An Improved 3-D Reconstruction Method based on Deep Neural Network

Current three-dimensional (3-D) reconstruction methods based on two-dimensional (2-D) inverse synthetic aperture (ISAR) image sequences usually consist of some sequential nonlinear steps, and they face with the error accumulation and transmission inevitably. To realize precise target reconstruction, an improved 3-D reconstruction method based on motion parameters and deep neural network (DNN) is proposed. The proposed method could realize the end-to-end transformation from the motion parameters to the 3-D target via DNN, and the error transmission and accumulation can be avoided. Results based on the synthetized data set validate the proposed method.