  The retrieval of the 3D pose and shape of objects from images is an ill-posed
problem. A common way to object reconstruction is to match entities such as
keypoints, edges, or contours of a deformable 3D model, used as shape prior, to
their corresponding entities inferred from the image. However, such approaches
are highly sensitive to model initialisation, imprecise keypoint localisations
and/or illumination conditions. In this paper, we present a probabilistic
approach for shape-aware 3D vehicle reconstruction from stereo images that
leverages the outputs of a novel multi-task CNN. Specifically, we train a CNN
that outputs probability distributions for the vehicle's orientation and for
both, vehicle keypoints and wireframe edges. Together with 3D stereo
information we integrate the predicted distributions into a common
probabilistic framework. We believe that the CNN-based detection of wireframe
edges reduces the sensitivity to illumination conditions and object contrast
and that using the raw probability maps instead of inferring keypoint positions
reduces the sensitivity to keypoint localisation errors. We show that our
method achieves state-of-the-art results, evaluating our method on the
challenging KITTI benchmark and on our own new 'Stereo-Vehicle' dataset.
