Implicit Function

Implicit functions, mathematical relationships where one variable is not explicitly defined in terms of others, are increasingly used to represent complex shapes and scenes in computer vision and robotics. Current research focuses on developing and improving neural implicit function models, often leveraging architectures like transformers and employing techniques such as bilevel optimization and preconditioning to enhance accuracy and efficiency. These advancements enable applications ranging from high-fidelity 3D reconstruction and animation to robust adversarial defenses in image classification and reactive robotic grasping, significantly impacting fields requiring continuous and flexible representations of data.