3D Model Inpainting Based on 3D Deep Convolutional Generative Adversarial Network
Xinying Wang, Dikai Xu, Fangming Gu
Abstract
In recent years, the problem of hole repairing in the 3D model has been widely concerned in related fields. As the Generative Adversarial Network (GAN) has achieved great success in generating realistic images, a 3D mesh model repair method based on the 3D Deep Convolutional Generative Adversarial Network (3D-DCGAN) is proposed in this paper. The algorithm contains two GANs: a local GAN and a global GAN. Four steps have been used to implement this concept. First, the 3D model is voxelized, and a mask is used to identify the repairing area; Second, the repairing area is generated by training local GAN; Third, the repaired region is combined with the 3D model to be repaired, thereafter, the global GAN is trained with the combined model. Finally, a decent repaired model is obtained with the perfect transition. The experimental results show that this algorithm can effectively generate the repairing area while retaining the details of the area and blend it with the model to be repaired.