| dc.description.abstract | Texture maps are ubiquitous in 3D rendering to encode material properties like albedo color, surface normal vectors, roughness coefficients and many more. Applying them to the surface of a 3D object requires a mapping from the object’s surface
to the flat texture plane (uv-mapping), which may introduce artifacts through unavoidable distortion and seams.
In this work, we propose a novel neural approach to encoding surface material parameters without the need for uv-mapping. We build on recent research in the field of neural function approximation in computer graphics [1] [2], which achieves efficient compression of texture data by training a machine learning model. By parameterizing surface positions in relation to their mesh triangle, we adapt previous approaches to circumvent the uv-mapping step.
The evaluation of our prototype shows that our method is capable of encoding detailed, high-resolution textures at satisfying quality, while encoding multiple material channels in a single representation. We evaluate our method on a selection
of datasets with a broad range of geometry and texture characteristics. We observe that certain characteristics challenge our method more than others. Compression rates range from 66.6% to 8.3% across the examined datasets.
Our outlook discusses, among other points, how limitations regarding subpar performance on meshes with low vertex-density could be overcome in future work. Furthermore, we lay out a possibility how our method’s hierarchical structure could be leveraged to realize low-pass texture filtering. | sv |
