3D reconstruction of aircraft structures via 2D multi-view images

Aircraft pose is of great importance to the monitor during flight test. Commonly, traditional pose measurement is based on various sensors, which has the inconvenience of repair and replacement because of damage. Two-dimension (2D) single image processing method alleviate the inconvenience, but it has the ambiguity of single image three-dimension (3D) reconstruction. To address these problems, we accomplish 3D reconstruction of the aircraft's structures via 2D multi-view images. Structures are obtained from 2D multi-view images of aircraft by a convolutional neural network (CNN) and then used to accomplish reconstruction. Structures typically represent the topological relationship between components of aircraft, reducing the self-occlusion of point features. To more precise evaluation of the experimental results, we propose a new Mean Per Frame Position Error (MPFPE) calculation for the structures position. Compared with the Mean Per Joint Position Error (MPJPE), the MPFPE takes the length of structures into account and mixes the multi-view images. Experiments show the mean error of our method is 1.47%, which shows great potential for aircraft pose estimation.