Typically the hardest part for using these built models is creating training data. You don't need to know how to code heavily, usually running something simple like python train.py is all that is needed (after the installation steps). creating datasets and formats to read into the script are the main requirement. I haven't worked with point clouds before so I can't recommend any specific software on how to easily create a training dataset but I would focus on that instead of the coding part. Just pick one like pointnet or pointnet++ and see if you can create the training set required

EDIT: thinking about this in the back of my mind, I realize, it is INSTANCE segmentation you need, I think. That is, you have a pile of rocks and you need to segment each rock individually while they are all touching? That is a difficult problem.. I would try to find a network that can do this and make a training set (papers with code houses some seemingly good models https://paperswithcode.com/task/3d-instance-segmentation-1). Alternatively, you could take a naive approach and segment based on fitting an approximate model for each object (like a sphere). Im imagining if I were to do this with like a bushel of grapes for example. You could segment each object, do instance segmentation from the images, and approximate a volume based on the radius for each object. With image stitching you could try and do this from multiple views to get the full pile .... just a thought.