|
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.
|
