In this paper, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model situated latent because its behavior is determined by the intrinsic state -encoded as a latent space of a deep neural model- and by the extrinsic (environmental) data that is situated as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.

翻译：本文提出了一种名为DeepTree的新型树木建模方法，其核心在于通过学习分支结构的发育规则而非人工定义规则进行建模。我们将所提出的深度神经模型称为"情境隐变量"，因为其行为受内在状态（编码为深度神经模型的隐空间）与外在（环境）数据共同决定——其中环境数据被情境化为三维空间位置及树木结构中的具体位置。我们构建了神经网络管线来训练情境隐空间，使其能够仅基于树木分支图中单个节点信息，即可局部预测分支生长。通过这种表征，我们逐步生成新的分支节点，从而模拟树木的生长过程：从根节点开始，通过迭代对新增节点进行神经网络查询，最终生成完整树木的分支结构。本方法无需定义控制生长与行为的复杂参数，即可生成形态各异的树木模型。此外，我们证明情境隐变量还可编码树木模型对环境因素的响应（例如树木靠近障碍物生长时的形态变化）。通过基于树木形态学多项既有指标，将本方法生成的树木模型与程序化生成的模型进行相似度测量，验证了其有效性。