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

2025-10-11

AI Summary

Existing 3D referring localization methods model only pairwise object relationships, failing to capture complex multi-object spatial relations commonly expressed in natural language—leading to insufficient cross-modal alignment. To address this, we propose an *N-ary relation-aware framework* that progressively learns scene relationships from binary to N-ary configurations, constructing a globally semantically enhanced multimodal scene graph. Our method integrates a hybrid attention mechanism with group-wise supervision loss to explicitly model joint spatial and semantic constraints among object groups. Evaluated on the ReferIt3D and ScanRefer benchmarks, our approach significantly outperforms state-of-the-art methods, demonstrating that explicit N-ary relational modeling is critical for improving comprehension of intricate spatial configurations and achieving precise 3D referring localization.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Localizing 3D objects using natural language descriptions
• Modeling n-ary spatial relationships for global scene understanding
• Addressing absence of specific annotations for referred objects

Innovation

• Progressive relational learning from binary to n-ary
• Grouped supervision loss for n-ary relational learning
• Multi-modal network with hybrid attention mechanisms