Shape structure is about the arrangement and relations between shape parts. Structure-aware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of shape rather than on their local geometry.

With recent developments in easy shape acquisition, access to vast repositories of 3D models, and simple-to-use desktop fabrication possibilities, the study of structure in shapes has become a central research topic in shape analysis, editing, and modeling. A whole new line of structure-aware shape processing algorithms has emerged that base their operation on an attempt to understand such structure in shapes. The algorithms broadly consist of two key phases: an analysis phase, which extracts structural information from input data; and a (smart) processing phase, which utilizes the extracted information for exploration, editing, and synthesis of novel shapes.

In this course, we will organize, summarize, and present the key concepts and methodological approaches towards efficient structure-aware shape processing. We discuss common models of structure, their implementation in terms of mathematical formalism and algorithms, and explain the key principles in the context of a number of state-of-the-art approaches. Further, we attempt to list the key open problems and challenges, both at the technical and at the conceptual level, to make it easier for new researchers to better explore and contribute to this topic.

Our goal is to both give the practitioner an overview of available structure-aware shape processing techniques, as well as identify future research questions in this important, emerging, and fascinating research area.

Level

Intermediate

Intended Audience

This course is targeted to people from both industry and academia. The tutorial-styled lectures will focus on key algorithms and their design principles, talk about current challenges, and should provide a good overview both for implementing state-of-the-art methods and also identifying open challenges in this emerging area.

Prerequisites

Basic knowledge of computer graphics, linear algebra, and data manipulation. Experience in 3D modeling and processing of repositories of 3D models (e.g., Trimble warehouse) will be useful, but not required.

Presenter(s)

Niloy Mitra, University College London
Michael Wand, Max Planck Institut fuer Informatik
Hao (Richard) Zhang, Simon Fraser University
Daniel Cohen-Or, Tel Aviv University
Vladimir Kim, Princeton University
Qi-Xing Huang, Stanford University

Niloy Mitra is an associate professor at UCL and leads the Geometry Processing group. His work spans the area of symmetry detection, shape analysis, and their applications to semantic object manipulations.

Michael Wand is a senior scientist and junior research group leader at the MPII. His research interests include statistical techniques for geometry processing and geometric correspondence problems.

Hao (Richard) Zhang is an associate professor at Simon Fraser University, Canada. His research interests include geometry processing, shape analysis, and computer graphics.

Daniel Cohen-Or is a professor at the School of Computer Science at Tel Aviv University.

Vladimir G. Kim is a Ph.D. candidate in the Computer Science Department at Princeton University. His research interests include geometry processing and analysis of shapes and collections of 3D models. Vladimir is a recipient of the Siebel Scholarship and the NSERC Postgraduate Scholarship. He is also on the International Program Committee for SGP 2013.

Qi-Xing Huang is a post doctoral scholar in the Computer Science Department at Stanford University. His research interests include data-driven geometry processing and co-analysis of shapes and collections of 3D models using convex optimization techniques.