We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpose we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is a false positive or not and a meta regression model to predict the segmentwise intersection over union. Both models can then be applied to semantic segmentation inferences without knowing the ground truth. In our experiments we use different LiDAR segmentation models and datasets and analyze the power of our method. We show that our results outperform other standard approaches.

翻译：我们为LiDAR点云数据提供了一个新的语义分解处理工具,称为LidarMetaSeg,用于估算预测质量部分。为此,我们根据网络概率输出和基于点云输入特性的特性测量来计算分散措施,并将其汇总到分层上。这些汇总措施用于培训元分类模型,以预测预测预测的分解是否为假正数,并使用元回归模型来预测交错的分解。两种模型都可以应用到语义分解推断中,而不了解地面真相。在实验中,我们使用不同的分解模型和数据集,分析我们方法的力量。我们显示,我们的结果优于其他标准方法。