mlsanityData to 7 Oct 2026

3DSceneEditor: Controllable 3D Scene Editing with Gaussian Splatting

arXiv 2412.01583 ↗First posted 2 Dec 2024cs.CVcited by 20

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Abstract (arXiv)

The creation of 3D scenes has traditionally been both labor-intensive and costly, requiring designers to meticulously configure 3D assets and environments. Recent advancements in generative AI, including text-to-3D and image-to-3D methods, have dramatically reduced the complexity and cost of this process. However, current techniques for editing complex 3D scenes continue to rely on generally interactive multi-step, 2D-to-3D projection methods and diffusion-based techniques, which often lack precision in control and hamper interactive-rate performance. In this work, we propose ***3DSceneEditor***, a fully 3D-based paradigm for interactive-rate, precise editing of intricate 3D scenes using Gaussian Splatting. Unlike conventional methods, 3DSceneEditor operates through a streamlined 3D pipeline, enabling direct Gaussian-based manipulation for efficient, high-quality edits based on input prompts. The proposed framework (i) integrates a pre-trained instance segmentation model for semantic labeling; (ii) employs a zero-shot grounding approach with CLIP to align target objects with user prompts; and (iii) applies scene modifications, such as object addition, repositioning, recoloring, replacing, and removal--directly on Gaussians. Extensive experimental results show that 3DSceneEditor surpasses existing state-of-the-art techniques in terms of both editing precision and efficiency, establishing a new benchmark for efficient and interactive 3D scene customization.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 79 of its 99 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

79 of 99 references.

References · 79

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38 more
  1. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
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  16. 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 482
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 12.

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  1. 2026Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentationcited by 23
  2. 2025A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generationcited by 19
  3. 2025In-Context Meta LoRA Generationcited by 14
  4. 2025Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenescited by 8
  5. 2025EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selectioncited by 5
  6. 2026VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Imagecited by 4
  7. 2025GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Expertscited by 3
  8. 20253D Scene Change Modeling With Consistent Multi-View Aggregationcited by 3
  9. 2026Variation-aware Flexible 3D Gaussian Editingcited by 2
  10. 2026TRACE: High-Fidelity 3D Scene Editing via Tangible Reconstruction and Geometry-Aligned Contextual Video Maskingcited by 2
  11. 2026Self-Evolving Deep Research via Joint Generation and Evaluationcited by 2
  12. 2025GP-GS: Gaussian Processes Densification for 3D Gaussian Splattingcited by 1
8 more
  1. 2026Sparse Code Uplifting for Efficient 3D Language Gaussian Splattingcited by 1
  2. 2026Physics-Aware 3D Gaussian Editing for Driving Scene Generationcited by 1
  3. 2026EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learningcited by 1
  4. 2025Error-Driven Scene Editing for 3D Grounding in Large Language Modelscited by 0
  5. 2026AdvSplat: Adversarial Attacks on Feed-Forward Gaussian Splatting Modelscited by 0
  6. 2026Improving Multimodal Reasoning via Worst Dimension Optimizationcited by 0
  7. 2026AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splattingcited by 0
  8. 2026Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstructioncited by 0