  We present a novel learning framework for vehicle recognition from a single
RGB image. Unlike existing methods which only use attention mechanisms to
locate 2D discriminative information, our work learns a novel 3D perspective
feature representation of a vehicle, which is then fused with 2D appearance
feature to predict the category. The framework is composed of a global network
(GN), a 3D perspective network (3DPN), and a fusion network. The GN is used to
locate the region of interest (RoI) and generate the 2D global feature. With
the assistance of the RoI, the 3DPN estimates the 3D bounding box under the
guidance of the proposed vanishing point loss, which provides a perspective
geometry constraint. Then the proposed 3D representation is generated by
eliminating the viewpoint variance of the 3D bounding box using perspective
transformation. Finally, the 3D and 2D feature are fused to predict the
category of the vehicle. We present qualitative and quantitative results on the
vehicle classification and verification tasks in the BoxCars dataset. The
results demonstrate that, by learning such a concise 3D representation, we can
achieve superior performance to methods that only use 2D information while
retain 3D meaningful information without the challenge of requiring a 3D CAD
model.
