A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding
- URL: http://arxiv.org/abs/2508.05064v1
- Date: Thu, 07 Aug 2025 06:33:08 GMT
- Title: A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding
- Authors: Mahmoud Chick Zaouali, Todd Charter, Yehor Karpichev, Brandon Haworth, Homayoun Najjjaran,
- Abstract summary: Gaussian Splatting has emerged as a transformative technique for real-time 3D scene representation.<n>Its ability to render complex scenes with high fidelity has enabled progress across domains such as scene reconstruction, robotics, and interactive content creation.<n>This survey presents a structured review of current research efforts that combine language guidance with 3D Gaussian Splatting.
- Score: 0.48065284262360775
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Gaussian Splatting has rapidly emerged as a transformative technique for real-time 3D scene representation, offering a highly efficient and expressive alternative to Neural Radiance Fields (NeRF). Its ability to render complex scenes with high fidelity has enabled progress across domains such as scene reconstruction, robotics, and interactive content creation. More recently, the integration of Large Language Models (LLMs) and language embeddings into Gaussian Splatting pipelines has opened new possibilities for text-conditioned generation, editing, and semantic scene understanding. Despite these advances, a comprehensive overview of this emerging intersection has been lacking. This survey presents a structured review of current research efforts that combine language guidance with 3D Gaussian Splatting, detailing theoretical foundations, integration strategies, and real-world use cases. We highlight key limitations such as computational bottlenecks, generalizability, and the scarcity of semantically annotated 3D Gaussian data and outline open challenges and future directions for advancing language-guided 3D scene understanding using Gaussian Splatting.
Related papers
- OpenGS-Fusion: Open-Vocabulary Dense Mapping with Hybrid 3D Gaussian Splatting for Refined Object-Level Understanding [17.524454394142477]
We present OpenGS-Fusion, an innovative open-vocabulary dense mapping framework that improves semantic modeling and refines object-level understanding.<n>We also introduce a novel multimodal language-guided approach named MLLM-Assisted Adaptive Thresholding, which refines the segmentation of 3D objects by adaptively adjusting similarity thresholds.<n>Our method outperforms existing methods in 3D object understanding and scene reconstruction quality, as well as showcasing its effectiveness in language-guided scene interaction.
arXiv Detail & Related papers (2025-08-02T02:22:36Z) - LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion [21.828264119753502]
LangScene-X is a novel generative framework to unify and generate 3D consistent multi-modality information.<n>Powered by the generative capability of creating more consistent novel observations, we can build generalizable 3D language-embedded scenes from only sparse views.
arXiv Detail & Related papers (2025-07-03T17:21:23Z) - GaussianVLM: Scene-centric 3D Vision-Language Models using Language-aligned Gaussian Splats for Embodied Reasoning and Beyond [56.677984098204696]
multimodal language models are driving the development of 3D Vision-Language Models (VLMs)<n>We propose a scene-centric 3D VLM for 3D Gaussian splat scenes that employs language- and task-aware scene representations.<n>We present the first Gaussian splatting-based VLM, leveraging photorealistic 3D representations derived from standard RGB images.
arXiv Detail & Related papers (2025-07-01T15:52:59Z) - Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs [16.153129392697885]
We introduce a training-free framework that constructs a superpoint graph directly from Gaussian primitives.<n>The superpoint graph partitions the scene into spatially compact and semantically coherent regions, forming view-consistent 3D entities.<n>Our method achieves state-of-the-art open-vocabulary segmentation performance, with semantic field reconstruction completed over $30times$ faster.
arXiv Detail & Related papers (2025-04-17T17:56:07Z) - CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting [18.581169318975046]
3D Gaussian Splatting (3DGS) offers a powerful representation for scene reconstruction, but cross-view granularity inconsistency is a problem.<n>We propose Context-Aware Gaussian Splatting (CAGS), a novel framework that addresses this challenge by incorporating spatial context into 3DGS.<n>CAGS significantly improves 3D instance segmentation and reduces fragmentation errors on datasets like LERF-OVS and ScanNet.
arXiv Detail & Related papers (2025-04-16T09:20:03Z) - SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians [77.77265204740037]
3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering.<n>We introduce SuperGSeg, a novel approach that fosters cohesive, context-aware scene representation.<n>SuperGSeg outperforms prior works on both open-vocabulary object localization and semantic segmentation tasks.
arXiv Detail & Related papers (2024-12-13T16:01:19Z) - Occam's LGS: An Efficient Approach for Language Gaussian Splatting [57.00354758206751]
We show that the complicated pipelines for language 3D Gaussian Splatting are simply unnecessary.<n>We apply Occam's razor to the task at hand, leading to a highly efficient weighted multi-view feature aggregation technique.
arXiv Detail & Related papers (2024-12-02T18:50:37Z) - Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns.
A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
arXiv Detail & Related papers (2024-06-17T07:43:53Z) - DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling [23.06464506261766]
We present DreamScape, a method for generating 3D scenes from text.<n>We use 3D Gaussian Guide that encodes semantic primitives, spatial transformations and relationships from text using LLMs.<n>DreamScape achieves state-of-the-art performance, enabling high-fidelity, controllable 3D scene generation.
arXiv Detail & Related papers (2024-04-14T12:13:07Z) - HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting [53.6394928681237]
holistic understanding of urban scenes based on RGB images is a challenging yet important problem.
Our main idea involves the joint optimization of geometry, appearance, semantics, and motion using a combination of static and dynamic 3D Gaussians.
Our approach offers the ability to render new viewpoints in real-time, yielding 2D and 3D semantic information with high accuracy.
arXiv Detail & Related papers (2024-03-19T13:39:05Z) - A Survey on 3D Gaussian Splatting [51.96747208581275]
3D Gaussian splatting (GS) has emerged as a transformative technique in explicit radiance field and computer graphics.<n>We provide the first systematic overview of the recent developments and critical contributions in the domain of 3D GS.<n>By enabling unprecedented rendering speed, 3D GS opens up a plethora of applications, ranging from virtual reality to interactive media and beyond.
arXiv Detail & Related papers (2024-01-08T13:42:59Z) - Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding [2.517953665531978]
We introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary query tasks.
Our representation achieves the best visual quality and language querying accuracy across current language-embedded representations.
arXiv Detail & Related papers (2023-11-30T11:50:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.