@inproceedings{Sickert17_SSO,
    type = {inproceedings},
    key = {Sickert17_SSO},
    title = {Semantic Segmentation of Outdoor Areas using 3D Moment Invariants and Contextual Cues},
    author = {Sven Sickert and Joachim Denzler},
    booktitle = {DAGM German Conference on Pattern Recognition (DAGM-GCPR)},
    year = {2017},
    pages = {165-176},
    doi = {10.1007/978-3-319-66709-6_14},
    abstract = {In this paper, we propose an approach for the semantic segmentation of a 3D point cloud using local 3D moment invariants and the integration of contextual information. Specifically, we focus on the task of analyzing forestal and urban areas which were recorded by terrestrial LiDAR scanners. We demonstrate how 3D moment invariants can be leveraged as local features and that they are on a par with established descriptors. Furthermore, we show how an iterative learning scheme can increase the overall quality by taking neighborhood relationships between classes into account. Our experiments show that the approach achieves very good results for a variety of tasks including both binary and multi-class settings.},
    groups = {semanticsegmentation,3dpcl_semseg},
}