SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
This work addresses the need for accurate and adaptable pose refinement in robotics and computer vision, though it is incremental as it builds on the SCFlow model.
The paper tackles the problem of refining 6D object pose estimation by introducing SCFlow2, a plug-and-play framework that uses shape-constrained scene flow and geometry constraints to improve accuracy without retraining, resulting in significantly better results for state-of-the-art methods on BOP datasets with novel objects.
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 refinement methods either suffer from noises in establishing correspondences, or rely on retraining for novel objects. SCFlow2 is based on the SCFlow model designed for 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 matching network, by combining the rigid-motion embeddings in 3D scene flow and 3D shape prior of the target. We train SCFlow2 on a combination of dataset Objaverse, GSO and ShapeNet, and evaluate on BOP datasets with novel objects. After using our method as a post-processing, most state-of-the-art methods produce significantly better results, without any retraining or fine-tuning. The source code is available at https://scflow2.github.io.