Terrain Amplification with Implicit 3D Features
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| Title: | Terrain Amplification with Implicit 3D Features |
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| Authors: | Paris, Axel, Galin, Eric, Peytavie, Adrien, Guérin, Eric, Gain, James |
| Contributors: | Paris, Axel |
| Source: | ACM Transactions on Graphics. 38:1-15 |
| Publisher Information: | Association for Computing Machinery (ACM), 2019. |
| Publication Year: | 2019 |
| Subject Terms: | Implicit Surfaces, Landscapes, Virtual Worlds, [INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR], 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, [INFO] Computer Science [cs], 15. Life on land |
| Description: | While three-dimensional landforms, such as arches and overhangs, occupy a relatively small proportion of most computer-generated landscapes, they are distinctive and dramatic and have an outsize visual impact. Unfortunately, the dominant heightfield representation of terrain precludes such features, and existing in-memory volumetric structures are too memory intensive to handle larger scenes. In this article, we present a novel memory-optimized paradigm for representing and generating volumetric terrain based on implicit surfaces. We encode feature shapes and terrain geology using construction trees that arrange and combine implicit primitives. The landform primitives themselves are positioned using Poisson sampling, built using open shape grammars guided by stratified erosion and invasion percolation processes, and, finally, queried during polygonization. Users can also interactively author landforms using high-level modeling tools to create or edit the underlying construction trees, with support for iterative cycles of editing and simulation. We demonstrate that our framework is capable of importing existing large-scale heightfield terrains and amplifying them with such diverse structures as slot canyons, sea arches, stratified cliffs, fields of hoodoos, and complex karst cave networks. |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 1557-7368 0730-0301 |
| DOI: | 10.1145/3342765 |
| Access URL: | https://hal.archives-ouvertes.fr/hal-02273097/file/2019-tog.pdf https://dl.acm.org/citation.cfm?id=3342765 https://dl.acm.org/doi/10.1145/3342765 https://dblp.uni-trier.de/db/journals/tog/tog38.html#ParisGPGG19 https://hal.archives-ouvertes.fr/hal-02273097 https://doi.org/10.1145/3342765 https://hal.science/hal-02273097v1/document https://hal.science/hal-02273097v1 https://doi.org/10.1145/3342765 |
| Rights: | URL: https://www.acm.org/publications/policies/copyright_policy#Background |
| Accession Number: | edsair.doi.dedup.....c8cfe9c475d334dc1ca7b7157844b662 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | While three-dimensional landforms, such as arches and overhangs, occupy a relatively small proportion of most computer-generated landscapes, they are distinctive and dramatic and have an outsize visual impact. Unfortunately, the dominant heightfield representation of terrain precludes such features, and existing in-memory volumetric structures are too memory intensive to handle larger scenes. In this article, we present a novel memory-optimized paradigm for representing and generating volumetric terrain based on implicit surfaces. We encode feature shapes and terrain geology using construction trees that arrange and combine implicit primitives. The landform primitives themselves are positioned using Poisson sampling, built using open shape grammars guided by stratified erosion and invasion percolation processes, and, finally, queried during polygonization. Users can also interactively author landforms using high-level modeling tools to create or edit the underlying construction trees, with support for iterative cycles of editing and simulation. We demonstrate that our framework is capable of importing existing large-scale heightfield terrains and amplifying them with such diverse structures as slot canyons, sea arches, stratified cliffs, fields of hoodoos, and complex karst cave networks. |
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| ISSN: | 15577368 07300301 |
| DOI: | 10.1145/3342765 |