Learning Lightness from Human Judgement on Relative Reflectance

TitleLearning Lightness from Human Judgement on Relative Reflectance
Publication TypeConference Paper
Year of Publication2015
AuthorsNarihira, T., Maire M., & Yu S. X.
Other Numbers3765

We develop a new approach to inferring lightness, the perceived reflectance of surfaces, from a single image. Classic methods view this problem from the perspective of intrinsic image decomposition, where an image is separated into reflectance and shading components. Rather than reason about reflectance and shading together, we learn to directly predict lightness differences between pixels. Large-scale training from human judgement data on relative reflectance, and patch representations built using deep networks, provide the foundation for our model. Benchmarked on the Intrinsic Images in the Wild dataset, our local lightness model achieves on-par performance with the state-of-the-art global lightness model, which incorporates multiple shading/reflectance priors and simultaneous reasoning between pairs of pixels in a dense conditional random field formulation.

Bibliographic Notes

To appear in the proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, Massachusetts

Abbreviated Authors

T. Narihira, M. Maire, and S. X. Yu

ICSI Research Group


ICSI Publication Type

Article in conference proceedings