Implicit Representation

Implicit representations are a rapidly developing area of research focused on encoding data—from images and videos to 3D shapes and even tactile sensor feedback—as continuous functions, rather than discrete data points. Current research emphasizes developing efficient and scalable neural network architectures, such as Implicit Neural Representations (INRs) and Neural Radiance Fields (NeRFs), to learn these functions, often incorporating techniques like knowledge distillation and hierarchical structures to improve speed and generalization. This approach offers significant advantages in data compression, efficient scene representation, and enables novel applications in robotics, medical imaging, and 3D reconstruction by providing compact, continuous representations suitable for various downstream tasks.