Multiple-view D 2 NNs array: realizing robust 3D object recognition
Jiashuo Shi, Liang Zhou, Taige Liu, Chai Hu, Kewei Liu, Jun Luo, Haiwei Wang, Changsheng Xie, Xinyu Zhang · Optics Letters · 2021
As an optical-based classifier of the physical neural network, the independent diffractive deep neural network ( D 2 N N ) can be utilized to learn the single-view spatial featured mapping between the input lightfields and the truth labels by preprocessing a large number of training samples. However, it is still not enough to approach or even reach a satisfactory classification accuracy on three-dimensional (3D) targets owing to already losing lots of effective lightfield information on other view fields. This Letter presents a multiple-view D 2 N N s array (MDA) scheme that provides a significant inference improvement compared with individual D 2 N N or Res- D 2 N N by constructing a different complementary mechanism and then merging all base learners of distinct views on an electronic computer. Furthermore, a robust multiple-view D 2 N N s array (r-MDA) framework is demonstrated to resist the redundant spatial features of invalid lightfields due to severe optical disturbances.