3D Reconstruction

3D reconstruction aims to create three-dimensional models from various two-dimensional data sources, such as images or videos, with applications spanning diverse fields. Current research emphasizes improving accuracy and efficiency, particularly in challenging scenarios like sparse viewpoints, dynamic scenes, and occluded objects. Popular approaches utilize neural radiance fields (NeRFs), Gaussian splatting, and other deep learning architectures, often incorporating techniques like active view selection and multi-view stereo to enhance reconstruction quality. These advancements are driving progress in areas such as robotics, medical imaging, and remote sensing, enabling more accurate and detailed 3D models for various applications.