Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization
This work addresses 3D tree modeling for applications like forestry or computer graphics, but it is incremental, building on existing skeletonization techniques with specific enhancements.
The paper tackles the problem of approximating medial axes for 3D tree skeletonization from point clouds, introducing Smart-Tree, a supervised method that uses a sparse voxel CNN and greedy algorithm to improve robustness and fidelity over the state-of-the-art, as demonstrated on synthetic and real-world datasets.
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.