Text-Scene: A Scene-to-Language Parsing Framework for 3D Scene Understanding

• URL: http://arxiv.org/abs/2509.16721v1
• Date: Sat, 20 Sep 2025 15:10:45 GMT
• Title: Text-Scene: A Scene-to-Language Parsing Framework for 3D Scene Understanding
• Authors: Haoyuan Li, Rui Liu, Hehe Fan, Yi Yang,
• Abstract summary: Text-Scene is a framework that automatically parses 3D scenes into textual descriptions for scene understanding.<n>By leveraging both geometric analysis and MLLMs, Text-Scene produces descriptions that are accurate, detailed, and human-interpretable.
• Score: 38.97818584066075
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Enabling agents to understand and interact with complex 3D scenes is a fundamental challenge for embodied artificial intelligence systems. While Multimodal Large Language Models (MLLMs) have achieved significant progress in 2D image understanding, extending such capabilities to 3D scenes remains difficult: 1) 3D environment involves richer concepts such as spatial relationships, affordances, physics, layout, and so on, 2) the absence of large-scale 3D vision-language datasets has posed a significant obstacle. In this paper, we introduce Text-Scene, a framework that automatically parses 3D scenes into textual descriptions for scene understanding. Given a 3D scene, our model identifies object attributes and spatial relationships, and then generates a coherent summary of the whole scene, bridging the gap between 3D observation and language without requiring human-in-the-loop intervention. By leveraging both geometric analysis and MLLMs, Text-Scene produces descriptions that are accurate, detailed, and human-interpretable, capturing object-level details and global-level context. Experimental results on benchmarks demonstrate that our textual parses can faithfully represent 3D scenes and benefit downstream tasks. To evaluate the reasoning capability of MLLMs, we present InPlan3D, a comprehensive benchmark for 3D task planning, consisting of 3174 long-term planning tasks across 636 indoor scenes. We emphasize clarity and accessibility in our approach, aiming to make 3D scene content understandable through language. Code and datasets will be released.

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