In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.

翻译：在植物表型分析中，从树木的三维点云中准确提取性状仍然是一个未解决的问题。为了实现花朵和果实等树木器官的自动建模与性状提取，需要树木的语义分割点云及其骨架。为此，我们提出了樱桃采摘器（CherryPicker），一种自动处理流程，能够重建树木的光度点云，执行语义分割，并以骨架形式提取其拓扑结构。我们的系统结合了多种先进算法，以实现自动化处理，从而进一步用于三维植物表型应用。在该流程中，我们提出了一种自动估计单目重建比例因子的方法，以克服尺度模糊性并获得公制准确的点云。此外，我们提出了一种基于拉普拉斯收缩的语义骨架化算法。同时，我们证明，通过对不同树木器官进行语义加权，我们的方法能够有效消除由遮挡和结构尺寸变化引起的伪影。樱桃采摘器能够获得具有精确细节的高质量樱桃树拓扑重建结果。