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3D Inpainting
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3D Inpainting is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Constructing a 3D Town from a Single Image
Visibility-Uncertainty-guided 3D Gaussian Inpainting via Scene Conceptional Learning
IMFine: 3D Inpainting via Geometry-guided Multi-view Refinement
Geometry-Aware Diffusion Models for Multiview Scene Inpainting
3D Gaussian Inpainting with Depth-Guided Cross-View Consistency
MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D
3D StreetUnveiler with Semantic-Aware 2DGS
MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion
Advancing Brain Tumor Inpainting with Generative Models
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation
NeRFiller: Completing Scenes via Generative 3D Inpainting
Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion
InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360-degree Neural Radiance Fields
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
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