Sparse View

Sparse view reconstruction aims to create high-quality 3D models or images from a limited number of input views, addressing challenges in data acquisition and radiation exposure. Current research focuses on improving existing methods like 3D Gaussian Splatting and Neural Radiance Fields, often incorporating techniques such as diffusion models, implicit neural representations, and novel regularization strategies to mitigate artifacts and enhance detail in reconstructions from sparse data. This field is significant for applications ranging from medical imaging (reducing radiation dose in CT scans) to robotics and computer vision (efficient 3D scene understanding), with ongoing efforts to improve accuracy, efficiency, and robustness across diverse data types and scenarios.