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.

本文介绍了使用一维全卷积网络对点云数据进行语义标记的方法，该方法直接分析经过地形归一化后的点，并结合光谱数据以学习上下文特征。我们的方法不需要手动提取特征，且在多个方面上比传统方法表现更优越，其中包括利用 ISPRS 3D 语义标注竞赛数据获得了 81.6% 的准确率，F1 分数比最高准确率方法高 1.69%。此外，本文还展示了该方法可轻松扩展到 2D 语义分割任务。