Probabilistic Rotation

Probabilistic rotation modeling focuses on representing and estimating 3D rotations, accounting for inherent uncertainties and ambiguities often present in real-world data, such as those arising from occlusions or object symmetries. Current research emphasizes developing robust and efficient probabilistic distributions over the rotation manifold SO(3), employing methods like Bingham distributions, matrix Fisher distributions, and novel Laplace-inspired distributions, often integrated within deep learning frameworks for tasks like pose estimation. These advancements improve the accuracy and reliability of rotation estimation in applications ranging from robotics and computer vision to augmented reality, particularly in scenarios with noisy or incomplete data.