Point Cloud Normal Estimation

Point cloud normal estimation aims to determine the orientation of a surface at each point within a 3D point cloud, a crucial preprocessing step for many applications in 3D computer vision and geometry processing. Recent research emphasizes improving the robustness and accuracy of normal estimation, particularly in the presence of noise and varying point densities, using deep learning models such as Siamese networks and graph convolutional networks. These methods often incorporate weighted least-squares fitting, adaptive sampling strategies, and contrastive learning techniques to enhance performance. Accurate normal estimation is vital for downstream tasks like surface reconstruction, shape analysis, and object recognition, impacting fields ranging from robotics to medical imaging.