We introduce SCFlow2, a plug-and-play refinement framework for 6D object pose estimation. Most recent 6D object pose methods rely on refinement to get accurate results. However, most existing refinements either suffer from noises in establishing correspondences, or rely on retraining for novel objects. SCFlow2 is based on the SCFlow model designed for iterative RGB refinement with shape constraint, but formulates the additional depth as a regularization in the iteration via 3D scene flow for RGBD frames. The key design of SCFlow2 is an introduction of geometry constraints into the training of recurrent match network, by combining the rigid-motion embeddings in 3D scene flow and 3D shape prior of the target. We train the refinement network on a combination of dataset Objaverse, GSO and ShapeNet, and demonstrate on BOP datasets with novel objects that, after using our method, the result of most state-of-the-art methods improves significantly, without any retraining or fine-tuning.