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 environmental data, which is pivotal for the safety and efficiency of autonomous vehicles. Moreover, the enhanced detection accuracy, particularly in distinguishing pedestrians from other objects, highlights the potential of these models to contribute substantially to the advancement of autonomous vehicle technology.
Abstract (translated)
本研究探讨了点Net和点Net++在LiDAR生成的点云数据分类中的应用，这对实现完全自动驾驶车辆至关重要。利用来自Lyft 3D物体检测挑战的修改后的数据集，我们研究了模型在处理自动驾驶导航所需的关键动态和复杂环境的能力。我们的分析显示，点Net和点Net++的准确率分别为79.53%和84.24%。这些结果强调了模型在解释复杂环境数据方面的稳健性，这对自动驾驶车辆的安全和效率至关重要。此外，增强的检测精度，特别是对行人与其他物体进行区分，突出了这些模型对自动驾驶车辆技术进步的潜在贡献。
URL
https://arxiv.org/abs/2404.18665
