Object Reconstruction

Object reconstruction aims to create accurate 3D models of objects from various input data, such as images, LiDAR scans, or sensor readings, with primary objectives of achieving high fidelity and efficiency. Current research emphasizes developing robust methods for handling dynamic objects, sparse data, and challenging real-world conditions, often employing neural networks (including transformers and diffusion models), neural radiance fields (NeRFs), and Gaussian splatting for representation and reconstruction. These advancements have significant implications for robotics, augmented reality, medical imaging, and other fields requiring accurate 3D scene understanding and object manipulation.