# Removing Objects From Neural Radiance Fields

arXiv: https://arxiv.org/abs/2212.11966 · HTML: https://mlsanity.com/paper/2212.11966/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 85 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 30 of 61)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [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)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (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)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2112.00724/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021)
- [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://mlsanity.com/paper/2111.08897/) (2021)
- [Decomposing NeRF for Editing via Feature Field Distillation](https://mlsanity.com/paper/2205.15585/) (2022)
- [Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion](https://mlsanity.com/paper/2003.01456/) (2020)
- [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021)
- [Neural Scene Graphs for Dynamic Scenes](https://mlsanity.com/paper/2011.10379/) (2020)
- [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021)
- [Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering](https://mlsanity.com/paper/2109.01847/) (2021)
- [Editing Conditional Radiance Fields](https://mlsanity.com/paper/2105.06466/) (2021)

## Built on by (most cited first, 30 of 66)

- [DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editing](https://mlsanity.com/paper/2404.18929/) (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)
- [PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF](https://mlsanity.com/paper/2311.13099/) (2023)
- [Watch Your Steps: Local Image and Scene Editing by Text Instructions](https://mlsanity.com/paper/2308.08947/) (2023)
- [PERF: Panoramic Neural Radiance Field from a Single Panorama](https://mlsanity.com/paper/2310.16831/) (2023)
- [GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal](https://mlsanity.com/paper/2404.13679/) (2024)
- [SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds](https://mlsanity.com/paper/2312.09246/) (2023)
- [OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields](https://mlsanity.com/paper/2305.10503/) (2023)
- [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)
- [NeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation](https://mlsanity.com/paper/2403.17537/) (2024)
- [MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior](https://mlsanity.com/paper/2405.02859/) (2024)
- [3D Gaussian Inpainting with Depth-Guided Cross-View Consistency](https://mlsanity.com/paper/2502.11801/) (2025)
- [RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting](https://mlsanity.com/paper/2404.10765/) (2024)
- [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)
- [RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models](https://mlsanity.com/paper/2306.05668/) (2023)
- [Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting](https://mlsanity.com/paper/2404.01168/) (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)
- [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024)
- [InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes](https://mlsanity.com/paper/2401.05335/) (2024)
- [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)
- [Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting](https://mlsanity.com/paper/2304.03763/) (2023)
- [Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates](https://mlsanity.com/paper/2309.11281/) (2023)
- [Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review](https://mlsanity.com/paper/2402.11141/) (2024)
- [DerainNeRF: 3D Scene Estimation with Adhesive Waterdrop Removal](https://mlsanity.com/paper/2403.20013/) (2024)
- [Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes](https://mlsanity.com/paper/2511.06457/) (2025)

## Abstract (arXiv)

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.