无需重训练，一键初始化实现工业场景下稳定6D位姿估计。
SuperPose: Improved 6D Pose Estimation with Robust Tracking and Mask-Free Initialization
- 结合SAM2与FoundationPose，通过点击定位自动完成初始提示。
- 在YCB及工业部件上实现稳定跟踪，即使遮挡或快速移动也有效。
- 支持对称物体，避免旋转错误，适合工业质检与机器人抓取。
我们提出一种实时6D目标检测的鲁棒解决方案，整合FoundationPose、SAM2和LightGlue，无需重新训练。针对FoundationPose需首帧掩码及对称物体旋转误判的问题，仅需目标物体的CAD模型，用户在初始设置中点击其位置即可。系统自动保存参考图像，并在后续运行中使用LightGlue在真实场景与目标间进行特征匹配，生成检测初始提示。在YCB数据集及洗洁精瓶、齿轮等工业部件上测试表明，该方法具备可靠的6D检测与跟踪能力。通过融合SAM2与FoundationPose，有效缓解了跟踪丢失问题，在遮挡或快速运动条件下仍能保持连续准确追踪。
原文摘要 · Abstract (English)
We developed a robust solution for real-time 6D object detection in industrial applications by integrating FoundationPose, SAM2, and LightGlue, eliminating the need for retraining. Our approach addresses two key challenges: the requirement for an initial object mask in the first frame in FoundationPose and issues with tracking loss and automatic rotation for symmetric objects. The algorithm requires only a CAD model of the target object, with the user clicking on its location in the live feed during the initial setup. Once set, the algorithm automatically saves a reference image of the object and, in subsequent runs, employs LightGlue for feature matching between the object and the real-time scene, providing an initial prompt for detection. Tested on the YCB dataset and industrial components such as bleach cleanser and gears, the algorithm demonstrated reliable 6D detection and tracking. By integrating SAM2 and FoundationPose, we effectively mitigated common limitations such as the problem of tracking loss, ensuring continuous and accurate tracking under challenging conditions like occlusion or rapid movement.
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