ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 63–70, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-63-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 63–70, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-63-2019

  16 Sep 2019

16 Sep 2019

SEMANTIC LABELING AND REFINEMENT OF LIDAR POINT CLOUDS USING DEEP NEURAL NETWORK IN URBAN AREAS

R. Huang1, Z. Ye1, D. Hong2, Y. Xu1, and U. Stilla1 R. Huang et al.
  • 1Photogrammetry and Remote Sensing, Technical University of Munich (TUM), Germany
  • 2Remote Sensing Technology Institute, German Aerospace Center, Weisseling, Germany

Keywords: point clouds, MLS, semantic labeling, deep learning, optimization

Abstract. In this paper, we propose a framework for obtaining semantic labels of LiDAR point clouds and refining the classification results by combining a deep neural network with a graph-structured smoothing technique. In general, the goal of the semantic scene analysis is to assign a semantic label to each point in the point cloud. Although various related researches have been reported, due to the complexity of urban areas, the semantic labeling of point clouds in urban areas is still a challenging task. In this paper, we address the issues of how to effectively extract features from each point and its local surrounding and how to refine the initial soft labels by considering contextual information in the spatial domain. Specifically, we improve the effectiveness of classification of point cloud in two aspects. Firstly, instead of utilizing handcrafted features as input for classification and refinement, the local context of a point is embedded into deep dimensional space and classified via a deep neural network (PointNet++), and simultaneously soft labels are obtained as initial results for next refinement. Secondly, the initial label probability set is improved by taking the context both in the spatial domain into consideration by constructing a graph structure, and the final labels are optimized by a graph cuts algorithm. To evaluate the performance of our proposed framework, experiments are conducted on a mobile laser scanning (MLS) point cloud dataset. We demonstrate that our approach can achieve higher accuracy in comparison to several commonly-used state-of-the-art baselines. The overall accuracy of our proposed method on TUM dataset can reach 85.38% for labeling eight semantic classes.