Evaluation of 2D affine – hand-crafted detectors for feature-based TLS point cloud registration
More details
Hide details
Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Faculty of Geodesy and Cartography, Warsaw University of Technology, Pl. Politechniki 1, 00-661, Warsaw, Poland
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2023-11-28
Final revision date: 2024-04-05
Acceptance date: 2024-04-10
Publication date: 2024-05-23
Corresponding author
Jakub Markiewicz   

Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Faculty of Geodesy and Cartography, Warsaw University of Technology, Pl. Politechniki 1, 00-661, Warsaw, Poland
Reports on Geodesy and Geoinformatics 2024;117:69-88
The development of modern surveying methods, particularly, Terrestrial Laser Scanning (TLS), has found wide application in protecting and monitoring engineering and objects and sites of cultural heritage. For this reason, it is crucial that several factors affecting the correctness of point cloud registration are considered, including the correctness of the distribution of control points (both signalised and natural), the quality of the process, and robustness analysis. The aim of this article is to evaluate the quality and correctness of TLS registration based on point clouds converted to raster form (in spherical mapping) and hand-crafted detectors. The expanded Structure-from-Motion (SfM) was used to detect the tie points for TLS registration and reliability assessment. The results demonstrated that affine detectors are useful in detecting a high number of key points (increased for point detectors by 8-12 times and for blob detectors by about 10-24 times), improving the quality and TLS registration completeness. For the registration accuracy of point cloud on signalised check points, the lower values can be noted for maximum RMSE errors for blob affine detectors than detectors and larger values for corner detectors and affine detectors (not more than 4 mm in the extreme cases, typically 2 mm). The commonly-applied target-based registration method yields similar results (differences do not exceed - in extreme cases - 3.5 mm, typically less than 2 mm), proving that using affine detectors in the TLS registration process is and reasonable and can be recommended.
The research was funded by the Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) programme (No. 1820/55/Z01/2021).
Abbate, E., Sammartano, G., and Spanò, A. (2019). Prospective upon multi-source urban scale data for 3D documentation and monitoring of urban legacies. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W11:11–19, doi:10.5194/isprs-archives-xlii-2-w11-11-2019.
Agrawal, M., Konolige, K., and Blas, M. R. (2008). Censure: Center surround extremas for realtime feature detection and matching. In European conference on computer vision, pages 102–115. Springer, doi:10.1007/978-3-540-88693-8_8.
Arif, R. and Essa, K. (2017). Evolving techniques of documentation of a world heritage site in Lahore. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W5:33–40, doi:10.5194/isprs-archives-xlii-2-w5-33-2017.
Bae, K.-H. and Lichti, D. D. (2008). A method for automated registration of unorganised point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 63(1):36–54, doi:10.1016/j.isprsjprs.2007.05.012.
Bay, H., Tuytelaars, T., and Van Gool, L. (2006). SURF: Speeded Up Robust Features, pages 404–417. Springer Berlin Heidelberg, doi:10.1007/11744023_32.
Bianco, S., Ciocca, G., and Marelli, D. (2018). Evaluating the performance of Structure from Motion pipelines. Journal of Imaging, 4(8):98, doi:10.3390/jimaging4080098.
Biber, P. and Straßer, W. (2003). The normal distributions transform: A new approach to laser scan matching. In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453), volume 3, pages 2743–2748. IEEE, doi:10.1109/IROS.2003.1249285.
Boehler, W., Vicent, M. B., Marbs, A., et al. (2003). Investigating laser scanner accuracy. The international archives of photogrammetry, remote sensing and spatial information sciences, 34(Part 5):696–701.
Bosché, F. (2010). Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Advanced Engineering Informatics, 24(1):107–118, doi:10.1016/j.aei.2009.08.006.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698, doi:10.1109/tpami.1986.4767851.
Chen, Y., Chen, Y., and Wang, G. (2019). Bundle adjustment revisited. doi:10.48550/ARXIV.1912.03858.
Cheng, L., Chen, S., Liu, X., Xu, H., Wu, Y., Li, M., and Chen, Y. (2018). Registration of laser scanning point clouds: A review. Sensors, 18(5):1641, doi:10.3390/s18051641.
Cloudcompare (2024). CloudCompare project. https://cloudcompare-org.danie....
Das, A. and Waslander, S. L. (2012). Scan registration with multi-scale k-means normal distributions transform. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, doi:10.1109/iros.2012.6386185.
Dong, Z., Yang, B., Liang, F., Huang, R., and Scherer, S. (2018). Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor. ISPRS Journal of Photogrammetry and Remote Sensing, 144:61–79, doi:10.1016/j.isprsjprs.2018.06.018.
Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381–395, doi:10.1145/358669.358692.
Giżyńska, J., Komorowska, E., and Kowalczyk, M. (2022). The comparison of photogrammetric and terrestrial laser scanning methods in the documentation of small cultural heritage object – case study. Journal of Modern Technologies for Cultural Heritage Preservation, 1(1), doi:10.33687/jmtchp.001.01.0013.
Guo, Y., Sohel, F., Bennamoun, M., Lu, M., and Wan, J. (2013). Rotational projection statistics for 3D local surface description and object recognition. International Journal of Computer Vision, 105(1):63–86, doi:10.1007/s11263-013-0627-y.
Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Procedings of the Alvey Vision Conference 1988, AVC 1988. Alvey Vision Club, doi:10.5244/c.2.23.
Hekimoglu, S., Demirel, H., and Aydin, C. (2002). Reliability of the conventional deformation analysis methods for vertical networks. In FIG XXII International Congress, pages 1–13. International Federation of Surveyors Washington, DC.
Karwel, A. K. and Markiewicz, J. (2022). The methodology of the archival aerial image orientation based on the SfM method. Sensors and Machine Learning Applications, 1(2), doi:10.55627/smla.001.02.0015.
Łapiński, S. (2011). Method of network reliability analysis based on accuracy characteristics. Reports on Geodesy, 90(1):265–270.
Leutenegger, S., Chli, M., and Siegwart, R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In 2011 International Conference on Computer Vision. IEEE, doi:10.1109/iccv.2011.6126542.
Lowe, D. (1999). Object recognition from local scale-invariant features. In Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, doi:10.1109/iccv.1999.790410.
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110, doi:10.1023/b:visi.0000029664.99615.94.
Lu-Xingchang and Liu-Xianlin (2006). Reconstruction of 3D Model Based on Laser Scanning, pages 317–332. Springer Berlin Heidelberg, doi:10.1007/978-3-540-36998-1_25.
Markiewicz, J., Łapiński, S., Bocheńska, A., and Kot, P. (2021). The reliability assessment of the TLS registration methods – the case study of the Royal Castle in Warsaw. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2021:855–861, doi:10.5194/isprs-archives-xliii-b2-2021-855-2021.
Markiewicz, J., Kot, P., Markiewicz, u., and Muradov, M. (2023). The evaluation of hand-crafted and learned-based features in Terrestrial Laser Scanning-Structure-from-Motion (TLS-SfM) indoor point cloud registration: the case study of cultural heritage objects and public interiors. Heritage Science, 11(1), doi:10.1186/s40494-023-01099-9.
Markiewicz, J. and Zawieska, D. (2019). The influence of the cartographic transformation of TLS data on the quality of the automatic registration. Applied Sciences, 9(3):509, doi:10.3390/app9030509.
Markiewicz, J. S. (2016). The use of computer vision algorithms for automatic orientation of Terrestrial Laser Scanning data. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B3:315–322, doi:10.5194/isprsarchives-xli-b3-315-2016.
Moisan, L. and Stival, B. (2004). A probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix. International Journal of Computer Vision, 57(3):201–218, doi:10.1023/b:visi.0000013094.38752.54.
Moussa, W. (2014). Integration of digital photogrammetry and terrestrial laser scanning for cultural heritage data recording. PhD thesis, University Of Stuttgart, Germany.
Mukupa, W., Roberts, G. W., Hancock, C. M., and Al-Manasir, K. (2016). A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures. Survey Review, pages 1–18, doi:10.1080/00396265.2015.1133039.
Muradov, M., Kot, P., Markiewicz, J., Łapiński, S., Tobiasz, A., Onisk, K., Shaw, A., Hashim, K., Zawieska, D., and Mohi-Ud-Din, G. (2022). Non-destructive system for in-wall moisture assessment of cultural heritage buildings. Measurement, 203:111930, doi:10.1016/j.measurement.2022.111930.
Nowak, E. and Odziemczyk, W. (2018). Adjustment of observation accuracy harmonisation parameters in optimising the network’s reliability. Reports on Geodesy and Geoinformatics, 105(1):53–59, doi:10.2478/rgg-2018-0006.
Pavlov, A. L., Ovchinnikov, G. V., Derbyshev, D. Y., Tsetserukou, D., and Oseledets, I. V. (2017). AA-ICP: Iterative Closest Point with Anderson Acceleration. 2018 IEEE International Conference On Robotics And Automation (ICRA), doi:10.48550/ARXIV.1709.05479.
Pomerleau, F., Colas, F., and Siegwart, R. (2015). A review of point cloud registration algorithms for mobile robotics. Foundations and Trends in Robotics, 4(1):1–104, doi:10.1561/2300000035.
Prószyński, W. and Łapiński, S. (2018). Reliability analysis for non-distorting connection of engineering survey networks. Survey Review, 51(366):219–224, doi:10.1080/00396265.2018.1425605.
Rashidi, M., Mohammadi, M., Sadeghlou Kivi, S., Abdolvand, M. M., Truong-Hong, L., and Samali, B. (2020). A decade of modern bridge monitoring using Terrestrial Laser Scanning: Review and future directions. Remote Sensing, 12(22):3796, doi:10.3390/rs12223796.
Rofatto, V. F., Matsuoka, M. T., Klein, I., Veronez, M. R., Bonimani, M. L., and Lehmann, R. (2018). A half-century of Baarda’s concept of reliability: a review, new perspectives, and applications. Survey Review, 52(372):261–277, doi:10.1080/00396265.2018.1548118.
Rosten, E. and Drummond, T. (2006). Machine Learning for High-Speed Corner Detection, pages 430–443. Springer Berlin Heidelberg, doi:10.1007/11744023_34.
Salvi, J., Matabosch, C., Fofi, D., and Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image and Vision computing, 25(5):578–596, doi:10.1016/j.imavis.2006.05.012.
Staiger, R. (2005). The geometrical quality of Terrestrial Laser Scanner (TLS). In Proceedings of FIG Working Week and GSDI-8, Cairo, Egypt, pages 1–11.
Takeuchi, E. and Tsubouchi, T. (2006). A 3-D scan matching using improved 3-D normal distributions transform for mobile robotic mapping. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, doi:10.1109/iros.2006.282246.
Tam, G. K. L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X.-F., and Rosin, P. L. (2013). Registration of 3D point clouds and meshes: A survey from rigid to nonrigid. IEEE Transactions on Visualization and Computer Graphics, 19(7):1199–1217, doi:10.1109/tvcg.2012.310.
Tazir, M. L., Gokhool, T., Checchin, P., Malaterre, L., and Trassoudaine, L. (2019). Cluster ICP: Towards sparse to dense registration. In Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, volume 867, pages 730–747. Springer, doi:10.1007/978-3-030-01370-7_57.
Tobiasz, Markiewicz, Łapiński, Nikel, Kot, and Muradov (2019). Review of methods for documentation, management, and sustainability of cultural heritage. Case Study: Museum of King Jan III’s Palace at Wilanów. Sustainability, 11(24):7046, doi:10.3390/su11247046.
Tola, E., Lepetit, V., and Fua, P. (2010). DAISY: An efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5):815–830, doi:10.1109/tpami.2009.77.
Tuytelaars, T. and Mikolajczyk, K. (2007). Local invariant feature detectors: A survey. Foundations and Trends® in Computer Graphics and Vision, 3(3):177–280, doi:10.1561/0600000017.
Urban, S. and Weinmann, M. (2015). Finding a good feature detector-descriptor combination for the 2D keypoint-based registration of TLS point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3/W5:121–128, doi:10.5194/isprsannals-ii-3-w5-121-2015.
Vacca, G., Mistretta, F., Stochino, F., and Dessi, A. (2016). Terrestrial laser scanner for monitoring the deformations and the damages of buildings. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B5:453–460, doi:10.5194/isprs-archives-xli-b5-453-2016.
Wang, W., Zhao, W., Huang, L., Vimarlund, V., and Wang, Z. (2014). Applications of terrestrial laser scanning for tunnels: a review. Journal of Tra c and Transportation Engineering (English Edition), 1(5):325–337, doi:10.1016/s2095-7564(15)30279-8.
Weinmann, M. (2016). From Irregularly Distributed 3D Points To Object Classes. Reconstruction And Analysis Of 3D Scenes. Springer International Publishing, doi:10.1007/978-3-319-29246-5.
Wojtkowska, M., Kedzierski, M., and Delis, P. (2021). Validation of terrestrial laser scanning and artificial intelligence for measuring deformations of cultural heritage structures. Measurement, 167:108291, doi:10.1016/j.measurement.2020.108291.
Xu, Y., Boerner, R., Yao, W., Hoegner, L., and Stilla, U. (2019). Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets. ISPRS Journal of Photogrammetry and Remote Sensing, 151:106–123, doi:10.1016/j.isprsjprs.2019.02.015.
Yu, G. and Morel, J.-M. (2011). ASIFT: An algorithm for fully affine invariant comparison. Image Processing On Line, 1:11–38, doi:10.5201/ipol.2011.my-asift.
Z+F (2024). Zoller + Fröhlich. https://www.zofre.de/en/.
Journals System - logo
Scroll to top