  Three-dimensional object detection from a single view is a challenging task
which, if performed with good accuracy, is an important enabler of low-cost
mobile robot perception. Previous approaches to this problem suffer either from
an overly complex inference engine or from an insufficient detection accuracy.
To deal with these issues, we present SS3D, a single-stage monocular 3D object
detector. The framework consists of (i) a CNN, which outputs a redundant
representation of each relevant object in the image with corresponding
uncertainty estimates, and (ii) a 3D bounding box optimizer. We show how
modeling heteroscedastic uncertainty improves performance upon our baseline,
and furthermore, how back-propagation can be done through the optimizer in
order to train the pipeline end-to-end for additional accuracy. Our method
achieves SOTA accuracy on monocular 3D object detection, while running at 20
fps in a straightforward implementation. We argue that the SS3D architecture
provides a solid framework upon which high performing detection systems can be
built, with autonomous driving being the main application in mind.
