  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.
