Beyond Categorical Matching: Intra-Class Graded Relevance Estimation for Cross-Modal 3D Retrieval

Mainstream 3D asset retrieval relies predominantly on category-level matching, overlooking intra-class variations in geometry, style, andtexture critical for real-world applications. To address this, we redefineretrieval from binary categorical matching to intra-class graded relevanceestimation, treating relevance as a continuous variable to capture fine-grained semantic nuances. We propose a tri-modal encoder incorporatinga Semantic Conditioned Interaction module for cross-modal fusion and aQuery-Guided Mixture-of-Experts module for dynamic modality weight-ing. This design explicitly decouples intra-class variations and preventssemantic collapse via a re-parameterization mechanism. Furthermore,we introduce an automated, scalable benchmark generation frameworkdriven by a Multimodal Large Language Model to synthesize high-qualitysoft relevance labels, proposing a new benchmark: INGRE. Extensive ex-periments demonstrate our method achieves state-of-the-art results onINGRE while maintaining superior performance and robustness on stan-dard classification tasks.