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Yunfeng Nie
Researcher at Vrije Universiteit Brussel
Publications - 43
Citations - 197
Yunfeng Nie is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Computer science & Lens (optics). The author has an hindex of 5, co-authored 28 publications receiving 101 citations. Previous affiliations of Yunfeng Nie include Chinese Academy of Sciences & VU University Amsterdam.
Papers
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Journal ArticleDOI
Multifield direct design method for ultrashort throw ratio projection optics with two tailored mirrors
Yunfeng Nie,Rubén Mohedano,Pablo Benítez,Julio Chaves,Juan C. Miñano,Hugo Thienpont,Fabian Duerr +6 more
TL;DR: This work presents a multifield direct design method for ultrashort throw ratio projection optics, which allows us to directly calculate two freeform mirror profiles, which are fitted by odd polynomials and imported into an optical design program as an excellent starting point.
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Freeform optical design for a nonscanning corneal imaging system with a convexly curved image.
TL;DR: In order to view the full cornea and sclera with snapshot imaging, qualified two- and three-mirror solutions from Seidel aberration theory are calculated.
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Direct design approach to calculate a two-surface lens with an entrance pupil for application in wide field-of-view imaging
TL;DR: A multifields optical design method aiming to calculate two high-order aspheric lens profiles with an embedded entrance pupil capable of partially coupling more than three ray bundles that enter the same pupil with only two surfaces is proposed.
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Automated freeform imaging system design with generalized ray tracing and simultaneous multi-surface analytic calculation.
TL;DR: In this article, a generalized differentiable ray tracing approach was proposed for most optical surfaces, and a double-pass surface strategy with desired overlap (not mutually centered) was proposed to enable a component reduction for very compact yet high-performing freeform optical systems.
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Deep Convolutional Network for Stereo Depth Mapping in Binocular Endoscopy
TL;DR: An efficient scale-invariant loss function is introduced in this paper to accommodate the characteristics of endoscope images, which improves the accuracy of achieved depth mapping results.