{
  "annotation_id": "a97c2c6e-8e3f-473c-be32-8260c8ae363b",
  "date_created": "2026-02-17T05:53:24.386000Z",
  "date_modified": "2026-02-17T05:53:24.386000Z",
  "file_hash": "4c092f26dfb67da15d442b6c6f2f776dfe7c53f499b174b50e2a851b05b1cea7",
  "private": false,
  "record": {
    "abstract": "Open-vocabulary 3D Scene Graph (3DSG) generation can enhance various downstream tasks in robotics, such as manipulation and navigation, by leveraging structured semantic representations. A 3DSG is constructed from multiple images of a scene, where objects are represented as nodes and relationships as edges. However, existing works for open-vocabulary 3DSG generation suffer from both low object-level recognition accuracy and speed, mainly due to constrained viewpoints, occlusions, and redundant surface density. To address these challenges, we propose RAG-3DSG to mitigate aggregation noise through re-shot guided uncertainty estimation and support object-level Retrieval-Augmented Generation (RAG) via reliable low-uncertainty objects. Furthermore, we propose a dynamic downsample-mapping strategy to accelerate cross-image object aggregation with adaptive granularity. Experiments on Replica dataset demonstrate that RAG-3DSG significantly improves node captioning accuracy in 3DSG generation while reducing the mapping time by two-thirds compared to the vanilla version.",
    "arxiv_id": "2601.10168",
    "authors": [
      "Yue Chang",
      "Rufeng Chen",
      "Zhaofan Zhang",
      "Yi Chen",
      "Sihong Xie"
    ],
    "categories": [
      "cs.CV",
      "cs.AI",
      "cs.RO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "RAG-3DSG: Enhancing 3D Scene Graphs with Re-Shot Guided Retrieval-Augmented Generation",
    "url": "https://arxiv.org/abs/2601.10168",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "bbfe54b3-0d15-47c8-ae75-858dbaf9f0b5",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}