Fast Reconstruction

Fast reconstruction aims to rapidly generate high-quality images or 3D models from incomplete or undersampled data, a crucial goal across diverse fields like medical imaging and computer vision. Current research emphasizes efficient neural network architectures, including those based on implicit neural representations, wavelet transforms, and transformers, often incorporating techniques like meta-learning and coarse-to-fine refinement to accelerate training and inference. These advancements are significantly impacting applications by enabling real-time processing in areas such as surgical planning, video compression, and medical diagnosis, ultimately improving speed and accuracy in various scientific and clinical workflows.