# SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition

arXiv: https://arxiv.org/abs/2401.17857 · HTML: https://mlsanity.com/paper/2401.17857/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 88 of its 121 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 30 of 88)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [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)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)

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

- [Recent Advances in 3D Gaussian Splatting](https://mlsanity.com/paper/2403.11134/) (2024)
- [PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond](https://mlsanity.com/paper/2504.11451/) (2025)
- [DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds](https://mlsanity.com/paper/2503.18402/) (2025)
- [NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting](https://mlsanity.com/paper/2507.12621/) (2025)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [Trace3D: Consistent Segmentation Lifting via Gaussian Instance Tracing](https://mlsanity.com/paper/2508.03227/) (2025)
- [TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting](https://mlsanity.com/paper/2507.13586/) (2025)
- [Digital Twin Generation from Visual Data: A Survey](https://mlsanity.com/paper/2504.13159/) (2025)
- [Zero-Shot Visual Grounding in 3D Gaussians via View Retrieval](https://mlsanity.com/paper/2509.15871/) (2025)
- [Gradient-Weighted Feature Back-Projection: A Fast Alternative to Feature Distillation in 3D Gaussian Splatting](https://mlsanity.com/paper/2411.15193/) (2024)
- [DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting](https://mlsanity.com/paper/2412.10972/) (2024)
- [GS-Checker: Tampering Localization for 3D Gaussian Splatting](https://mlsanity.com/paper/2511.20354/) (2025)
- [3D Audio-Visual Segmentation](https://mlsanity.com/paper/2411.02236/) (2024)
- [GaussianTrimmer: Online Trimming Boundaries for 3DGS Segmentation](https://mlsanity.com/paper/2601.12683/) (2026)
- [SceneX: Procedural Controllable Large-scale Scene Generation](https://mlsanity.com/paper/2403.15698/) (2024)
- [CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus](https://mlsanity.com/paper/2509.04859/) (2025)
- [Online Segment 3D Gaussians via Launching Virtual Drones](https://mlsanity.com/paper/2607.01628/) (2026)
- [CrashSplat: 2D to 3D Vehicle Damage Segmentation in Gaussian Splatting](https://mlsanity.com/paper/2509.23947/) (2025)
- [Direct Object-Level Reconstruction via Probabilistic Gaussian Splatting](https://mlsanity.com/paper/2603.14316/) (2026)
- [Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation](https://mlsanity.com/paper/2604.05070/) (2026)
- [NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation](https://mlsanity.com/paper/2604.14706/) (2026)
- [BEA-GS: BEyond RAdiance Supervision in 3DGS for Precise Object Extraction](https://mlsanity.com/paper/2605.09662/) (2026)
- [Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting](https://mlsanity.com/paper/2605.16065/) (2026)
- [Consistent Scene Understanding in 3D Gaussian Splatting via Multi-Cue Mask Refinement](https://mlsanity.com/paper/2607.01708/) (2026)
- [GaussianSelector: Lightweight Human-Guided Object Selection in 3D Gaussian Splatting with Graph Optimization](https://mlsanity.com/paper/2608.01492/) (2026)
- [LEGO: Leveled Language Gaussian Splatting](https://mlsanity.com/paper/2608.10057/) (2026)
- [ObjectSplat: Improving Mesh Fidelity and Interactivity for 3D Scenes via Object-Level Mesh Splatting](https://mlsanity.com/paper/2608.30423/) (2026)
- [VCAR: Training-Free 3DGS Segmentation via View Completeness and Axis-Aware Boundary Refinement](https://mlsanity.com/paper/2608.30870/) (2026)
- [MEGA: Object-Level Mesh Extraction from 3D Gaussian Splatting via Spatial Visual Distillation](https://mlsanity.com/paper/2610.01707/) (2026)
- [PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2610.17936/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis, benefiting from its high-quality rendering results and real-time rendering speed. However, the 3D Gaussians learned by 3D-GS have ambiguous structures without any geometry constraints. This inherent issue in 3D-GS leads to a rough boundary when segmenting individual objects. To remedy these problems, we propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS to improve segmentation accuracy while preserving segmentation speed. Specifically, we introduce a Gaussian Decomposition scheme, which ingeniously utilizes the special structure of 3D Gaussian, finds out, and then decomposes the boundary Gaussians. Moreover, to achieve fast interactive 3D segmentation, we introduce a novel training-free pipeline by lifting a 2D foundation model to 3D-GS. Extensive experiments demonstrate that our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.