NeRF Based 3D Reconstruction

Neural Radiance Fields (NeRFs) are revolutionizing 3D scene reconstruction by learning a continuous representation of a scene from multiple 2D images. Current research focuses on improving NeRF robustness and efficiency, particularly addressing challenges like reconstructing transparent objects and handling limited input data, often employing techniques like residual networks and diffusion priors to enhance accuracy and reduce the number of images needed. These advancements are driving progress in robotics (e.g., object manipulation), computer vision (e.g., improved depth estimation), and other fields requiring accurate 3D models, facilitated by the development of standardized benchmark datasets for evaluating NeRF performance.