# LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes

arXiv: https://arxiv.org/abs/2410.14462 · HTML: https://mlsanity.com/paper/2410.14462/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 59 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 30 of 44)

- [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)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction](https://mlsanity.com/paper/2109.00512/) (2021)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [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)
- [Decomposing NeRF for Editing via Feature Field Distillation](https://mlsanity.com/paper/2205.15585/) (2022)
- [Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields](https://mlsanity.com/paper/2312.03203/) (2023)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces](https://mlsanity.com/paper/2311.17977/) (2023)
- [Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding](https://mlsanity.com/paper/2311.18482/) (2023)
- [Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations](https://mlsanity.com/paper/2209.03494/) (2022)
- [Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images](https://mlsanity.com/paper/2311.13398/) (2023)
- [GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions](https://mlsanity.com/paper/2311.16037/) (2023)
- [Segment Any 3D Gaussians](https://mlsanity.com/paper/2312.00860/) (2023)
- [OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding](https://mlsanity.com/paper/2406.02058/) (2024)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [Weakly Supervised 3D Open-vocabulary Segmentation](https://mlsanity.com/paper/2305.14093/) (2023)
- [GARField: Group Anything with Radiance Fields](https://mlsanity.com/paper/2401.09419/) (2024)
- [FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization](https://mlsanity.com/paper/2403.06908/) (2024)
- [CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation](https://mlsanity.com/paper/2312.12359/) (2023)

## Built on by (most cited first, 30 of 37)

- [C3G: Learning Compact 3D Representations with 2K Gaussians](https://mlsanity.com/paper/2512.04021/) (2025)
- [PanSt3R: Multi-view Consistent Panoptic Segmentation](https://mlsanity.com/paper/2506.21348/) (2025)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting](https://mlsanity.com/paper/2506.08710/) (2025)
- [CF3: Compact and Fast 3D Feature Fields](https://mlsanity.com/paper/2508.05254/) (2025)
- [Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding](https://mlsanity.com/paper/2512.17817/) (2025)
- [ExtrinSplat: Decoupling Geometry and Semantics for Open-Vocabulary Understanding in 3D Gaussian Splatting](https://mlsanity.com/paper/2509.22225/) (2025)
- [ReLaGS: Relational Language Gaussian Splatting](https://mlsanity.com/paper/2603.17605/) (2026)
- [OpenVoxel: Training-Free Grouping and Captioning Voxels for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2601.09575/) (2026)
- [LightSplat: Fast and Memory-Efficient Open-Vocabulary 3D Scene Understanding in Five Seconds](https://mlsanity.com/paper/2603.24146/) (2026)
- [Hierarchical Scoring with 3D Gaussian Splatting for Instance Image-Goal Navigation](https://mlsanity.com/paper/2506.07338/) (2025)
- [SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting](https://mlsanity.com/paper/2507.23772/) (2025)
- [GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis](https://mlsanity.com/paper/2603.14965/) (2026)
- [ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting](https://mlsanity.com/paper/2607.18801/) (2026)
- [B3-Seg: Camera-Free, Training-Free 3DGS Segmentation via Analytic EIG and Beta-Bernoulli Bayesian Updates](https://mlsanity.com/paper/2602.17134/) (2026)
- [Benchmarking PhD-Level Coding in 3D Geometric Computer Vision](https://mlsanity.com/paper/2603.30038/) (2026)
- [Visually-grounded Humanoid Agents](https://mlsanity.com/paper/2604.08509/) (2026)
- [Sparse Code Uplifting for Efficient 3D Language Gaussian Splatting](https://mlsanity.com/paper/2605.13600/) (2026)
- [CDSeg: A Renderable Gaussian Carrier for Image-to-3D Label Transfer](https://mlsanity.com/paper/2608.05482/) (2026)
- [CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus](https://mlsanity.com/paper/2509.04859/) (2025)
- [Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation](https://mlsanity.com/paper/2602.06032/) (2026)
- [EmbodiedSplat: Online Feed-Forward Semantic 3DGS for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2603.04254/) (2026)
- [Scenes as Objects, Not Primitives: Instance-Structured 3D Tokenization from Unposed Views](https://mlsanity.com/paper/2606.29513/) (2026)
- [Relation-Centric Open-Vocabulary 3D Gaussian Segmentation](https://mlsanity.com/paper/2607.01140/) (2026)
- [CoSAG: Compact Semantic Anchor Gaussians via Training-Free Rate-Distortion Coding](https://mlsanity.com/paper/2607.10237/) (2026)
- [OutLangSplat: 3D Language Gaussian Splatting for UAV Outdoor Scenes](https://mlsanity.com/paper/2608.04560/) (2026)
- [HumanCrafter: Synergizing Generalizable Human Reconstruction and Semantic 3D Segmentation](https://mlsanity.com/paper/2511.00468/) (2025)
- [CUS-GS: A Compact Unified Structured Gaussian Splatting Framework for Multimodal Scene Representation](https://mlsanity.com/paper/2511.17904/) (2025)
- [NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting](https://mlsanity.com/paper/2604.22439/) (2026)
- [Disambiguating 2D-3D Correspondences in Gaussian Splatting-based Feature Fields for Visual Localization](https://mlsanity.com/paper/2605.07351/) (2026)

## Abstract (arXiv)

We address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D image features into Gaussian Splatting representations of 3D scenes. Unlike traditional approaches that rely on minimizing a reconstruction loss, our method employs a simpler and more efficient feature aggregation technique, augmented by a graph diffusion mechanism. Graph diffusion refines 3D features, such as coarse segmentation masks, by leveraging 3D geometry and pairwise similarities induced by DINOv2. Our approach achieves performance comparable to the state of the art on multiple downstream tasks while delivering significant speed-ups. Notably, we obtain competitive segmentation results using only generic DINOv2 features, despite DINOv2 not being trained on millions of annotated segmentation masks like SAM. When applied to CLIP features, our method demonstrates strong performance in open-vocabulary object segmentation tasks, highlighting the versatility of our approach.