Abstract
Abstract. In this work, we propose a classification method designed for the labeling of MLS point clouds, with detrended geometric features extracted from the points of the supervoxel-based local context. To achieve the analysis of complex 3D urban scenes, acquired points of the scene should be tagged with individual labels of different classes. Thus, assigning a unique label to the points of an object that belong to the same category plays an essential role in the entire 3D scene analysis workflow. Although plenty of studies in this field have been reported, this work is still a challenging task. Specifically, in this work: 1) A novel geometric feature extraction method, detrending the redundant and in-salient information in the local context, is proposed, which is proved to be effective for extracting local geometric features from the 3D scene. 2) Instead of using individual point as basic element, the supervoxel-based local context is designed to encapsulate geometric characteristics of points, providing a flexible and robust solution for feature extraction. 3) Experiments using complex urban scene with manually labeled ground truth are conducted, and the performance of proposed method with respect to different methods is analyzed. With the testing dataset, we have obtained a result of 0.92 for overall accuracy for assigning eight semantic classes.
Highlights
In the past decade, automatic 3D scene analysis using LiDAR point clouds has been a challenging task in research fields of photogrammetry (Vosselman and Maas, 2010), remote sensing (Lefsky et al, 1999), computer vision (Buch et al, 2011), and robotics (Rusu et al, 2009)
As a generally utilized data type, LiDAR point clouds can be acquired through different acquisition techniques such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), and airborne laser scanning (ALS)
We propose a classification method designed for the labeling of MLS point clouds, focusing on detrended geometric features extracted from points of supervoxel-based local contexts
Summary
Automatic 3D scene analysis using LiDAR point clouds has been a challenging task in research fields of photogrammetry (Vosselman and Maas, 2010), remote sensing (Lefsky et al, 1999), computer vision (Buch et al, 2011), and robotics (Rusu et al, 2009). ALS datasets are usually used for large scale scene description and analysis, with a relatively low point density. For dense and accurate 3D scene analysis and interpretation, especially in the context of urban areas, TLS and MLS, which have higher scanning density and more stable carrier platform (e.g., static scanning stations of TLS), are considerably more reliable. Point clouds obtained via TLS, which have normally a high point density and a corresponding high spatial resolution, show a great potential of being used as datasets for interpreting 3D scenes in urban areas. For further 3D analysis of large scale urban scene, especially for acquiring To this end, we propose a classification method designed for the labeling of MLS point clouds, focusing on detrended geometric features extracted from points of supervoxel-based local contexts.
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