3D modeling of non-linear objects from stylized sketches is a challenge even for experts in computer graphics. The extrapolation of objects parameters from a stylized sketch is a very complex and cumbersome task. In the present study, we propose a broker system that mediates between the modeler and the 3D modelling software and can transform a stylized sketch of a tree into a complete 3D model. The input sketches do not need to be accurate or detailed, and only need to represent a rudimentary outline of the tree that the modeler wishes to 3D-model. Our approach is based on a well-defined Deep Neural Network (DNN) architecture, we called TreeSketchNet (TSN), based on convolutions and able to generate Weber and Penn parameters that can be interpreted by the modelling software to generate a 3D model of a tree starting from a simple sketch. The training dataset consists of synthetically-generated sketches that are associated with Weber-Penn parameters generated by a dedicated Blender modelling software add-on. The accuracy of the proposed method is demonstrated by testing the TSN with both synthetic and hand-made sketches. Finally, we provide a qualitative analysis of our results, by evaluating the coherence of the predicted parameters with several distinguishing features.

本文提出了一种使用基于卷积的深度神经网络架构(TreeSketchNet)将树木的简单草图转换为完整三维模型的中介模型，并基于合成和手工制作的草图对其进行测试，结果表明其结果较为准确。

TreeSketchNet: 从草图生成 3D 树模型参数