Recovering 3D shape and motion from image streams using nonlinear least squares

A shape and motion estimation algorithm based on nonlinear least squares applied to the tracks of features through time is presented. While the authors´ approach requires iteration, it quickly converges to the desired solution, even in the absence of a priori knowledge about the shape or motion. Important features of the algorithm include its ability to handle partial point tracks and true perspective, its ability to use line segment matches and point matches simultaneously, and its use of an object-centered representation for faster and more accurate structure and motion recovery