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.

The Large Hadron Collider (LHC), which was built by the European Organization for Nuclear Research (CERN), is the world’s largest collider. The LHCb experiment at the LHC focuses on studying heavy flavor physics, making precise measurements of CP violation, and investigating other effects within and beyond the Standard Model.The LHCb detector consists of several components, including an electromagnetic calorimeter (ECAL). Simulating the expected detector response is crucial for the physical analysis of the collected data and deriving physical results. Using theGeant4 package to simulate detector responses is computationally expensive and resource intensive, so there is a need to speed up this process. This research paper explores the potential of using generative adversarial networks (GANs) to accelerate the simulation process of calorimeter response in high-energy physics experiments. The thesis contribution consists of three essential parts. The model’s performance is highly sensitive to its architecture, so it was improved comparing to the previously published once. The second contribution is a method that helps the model to take particular properties of generated objects into account and improve its generation quality. This method requires increasing models capacity, so an other approach that allows to balance between training stability and expressivity is proposed. By proposing methods to enhance simulation speed and improve calorimeter response accuracy, this work holds significant implications for the advancement ofLHCb and other high-energy physics projects.