Shape Modeling International (SMI 2019), which this year is part of the International Geometry Summit, provides an international forum for the dissemination of new mathematical theories and computational techniques for modeling, simulating and processing digital representations of shapes and their properties to a community of researchers, developers, students, and practitioners across a wide range of fields. Conference proceedings (long and short papers) will be published in a Special Issue of Computer & Graphics Journal, Elsevier. Papers presenting original research are being sought in all areas of shape modeling and its applications.
More information on topics, submission guidelines, and important dates are given below.
SMI 2019 will be co-located with the Symposium on Solid and Physical Modeling (SPM 2019), the SIAM Conference on Computational Geometric Design (SIAM 2019), the International Conference on Geometric Modelling and Processing (GMP2019), as part of the Geometry Summit 2019.
The Fabrication and Sculpting Event FASE (2019) will be organized in co-location with SMI 2019. It presents original research at the intersection of theory and practice in shape modeling, fabrication and sculpting.
The symposium will take place from June 19th to 21th at Vancouver, Canada.
Full paper submission: Tuesday, March 19th
First review notification: Monday, April 15th
Revised papers: Thursday, May 2nd
Second review notification: Wednesday, May 15th
Camera ready full papers due: Monday, May 20th
Conference: Wednesday to Friday,June 19th-21st
All deadlines at 23:59 UTC/GMT
Papers should present previously unpublished, original results that are not simultaneously submitted elsewhere.
Submissions should be formatted according to the style guidelines for the Computers &Graphics Journal and should not exceed 12 pages, including figures and references. We strongly recommend using the LaTeX template to format your paper. We also accept papers formatted by MS Word according to the style guidelines for Computers & Graphics. The file must be exported to pdf file for the first round of submission. For format details, please refer to Computers & Graphics Guide for Authors.
The SMI 2019 conference will use a double-blind review process. Consequently, all submissions must be anonymous. All papers should be submitted directly via the journal online submission system of Computers & Graphics (click here). When submitting your paper to SMI 2019, please make sure that the type of article is specified as "SI: SMI 2019”.
Any accepted paper is required to have at least one registered author to attend and present the paper at the conference.
SMI participates in the Replicability Stamp Initiative, an additional recognition for authors who are willing to go one step further, and in addition to publishing the paper, provide a complete open-source implementation. The Graphics Replicability Stamp Initiative (GRSI) is an independent group of volunteers who want to help the community by enabling sharing of code and data as a community resource for non-commercial use. The volunteers review the submitted code and certify its replicability, awarding a replicability stamp, which is an additional recognition for authors of accepted papers who are willing to provide a complete implementation of their algorithm, to replicate the results presented in their paper. The replicability stamp is not meant to be a measure of the scientific quality of the paper or of the usefulness of presented algorithms. Rather, it is meant to be an endorsement of the replicability of the results presented in it!
The paper and the recognition of the service provided to the community by releasing the code. Submissions for the replicability stamp will be considered only after the paper has been fully accepted. Submissions that are awarded the replicability stamp will receive additional exposure by being listed on this website. The purpose of this stamp is to promote reproducibility of research results and to allow scientists and practitioners to immediately benefit from state-of-the-art research results, without spending months re-implementing the proposed algorithms and trying to find the right parameter values. We also hope that it will indirectly foster scientific progress, since it will allow researchers to reliably compare with and to build upon existing techniques, knowing that they are using exactly the same implementation. This is an initiative supported by a growing list of publishers, journals, and conferences.
The submission procedure is lightweight (click here to see requirements) and we encourage the authors of accepted papers to participate by filling the form that they received in the acceptance letter. The papers with the replicability stamp will receive additional exposure during SMI, and will be listed on the replicability stamp website.
The qualified papers will be decorated with the 
logo in the program
(logo design by Michela Mortara)
Bio:Holly Rushmeier is the John C. Malone Professor of Computer Science at Yale University. Her research interests include shape and appearance capture, applications of perception in computer graphics, modeling material appearance and developing computational tools for cultural heritage. She received the BS, MS and PhD degrees in Mechanical Engineering from Cornell University in 1977, 1986 and 1988 respectively. After receiving her PhD she was at Georgia Tech, NIST and IBM TJ Watson Research before joining Yale in 2004. Rushmeier was Editor-in-Chief of ACM Transactions on Graphics from 1996-99 and co-EiC of Computer Graphics Forum (2010-2014). She received the ACM SIGGRAPH Computer Graphics Achievement Award in 2013, and is a fellow of the ACM and of the Eurographics Association.
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Date:19th June, 9:45-10:45
Abstract:Recent research in textures for rendering has led to some interesting problems that cross the boundaries between shape modeling, rendering and animation. I will discuss a few projects in this area. Inverse modeling of small cale geometric textures is part of a project in appearance modeling. Understanding how people expect to interact with shapes is part of a project on how to model texture and reflectance consistent with shape. Modeling 3D textures has led to a project in exploring new shapes for effective materials in storage batteries. Finally, designing and printing textures on shapes is related to a project in creating objects that carry encoded animations.
Bio:Scott Schaefer is a Professor and Associate Department Head of the Computer Science & Engineering Department at Texas A&M University. He received a bachelor's degree in Computer Science/Mathematics from Trinity University in 2000 and an M.S. and PhD. in Computer Science from Rice University in 2003 and 2006 respectively. His research interests include graphics, geometry processing, curve and surface representations, and barycentric coordinates. Dr. Schaefer received the Günter Enderle Award in 2011 and an NSF CAREER Award in 2012. His research has been used by various companies including Pixar, Nvidia, Microsoft, and Adobe.
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Date:20th June, 13:30-14:30
Abstract:This talk describes the motivation and geometry of parameterization in Computer Graphics. In particular, we focus on the difficulty of computing low distortion bijective maps between triangulated surfaces and the two dimensional plane. To do so, we describe an isometric distortion metric and describe how to specialize nonlinear optimization procedures by directly computing all singularities of the function explicitly. We guarantee bijectivity through the use of a barrier function and show how to obtain fast optimization times through the use of a spatial hash. The result is an efficient method for computing a bijective map that obtains low distortion without constraining the boundary.
Bio:Alec Jacobson is an Assistant Professor and Canada Research Chair in the Department of Computer Science at University of Toronto. Before that he was a post-doctoral researcher at Columbia University working with Prof. Eitan Grinspun. He received a PhD in Computer Science from ETH Zurich advised by Prof. Olga Sorkine-Hornung, and an MA and BA in Computer Science and Mathematics from the Courant Institute of Mathematical Sciences, New York University. His PhD thesis on real-time deformation techniques for 2D and 3D shapes was awarded the ETH Medal and the Eurographics Best PhD award. Leveraging ideas from differential geometry and finite-element analysis, his work in geometry processing improves exposure of geometric quantities, while his novel user interfaces reduce human effort and increase exploration. He has published several papers in the proceedings of SIGGRAPH. He leads development of the widely used geometry processing library, libigl, winner of the 2015 SGP software award. In 2017, he received the Eurographics Young Researcher Award.
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Date:21st June, 9:00-10:00
Abstract:Geometric data abounds, but our algorithms for geometry processing are failing. Whether from medical imagery, free-form architecture, self-driving cars, or 3D-printed parts, geometric data is often messy, riddled with "defects" that cause algorithms to crash or behave unpredictably. The traditional philosophy assumes geometry is given with 100% certainty and that algorithms can use whatever discretization is most convenient. As a result, geometric pipelines are leaky patchworks requiring esoteric training and involving many different people. Instead, we adapt fundamental mathematics to work directly on messy geometric data. As an archetypical example, I will discuss how to generalize the classic formula for determining the inside from the outside of a curve to messy representations of a 3D surface. This work helps keep the geometry processing pipeline flowing, as validated on our large-scale geometry benchmarks. Our long term vision is to replace the current leaky geometry processing pipeline with a robust workflow where processing operates directly on real geometric data found "in the wild". To do this, we need to rethink how algorithms should gracefully degrade when confronted with imprecision and uncertainty. Our most recent work on differentiable rendering and geometry-based adversarial attacks on image classification demonstrates the potential power of this philosophy.
