Home
NNDT is a framework for physically informed neural networks and implicit neural representation of 3D objects. The main scope of this code is computational anatomy and computational physiology. We try to implement as many as possible methods from these fields in a neural-powered way. The framework includes basic building blocks, such as data loaders, layers, neural network models, as well as, end-user inference tools. We expect undergraduate students and junior researchers as early adopters of this project. For this reason, it should provide an easy-to-start experience. Also, we expect Ph.D. students, postdocs, and industry researchers as the main target audience for this software. For this reason, a solution must include the most prominent and advanced approach in the target scope.
The basic code are based on Vladislav Dordiuk research notebooks, that were reevaluated and rewritten as a single project by Konstantin Ushenin. Small improvements, quality of code, and CI/CD are being supported by Maksim Dzhigil.
Figure 2. The software architecture. Stars mark files that is not implemented in v0.0.0.
- space_model is the 3D scene representation. It keep data consistency and provide function to access to data.
- generators is an out-of-the-box data generators for NN training
- layers is classes inherited from haiku.Modules. That class include new layers and
Figure 1. The shape interpolation demo (sdf_multiple_files.py). first and forth shape are original, the second and third are generated by neural network.