DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpainting to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geometry (i.e., depth), in particular for advanced applications, such as 3D scene editing. In this paper, we propose DeepDR, a first RGB-D inpainting framework fulfilling all requirements of DR: Plausible image and geometry inpainting with coherent structure, running at real-time frame rates, with minimal temporal artifacts. Our structure-aware generative network allows us to explicitly condition color and depth outputs on the scene semantics, overcoming the difficulty of reconstructing sharp and consistent boundaries in regions with complex backgrounds. Experimental results show that the proposed framework can outperform related work qualitatively and quantitatively.

Video

Results

InteriorNet (synthetic data)

DynaFill (synthetic data)

ScanNet (real data)

Bibtex

@article{gsaxner2023deepdr,
    title={DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality},
    author={Gsaxner, Christina and Mori, Shohei and Schmalstieg, Dieter and Egger, Jan and Paar, Gerhard and Bailer, Werner and Kalkofen, Denis},
    journal={arXiv preprint arXiv:2312.00532},
    year={2023}
}