  We introduce a method for 3D object detection using a single monocular image.
Starting from a synthetic dataset, we pre-train an RGB-to-Depth Auto-Encoder
(AE). The embedding learnt from this AE is then used to train a 3D Object
Detector (3DOD) CNN which is used to regress the parameters of 3D object poses
after the encoder from the AE generates a latent embedding from the RGB image.
We show that we can pre-train the AE using paired RGB and depth images from
simulation data once and subsequently only train the 3DOD network using real
data, comprising of RGB images and 3D object pose labels (without the
requirement of dense depth). Our 3DOD network utilizes a particular
`cubification' of 3D space around the camera, where each cuboid is tasked with
predicting N object poses, along with their class and confidence values. The AE
pre-training and this method of dividing the 3D space around the camera into
cuboids give our method its name - CubifAE-3D. We demonstrate results for
monocular 3D object detection in the Autonomous Vehicle (AV) use-case with the
Virtual KITTI 2 and the KITTI datasets.
