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 000m2 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: this https URL

利用多层次结构的 LiDAR 对野生森林进行 3D 注释，提出了一个同时预测 3D 点云标签和高分辨率层占用栅格的深度网络结构，以此实现对每层厚度和相应的闭合网格的精确估计，并发布了相关数据集和模型。