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.
Papers - Page 3
Reconstruction of frequency-localized functions from pointwise samples via least squares and deep learning
A. Martina Neuman, Andres Felipe Lerma Pineda, Jason J. Bramburger, Simone BrugiapagliaSelf-Calibrating Gaussian Splatting for Large Field of View Reconstruction
Youming Deng, Wenqi Xian, Guandao Yang, Leonidas Guibas, Gordon Wetzstein, Steve Marschner, Paul Debevec
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction
Valiyeh A. Nezhad, Gokberk Elmas, Bilal Kabas, Fuat Arslan, Tolga ÇukursshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D Reconstruction from Sparse-Views
Eyvaz Najafli, Marius Kästingschäfer, Sebastian Bernhard, Thomas Brox, Andreas Geiger
Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction
Eric Bezzam, Yohann Perron, Martin VetterliWonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction
Zilong Wang, Zhiyang Dou, Yuan Liu, Cheng Lin, Xiao Dong, Yunhui Guo, Chenxu Zhang, Xin Li, Wenping Wang, Xiaohu Guo
Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction
Dayoung Baik, Jaejun YooSubject Disentanglement Neural Network for Speech Envelope Reconstruction from EEG
Li Zhang, Jiyao LiuScalable and High-Quality Neural Implicit Representation for 3D Reconstruction
Leyuan Yang, Bailin Deng, Juyong Zhang