Elwira Hołowko
Abstract
In this article, a method of merging point clouds using the modified Harris corner detection algorithm for extracting interest points of textured 3D point clouds is proposed. A new descriptor characterizing point features for identifying corresponding points in datasets is presented. The merging process is based on the Random Sample Consensus (RANSAC) algorithm, which enables calculation of the geometric transformation between point clouds based on a set of interest points that includes incorrect samples, called outliers. The proposed processing path is designed to integrate many directional measurements, which are acquired with a 3D scanner and are represented as unsorted point clouds (x, y, z) with color information (R, G, B). Exemplary measurements shown in this article represent sections of ceiling in the King's Chinese Cabinet of the Museum of King Jan III's Palace at Wilanow in Warsaw, Poland, as well as some more complex objects. Experimental verification confirms the effectiveness of the proposed method in integrating directional measurements of objects with detailed texture, particularly if they have no unique geometric features.
- G. H. Bendels, P. Degener, R. Wahl, M. Kortgen, and R. Klein. 2004. Image-based registration of 3D-range data using feature surface elements. In Proceedings of the 5th International Conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage. 115--124. Google Scholar
Digital Library
- P. J. Besl and N. D. Mckay. 1992. A method for registration of 3-D l. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239--256. Google ScholarDigital Library
- J. Gómez-García-Bermejo, E. Zalama, and R. Feliz. 2013. Automated registration of 3D scans using geometric features and normalized color data. Computer-Aided Civil and Infrastructure Engineering 28, 98--111.Google ScholarCross Ref
- C. Harris and M. Stephens. 1988. A combined corner and edge detection. Proceedings of the Fourth Alvey Vision Conference, Manchester, 147--151.Google Scholar
- K. Ikeuchi and D. Miyazaki. 2008. Digitally Archiving Cultural Objects. Springer. Google ScholarDigital Library
- A. E. Johnson and M. Hebert. 1999. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 433--449. Google ScholarDigital Library
- I. T. Jolliffe. 2002. Principal Component Analysis (2nd Ed). Springer.Google Scholar
- T. Jost. 2002. Fast Geometric Matching For Shape Registration. Retrieved from http://wwwa.unine.ch/parlab/pub/pdf/2002-thTJ.pdf.Google Scholar
- S. Lazebnik, C. Schmid, and J. Ponce. 2005. A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1265--1278. Google ScholarDigital Library
- D. G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91--110. Google ScholarDigital Library
- A. Makadia, A. I. Patterson, and K. Daniilidis. 2006. Fully automatic registration of 3D point clouds. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 1297--1304. Google ScholarDigital Library
- K. Mikolajczyk and C. Schmid. 2005. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence 27, 1615--1630. Google ScholarDigital Library
- H. Moravec. 1980. Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Robotics Institute, Carnegie Mellon University.Google Scholar
- K. Pulli. 1999. Multiview registration for large data sets. In Proceedings of the 2nd International Conference on 3-D Digital Imaging and Modeling. 160--168. Google ScholarDigital Library
- G. Roth. 1999. Registering two overlapping range images. In Proceedings of the 2nd International Conference on 3-D Digital Imaging and Modeling. 191--200. Google ScholarDigital Library
- R. B. Rusu, N. Blodow, and M. Beetz. 2009. Fast point feature histograms (FPFH) for 3D registration. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’09). 3212--3217. Google ScholarDigital Library
- R. B. Rusu, Z. C. Marton, N. Blodow, and M. Beetz. 2008. Persistent point feature histograms for 3D point clouds. In Proceedings of the 10th International Conference on Intelligent Autonomous Systems. 154, 477.Google Scholar
- I. Sipiran and B. Bustos. 2010. A robust 3D interest points detector based on Harris operator. In Proceedings of the 3rd Eurographics Conference on 3D Object Retrieval. 7--14. Google ScholarDigital Library
- R. Sitnik, P. Bolewicki, J. Rutkiewicz, J. Michonski, M. Karaszewski, J. Lenar, K. Mularczyk, and W. Zaluski. 2010. Project “Revitalization and Digitization of the Seventeenth Century Palace Complex and Garden In Wilanow -- Phase III” task 3D Digitalization of Selected Exhibits Collection. In Proceedings of the Euromed 2010. 7--13.Google Scholar
- R. Sitnik, M. Kujawinska, and J. Woznicki. 2002. Digital fringe projection system for large-volume 360-degree shape measurement. Optical Engineering 41, 443--449.Google ScholarCross Ref
- B. Steder, R. Bogdan, R. Kurt, and K. W. Burgard. 2011. Point feature extraction on 3D range scans taking into account object boundaries. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’11). 2601--2608.Google Scholar
- F. Tombari, S. Salti, and L. Di Stefano, 2011. A combined texture-shape descriptor for enhanced 3D feature matching. IEEE 18th International Conference on Image Processing (ICIP’11). 809--812.Google Scholar
- A. Zaharescu, E. Boyer, K. Varanasi, and R. Horaud. 2009. Surface feature detection and description with applications to mesh matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). 373--380.Google Scholar
- F. Zhao. 2006. Image matching by normalized cross-correlation. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’06).Google Scholar
- Y. Zhong. 2009. Intrinsic shape signatures: A shape descriptor for 3D object recognition. In Proceedings of the IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops). 689--696.Google ScholarCross Ref
- M. Zuliani, Ed. 2008. RANSAC for Dummies. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.139.2808.Google Scholar
Index Terms
- Color-Based Algorithm for Automatic Merging of Multiview 3D Point Clouds
Recommendations
Multi-scale region perpendicular local binary pattern: an effective feature for interest region description
This paper proposes the perpendicular local binary pattern (PLBP) for efficiently describing textures in an interest region. Its novelty is two-fold: (1) the candidate generation scheme provides a set of patterns for each pixel, instead of ...
Compact color-texture description for texture classification
We show that combining multiple texture description methods significantly improves the performance compared to using the single best texture method alone.We further propose to use information theoretic compression approach to compress high-dimensional ...
A Performance Evaluation of Local Descriptors
In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed in the literature. It is unclear which descriptors ...
Comments