  The 3D reconstruction of objects is a prerequisite for many highly relevant
applications of computer vision such as mobile robotics or autonomous driving.
To deal with the inverse problem of reconstructing 3D objects from their 2D
projections, a common strategy is to incorporate prior object knowledge into
the reconstruction approach by establishing a 3D model and aligning it to the
2D image plane. However, current approaches are limited due to inadequate shape
priors and the insufficiency of the derived image observations for a reliable
alignment with the 3D model. The goal of this paper is to show how 3D object
reconstruction can profit from a more sophisticated shape prior and from a
combined incorporation of different observation types inferred from the images.
We introduce a subcategory-aware deformable vehicle model that makes use of a
prediction of the vehicle type for a more appropriate regularisation of the
vehicle shape. A multi-branch CNN is presented to derive predictions of the
vehicle type and orientation. This information is also introduced as prior
information for model fitting. Furthermore, the CNN extracts vehicle keypoints
and wireframes, which are well-suited for model-to-image association and model
fitting. The task of pose estimation and reconstruction is addressed by a
versatile probabilistic model. Extensive experiments are conducted using two
challenging real-world data sets on both of which the benefit of the developed
shape prior can be shown. A comparison to state-of-the-art methods for vehicle
pose estimation shows that the proposed approach performs on par or better,
confirming the suitability of the developed shape prior and probabilistic model
for vehicle reconstruction.
