# 3D Vision-Language Gaussian Splatting

arXiv: https://arxiv.org/abs/2410.07577 · HTML: https://mlsanity.com/paper/2410.07577/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 29 of its 33 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [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)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [Scaling Open-Vocabulary Image Segmentation with Image-Level Labels](https://mlsanity.com/paper/2112.12143/) (2021)
- [LangSplat: 3D Language Gaussian Splatting](https://mlsanity.com/paper/2312.16084/) (2023)
- [Gaussian Grouping: Segment and Edit Anything in 3D Scenes](https://mlsanity.com/paper/2312.00732/) (2023)
- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields](https://mlsanity.com/paper/2312.03203/) (2023)
- [Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding](https://mlsanity.com/paper/2311.18482/) (2023)
- [Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images](https://mlsanity.com/paper/2311.13398/) (2023)
- [HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting](https://mlsanity.com/paper/2403.12722/) (2024)
- [Weakly Supervised 3D Open-vocabulary Segmentation](https://mlsanity.com/paper/2305.14093/) (2023)
- [FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding](https://mlsanity.com/paper/2401.01970/) (2024)
- [OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views](https://mlsanity.com/paper/2404.03650/) (2024)
- [Aligning and Prompting Everything All at Once for Universal Visual Perception](https://mlsanity.com/paper/2312.02153/) (2023)
- [GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane](https://mlsanity.com/paper/2405.17596/) (2024)
- [Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting](https://mlsanity.com/paper/2405.04378/) (2024)
- [Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot](https://mlsanity.com/paper/2409.18108/) (2024)

## Built on by (most cited first, 30 of 31)

- [SplatTalk: 3D VQA with Gaussian Splatting](https://mlsanity.com/paper/2503.06271/) (2025)
- [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)
- [Occam's LGS: An Efficient Approach for Language Gaussian Splatting](https://mlsanity.com/paper/2412.01807/) (2024)
- [Tackling View-Dependent Semantics in 3D Language Gaussian Splatting](https://mlsanity.com/paper/2505.24746/) (2025)
- [CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting](https://mlsanity.com/paper/2505.20469/) (2025)
- [Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding](https://mlsanity.com/paper/2512.17817/) (2025)
- [Pointmap Association and Piecewise-Plane Constraint for Consistent and Compact 3D Gaussian Segmentation Field](https://mlsanity.com/paper/2502.16303/) (2025)
- [Splat Feature Solver](https://mlsanity.com/paper/2508.12216/) (2025)
- [Consistent Instance Field for Dynamic Scene Understanding](https://mlsanity.com/paper/2512.14126/) (2025)
- [OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2601.09575/) (2026)
- [Universal Beta Splatting](https://mlsanity.com/paper/2510.03312/) (2025)
- [3AM: 3egment Anything with Geometric Consistency in Videos](https://mlsanity.com/paper/2601.08831/) (2026)
- [SparseGS-W: Sparse-View 3D Gaussian Splatting in the Wild with Generative Priors](https://mlsanity.com/paper/2503.19452/) (2025)
- [MMOne: Representing Multiple Modalities in One Scene](https://mlsanity.com/paper/2507.11129/) (2025)
- [ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting](https://mlsanity.com/paper/2607.18801/) (2026)
- [Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding](https://mlsanity.com/paper/2601.02339/) (2026)
- [DualSplat: Robust 3D Gaussian Splatting via Pseudo-Mask Bootstrapping from Reconstruction Failures](https://mlsanity.com/paper/2604.21631/) (2026)
- [Relation-Centric Open-Vocabulary 3D Gaussian Segmentation](https://mlsanity.com/paper/2607.01140/) (2026)
- [Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation](https://mlsanity.com/paper/2412.20538/) (2024)
- [CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting](https://mlsanity.com/paper/2601.12814/) (2026)
- [Language and Geometry Grounded Sparse Voxel Representations for Holistic Scene Understanding](https://mlsanity.com/paper/2602.15734/) (2026)
- [No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency](https://mlsanity.com/paper/2602.23559/) (2026)
- [NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation](https://mlsanity.com/paper/2604.14706/) (2026)
- [AnythingReality: Robust Online Gaussian Splatting SLAM for Open-Vocabulary VR Scene Exploration](https://mlsanity.com/paper/2607.09260/) (2026)
- [E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding](https://mlsanity.com/paper/2607.15536/) (2026)
- [LEGO: Leveled Language Gaussian Splatting](https://mlsanity.com/paper/2608.10057/) (2026)
- [CausalSplat: Towards Comprehensive Hierarchical Reasoning in 3D Gaussian Splatting](https://mlsanity.com/paper/2608.11150/) (2026)
- [InterHier: Learning Interconnected Hierarchical Semantics for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2609.24026/) (2026)
- [EviSplat: Preserving Multi-View Evidence in 3D Gaussian Splatting for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2609.34853/) (2026)

## Abstract (arXiv)

Recent advancements in 3D reconstruction methods and vision-language models have propelled the development of multi-modal 3D scene understanding, which has vital applications in robotics, autonomous driving, and virtual/augmented reality. However, current multi-modal scene understanding approaches have naively embedded semantic representations into 3D reconstruction methods without striking a balance between visual and language modalities, which leads to unsatisfying semantic rasterization of translucent or reflective objects, as well as over-fitting on color modality. To alleviate these limitations, we propose a solution that adequately handles the distinct visual and semantic modalities, i.e., a 3D vision-language Gaussian splatting model for scene understanding, to put emphasis on the representation learning of language modality. We propose a novel cross-modal rasterizer, using modality fusion along with a smoothed semantic indicator for enhancing semantic rasterization. We also employ a camera-view blending technique to improve semantic consistency between existing and synthesized views, thereby effectively mitigating over-fitting. Extensive experiments demonstrate that our method achieves state-of-the-art performance in open-vocabulary semantic segmentation, surpassing existing methods by a significant margin.