Диссертации, представленные на защиту и подготовленные в НИУ ВШЭ

Implicit Neural Representations (INRs) recently emerged as a powerful and compact 3D representation, well fitted for the needs of differential rendering. On the other hand, basic INRs and algorithms for their optimization lack the number of qualities especially important for the tasks of multi-view reconstruction and 3D generation. These qualities include the inability of INRs to represent a large variety of objects simultaneously and excessive rendering time. Also, when combined with the generation approaches, INRs often fail to produce results which are multi-view consistent, or consistent with each other, in the case of the generation of multiple instances. In order to overcome the described drawback, a number of methods is developed and described in the current thesis: a method for training hypernetwork INR in the role of Generative Adversarial Network (GAN) generator; a method for obtaining aligned 3D models, parameterized by a single INR, given a set of text prompts; an algorithm for fast rendering and reconstruction of the implicit surface; an approach for completing the surface, which is observed only partially; a GAN-based image generation method for unsupervised shape/appearance disentanglement, where deformation maps and textures are produced by separate INR generators. Developed methods widen the scope of the application for the Implicit Neural Representations, allowing to model complex structures of the significant variety. They also allow to manipulate existing 3D objects, edit them and complete the missing parts, producing the assets which could be used in computer graphics applications.

This research focuses on developing methods for converting raster images and three-dimensional objects into vector representations using deep learning. Vectorization of objects involves finding object representations using mathematical primitives and relationships between them.To achieve this goal, the following tasks were addressed: data collection, construction of mathematical models, and development of vectorization algorithms. Data collection was performed by processing scanned images of 2D and 3D objects and generating synthetic data. Automatic algorithms using computer vision methods were developed for data cleaning and processing, along with procedures for manual data processing. These algorithms facilitate semi-automatic annotation of data, opening up the possibility to train neural networks using deep learning methods. Various neural network architectures, including convolutional neural networks and transformers, are explored to create models capable of accurately and efficiently vectorizing technical drawings and 3D point clouds. The proposed algorithms demonstrate high accuracy and efficiency in solving object vectorization tasks, with potential applications in computer vision, robotics, and data visualization.