PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes

TL;DR

This paper introduces PanoDR, a structure-aware method for indoor scene diminished reality using 360-degree panoramas, which preserves scene structure during object removal and outperforms existing methods in accuracy and convergence speed.

Contribution

The paper presents a novel structure-guided inpainting model for indoor panoramic scenes that ensures realistic and structure-preserving object removal, with improved convergence and performance.

Findings

Outperforms state-of-the-art methods in quantitative metrics

Achieves faster convergence during training

Produces more realistic and structure-preserving inpainted scenes

Abstract

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/ .