REF$^2$ NeRF

REF$^2$-NeRF (and related NeRF advancements) focuses on improving the accuracy and efficiency of Neural Radiance Fields (NeRFs) for 3D scene reconstruction from images. Current research emphasizes addressing limitations such as blurry input images, privacy concerns during training, and the need for more robust handling of complex phenomena like reflections, refractions, and dynamic objects. This involves developing novel architectures and algorithms, including those incorporating event streams, multi-modal data (text and images), generative models, and view morphing techniques, to enhance scene representation and view synthesis. These improvements have significant implications for various applications, including virtual and augmented reality, computer graphics, and robotics.