Euclidean 3D reconstruction based on structure from motion of matching adjacent images

Traditional incremental structure from motion is susceptible to scale change, and the reconstructed point cloud is hierarchical and has no units. A new Euclidean 3D reconstruction method was proposed by improving the reconstruction topology and the scaling iterative closest point algorithm. First, a new reconstruction topology, reconstructing a point cloud from two adjacent pictures and then merging it into the main point cloud, was presented; then, corresponding tables were established aiming to find the corresponding 3D point pairs of a world point between the newly created point cloud and the main point cloud; subsequently, combining Geman-McClure norm, an anti-noise scaling iterative closest point method was proposed; finally, ground control points were set up to introduce scale for the reconstructed point cloud. Experiment results show that the point cloud reconstructed by proposed method is more accurate than that reconstructed by traditional incremental structure from motion, and the absolute error of length for the point cloud is about 1%-2%. The proposed method is suitable for precise Euclidean reconstruction of objects in close scene.