decomposition results.

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 reflectance 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 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 have been over-estimated so far. Explicit prior knowledge is still at least as important to obtain high performance in intrinsic image decompositions.