This letter presents KGpose, a novel end-to-end framework for 6D pose estimation of multiple objects. Our approach combines keypoint-based method with learnable pose regression through `keypoint-graph', which is a graph representation of the keypoints. KGpose first estimates 3D keypoints for each object using an attentional multi-modal feature fusion of RGB and point cloud features. These keypoints are estimated from each point of point cloud and converted into a graph representation. The network directly regresses 6D pose parameters for each point through a sequence of keypoint-graph embedding and local graph embedding which are designed with graph convolutions, followed by rotation and translation heads. The final pose for each object is selected from the candidates of point-wise predictions. The method achieves competitive results on the benchmark dataset, demonstrating the effectiveness of our model. KGpose enables multi-object pose estimation without requiring an extra localization step, offering a unified and efficient solution for understanding geometric contexts in complex scenes for robotic applications.

KGpose是一个新的端到端框架，用于多个物体的6D位姿估计。通过关键点图的学习姿势回归，KGpose首先使用RGB和点云特征的多模态特征融合来估计每个物体的3D关键点，然后将这些关键点转换为图表示。网络通过一系列的关键点图嵌入和局部图嵌入直接回归每个点的6D位姿参数，最终选择每个物体的姿态。KGpose在基准数据集上取得了竞争性的结果，展示了我们模型的有效性。为机器人应用提供了一种无需额外定位步骤的多物体姿态估计的统一且高效的解决方案，用于理解复杂场景中的几何背景。

KGpose: 基于关键点图驱动的端到端多物体6D姿态估计通过点位姿投票