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3D Gaussian Inpainting with Depth-Guided Cross-View Consistency
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When performing 3D inpainting using novel-view rendering methods like Neural Radiance Field (NeRF) or 3D Gaussian Splatting (3DGS), how to achieve texture and geometry consistency across camera views has been a challenge. In this paper, we propose a framework of 3D Gaussian Inpainting with Depth-Guided Cross-View Consistency (3DGIC) for cross-view consistent 3D inpainting. Guided by the rendered depth information from each training view, our 3DGIC exploits background pixels visible across different views for updating the inpainting mask, allowing us to refine the 3DGS for inpainting purposes.Through extensive experiments on benchmark datasets, we confirm that our 3DGIC outperforms current state-of-the-art 3D inpainting methods quantitatively and qualitatively.
Forward citations
Cited by 2 Pith papers
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DSG-World: Learning a 3D Gaussian World Model from Dual State Videos
DSG-World builds two segmented 3D Gaussian fields from two scene states and trains them with mutual consistency, enabling novel-state simulation without inpainting or dense capture.
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VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal
VEIGAR is a pipeline for 3D object removal in Gaussian Splatting that uses deep stereo depth projection and a scale-invariant depth loss to achieve faster training and comparable quality to prior state-of-the-art.
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