  Blue noise sampling has proved useful for many graphics applications, but
remains underexplored in high-dimensional spaces due to the difficulty of
generating distributions and proving properties about them. We present a blue
noise sampling method with good quality and performance across different
dimensions. The method, spoke-dart sampling, shoots rays from prior samples and
selects samples from these rays. It combines the advantages of two major
high-dimensional sampling methods: the locality of advancing front with the
dimensionality-reduction of hyperplanes, specifically line sampling. We prove
that the output sampling is saturated with high probability, with bounds on
distances between pairs of samples and between any domain point and its nearest
sample. We demonstrate spoke-dart applications for approximate Delaunay graph
construction, global optimization, and robotic motion planning. Both the
blue-noise quality of the output distribution and the adaptability of the
intermediate processes of our method are useful in these applications.
