3D visual grounding aims to localize the unique target described by natural languages in 3D scenes. The significant gap between 3D and language modalities makes it a notable challenge to distinguish multiple similar objects through the described spatial relationships. Current methods attempt to achieve cross-modal understanding in complex scenes via a target-centered learning mechanism, ignoring the perception of referred objects. We propose a novel 2D-assisted 3D visual grounding framework that constructs semantic-spatial scene graphs with referred object discrimination for relationship perception. The framework incorporates a dual-branch visual encoder that utilizes 2D pre-trained attributes to guide the multi-modal object encoding. Furthermore, our cross-modal interaction module uses graph attention to facilitate relationship-oriented information fusion. The enhanced object representation and iterative relational learning enable the model to establish effective alignment between 3D vision and referential descriptions. Experimental results on the popular benchmarks demonstrate our superior performance compared to state-of-the-art methods, especially in addressing the challenges of multiple similar distractors.
View on arXiv@article{xiao2025_2505.04058,
title={ AS3D: 2D-Assisted Cross-Modal Understanding with Semantic-Spatial Scene Graphs for 3D Visual Grounding },
author={ Feng Xiao and Hongbin Xu and Guocan Zhao and Wenxiong Kang },
journal={arXiv preprint arXiv:2505.04058},
year={ 2025 }
}