Twin-Channel Gan: Repair Shape with Twin-Channel Generative Adversarial Network and Structural Constraints

The establishment of 3D content with deep learning has been a focus of research in computer graphics during past years. Recently, researchers analyze 3D shapes through the dividing-and-conquer strategy with the geometry information and the structure information. Although many works perform well, there are still several problems. For example, the geometry information missing and not plausible in structure. In this work, we propose the Twin-channel GAN for the 3D shape completion. In this framework, the structure information is well studied via the structural constraints for optimizing the details of 3D shapes. The experimental results also demonstrated that our method achieves better performance.