  Point patterns are characterized by their density and correlation. While
spatial variation of density is well-understood, analysis and synthesis of
spatially-varying correlation is an open challenge. No tools are available to
intuitively edit such point patterns, primarily due to the lack of a compact
representation for spatially varying correlation. We propose a low-dimensional
perceptual embedding for point correlations. This embedding can map point
patterns to common three-channel raster images, enabling manipulation with
off-the-shelf image editing software. To synthesize back point patterns, we
propose a novel edge-aware objective that carefully handles sharp variations in
density and correlation. The resulting framework allows intuitive and
backward-compatible manipulation of point patterns, such as recoloring,
relighting to even texture synthesis that have not been available to 2D point
pattern design before. Effectiveness of our approach is tested in several user
experiments.
