# 3D Gaussian Inpainting with Depth-Guided Cross-View Consistency

arXiv: https://arxiv.org/abs/2502.11801 · HTML: https://mlsanity.com/paper/2502.11801/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 49 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 30 of 40)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [LangSplat: 3D Language Gaussian Splatting](https://mlsanity.com/paper/2312.16084/) (2023)
- [Gaussian Grouping: Segment and Edit Anything in 3D Scenes](https://mlsanity.com/paper/2312.00732/) (2023)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [Removing Objects From Neural Radiance Fields](https://mlsanity.com/paper/2212.11966/) (2022)

## Built on by (most cited first, 24 of 24)

- [Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes](https://mlsanity.com/paper/2511.06457/) (2025)
- [OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2601.09575/) (2026)
- [High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details](https://mlsanity.com/paper/2507.18023/) (2025)
- [IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion](https://mlsanity.com/paper/2508.13153/) (2025)
- [WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes](https://mlsanity.com/paper/2605.15843/) (2026)
- [DeclutterNeRF: Generative-Free 3D Scene Recovery for Occlusion Removal](https://mlsanity.com/paper/2504.04679/) (2025)
- [DSG-World: Learning a 3D Gaussian World Model from Dual State Videos](https://mlsanity.com/paper/2506.05217/) (2025)
- [CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus](https://mlsanity.com/paper/2509.04859/) (2025)
- [GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space](https://mlsanity.com/paper/2605.00498/) (2026)
- [GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments](https://mlsanity.com/paper/2606.17520/) (2026)
- [GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors](https://mlsanity.com/paper/2608.21849/) (2026)
- [VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal](https://mlsanity.com/paper/2506.15821/) (2025)
- [CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting](https://mlsanity.com/paper/2601.12814/) (2026)
- [Transformer-Based Inpainting for Real-Time 3D Streaming in Sparse Multi-Camera Setups](https://mlsanity.com/paper/2603.05507/) (2026)
- [Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment](https://mlsanity.com/paper/2603.21936/) (2026)
- [BEA-GS: BEyond RAdiance Supervision in 3DGS for Precise Object Extraction](https://mlsanity.com/paper/2605.09662/) (2026)
- [Benchmarking Single-Step Inpainting Methods for Multi-Object 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2605.30987/) (2026)
- [Lighting-Consistent Object Transfer Across Radiance Fields](https://mlsanity.com/paper/2606.22481/) (2026)
- [CoIn: Comprehensive 2D-3D Inpainting with Gaussian Splatting Guidance](https://mlsanity.com/paper/2606.27584/) (2026)
- [Restore3D: Breathing Life into Broken Objects with Shape and Texture Restoration](https://mlsanity.com/paper/2607.00522/) (2026)
- [SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting](https://mlsanity.com/paper/2607.05598/) (2026)
- [Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction](https://mlsanity.com/paper/2607.16286/) (2026)
- [GSComplete: Gaussian Splat Completion with 2D Diffusion Priors](https://mlsanity.com/paper/2609.08449/) (2026)
- [WINGS: Reference-Free Gaussian Splatting Inpainting with 3D-Native Generative Priors](https://mlsanity.com/paper/2609.37816/) (2026)

## Abstract (arXiv)

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.