The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 263–270, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-263-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 263–270, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-263-2020

  12 Aug 2020

12 Aug 2020

FEATURE-EXTRACTION FROM ALL-SCALE NEIGHBORHOODS WITH APPLICATIONS TO SEMANTIC SEGMENTATION OF POINT CLOUDS

A. Leichter1, M. Werner2, and M. Sester1 A. Leichter et al.
  • 1Institute of Cartography and Geoinformatics, Leibniz University Hannover, Germany
  • 2Professorship for Big Geospatial Data Management, Technical University of Munich, Germany

Keywords: Point Cloud, Adaptive Neighborhood, Scale Selection, Multi-scale Analysis, PCA, Eigenvalues, Dimensionality, 3D Scene Analysis, Semantic Segmentation

Abstract. Feature extraction from a range of scales is crucial for successful classification of objects of different size in 3D point clouds with varying point density. 3D point clouds have high relevance in application areas such as terrain modelling, building modelling or autonomous driving. A large amount of such data is available but also that these data is subject to investigation in the context of different tasks like segmentation, classification, simultaneous localisation and mapping and others. In this paper, we introduce a novel multiscale approach to recover neighbourhood in unstructured 3D point clouds. Unlike the typical strategy of defining one single scale for the whole dataset or use a single optimised scale for every point, we consider an interval of scales. In this initial work our primary goal is to evaluate the information gain through the usage of the multiscale neighbourhood definition for the calculation of shape features, which are used for point classification. Therefore, we show and discuss empirical results from the application of classical classification models to multiscale features. The unstructured nature of 3D point cloud makes it necessary to recover neighbourhood information before meaningful features can be extracted. This paper proposes the extraction of geometrical features from a range of neighbourhood with different scales, i.e. neighborhood ranges. We investigate the utilisation of the large set of features in combination with feature aggregation/selection algorithms and classical machine learning techniques. We show that the all-scale-approach outperform single scale approaches as well as the approach with an optimised per point selected scale.