  Estimating camera pose from a single image is a fundamental problem in
computer vision. Existing methods for solving this task fall into two distinct
categories, which we refer to as direct and indirect. Direct methods, such as
PoseNet, regress pose from the image as a fixed function, for example using a
feed-forward convolutional network. Such methods are desirable because they are
deterministic and run in constant time. Indirect methods for pose regression
are often non-deterministic, with various external dependencies such as image
retrieval and hypothesis sampling. We propose a direct method that takes
inspiration from structure-based approaches to incorporate explicit 3D
constraints into the network. Our approach maintains the desirable qualities of
other direct methods while achieving much lower error in general.
