Special Seminar in Computer Graphics

Accurately controllable shading detail is a crucial aspect of realistic appearance modelling. Two fundamental building blocks for this are microfacet BRDFs, which describe the statistical behaviour of infinitely small facets, and normal maps, which provide user-controllable spatio-directional surface features. We analyse the filtering of the combined effect of a microfacet BRDF and a normal map. By partitioning the half-vector domain into bins we show that the filtering problem can be reduced to evaluation of an integral histogram (IH), a generalization of a summed-area table (SAT). Integral histograms are known for their large memory requirements, which are usually proportional to the number of bins. To alleviate this, we introduce Inverse Bin Maps, a specialised form of IH with a memory footprint that is practically independent of the number of bins. Based on these, we present a memory-efficient, production-ready approach for filtering of high resolution normal maps with arbitrary Beckmann flake roughness.

With the wider availability of full-color 3D printers, color-accurate 3D-print preparation has received increased attention. A key
challenge lies in the inherent translucency of commonly used print materials that blurs out details of the color texture. Previous
work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels.
To date, the highest-quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light
transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution;
that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by
replacing the light transport simulation with a data-driven approach. Leveraging a deep neural network to predict the scattering
within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering
while yielding optimization results of similar quality level. The network is based on an established method from atmospheric
cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces
the network size. We analyze its performance in an end-to-end print preparation pipeline and compare quality and runtime to
alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables
full heterogenous material optimization for 3D-print preparation within time frames in the order of the actual printing time.

Filip Jurčák
Material Picker: Material recognition in images using machine learning

One of the important steps in modeling realistic 3D scenes is setting material appearance of the various scene objects. The goal of our ongoing project is to simplify this often tedious task by providing the 3D artist with an intelligent material picker tool. The tool should allow user to ‘pick’ a material from any given input image by simply pointing to an object. A deep neural network will be trained to achieve this nontrivial goal. An extensive set of training data will be provided, where the complex correspondence between the image pixels and the underlying object material will be available. The network should be then able to recover this pixel-material correspondence from new, previously unseen images.