Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies
This addresses the difficulty of obtaining tree skeletons for agricultural manipulation planning due to occlusion by leaves and branches, but it is incremental as it builds on existing segmentation and path search techniques.
The paper tackles the problem of extracting tree skeletons from self-occluded canopies by estimating unobserved structures, showing that their method outperforms baselines in highly occluded scenes on a synthetic dataset.
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.