  This article introduces Point2Tree, a novel framework that incorporates a
three-stage process involving semantic segmentation, instance segmentation,
optimization analysis of hyperparemeters importance. It introduces a
comprehensive and modular approach to processing laser points clouds in
Forestry. We tested it on two independent datasets. The first area was located
in an actively managed boreal coniferous dominated forest in V{\aa}ler, Norway,
16 circular plots of 400 square meters were selected to cover a range of forest
conditions in terms of species composition and stand density. We trained a
model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic
segmentation. As a second step in our pipeline we used graph-based approach for
instance segmentation which reached F1-score approx. 0.6. The optimization
allowed to further boost the performance of the pipeline by approx. 4 \%
points.
