3D Polygon Mesh Compression with Multi Layer Feed Forward Neural Networks

In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information in one compact form, converting the discrete and surface polygon representation into an analytic, solid colloquial. Furthermore, the 3D object in its compressed neural form can be directly without decompression used for rendering. The neural compression representation is viable to 3D transformations without the need of any anti-aliasing techniques transformations do not disrupt the accuracy of the geometry. Our method does not suffer any scaling problem and was tested with objects of 300 to 10 polygons such as the David of Michelangelo achieving in all cases an order of O(b) less bits for the representation than any other commonly known compression method. The simplicity of our algorithm and the established mathematical background of neural networks combined with their aptness for hardware implementation can establish this method as a good solution for polygon compression and if further investigated, a novel approach for 3D collision, animation and morphing.