The segmentation of individual trees from forest point clouds is a crucial
task for downstream analyses such as carbon sequestration estimation. Recently,
deep-learning-based methods have been proposed which show the potential of
learning to segment trees. Since these methods are trained in a supervised way,
the question arises how general models can be obtained that are applicable
across a wide range of settings. So far, training has been mainly conducted
with data from one specific laser scanning type and for specific types of
forests. In this work, we train one segmentation model under various
conditions, using seven diverse datasets found in literature, to gain insights
into the generalization capabilities under domain-shift. Our results suggest
that a generalization from coniferous dominated sparse point clouds to
deciduous dominated high-resolution point clouds is possible. Conversely,
qualitative evidence suggests that generalization from high-resolution to
low-resolution point clouds is challenging. This emphasizes the need for forest
point clouds with diverse data characteristics for model development. To enrich
the available data basis, labeled trees from two previous works were propagated
to the complete forest point cloud and are made publicly available at
this https URL

研究了深度学习方法在树木分割中的应用，通过使用七个不同数据集的训练，发现从针叶树为主的稀疏点云到阔叶树为主的高分辨率点云的泛化是可能的，但由高分辨率到低分辨率点云的泛化却具有挑战性，强调了模型开发中需要具备多样性数据特征的森林点云。

通往通用基于深度学习的树实例分割模型

Towards general deep-learning-based tree instance segmentation models

Laser-scanned point clouds of forests make it possible to extract valuable
information for forest management. To consider single trees, a forest point
cloud needs to be segmented into individual tree point clouds. Existing
segmentation methods are usually based on hand-crafted algorithms, such as
identifying trunks and growing trees from them, and face difficulties in dense
forests with overlapping tree crowns. In this study, we propose
\mbox{TreeLearn}, a deep learning-based approach for semantic and instance
segmentation of forest point clouds. Unlike previous methods, TreeLearn is
trained on already segmented point clouds in a data-driven manner, making it
less reliant on predefined features and algorithms. Additionally, we introduce
a new manually segmented benchmark forest dataset containing 156 full trees,
and 79 partial trees, that have been cleanly segmented by hand. This enables
the evaluation of instance segmentation performance going beyond just
evaluating the detection of individual trees. We trained TreeLearn on forest
point clouds of 6665 trees, labeled using the Lidar360 software. An evaluation
on the benchmark dataset shows that TreeLearn performs equally well or better
than the algorithm used to generate its training data. Furthermore, the
method's performance can be vastly improved by fine-tuning on the cleanly
labeled benchmark dataset. The TreeLearn code is availabe from
this https URL The data as well as trained models can
be found at this https URL

利用深度学习的方法对森林点云进行语义和实例分割，通过已标注的数据和引入全新的参考数据集，该方法在提高树木分割性能方面表现出良好的成绩。