For object detection in a point cloud, I'll propose a process for you to follow instead of just providing links. I assume you don't have experience with machine learning and that is driving your question. If I'm wrong about that, my apologies.
I would turn the 3D point cloud into a depth map oriented with the viewer at the lidar. This would give you a 2D 'image' with each pixel representing depth. Use an available ML object detection algo like 2D YOLO that is pretrained and well documented. Study the tutorials, get it working, and then train the last few layers on training data you would label by hand.
The results from 2D processing like YOLO may or may not be good enough for your case but at least you have learned a bit about machine learning, have your PC setup for machine learning, and understand labeling data sets.
Having experience with the easy stuff above, what you find with google will be easier to understand:
All of this is to explain that you're not going to get a suitable answer without some underlying understanding of the subject. If you want to use ML for object detection, learn about object detection and machine learning first, then try to leverage the work of those coming before you.
I have integrated YOLOV4 running on DARKNET into a ROS2 node for realtime 2D custom-object detection, but I started with learning the DARKNET separately and only after I was comfortable with it, did I think about integrating with ROS. It was more work than I expected (but I suck at software and it may be faster for you).