Abstract
Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations, which aims to enhance the extraction and alignment of geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics.
Abstract (translated)
对象姿态优化对于稳健的目标姿态估计是至关重要的。之前的工作在实例级别的对象姿态优化方面取得了显著的进展。然而，由于类别内形状的较大差异以及目标对象和形状先前的差异，类别级别的姿态优化是一个更具挑战性的问题。为了应对这些挑战，我们引入了一种新颖的类别级别对象姿态优化架构。我们的方法结合了HS层和学习可变变换，旨在增强几何信息的提取和匹配。此外，我们还引入了一种跨云变换机制，有效地合并了多样数据源。最后，我们通过引入平移和大小误差预测的形状先验信息，将模型的极限推向了更高的水平。我们进行了广泛的实验来证明所提出的框架的有效性。通过广泛的定量实验，我们证明，与基线方法相比，我们的框架在所有指标上显著取得了更大的改进。
URL
https://arxiv.org/abs/2404.11139
