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abstract

Spatio-temporal Super-resolution with Photographic and Depth Data using GANs

Published: 18 April 2019 Publication History

Abstract

LiDAR technology is essential for self-driving cars, which have seen a surge in interest and investments from startups and established automotive corporations alike. However, the task of automated driving requires high resolution and significant depth-range capabilities of the sensor, keeping its cost prohibitive. Super-resolution of depth maps has been explored as a potential circumvention of these problems, with a substantial number of methods being analyzed in the past few years, yielding various levels of success. We propose a super-resolution algorithm trained for depth-map data and LiDAR compatibility using Generative Adversarial Networks (GANs).

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Cited By

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  • (2021)Generative Adversarial Network Performance in Low-Dimensional SettingsJournal of Research of the National Institute of Standards and Technology10.6028/jres.126.008126Online publication date: 2021

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  1. Spatio-temporal Super-resolution with Photographic and Depth Data using GANs

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    cover image ACM Conferences
    ACMSE '19: Proceedings of the 2019 ACM Southeast Conference
    April 2019
    295 pages
    ISBN:9781450362511
    DOI:10.1145/3299815
    • Conference Chair:
    • Dan Lo,
    • Program Chair:
    • Donghyun Kim,
    • Publications Chair:
    • Eric Gamess
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 18 April 2019

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    Author Tags

    1. Deep Learning
    2. Depth Image
    3. GAN
    4. LiDAR
    5. Machine Learning
    6. Super Resolution

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    ACM SE '19
    Sponsor:
    ACM SE '19: 2019 ACM Southeast Conference
    April 18 - 20, 2019
    GA, Kennesaw, USA

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    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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    • (2021)Generative Adversarial Network Performance in Low-Dimensional SettingsJournal of Research of the National Institute of Standards and Technology10.6028/jres.126.008126Online publication date: 2021

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