论文标题
空中激光扫描的茂密植被的多层建模
Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans
论文作者
论文摘要
对野生森林的多层结构的分析是自动大规模林业的重要挑战。虽然现代的空中激光雷达提供了所有植被层的几何信息，但大多数数据集和方法仅着重于冠层顶部的分割和重建。我们释放了Wildforest3D，其中包括29个研究地块和47 000m2的2000多个单独的树木，并具有致密的3D注释，以及3个植被层的占用和高度图：地面植被，植物，底层和整体。我们首次同时提出了一个3D深网架构，该架构首次预测3D点标签和高分辨率层占用率rasters。这使我们能够对每个植被层的厚度以及相应的水密网格进行精确估计，从而满足大多数林业目的。数据集和模型都在开放访问中发布：https：//github.com/ekalinicheva/multi_layer_vegetation。
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: https://github.com/ekalinicheva/multi_layer_vegetation.
