Real-Time 3D Scene Understanding with Vision-Language Models

Abstract

Real-time 3D scene understanding is a cornerstone for applications like robotics, augmented reality, and autonomous navigation. Traditional methods focus on geometric reconstruction from LiDAR or RGB-D sensors but often lack semantic context. The emergence of vision-language models (VLMs) offers a promising direction to imbue 3D understanding with rich semantic reasoning. This paper explores how multi-modal models that combine vision and language can enhance real-time scene comprehension by integrating semantic labeling, spatial reasoning, and efficient inference. We review recent advancements in neural rendering—particularly Neural Radiance Fields (NeRF) and its real-time variants such as PlenOctrees and SNeRG—that enable fast capture and rendering of 3D scenes from 2D images. Simultaneously, we examine the evolution of vision-language alignment techniques (e.g., CLIP) and their adaptations for 3D understanding, such as semantic labeling of point clouds or volumetric data. Together, these technologies pave the way for scene parsing that is both spatially accurate and semantically meaningful. Our methodology section proposes a hybrid system combining real-time NeRF extensions (e.g., PlenOctrees) with semantic embedding derived from VLMs to achieve real-time, language-aware 3D scene understanding. We detail experimental setups using standard benchmarks, measuring metrics such as rendering speed, semantic classification accuracy, and latency. Results suggest that VLM-augmented 3D pipelines can achieve near real-time performance (interactive rates) while delivering semantic understanding, outperforming purely geometric approaches in conveying context. We also discuss challenges such as heavy compute requirements, limited 3D-language aligned datasets, and the semantic gaps between visual representations and linguistic descriptions. In conclusion, fusing efficient 3D reconstruction techniques with vision-language models offers an effective route to real-time, context-aware scene understanding. Future work should focus on lightweight backbone models, improved dataset generation, and cross-modal pretraining.