  3D pose estimation from a single 2D image is an important and challenging
task in computer vision with applications in autonomous driving, robot
manipulation and augmented reality. Since 3D pose is a continuous quantity, a
natural formulation for this task is to solve a pose regression problem.
However, since pose regression methods return a single estimate of the pose,
they have difficulties handling multimodal pose distributions (e.g. in the case
of symmetric objects). An alternative formulation, which can capture multimodal
pose distributions, is to discretize the pose space into bins and solve a pose
classification problem. However, pose classification methods can give large
pose estimation errors depending on the coarseness of the discretization. In
this paper, we propose a mixed classification-regression framework that uses a
classification network to produce a discrete multimodal pose estimate and a
regression network to produce a continuous refinement of the discrete estimate.
The proposed framework can accommodate different architectures and loss
functions, leading to multiple classification-regression models, some of which
achieve state-of-the-art performance on the challenging Pascal3D+ dataset.
