  In this paper, we present SPVLoc, a global indoor localization method that
accurately determines the six-dimensional (6D) camera pose of a query image and
requires minimal scene-specific prior knowledge and no scene-specific training.
Our approach employs a novel matching procedure to localize the perspective
camera's viewport, given as an RGB image, within a set of panoramic semantic
layout representations of the indoor environment. The panoramas are rendered
from an untextured 3D reference model, which only comprises approximate
structural information about room shapes, along with door and window
annotations. We demonstrate that a straightforward convolutional network
structure can successfully achieve image-to-panorama and ultimately
image-to-model matching. Through a viewport classification score, we rank
reference panoramas and select the best match for the query image. Then, a 6D
relative pose is estimated between the chosen panorama and query image. Our
experiments demonstrate that this approach not only efficiently bridges the
domain gap but also generalizes well to previously unseen scenes that are not
part of the training data. Moreover, it achieves superior localization accuracy
compared to the state of the art methods and also estimates more degrees of
freedom of the camera pose. Our source code is publicly available at
https://fraunhoferhhi.github.io/spvloc .
