  Absolute camera pose regressors estimate the position and orientation of a
camera from the captured image alone. Typically, a convolutional backbone with
a multi-layer perceptron head is trained with images and pose labels to embed a
single reference scene at a time. Recently, this scheme was extended for
learning multiple scenes by replacing the MLP head with a set of fully
connected layers. In this work, we propose to learn multi-scene absolute camera
pose regression with Transformers, where encoders are used to aggregate
activation maps with self-attention and decoders transform latent features and
scenes encoding into candidate pose predictions. This mechanism allows our
model to focus on general features that are informative for localization while
embedding multiple scenes in parallel. We evaluate our method on commonly
benchmarked indoor and outdoor datasets and show that it surpasses both
multi-scene and state-of-the-art single-scene absolute pose regressors. We make
our code publicly available from here.
