Abstract
Visual recognition of wet surfaces and their degrees of
wetness is important for many computer vision applications. It can inform slippery spots on a road to autonomous
vehicles, muddy areas of a trail to humanoid robots, and
the freshness of groceries to us. In the past, monochromatic
appearance change, the fact that surfaces darken when wet,
has been modeled to recognize wet surfaces. In this paper,
we show that color change, particularly in its spectral behavior, carries rich information about a wet surface. We
derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening
due to multiple scattering and absorption in the surface. We
derive a novel method for estimating key parameters of this
spectral appearance model, which enables the recovery of
the original surface color and the degree of wetness from
a single observation. Applied to a multispectral image, the
method estimates the spatial map of wetness together with
the dry spectral distribution of the surface. To our knowledge, this work is the first to model and leverage the spectral characteristics of wet surfaces to revert its appearance.
We conduct comprehensive experimental validation with a
number of wet real surfaces. The results demonstrate the
accuracy of our model and the effectiveness of our method
for surface wetness and color estimation