Abstract
AbstractRecent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.
Funder
W. M. Keck Foundation
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Reference25 articles.
1. Schwarz, B. LIDAR: mapping the world in 3D. Nat. Photonics 4, 429 (2010).
2. Oggier, T., Scott T. S. & A. Herrington. Line scan depth sensor. U.S. Patent Application No. 15/700,231.
3. Niclass, C. L. et al. Light detection and ranging sensor. U.S. Patent Application No. 15/372,411.
4. Ng, R. et al. Light field photography with a hand-held plenoptic camera. Computer Sci. Tech. Rep. CSTR2. 11, 1–11 (2005).
5. Navarro, H. et al. High-resolution far-field integral-imaging camera by double snapshot. Opt. Express 20, 890–895 (2012).
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献