As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness enabled by a Shape Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the limitations of sparsity and ambiguity of the sketches. The network design also brings high robustness to partial sketch input in industrial applications.Our approach has undergone extensive experiments, demonstrating its state-of-the-art (SOTA) performance on both synthetic and real-world datasets. These results validate the effectiveness and superiority of our method compared to existing techniques. We have demonstrated the conversion of free-hand sketches into physical 3D objects using additive manufacturing. We believe that our approach has the potential to accelerate product design and democratize customized manufacturing.

翻译：摘要：随着三维模型在当今制造业和产品设计中变得至关重要，基于计算机辅助设计（CAD）的传统三维建模方法劳动密集、耗时且对创作者要求极高。本研究旨在引入一种替代性三维建模方法，利用手绘草图获取所需的三维模型。我们提出Deep3DSketch+，这是一种深度学习算法，能够以单张手绘草图为输入，生成与之匹配的完整且高保真度的模型。该神经网络通过形状判别器（SD）和笔画增强模块（SEM）实现了视图感知与结构感知，从而克服了草图稀疏性和模糊性的限制。网络设计还使其在工业应用中对部分草图输入具有高鲁棒性。经过大量实验验证，我们的方法在合成数据集和真实世界数据集上均展现出最先进（SOTA）的性能。这些结果证实了该方法相较于现有技术的有效性和优越性。我们还展示了通过增材制造将手绘草图转化为物理三维物体的过程。我们相信，本方法有望加速产品设计并推动定制化制造的普及。