Abstract
Throughout the course of several years, significant progress has been made with regard to the accuracy and performance of pair-wise alignment techniques; however when considering low-resolution scans with minimal pair-wise overlap, and scans with high levels of symmetry, the process of successfully performing sequential alignments in the object reconstruction process remains a challenging task. Even with the improvements in surface point sampling and surface feature correspondence estimation, existing techniques do not guarantee an alignment between arbitrary point-cloud pairs due to statistically-driven estimation models. In this paper we define a robust and intuitive painting-based feature correspondence selection methodology that can refine input sets for these existing techniques to ensure alignment convergence. Additionally, we consolidate this painting process into a semi-automated alignment compilation technique that can be used to ensure the proper reconstruction of scanned models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rusu, R.B., Blodow, N., Beetz, M.: Fast Point Feature Histograms (FPFH) for 3D registration. In: International Conference on Robotics and Automation, ICRA, pp. 3212–3217 (2009)
Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14 (1992)
ter Haar, F.B., Veltkamp, R.C.: Automatic multiview quadruple alignment of unordered range scans. In: IEEE International Conference on Shape Modeling and Applications, SMI 2007, June 13-15, pp. 137–146 (2007)
Cho, M., Lee, J., Lee, K.M.: Feature Correspondence and deformable Object Matching via Agglomerative Correspondence Clustering. In: IEEE International Conference of Computer Vision (ICCV) (2009)
Transue, S., Choi, M.-H.: Enhanced Pre-conditioning Algorithm for the Accurate Alignment of 3D Range Scans. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (2013)
Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning Point Cloud Views using Persistent Feature Histograms. In: Proceedings of the 21st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France, September 22-26 (2008)
Rusinkiewicz, S., Levoy, M.: Efficient Variants of the ICP Algorithm. In: Proceedings of the International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)
Chen, Y., Medioni, G.: Object Modeling by Registration of Multiple Range Images. International Journal of Image and Vision Computing 10(3), 145–155 (1992)
Torsello, A., Rodola‘, E., Albarelli, A.: Sampling Relevant Points for Surface Registration. In: 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), May 16-19, pp. 290–295 (2011)
Chatterjee, A., Jain, S., Govindu, V.M.: A pipeline for building 3D models using depth cameras. In: ICVGIP 2012 Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing. Article No. 38 (2012)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Scanalyze: A system for aligning and merging range data. Stanford University (1997-2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Transue, S., Choi, MH. (2014). Intuitive Alignment of Point-Clouds with Painting-Based Feature Correspondence. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-14364-4_72
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
eBook Packages: Computer ScienceComputer Science (R0)