Robust Registration

Robust registration aims to accurately align data from different sources, such as images or point clouds, despite challenges like noise, outliers, and large initial misalignments. Current research focuses on developing robust algorithms and model architectures, including those based on deep learning (e.g., transformers, equivariant networks), graph-based methods, and improved versions of classical techniques like ICP. These advancements are crucial for various applications, including robotics (SLAM, object pose estimation), medical imaging (monitoring disease progression), and 3D reconstruction, where accurate and reliable registration is essential for successful operation.