Pose Distribution

Pose distribution estimation aims to determine not just the single most likely position and orientation of an object (its pose), but the entire probability distribution of possible poses, accounting for uncertainties like occlusion or object symmetry. Current research focuses on developing methods that leverage geometric constraints, CAD models, and neural networks (including transformers and normalizing flows) to accurately and efficiently estimate these distributions, often using probabilistic losses and novel evaluation metrics. This improved understanding of pose uncertainty is crucial for robotics, augmented reality, and other applications requiring robust object manipulation and interaction in complex, real-world scenarios.