KALINICHEVA, Ekaterina; LANDRIEU, Loic; MALLET, Clement; CHEHATA, Nesrine

doi:10.1109/CVPRW56347.2022.00140

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KALINICHEVA, Ekaterina
Biodiversité, Gènes & Communautés [BioGeCo]
Ecole Nationale Supérieure de Géologie [ENSG]
IGN-France International [IGN FI]

LANDRIEU, Loic
Ecole Nationale Supérieure de Géologie [ENSG]
IGN-France International [IGN FI]

MALLET, Clement
Ecole Nationale Supérieure de Géologie [ENSG]
IGN-France International [IGN FI]

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Communication dans un congrès

Este ítem está publicado en

2022-06-19, New Orleans. p. 1341-1350

IEEE

Resumen en inglés

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m 2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://github.com/ekalinicheva/multi_layer_vegetation.< Leer menos

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Computer vision

Solid modeling

Three-dimensional displays

Laser radar

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Atmospheric modeling

Time series analysis

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