  Registering point clouds of forest environments is an essential prerequisite
for LiDAR applications in precision forestry. State-of-the-art methods for
forest point cloud registration require the extraction of individual tree
attributes, and they have an efficiency bottleneck when dealing with point
clouds of real-world forests with dense trees. We propose an automatic, robust,
and efficient method for the registration of forest point clouds. Our approach
first locates tree stems from raw point clouds and then matches the stems based
on their relative spatial relationship to determine the registration
transformation. The algorithm requires no extra individual tree attributes and
has quadratic complexity to the number of trees in the environment, allowing it
to align point clouds of large forest environments. Extensive experiments on
forest terrestrial point clouds have revealed that our method inherits the
effectiveness and robustness of the stem-based registration strategy while
exceedingly increasing its efficiency. Besides, we introduce a new benchmark
dataset that complements the very few existing open datasets for the
development and evaluation of registration methods for forest point clouds. The
source code of our method and the dataset are available at
https://github.com/zexinyang/GlobalMatch.
