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Implicit Map Augmentation for Relocalization

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Book cover Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Learning neural radiance fields (NeRF) has recently revolutionized novel view synthesis and related topics. The fact that the implicit scene models learned via NeRF greatly extend the representational capability compared with sparse maps, however, is largely overlooked. In this paper, we propose implicit map augmentation (IMA) that utilizes implicit scene representations to augment the sparse maps and help with visual relocalization. Given a sparse map reconstructed by structure-from-motion (SfM) or SLAM, the method first trains a NeRF model conditioned on the sparse reconstruction. Then an augmented sparse map representation can be sampled from the NeRF model to render better relocalization performance. Unlike the existing implicit mapping and pose estimation methods based on NeRF, IMA takes a hybrid approach by bridging the sparse map representation with MLP-based implicit representation in a non-intrusive way. The experiments demonstrate that our approach achieves better relocalization results with the augmented maps on challenging views. We also show that the resulting augmented maps not only remove the noisy 3D points but also bring back missing details that get discarded during the sparse reconstruction, which helps visual relocalization in wide-baseline scenarios.

Y. Hou—Work done during internship at Reality Labs, Meta.

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Authors and Affiliations

  1. Aalto University, Espoo, Finland

    Yuxin Hou

  2. Niantic, San Francisco, USA

    Yuxin Hou

  3. Reality Labs, Meta, Redmond, USA

    Tianwei Shen, Tsun-Yi Yang, Daniel DeTone, Hyo Jin Kim, Chris Sweeney & Richard Newcombe

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  1. Yuxin Hou
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  2. Tianwei Shen
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  3. Tsun-Yi Yang
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  4. Daniel DeTone
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  5. Hyo Jin Kim
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  6. Chris Sweeney
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  7. Richard Newcombe
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Correspondence to Tianwei Shen .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Hou, Y. et al. (2023). Implicit Map Augmentation for Relocalization. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13803. Springer, Cham. https://doi.org/10.1007/978-3-031-25066-8_36

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