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DAttNet: monocular depth estimation network based on attention mechanisms

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Abstract

As autonomous vehicles get closer to our daily lives, the need for architectures that function as redundant pipelines is becoming increasingly critical. To address this issue without compromising the budget, researchers aim to avoid duplicating high-cost sensors such as LiDARs. In this work, we propose using monocular cameras, which are already essential for some modules of the autonomous platform, for 3D scene understanding. While many methods for depth estimation using single images have been proposed in the literature, they usually rely on complex neural network ensembles that extract dense feature maps, resulting in a high computational cost. Instead, we propose a novel and inherently efficient method for obtaining depth images that replace tangled neural architectures with attention mechanisms applied to basic encoder–decoder models. We evaluate our method on the KITTI public dataset and in real-world experiments on our automated vehicle. The obtained results prove the viability of our approach, which can compete with intricate state-of-the-art methods while outperforming most alternatives based on attention mechanisms.

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Data availability

The authors declare that the dataset used for training and validating the results presented in this study is openly accessible and available at: https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction [26].

Notes

  1. Additional results: https://www.youtube.com/watch?v=pQDc_AimYiU.

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Acknowledgements

This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M (“Fostering Young Doctors Research”, APBI-CM-UC3M), and in the context of the V PRICIT (Research and Technological Innovation Regional Programme). Carlos Guindel acknowledges the support of the Ministry of Universities and the Universidad Carlos III de Madrid’s Call for Grants for the requalification of the Spanish University System for 2021-2023, based on Royal Decree 289/2021 of April 20, 2021, which regulates the direct granting of subsidies to public universities for the requalification of the Spanish university system. This work has been supported by the Spanish Government through the projects ID2021-128327OA-I00, PID2021-124335OB-C21 and TED2021-129374A-I00 funded by MCIN/AEI/10.13039/501100011033, by the European Union NextGenerationEU/PRTR.

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Authors and Affiliations

  1. Autonomous Mobility and Perception Laboratory (AMPL), Universidad Carlos III de Madrid, Madrid, Spain

    Armando Astudillo, Alejandro Barrera, Carlos Guindel, Abdulla Al-Kaff & Fernando García

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  1. Armando Astudillo
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  2. Alejandro Barrera
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  3. Carlos Guindel
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  4. Abdulla Al-Kaff
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  5. Fernando García
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Correspondence to Armando Astudillo.

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Astudillo, A., Barrera, A., Guindel, C. et al. DAttNet: monocular depth estimation network based on attention mechanisms. Neural Comput & Applic 36, 3347–3356 (2024). https://doi.org/10.1007/s00521-023-09210-8

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