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How to use ConvNet to detect 3D object categories?

I am interested in using ConvNet to classify in 3D space a set of objects (footballs, basketballs, etc). I was wondering whether you can use a convnet to classify 3d shapes instead of image classification.
If that’s possible, what kind of input and what kind of parameters would have to be used?

Let’s say you want to classify in 3D space a set of objects (footballs,
basketballs, etc).

This does not follow.
If you were to classify objects of some known class (such as fruits or animals) in 2D space then indeed you could use a CNN.
However, if your task is to simply classify objects in 3D space then it doesn’t really make sense to do so with just a 3D CNN. To do so, you would need a convolutional neural network that operates in 3D space.
To do that, you would need to define a 3D convolution and a 3D pool
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