Hello, thanks for your work.
One of the features of your method is that it can predict the poses of multiple different objects in one inference. To achieve multi-object detection, you processed the class 2 (benchvise) data in the linemod dataset, which originally only annotated the pose and bounding box of class 2, and your class 2 data annotation included the pose and bounding box of all objects that appeared in the image. I wonder how did you achieve this effect? Can I do the same for other classes of data?
Thanks for your help!
Hi @sunyx33,
this is actually the Occlusion dataset. It is basically a subset of Linemod (scene 2) and includes GT annotations of all visible objects in this scene.
Hello, thanks for your work. One of the features of your method is that it can predict the poses of multiple different objects in one inference. To achieve multi-object detection, you processed the class 2 (benchvise) data in the linemod dataset, which originally only annotated the pose and bounding box of class 2, and your class 2 data annotation included the pose and bounding box of all objects that appeared in the image. I wonder how did you achieve this effect? Can I do the same for other classes of data? Thanks for your help!