DSG: Dynamic 3D Scene Graph Construction for Embodied Agents in Changing Indoor Environments

2026-08-31

AI Summary

本文针对室内环境物体位置变化导致场景图不一致的问题，提出DSG框架，通过3D高斯场景表示和双视角渲染检测物体变化，并结合多粒度视觉上下文进行空间关系推理。

Abstract

In indoor environments, object positions frequently change due to human activities or embodied-agent interactions, causing previously constructed scene graphs to become inconsistent with the current scene. To address this issue, we propose DSG, a dynamic 3D scene graph construction framework that detects object changes and performs spatial relationship reasoning. First, we construct a semantic-aware 3D Gaussian scene representation and develop a dual-view rendering-based object change detection method to enable reliable scene graph node updates. Second, we propose a spatial relationship reasoning method that incorporates multi-granularity visual context, enabling a large language model to identify a richer set of interobject spatial relationships. Furthermore, we introduce DynTHOR, a dynamic indoor scene graph benchmark built on the AI2-THOR simulation platform for evaluating scene graph construction in dynamic environments. Extensive experiments on Dyn-THOR, 3RScan, and real-world scenes demonstrate that DSG consistently outperforms existing methods in both object node construction and spatial relationship reasoning, significantly improving the accuracy of dynamic scene graph construction.

Problem

• Dynamic 3D Scene Graph
• Indoor Environments
• Object Changes
• Spatial Relationship Reasoning

Innovation

• Dynamic 3D Scene Graph
• Object Change Detection
• Spatial Relationship Reasoning
• Dual-View Rendering
• Multi-Granularity Visual Context