  This paper introduces an approach to produce accurate 3D detection boxes for
objects on the ground using single monocular images. We do so by merging 2D
visual cues, 3D object dimensions, and ground plane constraints to produce
boxes that are robust against small errors and incorrect predictions. First, we
train a single-shot convolutional neural network (CNN) that produces multiple
visual and geometric cues of interest: 2D bounding boxes, 2D keypoints of
interest, coarse object orientations and object dimensions. Subsets of these
cues are then used to poll probable ground planes from a pre-computed database
of ground planes, to identify the "best fit" plane with highest consensus. Once
identified, the "best fit" plane provides enough constraints to successfully
construct the desired 3D detection box, without directly predicting the 6DoF
pose of the object. The entire ground plane polling (GPP) procedure is
constructed as a non-parametrized layer of the CNN that outputs the desired
"best fit" plane and the corresponding 3D keypoints, which together define the
final 3D bounding box. Doing so allows us to poll thousands of different ground
plane configurations without adding considerable overhead, while also creating
a single CNN that directly produces the desired output without the need for
post processing. We evaluate our method on the 2D detection and orientation
estimation benchmark from the challenging KITTI dataset, and provide additional
comparisons for 3D metrics of importance. This single-stage, single-pass CNN
results in superior localization and orientation estimation compared to more
complex and computationally expensive monocular approaches.
