ABSTRACT
The use of a terrestrial laser scanner (TLS) has become a popular technique for the acquisition of 3D scenes in architecture and design. Surface reconstruction is used to generate a digital model from the acquired point clouds. However, the model often consists of excessive data, limiting real-time user experiences that make use of the model. In this study, we present a coarse to fine planar shape segmentation method for indoor point clouds, which results in the digital model of an indoor scene being represented by a small number of planar patches. First, the Gaussian map and region growing techniques are used to coarsely segment the planar shape from sampled point clouds. Then, the best-fit-plane is calculated by random sample consensus (RANSAC), avoiding the negative impact of outliers. Finally, the refinement of planar shape is produced by projecting point clouds onto the corresponding bestfit-plane. Our method has been demonstrated to be robust towards noise and outliers in the scanned point clouds and overcomes the limitations of over- and under-segmentation. We have tested our system and algorithms on real datasets and experiments show the reliability of the proposed method against existing region-growing methods.
- Adan, A., Quintana, B., V Zquez, A.S., Olivares, A., Parra, E. and Prieto, S. 2015. Towards the automatic scanning of indoors with robots. Sensors 15,5, 11551--11574.Google ScholarCross Ref
- Awwad, T.M., Zhu, Q., Du, Z. and Zhang, Y. 2010. An improved segmentation approach for planar surfaces from unstructured 3D point clouds. The Photogrammetric Record 25,129, 5--23.Google ScholarCross Ref
- Biosca, J.M. and Lerma, J.L. 2008. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods. ISPRS Journal of Photogrammetry and Remote Sensing 63,1, 84--98.Google ScholarCross Ref
- Buck, U., Naether, S., Rass, B., Jackowski, C., and Thali, M.J. 2013. Accident or homicide-virtual crime scene reconstruction using 3D methods. Forensic Sci. Int. 225, 1--3, 75--84.Google Scholar
- Crosilla, F., Visintini, D. and Sepic, F. 2009. Reliable automatic classification and segmentation of laser point clouds by statistical analysis of surface curvature values. Applied Geomatics 1, 17--30.Google ScholarCross Ref
- Deschaud, J., and Goulette, F. 2010. A fast and accurate plane detection algorithm for large noisy point clouds using filtered normals and voxel growing. In Proceedings of 3D Processing, Visualization and Transmission Conference (3DPVT2010).Google Scholar
- Douros, I. and Buxton, B.F. 2002. Three-dimensional surface curvature estimation using quadric surface patches. Scanning.Google Scholar
- 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. Google ScholarDigital Library
- Guo, J., Yan, D.M., Jia, X. and Zhang, X. 2015. Efficient maximal Poisson-disk sampling and remeshing on surfaces. Computers & Graphics 46, 72--79. Google ScholarDigital Library
- Hoppe, H., Derose, T., Duchamp, T., Mcdonald, J. and Stuetzle, W. 1992. Surface reconstruction from unorganized points. In Proceedings of SIGGRAPH'92, 71--78. Google ScholarDigital Library
- Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D. and Davison, A. 2011. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In Proceedings of the 24th annual ACM symposium on User interface software and technology, 559--568. Google ScholarDigital Library
- Kazhdan, M., Bolitho, M. and Hoppe, H. 2006. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 61--70. Google ScholarDigital Library
- Leng, X., Xiao, J. and Wang, Y. 2016. A multi-scale plane-detection method based on the Hough transform and region growing. The Photogrammetric Record 31, 154, 166--192.Google Scholar
- Liu, Y. and Xiong, Y. 2008. Automatic segmentation of unorganized noisy point clouds based on the Gaussian map. Computer-Aided Design 40, 5, 576--594. Google ScholarDigital Library
- Ma, L., Favier, R., Do, L., Bondarev, E. and De with, P.H. 2013. Plane segmentation and decimation of point clouds for 3D environment reconstruction. IEEE 10th Consumer Communications and Networking Conference (CCNC), 43--49.Google Scholar
- Mitra, N.J. and Nguyen, A. 2003. Estimating surface normals in noisy point cloud data. In Proceedings of the nineteenth annual symposium on Computational geometry, 322--328. Google ScholarDigital Library
- Mozos, O.M., Mizutani, H., Kurazume, R. and Hasegawa, T. 2012. Categorization of indoor places using the kinect sensor. Sensors 12, 5, 6695--6711.Google ScholarCross Ref
- Mura, C., Mattausch, O., Villanueva, A.J., Gobbetti, E. and Pajarola, R. 2014. Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts. Computers & Graphics 44, 20--32. Google ScholarDigital Library
- Nan, L., Xie, K. and Sharf, A. 2012. A search-classify approach for cluttered indoor scene understanding. ACM Transactions on Graphics 31, 6, Article 137. Google ScholarDigital Library
- Nurunnabi, A., West, G. and Belton, D. 2015. Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data. Pattern Recognition 48, 4, 1404--1419. Google ScholarDigital Library
- Ochmann, S., Vock, R., Wessel, R. and Klein, R. 2016. Automatic reconstruction of parametric building models from indoor point clouds. Computers & Graphics 54, 94--103. Google ScholarDigital Library
- Oesau, S., Lafarge, F. and Alliez, P. 2014. Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut. ISPRS Journal of Photogrammetry and Remote Sensing 90, 68--82.Google ScholarCross Ref
- Pauly, M., Gross, M. and Kobbelt, L.P. 2002. Efficient simplification of point-sampled surfaces. In Proceedings of the IEEE conference on Visualization'02, 163--170. Google ScholarDigital Library
- Pauly, M., Keiser, R. and Gross, M. 2003. Multi-scale Feature Extraction on Point-Sampled Surfaces. Computer Graphics Forum 22, 3, 281--289. Google ScholarDigital Library
- Pauly, M., Keiser, R., Kobbelt, L.P. and Gross, M. 2003. Shape modeling with point-sampled geometry. ACM Transactions on Graphics 22, 3, 641--650. Google ScholarDigital Library
- Rabbani, T., Van Den Heuvel, F. and Vosselmann, G. 2006. Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36, 248--253.Google Scholar
- Schnabel, R., Wahl, R. and Klein, R. 2007. Efficient RANSAC for Point-Cloud Shape Detection. Computer Graphics Forum 26, 2, 214--226.Google ScholarCross Ref
- Shao, T., Xu, W., Zhou, K., Wang, J., Li, D. and Guo, B. 2012. An interactive approach to semantic modeling of indoor scenes with an RGBD camera. ACM Transactions on Graphics 31, 6, Article 136. Google ScholarDigital Library
- Silberman, N. and Fergus, R. 2011. Indoor scene segmentation using a structured light sensor. Proceeding of IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 601--608.Google Scholar
- Silberman, N., Hoiem, D., Kohli, P. and Fergus, R. 2012. Indoor segmentation and support inference from RGBD images. Proceeding of European Conference on Computer Vision (ECCV'12), 746--760. Google ScholarDigital Library
- Tarsha-Kurdi, F., Landes, T. and Grussenmeyer, P. 2007. Hough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data. In Proceedings of the ISPRS Workshop on Laser Scanning, 407--412.Google Scholar
- VC, H.P. 1962. Method and means for recognizing complex patterns. In US Patent, 1962Google Scholar
- Vosselman, G. and Dijkman, S. 2001. 3D building model reconstruction from point clouds and ground plans. International archives of photogrammetry remote sensing and spatial information sciences 34, 37--44.Google Scholar
- Wang, Y., Feng, H.Y., Delorme, F.É. and Engin, S. 2013. An adaptive normal estimation method for scanned point clouds with sharp features. Computer-Aided Design 45, 11, 1333--1348. Google ScholarDigital Library
- Wang, Y., Hao, W., Ning, X., Zhao, M., Zhang, J., Shi, Z. and Zhang, X. 2013. Automatic segmentation of urban point clouds based on the Gaussian map. The Photogrammetric Record 28, 144, 342--361.Google Scholar
- Weber, C., Hahmann, S. and Hagen, H. 2010. Sharp feature detection in point clouds. In Proceeding of Shape Modeling International Conference (SMI' 10), 175--186. Google ScholarDigital Library
- Whelan, T., Kaess, M., Johannsson, H., Fallon, M., Leonard, J.J. and Mcdonald, J. 2015. Real-time large-scale dense RGB-D SLAM with volumetric fusion. The International Journal of Robotics Research 34, 598--626. Google ScholarDigital Library
- Xiong, X., Adan, A., Akinci, B. and Huber, D. 2013. Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction 31, 325--337.Google ScholarCross Ref
- Pang X., Pang., M, P. and Xiao, C. 2010. An algorithm for extracting and enhancing valley-ridge features from point sets. Acta Automatica Sinica 36, 1074--1083.Google ScholarCross Ref
- Zhang, X., Li, G., Xiong, Y. and He, F. 2008. 3D mesh segmentation using mean-shifted curvature. In International Conference on Geometric Modeling and Processing, 465--474. Google ScholarDigital Library
- Zou, Y., Chen, W., Wu, X. and Liu, Z. 2012. Indoor localization and 3D scene reconstruction for mobile robots using the Microsoft Kinect sensor. In IEEE 10th International Conference on Industrial Informatics, 1182--1187.Google Scholar
Index Terms
- Automatic planar shape segmentation from indoor point clouds
Recommendations
An adaptive normal estimation method for scanned point clouds with sharp features
Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation ...
Semi-automatic segmentation of 3d point clouds skeleton without explicit computation for critical points
PRICAI'12: Proceedings of the 12th Pacific Rim international conference on Trends in Artificial IntelligenceSegmentation of 3D point clouds is vigorously discussed in recent years. Many existing techniques pre-process the data to identify critical points for meaningful features. Very often, the critical points are predicted at curvature objects and this does ...
Geometric correction for projection on non planar surfaces using point clouds
ICDSC '18: Proceedings of the 12th International Conference on Distributed Smart CamerasImages projecting on non-planar surfaces result in the geometric distortion which is highly undesired in the projection of digital content. Geometric correction techniques pre-warps the image to be projected such that the projected image appears ...
Comments