  The rising availability of commercial $360^\circ$ cameras that democratize
indoor scanning, has increased the interest for novel applications, such as
interior space re-design. Diminished Reality (DR) fulfills the requirement of
such applications, to remove existing objects in the scene, essentially
translating this to a counterfactual inpainting task. While recent advances in
data-driven inpainting have shown significant progress in generating realistic
samples, they are not constrained to produce results with reality mapped
structures. To preserve the `reality' in indoor (re-)planning applications, the
scene's structure preservation is crucial. To ensure structure-aware
counterfactual inpainting, we propose a model that initially predicts the
structure of an indoor scene and then uses it to guide the reconstruction of an
empty -- background only -- representation of the same scene. We train and
compare against other state-of-the-art methods on a version of the Structured3D
dataset modified for DR, showing superior results in both quantitative metrics
and qualitative results, but more interestingly, our approach exhibits a much
faster convergence rate. Code and models are available at
https://vcl3d.github.io/PanoDR/ .
