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OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields
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The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has increased interest in 3D scene editing. An essential task in editing is removing objects from a scene while ensuring visual reasonability and multiview consistency. However, current methods face challenges such as time-consuming object labeling, limited capability to remove specific targets, and compromised rendering quality after removal. This paper proposes a novel object-removing pipeline, named OR-NeRF, that can remove objects from 3D scenes with user-given points or text prompts on a single view, achieving better performance in less time than previous works. Our method spreads user annotations to all views through 3D geometry and sparse correspondence, ensuring 3D consistency with less processing burden. Then recent 2D segmentation model Segment-Anything (SAM) is applied to predict masks, and a 2D inpainting model is used to generate color supervision. Finally, our algorithm applies depth supervision and perceptual loss to maintain consistency in geometry and appearance after object removal. Experimental results demonstrate that our method achieves better editing quality with less time than previous works, considering both quality and quantity.
Forward citations
Cited by 5 Pith papers
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EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning
An end-to-end 3D editing framework achieves high-fidelity local edits from coarse bounding boxes and 2D image prompts using region-aware loss reweighting and a large-scale parts-derived training dataset.
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High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details
A 3D Gaussian inpainting framework with automatic mask refinement and depth-initialized uncertainty weighting balances multi-view consistency and visual detail, reporting the best LPIPS on the SPIn-NeRF dataset.
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RePaintGS: Reference-Guided Gaussian Splatting for Realistic and View-Consistent 3D Scene Inpainting
RePaintGS uses one reference inpainting view to weight and warp other inpainted views while training 3D Gaussian Splatting, yielding more view-consistent object removal.
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InstaInpaint: Instant 3D-Scene Inpainting with Masked Large Reconstruction Model
A masked fine-tuning strategy adapts large reconstruction models to perform feed-forward 3D scene inpainting in 0.4 seconds with competitive state-of-the-art quality.
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SplatFill: 3D Scene Inpainting via Depth-Guided Gaussian Splatting
A depth-guided Gaussian Splatting inpainting method with soft depth clustering and selective guided refinement achieves modest quality gains and 24.5% faster training over GScream on SPIn-NeRF.
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