Dynamic NeRF

Dynamic NeRFs extend the capabilities of static Neural Radiance Fields (NeRFs) by enabling the representation and rendering of scenes undergoing changes over time. Current research focuses on improving the accuracy and efficiency of dynamic scene reconstruction, employing techniques like Kalman filtering to track motion and leveraging 3D scene flows or spline-based representations for continuous temporal modeling. These advancements are significant for applications such as high-quality video editing, event-based vision, and creating more realistic and interactive virtual environments. The development of robust and efficient dynamic NeRFs is pushing the boundaries of 3D scene representation and manipulation.