从三维扫描中学习推断植物的参数化表示
Learning to Infer Parameterized Representations of Plants from 3D Scans
用程序化模型合成虚拟植物训练递归网络，从无序点云直接回归带参数的树状结构（枝干拓扑+各器官参数），为细长、自遮挡严重的结构化解剖重建提供了「合成数据预训练+参数化输出」的可复用范式。
原文依据 train a recursive neural network with virtual plants generated using a procedural model
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论植物通常包含大量器官，这些器官组织成三维分枝系统，从而定义植物的结构。由于器官之间（往往是细长结构）存在自遮挡与空间邻近，从非结构化观测中重建植物结构具有挑战性。为完成这一困难任务，我们提出一种方法，能够从给定的植物三维扫描中推断植物结构的参数化表示。除植物的分枝结构外，该表示还包含每个植物器官的参数化信息，因此可直接用于多种任务。在这一数据驱动方法中，我们使用程序化模型生成的虚拟植物训练一个递归神经网络。训练完成后，该网络能够基于输入的三维点云推断出参数化的树状表示。我们的方法适用于任何可表示为二叉轴树的植物。我们在藜（Chenopodium Album）植株上针对重建、分割与骨架化这三项植物表型分析中的重要问题进行了定量评估。除可同时完成多项任务外，我们的方法在每项任务上均达到与强基线相当的结果。我们将仅在合成数据上训练的方法应用于真实三维扫描，并表明其具有良好的泛化能力。
英文原文摘要 1369 字符
Plants frequently contain numerous organs, organized in 3D branching systems defining the plant's architecture. Reconstructing the architecture of plants from unstructured observations is challenging because of self-occlusion and spatial proximity between organs, which are often thin structures. To achieve the challenging task, we propose an approach that allows to infer a parameterized representation of the plant's architecture from a given 3D scan of a plant. In addition to the plant's branching structure, this representation contains parametric information for each plant organ, and can therefore be used directly in a variety of tasks. In this data-driven approach, we train a recursive neural network with virtual plants generated using a procedural model. After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud. Our method is applicable to any plant that can be represented as binary axial tree. We quantitatively evaluate our approach on Chenopodium Album plants on reconstruction, segmentation and skeletonization, which are important problems in plant phenotyping. In addition to carrying out several tasks at once, our method achieves results on-par with strong baselines for each task. We apply our method, trained exclusively on synthetic data, to 3D scans and show that it generalizes well.