  Sampling autonomy for icy moon lander missions requires understanding of
topographic and photometric properties of the sampling terrain. Unavailability
of high resolution visual datasets (either bird-eye view or point-of-view from
a lander) is an obstacle for selection, verification or development of
perception systems. We attempt to alleviate this problem by: 1) proposing
Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for
versatile stereo dataset generation that spans the spectrum of bulk photometric
properties, and 2) focusing on a stereo-based visual perception system and
evaluating both traditional and deep learning-based algorithms for depth
estimation from stereo matching. The surface reflectance properties of icy moon
terrains (Enceladus and Europa) are inferred from multispectral datasets of
previous missions. With procedural terrain generation and physically valid
illumination sources, our framework can fit a wide range of hypotheses with
respect to visual representations of icy moon terrains. This is followed by a
study over the performance of stereo matching algorithms under different visual
hypotheses. Finally, we emphasize the standing challenges to be addressed for
simulating perception data assets for icy moons such as Enceladus and Europa.
Our code can be found here: https://github.com/nasa-jpl/guiss.
