Dongqiang Gou

Dongqiang Gou, Xuming He

Grounding natural language questions to functionally relevant regions in 3D objects -- termed language-driven 3D affordance grounding -- is essential for embodied intelligence and human-AI interaction. Existing methods, while progressing from label-based to language-driven approaches, still face challenges in open-vocabulary generalization, fine-grained geometric alignment, and part-level semantic consistency. To address these issues, we propose a novel two-stage cross-modal framework that enhances both semantic and geometric representations for open-vocabulary 3D affordance grounding. In the first stage, large language models generate part-aware instructions to recover missing semantics, enabling the model to link semantically similar affordances. In the second stage, we introduce two key components: Affordance Prototype Aggregation (APA), which captures cross-object geometric consistency for each affordance, and Intra-Object Relational Modeling (IORM), which refines geometric differentiation within objects to support precise semantic alignment. We validate the effectiveness of our method through extensive experiments on a newly introduced benchmark, as well as two existing benchmarks, demonstrating superior performance in comparison with existing methods.

Xinhang Wan, Dongqiang Gou, Xinwang Liu et al.

A core problem of Embodied AI is to learn object manipulation from observation, as humans do. To achieve this, it is important to localize 3D object affordance areas through observation such as images (3D affordance grounding) and understand their functionalities (affordance classification). Previous attempts usually tackle these two tasks separately, leading to inconsistent predictions due to lacking proper modeling of their dependency. In addition, these methods typically only ground the incomplete affordance areas depicted in images, failing to predict the full potential affordance areas, and operate at a fixed scale, resulting in difficulty in coping with affordances significantly varying in scale with respect to the whole object. To address these issues, we propose a novel approach that learns an affordance-aware 3D representation and employs a stage-wise inference strategy leveraging the dependency between grounding and classification tasks. Specifically, we first develop a cross-modal 3D representation through efficient fusion and multi-scale geometric feature propagation, enabling inference of full potential affordance areas at a suitable regional scale. Moreover, we adopt a simple two-stage prediction mechanism, effectively coupling grounding and classification for better affordance understanding. Experiments demonstrate the effectiveness of our method, showing improved performance in both affordance grounding and classification.