Category-level object pose estimation involves estimating the 6D pose and the
3D metric size of objects from predetermined categories. While recent
approaches take categorical shape prior information as reference to improve
pose estimation accuracy, the single-stage network design and training manner
lead to sub-optimal performance since there are two distinct tasks in the
pipeline. In this paper, the advantage of two-stage pipeline over single-stage
design is discussed. To this end, we propose a two-stage deformation-and
registration pipeline called DR-Pose, which consists of completion-aided
deformation stage and scaled registration stage. The first stage uses a point
cloud completion method to generate unseen parts of target object, guiding
subsequent deformation on the shape prior. In the second stage, a novel
registration network is designed to extract pose-sensitive features and predict
the representation of object partial point cloud in canonical space based on
the deformation results from the first stage. DR-Pose produces superior results
to the state-of-the-art shape prior-based methods on both CAMERA25 and REAL275
benchmarks. Codes are available at this https URL

本文讨论了两阶段管道相对于单阶段设计的优势，提出了一种名为 DR-Pose 的两阶段变形和配准管道，包括辅助完成阶段和缩放配准阶段，通过点云完成方法生成目标对象的未见部分并引导形状先验的后续形变，并设计了一种新颖的配准网络来提取姿态敏感特征，并根据第一阶段的形变结果预测规范空间中对象部分点云的表示方法。 DR-Pose 在 CAMERA25 和 REAL275 基准测试上优于最先进的基于形状先验的方法。