2 Dimensional Projection

Two-dimensional (2D) projections of 3D data are increasingly used to reconstruct and analyze complex structures in various fields, offering computational efficiency and reduced annotation needs. Current research focuses on developing deep learning models, including convolutional neural networks (CNNs) and graph neural networks (GNNs), to infer 3D information from 2D projections, often incorporating techniques like implicit neural representations and self-supervised learning. This approach is particularly valuable in medical imaging (e.g., reconstructing coronary arteries or segmenting brain structures) and other applications where acquiring or processing full 3D data is challenging, enabling faster and more efficient analysis while maintaining accuracy. The resulting improvements in speed and resource efficiency are significant for large-scale data processing and clinical applications.