Reference Frame

Reference frames, fundamental coordinate systems used to represent the position and orientation of objects, are crucial for various computer vision and robotics tasks. Current research focuses on developing robust and efficient methods for constructing and utilizing reference frames, particularly local reference frames (LRFs) for analyzing 3D point clouds and videos, often employing neural networks (e.g., transformers, graph neural networks) and optimization strategies to achieve rotation invariance and improved accuracy in tasks like pose estimation, video coding, and shape correspondence. These advancements have significant implications for improving the performance and generalizability of algorithms in fields ranging from autonomous driving to medical image analysis.