Perspective N Point

Perspective-n-Point (PnP) is a fundamental computer vision problem focusing on estimating the pose (position and orientation) of a camera relative to a known 3D scene using a minimum of three 2D-3D point correspondences. Current research emphasizes developing robust and efficient PnP solvers, addressing challenges like noisy data, unknown camera parameters (e.g., focal length, scaling), and the need for real-time performance in applications such as robotics and augmented reality. These advancements leverage techniques such as iterative least squares, Reproducing Kernel Hilbert Spaces (RKHS), and end-to-end trainable neural networks, improving accuracy and speed while handling increasingly complex scenarios. The resulting improvements in pose estimation have significant implications for various applications, including autonomous navigation, 3D reconstruction, and object manipulation.