Three-dimensional (3D) modeling and exploration are crucial for a wide range of applications.The authors propose a novel approach for sketch-based 3D exploration utilizing Stacked Generative Adversarial Networks (SGANs). The authors highlight the significance of three-dimensional (3D) modeling and exploration across various domains, such as computer-aided design, virtual reality, and gaming. The authors emphasize that traditional techniques for 3D modeling often necessitate expertise in complex software tools and considerable manual effort. The authors note that in recent years, deep learning methods, specifically Generative Adversarial Networks (GANs), have demonstrated impressive capabilities in generating realistic and high-quality 3D models. Building upon this progress, they present their method, which harnesses the power of GANs to produce 3D models from sketch-based inputs. This approach enables users to intuitively and interactively explore 3D scenes. The research contributes to the field of sketch-based 3D exploration by introducing a unique framework that combines the strengths of GANs with sketch input, resulting in the generation of realistic and interactive 3D models. The authors highlight that their approach holds the potential to transform the way users engage with 3D modeling tools, making the process more intuitive, accessible, and enjoyable.

Sketch-based modeling 3D shape generation Stacked Generative Adversarial Networks (SGANs) Interactive 3D exploration Sketch-based input Intuitive user interface.