AI 中文总结
提出一种确定性、地形感知的河流生成算法，利用板块域局部预处理，实现无限程序化世界中保证下坡流动的河流网络，支持高效按需评估。
AI 中文摘要
现有的程序化地形生成方法只能在固定域上生成连贯的、下坡流动的河流网络，而扩展到无限世界的方法通常会牺牲网络连贯性或下坡流动的保证。本工作提出了一种用于无限程序化世界的确定性、地形感知、下坡流动河流算法。构造板块种子定义了连续地形场和相应的有界Voronoi-like板块域，因此局部河流预处理可以直接在几何域上进行，而无需为了定位板块边界而采样地形场。在每个板块内，地形场在低分辨率网格上采样，河流从高地源节点沿下坡追踪到海岸线、局部最小值或现有汇合点。然后沿每条路径单调调整河流高度，使每个存储的线段都下降。生成的河流网络按板块惰性生成和缓存，因此每个板块独立于其邻居进行处理。在全分辨率地形采样期间，精确计算到缓存河流网络的距离，河流高度沿附近线段插值，并按需与局部评估的地形混合，而不缓存混合结果本身。由于板块边界的扭曲被限制在河流边界余量之下，这保证了最终地形的每条河流通道的下坡流动。总之，这些设计选择通过支持高效的按需评估，使地形感知的河流生成对于交互式程序化世界变得实用，正如本工作中呈现的性能分析所证明的那样。
英文摘要
Existing procedural terrain generation methods produce coherent, downhill-flowing river networks only on fixed domains, whereas approaches that extend to infinite worlds typically sacrifice network coherence or downhill-flow guarantees. This work presents a deterministic, terrain-aware, downhill-flowing river algorithm for infinite procedural worlds. Tectonic plate seeds define both continuous terrain fields and corresponding bounded Voronoi-like plate domains, so localized river preprocessing can operate directly on the geometric domain without sampling terrain fields for the purpose of locating plate boundaries. Within each plate, the terrain field is sampled on a low-resolution grid, and rivers are traced downhill from highland source nodes to coastlines, local minima, or existing confluences. River heights are then monotonically adjusted along each path so that every stored segment descends. The resulting river network is generated and cached lazily per plate, so each plate is processed independently of its neighbors. During full-resolution terrain sampling, the distance to the cached river network is computed exactly, and river height is interpolated along nearby segments and blended with the locally evaluated terrain on demand, without caching the blended result itself. Because the warp of the plate borders is bounded below the river border margin, this guarantees downhill flow along every river channel of the final terrain. Together, these design choices make terrain-aware river generation practical for interactive procedural worlds by supporting efficient on-demand evaluation, as demonstrated by the performance analysis presented in this work.
