PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

Vasileios Gkitsas, Vladimiros Sterzentsenko, Nikolaos Zioulis, Georgios Albanis, Dimitrios Zarpalas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 3716-3726


The rising availability of commercial 360o 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 a 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 [47] 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

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[pdf] [arXiv]
@InProceedings{Gkitsas_2021_CVPR, author = {Gkitsas, Vasileios and Sterzentsenko, Vladimiros and Zioulis, Nikolaos and Albanis, Georgios and Zarpalas, Dimitrios}, title = {PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {3716-3726} }