结构化约束增强的6D物体位姿估计

    6D Object Pose Estimation Enhanced by Structural Constraint

    • 摘要: 针对基于投票策略的6D物体位姿估计方法忽略了关键点间结构信息的问题,提出结构化约束增强的6D物体位姿估计方法——SC-Pose。该方法定义了一种用于描述物体2D关键点间结构化信息的形状描述符,通过增加关键点结构化损失约束形状描述符的预测值与真值相近,从而使2D关键点的定位更加准确,提升了6D物体位姿估计的精度。在LINEMOD、OCC-LINEMOD和TruncationLINEMOD数据集上进行了实验,结果表明,SC-Pose方法可以明显提升6D物体位姿估计的性能。

       

      Abstract: Aiming at the problem that the 6D object pose estimation method based on the voting strategy ignores the structural information between keypoints, a 6D object pose estimation method enhanced by structural constraint (SC-Pose) is proposed. This method defines a shape descriptor designed to describe the structured information between the 2D keypoints of the object. By enhancing the keypoint structural loss to constrain the predicted shape descriptor to be close to the ground-truth shape descriptor, the positioning of the 2D keypoints is more accurate, and thereby ultimately e incorporating the accuracy of 6D object pose estimation. Results on the LINEMOD, OCC-LINEMOD and TruncationLINEMOD datasets show that SC-Pose can significantly boost the accuracy of 6D object pose estimation.

       

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