3D object detection is a problem that has gained popularity among the research community due to
its extensiveset of application on autonomous navigation, surveillance and pick-and-place. Most
of the solutions proposed in the state-of-the-art are based on deep learning techniques and
present astonishing results in terms of accuracy. Nevertheless, a set of problems inherits from
this sort of solutions such as the need of enormous tagged datasets, extensive computational 
resources due to the complexity of the model and most of the time, no real-time inference. 
This work proposes an end-to-end classic Machine Learning (ML) pipeline to solve the
3D object detection problem for cars. The proposed method is leveraged on the use of frustum
region proposals to segment and estimate the parameters of the amodal 3D bouning box. Here we
do not deal with the problem of 2D object detection as for most of the research community this
is considered solved with ConvolutionalNeural Networks (CNN).

This task is addressed employing different ML techniques such as RANSAC for road segmentation and
DBSCAN for clustering. Global features are extracted out of the segmented point cloud using 
The Ensemble of Shape Functions (ESF). Some feature are engineered through PCA and statistics.
Finally, the amodal 3D bounding box parameters are estimated through a SVR regressor.