Primitive Based

Primitive-based approaches represent a growing trend in various fields, aiming to decompose complex systems or data into simpler, fundamental building blocks for improved efficiency and understanding. Current research focuses on developing algorithms to automatically generate and utilize these primitives, exploring applications in areas such as image processing (e.g., using Gaussian splats or adapting pre-trained models for segmentation), robotics (e.g., for imitation learning and manipulation), and computer vision (e.g., shape abstraction from signed distance functions). This focus on primitives offers significant potential for advancing fields like AI, computer graphics, and medical image analysis through more efficient representations, improved scalability, and enhanced data efficiency in complex tasks.