Browsing by Author "Schneider, Teseo"

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    Black Box Geometric Computing with Python: From Theory to Practice
    (The Eurographics Association, 2020) Koch, Sebastian; Schneider, Teseo; Li, Chengchen; Panozzo, Daniele; Fjeld, Morten and Frisvad, Jeppe Revall
    The first part of the course is theoretical, and introduces the finite element method trough interactive Jupyter notebooks. It also covers recent advancements toward an integrated pipeline, considering meshing and element design as a single challenge, leading to a black box pipeline that can solve simulations on ten thousand in the wild meshes, without any parameter tuning. In the second part we will move to practice, introducing a set of easy-to-use Python packages for applications in geometric computing. The presentation will have the form of live coding in a Jupyter notebook. We have designed the presented libraries to have a shallow learning curve, while also enabling programmers to easily accomplish a wide variety of complex tasks. Furthermore, these libraries utilize 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.
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    EGGS: Sparsity-Specific Code Generation
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Tang, Xuan; Schneider, Teseo; Kamil, Shoaib; Panda, Aurojit; Li, Jinyang; Panozzo, Daniele; Jacobson, Alec and Huang, Qixing
    Sparse matrix computations are among the most important computational patterns, commonly used in geometry processing, physical simulation, graph algorithms, and other situations where sparse data arises. In many cases, the structure of a sparse matrix is known a priori, but the values may change or depend on inputs to the algorithm. We propose a new methodology for compile-time specialization of algorithms relying on mixing sparse and dense linear algebra operations, using an extension to the widely-used open source Eigen package. In contrast to library approaches optimizing individual building blocks of a computation (such as sparse matrix product), we generate reusable sparsity-specific implementations for a given algorithm, utilizing vector intrinsics and reducing unnecessary scanning through matrix structures. We demonstrate the effectiveness of our technique on a benchmark of artificial expressions to quantitatively evaluate the benefit of our approach over the state-ofthe- art library Intel MKL. To further demonstrate the practical applicability of our technique we show that our technique can improve performance, with minimal code changes, for mesh smoothing, mesh parametrization, volumetric deformation, optical flow, and computation of the Laplace operator.
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    Fast and Exact Root Parity for Continuous Collision Detection
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Bolun; Ferguson, Zachary; Jiang, Xin; Attene, Marco; Panozzo, Daniele; Schneider, Teseo; Chaine, Raphaƫlle; Kim, Min H.
    We introduce the first exact root parity counter for continuous collision detection (CCD). That is, our algorithm computes the parity (even or odd) of the number of roots of the cubic polynomial arising from a CCD query. We note that the parity is unable to differentiate between zero (no collisions) and the rare case of two roots (collisions). Our method does not have numerical parameters to tune, has a performance comparable to efficient approximate algorithms, and is exact. We test our approach on a large collection of synthetic tests and real simulations, and we demonstrate that it can be easily integrated into existing simulators.
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    Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference: Frontmatter
    (The Eurographics Association, 2022) Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo