Σχεδίαση και ανάπτυξη αμφίδρομων γεννητικών ανταγωνιστικών δικτύων υπό όρους για την ολοκλήρωση τρισδιάστατων σχημάτων

Abstract

In a brief period, the rapid increase in interest in Computer Vision and Deep Learning has led to a plethora of different applications on text, images, and videos. Those applications range from simple problems such as “motion detection in static cameras” or “Spam Filtering” to more complex ones such as “Robot Object Grasping through Vision” or “Image Caption Generation.” With the expeditious development of Computer Graphics, and 3D models acquisition technologies, Deep Learning applications on 3D Object Models have attracted increased attention. Sensors can provide us with 3D data with rich geometry, shape, and scale information, and accompanied by 2D images, can grant us with a better understanding of a certain environment. Three-Dimensional Data can be represented in a variety of ways, one of the most commonly used being point clouds (PC), which preserve the primary geometric information in 3D space without any discretization. However, even Deep Learning on Point Clouds is still in its preliminary stages due to challenges encountered when processing PCs with Deep Neural Networks. One of the main challenges is that decent quality PC Models are hard to obtain and often miss parts of data. A way to solve this problem is to create a 3D Shape Completion Model capable of restoring those missing parts. In this dissertation, we will research a family of models, with a variety of applications in Computer Vision, called Deep Generative Models. Specifically, we will expand indepth on Generative Adversarial Networks (GANs) as well as Variational Autoencoders (VAE). GANs create new data based on a set of given training data, with which they have the same statistics, whereas VAEs ensure that their encodings distribution is regularized during the training so that the latent space is of sufficient quality, to generate new data. The main objective is to create a point-based shape completion network based on Deep Generative Models that can complete a partial scheme with reasonable results.