  This paper aims to design a 3D object detection model from 2D images taken by
monocular cameras by combining the estimated bird's-eye view elevation map and
the deep representation of object features. The proposed model has a
pre-trained ResNet-50 network as its backend network and three more branches.
The model first builds a bird's-eye view elevation map to estimate the depth of
the object in the scene and by using that estimates the object's 3D bounding
boxes. We have trained and evaluate it on two major datasets: a syntactic
dataset and the KIITI dataset.
