Normal Vector

Normal vectors, representing the direction perpendicular to a surface at a given point, are fundamental in various fields, primarily aiming to describe and analyze surface geometry and relationships between data points. Current research focuses on improving normal vector estimation techniques, particularly for point clouds, using methods like neural networks that learn angle fields or leverage normal vector distributions for robust registration in applications such as simultaneous localization and mapping (SLAM). These advancements enhance the accuracy and robustness of algorithms in 3D computer vision, robotics, and spatial data analysis, leading to improved performance in tasks ranging from object recognition to environmental risk assessment.