# Reference-guided Controllable Inpainting of Neural Radiance Fields

arXiv: https://arxiv.org/abs/2304.09677 · HTML: https://mlsanity.com/paper/2304.09677/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 90 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 30 of 71)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [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)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [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)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)

## Built on by (most cited first, 30 of 40)

- [RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion](https://mlsanity.com/paper/2404.07199/) (2024)
- [NeRFiller: Completing Scenes via Generative 3D Inpainting](https://mlsanity.com/paper/2312.04560/) (2023)
- [InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior](https://mlsanity.com/paper/2404.11613/) (2024)
- [Watch Your Steps: Local Image and Scene Editing by Text Instructions](https://mlsanity.com/paper/2308.08947/) (2023)
- [GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal](https://mlsanity.com/paper/2404.13679/) (2024)
- [MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing](https://mlsanity.com/paper/2408.08000/) (2024)
- [Point'n Move: Interactive Scene Object Manipulation on Gaussian Splatting Radiance Fields](https://mlsanity.com/paper/2311.16737/) (2023)
- [MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior](https://mlsanity.com/paper/2405.02859/) (2024)
- [FastScene: Text-Driven Fast 3D Indoor Scene Generation via Panoramic Gaussian Splatting](https://mlsanity.com/paper/2405.05768/) (2024)
- [3D Gaussian Inpainting with Depth-Guided Cross-View Consistency](https://mlsanity.com/paper/2502.11801/) (2025)
- [AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360deg Unbounded Scene Inpainting](https://mlsanity.com/paper/2502.05176/) (2025)
- [Taming Latent Diffusion Model for Neural Radiance Field Inpainting](https://mlsanity.com/paper/2404.09995/) (2024)
- [InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360-degree Neural Radiance Fields](https://mlsanity.com/paper/2305.15094/) (2023)
- [Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion](https://mlsanity.com/paper/2312.03869/) (2023)
- [ExtraNeRF: Visibility-Aware View Extrapolation of Neural Radiance Fields with Diffusion Models](https://mlsanity.com/paper/2406.06133/) (2024)
- [InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes](https://mlsanity.com/paper/2401.05335/) (2024)
- [LucidDreaming: Controllable Object-Centric 3D Generation](https://mlsanity.com/paper/2312.00588/) (2023)
- [IMFine: 3D Inpainting via Geometry-guided Multi-view Refinement](https://mlsanity.com/paper/2503.04501/) (2025)
- [InstaInpaint: Instant 3D-Scene Inpainting with Masked Large Reconstruction Model](https://mlsanity.com/paper/2506.10980/) (2025)
- [Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review](https://mlsanity.com/paper/2402.11141/) (2024)
- [ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields](https://mlsanity.com/paper/2401.17895/) (2024)
- [LLaNA: Large Language and NeRF Assistant](https://mlsanity.com/paper/2406.11840/) (2024)
- [IE-NeRF: Inpainting Enhanced Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2407.10695/) (2024)
- [Geometry-Aware Diffusion Models for Multiview Scene Inpainting](https://mlsanity.com/paper/2502.13335/) (2025)
- [DreamInsert: Zero-Shot Image-to-Video Object Insertion from A Single Image](https://mlsanity.com/paper/2503.10342/) (2025)
- [4D-Editor: Interactive Object-level Editing in Dynamic Neural Radiance Fields via Semantic Distillation](https://mlsanity.com/paper/2310.16858/) (2023)
- [Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations](https://mlsanity.com/paper/2310.17880/) (2023)
- [OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2601.09575/) (2026)
- [GO-NeRF: Generating Objects in Neural Radiance Fields for Virtual Reality Content Creation](https://mlsanity.com/paper/2401.05750/) (2024)
- [Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes](https://mlsanity.com/paper/2503.15742/) (2025)

## Abstract (arXiv)

The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and controllable manner. In addition to the typical NeRF inputs and masks delineating the unwanted region in each view, we require only a single inpainted view of the scene, i.e., a reference view. We use monocular depth estimators to back-project the inpainted view to the correct 3D positions. Then, via a novel rendering technique, a bilateral solver can construct view-dependent effects in non-reference views, making the inpainted region appear consistent from any view. For non-reference disoccluded regions, which cannot be supervised by the single reference view, we devise a method based on image inpainters to guide both the geometry and appearance. Our approach shows superior performance to NeRF inpainting baselines, with the additional advantage that a user can control the generated scene via a single inpainted image. Project page: https://ashmrz.github.io/reference-guided-3d