Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

• URL: http://arxiv.org/abs/2105.14021v1
• Date: Fri, 28 May 2021 17:55:15 GMT
• Title: Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging
• Authors: S. Mahdi H. Miangoleh, Sebastian Dille, Long Mai, Sylvain Paris, Ya\u{g}{\i}z Aksoy
• Abstract summary: We show how a consistent scene structure and high-frequency details affect depth estimation performance. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail.
• Score: 14.279471205248534
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects depth estimation performance. We demonstrate that there is a trade-off between a consistent scene structure and the high-frequency details, and merge low- and high-resolution estimations to take advantage of this duality using a simple depth merging network. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details to the final result. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail using a pre-trained model.

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