  3D object detection based on monocular camera data is a key enabler for
autonomous driving. The task however, is ill-posed due to lack of depth
information in 2D images. Recent deep learning methods show promising results
to recover depth information from single images by learning priors about the
environment. Several competing strategies tackle this problem. In addition to
the network design, the major difference of these competing approaches lies in
using a supervised or self-supervised optimization loss function, which require
different data and ground truth information. In this paper, we evaluate the
performance of a 3D object detection pipeline which is parameterizable with
different depth estimation configurations. We implement a simple distance
calculation approach based on camera intrinsics and 2D bounding box size, a
self-supervised, and a supervised learning approach for depth estimation.
  Ground truth depth information cannot be recorded reliable in real world
scenarios. This shifts our training focus to simulation data. In simulation,
labeling and ground truth generation can be automatized. We evaluate the
detection pipeline on simulator data and a real world sequence from an
autonomous vehicle on a race track. The benefit of simulation training to real
world application is investigated. Advantages and drawbacks of the different
depth estimation strategies are discussed.
