Fractal bubble algorithm for simplification of 3D point cloud data
Authors:
- 24 October 2019
- journal article
- research article
- Published by SAGE Publications in Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
- Vol. 37 (6) , 7815-7830
Abstract
We present a novel technique for 3D point cloud simplification — the so-called fractal bubble algorithm — to minimize the computational time and overall storage space. The proposed fractal bubble algorithm generates 2D elastic bubbles and copies of themselves through 2D data sets representing planar geometric contours. Each of the bubbles, as it grows, is made to select a single point of its first contact, and all the selected points become the simplified set of points. The fractal bubble algorithm is repeatedly applied to the simplification of planar slices of general 3D point clouds corresponding to 3D geometric objects, leading to the global simplification of 3D point clouds. The benefits of the algorithm are: first the algorithm is computationally light and memory efficient, second it is simple to implement and inherently allows the organized selection of the points of contact and finally it enables us to simplify the point cloud data through a multi-scale fashion by varying a set of user-controlled algorithm parameters. Numerical results verify the effectiveness of the proposed algorithm.Keywords
This publication has 22 references indexed in Scilit:
- Fractal and multifractal analysis: A reviewMedical Image Analysis, 2009
- A progressive point cloud simplification algorithm with preserved sharp edge dataThe International Journal of Advanced Manufacturing Technology, 2009
- A global clustering approach to point cloud simplification with a specified data reduction ratioComputer-Aided Design, 2008
- Efficient biased sampling for approximate clustering and outlier detection in large data setsIEEE Transactions on Knowledge and Data Engineering, 2003
- Modeling and rendering of points with local geometryIEEE Transactions on Visualization and Computer Graphics, 2003
- Coarse-to-fine surface simplification with geometric guaranteesComputer Graphics Forum, 2001
- Point Data Reduction Using 3D GridsThe International Journal of Advanced Manufacturing Technology, 2001
- Applications of fractal analysis to food structureLWT, 1995
- The application of fractal analysis to mammographic tissue classificationCancer Letters, 1994
- Fractal geometry model for wear predictionWear, 1993