  We introduce PREDATOR, a model for pairwise point-cloud registration with
deep attention to the overlap region. Different from previous work, our model
is specifically designed to handle (also) point-cloud pairs with low overlap.
Its key novelty is an overlap-attention block for early information exchange
between the latent encodings of the two point clouds. In this way the
subsequent decoding of the latent representations into per-point features is
conditioned on the respective other point cloud, and thus can predict which
points are not only salient, but also lie in the overlap region between the two
point clouds. The ability to focus on points that are relevant for matching
greatly improves performance: PREDATOR raises the rate of successful
registrations by more than 20% in the low-overlap scenario, and also sets a new
state of the art for the 3DMatch benchmark with 89% registration recall.
