# GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal

arXiv: https://arxiv.org/abs/2404.13679 · HTML: https://mlsanity.com/paper/2404.13679/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 21 Apr 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [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)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://mlsanity.com/paper/2312.02145/) (2023)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis](https://mlsanity.com/paper/1612.00101/) (2016)
- [MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes](https://mlsanity.com/paper/2302.12249/) (2023)
- [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans](https://mlsanity.com/paper/1712.10215/) (2017)
- [SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans](https://mlsanity.com/paper/1912.00036/) (2019)
- [Removing Objects From Neural Radiance Fields](https://mlsanity.com/paper/2212.11966/) (2022)
- [NeRF-In: Free-Form NeRF Inpainting with RGB-D Priors](https://mlsanity.com/paper/2206.04901/) (2022)
- [Reference-guided Controllable Inpainting of Neural Radiance Fields](https://mlsanity.com/paper/2304.09677/) (2023)
- [OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields](https://mlsanity.com/paper/2305.10503/) (2023)

## Built on by (most cited first, 30 of 39)

- [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)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction](https://mlsanity.com/paper/2503.05484/) (2025)
- [CLIP-GS: CLIP-Informed Gaussian Splatting for View-Consistent 3D Indoor Semantic Understanding](https://mlsanity.com/paper/2404.14249/) (2024)
- [Dynamics-Aware Gaussian Splatting Streaming Towards Fast On-the-Fly 4D Reconstruction](https://mlsanity.com/paper/2411.14847/) (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)
- [CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization](https://mlsanity.com/paper/2506.21117/) (2025)
- [Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes](https://mlsanity.com/paper/2511.06457/) (2025)
- [Geometry-Aware Diffusion Models for Multiview Scene Inpainting](https://mlsanity.com/paper/2502.13335/) (2025)
- [Omni-3DEdit: Generalized Versatile 3D Editing in One-Pass](https://mlsanity.com/paper/2603.17841/) (2026)
- [Multiview Geometric Regularization of Gaussian Splatting for Accurate Radiance Fields](https://mlsanity.com/paper/2506.13508/) (2025)
- [OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2601.09575/) (2026)
- [3D StreetUnveiler with Semantic-aware 2DGS -- a simple baseline](https://mlsanity.com/paper/2405.18416/) (2024)
- [Attention in Diffusion Model: A Survey](https://mlsanity.com/paper/2504.03738/) (2025)
- [Learning Heterogeneous Mixture of Scene Experts for Large-scale Neural Radiance Fields](https://mlsanity.com/paper/2505.02005/) (2025)
- [F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Aggregative Gaussian Splatting](https://mlsanity.com/paper/2501.06714/) (2025)
- [Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation](https://mlsanity.com/paper/2503.11731/) (2025)
- [High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details](https://mlsanity.com/paper/2507.18023/) (2025)
- [HOMER: Homography-Based Efficient Multi-view 3D Object Removal](https://mlsanity.com/paper/2501.17636/) (2025)
- [CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting](https://mlsanity.com/paper/2503.12836/) (2025)
- [DeclutterNeRF: Generative-Free 3D Scene Recovery for Occlusion Removal](https://mlsanity.com/paper/2504.04679/) (2025)
- [VIRGi: View-dependent Instant Recoloring of 3D Gaussians Splats](https://mlsanity.com/paper/2603.02986/) (2026)
- [VistaGEN: Consistent Driving Video Generation with Fine-Grained Control Using Multiview Visual-Language Reasoning](https://mlsanity.com/paper/2603.28353/) (2026)
- [JointEdit3D: Feed-Forward 3D Scene Editing in a Unified Latent Space](https://mlsanity.com/paper/2606.13345/) (2026)
- [GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments](https://mlsanity.com/paper/2606.17520/) (2026)
- [3D-GIMP: When 3D Gaussian Inpainting Meets PatchMatch](https://mlsanity.com/paper/2607.20789/) (2026)
- [VEIGAR: View-consistent Explicit Inpainting and Geometry Alignment for 3D object Removal](https://mlsanity.com/paper/2506.15821/) (2025)
- [MILD: Multi-Layer Diffusion Strategy for Complex and Precise Multi-IP Aware Human Erasing](https://mlsanity.com/paper/2508.06543/) (2025)

## Abstract (arXiv)

This paper tackles the intricate challenge of object removal to update the radiance field using the 3D Gaussian Splatting. The main challenges of this task lie in the preservation of geometric consistency and the maintenance of texture coherence in the presence of the substantial discrete nature of Gaussian primitives. We introduce a robust framework specifically designed to overcome these obstacles. The key insight of our approach is the enhancement of information exchange among visible and invisible areas, facilitating content restoration in terms of both geometry and texture. Our methodology begins with optimizing the positioning of Gaussian primitives to improve geometric consistency across both removed and visible areas, guided by an online registration process informed by monocular depth estimation. Following this, we employ a novel feature propagation mechanism to bolster texture coherence, leveraging a cross-attention design that bridges sampling Gaussians from both uncertain and certain areas. This innovative approach significantly refines the texture coherence within the final radiance field. Extensive experiments validate that our method not only elevates the quality of novel view synthesis for scenes undergoing object removal but also showcases notable efficiency gains in training and rendering speeds.