用高程信息提升点云分割精度，尤其在城市区域表现显著
Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentation
- 引入相对高程特征，建立地面与物体间的长距离依赖关系
- 在三个数据集上平均F1提升3.7个百分点，最高达76.01%
- 适合做遥感点云语义分割的研究者和工程师参考
本文分析了利用高程数据辅助遥感中城市及建成区大范围点云语义分割的可行性。在密集室外点云中，模型感受野可能过小，难以准确判断点的上下文。通过从点云生成数字地形模型（DTMs），提取相对高程特征（即点到地面的垂直距离）。采用RandLA-Net对大规模点云进行高效语义分割，在三种不同传感器技术与位置采集的户外数据集上评估其性能。融合相对高程数据后，所有数据集均实现一致性能提升，尤其在Hessigheim数据集上，平均F1得分从72.35%提升至76.01%，增幅达3.7个百分点。此外，探索了平面度、法向量、2D特征等局部特征，但其效果因点云特性而异。研究强调了非局部相对高程特征在遥感点云分割中的关键作用。
原文摘要 · Abstract (English)
This paper presents an analysis of utilizing elevation data to aid outdoor point cloud semantic segmentation through existing machine-learning networks in remote sensing, specifically in urban, built-up areas. In dense outdoor point clouds, the receptive field of a machine learning model may be too small to accurately determine the surroundings and context of a point. By computing Digital Terrain Models (DTMs) from the point clouds, we extract the relative elevation feature, which is the vertical distance from the terrain to a point. RandLA-Net is employed for efficient semantic segmentation of large-scale point clouds. We assess its performance across three diverse outdoor datasets captured with varying sensor technologies and sensor locations. Integration of relative elevation data leads to consistent performance improvements across all three datasets, most notably in the Hessigheim dataset, with an increase of 3.7 percentage points in average F1 score from 72.35% to 76.01%, by establishing long-range dependencies between ground and objects. We also explore additional local features such as planarity, normal vectors, and 2D features, but their efficacy varied based on the characteristics of the point cloud. Ultimately, this study underscores the important role of the non-local relative elevation feature for semantic segmentation of point clouds in remote sensing applications.
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