SALIENCY CHARACTERISTICS-BASED SIMULATION INCOMPLETE POINT CLOUD MASK GENERATION METHOD

摘要

A saliency characteristics-based simulation incomplete point cloud mask generation method. The method comprises: step 1, obtaining the number of current point cloud points, a loss function measurement mode, a discard rate and the cycle number of times; step 2, when being not within the cycle number of times, jumping out of the cycle and outputting a point cloud mask M; step 3, when being within the cycle number of times, calculating coordinates xc of a sphere center (S3), wherein the position of the sphere center of the point cloud can be roughly measured by using the median or average value of all the coordinates; step 4, calculating a gradient g of the current point cloud according to the loss function (S4); step 5, calculating a change rate δ of each point in each current point cloud with respect to the sphere center (S5); step 6, calculating scores s=-w*δ (S6) of each point in the current point cloud; and step 7, sorting all the scores from low to high, and deleting preceding [pN/T] points from the sorted points in the point cloud (S7). According to the method, a multi-region missing point cloud condition can be generated, and more flexible, changeable and complex masks can be generated.