COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for 3D Retrieval
<|reference_start|>COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for 3D Retrieval: In this paper, we investigate an open research task of cross-modal retrieval between 3D shapes and textual descriptions. Previous approaches mainly rely on point cloud encoders for feature extraction, which may ignore key inherent features of 3D shapes, including depth, spatial hierarchy, geometric continuity, etc. To address this issue, we propose COM3D, making the first attempt to exploit the cross-view correspondence and cross-modal mining to enhance the retrieval performance. Notably, we augment the 3D features through a scene representation transformer, to generate cross-view correspondence features of 3D shapes, which enrich the inherent features and enhance their compatibility with text matching. Furthermore, we propose to optimize the cross-modal matching process based on the semi-hard negative example mining method, in an attempt to improve the learning efficiency. Extensive quantitative and qualitative experiments demonstrate the superiority of our proposed COM3D, achieving state-of-the-art results on the Text2Shape dataset.<|reference_end|>
@article{wu2024com3d:,
    title={COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for
  3D Retrieval},
    author={Hao Wu, Ruochong LI, Hao Wang, Hui Xiong},
    journal={arXiv preprint arXiv:2405.04103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04103},
    primaryClass={cs.CV}
}