论文标题
没有GPU？没问题：具有简单的建议生成器和基于能量的分布点网的超快速3D检测道路使用者
No GPU? No problem: an ultra fast 3D detection of road users with a simple proposal generator and energy-based out-of-distribution PointNets
论文作者
论文摘要
本文介绍了一个新颖的构建，用于点云道路用户检测，该架构基于经典的点云提案生成器方法，该方法利用了简单的几何规则。新方法与该技术相结合，以达到极小的计算要求，并映射与最新的计算要求相当。这个想法是要专门利用几何规则，以期更快的性能。这种方法的典型弊端，例如本文解决了全球环境损失，并提出了解决方案。这种方法允许在单个核心CPU上实时性能，而最先进的端到端解决方案并非如此。我们已经通过公共KITTI数据集评估了该方法的性能，并使用我们自己的带有小型移动机器人平台收集的注释数据集进行了评估。此外，我们还提出了一种新颖的地面分割方法，该方法通过公共Semantickitti数据集进行了评估。
This paper presents a novel architecture for point cloud road user detection, which is based on a classical point cloud proposal generator approach, that utilizes simple geometrical rules. New methods are coupled with this technique to achieve extremely small computational requirement, and mAP that is comparable to the state-of-the-art. The idea is to specifically exploit geometrical rules in hopes of faster performance. The typical downsides of this approach, e.g. global context loss, are tackled in this paper, and solutions are presented. This approach allows real-time performance on a single core CPU, which is not the case with end-to-end solutions presented in the state-of-the-art. We have evaluated the performance of the method with the public KITTI dataset, and with our own annotated dataset collected with a small mobile robot platform. Moreover, we also present a novel ground segmentation method, which is evaluated with the public SemanticKITTI dataset.
