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Towards Domain-agnostic Depth Completion

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Abstract

Existing depth completion methods are often targeted at a specific sparse depth type and generalize poorly across task domains. We present a method to complete sparse/semi-dense, noisy, and potentially low-resolution depth maps obtained by various range sensors, including those in modern mobile phones, or by multi-view reconstruction algorithms. Our method leverages a data-driven prior in the form of a single image depth prediction network trained on large-scale datasets, the output of which is used as an input to our model. We propose an effective training scheme where we simulate various sparsity patterns in typical task domains. In addition, we design two new benchmarks to evaluate the generalizability and robustness of depth completion methods. Our simple method shows superior cross-domain generalization ability against state-of-the-art depth completion methods, introducing a practical solution to high-quality depth capture on a mobile device.

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  1. These authors contribute equally to this work

Authors and Affiliations

  1. Zhejiang University, Hangzhou, 310058, China

    Guangkai Xu

  2. DaJiang Technology, Shenzhen, 518057, China

    Wei Yin

  3. Adobe Research, California, 95110, USA

    Jianming Zhang, Oliver Wang, Simon Niklaus & Simon Chen

  4. University of Oxford, Oxford, OX1 2JD, UK

    Jia-Wang Bian

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  1. Guangkai Xu
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Additional information

Guangkai Xu received the B. Eng. degree in automation in the School of Automation Engineering, the University of Electronic Science and Technology of China (UESTC), China in 2020, and M. Eng. degree in control science and engineering in the School of Information Science and Technology, University of Science and Technology of China (USTC), China in 2023. Currently, he is a Ph. D. degree candidate in the College of Computer Science and Technology, Zhejiang University, China.

His research interests include monocular depth estimation, 3D scene reconstruction and rendering, and visual-language models.

Wei Yin received the Ph. D. degree in computer science from University of Adelaide, Australia in 2022. Currently, he is a senior research engineer at Dajiang Technology in ShenZhen, China.

His research interests include autonomous driving and 3D reconstruction.

Jianming Zhang received the Ph. D. degree in computer vision with Prof. Stan Sclaroff at Boston University, USA in 2016. Currently, he is a researcher at Adobe in California, USA.

His research interests include deep learning, image processing and intelligent systems.

Oliver Wang received the B. Sc. degree in computer science from Cornell University, USA in 2003, the M. Sc. and Ph. D. degrees in computer science from University of California, Santa Cruz, USA in 2006 and 2010, respectively. He is currently a senior staff research scientist at Google research, USA.

His research interests include image and video processing/editing, computer vision, machine learning and photography.

Simon Niklaus received the Ph. D. degree in computer science from Portland State University, USA in 2020. He is a researcher at Adobe, USA. He is a student of Feng Liu and is grateful for his internship at Adobe while working with Long Mai on the 3D Ken Burns project, and his internship at Google while working with Tianfan Xue on an undisclosed project within Marc Levoy’s team, and his first years at Adobe when he was reporting to Oliver Wang.

His research interests include AI & machine learning, computer vision, imaging & video, graphics (2D & 3D)

Simon Chen received the Ph. D. degree in electrical engineering with Prof. Robert Haralick, University of Washington, USA in 1995. He is currently a senior principal scientist at Adobe, USA.

His research interests include image processing, computer vision, deep learning and applications.

Jia-Wang Bian received the B. Eng. degree in computer science from Nankai University, China in 2016. After that, he did a research assistant job at the Singapore University of Technology and Design, Singapore. He received the Ph. D. degree in computer science from the University of Adelaide, Australia in 2022. Also, he did research intern jobs in research institutes/companies, including the Advanced Digital Sciences Center, Tusimple, Amazon, and Facebook. He is currently a postdoctoral researcher at the University of Oxford, UK.

His research interest is 3D computer vision.

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Xu, G., Yin, W., Zhang, J. et al. Towards Domain-agnostic Depth Completion. Mach. Intell. Res. (2024). https://doi.org/10.1007/s11633-024-1494-4

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