Canonical Mapping

Canonical mapping focuses on creating standardized, viewpoint-independent representations of objects or data, aiming to improve efficiency and robustness in various applications. Current research emphasizes learning these mappings automatically, often using deep neural networks such as transformers and neural operators, and incorporating techniques like adversarial training and self-supervised learning to improve representation quality. This work has significant implications for diverse fields, including robotics (improving object manipulation and perception), computer vision (enhancing pose estimation and shape analysis), and scientific machine learning (facilitating efficient data representation and analysis of complex systems).