Rotation Regression

Rotation regression focuses on accurately estimating 3D rotations from various data sources, such as images or sensor readings, aiming for robust and efficient algorithms. Current research emphasizes developing end-to-end methods, leveraging probabilistic models like the Laplace distribution to handle noisy data and outliers, and employing semi-supervised learning techniques to reduce reliance on large labeled datasets. These advancements are crucial for applications in computer vision, robotics, and remote sensing, improving tasks like object pose estimation, 3D scene reconstruction, and autonomous navigation.