Pose Regression

Pose regression aims to estimate the position and orientation (pose) of an object or camera from visual data, such as images or point clouds. Current research focuses on improving accuracy and efficiency through various approaches, including the use of convolutional neural networks, transformers, and graph convolutional networks, often incorporating techniques like keypoint detection, multi-modal fusion (RGB-D, LiDAR), and uncertainty quantification. These advancements have significant implications for robotics, augmented reality, and autonomous systems, enabling more robust and reliable visual localization and object manipulation. Furthermore, research is actively exploring methods to handle challenging scenarios like occlusions, varying lighting conditions, and limited data availability.