无需重训练，可提升6D姿态估计精度的插件式优化框架
SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
- 引入3D场景流与形状先验约束，迭代优化姿态匹配
- 在BOP数据集上对新物体实现显著性能提升
- 适合已有的6D姿态估计模型作为后处理模块使用
我们提出SCFlow2，一种用于6D物体姿态估计的即插即用精修框架。现有方法多依赖精修以获得高精度结果，但多数方法在建立对应关系时受噪声影响，或需为新物体重新训练。SCFlow2基于原版SCFlow模型，通过3D场景流将深度信息作为迭代中的正则化项，引入几何约束。其核心设计是将3D场景流中的刚体运动嵌入与目标物体的3D形状先验结合，用于循环匹配网络的训练。模型在Objaverse、GSO和ShapeNet数据集组合上训练，并在BOP数据集上评估新物体表现。作为后处理使用后，多数最先进方法均显著提升性能，且无需任何重训练或微调。代码开源地址：https://scflow2.github.io。
原文摘要 · Abstract (English)
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.
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