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Titel:

Peeking Behind Objects: Layered Depth Prediction from a Single Image

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Dhamo, H.; Tateno, K.; Laina, I.; Navab, N.; Tombari, F.
Abstract:
While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limits the use of depth prediction in augmented and virtual reality applications, that aim at scene exploration by synthesizing the scene from a different vantage point, or at diminished reality. To address this issue, we shift the focus from conventional depth map prediction to the regression of a specific data representa...     »
Stichworte:
Layered depth image, RGB-D inpainting, Generative adversarial networks, Occlusion
Zeitschriftentitel:
Pattern Recognition Letters
Jahr:
2019
Band / Volume:
125
Seitenangaben Beitrag:
333--340
Volltext / DOI:
doi:10.1016/j.patrec.2019.05.007
Print-ISSN:
0167-8655
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