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In architecture and computer-aided design, wireframes (i.e., line-basedmodels) are widely used as basic 3D models for design evaluation and fastdesign iterations. However, unlike a full design file, a wireframe model lackscritical information, such as detailed shape, texture, and materials, needed bya conventional renderer to produce 2D renderings of the objects or scenes. Inthis paper, we bridge the information gap by generating photo-realisticrendering of indoor scenes from wireframe models in an image translationframework. While existing image synthesis methods can generate visuallypleasing images for common objects such as faces and birds, these methods donot explicitly model and preserve essential structural constraints in awireframe model, such as junctions, parallel lines, and planar surfaces. Tothis end, we propose a novel model based on a structure-appearance jointrepresentation learned from both images and wireframes. In our model,structural constraints are explicitly enforced by learning a jointrepresentation in a shared encoder network that must support the generation ofboth images and wireframes. Experiments on a wireframe-scene dataset show thatour wireframe-to-image translation model significantly outperforms thestate-of-the-art methods in both visual quality and structural integrity ofgenerated images.

Neural Wireframe Renderer: Learning Wireframe to Image Translations