从3D扫描中学习植物参数化结构，一次完成重建、分割与骨架提取。
Learning to Infer Parameterized Representations of Plants from 3D Scans
- 用递归神经网络从点云推断植物参数化分支结构。
- 在藜科植物上达到与强基线相当的重建、分割和骨架化精度。
- 仅用合成数据训练，仍能良好泛化到真实3D扫描数据。
植物通常包含大量器官，以三维分枝系统组织，构成其整体架构。由于器官间自遮挡和空间紧密相邻（常为细长结构），从非结构化观测中重建植物架构极具挑战。为此，我们提出一种方法，可从给定的植物3D扫描中推断其参数化架构表示。该表示不仅包含分枝结构，还为每个器官提供参数信息，可直接用于多种任务。本数据驱动方法使用基于过程模型生成的虚拟植物训练递归神经网络。训练后，网络可根据输入3D点云推断出参数化的树状表示。本方法适用于可表示为二叉轴向树的任意植物。我们在藜科植物（Chenopodium Album）上对重建、分割和骨架化进行定量评估，这些是植物表型分析中的关键问题。所提方法可同时完成多项任务，且各项性能均达到与强基线相当的水平。模型仅在合成数据上训练，但应用于真实3D扫描时仍表现出良好泛化能力。
原文摘要 · Abstract (English)
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.
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