This thesis presents an improved normal-guided pointcloud denoising pipeline that enhances the quality and efficiency of 3D pointcloud reconstruction. Building on the Constraint-based Point Set Denoising (CPSD) method by (Yadav et al., 2018), several modifications and extensions are proposed to improve the denoising process. The key contributions include a revised point classification approach, dedicated point update formulas for different point classes and a pipeline optimization and evaluation. Experiments were performed on synthetic and real-world scanned datasets, using Chamfer Distance (CD) and single-sided Chamfer Distance (sCD) as evaluation metrics. Results demonstrate that the proposed method achieves lower error scores than existing pipelines while requiring fewer iterations. Additionally, the modular nature of the pipeline enables future integration of neural networks or curvature-aware point update functions, opening pathways for further improvements in denoising pipelines.