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Accelerating stereo image simulation for automotive applications using neural stereo super resolution
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Haghighi, Hamed, Dianati, Mehrdad, Donzella, Valentina and Debattista, Kurt (2023) Accelerating stereo image simulation for automotive applications using neural stereo super resolution. IEEE Transactions on Intelligent Transportation Systems, 24 (11). pp. 12627-12636. doi:10.1109/TITS.2023.3287912 ISSN 1524-9050.
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WRAP-accelerating-stereo-image-simulation-automotive-applications-using-neural-stereo-super-resolution-Haghighi-2023.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TITS.2023.3287912
Abstract
Camera image simulation is integral to the virtual validation of autonomous vehicles and robots that use visual perception to understand their environment. It also has applications in creating image datasets for training learning-based vision models. As camera image simulation takes into account a wide variety of external and internal parameters, achieving a high-fidelity simulation is a computationally expensive process. Recently, several neural network-based techniques have been proposed to reduce the computational complexity of image rendering, a critical element of the camera simulation pipeline. However, the existing methods are tailored for monocular camera images and are not optimised for stereo images, which are widely used in autonomous driving applications. To address this, we propose a technique based on Stereo Super Resolution (SSR) to speed up the simulation of stereo images. The proposed method first simulates stereo images at a lower resolution, then super-resolves them to their original resolution using our introduced SSR model, ETSSR. We evaluated the performance of our technique using the CARLA driving simulator and created our own synthetic dataset for training ETSSR. The evaluations indicate that our approach can speed up stereo image simulation by a factor of up to 2.57 over various resolutions. Moreover, it shows that our ETSSR achieves on-par or superior performance compared to the state-of-the-art models, using significantly fewer parameters and FLOPs. We have made our source code and dataset available at https://github.com/hamedhaghighi/ETSSR.
Item Type: | Journal Article | |||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
Library of Congress Subject Headings (LCSH): | Automated vehicles, Imaging systems, Digital cameras, Image processing -- Digital techniques | |||||||||
Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 1524-9050 | |||||||||
Official Date: | November 2023 | |||||||||
Dates: |
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Volume: | 24 | |||||||||
Number: | 11 | |||||||||
Page Range: | pp. 12627-12636 | |||||||||
DOI: | 10.1109/TITS.2023.3287912 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Re-use Statement: | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 14 July 2023 | |||||||||
Date of first compliant Open Access: | 17 July 2023 | |||||||||
RIOXX Funder/Project Grant: |
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