# Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest

arXiv: https://arxiv.org/abs/2305.02651 · HTML: https://mlsanity.com/paper/2305.02651/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 2 of 2)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass](https://mlsanity.com/paper/2112.11335/) (2021)

## Built on by (most cited first, 14 of 14)

- [Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning](https://mlsanity.com/paper/2312.15084/) (2023)
- [FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees](https://mlsanity.com/paper/2309.01279/) (2023)
- [SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data](https://mlsanity.com/paper/2401.15739/) (2024)
- [TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds](https://mlsanity.com/paper/2309.08471/) (2023)
- [ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds](https://mlsanity.com/paper/2506.16991/) (2025)
- [Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds](https://mlsanity.com/paper/2502.06227/) (2025)
- [Scaling Up Forest Vision with Synthetic Data](https://mlsanity.com/paper/2509.11201/) (2025)
- [SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests](https://mlsanity.com/paper/2606.08206/) (2026)
- [Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches](https://mlsanity.com/paper/2503.05630/) (2025)
- [Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS](https://mlsanity.com/paper/2503.14273/) (2025)
- [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025)
- [Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds](https://mlsanity.com/paper/2508.15646/) (2025)
- [treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds](https://mlsanity.com/paper/2509.03633/) (2025)
- [A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning](https://mlsanity.com/paper/2607.22129/) (2026)

## Abstract (arXiv)

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 Vaaler, 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.