A Point Clouds Fast Thinning Algorithm Based on Sample Point Spatial Neighborhood
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초록
Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involvessome uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensablestep in point clouds model reconstruction and other applications. To overcome the shortcomingsof complex classification index and long time consuming in existing point clouds thinning algorithms, thispaper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established inplane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance differenceand height difference are employed to further delete or retain the selected sample point. Sequentially, the indexof sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds arebuilt in advance. Besides, the new method also performs superiority in time consuming, modelling accuracyand feature retention by comparing with octree thinning algorithm.
Abstract
Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involvessome uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensablestep in point clouds model reconstruction and other applications. To overcome the shortcomingsof complex classification index and long time consuming in existing point clouds thinning algorithms, thispaper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established inplane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance differenceand height difference are employed to further delete or retain the selected sample point. Sequentially, the indexof sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds arebuilt in advance. Besides, the new method also performs superiority in time consuming, modelling accuracyand feature retention by comparing with octree thinning algorithm.
참고문헌 (21)
- X. Yang, "Stereovision-based integrated system for point cloud reconstruction and simulated brain shift validation", Journal of Medical Imaging, 4(3) (2017)
- P. Grussenmeyer, "From metric image archives to point cloud reconstruction : case study of the Great Mosque of Aleppo in Syria", Proceedings of International Archives of the Photogrammetry, pp.295~301 (2017)
- A. K. Patil, "An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds", Automation in Construction, 75, pp.65~78 (2017)
- E. El-Sayed, "Plane detection in 3D point cloud using octreebalanced density down-sampling and iterative adaptive plane extraction", IET Image Processing, 12(9), pp.1595~1605 (2018)
- M. Pauly, "Efficient simplification of point-sampled surfaces", Proceedings of IEEE Visualization, pp.163~170 (2002)
- A. V. Vo, "Octree-based region growing for point cloud segmentation", ISPRS Journal of Photogrammetry and Remote Sensing, 104, pp.88~100 (2015)
- S. Pamela, "A data density reduction algorithm for Post-processed airborne lidar bathymetric survey data", Ph. D., pp.2000
- H. Woo, "A new segmentation method for point cloud data", International Journal of Machine Tools and Manufacture, 42(2), pp.167~178 (2002)
- T. Liu, "Curvature estimation of scattered-point cloud data based on bounding box method", Science Technology and Engineering, 9(12), pp.3333~3336 (2009)
- P. Burger, "Fast multi-pass 3D point segmentation based on a structured mesh graph for ground vehicles", Proceedings of 2018 IEEE Intelligent Vehicles Symposium(IV), pp.2150~2156 (2018)
- L. Pagani, "Curvature based sampling of curves and surfaces", Computer Aided Geometric Design, 59, pp.32~48 (2018)
- Y. Wang, "Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information", ISPRS Journal of Photogrammetry and Remote Sensing, 139, pp.146~153 (2018)
- R. Schnabel, "Efficient RANSAC for point-cloud shape detection", Computer Graphics Forum, 26(2), pp.214~226 (2007)
- R. Schnabel, "Octree-based point-cloud compression", Proceedings of 3rd Eurographics, 2009, 111~120
- H. Song, "A global clustering approach to point cloud simplification with a specified data reduction ratio", Computer-Aided Design, 40(3), pp.281~292 (2008)
- H. Han, "Point cloud simplification with preserved edge based on normal vector", Optik-International Journal for Light and Electron Optics, 126(19), pp.2157~2162 (2015)
- X. Yang, "Feature-preserving simplification of point cloud by using clustering approach based on mean curvature", The Society for Art and Science, 14(4), pp.117~128 (2015)
- T. Whelan, "Incremental and batch planar simplification of dense point cloud maps", Robotics and Autonomous Systems, 69, pp.3~14 (2015)
- F. N. Fritsch, "Monotone piecewise cubic interpolation", SIAM Journal on Numerical Analysis, 17(2), pp.238~246 (1980)
- H. T. Huynh, "Accurate monotone cubic interpolation", SIAM Journal on Numerical Analysis, 30(1), pp.57~100 (1993)
- K. W. Brodlie, "Preserving convexity using piecewise cubic interpolation", Computers &Graphics, 15(1), pp.15~23 (1991)