Coordinate Frame

Coordinate frames are fundamental reference systems used to represent the position and orientation of objects in various domains, from computer vision and robotics to color science. Current research focuses on optimizing coordinate frame selection for improved accuracy and efficiency in applications like dynamic SLAM, robot modeling (using methods like exponential maps), and deep learning architectures (e.g., MLPs with multi-coordinate frame receptive fields). The choice of coordinate frame significantly impacts performance, particularly in tasks involving multiple objects or dynamic environments, with recent work emphasizing the trade-offs between agent-centric and scene-centric approaches and exploring knowledge distillation techniques to bridge the performance gap. These advancements have implications for improving the accuracy and efficiency of autonomous systems, computer vision algorithms, and other applications requiring precise spatial representation.