A GPU Framework for the Visualization and On-the-fly Amplification of Real Terrains. Amara, Y., Meunier, S., & Marsault, X. In pages 586–597, 2007. ![A GPU Framework for the Visualization and On-the-fly Amplification of Real Terrains [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex This paper describes a GPU framework for the real-time visualization of natural textured terrains, as well as the steps that are needed to populate them on-the-fly with tens of thousands of plant and/or mineral objects. Our main contribution is a robust modular architecture developed for the G80 and later GPUs, that performs texture/seed selection and rendering. It does not deal with algorithms that procedurally model or render either terrain or specific natural objects, but uses them for demonstration purposes. It can be used to calculate and display realistic landscapes and ecosystems with minimal pre-stored data, CPU load and popping artefacts. It works in tandem with a pre-classification of available aerial images, and makes it possible to fine-tune the properties of objects added to the scene
@inproceedings{amara_gpu_2007,
title = {A {GPU} {Framework} for the {Visualization} and {On}-the-fly {Amplification} of {Real} {Terrains}},
url = {http://halshs.archives-ouvertes.fr/halshs-00265125},
abstract = {This paper describes a GPU framework for the real-time visualization of natural textured terrains, as well as the steps that are needed to populate them on-the-fly with tens of thousands of plant and/or mineral objects. Our main contribution is a robust modular architecture developed for the G80 and later GPUs, that performs texture/seed selection and rendering. It does not deal with algorithms that procedurally model or render either terrain or specific natural objects, but uses them for demonstration purposes. It can be used to calculate and display realistic landscapes and ecosystems with minimal pre-stored data, CPU load and popping artefacts. It works in tandem with a pre-classification of available aerial images, and makes it possible to fine-tune the properties of objects added to the scene},
urldate = {2014-07-02},
author = {Amara, Yacine and Meunier, Sylvain and Marsault, Xavier},
year = {2007},
keywords = {Algorithm Analysis and Problem Complexity, Artificial Intelligence (incl. Robotics), Biometrics, Computer Graphics, GPU quadtree, Image Processing and Computer Vision, Pattern Recognition, cloud billboards, seed model, terrain amplification, tree instancing},
pages = {586--597}
}
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