F-PointNet multi-modal 3D object detection based on RGB images and LiDAR point cloud

This project explores the integration of image and point cloud data for 3D object detection using the F-PointNet model, aiming to enhance accuracy and reliability in autonomous driving applications. F-PointNet leverages multimodal data from RGB cameras and LiDAR to improve environmental perception and object localisation under varied operational conditions. Employing a rigorous methodology, the model incorporates preprocessing and network components such as frustum rotation and T-net adjustments to refine the detection process. Experiments were conducted on the KITTI dataset, which included applying both random and designated perturbations, and assessing their impact on the models performance. Results show that random perturbations generally outperform designated ones, especially in complex scenarios, by enhancing the models adaptability and capability for generalisation. This study highlights the critical role of methodological innovations and data perturbation strategies in advancing 3D object detection technologies, suggesting that further research is needed to optimise these approaches for broader applications. Furthermore, this research contributes to the development of autonomous systems, emphasising the importance of robust and accurate 3D object detection in enhancing the safety and reliability of autonomous vehicles.