  We present a novel approach to detect, segment, and reconstruct complete
textured 3D models of vehicles from a single image for autonomous driving. Our
approach combines the strengths of deep learning and the elegance of
traditional techniques from part-based deformable model representation to
produce high-quality 3D models in the presence of severe occlusions. We present
a new part-based deformable vehicle model that is used for instance
segmentation and automatically generate a dataset that contains dense
correspondences between 2D images and 3D models. We also present a novel
end-to-end deep neural network to predict dense 2D/3D mapping and highlight its
benefits. Based on the dense mapping, we are able to compute precise 6-DoF
poses and 3D reconstruction results at almost interactive rates on a commodity
GPU. We have integrated these algorithms with an autonomous driving system. In
practice, our method outperforms the state-of-the-art methods for all major
vehicle parsing tasks: 2D instance segmentation by 4.4 points (mAP), 6-DoF pose
estimation by 9.11 points, and 3D detection by 1.37. Moreover, we have released
all of the source code, dataset, and the trained model on Github.
