Motion Averaging

Motion averaging is a technique used to estimate global camera poses or object positions from a set of relative motion measurements, offering a computationally efficient alternative to traditional methods like bundle adjustment. Current research focuses on improving the robustness and accuracy of motion averaging algorithms, particularly in the presence of outliers, through techniques such as weighted averaging based on local Hessian matrices and the use of robust cost functions like the maximum correntropy criterion. These advancements are significant for applications in computer vision and robotics, enabling faster and more reliable 3D scene reconstruction and visual localization, even with limited computational resources.