Actually it is quite a hard topic. Classical multi-view 3d reconstruction deals with point matching in the first place, i.e. find the same point on every image. Given the camera (view) parameters for each image, the original 3d point can be reconstructed. (Using a laser or a projector the scene can be lit so the matching can be done relatively easily.)
The bible of the field is Multiple View Geometry in Computer Vision by Hartley and Zisserman
In the book there is a section about the trifocal tensor, which is a multilinear constraint between 3 views. It contains not just point but line correspondence constraints as well. It can be used for building reconstruction very well.
So your contours should be matched at the first place, and maybe can be reconstructed knowing the camera parameters (camera calibration is also covered in the book). Then you will have contours in 3d but nothing more. For real surfaces you have to do dense point matching. Though the tensor I mentioned look good it is used for straight lines and I am sure that a modern car has curved lines all over.
I do not know how you got those contours but seeing the image you have posted I am quite sceptic about the robustness of that algorithm, so the reconstruction will be poor.
Another method it came to my mind is visual hull or space carving. The contour mathcing should also be done. Running the method on each contour you can have the model.