I'm having some basic trouble understanding how some of the Neural Networks for point cloud instance segmentation are implemented. For instance, some networks trained and tested on the Stanford Indoor dataset are trained on the whole indoor scene annotated with different objects and then during test when given another indoor scene, the networks produce a instance segmented point cloud.
My question is, what if I have a dataset containing all the objects that can be found in my test scene as point clouds and I train the network on this dataset. To be clear, I don't have a scene annotated with different classes like the Standford dataset. I only have objects as point clouds without any background details.
While testing I give it a scene. Can the networks detect and segment the test scene point cloud to recognise only the objects it was trained for and the rest of the scene understanding is not that import for my use case.
It would be really helpful if someone could tell me what I'm not understanding properly.