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Proceedings Paper

Feature enhancing aerial lidar point cloud refinement
Author(s): Zhenzhen Gao; Ulrich Neumann
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Paper Abstract

Raw aerial LiDAR point clouds often suffer from noise and under-sampling, which can be alleviated by feature preserving refinement. However, existing approaches are limited to only preserving normal discontinuous features (ridges, ravines and crest lines) while position discontinuous features (boundaries) are also universal in urban scenes. We present a new refinement approach to accommodate unique properties of aerial LiDAR building points. By extending recent developments in geometry refinement to explicitly regularize boundary points, both normal and position discontinuous features are preserved and enhanced. The refinement includes two steps: i) the smoothing step applies a two-stage feature preserving bilateral filtering, which first filters normals and then updates positions under the guidance of the filtered normals. In a separate similar process, boundary points are smoothed directed by tangent directions of underlying lines, and ii) the up-sampling step interpolates new points to fill gaps/holes for both interior surfaces and boundary lines, through a local gap detector and a feature-aware bilateral projector. Features can be further enhanced by limiting the up-sampling near discontinuities. The refinement operates directly on points with diverse density, shape and complexity. It is memory-efficient, easy to implement, and easily extensible.

Paper Details

Date Published: 6 March 2014
PDF: 15 pages
Proc. SPIE 9013, Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2014, 901303 (6 March 2014); doi: 10.1117/12.2035749
Show Author Affiliations
Zhenzhen Gao, The Univ. of Southern California (United States)
Ulrich Neumann, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 9013:
Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2014
Atilla M. Baskurt; Robert Sitnik, Editor(s)

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