Point Cloud Foot Model Extraction Algorithm for 3D Foot Model Scanner

Abstract

Point cloud is one of the data sources widely used in many fields, such as 3D scanning calculation and computer vision, and information extraction is a necessary link in point cloud processing, analysis, and application. The experimental data is the dense point cloud model scanned by a 3D scanner. According to the characteristics of the model data, this paper proposes a dense point cloud foot model extraction method based on Euclidean distance, that is, judge the adjacent points of the dense point cloud data based on Euclidean distance, identify the redundant parts outside the foot model, and then extract the foot model. The results show that this method can identify the redundant part well, and the extracted foot model is also effective.