Geometric modeling with primitives
| dc.contributor.author | Angles, Baptiste | |
| dc.contributor.supervisor | Tagliasacchi, Andrea | |
| dc.contributor.supervisor | Barthe, Loic | |
| dc.date.accessioned | 2019-04-29T21:31:21Z | |
| dc.date.available | 2019-04-29T21:31:21Z | |
| dc.date.copyright | 2019 | en_US |
| dc.date.issued | 2019-04-29 | |
| dc.degree.department | Department of Computer Science | |
| dc.degree.level | Doctor of Philosophy Ph.D. | en_US |
| dc.description.abstract | Both man-made or natural objects contain repeated geometric elements that can be interpreted as primitive shapes. Plants, trees, living organisms or even crystals, showcase primitives that repeat themselves. Primitives are also commonly found in man-made environments because architects tend to reuse the same patterns over a building and typically employ simple shapes, such as rectangular windows and doors. During my PhD I studied geometric primitives from three points of view: their composition, simulation and autonomous discovery. In the first part I present a method to reverse-engineer the function by which some primitives are combined. Our system is based on a composition function template that is represented by a parametric surface. The parametric surface is deformed via a non-rigid alignment of a surface that, once converged, represents the desired operator. This enables the interactive modeling of operators via a simple sketch, solving a major usability gap of composition modeling. In the second part I introduce the use of a novel primitive for real-time physics simulations. This primitive is suitable to efficiently model volume-preserving deformations of rods but also of more complex structures such as muscles. One of the core advantages of our approach is that our primitive can serve as a unified representation to do collision detection, simulation, and surface skinning. In the third part I present an unsupervised deep learning framework to learn and detect primitives. In a signal containing a repetition of elements, the method is able to automatically identify the structure of these elements (i.e. primitives) with minimal supervision. In order to train the network that contains a non-differentiable operation, a novel multi-step training process is presented. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/10794 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | geometric modeling | en_US |
| dc.subject | computer graphics | en_US |
| dc.subject | computer vision | en_US |
| dc.subject | geometric primitives | en_US |
| dc.subject | machine learning | en_US |
| dc.title | Geometric modeling with primitives | en_US |
| dc.type | Thesis | en_US |