  We introduce UprightNet, a learning-based approach for estimating 2DoF camera
orientation from a single RGB image of an indoor scene. Unlike recent methods
that leverage deep learning to perform black-box regression from image to
orientation parameters, we propose an end-to-end framework that incorporates
explicit geometric reasoning. In particular, we design a network that predicts
two representations of scene geometry, in both the local camera and global
reference coordinate systems, and solves for the camera orientation as the
rotation that best aligns these two predictions via a differentiable least
squares module. This network can be trained end-to-end, and can be supervised
with both ground truth camera poses and intermediate representations of surface
geometry. We evaluate UprightNet on the single-image camera orientation task on
synthetic and real datasets, and show significant improvements over prior
state-of-the-art approaches.
