Many object pose estimation algorithms rely on the analysis-by-synthesis
framework which requires explicit representations of individual object
instances. In this paper we combine a gradient-based fitting procedure with a
parametric neural image synthesis module that is capable of implicitly
representing the appearance, shape and pose of entire object categories, thus
rendering the need for explicit CAD models per object instance unnecessary. The
image synthesis network is designed to efficiently span the pose configuration
space so that model capacity can be used to capture the shape and local
appearance (i.e., texture) variations jointly. At inference time the
synthesized images are compared to the target via an appearance based loss and
the error signal is backpropagated through the network to the input parameters.
Keeping the network parameters fixed, this allows for iterative optimization of
the object pose, shape and appearance in a joint manner and we experimentally
show that the method can recover orientation of objects with high accuracy from
2D images alone. When provided with depth measurements, to overcome scale
ambiguities, the method can accurately recover the full 6DOF pose successfully.

本文提出了一种基于图像合成模块和基于梯度的拟合过程相结合的方法，能够通过显式地表示个别物体实例的需求，来隐式地表示整个物体类别的外观、形状和姿态，从而无需每个物体实例都有一个显式的 CAD 模型。该方法包括图像合成台阶、对应误差度量的拟合台阶和对输入参数的反向传播优化台阶，且实验证明该方法可以仅通过 2D 图像就能准确地恢复物体的方向和全姿态。