REVIEW 14 cited by
InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior
read the original abstract
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 14 Pith papers
-
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.
-
CoIn: Comprehensive 2D-3D Inpainting with Gaussian Splatting Guidance
CoIn introduces a multi-stage pipeline using diffusion models for initial 2D inpainting, Reference Adaptive GS for 3D reconstruction, and GS-based warping plus a discriminator for multi-view consistent 3D scene inpain...
-
GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space
GOR-IS removes objects from 3D Gaussian Splatting reconstructions by performing inpainting in an intrinsic decomposition space that explicitly models light transport for consistent global lighting and non-Lambertian surfaces.
-
GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
GA-GS uses motion segmentation, diffusion-based inpainting for pseudo-ground-truth, and per-Gaussian authenticity scalars to achieve SOTA static scene reconstruction from videos with dynamic occlusions.
-
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.
-
SphericalDreamer: Generating Navigable Immersive 3D Worlds with Panorama Fusion
SphericalDreamer creates long-range navigable immersive 3D environments from text by generating and fusing multiple panoramic images into consistent 3D structures.
-
WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes
WorldAct activates monolithic 3D worlds into interactive scenes via multimodal agent-guided decomposition, geometrically aligned mesh reconstruction, and 3D inpainting.
-
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.
-
LIVE-GS: LLM Powers Interactive VR Experience with Physics-Aware Gaussian Splatting
LIVE-GS uses an LLM to predict physical parameters from static Gaussian assets in 10 seconds for physics-aware VR interactions, validated by interviews, baseline comparisons, and user studies.
-
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.
-
Occlusion-Robust Multi-Object Decoupling for Physics-Based Robotic Interaction
A pipeline combining SAM2 segmentation, 3D Gaussian Splatting, and joint Score Distillation Sampling with 2D/3D diffusion priors reconstructs decoupled multi-object geometries from occluded sparse views for MPM simulation.
-
Occlusion-Robust Multi-Object Decoupling for Physics-Based Robotic Interaction
A new pipeline for occlusion-robust multi-object 3D reconstruction from sparse views supports physics-based robotic interaction.
-
A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.
-
A Survey on 3D Gaussian Splatting
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.