Open-Vocabulary 3D Affordance Understanding via Functional Text Enhancement and Multilevel Representation Alignment

Understanding 3D affordance is essential for agents to effectively interact with real-world environments, encompassing tasks such as manipulation and navigation. Existing methods typically support open-vocabulary queries through label-based language descriptions but often suffer from limited generalization and weak discriminative ability in their representations. However, affordance understanding requires constructing a coherent semantic landscape from fragmented linguistic expressions-one that preserves intra-class diversity while minimizing inter-class overlap. To address these challenges, we introduce Aff3DFunc, a framework designed to enhance the alignment between affordance and 3D geometry. It begins with a functional text enhancement module grounded in the Information Bottleneck (IB) principle, which strategically enriches affordance semantics by maximizing both relevance and diversity. A dual-encoder architecture is then employed to extract embeddings from both point clouds and text. To bridge the modality gap, we further propose a multilevel representation alignment strategy that incorporates supervised contrastive learning, reinforcing semantic-geometric correspondence in a part-to-whole manner. Extensive experiments demonstrate that our approach significantly enhances the understanding of affordance complexity. The learned representations exhibit high adaptability to diverse text queries, particularly in zero-shot settings. Furthermore, the real-world robot validation confirms that our method improves affordance understanding, enabling more fine-grained manipulation tasks.