论文标题
LRPD：长距离3D行人检测利用激光雷达和RGB的特定强度
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
论文作者
论文摘要
虽然短距离3D行人检测足以应付紧急破裂，但需要远程检测才能平稳破裂并获得对自动驾驶汽车的信任。 KITTI基准测试的当前最新技术在检测行人在远距离的位置方面表现出色。因此，我们提出了一种专门针对远距离3D行人检测（LRPD）的方法，利用RGB的密度和LIDAR的精度。因此，对于建议，将RGB实例分割和基于激光雷达点的提案生成组合，然后使用两种传感器模态对称地使用第二阶段。与当前的最新技术相比，这导致了远距离地图的显着改善。我们的LRPD方法的评估是对Kitti基准测试的行人进行的。
While short range 3D pedestrian detection is sufficient for emergency breaking, long range detections are required for smooth breaking and gaining trust in autonomous vehicles. The current state-of-the-art on the KITTI benchmark performs suboptimal in detecting the position of pedestrians at long range. Thus, we propose an approach specifically targeting long range 3D pedestrian detection (LRPD), leveraging the density of RGB and the precision of LiDAR. Therefore, for proposals, RGB instance segmentation and LiDAR point based proposal generation are combined, followed by a second stage using both sensor modalities symmetrically. This leads to a significant improvement in mAP on long range compared to the current state-of-the art. The evaluation of our LRPD approach was done on the pedestrians from the KITTI benchmark.
