3D Rotation

3D rotation is a fundamental problem across numerous scientific fields, focusing on efficiently and accurately representing and manipulating rotations in three-dimensional space. Current research emphasizes developing robust and efficient algorithms for 3D rotation estimation and prediction, often employing neural network architectures like diffusion models, equivariant networks, and those leveraging spherical or icosahedral projections, alongside optimization techniques such as semidefinite relaxations. These advancements are crucial for applications ranging from medical image processing and robotic manipulation to knowledge graph embedding and computer vision tasks like pose estimation, improving accuracy and efficiency in handling complex 3D data.