One of the important steps in modeling realistic 3D scenes is setting material appearance of the various scene objects. The goal of this thesis is to simplify this often tedious task by providing the 3D artist with an intelligent material picker tool. The tool should user allow to 'pick' a material from any given input image by simply pointing to an object. A deep neural network should 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 able to recover this pixel-material correspondence from new, previously unseen images.
Seznam odborné literatury
Goodfellow, Bengio, Courville: Deep Learning. MIT Press, 2016.
Sengupta, Gu, Kim, Liu, Jacobs, Kautz: Neural inverse rendering of an indoor scene from a single image. International Conference on Computer Vision, 2019.
- zadáno a potvrzeno stud. odd.