Abstract
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.
Highlights
We introduce a novel multiscale approach to recover neighbourhood in unstructured 3D point clouds
The scale is considered to be k the number of neighbours in the first case and r the radius of the ball in second case. In this initial work we evaluate the information gain through the usage of the multiscale neighbourhood definition for the calculation of shape features, which are used for classification
We are going to compute all features for a large set of scale parameters k or r. All of those features provide a high-dimensional classification problem with many highly-correlated features and the classification system must take care to select suitable subsets of features. With respect to this problem, we propose to use Support Vector Machines with l1 and l2 regularization, as they are known to deal well with high-dimensional classification problems, to perform a principal component analysis (PCA) on the set of features in order to reduce the correlation between features, to use random forests and Correlation-based Feature Selection (CFS) (Hall, 1999)to assess the feature importance in a first step and truncate the classification problem to include only the top features, and to empirically test the improved performance of the model trained on subset of features
Summary
We introduce a novel multiscale approach to recover neighbourhood in unstructured 3D point clouds. The size of the neighborhood can be related to a scale, where the 3D pointcloud is investigated. Such approaches are well known in image processing, where image pyramids are used to extend the pull-in range for different kinds of analysis operations. The scale is considered to be k the number of neighbours in the first case and r the radius of the ball in second case. In this initial work we evaluate the information gain through the usage of the multiscale neighbourhood definition for the calculation of shape features, which are used for classification. We show and discuss empirical results from the application of classical classification models to multiscale features
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