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IJAT Vol.19 No.3 pp. 226-236
doi: 10.20965/ijat.2025.p0226
(2025)

Research Paper:

Efficient Distortion Mitigation in Equirectangular Images for Two-View Pose Estimation

Taisei Ando*,† ORCID Icon, Junwoon Lee* ORCID Icon, Mitsuru Shinozaki**, Toshihiro Kitajima**, Qi An* ORCID Icon, and Atsushi Yamashita* ORCID Icon

*The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8563, Japan

Corresponding author

**Technology Innovation R&D Dept. II, Research & Development Headquarters, KUBOTA Corporation
Sakai, Japan

Received:
November 18, 2024
Accepted:
January 9, 2025
Published:
May 5, 2025
Keywords:
equirectangular image, feature matching, two-view pose estimation
Abstract

This study proposes a method to efficiently reduce distortion effects in equirectangular images. Spherical cameras provide a wide field of view, advantageous for localization tasks. When applying standard image processing techniques to spherical images, they are commonly converted into equirectangular images by equirectangular projection, which introduces geometric distortions that can impair localization accuracy. Existing approaches for distortion mitigation frequently encounter a trade-off between accuracy and processing speed. We propose a method that mitigates distortion effects while reducing computational costs to overcome these limitations. Our method incorporates an innovative strategy for image rotation and region selection, improving computational efficiency in feature detection and description. Experimental results for two-view pose estimation, an essential component of localization, showed that our method achieves the fastest processing speed while maintaining accuracy comparable to that of distortion mitigation techniques.

Cite this article as:
T. Ando, J. Lee, M. Shinozaki, T. Kitajima, Q. An, and A. Yamashita, “Efficient Distortion Mitigation in Equirectangular Images for Two-View Pose Estimation,” Int. J. Automation Technol., Vol.19 No.3, pp. 226-236, 2025.
Data files:
References
  1. [1] A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “MonoSLAM: Real-Time Single Camera SLAM,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.29, No.6, pp. 1052-1067, 2007. https://doi.org/10.1109/TPAMI.2007.1049
  2. [2] C. Campos, R. Elvira, J. J. Gomez, J. M. M. Montiel, and J. D. Tardós, “ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM,” IEEE Trans. on Robotics, Vol.37, No.6, pp. 1874-1890, 2021. https://doi.org/10.1109/TRO.2021.3075644
  3. [3] B. K. Horn and B. G. Schunck, “Determining optical flow,” Artificial Intelligence, Vol.17, No.1, pp. 185-203, 1981. https://doi.org/10.1016/0004-3702(81)90024-2
  4. [4] T. Qin, P. Li, and S. Shen, “VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator,” IEEE Trans. on Robotics, Vol.34, No.4, pp. 1004-1020, 2018. https://doi.org/10.1109/TRO.2018.2853729
  5. [5] T. Shan, B. Englot, C. Ratti, and R. Daniela, “LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping,” Proc. of the 2021 IEEE Int. Conf. on Robotics and Automation (ICRA2021), pp. 5692-5698, 2021. https://doi.org/10.1109/ICRA48506.2021.9561996
  6. [6] J. Lee, R. Komatsu, M. Shinozaki, T. Kitajima, H. Asama, Q. An, and A. Yamashita, “Switch-SLAM: Switching-Based LiDAR-Inertial-Visual SLAM for Degenerate Environments,” IEEE Robotics and Automation Letters, Vol.9, No.8, pp. 7270-7277, 2024. https://doi.org/10.1109/LRA.2024.3421792
  7. [7] L. Valgaerts, A. Bruhn, M. Mainberger, and J. Weickert, “Dense versus sparse approaches for estimating the fundamental matrix,” Int. J. of Computer Vision, Vol.96, pp. 212-234, 2012. https://doi.org/10.1007/s11263-011-0466-7
  8. [8] S. Pathak, A. Moro, H. Fujii, A. Yamashita, and H. Asama, “Spherical Video Stabilization by Estimating Rotation from Dense Optical Flow Fields,” J. Robot. Mechatron., Vol.29, No.3, pp. 566-579, 2017. https://doi.org/10.20965/jrm.2017.p0566
  9. [9] H. Shi, Y. Zhou, K. Yang, X. Yin, Z. Wang, Y. Ye, Z. Yin, S. Meng, P. Li, and K. Wang, “PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding,” IEEE Trans. on Intelligent Transportation Systems, Vol.24, No.5, pp. 5570-5585, 2023. https://doi.org/10.1109/TITS.2023.3241212
  10. [10] J. Murrugarra-Llerena, T. L. T. Da Silveira, and C. R. Jung, “Pose Estimation for Two-View Panoramas based on Keypoint Matching: A Comparative Study and Critical Analysis,” Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR2022) Workshops, pp. 5198-5207, 2022. https://doi.org/10.1109/CVPRW56347.2022.00568
  11. [11] S. Jiang, K. You, L. Yaxin, D. Weng, and W. Chen, “3D Reconstruction of Spherical Images: A Review of Techniques, Applications, and Prospects,” Geo-spatial Information Science, Vol.27, No.6, pp. 1959-1988, 2024. https://doi.org/10.1080/10095020.2024.2313328
  12. [12] H. Huang and S.-K. Yeung, “360VO: Visual Odometry Using A Single 360 Camera,” Proc. of the 2022 IEEE Int. Conf. on Robotics and Automation (ICRA2022), pp. 5594-5600, 2022. https://doi.org/10.1109/ICRA46639.2022.9812203
  13. [13] Q. Wu, X. Xu, X. Chen, L. Pei, C. Long, J. Deng, G. Liu, S. Yang, S. Wen, and W. Yu, “360-VIO: A Robust Visual-Inertial Odometry Using a 360° Camera,” IEEE Trans. on Industrial Electronics, Vol.71, No.9, pp. 11136-11145, 2024. https://doi.org/10.1109/TIE.2023.3337541
  14. [14] J. Cruz-Mota, I. Bogdanova, B. Paquier, M. Bierlaire, and J.-P. Thiran, “Scale Invariant Feature Transform on the Sphere: Theory and Applications,” Int. J. of Computer Vision, Vol.98, No.2, pp. 217-241, 2012. https://doi.org/10.1007/s11263-011-0505-4
  15. [15] Q. Zhao, W. Feng, L. Wan, and J. Zhang, “SPHORB: A Fast and Robust Binary Feature on the Sphere,” Int. J. of Computer Vision, Vol.113, No.2, pp. 1573-1405, 2015. https://doi.org/10.1007/s11263-014-0787-4
  16. [16] H. Guan and W. A. P. Smith, “BRISKS: Binary Features for Spherical Images on a Geodesic Grid,” Proc. of the 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR2017), pp. 4886-4894, 2017. https://doi.org/10.1109/CVPR.2017.519
  17. [17] B. Coors, A. P. Condurache, and A. Geiger, “SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images,” Proc. of the 13th European Conf. on Computer Vision (ECCV2018), pp. 518-533, 2018. https://doi.org/10.1007/978-3-030-01240-3_32
  18. [18] Y. Li, C. Barnes, K. Huang, and F.-L. Zhang, “Deep 360° Optical Flow Estimation Based on Multi-projection Fusion,” Proc. of the 17th European Conf. on Computer Vision (ECCV2022), pp. 336-352, 2022. https://doi.org/10.1007/978-3-031-19833-5_20
  19. [19] Y. Wang, S. Cai, S.-J. Li, Y. Liu, Y. Guo, T. Li, and M.-M. Cheng, “CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System,” Proc. of the Asian Conf. on Computer Vision, pp. 34-49, 2018. https://doi.org/10.1007/978-3-030-20876-9_3
  20. [20] M. Eder, M. Shvets, J. Lim, and J.-M. Frahm, “Tangent Images for Mitigating Spherical Distortion,” Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR2020), pp. 12423-12431, 2020. https://doi.org/10.1109/CVPR42600.2020.01244
  21. [21] H. Taira, Y. Inoue, A. Torii, and M. Okutomi, “Robust feature matching for distorted projection by spherical cameras,” IPSJ Trans. on Computer Vision and Applications, Vol.7, pp. 84-88, 2015. https://doi.org/10.2197/ipsjtcva.7.84
  22. [22] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. of Computer Vision, Vol.60, No.2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  23. [23] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An Efficient Alternative to SIFT or SURF,” Proc. of the 2011 Int. Conf. on Computer Vision (ICCV2011), pp. 2564-2571, 2011. https://doi.org/10.1109/ICCV.2011.6126544
  24. [24] P. F. Alcantarilla, J. Nuevo, and A. Bartoli, “Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces,” Proc. of British Machine Vision Conf. (BMVC2013), 2013.
  25. [25] D. DeTone, T. Malisiewicz, and A. Rabinovich, “SuperPoint: Self-Supervised Interest Point Detection and Description,” Proc. of the 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR2018) Workshops, pp. 337-349, 2018. https://doi.org/10.1109/CVPRW.2018.00060
  26. [26] X. Zhao, X. Wu, W. Chen, P. C. Y. Chen, Q. Xu, and Z. Li, “ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation,” IEEE Trans. on Instrumentation and Measurement, pp. 1-16, 2023. https://doi.org/10.1109/TIM.2023.3271000
  27. [27] J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou, “LoFTR: Detector-Free Local Feature Matching with Transformers,” Proc. of the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR2021), pp. 8918-8927, 2021. https://doi.org/10.1109/CVPR46437.2021.00881
  28. [28] P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “SuperGlue: Learning Feature Matching with Graph Neural Networks,” Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR2020), pp. 4937-4946, 2020. https://doi.org/10.1109/CVPR42600.2020.00499
  29. [29] C. Gava, V. Mukunda, T. Habtegebrial, F. Raue, S. Palacio, and A. Dengel, “SphereGlue: Learning Keypoint Matching on High Resolution Spherical Images,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR2023) Workshops, pp. 6133-6143, 2023. https://doi.org/10.1109/CVPRW59228.2023.00653
  30. [30] T. Ando, J. Lee, M. Shinozaki, T. Kitajima, Q. An, and A. Yamashita, “Highly Accurate and Fast Two-view Pose Estimation by Fast Reduction of Spherical Image Distortion Effects,” Proc. of the 24th Int. Conf. on Control, Automation and Systems (ICCAS2024), pp. 774-779, 2024.
  31. [31] H. Huang, C. Liu, Y. Zhu, C. Hui, T. Braud, and S.-K. Yeung, “360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 22314-22324, 2024. https://doi.org/10.1109/CVPR52733.2024.02106
  32. [32] M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, Vol.24, No.6, pp. 381-395, 1981. https://doi.org/10.1145/358669.358692
  33. [33] R. Hartley, “In Defense of the Eight-Point Algorithm,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.6, pp. 580-593, 1997. https://doi.org/10.1109/34.601246

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Last updated on May. 08, 2025