Pose Estimate

Pose estimation, the task of determining an object's 3D position and orientation from visual or sensor data, aims to accurately and reliably locate objects within a scene. Current research emphasizes improving accuracy and robustness, particularly in challenging scenarios like low-light conditions, occlusions, and noisy data, often employing techniques like neural radiance fields (NeRFs), graph convolutional networks (GCNs), and deep ensembles for uncertainty quantification. These advancements have significant implications for robotics (e.g., manipulation, navigation), augmented reality (AR), and autonomous systems, enabling more precise and reliable interaction with the physical world.