SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests

2026-06-06

AI Summary

This work addresses the limited generalization of tree instance segmentation in forest point clouds across diverse sensors, platforms, and forest types by proposing a unified, sensor- and platform-agnostic framework. Built upon the Point Transformer v3 backbone, the method integrates a lightweight semantic head with a tree-focused cross-attention mask decoder, enhanced by tree-aware query initialization, one-to-many seed supervision, and an asymmetric mask scoring mechanism to significantly improve instance separation accuracy in dense stands. The study also introduces FOR-instance v3, a large-scale benchmark dataset encompassing diverse ecosystems. Evaluated on the FOR-instance v2 test set, the approach achieves 90.5% precision, 80.2% recall, 85.0% F1 score, 90.7% coverage, and 87.6% semantic mIoU, substantially outperforming existing methods and demonstrating exceptional cross-domain generalization capability.

Abstract

We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds. The model combines a serialization-based Point Transformer v3 backbone with a lightweight semantic head and a tree-focused cross-attention mask decoder. Semantic predictions restrict instance decoding to tree-class voxels, while instance-aware query initialization, one-to-many seed supervision, and asymmetric mask scoring improve separation in dense and structurally complex stands. We further introduce FOR-instance v3, an expanded benchmark comprising 427 scenes and 26,496 annotated trees across diverse biomes, forest structures, and LiDAR platforms. On the FOR-instanceV2 test split, SegmentAnyTreeV2 achieves 90.5% precision, 80.2% recall, 85.0% F1, 90.7% coverage, and 87.6% semantic mIoU, outperforming previous learning-based methods in both instance detection and mask completeness. Zero-shot evaluation on independent sites further demonstrates strong cross-domain generalization.

Problem

• tree instance segmentation
• forest point clouds
• cross-sensor generalization
• LiDAR
• semantic segmentation

Innovation

• Transformer-based segmentation
• cross-attention mask decoder
• instance-aware query initialization
• zero-shot generalization
• forest point cloud