Innovative 3D Cone Point Cloud Fitting via Landweber Iteration

Cone surface fitting is essential in various fields, including computer graphics, computer vision, and robotics.  However, factors such as noise, initial parameter selection, and the varying distribution of point clouds can significantly impact fitting accuracy and stability.  To address these challenges, we propose a novel optimization method based on Landweber iteration.  This method solves an objective function that includes the cone vertex.  Initially, Landweber iteration is used to calculate high-precision initial values for the conical surface by leveraging all point cloud data.  Subsequently, the RANSAC algorithm filters the point cloud data based on these initial values, eliminating points that do not meet the distance threshold.  Finally, an error equation is formulated, and the cone surface parameters are further refined using an optimization algorithm based on Landweber iteration.  Simulation experiments validate the feasibility and robustness of our approach, demonstrating superior accuracy and stability in 3D cone surface fitting.