3D structure recovery from a collection of 2D photos is a classical problem in computer vision that requires the estimation of the camera orientations and positions, i.e. the camera motion. For a large, irregular collection of images, high quality camera motion estimation turns out to be a complex, time consuming problem. In our work, we introduce a computationally efficient algorithm composed of iterative-global convex programming for camera orientation estimation and robust-distributed convex programming for camera location estimation. Our main contribution is a robust distributed convex program for camera position estimation, which is considered to be the most challenging part of the structure from motion problem. We introduce the concept of "global parallel-rigidity" to the camera location estimation problem, show how to extract maximally global parallel-rigid components of the available location information and formulate a stable semidefinite program (SDP) for high levels of pairwise direction information noise. For large sets of images, we also formulate fast convex programs to produce the global camera location solution from partial solutions. This is a joint work with Amit Singer (Princeton University) and Ronen Basri (Weizmann Institute of Science).