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SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices

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Τίτλος: SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices
Συγγραφείς: Luginov, Albert, Makarov, Ilya
Πηγή: 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) ISMAR-ADJUNCT Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2023 IEEE International Symposium on. :642-647 Oct, 2023
Relation: 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
Βάση Δεδομένων: IEEE Xplore Digital Library
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      – Type: doi
        Value: 10.1109/ISMAR-Adjunct60411.2023.00137
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        PageCount: 6
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      – TitleFull: SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices
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              Y: 2023
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