发表机构
University of California, Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于流匹配模型的联合冠层生成方法，成功再现树冠羞避现象，其间隙分布误差仅为单独生成树木的模型的一半，且与热带橡树林实地测量结果匹配
AI 中文摘要
在密闭森林中，相邻树木的树冠常停止生长至相互接触，留下由狭窄间隙构成的网络，这一现象被称为树冠羞避。该模式属于整个林分而非单棵树，这为生成建模中的一个问题提供了自然探究：学习得到的模型能否生成一组仅在对象之间存在定义结构的对象？我们将林分级冠层生成建模为基于流匹配模型的集合生成，其中树木间的注意力是产生耦合的唯一通道。该模型在资源竞争模拟生成的林分上进行训练，而该模拟生成的林分可证明无法简化为单棵树的几何特征。与容量相同、仅单独生成每棵树的模型相比，联合模型的间隙分布误差减半，且该优势在训练范围外的茎密度下依然存在。针对热带橡树林的实地测量结果，仅需一个校准标量即可在间隙大小和树冠不对称性上达成未见过的一致性。间隙的方向统计由茎的位置而非生长规则控制，在校准茎抖动后与实地测量结果匹配。无论是模拟结果还是学习得到的模型，树冠羞避都是林分的属性而非单棵树的属性。
英文摘要
In closed forests, neighboring tree crowns often stop short of touching, leaving a network of narrow gaps known as crown shyness. The pattern belongs to the stand rather than to any single tree, which makes it a natural probe of a question in generative modeling: can a learned model produce a set of objects whose defining structure exists only between them? We formulate stand-level canopy generation as set generation with a flow-matching model, in which attention between trees is the only channel through which coupling can arise. Trained on stands grown by a resource-competition simulation that is provably not reducible to per-tree geometry, the joint model halves the clearance distribution error of an identical-capacity model that generates each tree alone, and the advantage persists at stem densities outside the training range. Against field measurements of a tropical oak forest, a single calibrated scalar yields held-out agreement in gap magnitude and crown asymmetry. The directional statistics of the gaps are controlled by stem placement rather than by the growth rule, and match the field once stem jitter is calibrated. Crown shyness, in both the simulation and the learned model, is a property of the stand and not of the tree.
Comments8 pages plus references, 8 figures
