Abstract
Pure rotational anomaly recognition is a critical problem in 3D visual computation, requiring precise recognition for reliable camera pose estimation and robust 3D reconstruction. Current techniques primarily focus on model selection, parallax angle, and intersection constraints within two-view geometric models when identifying pure rotational motion. This paper proposes a multi-view pure rotational detection method that draws upon two-view rotation-only recognition indicators to identify pure rotational views that cause pose estimation anomalies. An automatic data annotation and training strategy for rotation-only anomaly recognition in multi-view pose estimation data is also introduced. Our experiments demonstrate that our proposed model for rotation-only anomaly recognition achieves an accuracy of 91% on the test set and is highly effective in improving the precision, resilience, and performance of camera pose estimation, 3D reconstruction, object tracking, and other computer vision tasks. The effectiveness of our approach is validated through comparison with related approaches in simulated camera motion trajectory experiments and Virtual KITTI dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cai, Q., Wu, Y., Zhang, L., Zhang, P.: Equivalent constraints for two-view geometry: pose solution/pure rotation identification and 3D reconstruction. Int. J. Comput. Vision 127(2), 163–180 (2019)
Cai, Q., Zhang, L., Wu, Y., Yu, W., Hu, D.: A pose-only solution to visual reconstruction and navigation. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 73–86 (2023)
Chatterjee, A., Govindu, V.M.: Efficient and robust large-scale rotation averaging. In: 2013 IEEE International Conference on Computer Vision, pp. 521–528. IEEE (2013)
Civera, J., Davison, A.J., Montiel, J.M.M.: Interacting multiple model monocular slam. In: 2008 IEEE International Conference on Robotics and Automation, pp. 3704–3709. IEEE (2008)
Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2625–2634. IEEE (2015)
Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016)
Gauglitz, S., Sweeney, C., Ventura, J., Turk, M., Höllerer, T.: Live tracking and mapping from both general and rotation-only camera motion. In: 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 13–22. IEEE (2012)
Gauglitz, S., Sweeney, C., Ventura, J., Turk, M., Höllerer, T.: Model estimation and selection towards unconstrained real-time tracking and mapping. IEEE Trans. Visual Comput. Graphics 20(6), 825–838 (2014)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE (2016)
Herrera, D.C., Kim, K., Kannala, J., Pulli, K., Heikkilä, J.: DT-slam: deferred triangulation for robust slam. In: 2014 2nd International Conference on 3D Vision, vol. 1, pp. 609–616. IEEE (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017). https://arxiv.org/abs/1412.6980
Liu, C., Ma, X., He, X., Xu, T.: Hierarchical multimodal attention network based on semantically textual guidance for video captioning. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) ICONIP 2022. LNCS, vol. 13625, pp. 158–169. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-30111-7_14
Ma, C.Y., Chen, M.H., Kira, Z., AlRegib, G.: TS-LSTM and temporal-inception: exploiting spatiotemporal dynamics for activity recognition. Signal Process. Image Commun. 71, 76–87 (2019)
Özyeşil, O., Singer, A.: Robust camera location estimation by convex programming. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015)
Özyeşil, O., Voroninski, V., Basri, R., Singer, A.: A survey of structure from motion. Acta Numer. 26, 305–364 (2017)
Pirchheim, C., Schmalstieg, D., Reitmayr, G.: Handling pure camera rotation in keyframe-based slam. In: 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 229–238. IEEE (2013)
Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362–373. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_33
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 68–576. Curran Associates, Inc. (2014)
Torr, P.H.: Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. Int. J. Comput. Vision 50(1), 35–61 (2002)
Zhao, J.: An efficient solution to non-minimal case essential matrix estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(4), 1777–1792 (2022)
Zhou, Y., Yan, F., Zhou, Z.: Handling pure camera rotation in semi-dense monocular SLAM. Vis. Comput. 35(1), 123–132 (2019)
Acknowledgements
This work was partially funded by the National Natural Science Foundation of China (61876034).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, S., Cai, Q., Hu, Y., Zhong, X. (2024). Enhancing Camera Position Estimation by Multi-view Pure Rotation Recognition and Automated Annotation Learning. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_42
Download citation
DOI: https://doi.org/10.1007/978-981-99-8148-9_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8147-2
Online ISBN: 978-981-99-8148-9
eBook Packages: Computer ScienceComputer Science (R0)