Multiple Registration Output

Multiple registration output methods aim to improve the accuracy and efficiency of aligning multiple data sources, such as point clouds, images, or digital surface models. Current research focuses on developing incremental and iterative algorithms, often employing graph-based representations or deep learning architectures like neural networks and transformers, to handle large datasets and complex deformations. These advancements are crucial for applications ranging from 3D reconstruction and medical image analysis to remote sensing, enabling more precise and robust analysis of multi-source data. The development of multi-objective approaches further enhances the clinical utility by providing multiple registration outputs reflecting different trade-offs between competing objectives.