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Proceedings Paper

Edge-crease detection and surface reconstruction from point clouds using a second-order variational model
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Paper Abstract

The automatic detection of geometric features, such as edges and creases, from objects represented by 3D point clouds (e.g., LiDAR measurements, Tomographic SAR) is a very important issue in different application domains including urban monitoring and building reconstruction. A limitation of many methods in the literature is that they rely on rasterization or interpolation of the original grid, with consequent potential loss of detail. Recently, a second-order variational model for edge and crease detection and surface regularization has been presented in literature and succesfully applied to DSMs. In this paper we address the generalization of this model to unstructured grids. The model is based on the Blake-Zisserman energy and allows to obtain a regularization of the original data (noise reduction) which does not affect crucial regions containing jumps and creases. Specifically, we focus on the detection of these features by means of two auxiliary functions that are computable by solving specific differential equations. Results obtained on LiDAR data by solving the equations via Finite Element Method are presented.

Paper Details

Date Published: 15 October 2014
PDF: 11 pages
Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 924409 (15 October 2014); doi: 10.1117/12.2069666
Show Author Affiliations
Massimo Zanetti, Univ. of Trento (Italy)
Lorenzo Bruzzone, Univ. of Trento (Italy)


Published in SPIE Proceedings Vol. 9244:
Image and Signal Processing for Remote Sensing XX
Lorenzo Bruzzone, Editor(s)

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