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.

翻译：摘要：点模式以其密度和相关性为特征。尽管密度的空间变化已被充分理解，但空间变化相关性的分析与综合仍是一个开放的挑战。目前缺乏直观编辑此类点模式的工具，主要原因是缺乏对空间变化相关性的紧凑表示。我们提出了一种用于点相关性的低维感知嵌入。该嵌入可将点模式映射为常见的三通道光栅图像，从而能够利用现成的图像编辑软件进行操作。为了合成回点模式，我们提出了一种新颖的边缘感知目标函数，该函数能妥善处理密度和相关性中的剧烈变化。由此产生的框架支持对点模式进行直观且向后兼容的操作，例如重新着色、重新光照，甚至实现此前在二维点模式设计中无法达成的纹理合成。我们通过多项用户实验验证了该方法的效果。