Cross-Modal 3D Shape Retrieval via Heterogeneous Dynamic Graph Representation

Cross-modal 3D shape retrieval is a crucial and widely applied task in the field of 3D vision. Its goal is to construct retrieval representations capable of measuring the similarity between instances of different 3D modalities. However, existing methods face challenges due to the performance bottlenecks of single-modal representation extractors and the modality gap across 3D modalities. To tackle these issues, we propose a Heterogeneous Dynamic Graph Representation (HDGR) network, which incorporates context-dependent dynamic relations within a heterogeneous framework. By capturing correlations among diverse 3D objects, HDGR overcomes the limitations of ambiguous representations obtained solely from instances. Within the context of varying mini-batches, dynamic graphs are constructed to capture proximal intra-modal relations, and dynamic bipartite graphs represent implicit cross-modal relations, effectively addressing the two challenges above. Subsequently, message passing and aggregation are performed using Dynamic Graph Convolution (DGConv) and Dynamic Bipartite Graph Convolution (DBConv), enhancing features through heterogeneous dynamic relation learning. Finally, intra-modal, cross-modal, and self-transformed features are redistributed and integrated into a heterogeneous dynamic representation for cross-modal 3D shape retrieval. HDGR establishes a stable, context-enhanced, structure-aware 3D shape representation by capturing heterogeneous inter-object relationships and adapting to varying contextual dynamics. Extensive experiments conducted on the ModelNet10, ModelNet40, and real-world ABO datasets demonstrate the state-of-the-art performance of HDGR in cross-modal and intra-modal retrieval tasks. Moreover, under the supervision of robust loss functions, HDGR achieves remarkable cross-modal retrieval against label noise on the 3D MNIST dataset. The comprehensive experimental results highlight the effectiveness and efficiency of HDGR on cross-modal 3D shape retrieval.