4 Dimensional

Four-dimensional (4D) research encompasses the modeling and analysis of three spatial dimensions plus time, aiming to represent and understand dynamic phenomena. Current research focuses on developing methods for generating and analyzing 4D data, employing techniques like Gaussian splatting, neural radiance fields (NeRFs), and diffusion models, often incorporating geometric, topological, and physical priors to improve accuracy and efficiency. This field is significant for its applications in diverse areas such as autonomous driving, medical imaging (e.g., 4D CT scans), human motion capture, and virtual/augmented reality, enabling more realistic and dynamic simulations and analyses.