Absolute Pose Regression

Absolute pose regression (APR) aims to directly estimate a camera's 3D position and orientation from a single image, a crucial task in robotics, augmented reality, and autonomous driving. Current research focuses on improving APR accuracy and robustness, particularly in challenging outdoor environments, through techniques like incorporating 3D geometric constraints (e.g., using neural radiance fields or volumetric features), leveraging relative pose information, and employing efficient model architectures such as lightweight convolutional neural networks and transformers. These advancements are driving progress towards more accurate and computationally efficient camera localization, enabling wider adoption in real-world applications.