Photonic Lanterns, 3-D Waveguides, Multiplane Light Conversion, and Other Components That Enable Space-Division Multiplexing

Fontaine, Nicolas K.; Carpenter, Joel; Gross, Simon; Leon-Saval, Sergio G.; Jung, Yongmin; Richardson, David J.; Amezcua‐Correa, Rodrigo

doi:10.1109/jproc.2022.3207046

Cited by 39 publications

(14 citation statements)

References 111 publications

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“…Additionally, IBRNet (Wang et al 2021), SRF (Chibane et al 2021) and MatchNeRF (Chen et al 2023b) contributed to scene reconstruction through the feature alignment of projected points from diverse perspectives. Besides, researchers have also explored novel forms of explicit 3D representation established from sparse images (Fang et al 2023), such as voxel mesh (Maturana and Scherer 2015;Sun, Sun, and Chen 2022;Huang et al 2019;Deng et al 2021), multiplane images (MPI) (Li et al 2021;Fontaine et al 2022), or layered depth images (LDI) (Tulsiani, Tucker, and Snavely 2018;Shih et al 2020). To address the challenge of inaccurate geometric information under sparse input settings, regularization methods targeting volume density have…”

Section: Related Workmentioning

confidence: 99%

ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field

Ni,

Yang,

Yang

et al. 2024

AAAI

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Neural Radiance Fields (NeRF) have demonstrated impressive potential in synthesizing novel views from dense input, however, their effectiveness is challenged when dealing with sparse input. Existing approaches that incorporate additional depth or semantic supervision can alleviate this issue to an extent. However, the process of supervision collection is not only costly but also potentially inaccurate. In our work, we introduce a novel model: the Collaborative Neural Radiance Fields (ColNeRF) designed to work with sparse input. The collaboration in ColNeRF includes the cooperation among sparse input source images and the cooperation among the output of the NeRF. Through this, we construct a novel collaborative module that aligns information from various views and meanwhile imposes self-supervised constraints to ensure multi-view consistency in both geometry and appearance. A Collaborative Cross-View Volume Integration module (CCVI) is proposed to capture complex occlusions and implicitly infer the spatial location of objects. Moreover, we introduce self-supervision of target rays projected in multiple directions to ensure geometric and color consistency in adjacent regions. Benefiting from the collaboration at the input and output ends, ColNeRF is capable of capturing richer and more generalized scene representation, thereby facilitating higher-quality results of the novel view synthesis. Our extensive experimental results demonstrate that ColNeRF outperforms state-of-the-art sparse input generalizable NeRF methods. Furthermore, our approach exhibits superiority in fine-tuning towards adapting to new scenes, achieving competitive performance compared to per-scene optimized NeRF-based methods while significantly reducing computational costs. Our code is available at: https://github.com/eezkni/ColNeRF.

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Section: Related Workmentioning

confidence: 99%

ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field

Ni,

Yang,

Yang

et al. 2024

AAAI

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“…A PL is a passive fiber device that converts the many modes of a multimoded input to an array of single mode or multimode fibers with minimal loss. [21][22][23][24][25] In general, each output fiber of the photonic lantern corresponds to an orthogonal combination of modes within the MM input and vice versa. One way to fabricate this fiber device is by bundling several optical fibers together and fusing and tapering them down to a single multimode output.…”

Section: Photonic Lanternsmentioning

confidence: 99%

Photonic lantern based real-time modal instability diagnostic tool for high power lasers

Cooper¹,

Yerolastsitis²,

Lopez-Zelaya³

et al. 2023

Fiber Lasers XX: Technology and Systems

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We demonstrate a fast and versatile approach to analyze the modal content of a high power fiber amplifier using a low-loss photonic lantern. By monitoring the first three modes of the photonic lantern on a photodetector we can directly determine the modal content of a laser beam, enabling real time diagnostics of the output and its corresponding beam quality factor, M2. We first investigate the beam quality and modal content of the output of a passive LMA double clad fiber commonly used as a delivery fiber in high power fiber laser amplifiers. The output of the fiber is analyzed by both a 6-mode mode-selective photonic lantern and a conventional M2 setup utilizing a translation stage and beam profiler. The modal content and beam quality measurements produced in real-time by the photonic lantern are compared to the M2 measurements resulting in an RMS error less than 0.098 across M2 values between 1.020 to 2.260. We then conduct a follow on experiment using the same photonic lantern to monitor modal instability in a large mode area fiber laser amplifier. In this case, we compare our photonic lantern mode analysis approach versus the commonly used RIN/pinhole method evaluating modal instabilities. Not only does the photonic lantern estimate the modal content and beam quality in real-time but the modal content trends with the RIN metric as the fiber laser amplifier progresses from stable regime below 300W through the chaotic transverse modal instability regime above 400W.

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“…This section briefly introduces the main concepts of SDM integration that have been proposed and discusses various tradeoffs. Further details are provided in other papers contained within this Special Issue, including [60], [67], [143], [144], [145], and [146].…”

Section: P a R A L L E L S Y S T E M A R C H I T E C T U R E S A N D ...mentioning

confidence: 99%