{
  "id": "1710.01408",
  "version": "v1",
  "published": "2017-10-03T22:35:25.000Z",
  "updated": "2017-10-03T22:35:25.000Z",
  "title": "A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds",
  "authors": [
    "Mohammed Yousefhussien",
    "David J. Kelbe",
    "Emmett J. Ientilucci",
    "Carl Salvaggio"
  ],
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "abstract": "When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features - usually derived from the 3D-covariance matrix - are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming, and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data,if available, to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds,i.e. from muliple-view imagery. We train our network by splitting the data into square regions, and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-10-03T22:35:25.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "3d point clouds",
      "fully convolutional network",
      "spectral features",
      "features capture local information",
      "isprs 3d semantic labeling contest"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}