The semantic interpretation using point clouds, especially regarding light detection and ranging (LiDAR) point cloud classification, has attracted a growing interest in the fields of photogrammetry, remote sensing, and computer vision. In this letter, we aim at tackling a general and typical feature learning problem in 3-D point cloud classification— how to represent geometric features by structurally considering a point and its surroundings in a more effective and discriminative fashion? Recently, enormous efforts have been made to design the geometric features, yet it is less investigated to fully explore the potentials of the features. For that, there have been many filter-based studies proposed by selecting a subset of the whole feature space for better representing the local geometry structure. However, such a hard-threshold selection strategy inevitably suffers from information loss. In addition, the construction of the geometric features is relatively sensitive to the size of the neighborhood. To this end, we propose to extract multi-scaled feature representations and locally embed them into a low-dimensional and robust subspace where a more compact representation with the intrinsic structure preservation of the data is expected to be obtained, thereby further yielding a better classification performance. In our case, we apply a popular manifold learning approach, that is, locality-preserving projections, for the task of learning low-dimensional embedding. Experimental results conducted on one LiDAR point cloud data set provided by the 2018 IEEE Data Fusion Contest demonstrate the effectiveness of the proposed method in comparison with several commonly used state-of-the-art baselines.

中文翻译：

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用于激光雷达点云分类的多尺度局部上下文嵌入

使用点云的语义解释，特别是关于光检测和测距（LiDAR）点云分类，在摄影测量、遥感和计算机视觉领域引起了越来越大的兴趣。在这封信中，我们的目标是解决 3-D 点云分类中的一个一般性和典型的特征学习问题——如何通过以更有效和更具判别性的方式从结构上考虑一个点及其周围环境来表示几何特征？最近，已经为设计几何特征做出了巨大的努力，但对充分探索这些特征的潜力的研究较少。为此，通过选择整个特征空间的一个子集来更好地表示局部几何结构，提出了许多基于过滤器的研究。然而，这种硬阈值选择策略不可避免地会遭受信息丢失。此外，几何特征的构建对邻域的大小相对敏感。为此，我们建议提取多尺度特征表示并将它们局部嵌入到低维和鲁棒的子空间中，在该子空间中有望获得具有数据内在结构保留的更紧凑的表示，从而进一步产生更好的分类表现。在我们的例子中，我们应用了一种流行的流形学习方法，即局部保留投影，用于学习低维嵌入的任务。