Innovative 3D Cone Point Cloud Fitting via Landweber Iteration

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