  Modern computer vision has moved beyond the domain of internet photo
collections and into the physical world, guiding camera-equipped robots and
autonomous cars through unstructured environments. To enable these embodied
agents to interact with real-world objects, cameras are increasingly being used
as depth sensors, reconstructing the environment for a variety of downstream
reasoning tasks. Machine-learning-aided depth perception, or depth estimation,
predicts for each pixel in an image the distance to the imaged scene point.
While impressive strides have been made in depth estimation, significant
challenges remain: (1) ground truth depth labels are difficult and expensive to
collect at scale, (2) camera information is typically assumed to be known, but
is often unreliable and (3) restrictive camera assumptions are common, even
though a great variety of camera types and lenses are used in practice. In this
thesis, we focus on relaxing these assumptions, and describe contributions
toward the ultimate goal of turning cameras into truly generic depth sensors.
