  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.
