12 papers with code • 0 benchmarks • 1 datasets
These leaderboards are used to track progress in Vector Graphics
The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket.
Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.
Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions.
As examples of applications, researchers can reconstruct ancestral ranges in historical biogeography or identify indicator species for targeted conservation.
A neural architecture first transforms a rasterized image to a set of junctions that represent low-level geometric and semantic information (e. g., wall corners or door end-points).
We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content.
The current alternative is to use specialized models that require explicit supervision on the vector graphics representation at training time.