EG 2019 - Tutorials
Permanent URI for this collection
Tutorials
Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and
Solids
] [
]
Dan Koschier, Jan Bender, Barbara Solenthaler, and Matthias Teschner
Deep Learning for Computer Graphics and Geometry Processing
Michael Bronstein, Leonidas Guibas, Iasonas Kokkinos, Or Litany, Niloy Mitra, Federico
Monti, and Emanuele RodolĂ
libigl: Prototyping Geometry Processing Research in C++
Daniele Panozzo and Alec Jacobson
Learning Generative Models of 3D Structures
Siddhartha Chaudhuri, Daniel Ritchie, Kai Xu, and Hao (Richard) Zhang
Browse
Recent Submissions
Item Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids(The Eurographics Association, 2019) Koschier, Dan; Bender, Jan; Solenthaler, Barbara; Teschner, Matthias; Jakob, Wenzel and Puppo, EnricoGraphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization technique, commonly used for the numerical simulation of continuum mechanical problems such as the simulation of fluids, highly viscous materials, and deformable solids. Recent advances in the field have made it possible to efficiently simulate massive scenes with highly complex boundary geometries on a single PC [Com16b, Com16a]. Moreover, novel techniques allow to robustly handle interactions among various materials [Com18,Com17]. As of today, graphics-inspired pressure solvers, neighborhood search algorithms, boundary formulations, and other contributions often serve as core components in commercial software for animation purposes [Nex17] as well as in computer-aided engineering software [FIF16]. This tutorial covers various aspects of SPH simulations. Governing equations for mechanical phenomena and their SPH discretizations are discussed. Concepts and implementations of core components such as neighborhood search algorithms, pressure solvers, and boundary handling techniques are presented. Implementation hints for the realization of SPH solvers for fluids, elastic solids, and rigid bodies are given. The tutorial combines the introduction of theoretical concepts with the presentation of actual implementations.Item Deep Learning for Computer Graphics and Geometry Processing(The Eurographics Association, 2019) Bronstein, Michael; Guibas, Leonidas; Kokkinos, Iasonas; Litany, Or; Mitra, Niloy; Monti, Federico; Rodolà , Emanuele; Jakob, Wenzel and Puppo, EnricoIn computer graphics and geometry processing, many traditional problems are now becoming increasingly handled by data-driven methods. In an increasing variety of problem settings, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.Item libigl: Prototyping Geometry Processing Research in C++(The Eurographics Association, 2019) Panozzo, Daniele; Jacobson, Alec; Jakob, Wenzel and Puppo, EnricoModern geometry processing algorithms depend on an ever-growing toolbox of fundamental sub-routines and data structures. Prototyping from scratch requires much time building basic tools rather than focusing on the novel research idea. Many existing code libraries have unsatisfactory APIs and the time spent implementing sub-routines is often replaced with time spent learning complex, templated object hierarchies or memory layouts. Libigl is a C++ library of geometry processing algorithms designed for and by researchers. Its wide functionality includes construction of common sparse discrete differential geometry operators (such as the cotangent Laplacian), simple facet- and edge-based topology data structures, mesh-viewing utilities for OpenGL and GLSL, and many core functions for matrix manipulation which make Eigen feel a lot more like MATLAB. Libigl places extreme importance on ease of use and experimentation. To this end, algorithms are directly exposed as functions taking simple matrix types as inputs and outputs. Libigl is a "header only" library and compiles on Windows, Mac, and Linux. In this course, we will walk through an introduction of libigl via readymade examples spanning the gamut of geometry processing applications and tasks. Attendees will be able to follow along on their laptops. We will explain the core functionality of libigl, how to piece together complex algorithms from library functions, and how to interface to libigl from Python and MATLAB. We will highlight some of libigl’'s most powerul features: including mesh booleans, quad remeshing, parameterization, and shape deformation. We will conclude with live coding sessions demonstrating libigl's effectiveness and ease-of-use.Item Learning Generative Models of 3D Structures(The Eurographics Association, 2019) Chaudhuri, Siddhartha; Ritchie, Daniel; Xu, Kai; Zhang, Hao (Richard); Jakob, Wenzel and Puppo, EnricoMany important applications demand 3D content, yet 3D modeling is a notoriously difficult and inaccessible activity. This tutorial provides a crash course in one of the most promising approaches for democratizing 3D modeling: learning generative models of 3D structures. Such generative models typically describe a statistical distribution over a space of possible 3D shapes or 3D scenes, as well as a procedure for sampling new shapes or scenes from the distribution. To be useful by non-experts for design purposes, a generative model must represent 3D content at a high level of abstraction in which the user can express their goals-that is, it must be structure-aware. In this tutorial, we will take a deep dive into the most exciting methods for building generative models of both individual shapes as well as composite scenes, highlighting how standard data-driven methods need to be adapted, or new methods developed, to create models that are both generative and structure-aware. The tutorial assumes knowledge of the fundamentals of computer graphics, linear algebra, and probability, though a quick refresher of important algorithmic ideas from geometric analysis and machine learning is included. Attendees should come away from this tutorial with a broad understanding of the historical and current work in generative 3D modeling, as well as familiarity with the mathematical tools needed to start their own research or product development in this area.Item EUROGRAPHICS 2019: Tutorials Frontmatter(Eurographics Association, 2019) Jakob, Wenzel; Puppo, Enrico; Jakob, Wenzel and Puppo, Enrico