105 – 3D/AR/VR Modelling and Communications
Šimon Tibenský
Annotation

This module provides a rigorous introduction to the theoretical and practical underpinnings of 3D modeling, Augmented Reality (AR), and Virtual Reality (VR). It delves into the computational methodologies, data structures, and algorithmic principles that govern the creation, manipulation, and visualization of three-dimensional digital content. Emphasis is placed on the interdisciplinary applications of these technologies, exploring their significance in fields such as engineering, medicine, entertainment, and advanced digital communication. The module also covers cutting-edge volumetric capture and representation techniques like Neural Radiance Fields (NeRF) and Gaussian Splatting, alongside an analysis of current trends and future challenges in immersive digital environments.

Objectives

Upon completing this module, university students will be able to: Articulate the fundamental concepts, historical evolution, and core terminology of 3D modeling. Analyze and differentiate between various 3D model types and their respective modeling methodologies. Evaluate the capabilities and limitations of common 3D modeling software and understand the principles of texturing and rendering. Differentiate between Augmented Reality (AR) and Virtual Reality (VR) based on their technical architectures and interaction paradigms. Apply principles of content creation and optimization for performance-critical AR/VR applications. Comprehend the algorithmic foundations and practical implications of advanced 3D reconstruction technologies such as NeRF and Gaussian Splatting. Critically assess the societal, ethical, and technological challenges and opportunities presented by the convergence of 3D, AR, VR, and the Metaverse.

Keywords
3D modeling, digital geometry, virtual reality (VR), augmented reality (AR), immersive technologies, computer graphics, rendering pipelines, polygonal meshes, NURBS, digital sculpting, Neural Radiance Fields (NeRF), Gaussian Splatting, volumetric capture, real-time graphics, human-computer interaction (HCI), Metaverse.
Date of Creation
20.07.2024
Duration
4 hours
Language
English
License
Creative Commons BY-SA 4.0
ISBN
Literature
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  1. Mildenhall, Ben P., et al. "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis." European Conference on Computer Vision (ECCV). Cham: Springer, 2020. S. 582-599.
  1. Kerbl, Bernhard, et al. "3D Gaussian Splatting for Real-Time Radiance Field Rendering." ACM Transactions on Graphics (SIGGRAPH). Vol. 42, č. 4. New York: ACM, 2023. Article 109.