Derin sinir ağları kullanılarak 3B modellerın üretilmesi ve düzenlenmesi

Artificial intelligence (AI) and particularly deep neural networks (DNN) have become very hot topics in the recent years and they have been shown to be successful in problems such as detection, recognition and segmentation. More recently DNNs have started to be popular in data generation problems by the invention of Generative Adversarial Networks (GAN). Using GANs, various types of data such as audio, image or 3D models could be generated. In this thesis, we aim to propose a system that creates artificial 3D models with given characteristics. For this purpose, we focus on latent modification and generation of 3D point cloud object models with respect to their semantic parts. Different to the existing methods which use separate networks for part generation and assembly, we propose a single end-to-end Autoencoder model that can handle generation and modification of both semantic parts, and global shapes. The proposed method supports part exchange between 3D point cloud models and composition by different parts to form new models by directly editing latent representations. This holistic approach does not need part-based training to learn part representations and does not introduce any extra loss besides the standard reconstruction loss. The experiments demonstrate the robustness of the proposed method with different object categories and varying number of points, rotations and scales. The method can generate new models by integration of generative models such as GANs and VAEs and can work with unannotated point clouds by integration of a segmentation module.