# FastLGS: Speeding up Language Embedded Gaussians with Feature Grid Mapping

arXiv: https://arxiv.org/abs/2406.01916 · HTML: https://mlsanity.com/paper/2406.01916/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://mlsanity.com/paper/2210.04150/) (2022)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [Gaussian Grouping: Segment and Edit Anything in 3D Scenes](https://mlsanity.com/paper/2312.00732/) (2023)
- [Decomposing NeRF for Editing via Feature Field Distillation](https://mlsanity.com/paper/2205.15585/) (2022)
- [Fast Dynamic Radiance Fields with Time-Aware Neural Voxels](https://mlsanity.com/paper/2205.15285/) (2022)
- [Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations](https://mlsanity.com/paper/2209.03494/) (2022)
- [Panoptic Lifting for 3D Scene Understanding with Neural Fields](https://mlsanity.com/paper/2212.09802/) (2022)
- [Weakly Supervised 3D Open-vocabulary Segmentation](https://mlsanity.com/paper/2305.14093/) (2023)
- [Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation](https://mlsanity.com/paper/2008.01550/) (2020)
- [Recurrent Slice Networks for 3D Segmentation of Point Clouds](https://mlsanity.com/paper/1802.04402/) (2018)
- [EgoLifter: Open-world 3D Segmentation for Egocentric Perception](https://mlsanity.com/paper/2403.18118/) (2024)

## Built on by (most cited first, 30 of 34)

- [DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input](https://mlsanity.com/paper/2409.12753/) (2024)
- [Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration](https://mlsanity.com/paper/2502.16652/) (2025)
- [InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception](https://mlsanity.com/paper/2411.19235/) (2024)
- [ReferSplat: Referring Segmentation in 3D Gaussian Splatting](https://mlsanity.com/paper/2508.08252/) (2025)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [SLGaussian: Fast Language Gaussian Splatting in Sparse Views](https://mlsanity.com/paper/2412.08331/) (2024)
- [GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting](https://mlsanity.com/paper/2412.13654/) (2024)
- [SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment](https://mlsanity.com/paper/2507.02705/) (2025)
- [GSemSplat: Generalizable Semantic 3D Gaussian Splatting from Uncalibrated Image Pairs](https://mlsanity.com/paper/2412.16932/) (2024)
- [UniForward: Unified 3D Scene and Semantic Field Reconstruction via Feed-Forward Gaussian Splatting from Only Sparse-View Images](https://mlsanity.com/paper/2506.09378/) (2025)
- [NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks](https://mlsanity.com/paper/2508.02046/) (2025)
- [COS3D: Collaborative Open-Vocabulary 3D Segmentation](https://mlsanity.com/paper/2510.20238/) (2025)
- [OpenGS-Fusion: Open-Vocabulary Dense Mapping with Hybrid 3D Gaussian Splatting for Refined Object-Level Understanding](https://mlsanity.com/paper/2508.01150/) (2025)
- [OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2601.09575/) (2026)
- [SparseLGS: Sparse View Language Embedded Gaussian Splatting](https://mlsanity.com/paper/2412.02245/) (2024)
- [Semantic Consistent Language Gaussian Splatting for Point-Level Open-vocabulary Querying](https://mlsanity.com/paper/2503.21767/) (2025)
- [Hi-LSplat: Hierarchical 3D Language Gaussian Splatting](https://mlsanity.com/paper/2506.06822/) (2025)
- [FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views via Compact Semantic Representation](https://mlsanity.com/paper/2512.17541/) (2025)
- [econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic Gaussians](https://mlsanity.com/paper/2504.06003/) (2025)
- [FMLGS: Fast Multilevel Language Embedded Gaussians for Part-level Interactive Agents](https://mlsanity.com/paper/2504.08581/) (2025)
- [EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation](https://mlsanity.com/paper/2606.08980/) (2026)
- [B3-Seg: Camera-Free, Training-Free 3DGS Segmentation via Analytic EIG and Beta-Bernoulli Bayesian Updates](https://mlsanity.com/paper/2602.17134/) (2026)
- [OnlineX: Unified Online 3D Reconstruction and Understanding with Active-to-Stable State Evolution](https://mlsanity.com/paper/2603.02134/) (2026)
- [Taking Language Embedded 3D Gaussian Splatting into the Wild](https://mlsanity.com/paper/2507.19830/) (2025)
- [Lang3D-XL: Language Embedded 3D Gaussians for Large-scale Scenes](https://mlsanity.com/paper/2512.07807/) (2025)
- [GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments](https://mlsanity.com/paper/2606.17520/) (2026)
- [A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding](https://mlsanity.com/paper/2508.05064/) (2025)
- [World2Minecraft: Occupancy-Driven Simulated Scenes Construction](https://mlsanity.com/paper/2604.27578/) (2026)
- [Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting](https://mlsanity.com/paper/2605.04506/) (2026)
- [GaussianSelector: Lightweight Human-Guided Object Selection in 3D Gaussian Splatting with Graph Optimization](https://mlsanity.com/paper/2608.01492/) (2026)

## Abstract (arXiv)

The semantically interactive radiance field has always been an appealing task for its potential to facilitate user-friendly and automated real-world 3D scene understanding applications. However, it is a challenging task to achieve high quality, efficiency and zero-shot ability at the same time with semantics in radiance fields. In this work, we present FastLGS, an approach that supports real-time open-vocabulary query within 3D Gaussian Splatting (3DGS) under high resolution. We propose the semantic feature grid to save multi-view CLIP features which are extracted based on Segment Anything Model (SAM) masks, and map the grids to low dimensional features for semantic field training through 3DGS. Once trained, we can restore pixel-aligned CLIP embeddings through feature grids from rendered features for open-vocabulary queries. Comparisons with other state-of-the-art methods prove that FastLGS can achieve the first place performance concerning both speed and accuracy, where FastLGS is 98x faster than LERF and 4x faster than LangSplat. Meanwhile, experiments show that FastLGS is adaptive and compatible with many downstream tasks, such as 3D segmentation and 3D object inpainting, which can be easily applied to other 3D manipulation systems.