“Geometric Computing With Python” by Koch, Schneider, Williams and Panozzo

  • ©Sebastian Koch, Teseo Schneider, Francis Williams, and Daniele Panozzo



Entry Number: 11


    Geometric Computing With Python

Course Organizer(s):



    The course will mainly use igl (Section 2), polyfem (Section 3), ABC Dataset CAD Processing (Section 4), TetWild and 3D Viewer.

    Many disciplines of computer science have access to high level libraries allowing researchers and engineers to quickly produce prototypes. For instance, in machine learning, one can construct complex, state-of-the-art models which run on the GPU in a few lines of Python. In the field of geometric computing, however such high-level libraries are sparse. As a result, writing prototypes in geometry is time consuming and difficult even for advanced users. In this course, we present a set of easy-to-use Python packages for applications in geometric computing. We have designed these libraries to have a shallow learning curve, while also enabling programmers to easily accomplish a wide variety of complex tasks. Furthermore, the libraries we present share NumPy arrays as a common interface, making them highly composable with each-other as well as existing scientific computing packages. Finally, our libraries are blazing fast, doing most of the heavy computations in C++ with a minimal constant-overhead interface to Python. In the course, we will present a set of real-world examples from geometry processing, physical simulation, and geometric deep learning. Each example is prototypical of a common task in research or industry and is implemented in a few lines of code. By the end of the course, attendees will have exposure to a swiss-army-knife of simple, composable, and high-performance tools for geometric computing.

Additional Information:

    Research & Education

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