Sparse Input View

Sparse input view research focuses on reconstructing 3D scenes and objects from a limited number of images, aiming to overcome the limitations of methods requiring dense view datasets. Current efforts center on adapting neural radiance fields (NeRFs) and Gaussian splatting, often incorporating techniques like multi-view stereo, diffusion models, and depth priors to improve reconstruction accuracy and efficiency. This research is significant for advancing 3D vision applications, particularly in scenarios where acquiring dense datasets is impractical, such as robotics, autonomous driving, and augmented reality.