Mastering Regional 3DGS: Locating, Initializing, and Editing with Diverse 2D Priors
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Abstract (arXiv)
Many 3D scene editing tasks focus on modifying local regions rather than the entire scene, except for some global applications like style transfer, and in the context of 3D Gaussian Splatting (3DGS), where scenes are represented by a series of Gaussians, this structure allows for precise regional edits, offering enhanced control over specific areas of the scene; however, the challenge lies in the fact that 3D semantic parsing often underperforms compared to its 2D counterpart, making targeted manipulations within 3D spaces more difficult and limiting the fidelity of edits, which we address by leveraging 2D diffusion editing to accurately identify modification regions in each view, followed by inverse rendering for 3D localization, then refining the frontal view and initializing a coarse 3DGS with consistent views and approximate shapes derived from depth maps predicted by a 2D foundation model, thereby supporting an iterative, view-consistent editing process that gradually enhances structural details and textures to ensure coherence across perspectives. Experiments demonstrate that our method achieves state-of-the-art performance while delivering up to a 4× speedup, providing a more efficient and effective approach to 3D scene local editing.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 52 of its 59 references that are in the map. First posted on arXiv 7 Jul 2025.
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References · 52
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2022Classifier-Free Diffusion Guidancecited by 5,847
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
- 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
- 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
38 more
- 2015Spatial Transformer Networkscited by 1,429
- 2024Depth Anything V2cited by 1,422
- 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
- 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,179
- 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
- 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
- 2023Zero-shot Image-to-Image Translationcited by 505
- 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 489
- 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 484
- 2023LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Modelscited by 349
- 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
- 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 335
- 2023FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attentioncited by 313
- 2023Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generationcited by 302
- 2023FSGS: Real-Time Few-shot View Synthesis using Gaussian Splattingcited by 279
- 2024A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasetscited by 216
- 2022Splicing ViT Features for Semantic Appearance Transfercited by 209
- 2023GaussianEditor: Editing 3D Gaussians Delicately with Text Instructionscited by 168
- 2022RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generationcited by 162
- 2023DreamEditor: Text-Driven 3D Scene Editing with Neural Fieldscited by 155
- 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
- 2024GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editingcited by 115
- 2024DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editingcited by 94
- 2024FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understandingcited by 86
- 2024TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Promptscited by 77
- 2024ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fieldscited by 68
- 2024View-Consistent 3D Editing with Gaussian Splattingcited by 67
- 2023NeRFiller: Completing Scenes via Generative 3D Inpaintingcited by 62
- 2023Learning Locally Editable Virtual Humanscited by 54
- 2024A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Modelscited by 52
- 2023Watch Your Steps: Local Image and Scene Editing by Text Instructionscited by 50
- 2022UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scenecited by 47
- 2024Deep Learning-based Image and Video Inpainting: A Surveycited by 44
- 2024EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splattingcited by 41
- 2025Training-Free Text-Guided Image Editing with Visual Autoregressive Modelcited by 21
- 2024StyleSplat: 3D Object Style Transfer with Gaussian Splattingcited by 18
- 2024Magic Insert: Style-Aware Drag-and-Dropcited by 17
- 2024Localized Gaussian Splatting Editing with Contextual Awarenesscited by 13
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