  Aligning partial views of a scene into a single whole is essential to
understanding one's environment and is a key component of numerous robotics
tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems
that can outperform traditional methods by leveraging pose supervision.
However, with the rising prevalence of cameras with depth sensors, we can
expect a new stream of raw RGB-D data without the annotations needed for
supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to
learning point cloud registration from raw RGB-D video. The key idea is to
leverage differentiable alignment and rendering to enforce photometric and
geometric consistency between frames. We evaluate our approach on indoor scene
datasets and find that we outperform existing traditional approaches with
classic and learned descriptors while being competitive with supervised
geometric point cloud registration approaches.
