SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds

2026-06-25

AI Summary

This work addresses the challenge of instance segmentation in forest LiDAR point clouds, where scarce annotations and overlapping tree crowns hinder accurate delineation. To overcome these limitations, the authors propose a promptable 3D tree instance segmentation model that integrates a Click-to-Query prompt encoder with a canopy height model (CHM)-guided initial prompting mechanism. The architecture further incorporates a state-space-based query decoder, local feature pooling, and a fusion strategy leveraging geometric and ecological priors. Requiring only a single user click or automatically generated treetop prompts, the method achieves efficient large-scale segmentation with minimal annotation dependency and linear time complexity. Evaluated across seven diverse forest regions and an independent test set, the model attains a 78.2% IoU with fewer parameters and faster inference than existing promptable approaches.

Abstract

Automated instance segmentation of forest LiDAR point clouds is increasingly critical as forest monitoring moves toward scalable, detailed, 3D measurement. Yet, progress is constrained by label scarcity for tree instances; a single hectare can hold millions of points and hundreds of overlapping, complex crowns, making manual annotation from scratch with raw data laborious and error-prone. Annotations are often corrected from automatic pre-segmentations, but remain costly as these provide no interactive or AI-assisted refinement. Inspired by the promptable paradigm of foundation segmentation models, we propose SelectAnyTree, a promptable instance segmentation model that delineates any individual tree in a 3D forest point cloud from a few clicks. It introduces two key components: Click-to-query prompt encoder and Canopy Height Model (CHM)-guided first prompt. The former turns each click into a single content query, encoding its 3D position and positive/negative polarity together with a pooled local backbone feature. The latter provides treetops as a geometry- and ecologically guided first prompt without any user input. The resulting prompt query is then decoded into one tree mask by a state-space query decoder to efficiently capture long-range context in large-scale forest scenes with linear-time complexity. We evaluate SelectAnyTree in interactive and instance-level settings across seven diverse forest regions and an independent held-out test dataset, demonstrating strong generalization beyond the training domains. It segments a target tree to 78.2 Intersection over Union (IoU) from a single click, 24.8 points above the strongest promptable baseline, and reaches every accuracy target with the fewest clicks, while using far fewer parameters and less inference time than prior promptable models. The source code is available at https://github.com/thanhhff/SelectAnyTree.

Problem

• instance segmentation
• LiDAR point clouds
• label scarcity
• forest monitoring
• tree crown

Innovation

• promptable segmentation
• 3D instance segmentation
• LiDAR point clouds
• Click-to-query encoder
• Canopy Height Model