Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866917652592590848 |
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| author | Doshi, Rajat K. |
| author_facet | Doshi, Rajat K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge Doshi, Rajat K. Computer Vision and Pattern Recognition 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. |
| title | Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.18665 |