Generation of Fake Mesh Structures Using GANs with SIFT-Based Feature Preservation

Abstract

The mechanism used in processing geometric information for graphs lies at the intersection of graph theory with engineering and data science; in this case, the structured graph data is processed and analyzed through topological and geometric concepts, thus generating artificial graphs that are similar to real data, taking into account when creating the data, the preservation of the existing properties of the original graphs, which are the basic features and structural patterns. The proposed approach uses a generative adversarial network (GAN) with the addition of a completely separate layer for each of the generator and discriminator networks, which is one of the deep learning techniques; in this way, the system is enabled to identify the patterns present in real data and reduce the possibility of overfitting resulting during the processing of data which is in the form of vectors. Applying this strategy, the model can build fake graphs with the number of points equal to the number of input data points and whose location is close to the points present in the real data graphs.  This study aims to produce graphs that are simulated to the original graphs. The simulated graph data can be used in the field of algorithm development, benchmarking and other fields such as testing and data augmentation.