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Boosting monocular depth estimation to high resolution

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2022-08-17
Authors/Contributors
Abstract
Convolutional neural networks have shown a remarkable ability to estimate depth from a single image. However, the estimated depth maps are low resolution due to network structure and hardware limitations, only showing the overall scene structure and lacking fine details, which limits their applicability. We demonstrate that there is a trade-off between the consistency of the scene structure and the high-frequency details concerning input content and resolution. Building upon this duality, we present a double estimation framework to improve the depth estimation of the whole image and a patch selection step to add more local details. Our approach obtains multi-megapixel depth estimations with sharp details by merging estimations at different resolutions based on image content. A key strength of our approach is that we can employ any off-the-shelf pre-trained CNN-based monocular depth estimation model without requiring further finetuning.
Document
Extent
66 pages.
Identifier
etd22069
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Aksoy, Yağız
Language
English
Member of collection
Download file Size
etd22069.pdf 47.85 MB

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