Full State Reconstruction

Full state reconstruction aims to recover complete and accurate representations of systems or objects from incomplete or noisy data. Current research focuses on diverse applications, employing various techniques including neural networks (e.g., UNets, Transformers, diffusion models), implicit neural representations, and spectral graph theory, often tailored to specific data modalities (images, point clouds, time series, etc.). These advancements improve accuracy and efficiency in diverse fields, ranging from robotics and computer vision (3D object reconstruction, hand tracking) to medical imaging (CT and MRI reconstruction) and speech processing (efficient speech separation). The resulting improvements in data analysis and system modeling have significant implications for various scientific and engineering disciplines.