# ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds

arXiv: https://arxiv.org/abs/2506.16991 · HTML: https://mlsanity.com/paper/2506.16991/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 74 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 30 of 38)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020)
- [Mask3D: Mask Transformer for 3D Semantic Instance Segmentation](https://mlsanity.com/paper/2210.03105/) (2022)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022)
- [OccuSeg: Occupancy-aware 3D Instance Segmentation](https://mlsanity.com/paper/2003.06537/) (2020)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/1906.01140/) (2019)
- [GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud](https://mlsanity.com/paper/1812.03320/) (2018)
- [3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation](https://mlsanity.com/paper/2003.13867/) (2020)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [Superpoint Transformer for 3D Scene Instance Segmentation](https://mlsanity.com/paper/2211.15766/) (2022)
- [OneFormer3D: One Transformer for Unified Point Cloud Segmentation](https://mlsanity.com/paper/2311.14405/) (2023)
- [Associatively Segmenting Instances and Semantics in Point Clouds](https://mlsanity.com/paper/1902.09852/) (2019)
- [JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields](https://mlsanity.com/paper/1904.00699/) (2019)
- [FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation](https://mlsanity.com/paper/2303.08594/) (2023)
- [Top-Down Beats Bottom-Up in 3D Instance Segmentation](https://mlsanity.com/paper/2302.02871/) (2023)
- [MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation](https://mlsanity.com/paper/2203.14662/) (2022)
- [MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation](https://mlsanity.com/paper/1902.04478/) (2019)
- [Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation](https://mlsanity.com/paper/2312.11269/) (2023)
- [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)
- [3D-BEVIS: Bird's-Eye-View Instance Segmentation](https://mlsanity.com/paper/1904.02199/) (2019)

## Built on by (most cited first, 13 of 13)

- [SelvaMask: Segmenting Trees in Tropical Forests and Beyond](https://mlsanity.com/paper/2602.02426/) (2026)
- [ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation](https://mlsanity.com/paper/2606.01549/) (2026)
- [Scaling Up Forest Vision with Synthetic Data](https://mlsanity.com/paper/2509.11201/) (2025)
- [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025)
- [treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds](https://mlsanity.com/paper/2509.03633/) (2025)
- [NormalView: tree species classification from backpack and aerial lidar data using geometric projections](https://mlsanity.com/paper/2512.05610/) (2025)
- [TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement](https://mlsanity.com/paper/2601.12823/) (2026)
- [FSKD: Monocular Forest Structure Inference via LiDAR-to-RGBI Knowledge Distillation](https://mlsanity.com/paper/2604.01766/) (2026)
- [A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning](https://mlsanity.com/paper/2607.22129/) (2026)
- [CedarCypress3D: an annotated UAV-LiDAR dataset of individual trees in planted cedar and cypress forests](https://mlsanity.com/paper/2608.30149/) (2026)
- [The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds](https://mlsanity.com/paper/2609.21903/) (2026)
- [Toward a foundation model for forest point clouds](https://mlsanity.com/paper/2609.24787/) (2026)
- [ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation](https://mlsanity.com/paper/2610.03403/) (2026)

## Abstract (arXiv)

The segmentation of forest LiDAR 3D point clouds, including both individual tree and semantic segmentation, is fundamental for advancing forest management and ecological research. However, current approaches often struggle with the complexity and variability of natural forest environments. We present ForestFormer3D, a new unified and end-to-end framework designed for precise individual tree and semantic segmentation. ForestFormer3D incorporates ISA-guided query point selection, a score-based block merging strategy during inference, and a one-to-many association mechanism for effective training. By combining these new components, our model achieves state-of-the-art performance for individual tree segmentation on the newly introduced FOR-instanceV2 dataset, which spans diverse forest types and regions. Additionally, ForestFormer3D generalizes well to unseen test sets (Wytham woods and LAUTx), showcasing its robustness across different forest conditions and sensor modalities. The FOR-instanceV2 dataset and the ForestFormer3D code are publicly available at https://bxiang233.github.io/FF3D/.