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Monocular depth map estimation based on a multi-scale deep architecture and curvilinear saliency feature boosting

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Abstract

Estimating depth from a monocular camera is a must for many applications, including scene understanding and reconstruction, robot vision, and self-driving cars. However, generating depth maps from single RGB images is still a challenge as object shapes are to be inferred from intensity images strongly affected by viewpoint changes, texture content and light conditions. Therefore, most current solutions produce blurry approximations of low-resolution depth maps. We propose a novel depth map estimation technique based on an autoencoder network. This network is endowed with a multi-scale architecture and a multi-level depth estimator that preserve high-level information extracted from coarse feature maps as well as detailed local information present in fine feature maps. Curvilinear saliency, which is related to curvature estimation, is exploited as a loss function to boost the depth accuracy at object boundaries and raise the performance of the estimated high-resolution depth maps. We evaluate our model on the public NYU Depth v2 and Make3D datasets. The proposed model yields superior performance on both datasets compared to the state-of-the-art, achieving an accuracy of \(~86\%\) and showing exceptional performance at the preservation of object boundaries and small 3D structures. The code of the proposed model is publicly available at https://github.com/SaddamAbdulrhman/MDACSFB.

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Acknowledgements

Financial support was given by the pre-doctoral grant (FI 2020) funded by the Catalan government.

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Author notes

  1. Hatem A. Rashwan, Miguel Angel Garcia, Armin Masoumian and Domenec Puig contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgil, Carretera de Valls, 43007, Tarragona, Tarragona, Spain

    Saddam Abdulwahab, Hatem A. Rashwan, Armin Masoumian & Domenec Puig

  2. Department of Electronic and Communications Technology, Universidad Autnoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049, Madrid, Madrid, Spain

    Miguel Angel Garcia

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Abdulwahab, S., Rashwan, H.A., Garcia, M.A. et al. Monocular depth map estimation based on a multi-scale deep architecture and curvilinear saliency feature boosting. Neural Comput & Applic 34, 16423–16440 (2022). https://doi.org/10.1007/s00521-022-07663-x

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