Can 3D Geometry-aware Features Help with Affordance Prediction?

Visual affordance prediction enables embodied agents to identify actionable objects and localize interaction regions from limited visual observations. However, existing affordance prediction methods typically rely on RGB appearance cues or explicit 3D representations. Since embodied agents often need to act before a complete 3D scene model is available, extracting useful geometric information directly from RGB observations is particularly valuable. To this end, we integrate reconstruction-pretrained geometry-aware features with visual representations without constructing an explicit 3D scene model. We evaluate this integration across multiple datasets, predictors, and prediction settings. Our experiments indicate that geometry-aware features can improve visual affordance prediction. Furthermore, the gains depend on data characteristics and view sampling and do not increase monotonically with the number of views. Notably, fine-grained geometric features provide greater benefits than global geometric features.