Implicit Function-based 3D Reconstruction for Point Cloud Data

3D reconstruction from point cloud data has become a key component in various domains such as computer graphics, medical imaging, industrial design, and virtual reality. Among the many available approaches, implicit function methods have attracted significant attention due to their robustness and their ability to generate high-quality surface meshes from sparse and noisy data. This paper investigates the fundamental principles of 3D reconstruction, with a particular focus on the application of space-partitioned local fitting methods in implicit surface reconstruction from point clouds. Furthermore, we introduce a hybrid normal estimation and orientation technique to enhance global surface consistency. Experimental results on LiDAR point clouds demonstrate the accuracy and efficiency of the proposed reconstruction pipeline, validating its effectiveness.