用因果学习和知识蒸馏提升未知物体的6D姿态估计精度
CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation
- 通过因果推断消除模型中的虚假关联
- 在多个基准上达到领先性能，最高提升4.2%
- 适合需要强泛化能力的机器人抓取场景
类别级物体姿态估计旨在恢复预定义类别中未见实例的旋转、平移和尺寸。现有基于深度神经网络的方法表现优异，但受模型中“不干净”混杂因素引发的虚假相关性影响，对具有显著差异的新实例性能下降。为此，我们提出CleanPose，融合因果学习与知识蒸馏以增强类别级姿态估计。为缓解未观测混杂因子的负面影响，设计基于前门调整的因果推断模块，减少潜在虚假相关性，实现无偏估计。此外，提出基于残差的知识蒸馏方法，有效传递全面的类别信息指导。在REAL275、CAMERA25和HouseCat6D等多个基准上的实验验证了其优越性，优于当前最先进方法。
原文摘要 · Abstract (English)
Category-level object pose estimation aims to recover the rotation, translation and size of unseen instances within predefined categories. In this task, deep neural network-based methods have demonstrated remarkable performance. However, previous studies show they suffer from spurious correlations raised by "unclean" confounders in models, hindering their performance on novel instances with significant variations. To address this issue, we propose CleanPose, a novel approach integrating causal learning and knowledge distillation to enhance category-level pose estimation. To mitigate the negative effect of unobserved confounders, we develop a causal inference module based on front-door adjustment, which promotes unbiased estimation by reducing potential spurious correlations. Additionally, to further improve generalization ability, we devise a residual-based knowledge distillation method that has proven effective in providing comprehensive category information guidance. Extensive experiments across multiple benchmarks (REAL275, CAMERA25 and HouseCat6D) hightlight the superiority of proposed CleanPose over state-of-the-art methods. Code will be available at https://github.com/chrislin0621/CleanPose.
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