论文标题
分段激光雷达实例的快速对象分类和有意义的数据表示
Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances
论文作者
论文摘要
近年来，龙达数据的对象检测算法已经看到了许多出版物，在针对汽车需求方向的数据集基准上报告了良好的结果。然而，其中许多无法部署到嵌入式车辆系统，因为它们需要巨大的计算能力才能接近实时执行。在这项工作中，我们提出了一种促进CPU实时LIDAR对象分类的方法。我们展示了我们的方法如何使用分段对象实例来提取重要功能，从而实现了计算有效的批次分类。为此，我们介绍了一个数据表示形式，该数据表示使用分解的正常矢量图像将三维信息转化为小图像贴片。我们将其与专用对象统计信息一起处理以处理边缘案例。我们将我们的方法应用于对象检测和语义分割的任务，以及泛型分割的相对较新的挑战。通过评估，我们表明，我们的算法能够在无需使用特定优化的情况下实时在CPU上实时运行，能够在公共数据上产生良好的结果。
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements. Nevertheless, many of these are not deployable to embedded vehicle systems, as they require immense computational power to be executed close to real time. In this work, we propose a way to facilitate real-time Lidar object classification on CPU. We show how our approach uses segmented object instances to extract important features, enabling a computationally efficient batch-wise classification. For this, we introduce a data representation which translates three-dimensional information into small image patches, using decomposed normal vector images. We couple this with dedicated object statistics to handle edge cases. We apply our method on the tasks of object detection and semantic segmentation, as well as the relatively new challenge of panoptic segmentation. Through evaluation, we show, that our algorithm is capable of producing good results on public data, while running in real time on CPU without using specific optimisation.
