Photogrammetry and Robotics Lab, University of Bonn, Bonn, Germany.
PLoS One. 2021 Feb 25;16(2):e0247243. doi: 10.1371/journal.pone.0247243. eCollection 2021.
Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.
植物表型分析是作物科学和植物育种的核心任务。它涉及测量植物性状，以描述植物的解剖结构和生理特性，并用于衍生性状和评估植物表现。传统的表型分析方法通常是费时费力的操作，需要大量的人工劳动。激光扫描仪或现代深度相机获取的植物 3D 传感器数据的可用性为自动化这些表型分析任务中的几个任务提供了潜力。这种自动化可以扩大表型测量和评估的规模，以便对更多的植物样本进行更精细的空间和时间分辨率的测量和评估。在本文中，我们研究了随时间和空间注册植物的 3D 点云的问题。这意味着我们确定了在不同时间点采集的植物点云之间的对应关系，并使用新的非刚性配准方法对它们进行配准。这种方法有可能成为旨在随时间跟踪植物性状的表型应用的核心。配准任务涉及在植物生长和外观不断变化的情况下，在不同时间采集的测量值之间找到数据关联，从而允许将在不同时间点采集的 3D 模型相互比较。由于植物的各向异性生长、拓扑变化和测量之间的非刚性运动，随时间对植物进行配准具有挑战性。因此，我们提出了一种新的方法，该方法首先以骨架的形式提取植物的紧凑表示形式，该骨架同时编码拓扑结构和语义信息，然后使用该骨架结构来确定随时间的对应关系并驱动配准过程。通过这种方法，我们可以有效地解决植物时间序列点云数据的关联问题。我们在不同的随时间采集的数据集上测试了我们的方法，并成功地注册了激光扫描仪记录的 3D 植物点云。我们证明，我们的方法允许通过在器官水平上跟踪植物性状来开发自动时间植物性状分析系统。
