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SC-wLS: Towards Interpretable Feed-forward Camera Re-localization

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality. Feed-forward absolute camera pose regression methods directly output poses by a network, but suffer from low accuracy. Meanwhile, scene coordinate based methods are accurate, but need iterative RANSAC post-processing, which brings challenges to efficient end-to-end training and inference. In order to have the best of both worlds, we propose a feed-forward method termed SC-wLS that exploits all scene coordinate estimates for weighted least squares pose regression. This differentiable formulation exploits a weight network imposed on 2D-3D correspondences, and requires pose supervision only. Qualitative results demonstrate the interpretability of learned weights. Evaluations on 7Scenes and Cambridge datasets show significantly promoted performance when compared with former feed-forward counterparts. Moreover, our SC-wLS method enables a new capability: self-supervised test-time adaptation on the weight network. Codes and models are publicly available.

X. Wu and H. Zhao—Equal contribution.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant 62176010.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of AI, Peking University, China, China

    Xin Wu, Hao Zhao, Yingdian Cao & Hongbin Zha

  2. PKU-SenseTime Machine Vision Joint Lab, China, China

    Xin Wu, Yingdian Cao & Hongbin Zha

  3. Intel Labs China, Beijing, China

    Hao Zhao

  4. Kuaishou Technology, Beijing, China

    Shunkai Li

Authors
  1. Xin Wu
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  5. Hongbin Zha
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Corresponding author

Correspondence to Xin Wu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Wu, X., Zhao, H., Li, S., Cao, Y., Zha, H. (2022). SC-wLS: Towards Interpretable Feed-forward Camera Re-localization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_34

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