GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects
This addresses the problem of accurate and generalizable 6D pose estimation for novel objects in robotics and computer vision, representing a novel method for a known bottleneck.
The paper tackles the trade-off between accuracy and scalability for novel objects in 6D pose estimation by introducing GenFlow, which uses the target object's 3D shape to predict optical flow for iterative refinement, achieving first place on unseen object benchmarks and competitive performance on seen objects without fine-tuning.
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.