A comparison of neural networks architectures for geometric modelling of 3D objects

This paper presents a critical comparison between three neural architectures for 3D object representation in terms of purpose, computational cost, complexity, conformance and convenience, ease of manipulation and potential uses in the context of virtualized reality. The models can be easily transformed in size, position and shape. Potential uses of the presented architectures in the context of virtualized reality are for the modeling of set operations and object morphing, for the detection of objects collision, for object recognition, object motion estimation and segmentation.