  In this work, we present a method to extract the skeleton of a self-occluded
tree canopy by estimating the unobserved structures of the tree. A tree
skeleton compactly describes the topological structure and contains useful
information such as branch geometry, positions and hierarchy. This can be
critical to planning contact interactions for agricultural manipulation, yet is
difficult to gain due to occlusion by leaves, fruits and other branches. Our
method uses an instance segmentation network to detect visible trunk, branches,
and twigs. Then, based on the observed tree structures, we build a custom 3D
likelihood map in the form of an occupancy grid to hypothesize on the presence
of occluded skeletons through a series of minimum cost path searches. We show
that our method outperforms baseline methods in highly occluded scenes,
demonstrated through a set of experiments on a synthetic tree dataset.
Qualitative results are also presented on a real tree dataset collected from
the field.
