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经验的任务。为简化三维形状建模，我们提出了一种以3D VR草图作为条件的三维形状生成网络。假设草图由未经艺术训练的初学者绘制，我们的目标是重建指定类别中几何真实的三维形状。为处理草图潜在的歧义性，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征，在有限训练数据条件下支持模型训练。为保证生成三维形状的真实性，我们采用归一化流建模三维形状隐空间分布。为增强生成三维形状与输入草图的结构保真度，我们提出了一种专用损失函数，并将其部署于训练过程的不同阶段。相关代码已开源在 https://github.com/Rowl1ng/3Dsketch2shape。