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.

在图形社区中，逼真的 3D 树重建仍然是一项繁琐且耗时的任务。在本文中，我们提出了一种简单有效的方法，用于从单个图像重建具有高保真度的 3D 树模型。基于单幅图​​像的树重建的关键是通过从一组合成树模型中学习到的深度神经网络来恢复树的 3D 形状信息。我们采用条件生成对抗网络 (cGAN) 分别从从图像中提取的边缘和用户绘制的简单 2D 笔画中推断出树的 3D 轮廓和骨架。基于预测的 3D 轮廓和骨架，可以使用过程建模技术生成一个真实的树模型，该模型继承了输入图像中的树形。