Real-Time Reconstruction of 3D Compressive Samples with Deep Learning

Vladislav Kravets; Adrian Stern

doi:10.1364/3D.2022.3F2A.2

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper 3F2A.2
•https://doi.org/10.1364/3D.2022.3F2A.2

Real-Time Reconstruction of 3D Compressive Samples with Deep Learning

Vladislav Kravets and Adrian Stern

3D Image Acquisition and Display: Technology, Perception and Applications 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-09-8

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3D Reconstruction and Display (3F2A)

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V. Kravets and A. Stern, "Real-Time Reconstruction of 3D Compressive Samples with Deep Learning," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper 3F2A.2.

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Abstract

We present a real-time 3D compressive samples reconstruction by using a deep learning network for variable density Hadamard samples. Our method is able to successfully recover 3D examples from as few as 10 samples.

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