Is it possible to train a convolutional neural network (CNN) to predict the dimensions of primitive objects such as (spheres, cylinders, cuboids, etc.) from point clouds?
The input to the CNN will be the point cloud of a single object and the output will be the dimensions of the object (for example, radius and height of the cylinder). The training data will be the point cloud of the object with the ground truth dimensions in a regression final layer?
I think it is possible for images since it will similar to a bounding box detection, but I am not sure with point clouds.