  Part information has been shown to be resistant to occlusions and viewpoint
changes, which is beneficial for various vision-related tasks. However, we
found very limited work in car pose estimation and reconstruction from street
views leveraging the part information. There are two major contributions in
this paper. Firstly, we make the first attempt to build a framework to
simultaneously estimate shape, translation, orientation, and semantic parts of
cars in 3D space from a single street view. As it is labor-intensive to
annotate semantic parts on real street views, we propose a specific approach to
implicitly transfer part features from synthesized images to real street views.
For pose and shape estimation, we propose a novel network structure that
utilizes both part features and 3D losses. Secondly, we are the first to
construct a high-quality dataset that contains 348 different car models with
physical dimensions and part-level annotations based on global and local
deformations. Given these models, we further generate 60K synthesized images
with randomization of orientation, illumination, occlusion, and texture. Our
results demonstrate that our part segmentation performance is significantly
improved after applying our implicit transfer approach. Our network for pose
and shape estimation achieves the state-of-the-art performance on the
ApolloCar3D dataset and outperforms 3D-RCNN and DeepMANTA by 12.57 and 8.91
percentage points in terms of mean A3DP-Abs.
