  We present a new learning-based framework to recover vehicle pose in SO(3)
from a single RGB image. In contrast to previous works that map from local
appearance to observation angles, we explore a progressive approach by
extracting meaningful Intermediate Geometrical Representations (IGRs) to
estimate egocentric vehicle orientation. This approach features a deep model
that transforms perceived intensities to IGRs, which are mapped to a 3D
representation encoding object orientation in the camera coordinate system.
Core problems are what IGRs to use and how to learn them more effectively. We
answer the former question by designing IGRs based on an interpolated cuboid
that derives from primitive 3D annotation readily. The latter question
motivates us to incorporate geometry knowledge with a new loss function based
on a projective invariant. This loss function allows unlabeled data to be used
in the training stage to improve representation learning. Without additional
labels, our system outperforms previous monocular RGB-based methods for joint
vehicle detection and pose estimation on the KITTI benchmark, achieving
performance even comparable to stereo methods. Code and pre-trained models are
available at this https URL.
