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The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard(More)
This paper proposes to summarize researches and new advances in full waveform lidar data. After a description of full waveform lidar systems, we will review different methodologies developed to process the waveforms (modelling, correlation, stacking). Applications on urban and vegetated areas are then presented. The paper ends up with recommendations on(More)
We propose a context-based classification method for point clouds acquired by full waveform airborne laser scanners. As these devices provide a higher point density and additional information like echo width or type of return, an accurate distinction of several object classes is possible. However, especially in dense urban areas correct labelling is a(More)
In this paper, we address tree segmentation and localization in the scope of the IQmulus Processing Contest IQPC'15. Based on the part of pre-classified 3D point cloud data which corresponds to trees, we present a novel framework which involves a downsampling of the original data, a projection of the downsampled data onto a horizontally oriented plane, a(More)
The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an(More)
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