QUERY-CONDITIONED RECONSTRUCTION REFINEMENT FOR 3D AFFORDANCE GROUNDING UNDER OCCLUSION

3D affordance grounding aims to localize affordance regions specified by natural-language instructions. However, partial observations and severe occlusion often result in missing functional surfaces, while reconstructed geometry may contain unreliable local structures that adversely affect affordance grounding. To address this issue, we propose a query-conditioned reconstruction refinement framework for 3D affordance grounding under occlusion. Given partial multi-view RGB-D observations, we first perform amodal geometry completion to recover the complete object structure. We then introduce query-conditioned localization reliability estimation to assess the reliability of reconstructed local regions with respect to the target affordance, and use the estimated reliability to guide adaptive local detail refinement. Furthermore, we develop query-guided spatial-functional reasoning to jointly exploit geometric spatial relations and query-conditioned functional relations among local regions. By selectively refining reconstructed geometry according to its reliability for the target affordance, our framework enables robust affordance grounding under occlusion. Experimental results on the public dataset demonstrate the effectiveness of the proposed framework, achieving 24.0 IoU on the 3D affordance grounding task under occlusion.