High Fidelity Geometry

High-fidelity geometry research aims to create accurate and detailed 3D models from various data sources, such as images, point clouds, and sensor data. Current efforts focus on improving the accuracy and efficiency of algorithms like neural radiance fields (NeRFs) and generative adversarial networks (GANs), often incorporating techniques like optimal transport and geometric regularization to enhance model fidelity and address challenges like multi-view consistency and topological accuracy. These advancements are crucial for applications ranging from 3D modeling and computer-aided design (CAD) to robotics and virtual/augmented reality, enabling more realistic and detailed simulations and interactions with digital environments.