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

TL;DR

ForestFormer3D is a novel end-to-end framework that achieves state-of-the-art segmentation of forest LiDAR 3D point clouds, effectively handling diverse forest environments and types.

Contribution

The paper introduces ForestFormer3D, a unified model with new components for improved forest LiDAR point cloud segmentation, including ISA-guided query selection and a score-based merging strategy.

Findings

Achieves state-of-the-art performance on FOR-instanceV2 dataset.

Generalizes well to unseen forest datasets like Wytham woods and LAUTx.

Provides publicly available dataset and code for further research.

Abstract

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/.