| Popis: |
In the problem of object pose estimation, one way to cope with the effect of ambiguity is to use multiple hypotheses. In this work, rather than generating the output pose based on a single object pose, our objective is to enable the system to be aware of the potential object ambiguity through maintaining multiple pose hypotheses. Firstly, we propose a pipeline for 6D object pose tracking on RGB images, wherein a key design is a fuzzy TOPSIS module that takes full advantage of multi-criteria decision making under uncertainties. Secondly, using decision variables determined on features that are frequently utilized in object pose estimation or tracking like segmented masks, fiducial keypoints, and distance transform the proposed method permits achieving tangible performance gains. An hourglass-based neural network is proposed to jointly detect object keypoints, predict the object’s non-occluded part, and to predict the object’s occluded part. To verify our designs, we conducted thorough experiments on the YCB-Video benchmark dataset. Besides, our method achieves competitive results in terms of ADD scores on the YCB-Video, showing that maintaining multiple pose hypotheses is beneficial to the task of object pose tracking. We observe that our method achieves competitive results against six recent methods estimating object pose from single frame and two SOTA object pose trackers. Extensive ablation studies verify our design choices. |
