  We present a novel approach to robustly detect and perceive vehicles in
different camera views as part of a cooperative vehicle-infrastructure system
(CVIS). Our formulation is designed for arbitrary camera views and makes no
assumptions about intrinsic or extrinsic parameters. First, to deal with
multi-view data scarcity, we propose a part-assisted novel view synthesis
algorithm for data augmentation. We train a part-based texture inpainting
network in a self-supervised manner. Then we render the textured model into the
background image with the target 6-DoF pose. Second, to handle various camera
parameters, we present a new method that produces dense mappings between image
pixels and 3D points to perform robust 2D/3D vehicle parsing. Third, we build
the first CVIS dataset for benchmarking, which annotates more than 1540 images
(14017 instances) from real-world traffic scenarios. We combine these novel
algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing
for CVIS. In practice, our approach outperforms SOTA methods on 2D detection,
instance segmentation, and 6-DoF pose estimation, by 4.5%, 4.3%, and 2.9%,
respectively. More details and results are included in the supplement. To
facilitate future research, we will release the source code and the dataset on
GitHub.
