@article{Contreras20_REC,
    type = {article},
    key = {Contreras20_REC},
    title = {Region-based Edge Convolutions with Geometric Attributes for the Semantic Segmentation of Large-scale 3D Point Clouds},
    author = {Jhonatan Contreras and Sven Sickert and Joachim Denzler},
    journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
    year = {2020},
    month = {December},
    number = {1},
    pages = {2598-2609},
    volume = {13},
    abstract = {In this paper, we present a semantic segmentation framework for large-scale 3D point clouds with high spatial resolution. For such data with huge amounts of points, the classification of each individual 3D point is an intractable task. Instead, we propose to segment the scene into meaningful regions as a first step. Afterward, we classify these segments using a combination of PointNet and geometric deep learning. This two-step approach resembles object-based image analysis. As an additional novelty, we apply surface normalization techniques and enrich features with geometric attributes. Our experiments show the potential of this approach for a variety of outdoor scene analysis tasks. In particular, we are able to reach 89.6\% overall accuracy and 64.4\% average intersection over union (IoU) in the Semantic3D benchmark. Furthermore, we achieve 66.7\% average IoU on Paris-Lille-3D. We also successfully apply our approach to the automatic semantic analysis of forestry data.},
    doi = {10.1109/JSTARS.2020.2998037},
    groups = {semanticsegmentation,3dpcl_semseg},
    publisher = {IEEE},
    url = {https://ieeexplore.ieee.org/document/9103287},
}