Abstract
Detecting multi-plane from 3D point clouds can provide concise and meaningful abstractions of 3D data and give users higher-level interaction possibilities. However, existing algorithms are deficient in accuracy and robustness, and highly dependent on thresholds. To overcome these deficiencies, a novel method is proposed, which detects multi-plane from 3D point clouds by labeling points instead of greedy searching planes. It first generates initial models. Second, it computes energy terms and constructs the energy function. Third, the point labeling problem is solved by minimizing the energy function. Then, it refines the labels and parameters of detected planes. This process is iterated until the energy does not decrease. Finally, multiple planes are detected. Experimental results validate the proposed method. It outperforms existing algorithms in accuracy and robustness. It also alleviates the high dependence on thresholds and the unknown number of planes in 3D point clouds.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)
Gallup, D., Frahm, J., Pollefeys, M.: Piecewise planar and non-planar stereo for urban scene reconstruction. Proc. IEEE CVPR II, 803–806 (2010)
Liu, J., Wu, Z.: An adaptive approach for primitive shape extraction from point clouds. Optik 125(9), 2000–2008 (2014)
Ogundana, O., Coggrave, C., Burguete, R., Huntley, J.: Automated detection of planes in 3-D point clouds using fast Hough transforms. Opt. Eng. 50(5), 053609-1–053609-11 (2011)
Trevor, A., Gedikli, S., Rusu, R., Christensen, H.: Efficient organized point cloud segmentation with connected components. In: 3rd Workshop on Semantic Perception Mapping and Exploration, Karlsruhe, Germany (2013)
Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)
Nister, D.: Preemptive RANSAC for live structure and motion estimation. Mach. Vis. Appl. 16(5), 321–329 (2005)
Fan, M., Lee, T.: Variants of seeded region growing. IET Image Process 6(9), 478–485 (2015)
Duan, F., Wang, L., Guo, P.: RANSAC based ellipse detection with application to catadioptric camera calibration. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part II. LNCS, vol. 6444, pp. 525–532. Springer, Heidelberg (2010)
Isack, H., Boykov, Y.: Energy-based geometric multi-model fitting. Int. J. Comput. Vis. 97(2), 123–147 (2012)
Delong, A., Osokin, A., Isack, H., Boykov, Y.: Fast approximate energy minimization with label costs. Int. J. Comput. Vis. 96(1), 1–27 (2012)
Rusu, R., Cousins, S.: 3D is here: point cloud library (PCL). In: Proceedings of IEEE ICRA, pp. 1–4 (2011)
Henry, P., Fox, D., Bhowmik, A., Mongia, R.: Patch volumes: segmentation-based consistent mapping with RGB-D cameras. In: Proceedings of IEEE 3DV, pp. 803–806 (2013)
Lai, K., Bo, L., Fox, D.: Unsupervised feature learning for 3D scene labeling. Proc. IEEE ICRA II, 803–806 (2014)
Acknowledgments
This work was supported by the NSFC under Grant 61572078, Program for New Century Excellent Talents in University under Grant NCET-13-0051, and Beijing Natural Science Foundation under Grant 4152027.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, L., Shen, C., Duan, F., Guo, P. (2016). Energy-Based Multi-plane Detection from 3D Point Clouds. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_80
Download citation
DOI: https://doi.org/10.1007/978-3-319-46672-9_80
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46671-2
Online ISBN: 978-3-319-46672-9
eBook Packages: Computer ScienceComputer Science (R0)