发表机构
PIT IN Co.（PIT IN公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RYOPO提出端到端可训练的查询式RGB-D集合预测器，联合检测分割物体并估计9自由度姿态，无需外部分割器，在NOCS上显著提升性能，并实现31.8 FPS的实时全帧估计。
AI 中文摘要
类别级物体姿态估计预测已知类别中未见实例的旋转、平移和度量尺寸。许多精确的RGB-D方法依赖外部实例分割和基于裁剪的姿态估计，引入了独立的阶段和依赖物体的处理成本，阻碍了实时推理。为了将精确的姿态估计带入实时，我们提出了\textit{RYOPO}，一个端到端可训练的基于查询的RGB-D集合预测器。它联合检测和分割物体，并估计其9自由度姿态，无需显式的CAD派生形状先验或单独训练的实例分割器。共享的图像和场景编码避免了重复的逐物体裁剪编码。查询条件几何路径将观察到的3D点和RGB特征与物体查询关联，并融入共享场景上下文。以物体为中心的细化使用所得点描述符，通过姿态条件交叉注意力和循环残差校正更新显式姿态状态。在NOCS上，\textit{RYOPO}显著改善了已发表的RGB-D联合检测和姿态估计结果。在REAL275和HouseCat6D的全物体评估中，它与两阶段方法相比具有竞争力的性能，同时在RTX A6000上实现了31.8 FPS的实时全帧姿态估计。项目页面：此https URL。
英文摘要
Category-level object pose estimation predicts the rotation, translation, and metric size of unseen instances within known categories. Many accurate RGB-D methods rely on external instance segmentation and crop-based pose estimation, introducing separate stages and object-dependent processing costs that hinder real-time inference. To bring accurate pose estimation into real time, we present \ours{}, an end-to-end trainable query-based RGB-D set predictor. It jointly detects and segments objects and estimates their \mbox{9-DoF} poses without explicit CAD-derived shape priors or a separately trained instance segmentor. Shared image and scene encoding avoids repeated per-object crop encoding. A query-conditioned geometry pathway associates observed 3D points and RGB features with object queries and incorporates shared scene context. Object-centric refinement uses the resulting point descriptors to update an explicit pose state through pose-conditioned cross-attention and recurrent residual corrections. On NOCS, \ours{} substantially improves on published RGB-D joint detection and pose estimation results. It achieves competitive performance compared with two-stage methods under all-object evaluation on REAL275 and HouseCat6D, while enabling real-time full-frame pose estimation at $31.8$ FPS on an RTX~A6000. Project page: https://yopo-series.github.io/RYOPO-project-page/.
CommentsProject page: https://yopo-series.github.io/RYOPO-project-page/
