mlsanityData to 7 Oct 2026

Explore Contextual Information for 3D Scene Graph Generation

arXiv 2210.06240 ↗First posted 12 Oct 2022cs.CVcited by 4

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Abstract (arXiv)

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 54 of its 71 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

54 of 71 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.

  1. 2023Frame-Event Alignment and Fusion Network for High Frame Rate Trackingcited by 43
  2. 2023SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstructioncited by 20
  3. 2023Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevancecited by 10
  4. 2025FROSS: Faster-than-Real-Time Online 3D Semantic Scene Graph Generation from RGB-D Imagescited by 10