This paper introduces Smart-Tree, a supervised method for approximating the medial axes of branch skeletons from a tree point cloud. Smart-Tree uses a sparse voxel convolutional neural network to extract the radius and direction towards the medial axis of each input point. A greedy algorithm performs robust skeletonization using the estimated medial axis. Our proposed method provides robustness to complex tree structures and improves fidelity when dealing with self-occlusions, complex geometry, touching branches, and varying point densities. We evaluate Smart-Tree using a multi-species synthetic tree dataset and perform qualitative analysis on a real-world tree point cloud. Our experimentation with synthetic and real-world datasets demonstrates the robustness of our approach over the current state-of-the-art method. The dataset and source code are publicly available.
@article{arxiv.2303.11560,
title = {Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization},
author = {Harry Dobbs and Oliver Batchelor and Richard Green and James Atlas},
journal= {arXiv preprint arXiv:2303.11560},
year = {2023}
}