Sökning: "3D Generative Models"
Visar resultat 1 - 5 av 20 uppsatser innehållade orden 3D Generative Models.
1. Design Automation of Air Intake Lips on an Aircraft : How to implement design automation for air intake lips in a later design concept phase
Master-uppsats, Linköpings universitet/ProduktrealiseringSammanfattning : Air intakes are complex components that are critical for the propulsion of the aircraft. The design has to consider requirements from several different departments, often contradictory. Additionally, the air intakes need to cooperate with other critical components. This makes testing of the models crucial, hence time-demanding. LÄS MER
2. Technology Acceptance for AI implementations : A case study in the Defense Industry about 3D Generative Models
Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)Sammanfattning : Advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has emerged into 3D object creation processes through the rise of 3D Generative Adversarial Networks (3D GAN). These networks contain 3D generative models capable of analyzing and constructing 3D objects. LÄS MER
3. Possibilities and Challenges of City Planning using 3D Visualization : A systematic literature review on the possibilities of city visualization using 3D computer graphics and the utility of parametric design
Kandidat-uppsats, Blekinge Tekniska HögskolaSammanfattning : There exist numerous previously conducted surveys, studies and written articles on the topic of 3D geo-visualization. The subject has been pursued increasingly for the last two decades. In the late 1960s, digital earth, the idea of a digital copy of the real world was first proposed. However, hardware capabilities were limited. LÄS MER
4. Exploring Normalizing Flow Modifications for Improved Model Expressivity
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. LÄS MER
5. Deep Generative Modeling : An Overview of Recent Advances in Likelihood-based Models and an Application to 3D Point Cloud Generation
Master-uppsats, Umeå universitet/Institutionen för matematik och matematisk statistikSammanfattning : Deep generative modeling refers to the process of constructing a model, parameterized by a deep neural network, that learns the underlying patterns and structures of the data generating process which produced the samples in a given dataset, in order to generate novel samples that resemble those in the original dataset. Deep generative models for 3D shape generation hold significant importance to various fields including robotics, medical imaging, manufacturing, computer animation and more. LÄS MER
