论文标题
生成激光雷达点云的现实主义公制
A Realism Metric for Generated LiDAR Point Clouds
论文作者
论文摘要
大量的研究与逼真的传感器数据的产生有关。激光点云是由复杂的模拟或学习的生成模型生成的。通常利用生成的数据来启用或改善下游感知算法。这些程序来自两个主要问题：首先，如何评估生成数据的现实主义？其次，更现实的数据还会导致更好的感知表现吗？本文解决了问题，并提出了一个新颖的指标，以量化激光点云的现实主义。通过训练代理分类任务，从现实世界和合成点云中学到了相关功能。在一系列实验中，我们证明了我们的指标的应用来确定生成的LiDar数据的现实主义，并将我们的度量标准的现实主义估计与分割模型的性能进行比较。我们确认我们的指标为下游细分性能提供了指示。
A considerable amount of research is concerned with the generation of realistic sensor data. LiDAR point clouds are generated by complex simulations or learned generative models. The generated data is usually exploited to enable or improve downstream perception algorithms. Two major questions arise from these procedures: First, how to evaluate the realism of the generated data? Second, does more realistic data also lead to better perception performance? This paper addresses both questions and presents a novel metric to quantify the realism of LiDAR point clouds. Relevant features are learned from real-world and synthetic point clouds by training on a proxy classification task. In a series of experiments, we demonstrate the application of our metric to determine the realism of generated LiDAR data and compare the realism estimation of our metric to the performance of a segmentation model. We confirm that our metric provides an indication for the downstream segmentation performance.
