LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments

• URL: http://arxiv.org/abs/2601.02905v2
• Date: Mon, 12 Jan 2026 09:08:59 GMT
• Title: LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments
• Authors: Sara Micol Ferraina, Michele Brienza, Francesco Argenziano, Emanuele Musumeci, Vincenzo Suriani, Domenico D. Bloisi, Daniele Nardi,
• Abstract summary: LOST-3DSG is a lightweight open-vocabulary 3D scene graph designed to track dynamic objects in real-world environments.<n>Our method adopts a semantic approach to entity tracking based on word2vec and sentence embeddings.<n>We evaluate our method through qualitative and quantitative experiments conducted in a real 3D environment using a TIAGo robot.
• Score: 1.5391321019692432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tracking objects that move within dynamic environments is a core challenge in robotics. Recent research has advanced this topic significantly; however, many existing approaches remain inefficient due to their reliance on heavy foundation models. To address this limitation, we propose LOST-3DSG, a lightweight open-vocabulary 3D scene graph designed to track dynamic objects in real-world environments. Our method adopts a semantic approach to entity tracking based on word2vec and sentence embeddings, enabling an open-vocabulary representation while avoiding the necessity of storing dense CLIP visual features. As a result, LOST-3DSG achieves superior performance compared to approaches that rely on high-dimensional visual embeddings. We evaluate our method through qualitative and quantitative experiments conducted in a real 3D environment using a TIAGo robot. The results demonstrate the effectiveness and efficiency of LOST-3DSG in dynamic object tracking. Code and supplementary material are publicly available on the project website at https://lab-rococo-sapienza.github.io/lost-3dsg/.

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