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High-Precision Self-supervised Monocular Depth Estimation with Rich-Resource Prior

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

In the area of self-supervised monocular depth estimation, models that utilize rich-resource inputs, such as high-resolution and multi-frame inputs, typically achieve better performance than models that use ordinary single image input. However, these rich-resource inputs may not always be available, limiting the applicability of these methods in general scenarios. In this paper, we propose Rich-resource Prior Depth estimator (RPrDepth), which only requires single input image during the inference phase but can still produce highly accurate depth estimations comparable to rich-resource based methods. Specifically, we treat rich-resource data as prior information and extract features from it as reference features in an offline manner. When estimating the depth for a single-image image, we search for similar pixels from the rich-resource features and use them as prior information to estimate the depth. Experimental results demonstrate that our model outperform other single-image model and can achieve comparable or even better performance than models with rich-resource inputs, only using low-resolution single-image input.

This work was supported in part by the FDCT grants 0102/2023/RIA2, 0154/2022/A3, and 001/2024/SKL, the MYRG-CRG2022-00013-IOTSC-ICI grant and the SRG2022-00023-IOTSC grant.

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Authors and Affiliations

  1. SKL-IOTSC, Computer and Information Science, University of Macau, Macau, China

    Wencheng Han & Jianbing Shen

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  1. Wencheng Han
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  2. Jianbing Shen
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Correspondence to Jianbing Shen .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Han, W., Shen, J. (2025). High-Precision Self-supervised Monocular Depth Estimation with Rich-Resource Prior. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15089. Springer, Cham. https://doi.org/10.1007/978-3-031-72751-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-72751-1_9

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