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PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage

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Computer Vision – ACCV 2024 (ACCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15476))

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Abstract

This work addresses the task of zero-shot monocular depth estimation. A recent advance in this field has been the idea of utilising Text-to-Image foundation models, such as Stable Diffusion [51]. Foundation models provide a rich and generic image representation, and therefore, little training data is required to reformulate them as a depth estimation model that predicts highly-detailed depth maps and has good generalisation capabilities. However, the realisation of this idea has so far led to approaches which are, unfortunately, highly inefficient at test-time due to the underlying iterative denoising process. In this work, we propose a different realisation of this idea and present PrimeDepth, a method that is highly efficient at test time while keeping, or even enhancing, the positive aspects of diffusion-based approaches. Our key idea is to extract from Stable Diffusion a rich, but frozen, image representation by running a single denoising step. This representation, we term preimage, is then fed into a refiner network with an architectural inductive bias, before entering the downstream task. We validate experimentally that PrimeDepth is two orders of magnitude faster than the leading diffusion-based method, Marigold [28], while being more robust for challenging scenarios and quantitatively marginally superior. Thereby, we reduce the gap to the currently leading data-driven approach, Depth Anything [72], which is still quantitatively superior, but predicts less detailed depth maps and requires 20 times more labelled data. Due to the complementary nature of our approach, even a simple averaging between PrimeDepth and Depth Anything predictions can improve upon both methods and sets a new state-of-the-art in zero-shot monocular depth estimation. In future, data-driven approaches may also benefit from integrating our preimage.

D. Zavadski and D. Kalšan—Equal Contribution.

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Notes

  1. 1.

    See Robust Vision Challenge 2020 (http://www.robustvision.net/rvc2020.php).

  2. 2.

    We take the average with respect to both measures, \(\delta _1\) and AbsRel.

  3. 3.

    Computed from 1000 runs of different image resolutions on an A100 GPU.

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Acknowledgements

We thank Yannick Pauler, Nicolas Bender and Friedrich Feiden for their help. The project has been supported by the Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA) funded by the German Academic Exchange Service (DAAD). The authors gratefully acknowledge the support by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK) through bwHPC, SDS@hd and the German Research Foundation (DFG) through the grants INST 35/1597-1 FUGG and INST 35/1503-1 FUGG.

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  1. Computer Vision and Learning Lab, IWR, Heidelberg University, Heidelberg, Germany

    Denis Zavadski, Damjan Kalšan & Carsten Rother

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  1. Denis Zavadski
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  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

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  4. National University of Singapore, Singapore, Singapore

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  5. Peking University, Beijing, China

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Zavadski, D., Kalšan, D., Rother, C. (2025). PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15476. Springer, Singapore. https://doi.org/10.1007/978-981-96-0917-8_2

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