2 Dimensional

Two-dimensional (2D) representations are central to many areas of computer vision and image processing, serving as foundational inputs for more complex 3D analyses. Current research focuses on leveraging 2D data for tasks like 3D reconstruction, object detection, and semantic segmentation, often employing deep learning models such as convolutional neural networks (CNNs), transformers, and diffusion models. These advancements improve efficiency and accuracy in applications ranging from medical image analysis and autonomous driving to architectural design and video synthesis, highlighting the continued importance of 2D data in tackling increasingly complex 3D problems.