  One of the key criticisms of deep learning is that large amounts of expensive
and difficult-to-acquire training data are required in order to train models
with high performance and good generalization capabilities. Focusing on the
task of monocular camera pose estimation via scene coordinate regression (SCR),
we describe a novel method, Domain Adaptation of Networks for Camera pose
Estimation (DANCE), which enables the training of models without access to any
labels on the target task. DANCE requires unlabeled images (without known
poses, ordering, or scene coordinate labels) and a 3D representation of the
space (e.g., a scanned point cloud), both of which can be captured with minimal
effort using off-the-shelf commodity hardware. DANCE renders labeled synthetic
images from the 3D model, and bridges the inevitable domain gap between
synthetic and real images by applying unsupervised image-level domain
adaptation techniques (unpaired image-to-image translation). When tested on
real images, the SCR model trained with DANCE achieved comparable performance
to its fully supervised counterpart (in both cases using PnP-RANSAC for final
pose estimation) at a fraction of the cost. Our code and dataset are available
at https://github.com/JackLangerman/dance
