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.

本研究提出了一种针对工业应用的实时6D对象检测的新方法，解决了FoundationPose中初始帧需提供对象掩码以及对称物体的跟踪丢失和自动旋转问题。该算法仅需目标对象的CAD模型，经过用户在实时视频中点击位置的初始设置后，便可实现准确的6D检测和跟踪，显示出在困难条件下的可靠性和稳定性。

GearTrack：自动化6D姿态估计