Tree branch level of detail models for forest navigation

Zhang Xiao-Peng, Bao Guanbo, Meng Weiliang, Jaeger Marc, Li Hongjun, Deussen Oliver, Chen Baoquan. 2017. Tree branch level of detail models for forest navigation. Computer Graphics Forum, 36 (8) : pp. 402-417.

Journal article ; Article de revue à facteur d'impact
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Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie; Staps

Abstract : We present a level of detail (LOD) method designed for tree branches. It can be combined with methods for processing tree foliage to facilitate navigation through large virtual forests. Starting from a skeletal representation of a tree, we fit polygon meshes of various densities to the skeleton while the mesh density is adjusted according to the required visual fidelity. For distant models, these branch meshes are gradually replaced with semi-transparent lines until the tree recedes to a few lines. Construction of these complete LOD models is guided by error metrics to ensure smooth transitions between adjacent LOD models. We then present an instancing technique for discrete LOD branch models, consisting of polygon meshes plus semi-transparent lines. Line models with different transparencies are instanced on the GPU by merging multiple tree samples into a single model. Our technique reduces the number of draw calls in GPU and increases rendering performance. Our experiments demonstrate that large-scale forest scenes can be rendered with excellent detail and shadows in real time. (Résumé d'auteur)

Mots-clés Agrovoc : Forêt, Branche, Anatomie végétale, Morphologie végétale, Bioinformatique, Biométrie, Croissance, Modèle de simulation, Modèle mathématique, Port de la plante, Espacement

Mots-clés complémentaires : Architecture des arbres, Architecture forestière, Architecture végétale

Classification Agris : K01 - Forestry - General aspects
F50 - Plant structure
F62 - Plant physiology - Growth and development
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

  • Zhang Xiao-Peng, Institut d'automatique (CHN)
  • Bao Guanbo, Institut d'automatique (CHN)
  • Meng Weiliang, Institut d'automatique (CHN)
  • Jaeger Marc, CIRAD-BIOS-UMR AMAP (FRA)
  • Li Hongjun, Institut d'automatique (CHN)
  • Deussen Oliver, Universität Konstanz (DEU)
  • Chen Baoquan, Shandong University (CHN)

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