Abstract
We present an efficient multi-resolution approach to segment a 3D point cloud into planar components. In order to gain efficiency, we process large point clouds iteratively from coarse to fine 3D resolutions: At each resolution, we rapidly extract surface normals to describe surface elements (surfels). We group surfels that cannot be associated with planes from coarser resolutions into co-planar clusters with the Hough transform. We then extract connected components on these clusters and determine a best plane fit through RANSAC. Finally, we merge plane segments and refine the segmentation on the finest resolution. In experiments, we demonstrate the efficiency and quality of our method and compare it to other state-of-the-art approaches.
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Oehler, B., Stueckler, J., Welle, J., Schulz, D., Behnke, S. (2011). Efficient Multi-resolution Plane Segmentation of 3D Point Clouds. In: Jeschke, S., Liu, H., Schilberg, D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science(), vol 7102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25489-5_15
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DOI: https://doi.org/10.1007/978-3-642-25489-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25488-8
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