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：三维形状建模是一项劳动密集、耗时且需要多年专业知识的工作。为简化三维形状建模流程，我们提出了一种以三维VR草图作为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，并旨在生成给定类别的几何真实三维形状。为应对草图可能存在的歧义性，该方法可生成多个与原始草图结构一致的三维形状。我们精心设计了方法流程，通过分步训练模型并利用多模态三维形状表示，支持在有限训练数据下完成模型训练。为保障生成三维形状的真实性，我们采用归一化流对三维形状潜在空间的分布进行建模。为提升生成形状与输入草图的一致性，我们设计了一种专用损失函数，并将其部署于训练过程的不同阶段。代码已开源：https://github.com/Rowl1ng/3Dsketch2shape