This paper presents a self-tuned two-stage framework for point cloud reconstruction. A parameter-free denoising module (TPDn) automatically selects thresholds through polynomial model fitting to remove noise and outliers without manual tuning. The denoised cloud is then upsampled by PU-Net to recover fine-grained geometry. This synergy enhances structural consistency and demonstrates qualitative robustness under various noise conditions. Experiments on synthetic datasets and real industrial scans show that the proposed method improves geometric accuracy and uniformity while maintaining low computational cost. The framework is simple, efficient, and easily scalable to large-scale point clouds.