Structure-from-Motion (SfM) is a powerful tool for computing 3D reconstructions from images of a scene and has wide applications in computer vision, scene recognition, and augmented and virtual reality. Standard SfM pipelines make strict assumptions about the capturing devices in order to simplify the process for estimating camera geometry and 3D structure. Specifically, most methods require monocular cameras with known focal length calibration. When considering large-scale SfM from internet photo collections, EXIF calibrations cannot used reliably. Further, the requirement of single camera systems limits the scalability of SfM.
This thesis proposes to remove these constraints by instead considering the collection of cameras as a "distributed camera" that encapsulates the image and geometric information of all cameras simultaneously. First, I provide full generalizations to the relative camera pose and absolute camera pose problems. These generalizations are more expressive and extend the traditional single-camera problems to distributed cameras, forming the basis for a novel hierarchical SfM pipeline that exhibits state-of-the-art performance on large-scale datasets. Second, I describe two efficient methods for estimating camera focal lengths for the distributed camera when calibration is not available. Finally, I show how removing these constraints enables a simpler, more scalable SFM pipeline that is capable of handling uncalibrated cameras at scale.