3D shape modeling is labor-intensive, time-consuming, and requires years of expertise. To facilitate 3D shape modeling, we propose a 3D shape generation network that takes a 3D VR sketch as a condition. We assume that sketches are created by novices without art training and aim to reconstruct geometrically realistic 3D shapes of a given category. To handle potential sketch ambiguity, our method creates multiple 3D shapes that align with the original sketch's structure. We carefully design our method, training the model step-by-step and leveraging multi-modal 3D shape representation to support training with limited training data. To guarantee the realism of generated 3D shapes we leverage the normalizing flow that models the distribution of the latent space of 3D shapes. To encourage the fidelity of the generated 3D shapes to an input sketch, we propose a dedicated loss that we deploy at different stages of the training process. The code is available at https://github.com/Rowl1ng/3Dsketch2shape.

翻译：三维形状建模劳动强度大、耗时且需要多年专业经验。为简化三维形状建模过程，本文提出一种以三维VR草图为输入条件的三维形状生成网络。我们假设草图由未经艺术训练的初学者绘制，目标是在给定类别下重建几何逼真的三维形状。为应对草图可能存在的歧义性，我们的方法能够生成多个与原始草图结构一致的三维形状。通过精心设计方法、分阶段训练模型，并利用多模态三维形状表示来支持有限训练数据下的学习。为保证生成三维形状的真实感，我们采用归一化流模型对三维形状潜在空间分布进行建模。为增强生成形状与输入草图的保真度，我们提出一个专用损失函数，并将其部署在训练过程的不同阶段。代码已开源：https://github.com/Rowl1ng/3Dsketch2shape