Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge

arXiv:2404.18665v1 Announce Type: new
Abstract: This study investigates the application of PointNet and PointNet++ in the classification of LiDAR-generated point cloud data, a critical component for achieving fully autonomous vehicles. Utilizing a modified dataset from the Lyft 3D Object Detection Challenge, we examine the models' capabilities to handle dynamic and complex environments essential for autonomous navigation. Our analysis shows that PointNet and PointNet++ achieved accuracy rates of 79.53% and 84.24%, respectively. These results underscore the models' robustness in interpreting intricate …

3d object 3d object detection abstract application arxiv autonomous autonomous vehicles capabilities challenge classification cloud cloud data cs.cv data dataset detection dynamic fully autonomous generated lidar lyft object study type vehicles