@inproceedings{contreras2019edgeconvolution,
    type = {inproceedings},
    key = {contreras2019edgeconvolution},
    title = {Edge-Convolution Point Net For Semantic Segmentation Of Large-Scale Point Clouds},
    author = {Jhonatan Contreras and Joachim Denzler},
    booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
    year = {2019},
    month = {July},
    pages = {5236-5239},
    abstract = {We propose a deep learning-based framework which can manage large-scale point clouds of outdoor scenes with high spatial resolution. Analogous to Object-Based Image Analysis (OBIA), our approach segments the scene by grouping similar points together to generate meaningful objects. Later, our net classifies segments instead of individual points using an architecture inspired by PointNet, which applies Edge convolutions, making our approach efficient. Usually, Light Detection and Ranging (LiDAR) data do not come together with RGB information. This approach was trained using both RBG and RGB+XYZ information. In some circumstances, LiDAR data presents patterns that do not correspond to the surface object. This mainly occurs when objects partially block beans of light, to address this issue, normalized elevation was included in the analysis to make the model more robust.},
    doi = {10.1109/IGARSS.2019.8899303},
    groups = {semanticsegmentation,3dpcl_semseg},
    url = {https://ieeexplore.ieee.org/document/8899303},
}