SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow

2025-04-12

AI Summary

Existing fine-tuning methods for 6D pose estimation suffer from noise sensitivity or require retraining for novel objects. This paper introduces SCFlow2—a plug-and-play, training-free RGB-D pose refinement framework. Methodologically, it pioneers the joint modeling of rigid-motion-embedded 3D scene flow and object-specific 3D shape priors, establishing a cyclic matching network constrained by both geometric consistency and shape fidelity, optimized robustly via iterative regularization. Crucially, SCFlow2 enables zero-shot generalization to unseen objects in the BOP benchmark, trained only once on multi-source 3D datasets (Objaverse, GSO, ShapeNet). As a post-processing module, it significantly boosts state-of-the-art pose estimators on BOP benchmarks, achieving over 15% average ADD(-S) improvement. The approach thus delivers strong generalizability, robustness to sensor noise and occlusion, and practical deployability without object-specific adaptation.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Scene Analysis & Understanding
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Refines 6D object pose estimation with shape constraints
• Addresses noise in correspondences without retraining for new objects
• Integrates geometry constraints via 3D scene flow and shape priors

Innovation

• Plug-and-play pose refinement with shape constraints
• 3D scene flow regularization for RGBD frames
• Geometry constraints in recurrent matching network