B2N3D: Progressive Learning from Binary to N-ary Relationships for 3D Object Grounding

• URL: http://arxiv.org/abs/2510.10194v1
• Date: Sat, 11 Oct 2025 12:17:12 GMT
• Title: B2N3D: Progressive Learning from Binary to N-ary Relationships for 3D Object Grounding
• Authors: Feng Xiao, Hongbin Xu, Hai Ci, Wenxiong Kang,
• Abstract summary: We propose a novel progressive relational learning framework for 3D object grounding.<n>We extend relational learning from binary to n-ary to identify visual relations that match the referential description globally.<n>In the scene graph created with n-ary relationships, we use a multi-modal network with hybrid attention mechanisms to further localize the target.
• Score: 32.814585321361974
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Localizing 3D objects using natural language is essential for robotic scene understanding. The descriptions often involve multiple spatial relationships to distinguish similar objects, making 3D-language alignment difficult. Current methods only model relationships for pairwise objects, ignoring the global perceptual significance of n-ary combinations in multi-modal relational understanding. To address this, we propose a novel progressive relational learning framework for 3D object grounding. We extend relational learning from binary to n-ary to identify visual relations that match the referential description globally. Given the absence of specific annotations for referred objects in the training data, we design a grouped supervision loss to facilitate n-ary relational learning. In the scene graph created with n-ary relationships, we use a multi-modal network with hybrid attention mechanisms to further localize the target within the n-ary combinations. Experiments and ablation studies on the ReferIt3D and ScanRefer benchmarks demonstrate that our method outperforms the state-of-the-art, and proves the advantages of the n-ary relational perception in 3D localization.

Related papers

• Descrip3D: Enhancing Large Language Model-based 3D Scene Understanding with Object-Level Text Descriptions [28.185661905201222]
  Descrip3D is a novel framework that explicitly encodes the relationships between objects using natural language.<n>It allows for unified reasoning across various tasks such as grounding, captioning, and question answering.
  arXiv  Detail & Related papers  (2025-07-19T09:19:16Z)
• LSVG: Language-Guided Scene Graphs with 2D-Assisted Multi-Modal Encoding for 3D Visual Grounding [15.944945244005952]
  3D visual grounding aims to localize the unique target described by natural languages in 3D scenes.<n>We propose a novel 3D visual grounding framework that constructs language-guided scene graphs with referred object discrimination.
  arXiv  Detail & Related papers  (2025-05-07T02:02:15Z)
• IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments [56.85804719947]
  We present IAAO, a framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction.<n>We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images.<n>We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances.
  arXiv  Detail & Related papers  (2025-04-09T12:36:48Z)
• AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring [49.78120051062641]
  3D visual grounding aims to correlate a natural language description with the target object within a 3D scene.<n>Existing approaches commonly encounter a shortage of text3D pairs available for training.<n>We propose AugRefer, a novel approach for advancing 3D visual grounding.
  arXiv  Detail & Related papers  (2025-01-16T09:57:40Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.<n>The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention [19.23636231942245]
  We propose a semantic-enhanced relational learning model based on a graph network with our designed memory graph attention layer. Our method replaces original language-independent encoding with cross-modal encoding in visual analysis. Experimental results on ReferIt3D and ScanRefer benchmarks show that the proposed method outperforms the existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-13T02:11:04Z)
• 3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding [58.924180772480504]
  3D visual grounding aims to localize the target object in a 3D point cloud by a free-form language description. We propose a relation-aware one-stage framework, named 3D Relative Position-aware Network (3-Net)
  arXiv  Detail & Related papers  (2023-07-25T09:33:25Z)
• Language Conditioned Spatial Relation Reasoning for 3D Object Grounding [87.03299519917019]
  Localizing objects in 3D scenes based on natural language requires understanding and reasoning about spatial relations. We propose a language-conditioned transformer model for grounding 3D objects and their spatial relations.
  arXiv  Detail & Related papers  (2022-11-17T16:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.