How do I prepare this 3D data for NN?

Question

How do I prepare the info of 3D models to use with NN? For example, I have thousands of models with boxes similar to the ones in the image below. I can extract the vertices and their normals that make up the faces of these boxes. Similarly, I would like to prepare the info of the red-shaded surfaces, again I have their vertices and their normals. For future studies, I will have more complex shapes such as cylinders, pyramids,...etc. What would be the best way to represent these complex shapes for NN?

Update: These boxes don't stay in the same position, see the second image I added. I will have different geometric models and different red-shaded areas on the surfaces of these objects. The NN output would be a number for each surface of these boxes/objects. The number represents the surface temperature. The input would be the following:

1- Some climate information such as (air temperature, humidity, ...etc.) 3- The location and size of the buildings that are represented in boxes/or maybe other shapes. 4- The size and the location of the red-shaded areas (red-shaded areas represent the shadow cast by buildings. 5- Material of each surface (concrete, brick,...etc).

Answer 1

I think, that the answer depends on the application, but a possible choice would be store it as a mesh - a list of vertices $V$ and edges $E$. Instead of edges, one can work with polygons, and define connectivity $F$ - for triplets of vertices $(v_i, v_j, v_k)$.

There is a nice paper on Mesh CNN that can handle various geometric object.

For the special case of boxes maybe there is a more educated approach, but since you would like to handle later more complex shapes, I would suggest to work with this architecture from the start.