Single Image Tree Reconstruction via Adversarial Network

Realistic 3D tree reconstruction is still a tedious and time-consuming task in the graphics community. In this paper, we propose a simple and efficient method for reconstructing 3D tree models with high fidelity from a single image. The key to single image-based tree reconstruction is to recover 3D shape information of trees via a deep neural network learned from a set of synthetic tree models. We adopted a conditional generative adversarial network (cGAN) to infer the 3D silhouette and skeleton of a tree respectively from edges extracted from the image and simple 2D strokes drawn by the user. Based on the predicted 3D silhouette and skeleton, a realistic tree model that inherits the tree shape in the input image can be generated using a procedural modeling technique. Experiments on varieties of tree examples demonstrate the efficiency and effectiveness of the proposed method in reconstructing realistic 3D tree models from a single image.