Point cloud global motion optimization method and device

Abstract

The invention discloses a point cloud global motion optimization method, and the method comprises the steps that a motion matrix initial value of each visual angle is reconstructed into a low-rank sparse matrix, and then the matrix recovery is carried out; under the condition that any two visual angles are known to move, a universal constraint condition is put forward to be added into an iterationprocess, the iteration frequency is effectively limited, the algorithm efficiency is improved, and the influence caused by random noise is reduced; a Cauchy weight item is added to measure the reliability of pairwise visual angle measurement, the influence of outliers is effectively reduced, and the robustness of the algorithm is improved; the method is suitable for solving an accurate global motion problem of a known multi-view point cloud relative motion initial value and real values of any two view changes. The method aims to solve the problems that a multi-view point cloud global optimization method in dimensional reconstruction excessively depends on registration initial values, robustness is not high and efficiency is low, random noise and abnormal values in initial motion are eliminated, point cloud global motion recovery precision is improved, and a more accurate reconstruction model is obtained.

Description

technical field [0001] The invention relates to the fields of three-dimensional reconstruction and computer vision, in particular to a point cloud global motion optimization method and device. Background technique [0002] With the development of unmanned driving, virtual reality, target recognition and other technologies, 3D reconstruction has become a hot issue in the field of computer vision. The core of 3D reconstruction is to move the point clouds from all perspectives into a model through translation and rotation. Various registration methods based on iterative closest point (ICP) can obtain the relative motion between point clouds of different views, but due to the influence of noise and outliers, these relative motions are restored to the global coordinate system There are certain errors, and there are also errors in the process of solving the relative motion in the registration, which leads to the low accuracy of the restored global motion and the difficulty in rec...

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a point cloud global motion optimization method and equipment, which is suitable for solving the precise global motion problem with the known initial value of multi-view point cloud motion and the real value of any two view changes, so as to solve the problem of multi-view point cloud in three-dimensional reconstruction The global optimization method relies too much on the registration initial value, the defect of low robustness, and low efficiency. It eliminates random noise and outliers in the initial motion, improves the global motion accuracy of the point cloud, and obtains a more accurate reconstruction model.

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