@inproceedings{a25dd0712f0f4ca7bb6ccbf2e896d246,
title = "Extraction of 3D planar Primitives from Raw Airborne Laser Data: A Normal Driven RANSAC Approach",
abstract = "Airborne laser data are nowadays well-known to provide regular and accurate altimetric data. Building reconstruction strategies from traditional stereo images may highly be enhanced us\textbackslash{}ng such data together with. The aim of this paper is to propose an efficient algorithm for extracting 3D planar primitives from a laser survey over urban areas, ft is based on a normal driven random sample consensus (ND-RANSAC) which consists of randomly selecting sets of three points within laser points sharing the same orientation of normal vectors. A robust plane is then estimated with laser points that are likely to belong to the real roof facet. The number of draws is managed automatically with a statistical analysis of the distribution of normal vectors within an approximation of the Gaussian sphere of the scene. Promising results are presented with laser data acquired over the city of Amiens, France.",
author = "Fr{\'e}d{\'e}ric Bretar and Michel Roux",
year = "2005",
month = jan,
day = "1",
language = "English",
isbn = "4901122045",
series = "Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005",
publisher = "Machine Vision Applications, MVA",
pages = "452--455",
booktitle = "Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005",
note = "9th IAPR Conference on Machine Vision Applications, MVA 2005 ; Conference date: 16-05-2005 Through 18-05-2005",
}