A Point Cloud Optimization Algorithm Based on Spatial Geometry Relationship and Siamese Network

This paper presents a robust point cloud optimization algorithm based on spatial geometry relationship and Siamese Network. The proposed algorithm is designed to be improve the integrity and accuracy of stereo matching. In order to approach the prior corresponding pixels in image pairs, an epipolar line constraint is employed to fix the effective matching range in searching images. Then a Siamese Network is utilized to calculate the similarity of matching templates between reference image and searching images to approach the optimal matching pixels. At last the corresponding pixel pairs are used to compute the coordinate of object points by the Space Intersection method. Comparison studies and experimental results prove the high integrity and accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.