# Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies

arXiv: https://arxiv.org/abs/2301.08387 · HTML: https://mlsanity.com/paper/2301.08387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Bumblebee: A Path Towards Fully Autonomous Robotic Vine Pruning](https://mlsanity.com/paper/2112.00291/) (2021)
- [Precision fruit tree pruning using a learned hybrid vision/interaction controller](https://mlsanity.com/paper/2109.13162/) (2021)
- [Fast tree skeleton extraction using voxel thinning based on tree point cloud](https://mlsanity.com/paper/2110.09028/) (2021)

## Built on by (most cited first, 1 of 1)

- [Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics](https://mlsanity.com/paper/2504.00032/) (2025)

## Abstract (arXiv)

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.