  Despite the progress of learning-based methods for 6D object pose estimation,
the trade-off between accuracy and scalability for novel objects still exists.
Specifically, previous methods for novel objects do not make good use of the
target object's 3D shape information since they focus on generalization by
processing the shape indirectly, making them less effective. We present
GenFlow, an approach that enables both accuracy and generalization to novel
objects with the guidance of the target object's shape. Our method predicts
optical flow between the rendered image and the observed image and refines the
6D pose iteratively. It boosts the performance by a constraint of the 3D shape
and the generalizable geometric knowledge learned from an end-to-end
differentiable system. We further improve our model by designing a cascade
network architecture to exploit the multi-scale correlations and coarse-to-fine
refinement. GenFlow ranked first on the unseen object pose estimation
benchmarks in both the RGB and RGB-D cases. It also achieves performance
competitive with existing state-of-the-art methods for the seen object pose
estimation without any fine-tuning.
