DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Neural Surface Priors for Editable Gaussian Splatting
GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Dense 3D Displacement Estimation for Landslide Monitoring via Fusion of TLS Point Clouds and Embedded RGB Images
ScaleLSD: Scalable Deep Line Segment Detection Streamlined
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
토론
이 논문과 연결된 포럼 글이 아직 없습니다.