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