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Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

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Computer Vision – ACCV 2020 (ACCV 2020)

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Abstract

Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB + LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.

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Acknowledgements

This research was supported by UK EPSRC IPALM project EP/S032398/1.

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Authors and Affiliations

  1. Imperial College London, London, UK

    Adrian Lopez-Rodriguez & Krystian Mikolajczyk

  2. Huawei Noah’s Ark Lab, London, UK

    Benjamin Busam

  3. Technical University of Munich, Munich, Germany

    Benjamin Busam

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  1. Adrian Lopez-Rodriguez
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  3. Krystian Mikolajczyk
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Correspondence to Adrian Lopez-Rodriguez .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Lopez-Rodriguez, A., Busam, B., Mikolajczyk, K. (2021). Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12622. Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_20

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