Fig. 3

Schematic method overview. Five main architectures are compared in this article: a utilizes a clustering algorithm followed by a recurrent neural network classifier. b makes the classification first via semantic segmentation and uses the extra information as additional input to a clusterer. c comprises an image-based object detector made accessible for point clouds via a grid mapping approach. d omits the grid mapping stage by using an object detector optimized for point clouds. Finally, e combines the first two methods in order to utilize the advantages of both architectures. All methods use the same point cloud as input, i.e., a cropped version of the scenario in Fig. 1. Due to space constraints, the point cloud are only displayed for the PointPillars method in (d). Dependent on the different methods, cluster formations of boxes are returned as object predictions