The CALIPSO satellite launched by NASA in 2006 uses an on-board LIDAR instrument to measure the vertical distribution
of clouds and aerosols along the orbital path. This satellite's dense vertical sampling of the atmosphere provides
previously unavailable information about the altitude and composition of clouds, including the polar stratospheric clouds
(PSCs) that play an important role in the annual formation of polar ozone holes. Reconstruction of cloud surfaces through
interpolation of CALIPSO data is challenging due to the sparsity of the data in the non-vertical dimensions and the complex
sampling pattern created by intersecting non-planar orbital paths. This paper presents a method for computing cloud
surfaces by reconstructing a continuous cloud surface distance field. The distance field reconstruction is performed via
shape-based interpolation of the cloud contours on each cross section using a medial axis representation of each contour.
The interpolation algorithm employs a projection operator that is defined in terms of (latitude, longitude, altitude) coordinates,
so that projection between cross sections follows the earth's curved atmosphere and preserves cloud altitude. This
process successfully interpolated cloud contours from CALIPSO data acquired during the 2006 polar winter and enabled
three-dimensional visualization of the PSCs.
Commercial eye-gaze trackers have the potential to be an important tool for quantifying the benefits of new visualization
techniques. The expense of such trackers has made their use relatively infrequent in visualization studies. As such, it is
difficult for researchers to compare multiple devices - obtaining several demonstration models is impractical in cost and
time, and quantitative measures from real-world use are not readily available. In this paper, we present a sample protocol
to determine the accuracy of a gaze-tacking device.