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This paper tackles the 3D object detection problem, which is of vitalimportance for applications such as autonomous driving. Our framework uses aMachine Learning (ML) pipeline on a combination of monocular camera and LiDARdata to detect vehicles in the surrounding 3D space of a moving platform. Ituses frustum region proposals generated by State-Of-The-Art (SOTA) 2D objectdetectors to segment LiDAR point clouds into point clusters which representpotentially individual objects. We evaluate the performance of classical MLalgorithms as part of an holistic pipeline for estimating the parameters of 3Dbounding boxes which surround the vehicles around the moving platform. Ourresults demonstrate an efficient and accurate inference on a validation set,achieving an overall accuracy of 87.1%.

High-level camera-LiDAR fusion for 3D object detection with machine learning