Point Cloud Upsampling for Accurate Surface Reconstruction via Attention-Guided Generation

Reconstructing accurate surfaces from sparse point clouds remains a fundamental challenge in 3D vision. In this paper, we propose a novel upsampling framework that focuses on surface-aware point generation. Our method comprises two key components. First, we introduce an adaptive query generation module that analyzes local attention information to determine where new points should be placed. These query points guide the generation toward regions of geometric significance. Second, we incorporate the Hyper Chamfer Distance (HCD) loss, which accounts for the distribution of point-wise distances to better capture complex surface structures. Unlike existing methods, our approach effectively combines attention-based guidance and loss-driven precision, leading to more accurate surface reconstruction. Extensive experiments on PU-GAN and PU1K datasets demonstrate that our method consistently outperforms state-of-the-art techniques, especially in terms of the P2F metric. Moreover, the proposed framework maintains robust performance across arbitrary upsampling ratios and under random noise, confirming its reliability and generalizability.