Sparse representation of terrains for procedural modeling.
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| Title: | Sparse representation of terrains for procedural modeling. |
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| Authors: | Guérin, Eric1,2, Digne, Julie1,3, Galin, Eric1,4, Peytavie, Adrien1,3 |
| Source: | Computer Graphics Forum. May2016, Vol. 35 Issue 2, p177-187. 11p. 3 Color Photographs, 2 Diagrams. |
| Subjects: | Computer graphics research, Computer art, Digital image processing, Graphic arts, Data visualization |
| Abstract: | In this paper, we present a simple and efficient method to represent terrains as elevation functions built from linear combinations of landform features (atoms). These features can be extracted either from real world data-sets or procedural primitives, or from any combination of multiple terrain models. Our approach consists in representing the elevation function as a sparse combination of primitives, a concept which we call Sparse Construction Tree, which blends the different landform features stored in a dictionary. The sparse representation allows us to represent complex terrains using combinations of atoms from a small dictionary, yielding a powerful and compact terrain representation and synthesis tool. Moreover, we present a method for automatically learning the dictionary and generating the Sparse Construction Tree model. We demonstrate the efficiency of our method in several applications: inverse procedural modeling of terrains, terrain amplification and synthesis from a coarse sketch. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In this paper, we present a simple and efficient method to represent terrains as elevation functions built from linear combinations of landform features (atoms). These features can be extracted either from real world data-sets or procedural primitives, or from any combination of multiple terrain models. Our approach consists in representing the elevation function as a sparse combination of primitives, a concept which we call Sparse Construction Tree, which blends the different landform features stored in a dictionary. The sparse representation allows us to represent complex terrains using combinations of atoms from a small dictionary, yielding a powerful and compact terrain representation and synthesis tool. Moreover, we present a method for automatically learning the dictionary and generating the Sparse Construction Tree model. We demonstrate the efficiency of our method in several applications: inverse procedural modeling of terrains, terrain amplification and synthesis from a coarse sketch. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01677055 |
| DOI: | 10.1111/cgf.12821 |
