CNN-based Tree Model Extraction

We propose a method for the segmentation and structural reconstruction of tree stems and branches in cluttered environments. We use single images of monocular cameras and convolutional neural networks for the segmentation, centerline, and contour detection of trees (trunks and branches), and a deterministic approach to build tree model graphs, defining the positions of vertices and edges, on the points of segments' centerlines. Compared to previous stochastic methods, this approach, tested on a synthetic dataset, gives better segmentation accuracy with a significantly smaller computational complexity.