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SemAttNet: Towards ...
SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion
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- Nazir, Danish (författare)
- Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) Trippstadter Str. 122, 67663 Kaiserslautern; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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- Pagani, Alain (författare)
- Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) Trippstadter Str. 122, 67663 Kaiserslautern
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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- Stricker, Didier (författare)
- Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) Trippstadter Str. 122, 67663 Kaiserslautern; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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- Afzal, Muhammad Zeshan (författare)
- Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) Trippstadter Str. 122, 67663 Kaiserslautern; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) Trippstadter Str 122, 67663 Kaiserslautern; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, University of Kaiserslautern, 67663 Kaiserslautern, Germany Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) Trippstadter Str. 122, 67663 Kaiserslautern (creator_code:org_t)
- IEEE, 2022
- 2022
- Engelska.
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 120781-120791
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the depth completion task suffers from sudden illumination changes in RGB images (e.g., shadows). In this paper, we propose a novel three-branch backbone comprising color-guided, semantic-guided, and depth-guided branches. Specifically, the color-guided branch takes a sparse depth map and RGB image as an input and generates color depth which includes color cues (e.g., object boundaries) of the scene. The predicted dense depth map of color-guided branch along-with semantic image and sparse depth map is passed as input to semantic-guided branch for estimating semantic depth. The depth-guided branch takes sparse, color, and semantic depths to generate the dense depth map. The color depth, semantic depth, and guided depth are adaptively fused to produce the output of our proposed three-branch backbone. In addition, we also propose to apply semantic-aware multi-modal attention-based fusion block (SAMMAFB) to fuse features between all three branches. We further use CSPN++ with Atrous convolutions to refine the dense depth map produced by our three-branch backbone. Extensive experiments show that our model achieves state-of-the-art performance in the KITTI depth completion benchmark at the time of submission.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Attention-based fusion for depth completion
- Benchmark testing
- Reliability
- Semantic-guided depth completion
- State-of-the-art Depth Completion approach on KITTI depth completion benchmark
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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