Pose Estimation

Pose estimation, the task of determining the position and orientation of objects in space, is a core problem in computer vision with applications ranging from robotics and augmented reality to autonomous driving and medical imaging. Current research focuses on improving accuracy and robustness in challenging scenarios, such as occlusions, low-quality images, and unstructured environments, often employing deep learning models like transformers and convolutional neural networks, along with techniques like bundle adjustment and graph optimization for pose refinement. These advancements are driving progress in various fields by enabling more precise and reliable object manipulation, scene understanding, and human-computer interaction.