Coarse-to-Fine Multi-Scene Pose Regression with Transformers
Yoli Shavit Ron Ferens Yosi Keller
Absolute camera pose regressors estimate the position and orientation of a
camera given the captured image alone. Typically, a convolutional backbone with
a multi-layer perceptron (MLP) head is trained using images and pose labels to
embed a single reference scene at a time. Recently, this scheme was extended to
learn 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 pose predictions. This allows our model to focus on general
features that are informative for localization, while embedding multiple scenes
in parallel. We extend our previous MS-Transformer approach
\cite{shavit2021learning} by introducing a mixed classification-regression
architecture that improves the localization accuracy. Our method is evaluated
on commonly benchmark indoor and outdoor datasets and has been shown to exceed
both multi-scene and state-of-the-art single-scene absolute pose regressors.