The creation of truly believable simulated natural environments remains an unsolved problem in Computer Graphics. This is, in part, due to a lack of visual variety. In nature, apart from variation due to abiotic and biotic growth factors, a significant role is played by disturbance events, such as fires, windstorms, disease, and death and decay processes, which give rise to both standing dead trees (snags) and downed woody debris (logs). For instance, snags constitute on average 10% of unmanaged forests by basal area, and logs account for 2\(\frac{1}{2}\) times this quantity.

While previous systems have incorporated individual elements of disturbance (e.g., forest fires) and decay (e.g., the formation of humus), there has been no unifying treatment, perhaps because of the challenge of matching simulation results with generated geometric models.

In this paper, we present a framework that combines an ecosystem simulation, which explicitly incorporates disturbance events and decay processes, with a model realization process, which balances the uniqueness arising from life history with the need for instancing due to memory constraints. We tested our hypothesis concerning the visual impact of disturbance and decay with a two-alternative forced-choice experiment (n = 116). Our findings are that the presence of dead wood in various forms, as snags or logs, significantly improves the believability of natural scenes, while, surprisingly, general variation in the number of model instances, with up to 8 models per species, and a focus on disturbance events, does not.

创建真正可信的模拟自然环境仍然是计算机图形学中尚未解决的问题。部分原因是缺乏视觉多样性。在自然界中，除了非生物和生物生长因子引起的变化外，干扰事件也发挥着重要作用，例如火灾、风暴、疾病以及死亡和腐烂过程，这些事件会导致直立枯死的树木（障碍物）和倒下的树木。木质碎片（原木）。例如，按断面面积计算，障碍物平均占未管理森林的 \(10\% \)，而原木占该数量的 \(2 \frac{1}{2} \) 倍。

虽然以前的系统已经纳入了干扰（例如森林火灾）和腐烂（例如腐殖质的形成）的各个元素，但没有统一的处理方法，这可能是因为将模拟结果与生成的几何模型相匹配的挑战。

在本文中，我们提出了一个框架，该框架将生态系统模拟与模型实现过程相结合，该模型明确地结合了干扰事件和衰退过程，该模型实现了平衡生命史产生的独特性与由于内存限制而需要实例化的需求。我们通过两个替代的强制选择实验（n = 116）检验了关于干扰和衰退的视觉影响的假设。我们的发现是，各种形式的死木（如障碍物或原木）的存在，显着提高了自然场景的可信度，而令人惊讶的是，模型实例数量的普遍变化，每个物种最多 8 个模型，并且焦点对于扰动事件，则不然。