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PanoAnnotator: a semi-automatic tool for indoor panorama layout annotation

Published: 04 December 2018 Publication History

Abstract

We present PanoAnnotator, a semi-automatic system that facilitates the annotation of 2D indoor panoramas to obtain high-quality 3D room layouts. Observing that fully-automatic methods are often restricted to a subset of indoor panoramas and generate room layouts with mediocre quality, we instead propose a hybrid method to recover high-quality room layouts by leveraging both automatic estimations and user edits. Specifically, our system first employs state-of-the-art methods to automatically extract 2D/3D features from input panorama, based on which an initial Manhattan world layout is estimated. Then, the user can further edit the layout structure via a set of intuitive operations, while the system will automatically refine the geometry according to the extracted features. The experimental results show that our automatic initialization outperforms a selected fully-automatic state-of-the-art method in producing room layouts with higher accuracy. In addition, our complete system reduces annotation time when comparing with a fully-manual tool for achieving the same high quality results.

References

[1]
Angel X. Chang, Angela Dai, Thomas A. Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. 2017. Matterport3D: Learning from RGB-D Data in Indoor Environments. In 3DV. IEEE Computer Society, 667--676.
[2]
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab. 2016. Deeper Depth Prediction with Fully Convolutional Residual Networks. CoRR abs/1606.00373 (2016).
[3]
Walkaboutworlds. 2016. Walkabout Worlds Panomodeller: Turn 360 panoramas into full 3D models.
[4]
Yinda Zhang, Shuran Song, Ping Tan, and Jianxiong Xiao. 2014. PanoContext: A Whole-Room 3D Context Model for Panoramic Scene Understanding. In ECCV (6) (Lecture Notes in Computer Science), Vol. 8694. Springer, 668--686.
[5]
Chuhang Zou, Alex Colburn, Qi Shan, and Derek Hoiem. 2018. LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image. CoRR abs/1803.08999 (2018). arXiv:1803.08999 http://arxiv.org/abs/1803.08999

Cited By

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  • (2024)PanoContext-Former: Panoramic Total Scene Understanding with a Transformer2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02653(28087-28097)Online publication date: 16-Jun-2024
  • (2022)360RAT: A Tool for Annotating Regions of Interest in 360-degree VideosProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3557930(272-280)Online publication date: 7-Nov-2022
  • (2021)Zillow Indoor Dataset: Annotated Floor Plans With 360° Panoramas and 3D Room Layouts2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00217(2133-2143)Online publication date: Jun-2021
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  1. PanoAnnotator: a semi-automatic tool for indoor panorama layout annotation

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    cover image ACM Conferences
    SA '18: SIGGRAPH Asia 2018 Posters
    December 2018
    166 pages
    ISBN:9781450360630
    DOI:10.1145/3283289
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 December 2018

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    Author Tags

    1. annotation
    2. layout
    3. modeling
    4. panorama
    5. scene

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    SA '18
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    SA '18: SIGGRAPH Asia 2018
    December 4 - 7, 2018
    Tokyo, Japan

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    Overall Acceptance Rate 178 of 869 submissions, 20%

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    Cited By

    View all
    • (2024)PanoContext-Former: Panoramic Total Scene Understanding with a Transformer2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02653(28087-28097)Online publication date: 16-Jun-2024
    • (2022)360RAT: A Tool for Annotating Regions of Interest in 360-degree VideosProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3557930(272-280)Online publication date: 7-Nov-2022
    • (2021)Zillow Indoor Dataset: Annotated Floor Plans With 360° Panoramas and 3D Room Layouts2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00217(2133-2143)Online publication date: Jun-2021
    • (2021)A Large-Scale Indoor Layout Reconstruction and Localization System for Spatial-Aware Mobile AR Applications2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR52153.2021.00054(237-241)Online publication date: Nov-2021
    • (2021)Manhattan Room Layout Reconstruction from a Single $$360^{\circ }$$ Image: A Comparative Study of State-of-the-Art MethodsInternational Journal of Computer Vision10.1007/s11263-020-01426-8129:5(1410-1431)Online publication date: 9-Feb-2021
    • (2020)AtlantaNet: Inferring the 3D Indoor Layout from a Single $$360^\circ $$ Image Beyond the Manhattan World AssumptionComputer Vision – ECCV 202010.1007/978-3-030-58598-3_26(432-448)Online publication date: 7-Nov-2020

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