  Pose regression networks predict the camera pose of a query image relative to
a known environment. Within this family of methods, absolute pose regression
(APR) has recently shown promising accuracy in the range of a few centimeters
in position error. APR networks encode the scene geometry implicitly in their
weights. To achieve high accuracy, they require vast amounts of training data
that, realistically, can only be created using novel view synthesis in a
days-long process. This process has to be repeated for each new scene again and
again. We present a new approach to pose regression, map-relative pose
regression (marepo), that satisfies the data hunger of the pose regression
network in a scene-agnostic fashion. We condition the pose regressor on a
scene-specific map representation such that its pose predictions are relative
to the scene map. This allows us to train the pose regressor across hundreds of
scenes to learn the generic relation between a scene-specific map
representation and the camera pose. Our map-relative pose regressor can be
applied to new map representations immediately or after mere minutes of
fine-tuning for the highest accuracy. Our approach outperforms previous pose
regression methods by far on two public datasets, indoor and outdoor. Code is
available: https://nianticlabs.github.io/marepo
