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InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior
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3D Gaussians have recently emerged as an efficient representation for novel view synthesis. This work studies its editability with a particular focus on the inpainting task, which aims to supplement an incomplete set of 3D Gaussians with additional points for visually harmonious rendering. Compared to 2D inpainting, the crux of inpainting 3D Gaussians is to figure out the rendering-relevant properties of the introduced points, whose optimization largely benefits from their initial 3D positions. To this end, we propose to guide the point initialization with an image-conditioned depth completion model, which learns to directly restore the depth map based on the observed image. Such a design allows our model to fill in depth values at an aligned scale with the original depth, and also to harness strong generalizability from largescale diffusion prior. Thanks to the more accurate depth completion, our approach, dubbed InFusion, surpasses existing alternatives with sufficiently better fidelity and efficiency under various complex scenarios. We further demonstrate the effectiveness of InFusion with several practical applications, such as inpainting with user-specific texture or with novel object insertion.
Forward citations
Cited by 13 Pith papers
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Super-Gaussian: Interactive Scene Editing for 3D Gaussian Splatting and NLI-Based Volume Visualization in Virtual Reality
A VR volume visualization framework that clusters Gaussian splats into selectable super-Gaussians and couples brush-based selection with natural language queries for interactive scene editing.
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Semantic-Guided Progressive Object Removal with Gaussian Splatting
Semantic block matching via DINOv2 plus selective high-frequency refinement yields higher-fidelity, multi-view-consistent object removal inside 3D Gaussian Splatting than prior one-shot Gaussian or NeRF inpainters.
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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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InstaScene: Towards Complete 3D Instance Decomposition and Reconstruction from Cluttered Scenes
InstaScene combines Gaussian-based instance decomposition with generative completion to produce complete, scene-aligned 3D object models from cluttered scenes.
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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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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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3D-GIMP: When 3D Gaussian Inpainting Meets PatchMatch
3D-GIMP removes objects from 3D Gaussian Splatting scenes by inpainting one reference view and propagating it to all views via a 3D-aware PatchMatch field, cutting optimization time from ~1 hour to ~6 minutes.
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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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ObjFiller3D: Scaling 3D Object Inpainting to Dense Multi-View Consistency
ObjFiller3D jointly optimizes a dense 360-degree ring of views to inpaint 3D objects with cross-view-consistent textures, reporting higher PSNR and LPIPS than per-view baselines at much lower runtime.
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DiGA3D: Coarse-to-Fine Diffusional Propagation of Geometry and Appearance for Versatile 3D Inpainting
DiGA3D performs text-guided 3D inpainting (removal, re-texturing, replacement) with a coarse-to-fine diffusion propagation scheme to improve multi-view appearance and geometry consistency.
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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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Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction
NDF treats a fixed-image depth estimator as an implicit field and optimizes it on observed depth at test time, improving inpainting accuracy and cross-view consistency.
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