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arXiv · 2510.10194

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

Feng Xiao·Hongbin Xu·Hai Ci·Wenxiong Kang
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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.

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Feng Xiao, Hongbin Xu, Hai Ci, Wenxiong Kang. 2025-12-01. B2N3D: Progressive Learning from Binary to N-ary Relationships for 3D Object Grounding. https://arxiv.org/abs/2510.10194

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