Abstract
Lidar became an important component of the perception systems in autonomous driving. But challenges of training data acquisition and annotation made emphasized the role of the sensor to sensor domain adaptation. In this work, we address the problem of lidar upsampling. Learning on lidar point clouds is rather a challenging task due to their irregular and sparse structure. Here we propose a method for lidar point cloud upsampling which can reconstruct fine-grained lidar scan patterns. The key idea is to utilize edge-aware dense convolutions for both feature extraction and feature expansion. Additionally applying a more accurate Sliced Wasserstein Distance facilitates learning of the fine lidar sweep structures. This in turn enables our method to employ a one-stage upsampling paradigm without the need for coarse and fine reconstruction. We conduct several experiments to evaluate our method and demonstrate that it provides better upsampling.
Abstract (translated)
激光成为自动驾驶感知系统的重要组件。然而,训练数据获取和标注的挑战强调了传感器到传感器领域的适应作用。在本研究中,我们解决了激光点云增加的问题。由于它们的不规则和稀疏结构,学习激光点云是非常具有挑战性的。我们提出了一种方法来增加激光点云,可以重构精细的激光扫描模式。关键思想是使用具有边缘意识的密集卷积来提取和扩展特征。此外,应用更精确的sliced Wasserstein距离促进了学习精细激光扫掠结构。这反过来使我们的方法能够使用一个阶段增加范式,而无需进行粗度和精细重建。我们进行了几项实验来评估我们的方法,并证明了它提供了更好的增加。
URL
https://arxiv.org/abs/2301.13558
